**Meet the editor**

Dr. Seth Appiah-Opoku is Associate Professor of Geography at the University of Alabama, Tuscaloosa, USA. He teaches Environmental Management, Land Use Regulation, Principles of Planning, Geography of Africa, Regional Planning and Analysis, and Field Studies in Africa course. He is a member of the American Institute of Certified Planners. He serves on the international edito-

rial board of the Journal of Environmental Impact Assessment Review and has published scholarly articles in several renowned journals including Environmental Management, Society and Natural Resources, The Environmentalist, Environments, Plan Canada, Journal of Environmental Impact Assessment Review, and Journal of Cultural Geography. He is also the author of The Need for Indigenous Knowledge in Environmental Impact Assessment: the Case of Ghana (Edwin Mellen Press, NY, June 2005).

Contents

**Preface IX** 

Imoke Eni

**Section 2 Analytical Methods/Tools** 

Chapter 6 **Environmental Land Use** 

Jing Shen and Hao Wang

Chapter 5 **An Integrated Land-Use System Model for the Jordan River Region 87**  Jennifer Koch, Florian Wimmer, Rüdiger Schaldach and Janina Onigkeit

**Section 1 Environmental Problems and Effects on Land Uses 1** 

Slavoljub Dragicevic, Nenad Zivkovic, Mirjana Roksandic, Stanimir Kostadinov, Ivan Novkovic, Radislav Tosic, Milomir Stepic, Marija Dragicevic and Borislava Blagojevic

**in the Boconó River Basin, North Venezuelan Andes, and Its Implications for the Natural Resources Management 35** 

Chapter 1 **Land Use Changes and Environmental Problems Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 3** 

Chapter 2 **Effects of Land Degradation on Soil Fertility:** 

Chapter 3 **Land Use and Land Cover (LULC) Change** 

**A Case Study of Calabar South, Nigeria 21** 

Joel Francisco Mejía and Volker Hochschild

**for Environmental Land Use Planning 69** 

**and the Ecological Footprint of Higher Learning 117** 

Chapter 4 **Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 71** 

Seth Appiah-Opoku and Crystal Taylor

## Contents

#### **Preface XI**


X Contents


Chapter 11 **The Role of Government in Environmental Land Use Planning: Towards an Integral Perspective 219**  Noelle Aarts and Anne Marike Lokhorst

## Preface

Environmental consideration is increasingly taking center-stage in planning and policy decisions at all levels of government. This is due to the growing concerns over the damage being caused to the environment by human activities. Today, the vital functions of the Earth are nearly all seriously compromised or moving in that direction. We now live in a riskier world with more consumption, more waste, more people and pollution but with dwindling biodiversity, fresh water and ozone layer. Thus, one thing is clear – our current destructive paths to development are unsustainable. There is an urgent need to reverse the trend and preserve the integrity of the environment, both for the current and future generations.

In response, environmental land use planning has evolved to provide thoughtful intervention tools and strategies aimed at reducing or minimizing the environmental burden on current and future generations; preserving or conserving our natural resources for current and future use; and minimizing environmental threats to human health and safety. With a more holistic view, environmental land use planning places emphasis on the biophysical environment and human communities. It adopts a perspective which recognizes all components of the earth, as well as the linkages between each and every one of them.

Environmental professionals need to have a basic understanding of environmental problems and their effects on land uses; analytical methods or tools to examine the problems; and understand the role of governments, community grants, and tradable permits in environmental land use planning. This book is intended to educate readers in these areas. The contributors to this volume have brought together a rich tapestry of experiences from all parts of the world. The issues covered in the volume range from land cover changes, environmental problems caused by river bank erosion, predicting changes in land use pattern, ecological footprint analysis, behavioral modelling, community grants and the role of government in environmental land use planning. The book is divided into three parts. Part I provides an overview of selected environmental problems and the effects on land uses. Part II presents analytical methods or tools for environmental land use planning. Part III discusses the role of governments, community grants, and tradable permits in environmental land use planning.

#### XII Preface

Although there are other significant issues pertaining to environmental planning, time and space have made it impossible to cover all in this volume. Therefore, this book should be seen as a wide brush stroke pointing the way to matters to be addressed in latter volumes. Written at a level that is understandable to most scholars, regardless of their technical background and education, this volume simplifies complex environmental problems and analytical tools. It challenges planners to overlook human-focused limits or boundaries, and plan with nature, including its functions and natural boundaries. Finally, the book recognizes the natural interdependence between the natural, human and social systems, and provides thoughtful and innovative approaches towards environmental sustainability.

> **Seth Appiah-Opoku** Geography Department University of Alabama Tuscaloosa, AL, USA

**Section 1** 

**Environmental Problems and Effects on Land Uses** 

**Chapter 1** 

© 2012 Dragicevic et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Land Use Changes and Environmental** 

**of the Kolubara River Basin in Serbia** 

Slavoljub Dragicevic, Nenad Zivkovic, Mirjana Roksandic,

Marija Dragicevic and Borislava Blagojevic

Additional information is available at the end of the chapter

Serbia, likewise in the region [5-7], and in the world [8-11].

http://dx.doi.org/10.5772/50580

**1. Introduction** 

[17,18].

Stanimir Kostadinov, Ivan Novkovic, Radislav Tosic, Milomir Stepic,

**Problems Caused by Bank Erosion: A Case Study** 

Geomorphological analysis of the dominant erosion processes and their intensity quantification were done in the previous researches of the Kolubara River basin [1-3]. The results showed that, the level of the landscape degradation and modification of geomorphologic processes by human activities has been increased in the past decades [4], and it was initiated by very fast demographic, socio-economic and technological changes in

According to level and type of degradation, the Kolubara River basin belongs to the most endangered areas in Serbia. Due to the lignite exploitation in the Kolubara River basin, human impact led to morphological change of the entire area, as well as to the changes of the intensity of different geomorphologic processes: changes in river course [12,13], the intensity of bank erosion [14,15], sediment deposition [16] and environmental problems

Unlike the other rivers with similar hydrological characteristics, the river network in the lower part of the Kolubara River basin were changed rapidly during the XX century because of direct human impact. Anthropogenic influences on the hydrological network in the study area were very intensive since 1959, when the huge river regulation works were done in the lower part of the Kolubara River. Spatial planning of the area, which included diverting of the Kolubara's river bed, had an aim to prepare the site for the lignite exploitation within the Kolubara mining basin. The Kolubara River divides the mining basin in two parts: eastern and western part. The productive area of the basin (geologic contours of lignite

and reproduction in any medium, provided the original work is properly cited.

**Chapter 1** 

## **Land Use Changes and Environmental Problems Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia**

Slavoljub Dragicevic, Nenad Zivkovic, Mirjana Roksandic, Stanimir Kostadinov, Ivan Novkovic, Radislav Tosic, Milomir Stepic, Marija Dragicevic and Borislava Blagojevic

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50580

## **1. Introduction**

Geomorphological analysis of the dominant erosion processes and their intensity quantification were done in the previous researches of the Kolubara River basin [1-3]. The results showed that, the level of the landscape degradation and modification of geomorphologic processes by human activities has been increased in the past decades [4], and it was initiated by very fast demographic, socio-economic and technological changes in Serbia, likewise in the region [5-7], and in the world [8-11].

According to level and type of degradation, the Kolubara River basin belongs to the most endangered areas in Serbia. Due to the lignite exploitation in the Kolubara River basin, human impact led to morphological change of the entire area, as well as to the changes of the intensity of different geomorphologic processes: changes in river course [12,13], the intensity of bank erosion [14,15], sediment deposition [16] and environmental problems [17,18].

Unlike the other rivers with similar hydrological characteristics, the river network in the lower part of the Kolubara River basin were changed rapidly during the XX century because of direct human impact. Anthropogenic influences on the hydrological network in the study area were very intensive since 1959, when the huge river regulation works were done in the lower part of the Kolubara River. Spatial planning of the area, which included diverting of the Kolubara's river bed, had an aim to prepare the site for the lignite exploitation within the Kolubara mining basin. The Kolubara River divides the mining basin in two parts: eastern and western part. The productive area of the basin (geologic contours of lignite

© 2012 Dragicevic et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

deposits) is 520 km2. Kolubara mining basin is situated about 40 km south-southeast of Belgrade and represents the largest lignite deposits in the central part of Serbia; the annual production is 30 million tons of lignite, and it is the opencast mine. The mine expansion caused the need for technical solutions of diverting and removing river beds in this area. According to "General project of diverting the Kolubara River and its tributaries for the purpose of lignite exploitation", the Kolubara's riverbed was diverted into the Pestan's riverbed (its right tributary). This caused many problems which were not predicted by the General project.

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 5

**Figure 1.** Position of the Kolubara River basin in Serbia (right) and study area (left)

changes, which represents the base for bank erosion intensity quantification.

In this research we used diffrent methods that can be devided into the field and lab work methods. The GIS methods were used for the modeling of terrain evolution and landscape

Analysis of topographical maps, aerial photo and orthophoto images were used in the previous researches aiming to determine the evolution of the riverbed [6,15,19-23]. The results showed that the application of GIS has an advantage in quantification of river

For the purposes of this study, comparative analyses have been made on the base of Cadastral maps scale 1:2500 from 1967 and orthophoto images from 2004; reconstruction of the hydrological system has been done for the periods from 1967 to 2004. By comparing the data from two periods, we determined the evolution of the Kolubara River course in 37 years. River bank lines were digitized and the extent of bank erosion was calculated under

**3. Methodology** 

migration processes.

In this way, anthropogenic factor modified existing natural conditions: the process of fluvial erosion was changed; bank erosion became stronger and resulted in soil loss, larger amounts of sediment load deposition, cutting off the meanders and fossilization of certain parts of the riverbed, floods, land use changes, landscape degradation, sediment load pollution, etc.

## **2. Research area**

Regarding to natural conditions, the Kolubara River basin is similar to the other river basins in the area. Tectonic movements had an influence on a morphological evolution of the river network in the past. During the Paleogene and the Early Neogene a small bay of the Pannonian Sea named the Kolubara's bay existed in the area of the Kolubara River basin. After the sea recession, the fluvial erosion started in this bay and it formed today's hydrological network of the Kolubara River. Tectonic characteristics of this area, more precisely Kolubarsko-pestanski fault and Posavski fault had influenced the orientation of the hydrological network in the Kolubara River basin. But today the Kolubara's hydrological network is influenced by fluvial erosion and anthropogenic factors.

The Kolubara River Basin encompasses the western part of Serbia and covers 4.12% of Serbia's surface area. The highest point of the drainage basin is at 1,346 m, and the lowest has altitude of 73 m. The Kolubara River is the last large right tributary of the Sava River, and according to the flow length (86.4 km) and the basin area (3,641 km2) it is classified as a middle-sized river on the territory of Serbia [3].

The lower part of the Kolubara River basin is called the Donjokolubarski basin (area of 1,810 km2) and is situated in the municipality of Obrenovac. The Donjokolubarski basin encompasses the catchment area of the Kolubara's confluences (the Pestan River, the Turija River with the Beljanica River, the Tamnava River with the Ub River and the Kladnica River) and the lower part of the Kolubara's valley. The average altitude of the Donjokolubarski basin is 168 m, the highest point is at 695 m, and the lowest has an altitude of 73 m.

According to the nearest meteorological station in Obrenovac, this area is characterized by continental climate, the average temperature was 11° C, and the mean annual precipitation from year 1925 to 2000 was 722 mm [12]. The average annual runoff of the Kolubara River (at Drazevac gauging station) for the period 1961-2005 was 21.8 m3/s.

**Figure 1.** Position of the Kolubara River basin in Serbia (right) and study area (left)

## **3. Methodology**

4 Environmental Land Use Planning

General project.

pollution, etc.

of 73 m.

**2. Research area** 

deposits) is 520 km2. Kolubara mining basin is situated about 40 km south-southeast of Belgrade and represents the largest lignite deposits in the central part of Serbia; the annual production is 30 million tons of lignite, and it is the opencast mine. The mine expansion caused the need for technical solutions of diverting and removing river beds in this area. According to "General project of diverting the Kolubara River and its tributaries for the purpose of lignite exploitation", the Kolubara's riverbed was diverted into the Pestan's riverbed (its right tributary). This caused many problems which were not predicted by the

In this way, anthropogenic factor modified existing natural conditions: the process of fluvial erosion was changed; bank erosion became stronger and resulted in soil loss, larger amounts of sediment load deposition, cutting off the meanders and fossilization of certain parts of the riverbed, floods, land use changes, landscape degradation, sediment load

Regarding to natural conditions, the Kolubara River basin is similar to the other river basins in the area. Tectonic movements had an influence on a morphological evolution of the river network in the past. During the Paleogene and the Early Neogene a small bay of the Pannonian Sea named the Kolubara's bay existed in the area of the Kolubara River basin. After the sea recession, the fluvial erosion started in this bay and it formed today's hydrological network of the Kolubara River. Tectonic characteristics of this area, more precisely Kolubarsko-pestanski fault and Posavski fault had influenced the orientation of the hydrological network in the Kolubara River basin. But today the Kolubara's

The Kolubara River Basin encompasses the western part of Serbia and covers 4.12% of Serbia's surface area. The highest point of the drainage basin is at 1,346 m, and the lowest has altitude of 73 m. The Kolubara River is the last large right tributary of the Sava River, and according to the flow length (86.4 km) and the basin area (3,641 km2) it is classified as a

The lower part of the Kolubara River basin is called the Donjokolubarski basin (area of 1,810 km2) and is situated in the municipality of Obrenovac. The Donjokolubarski basin encompasses the catchment area of the Kolubara's confluences (the Pestan River, the Turija River with the Beljanica River, the Tamnava River with the Ub River and the Kladnica River) and the lower part of the Kolubara's valley. The average altitude of the Donjokolubarski basin is 168 m, the highest point is at 695 m, and the lowest has an altitude

According to the nearest meteorological station in Obrenovac, this area is characterized by

from year 1925 to 2000 was 722 mm [12]. The average annual runoff of the Kolubara River

C, and the mean annual precipitation

hydrological network is influenced by fluvial erosion and anthropogenic factors.

middle-sized river on the territory of Serbia [3].

continental climate, the average temperature was 11°

(at Drazevac gauging station) for the period 1961-2005 was 21.8 m3/s.

In this research we used diffrent methods that can be devided into the field and lab work methods. The GIS methods were used for the modeling of terrain evolution and landscape changes, which represents the base for bank erosion intensity quantification.

Analysis of topographical maps, aerial photo and orthophoto images were used in the previous researches aiming to determine the evolution of the riverbed [6,15,19-23]. The results showed that the application of GIS has an advantage in quantification of river migration processes.

For the purposes of this study, comparative analyses have been made on the base of Cadastral maps scale 1:2500 from 1967 and orthophoto images from 2004; reconstruction of the hydrological system has been done for the periods from 1967 to 2004. By comparing the data from two periods, we determined the evolution of the Kolubara River course in 37 years. River bank lines were digitized and the extent of bank erosion was calculated under

Geomedia professional. The same software was used for the estimation of the Kolubara River lateral migration rate. This rate was estimated using the calculated area between river positions in 1967 and in 2004 (area of river migration), which was divided by the total length of the river course in 1967. The loss of land (*S*) is expressed as the ratio between area of endangered land parcels (ha) in 1967 (P1967) and area of endangered land parcels (ha) in 2004 (P2004) [15]:

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 7

The sediment samples were taken on two locations in the Kolubara's riverbed. For heavy metals and carbon analysis the soil was milled to a fine powder. Heavy metals were

**4.1. Natural conditions changes as a factor of bank erosion in the study area** 

times less comparing to the previous period of observation (1967-1981) [13].

analyzed to determine whether they have influenced the stronger bank erosion.

On the research sector (Fig. 1) the Kolubara River length in 1967 was 8.2 km and 10.6 km in 2004. This fact appoints to the river course evolution through the landscape. In the period between 1967 and 1981 the Kolubara River has migrated 50 m, actually 27 m into left and 23 m into right, and the average migration of the Kolubara River was 3.6 m per year. By further comparison of aerial photo image from 1981 and orthophoto image from 2004 it can be observed that the Kolubara's riverbed was stabilized and during 23 years migrated only 26 m. So, the Kolubara River average migration in this period was 1.1 m per year which is three

The rate of the Kolubara river lateral migration along the research sector is 47 m in average for the period of 37 years, which means 1.27 m per year. At the most endangered part (in the area of Drazevac village) the most intensive migration rate of the riverbed was 224 m in 37

The changes of fluvial erosion intensity may result from changes in climatic-hydrological characteristics of the river basin (which are manifested in discharge regime changes) and various human impacts. Therefore, the natural factors of the Donjokolubarski basin were

The results showed that average mean annual discharge of the Kolubara River measured in Drazevac was 22.3 m3/s in the observation period 1961-1990, and 21.3 m3/s in the observation period 1991-2005. Amplitude of average high and low flows in the period 1961-1990 was

To study water balance of the Donjokolubarski basin we used the following periods: 1925- 1960, 1961-1990 and 1991-2005. With this approach it was possible to determine the changes that may be occurred after diverting the Kolubara River into the Pestan's riverbed in 1959. Briefly, precipitation analysis showed that the second period was a bit wetter than the first, actually about 60 mm in the Pestan River basin and 80 mm in the Turija River basin and the Tamnava River basin. Meanwhile, higher air temperatures and higher evaporation caused almost the same specific discharges of these rivers. The last period was in mean values similar to the second, apart from intensified variation of extreme values of all climatic elements. The discharges were influenced by more frequent alternation of wet and dry

Monthly coefficients of variation of the period 1991-2005 are higher in all river sub basins except in July and August. These differences are significant, the variation of discharges in eight months are higher than the highest coefficients of variation of the period 1961-1990, which is 1.5. The more important is the fact that the period of appearances of unstable

determinate by AAS method.

**4. The intensity of bank erosion** 

years, with the average of 6.05 m per a year [15].

periods, which could be seen on figure 2.

77.94 m3/s, and in the period 1991-2005 it was 64.66 m3/s [13].

$$S = \frac{P\_{1967} - P\_{2004}}{P\_{1967}} \ast 100$$

River erosion and frequent floods make great material damages to people, villages and economy. The owners of the arable land parcels on the Kolubara River banks loose the parts of the parcels that river carries away. The reduction of parcels on the Kolubara River banks, land loss and land use changes were estimated comparing the cadastral maps from 1967 and orthophoto images from 2004.

Land use structure in the area of villages: Drazevac, Konatice and Poljane are characterized by: arable land (which people used for farming mostly wheat and corn-crop rotation practice), forests (alluvial forests of willows and poplars) and few pastures. The river dynamic is intensive in the Kolubara's alluvial zone, which influenced sandbank formation, mostly on the concave side of the river. By statistical analysis of a land use structure [24] in the three villages with degraded land parcels on the river banks, we obtained the results which show significant reduction in arable land. And by analyses of the questionnaire carried out among the owners of degraded land parcels in the villages Drazevac, Konatice and Poljane, it can be concluded that it was significant decrease in the agricultural production. The risks from the floods and further soil loss influenced the land owners' decision making about farming the degraded land parcels.

The change of fluvial erosion intensity was analyzed regarding to changes in water balance and sediment load transport on two hydrological profiles. Тhe results of water balance that D. Dukic [25] has made in his research for the period of 1925-1960 and the results obtained in this study were analyzed and compared. This comparative analysis appoints to the amount of water which Donjokolubarski basin disposed before regulatory changes of Kolubara in 1959/60 and after them. River flow regimes of different periods were compared because that could be a factor which has a significant influence on the observed process. All these efforts should confirm or eliminate the influence of natural factors on the river banks degradation in the Donjokolubarska valley.

Having data of extreme discharges, in order to estimate the impact of future floods on bank erosion, we have made a probability curve of maximum discharges of the Kolubara River and its tributaries.

Because of intense anthropogenic impacts in the Donjokolubarska valley, we have sampled the suspended sediments from the Kolubara's riverbed and later analyzed the pollution of the accumulated load. Since the processes of bank erosion and sediment accumulation occur close to the villages and that endangered land parcels are used for food production, such approach points to ecological aspect of researched problem.

The sediment samples were taken on two locations in the Kolubara's riverbed. For heavy metals and carbon analysis the soil was milled to a fine powder. Heavy metals were determinate by AAS method.

## **4. The intensity of bank erosion**

6 Environmental Land Use Planning

orthophoto images from 2004.

decision making about farming the degraded land parcels.

approach points to ecological aspect of researched problem.

degradation in the Donjokolubarska valley.

and its tributaries.

(P2004) [15]:

Geomedia professional. The same software was used for the estimation of the Kolubara River lateral migration rate. This rate was estimated using the calculated area between river positions in 1967 and in 2004 (area of river migration), which was divided by the total length of the river course in 1967. The loss of land (*S*) is expressed as the ratio between area of endangered land parcels (ha) in 1967 (P1967) and area of endangered land parcels (ha) in 2004

> 1967 2004 1967

River erosion and frequent floods make great material damages to people, villages and economy. The owners of the arable land parcels on the Kolubara River banks loose the parts of the parcels that river carries away. The reduction of parcels on the Kolubara River banks, land loss and land use changes were estimated comparing the cadastral maps from 1967 and

Land use structure in the area of villages: Drazevac, Konatice and Poljane are characterized by: arable land (which people used for farming mostly wheat and corn-crop rotation practice), forests (alluvial forests of willows and poplars) and few pastures. The river dynamic is intensive in the Kolubara's alluvial zone, which influenced sandbank formation, mostly on the concave side of the river. By statistical analysis of a land use structure [24] in the three villages with degraded land parcels on the river banks, we obtained the results which show significant reduction in arable land. And by analyses of the questionnaire carried out among the owners of degraded land parcels in the villages Drazevac, Konatice and Poljane, it can be concluded that it was significant decrease in the agricultural production. The risks from the floods and further soil loss influenced the land owners'

The change of fluvial erosion intensity was analyzed regarding to changes in water balance and sediment load transport on two hydrological profiles. Тhe results of water balance that D. Dukic [25] has made in his research for the period of 1925-1960 and the results obtained in this study were analyzed and compared. This comparative analysis appoints to the amount of water which Donjokolubarski basin disposed before regulatory changes of Kolubara in 1959/60 and after them. River flow regimes of different periods were compared because that could be a factor which has a significant influence on the observed process. All these efforts should confirm or eliminate the influence of natural factors on the river banks

Having data of extreme discharges, in order to estimate the impact of future floods on bank erosion, we have made a probability curve of maximum discharges of the Kolubara River

Because of intense anthropogenic impacts in the Donjokolubarska valley, we have sampled the suspended sediments from the Kolubara's riverbed and later analyzed the pollution of the accumulated load. Since the processes of bank erosion and sediment accumulation occur close to the villages and that endangered land parcels are used for food production, such

*P P <sup>S</sup> P* 

100

## **4.1. Natural conditions changes as a factor of bank erosion in the study area**

On the research sector (Fig. 1) the Kolubara River length in 1967 was 8.2 km and 10.6 km in 2004. This fact appoints to the river course evolution through the landscape. In the period between 1967 and 1981 the Kolubara River has migrated 50 m, actually 27 m into left and 23 m into right, and the average migration of the Kolubara River was 3.6 m per year. By further comparison of aerial photo image from 1981 and orthophoto image from 2004 it can be observed that the Kolubara's riverbed was stabilized and during 23 years migrated only 26 m. So, the Kolubara River average migration in this period was 1.1 m per year which is three times less comparing to the previous period of observation (1967-1981) [13].

The rate of the Kolubara river lateral migration along the research sector is 47 m in average for the period of 37 years, which means 1.27 m per year. At the most endangered part (in the area of Drazevac village) the most intensive migration rate of the riverbed was 224 m in 37 years, with the average of 6.05 m per a year [15].

The changes of fluvial erosion intensity may result from changes in climatic-hydrological characteristics of the river basin (which are manifested in discharge regime changes) and various human impacts. Therefore, the natural factors of the Donjokolubarski basin were analyzed to determine whether they have influenced the stronger bank erosion.

The results showed that average mean annual discharge of the Kolubara River measured in Drazevac was 22.3 m3/s in the observation period 1961-1990, and 21.3 m3/s in the observation period 1991-2005. Amplitude of average high and low flows in the period 1961-1990 was 77.94 m3/s, and in the period 1991-2005 it was 64.66 m3/s [13].

To study water balance of the Donjokolubarski basin we used the following periods: 1925- 1960, 1961-1990 and 1991-2005. With this approach it was possible to determine the changes that may be occurred after diverting the Kolubara River into the Pestan's riverbed in 1959. Briefly, precipitation analysis showed that the second period was a bit wetter than the first, actually about 60 mm in the Pestan River basin and 80 mm in the Turija River basin and the Tamnava River basin. Meanwhile, higher air temperatures and higher evaporation caused almost the same specific discharges of these rivers. The last period was in mean values similar to the second, apart from intensified variation of extreme values of all climatic elements. The discharges were influenced by more frequent alternation of wet and dry periods, which could be seen on figure 2.

Monthly coefficients of variation of the period 1991-2005 are higher in all river sub basins except in July and August. These differences are significant, the variation of discharges in eight months are higher than the highest coefficients of variation of the period 1961-1990, which is 1.5. The more important is the fact that the period of appearances of unstable

discharges is March-April (over 2.5), which is related to snow melting. That is the period of maximum discharges and any sudden disturbance of soil moisture resulting in serious disorder of river bank stability. Some of the natural factors have been changed in the last two decades, for example, March used to be, in Serbia, the month with the most stable discharges (and the highest). The differences in March discharges during the observed periods are over 20 m3/s (almost twice reduced), and it was followed by extreme discharge variations. These are the significant changes since the area of the Kolubara River basin is bigger than 3500 km2 and maximum discharges are higher than 500 m3/s. Although the mean values are not of crucial importance, they indicate some disturbances which should be kept in mind; particularly because the last period of observation is twice shorter than the previous one and all analyses in the world indicate that extreme values of natural phenomena are more pronounced and more frequent.

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 9

field "D" in 1961, "Tamnava-East Mining Field" in 1979, "Tamnava-West Mining Field" in

1994 and mining field "Veliki Crljeni" in 2008 [28].

**Figure 3.** Plan of the mining fields in Kolubara mining basin [28]

for opening new mining fields.

Beogradsko-posavska water community "Beograd" has made a project "Regulation of the Kolubara River and its right tributaries from Ćelije to Poljane (km 23+200 – km 55+506)" in 1957. Regulation works on the Kolubara River and its tributaries had begun in 1959/60. The diverting of the Kolubara River was done to clear the area for lignite exploitation, actually

From that moment the Kolubara River flows through the Pestan's riverbed (its right tributary), and previous Kolubara's riverbed is abandoned with the periodic flow. The length of the Kolubara River was shortened by 20 km because of diverting its riverbed, while the length of the Pestan River was also shortened because its confluence was moved to the South. By diverting the Kolubara's riverbed into the Pestan's riverbed, which

**Figure 2.** Mean monthly discharges (Q) and coefficients of variation (Cv) of the Kolubara River measured in Drazevac gauging station for the both periods of observation

Preliminary results of the Donjokolubarski basin annual flow variation show discrepancy in spring and summer monthly flow among two studied periods (Figure 2 - right). Further research should examine correlation of monthly flow and bank erosion intensity.

### **4.2. Anthropogenic influences as a factor of bank erosion in the study area**

The erosion control works in the river channel can cause changes in river morphology, since they influence the changes in river regimes, river bank characteristics and amount of sediment transport [26]. The consequences of these interventions are numerous, and often lead to riverbed widening and undermining concave sides of the river banks. The processes of river bank collapsing and erosion are complex since they are results of several factors, including sediment transport, ground lithology, stratigraphy, slope, flow geometry and anthropogenic activities [27].

Opencast lignite exploitation in Kolubara mining basin started in 1952 when the mining field "A" was open (it was exploited till 1966). Mining field "B" was opened in 1952, mining field "D" in 1961, "Tamnava-East Mining Field" in 1979, "Tamnava-West Mining Field" in 1994 and mining field "Veliki Crljeni" in 2008 [28].

8 Environmental Land Use Planning

phenomena are more pronounced and more frequent.

discharges is March-April (over 2.5), which is related to snow melting. That is the period of maximum discharges and any sudden disturbance of soil moisture resulting in serious disorder of river bank stability. Some of the natural factors have been changed in the last two decades, for example, March used to be, in Serbia, the month with the most stable discharges (and the highest). The differences in March discharges during the observed periods are over 20 m3/s (almost twice reduced), and it was followed by extreme discharge variations. These are the significant changes since the area of the Kolubara River basin is bigger than 3500 km2 and maximum discharges are higher than 500 m3/s. Although the mean values are not of crucial importance, they indicate some disturbances which should be kept in mind; particularly because the last period of observation is twice shorter than the previous one and all analyses in the world indicate that extreme values of natural

**Figure 2.** Mean monthly discharges (Q) and coefficients of variation (Cv) of the Kolubara River

research should examine correlation of monthly flow and bank erosion intensity.

**4.2. Anthropogenic influences as a factor of bank erosion in the study area** 

Preliminary results of the Donjokolubarski basin annual flow variation show discrepancy in spring and summer monthly flow among two studied periods (Figure 2 - right). Further

The erosion control works in the river channel can cause changes in river morphology, since they influence the changes in river regimes, river bank characteristics and amount of sediment transport [26]. The consequences of these interventions are numerous, and often lead to riverbed widening and undermining concave sides of the river banks. The processes of river bank collapsing and erosion are complex since they are results of several factors, including sediment transport, ground lithology, stratigraphy, slope, flow geometry and

Opencast lignite exploitation in Kolubara mining basin started in 1952 when the mining field "A" was open (it was exploited till 1966). Mining field "B" was opened in 1952, mining

measured in Drazevac gauging station for the both periods of observation

anthropogenic activities [27].

**Figure 3.** Plan of the mining fields in Kolubara mining basin [28]

Beogradsko-posavska water community "Beograd" has made a project "Regulation of the Kolubara River and its right tributaries from Ćelije to Poljane (km 23+200 – km 55+506)" in 1957. Regulation works on the Kolubara River and its tributaries had begun in 1959/60. The diverting of the Kolubara River was done to clear the area for lignite exploitation, actually for opening new mining fields.

From that moment the Kolubara River flows through the Pestan's riverbed (its right tributary), and previous Kolubara's riverbed is abandoned with the periodic flow. The length of the Kolubara River was shortened by 20 km because of diverting its riverbed, while the length of the Pestan River was also shortened because its confluence was moved to the South. By diverting the Kolubara's riverbed into the Pestan's riverbed, which

morphologically was not predisposed for kinetic energy of stronger flow, bank erosion became a dominating geomorphological process in the area and initiated processes of digging the riverbanks, transportation and deposition of eroded material. It is obvious that river system changes in lower part of the Kolubara River are demonstrated in domination of fluvial (lateral) erosion on one hand, and in cutting the meanders and fossilization of certain parts of the riverbed, on the other hand.

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 11

**Woods Pastures Meadows Sand** 

**banks** 

**Other (roads…)** 

on concave side of the Kolubara River often collapse and farmers who have arable land parcels on the river bank (in the area of three villages the Kolubara flows through) loose the parts of the parcels which were carried away by the river. Based on Cadastral maps from 1967 and orthophoto images from 2004 we have estimated the area of diminished land

Farmers who have land parcels in three villages (Drazevac, Konatice and Poljane) on the Kolubara river bank cannot farm them in whole, because the river has changed its course and took some parts of the land parcels away. The cadastral maps of the researched area scale 1:2500 from 1967 and orthophoto images from 2004 were compared. Using the results of this comparative analysis, the evolution of the hydrological system in the period from 1967 to 2004 was presented. The previous research showed that 60.37 ha was lost and degraded by the river bank erosion, which means that the land loss is 50.57 % of the land

**land** 

*Drazevac*  number of endagered parcels 95 39 16 1 5 34 -

area in 1967 (ha) 57.76 34.96 5.56 0.25 2.93 14.07 area in 2004 (ha) 28.23 21.76 2.48 0.02 1.30 2.67 loss of land (ha) 29.53 13.20 3.08 0.23 1.63 11.40 - *Konatice*  number of endagered parcels 86 56 2 2 - 25 1

area in 1967 (ha) 50.44 42.73 0.49 1.02 - 6.12 0.09 area in 2004 (ha) 32.13 29.57 0.34 0.55 - 1.60 0.06 loss of land (ha) 18.31 13.16 0.15 0.47 - 4.52 0.03

*Poljane*  number of endagered parcels 66 41 3 - - 21 1

On the basis of the recent and more accurate data from Obrenovac Municipality Cadastre we have determined land use structure of degraded land parcels on the Kolubara River banks. According to these data, total area of all 247 endangered land parcels was 148.3 ha in

1967, and 86.62 ha in 2004. Therefore, 61.68 ha of soil were lost within 37 years [13].

area in 1967 (ha) 40.09 33.91 0.82 - - 5.11 0.25 area in 2004 (ha) 26.26 25.15 0.19 - - 0.89 0.03 loss of land (ha) 13.83 8.76 0.63 - - 4.22 0.22

parcels and their land loss.

parcels from 1967 [15].

**Land use Total Arable**

**Table 1.** Land use structure in Drazevac, Konatice and Poljane.

## **5. The consequences of bank erosion**

## **5.1. Forming of meanders**

Map of the Kolubara's basin first trend of relief energy [2] shows that almost whole area of the Donjokolubarska valley is under tectonic movements of slowly sinking. For this reason the sediments are accumulated in the riverbed, river velocity decreases which cause the riverbed meandering and stronger bank erosion. This natural process became more intensive since the Kolubara River was diverted into the riverbed of Pestan. In the Donjokolubarski basin there are numerous sectors with abandoned riverbeds and cut off meanders.

Forming of meanders and cutting the "necks" are recent geomorphologic-hydrological process, which is dominated in the study area. According to results of the recent researches [13], there are 89 abandoned parts of the riverbeds and cut off meanders in the area of the Donjokolubarski basin. The Kolubara and its tributaries tend to move to the east because of the Kolubarsko-pestanski fault, which indicate more abandoned riverbeds and cut off meanders on the left side of the Kolubara valley (64), compared to the right side (25).

In the study area there are 40 cut off meanders with total length of 20.30 km while the number of abandoned parts of the riverbeds is 49 with total length of 76.03 km. Hence, the total length of all abandoned riverbeds and cut off meanders in the Donjokolubarski basin is 96.33 km, and their total surface is 3.35 km2. The longest cut off meander is 1.7 km long and the shortest is 185.7 m long. The longest abandoned riverbed is 6.49 km long.

The length of the Kolubara's riverbed is influenced by stronger bank erosion and formation of meanders, which is clearly perceived in the field. According to orthophoto image from 2004 and satellite image (Google Earth) the Kolubara River length (in the Donjokolubarska valley) is 66.52 km, while according to topographical map from 1970 it was 67.5 km, and according to topographical map from 1925 it was 87.6 km.

After cut off meander, the riverbed itself morphologically adjusts to the new state [29]. Morphological changes of the rivers are reflected in digging the concave river banks and sediment accumulation on the convex river banks.

### **5.2. Land use changes**

As we earlier indicated, river erosion and frequent floods can make great material damages to people, villages and economy. Since the lateral erosion has more intensity, the river banks on concave side of the Kolubara River often collapse and farmers who have arable land parcels on the river bank (in the area of three villages the Kolubara flows through) loose the parts of the parcels which were carried away by the river. Based on Cadastral maps from 1967 and orthophoto images from 2004 we have estimated the area of diminished land parcels and their land loss.

10 Environmental Land Use Planning

parts of the riverbed, on the other hand.

**5.1. Forming of meanders** 

meanders.

**5. The consequences of bank erosion** 

morphologically was not predisposed for kinetic energy of stronger flow, bank erosion became a dominating geomorphological process in the area and initiated processes of digging the riverbanks, transportation and deposition of eroded material. It is obvious that river system changes in lower part of the Kolubara River are demonstrated in domination of fluvial (lateral) erosion on one hand, and in cutting the meanders and fossilization of certain

Map of the Kolubara's basin first trend of relief energy [2] shows that almost whole area of the Donjokolubarska valley is under tectonic movements of slowly sinking. For this reason the sediments are accumulated in the riverbed, river velocity decreases which cause the riverbed meandering and stronger bank erosion. This natural process became more intensive since the Kolubara River was diverted into the riverbed of Pestan. In the Donjokolubarski basin there are numerous sectors with abandoned riverbeds and cut off

Forming of meanders and cutting the "necks" are recent geomorphologic-hydrological process, which is dominated in the study area. According to results of the recent researches [13], there are 89 abandoned parts of the riverbeds and cut off meanders in the area of the Donjokolubarski basin. The Kolubara and its tributaries tend to move to the east because of the Kolubarsko-pestanski fault, which indicate more abandoned riverbeds and cut off

In the study area there are 40 cut off meanders with total length of 20.30 km while the number of abandoned parts of the riverbeds is 49 with total length of 76.03 km. Hence, the total length of all abandoned riverbeds and cut off meanders in the Donjokolubarski basin is 96.33 km, and their total surface is 3.35 km2. The longest cut off meander is 1.7 km long and

The length of the Kolubara's riverbed is influenced by stronger bank erosion and formation of meanders, which is clearly perceived in the field. According to orthophoto image from 2004 and satellite image (Google Earth) the Kolubara River length (in the Donjokolubarska valley) is 66.52 km, while according to topographical map from 1970 it was 67.5 km, and

After cut off meander, the riverbed itself morphologically adjusts to the new state [29]. Morphological changes of the rivers are reflected in digging the concave river banks and

As we earlier indicated, river erosion and frequent floods can make great material damages to people, villages and economy. Since the lateral erosion has more intensity, the river banks

meanders on the left side of the Kolubara valley (64), compared to the right side (25).

the shortest is 185.7 m long. The longest abandoned riverbed is 6.49 km long.

according to topographical map from 1925 it was 87.6 km.

sediment accumulation on the convex river banks.

**5.2. Land use changes** 

Farmers who have land parcels in three villages (Drazevac, Konatice and Poljane) on the Kolubara river bank cannot farm them in whole, because the river has changed its course and took some parts of the land parcels away. The cadastral maps of the researched area scale 1:2500 from 1967 and orthophoto images from 2004 were compared. Using the results of this comparative analysis, the evolution of the hydrological system in the period from 1967 to 2004 was presented. The previous research showed that 60.37 ha was lost and degraded by the river bank erosion, which means that the land loss is 50.57 % of the land parcels from 1967 [15].


**Table 1.** Land use structure in Drazevac, Konatice and Poljane.

On the basis of the recent and more accurate data from Obrenovac Municipality Cadastre we have determined land use structure of degraded land parcels on the Kolubara River banks. According to these data, total area of all 247 endangered land parcels was 148.3 ha in 1967, and 86.62 ha in 2004. Therefore, 61.68 ha of soil were lost within 37 years [13].

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 13

In Serbia there is 4.25 million ha of arable land, and each year 500000 ha (which means 11.74 %) of arable land remain uncultivated [30]. In the above mentioned three villages 33.47 % of arable land (on the Kolubara River banks) remain uncultivated, which is three times more than the average in Republic of Serbia. During the field work, the interviewed owners of endangered arable land parcels pointed that they do not farm their land on the river banks because of flood risks. The Kolubara River floods almost every year and crop is ruined. Therefore, besides the loss of arable land, frequent floods are huge problem in this area.

The economic consequences of bank erosion in the area of the Donjokolubarski basin could be analyzed through losses that the owners of endangered arable land parcels had (because the arable land parcels were reduced). The area of arable land (on the river banks) was diminished by 35.12 ha within 37 years. In the research area the average annual yield is 3-4 t per hectare, so the annual losses of crops (mostly wheat and corn) in recent years are

Changes of land use structure and changes in sediment regimes are the direct consequences of the bank erosion [22]. The calculation of one-day sediment load discharge at the monitored hydrological profile includes the values of mean daily flow (Q – m3/s) and the relevant concentration of the suspended load (C–mg/l). The assessment of sediment deposition rate is based on the results of RHMSS [31] measurements and the results of own daily measurements of suspended load concentration during the period (1985-2004). The results show that 193253.8 tons of material was accumulated between two hydrological profiles. And the riverbed itself was raised for 36 cm, which is nine times enlarged comparing to previous research when it was raised for 4.2 cm (with a shorter time series) [2]. The extraction of the river deposited sediments from the Kolubara's riverbed was stopped. Although the river deposits were hand extracted with low intensity, it certainly had great positive effects from the aspect of maintaining the surface of riverbed profile. Simple solutions, like the river deposit extraction, do not need huge investments for the implementation and

REIK Kolubara has a negative ecological impact on the Donjokolubarska valley. There are lots of waste waters after the ore production. Waste waters from the mine "REIK Kolubara" are discharged without any treatment into the Kolubara River. Therefore, the Kolubara River contains waste waters from the mine and after each flood the soil on the river banks is contaminated by substances from the waste waters. The results of soil analysis in the Kolubara river basin show increased concentration of nickel, arsenic and lead in the area of

The eroded material from the river banks is accumulated downstream. The accumulated sediments can contain considerable concentrations of heavy metals and that is threat for the

Ecological aspects of mechanical water pollution by suspended sediment, chemical water pollution by organic and mineral fertilizers used in plant production in the catchment,

they can be carried out without limitation of the natural conditions.

between 100 and 140 t per year.

**5.3. Sediment load discharge** 

the Donjokolubarski basin [18].

aquatic habitats and for the people [8, 32-34].

**Figure 4.** Land use changes in total area (left) and area of arable land (right) in Drazevac, Konatice and Poljane between 1967 and 2004

**Figure 5.** Land use changes in area of woods (left) and area of pastures (right) in Drazevac, Konatice and Poljane between 1967 and 2004

From 247 endangered land parcels, 136 are arable land with the area of 111.6 ha in 1967, and 76.48 ha in 2004, which means that within 37 years 35.12 ha of arable land was lost for farming, and it is 31.5 % of the initial area (in 1967). The woods comprise 21 of all endangered land parcels with area of 6.87 ha in 1967, and 3.01 ha in 2004, which means that it has been lost 3.86 ha of woods. There are only the three endangered land parcels with pastures, and their area was 1.27 ha in 1967, and 0.57 ha in 2004. All five endangered land parcels with meadows are in the area of Drazevac village.

Analyzing the area of endangered parts in the three villages, one can conclude that erosion was the most intensive in the period 1967-1981, when 50.9 ha of soil was lost within 14 years. The riverbed was stabilized later and the erosion decreased. This appoints to the fact that diverting the Kolubara River into the Pestan's riverbed caused more intensive bank erosion since in time erosion was diminished which brought to the riverbed stabilization.

Three villages on the Kolubara River banks (Drazevac, Konatice and Poljane) were characterized by agricultural production and agricultural population. Analyzing the land use structure of endangered parcels one can conclude that arable land parcels are the most endangered and degraded by intensified lateral erosion of Kolubara.

In Serbia there is 4.25 million ha of arable land, and each year 500000 ha (which means 11.74 %) of arable land remain uncultivated [30]. In the above mentioned three villages 33.47 % of arable land (on the Kolubara River banks) remain uncultivated, which is three times more than the average in Republic of Serbia. During the field work, the interviewed owners of endangered arable land parcels pointed that they do not farm their land on the river banks because of flood risks. The Kolubara River floods almost every year and crop is ruined. Therefore, besides the loss of arable land, frequent floods are huge problem in this area.

The economic consequences of bank erosion in the area of the Donjokolubarski basin could be analyzed through losses that the owners of endangered arable land parcels had (because the arable land parcels were reduced). The area of arable land (on the river banks) was diminished by 35.12 ha within 37 years. In the research area the average annual yield is 3-4 t per hectare, so the annual losses of crops (mostly wheat and corn) in recent years are between 100 and 140 t per year.

## **5.3. Sediment load discharge**

12 Environmental Land Use Planning

Poljane between 1967 and 2004

and Poljane between 1967 and 2004

parcels with meadows are in the area of Drazevac village.

endangered and degraded by intensified lateral erosion of Kolubara.

**Figure 4.** Land use changes in total area (left) and area of arable land (right) in Drazevac, Konatice and

**Figure 5.** Land use changes in area of woods (left) and area of pastures (right) in Drazevac, Konatice

From 247 endangered land parcels, 136 are arable land with the area of 111.6 ha in 1967, and 76.48 ha in 2004, which means that within 37 years 35.12 ha of arable land was lost for farming, and it is 31.5 % of the initial area (in 1967). The woods comprise 21 of all endangered land parcels with area of 6.87 ha in 1967, and 3.01 ha in 2004, which means that it has been lost 3.86 ha of woods. There are only the three endangered land parcels with pastures, and their area was 1.27 ha in 1967, and 0.57 ha in 2004. All five endangered land

Analyzing the area of endangered parts in the three villages, one can conclude that erosion was the most intensive in the period 1967-1981, when 50.9 ha of soil was lost within 14 years. The riverbed was stabilized later and the erosion decreased. This appoints to the fact that diverting the Kolubara River into the Pestan's riverbed caused more intensive bank erosion since in time erosion was diminished which brought to the riverbed stabilization.

Three villages on the Kolubara River banks (Drazevac, Konatice and Poljane) were characterized by agricultural production and agricultural population. Analyzing the land use structure of endangered parcels one can conclude that arable land parcels are the most Changes of land use structure and changes in sediment regimes are the direct consequences of the bank erosion [22]. The calculation of one-day sediment load discharge at the monitored hydrological profile includes the values of mean daily flow (Q – m3/s) and the relevant concentration of the suspended load (C–mg/l). The assessment of sediment deposition rate is based on the results of RHMSS [31] measurements and the results of own daily measurements of suspended load concentration during the period (1985-2004). The results show that 193253.8 tons of material was accumulated between two hydrological profiles. And the riverbed itself was raised for 36 cm, which is nine times enlarged comparing to previous research when it was raised for 4.2 cm (with a shorter time series) [2]. The extraction of the river deposited sediments from the Kolubara's riverbed was stopped. Although the river deposits were hand extracted with low intensity, it certainly had great positive effects from the aspect of maintaining the surface of riverbed profile. Simple solutions, like the river deposit extraction, do not need huge investments for the implementation and they can be carried out without limitation of the natural conditions.

REIK Kolubara has a negative ecological impact on the Donjokolubarska valley. There are lots of waste waters after the ore production. Waste waters from the mine "REIK Kolubara" are discharged without any treatment into the Kolubara River. Therefore, the Kolubara River contains waste waters from the mine and after each flood the soil on the river banks is contaminated by substances from the waste waters. The results of soil analysis in the Kolubara river basin show increased concentration of nickel, arsenic and lead in the area of the Donjokolubarski basin [18].

The eroded material from the river banks is accumulated downstream. The accumulated sediments can contain considerable concentrations of heavy metals and that is threat for the aquatic habitats and for the people [8, 32-34].

Ecological aspects of mechanical water pollution by suspended sediment, chemical water pollution by organic and mineral fertilizers used in plant production in the catchment,

nutrients found in the soil as well as chemical pollution of water and sediment by pesticides and heavy metals are very important ecological problem in the study area. On two locations we have sampled the accumulated material from the Kolubara's riverbed to examine the transport of contamination and accumulation of contaminated sediments due to bank erosion processes.

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 15

mg.kg-1 for Cd; 36 - 120 mg.kg-1 for Cu; 123 - 1050 mg.kg-1 for Zn; 10 - 180 mg.kg-1 for Ni and 37 - 120 mg.kg-1 for Cr. These ranges are bigger then estimated ecotoxic criteria [35], which are: 5 - 50 mg.kg-1 for Pb, Ni i Cu; 0.1 – 1.0 mg.kg-1 for Cd; 50 - 500 mg.kg-1 for Zn; 10 - 100

**Sample Zn Cu Pb Cd Cr Ni** 

**Location 1** 39.3 15.9 23.0 0.0 94.0 198.3 **Location 2** 41.0 20.2 27.9 0.1 103.0 210.9

Respecting the above mentioned criteria, mean measured concentration of Pb, Zn and Cr are within the limits (after de Vries and Bakker [35]), while concentration of Cu and Cd are below the limits and concentration of Ni are above the limits. According to OSPAR limitation values [36], average concentrations of Pb, Cu and Cd are below the limits, average concentrations of Cr and Zn are within the limits, while average concentrations of Ni are

The Kolubara River is a good example which represents the existence of all conditions for frequent and large scale floods. As an indirect consequence of the anthropogenic influence on the hydrological system in the lower part of the Kolubara valley, once a year (sometimes twice a year) the Kolubara River overflows, and the area of lower part of the Kolubara River basin is endangered by floods. Catastrophic floods of the Kolubara River and its tributaries spread over the area of lower part of the Kolubara River basin during the spring of 1937, and they lasted two months approximately (from March to May). In this area large scale floods also happened in 1965, 1975, 1981, 1996, 1998, 1999, 2001, 2004, 2006, 2008 and 2010.

The highest discharge of the Kolubara River in the period of 1959-2000 was 646 m3/s and it was registered on Drazevac hydrological station. According to probability curve of high discharges the discharge of 646 m3/s may occur once in a 46 years. The lowest value of annual maximum discharge would be about 25 m3/s, the highest discharge in a hundred years would be 740 m3/s, and the highest discharge in a thousand years would be 960 m3/s. During the first decade of XXI century almost every two years the flood wave was bigger than the biggest one which occurs once in a fifty years. Huge flood waves were occurred in 2001, 2004, 2006, 2008 and 2010. The last flood in December 2010 had already reached the maximum value which occurs once in a hundred years (according to probability calculation (until and including) year of 2000)). Since the floods are directly and indirectly related to bank erosion these data should be included in bank erosion analysis because their analogy is proved, although there is no quantification of their correlation. Therefore, researches should be focused on causes of floods, and on reduction of bank erosion uncertainties. Many factors that influence the Kolubara River floods are already known. Firstly, there is a difference in

**Table 2.** Heavy metal contents in the deposited sediment load.

**mg.kg-1**

mg.kg-1 for Cr.

above the limits.

**5.4. Floods** 

**Figure 6.** Distribution of Ni (left) and As (right) in the soil of the Donjokolubarski basin [18].

**Figure 7.** Sampling of deposited sediments on location 1 (left) and 2 (right)

The deposited sediments have sandy-clay texture. Chemical characteristics of deposited sediments from Kolubara's riverbed are: mildly alkaline reaction, high bases saturation degree and low humus content. The average heavy metal concentration in sediments decreased in the order: Ni > Cr > Zn > Pb > Cu > Cd.

In Serbia there is no law defining limitation of heavy metals in suspended sediments. Some European countries have such laws [34], but the differences between countries are significant. In most of the cases the critical values are obtained using the equilibrium method and maximum acceptable concentration (MAC) for the surface waters with regard to direct and indirect effects on living organisms in the water-sediments systems. According to these data, the range for different elements is as follows: 15 - 100 mg.kg-1 for Pb; 0.6 – 2.4 mg.kg-1 for Cd; 36 - 120 mg.kg-1 for Cu; 123 - 1050 mg.kg-1 for Zn; 10 - 180 mg.kg-1 for Ni and 37 - 120 mg.kg-1 for Cr. These ranges are bigger then estimated ecotoxic criteria [35], which are: 5 - 50 mg.kg-1 for Pb, Ni i Cu; 0.1 – 1.0 mg.kg-1 for Cd; 50 - 500 mg.kg-1 for Zn; 10 - 100 mg.kg-1 for Cr.


**Table 2.** Heavy metal contents in the deposited sediment load.

Respecting the above mentioned criteria, mean measured concentration of Pb, Zn and Cr are within the limits (after de Vries and Bakker [35]), while concentration of Cu and Cd are below the limits and concentration of Ni are above the limits. According to OSPAR limitation values [36], average concentrations of Pb, Cu and Cd are below the limits, average concentrations of Cr and Zn are within the limits, while average concentrations of Ni are above the limits.

### **5.4. Floods**

14 Environmental Land Use Planning

erosion processes.

nutrients found in the soil as well as chemical pollution of water and sediment by pesticides and heavy metals are very important ecological problem in the study area. On two locations we have sampled the accumulated material from the Kolubara's riverbed to examine the transport of contamination and accumulation of contaminated sediments due to bank

**Figure 6.** Distribution of Ni (left) and As (right) in the soil of the Donjokolubarski basin [18].

**Figure 7.** Sampling of deposited sediments on location 1 (left) and 2 (right)

decreased in the order: Ni > Cr > Zn > Pb > Cu > Cd.

The deposited sediments have sandy-clay texture. Chemical characteristics of deposited sediments from Kolubara's riverbed are: mildly alkaline reaction, high bases saturation degree and low humus content. The average heavy metal concentration in sediments

In Serbia there is no law defining limitation of heavy metals in suspended sediments. Some European countries have such laws [34], but the differences between countries are significant. In most of the cases the critical values are obtained using the equilibrium method and maximum acceptable concentration (MAC) for the surface waters with regard to direct and indirect effects on living organisms in the water-sediments systems. According to these data, the range for different elements is as follows: 15 - 100 mg.kg-1 for Pb; 0.6 – 2.4 The Kolubara River is a good example which represents the existence of all conditions for frequent and large scale floods. As an indirect consequence of the anthropogenic influence on the hydrological system in the lower part of the Kolubara valley, once a year (sometimes twice a year) the Kolubara River overflows, and the area of lower part of the Kolubara River basin is endangered by floods. Catastrophic floods of the Kolubara River and its tributaries spread over the area of lower part of the Kolubara River basin during the spring of 1937, and they lasted two months approximately (from March to May). In this area large scale floods also happened in 1965, 1975, 1981, 1996, 1998, 1999, 2001, 2004, 2006, 2008 and 2010.

The highest discharge of the Kolubara River in the period of 1959-2000 was 646 m3/s and it was registered on Drazevac hydrological station. According to probability curve of high discharges the discharge of 646 m3/s may occur once in a 46 years. The lowest value of annual maximum discharge would be about 25 m3/s, the highest discharge in a hundred years would be 740 m3/s, and the highest discharge in a thousand years would be 960 m3/s. During the first decade of XXI century almost every two years the flood wave was bigger than the biggest one which occurs once in a fifty years. Huge flood waves were occurred in 2001, 2004, 2006, 2008 and 2010. The last flood in December 2010 had already reached the maximum value which occurs once in a hundred years (according to probability calculation (until and including) year of 2000)). Since the floods are directly and indirectly related to bank erosion these data should be included in bank erosion analysis because their analogy is proved, although there is no quantification of their correlation. Therefore, researches should be focused on causes of floods, and on reduction of bank erosion uncertainties. Many factors that influence the Kolubara River floods are already known. Firstly, there is a difference in

flows in the upper and lower part of the Kolubara River basin. The drainage conditions in the upper part are more favorable. The area of hydrological profile Slovac is less than 1/3 of the whole basin, but it drains a half of all waters in the Kolubara River basin. Downstream hydrological profile Beli Brod encompasses a half of the basin, and its discharge is 3/4 of Kolubara's discharge. In the Donjokolubarski basin the drainage conditions are different, and the most significant factor is slope (the slope of the river flow and the slope of the river basin). The distance between Beli Brod and the Kolubara's confluence with the Sava River is 50 km and the altitude difference is 20 m. The present slope of 40 cm/km (0.4 ‰) is declining every year due to intensive sediment accumulation in the riverbed. Relating these processes with the shape of the river basin and rapid concentration of water downstream of the Beli Brod, it does not surprise that Kolubara River "ramp" over its alluvial plain. Moreover, in the last decade there is simultaneity of frequent rains of high intensity with extended duration and sudden snowmelt, and that is the reason for increased concern. Considering that rivers in the sub basins are mostly torrential, this concern is even more enhanced. Additionally, in this area rivers were diverted to bring the economic benefits. Because of all these reasons, the life in the coastal zone of the Kolubara River basin is gloomy but real with lot of uncertainties.

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 17

Hydrological network of the Donjokolubarski basin is constantly changing due to natural factors and anthropogenic impacts. The damage which is done cannot be compensated, but even worse is the fact that no one feels responsible and that the population in this area is still left to the mercy of torrential river. Numerous calls for helping endangered people and goods were sent to the different addresses, but no one tried to help. Apparently, the problem goes beyond the "values" of a few villages and the state interest (lignite exploitation) has absolute priority, like in the case of neighboring Dubrava and unique sources of Obrenovac Municipality [34]. This situation lasted till the catastrophic floods in June 2010, when the shocking images of flood damage terrified the publicity, and problem could not be ignored anymore. As an attempt to repair the flood consequences, during 2010 two dikes were constructed with the length of 200 m in total. The first location was repaired for bridge protection, and the second one for household protection. The total cost of construction works was 100 000 euros. Since, the total length of all degraded river banks of first category is about 5 km; the economic profitability of this repairing method is questioned. It made sense in the initial phase of degradation, but now it goes beyond the reality of existing situation. It seems that, after the construction of two dikes, somebody tries to justify the negligence, because it is obvious that these two dykes are insufficient to solve the problem. In cases like this one, even not doing anything for protection of degraded areas represents a serious violation of principles of sustainable management of natural resources, actually that is an offence. The responsible for

effects of the changes in the Kolubara River basin is still unknown, is it nature or man?

fear from hazards.

responsible ones in finding the solution.

Making the constant pressure on state institutions through various appeals, indicating to unsustainability of current situation and stand by position of constant fear, this paper is one of many attempts to help the endangered population. In this context, the monitoring of the Kolubara River in the Donjokolubarski basin is a logical solution and our contribution, with particular results and recommendations, to fight for the basic human right to live without

What kind of message can be sent to people living in this area and dealing with above mentioned problems? As they say, finding that the state does not protect them from the problems that come upon them, they give up farming the parcels of endangered area (along the river). The even more irrational, is the fact that they still pay taxes on the parcels, which does not exist anymore or they are significantly reduced, because the taxes calculation is made according to Cadaster from 1967! The estimation of all unnecessary loss of land, land values, personal losses of individuals and damage done to whole community is in the course. At the time when the personal status is far beyond collective responsibility due to difficult economic situation, this scientific approach is the only way to inspire the

This research could be the warning for the future anthropogenic activities on the river system since the new changes on the hydrological network were planned in this area. The four new mining fields should be opened, and if it happens, the hydrological network will

be changed again and new problems will appear in the river basin.

In order to prevent the frequent floods there are a several plans to deal with the actual situation in the area. Construction of several small accumulations on the Kolubara's tributaries is at its first phase, but there is no indication for solving the existing water problems. There is an idea to channelize the Kolubara's riverbed for sailing (i.e. for the transportation of lignite from the Kolubara mine), but it is still in the early phase of planning, although the initiative appeared long time ago. The height difference between the Kolubara's River confluence with Sava River and the location of lignite exploitation in the Kolubara mine basin is 23 m [12]. This height difference and the wideness of the Kolubara's riverbed would facilitate its riverbed training works, enabling cheaper lignite transportation from the Kolubara mine basin. Training works the Kolubara's riverbed and its preparation for lignite transportation could easily be carried out, so the invested means would be economically justified. Thus, the meandering flow would be straightened, and the strong bank erosion in the riverbed would be regulated which means that some factors of flooding would be eliminated.

## **6. Conclusion**

Bank erosion, soil loss, sediment load deposition, changes in the river course, floods, landslides, soil and water pollution are the major environmental problems in the Kolubara River basin which could be aggravated by the land-use changes. The solutions for all mentioned environmental problems demand a complex analysis of the area characteristics and development of the strategy for solving the existing water problems in this area, but in the same time they have to provide necessary conditions for the further lignite exploitation. Some villages are located in the lower part of the Kolubara River basin, in the area which is planned for the expansion of the Kolubara mining basin, so it is an important factor for the future sustainable landscape planning.

Hydrological network of the Donjokolubarski basin is constantly changing due to natural factors and anthropogenic impacts. The damage which is done cannot be compensated, but even worse is the fact that no one feels responsible and that the population in this area is still left to the mercy of torrential river. Numerous calls for helping endangered people and goods were sent to the different addresses, but no one tried to help. Apparently, the problem goes beyond the "values" of a few villages and the state interest (lignite exploitation) has absolute priority, like in the case of neighboring Dubrava and unique sources of Obrenovac Municipality [34]. This situation lasted till the catastrophic floods in June 2010, when the shocking images of flood damage terrified the publicity, and problem could not be ignored anymore. As an attempt to repair the flood consequences, during 2010 two dikes were constructed with the length of 200 m in total. The first location was repaired for bridge protection, and the second one for household protection. The total cost of construction works was 100 000 euros. Since, the total length of all degraded river banks of first category is about 5 km; the economic profitability of this repairing method is questioned. It made sense in the initial phase of degradation, but now it goes beyond the reality of existing situation. It seems that, after the construction of two dikes, somebody tries to justify the negligence, because it is obvious that these two dykes are insufficient to solve the problem. In cases like this one, even not doing anything for protection of degraded areas represents a serious violation of principles of sustainable management of natural resources, actually that is an offence. The responsible for effects of the changes in the Kolubara River basin is still unknown, is it nature or man?

16 Environmental Land Use Planning

would be eliminated.

future sustainable landscape planning.

**6. Conclusion** 

flows in the upper and lower part of the Kolubara River basin. The drainage conditions in the upper part are more favorable. The area of hydrological profile Slovac is less than 1/3 of the whole basin, but it drains a half of all waters in the Kolubara River basin. Downstream hydrological profile Beli Brod encompasses a half of the basin, and its discharge is 3/4 of Kolubara's discharge. In the Donjokolubarski basin the drainage conditions are different, and the most significant factor is slope (the slope of the river flow and the slope of the river basin). The distance between Beli Brod and the Kolubara's confluence with the Sava River is 50 km and the altitude difference is 20 m. The present slope of 40 cm/km (0.4 ‰) is declining every year due to intensive sediment accumulation in the riverbed. Relating these processes with the shape of the river basin and rapid concentration of water downstream of the Beli Brod, it does not surprise that Kolubara River "ramp" over its alluvial plain. Moreover, in the last decade there is simultaneity of frequent rains of high intensity with extended duration and sudden snowmelt, and that is the reason for increased concern. Considering that rivers in the sub basins are mostly torrential, this concern is even more enhanced. Additionally, in this area rivers were diverted to bring the economic benefits. Because of all these reasons, the life in the

coastal zone of the Kolubara River basin is gloomy but real with lot of uncertainties.

In order to prevent the frequent floods there are a several plans to deal with the actual situation in the area. Construction of several small accumulations on the Kolubara's tributaries is at its first phase, but there is no indication for solving the existing water problems. There is an idea to channelize the Kolubara's riverbed for sailing (i.e. for the transportation of lignite from the Kolubara mine), but it is still in the early phase of planning, although the initiative appeared long time ago. The height difference between the Kolubara's River confluence with Sava River and the location of lignite exploitation in the Kolubara mine basin is 23 m [12]. This height difference and the wideness of the Kolubara's riverbed would facilitate its riverbed training works, enabling cheaper lignite transportation from the Kolubara mine basin. Training works the Kolubara's riverbed and its preparation for lignite transportation could easily be carried out, so the invested means would be economically justified. Thus, the meandering flow would be straightened, and the strong bank erosion in the riverbed would be regulated which means that some factors of flooding

Bank erosion, soil loss, sediment load deposition, changes in the river course, floods, landslides, soil and water pollution are the major environmental problems in the Kolubara River basin which could be aggravated by the land-use changes. The solutions for all mentioned environmental problems demand a complex analysis of the area characteristics and development of the strategy for solving the existing water problems in this area, but in the same time they have to provide necessary conditions for the further lignite exploitation. Some villages are located in the lower part of the Kolubara River basin, in the area which is planned for the expansion of the Kolubara mining basin, so it is an important factor for the Making the constant pressure on state institutions through various appeals, indicating to unsustainability of current situation and stand by position of constant fear, this paper is one of many attempts to help the endangered population. In this context, the monitoring of the Kolubara River in the Donjokolubarski basin is a logical solution and our contribution, with particular results and recommendations, to fight for the basic human right to live without fear from hazards.

What kind of message can be sent to people living in this area and dealing with above mentioned problems? As they say, finding that the state does not protect them from the problems that come upon them, they give up farming the parcels of endangered area (along the river). The even more irrational, is the fact that they still pay taxes on the parcels, which does not exist anymore or they are significantly reduced, because the taxes calculation is made according to Cadaster from 1967! The estimation of all unnecessary loss of land, land values, personal losses of individuals and damage done to whole community is in the course. At the time when the personal status is far beyond collective responsibility due to difficult economic situation, this scientific approach is the only way to inspire the responsible ones in finding the solution.

This research could be the warning for the future anthropogenic activities on the river system since the new changes on the hydrological network were planned in this area. The four new mining fields should be opened, and if it happens, the hydrological network will be changed again and new problems will appear in the river basin.

## **Author details**

Slavoljub Dragicevic\* , Nenad Zivkovic, Mirjana Roksandic and Ivan Novkovic *University of Belgrade, Faculty of Geography, Belgrade, Serbia* 

Land Use Changes and Environmental Problems

Caused by Bank Erosion: A Case Study of the Kolubara River Basin in Serbia 19

[6] Blanka V, Kiss T (2011) Effect of different water stages on bank erosion, case study of river Hernad, Hungary. Carpathian Journal of Earth and Environmental Sciences*.* 6(2):

[7] Milevski I (2011) Factors, Forms, Assessment and Human Impact on Excess Erosion and Deposition in Upper Bregalnica Watershed (Republic of Macedonia). In: Human Impact on Landscape, Eds. S. Harnischmachter and D. Loczy. Zeitschrift für

[8] Chen J, Chen J Z, Tan M Z, Gong Z T (2002) Soil degradation: a global problem endangering sustainable development. Journal of Geographical Sciences, 12(2): 243-252. [9] Goudie A (2006) The human impact on the natural environment: past, present and

[10] Li L, Lu X, Chen Z (2007) River channel change during the last 50 years in the middle

[11] Denes L (2010) Anthropogenic Geomorphology in Environmental Management. In: Anthropogenic Geomorphology - A Guide to Man-Made Landforms. Eds. József S.,

[12] Dragićević S, Živković N, Ducić V (2007) Factors of flooding on the territory of the Obrenovac municipality. Collection of the papers, Faculty of Geography, Belgrade, 55:

[13] Roksandić M (2012) Causes and consequences of changes of hydrographic network in Donjokolubarski basin. Unpublished PhD thesis, University of Belgrade, Faculty of

[14] Dragićević S, Živković N, Kostadinov S (2008a) Changes of hydrological system in the lower course of the Kolubara river. In proceedings of the XXIV Conference of the Danubian countries on the hydrological forecasting and hydrological bases of water

[15] Roksandić M, Dragićević S, Živković N, Kostadinov S, Zlatić M, Martinović M (2011) Bank erosion as a factor of soil loss and land use changes in the Kolubara river basin, Serbia. African journal of agricultural research, 6(32): 6604-6608, DOI:

[16] Dragićević S, (2002) Sediment Load balance in the Kolubara basin. Faculty of Geo-

[17] Dragićević S, Stepić M, Karić M (2008b) Natural potentials and degraded areas of Obrenovac municipality*.* Jantar groupe, Belgrade. pp 1-180. (in Serbian with summary

[18] Dragićević S, Živković N, Novković I (2011) Preparation of numerical and spatial data basis for the assessment of land and water diffuse pollution in the Kolubara River basin. Ministry of Environment, Mining and Spatial Planning, Environmental Protection

[19] Hooke J, Redmond C E (1989) River-channel changes in England and Wales. Journal

[20] Large R G A, Petts E G (1996) Historical channel-floodplain dynamics along the River Trent, Implications for river rehabilitation. Applied Geography, 16(3): 191-209.

Geomorphologie, Vol. 55, Suplementary issue 1, Stuttgart, p. 77-94

future. Blackwell Publishing, USA, sixth edition, p. 357.

Lóránt D., Dénes L., Springer, p .25-38

management, Bled, Slovenia.

Agency, Beograd (in Serbian)

10.5897/AJAR11.736

in English)

Yangtze River, the Jianli reach. Geomorpholgy. 85: 185-196.

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graphy, Belgrade. p. 184 (in Serbian with summary in English)

of *Institution* of Water and *Environmental Management,* 3: 328-335.

101–108.

39-54.

Stanimir Kostadinov *University of Belgrade, Faculty of Forestry, Belgrade, Serbia* 

Radislav Tosic *Faculty of Natural Sciences, Banja Luka, Republic of Srpska* 

Milomir Stepic *Institute for Political Studies,Belgrade, Serbia* 

Marija Dragicevic *First Elementary School in Obrenovac, Obrenovac, Serbia* 

Borislava Blagojevic *University of Nis, Faculty of Civil Engineering and Architecture, Serbia* 

## **Acknowledgement**

This paper was realized as a part of the projects "Studying climate change and its influence on the environment: impacts, adaptation and mitigation" (43007) and "The Democratic and National Capacities of Serbia's Institutions in the Process of International Integrations" (179009) financed by the Ministry of Education and Science of the Republic of Serbia within the framework of integrated and interdisciplinary research for the period 2011-2014. Translation and language correction was performed by Ljiljana Stanarevic.

## **7. References**


<sup>\*</sup> Corresponding Author

[6] Blanka V, Kiss T (2011) Effect of different water stages on bank erosion, case study of river Hernad, Hungary. Carpathian Journal of Earth and Environmental Sciences*.* 6(2): 101–108.

18 Environmental Land Use Planning

**Author details** 

Slavoljub Dragicevic\*

Stanimir Kostadinov

Radislav Tosic

Milomir Stepic

Marija Dragicevic

Borislava Blagojevic

**Acknowledgement** 

**7. References** 

 \*

Corresponding Author

Serbian with English abstract)

*University of Belgrade, Faculty of Geography, Belgrade, Serbia* 

*University of Belgrade, Faculty of Forestry, Belgrade, Serbia* 

*Faculty of Natural Sciences, Banja Luka, Republic of Srpska* 

*First Elementary School in Obrenovac, Obrenovac, Serbia* 

*University of Nis, Faculty of Civil Engineering and Architecture, Serbia* 

Translation and language correction was performed by Ljiljana Stanarevic.

*Institute for Political Studies,Belgrade, Serbia* 

, Nenad Zivkovic, Mirjana Roksandic and Ivan Novkovic

This paper was realized as a part of the projects "Studying climate change and its influence on the environment: impacts, adaptation and mitigation" (43007) and "The Democratic and National Capacities of Serbia's Institutions in the Process of International Integrations" (179009) financed by the Ministry of Education and Science of the Republic of Serbia within the framework of integrated and interdisciplinary research for the period 2011-2014.

[1] Dragićević S, Stepić M, (2006) Changes of the erosion intensity in the Ljig River basin – the influence of the antropogenic factor. Bull. Serbian Geogr. Soc*.* 85(2): 37-44 (in

[2] Dragićević S, (2007) Dominant Processes of Erosion in the Kolubara Basin. Faculty of Geography, Belgrade: Jantar groupe, p. 1-245 (in Serbian with summary in English). [3] Dragićević S, Carević I, Kostadinov S, Novković I, Abolmasov B, Milojković B, Simić D (2012) Landslide susceptibility zonation in the Kolubara river basin (western Serbia) analisys of input data. Carpathian Journal of Earth and Environmental Sciences 7(2): 37–47. [4] Dragićević S, Milevski I, (2010) Human Impact on the Landscape – Examples from Serbia and Macedonia. Advances in GeoEcology, no41, Global Change – Challenges for soil management (Editor M. Zlatic), CATENA VERLAG GMBH, Germany. pp. 298-309. [5] Tošić R (2006) Soil erosion in the catchment Ukrina. Geographic Society of the Republic of Srpska, Banja Luka. Special issue, No. 13, p. 150 (in Serbian with summary in English)


[21] Weng Q (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. Journal of Environmental Management*,* 3: 273-284.

**Chapter 2** 

© 2012 Eni, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Effects of Land Degradation on Soil Fertility:** 

Land degradation is a concept in which the value of the biophysical environment is affected by one or more combination of human induced processes acting upon the land. It literally refers to the impairment of natural quality of soil component of any ecosystem. Land degradation which is also seen as a decline in land quality caused by human activities, has been a major global issue since the 20th century and it has remained high on the international agenda in the 21st century. The importance of land degradation in Calabar South is enhanced because of its impact on food security and quality of the environments. The map of the

Land degradation can be viewed as any change or disturbance to the land perceived to be deleterious or undesirable (Eswaran, 2001). In the study area, the researcher observed loss of the biological and economic productivity and complexity of rain-fed cropland, irrigated cropland, range, forest and woodlands resulting from land uses or from a combination of processes arising from human activities and habitation patterns such as soil erosion caused by wind or water, deterioration of the physical, chemical, biological and economic properties of soil and long-term loss of natural vegetation. But there are also off-site effects,

Natural hazards are excluded as a cause of land degradation in Calabar South, however

Research has shown that up to 60% of agricultural land in Calabar South is seriously degraded. Furthermore, the main outcome of land degradation is a substantial reduction in

The major causes of land degradation include, land clearance poor farming practices, overgrazing, inappropriate irrigation, urban sprawl, and commercial development, land

and reproduction in any medium, provided the original work is properly cited.

such as loss of watershed functions which is a major problem in Calabar South.

human activities can indirectly affect phenomena such as floods and bush fires.

**A Case Study of Calabar South, Nigeria** 

Additional information is available at the end of the chapter

Imoke Eni

http://dx.doi.org/10.5772/51483

study area is presented on the next page.

the productivity of the land as shown in figure 2

**1. Introduction** 


## **Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria**

Imoke Eni

20 Environmental Land Use Planning

273-284.

96-110.

Belgrade (in Serbian).

www. rbkolubara.rs

hydrology, 321: 59-76.

InTech. pp. 283-300.

904426-52-2

Engineering Geology 55: 167-172.

Industry. 37(1): 87-99 (in Serbian)

[21] Weng Q (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. Journal of Environmental Management*,* 3:

[22] Kiss T, Fiala K, Sipos G (2008) Alterations of channel parameters in response to river regulation works since 1840 on the Lower Tisza River (Hungary). Geomorphology, 98:

[23] De Rose R C, Basher L R (2011) Measurement of river bank and cliff erosion from sequential LIDAR and historical aerial photography*.* Geomorphology, 126: 132-147.

[25] Dukić D (1974) Kolubara's regime and water management problems in its river basin. Bulletin of Serbian Academy of Science and Arts, Department of Science, vol. 36,

[26] Surian N, Rinaldi M (2003) Morphological response to river engineering and

[27] Youdeowei P O (1997) Bank collapse and erosion at the upper reaches of the Ekole creek in teh Niger delta area of Nigeria. Bulletin of the International Association of

[28] \*\*\*Diverting of Kolubara River: http://www.neshvyl.com/doc/prica\_o\_kolubari.pdf

[29] Hooke J (1995) River channel adjustment to meander cutoffs on the River Bollin and

[30] Pejanović R, Njegovan Z (2009) Actual problems of Serbian agriculture and villages.

[31] Republic Hydro Meteorological Service of Serbia. Values of precipitation, water

[32] Chen D, Duan J G (2006) Modeling with adjustment in meandring channels. Journal of

[33] Dragićević S, Nenadović S, Jovanović B, Milanović M, Novković I, Pavić D, Lješević M (2010*)* Degradation of Topciderska River water quality (Belgrade). Carpathian Journal

[34] Zivkovic N, Dragicevic S, Brceski I, Ristic R, Novkovic I, Jovanovic S, Djokic M, Simic S (2012) Groundwater Quality Degradation in Obrenovac Municipality, Serbia. In: K. Voudouris and D. Voutsa (Eds.), Water Quality Monitoring and Assessment, Rijeka:

[35] De Vries W, Bakker D J (1998) Manual for calculating critical loads of heavy metals for terestial ecosystems. Guidelines for critical limits, calculation methods and input data.

[36] OSPAR/ICES (2004) Workshop on the evaluation and update of background reference concentrations (B/RCs) and ecotoxicological assessment criteria (EACs) and how these assessment tools should be used in assessing contaminants in water, sediment and biota, 9–13 February 2004. The Hague, Final report, OSPAR Commission, ISBN 1-

discharge, sediment load concentration for Kolubara river basin (1961-2005).

management in alluvial channels in Italy. Geomorphology, 50: 307–326.

River Dane, northwest England. Geomorphology, 14: 235-253.

of Earth and Environmental Sciences, 5(2): 177 – 184

Wageningen, DLO Winand Staring Centre. Report 166. p. 144.

[24] Cadastral register of the Municipality of Obrenovac, 1967.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51483

## **1. Introduction**

Land degradation is a concept in which the value of the biophysical environment is affected by one or more combination of human induced processes acting upon the land. It literally refers to the impairment of natural quality of soil component of any ecosystem. Land degradation which is also seen as a decline in land quality caused by human activities, has been a major global issue since the 20th century and it has remained high on the international agenda in the 21st century. The importance of land degradation in Calabar South is enhanced because of its impact on food security and quality of the environments. The map of the study area is presented on the next page.

Land degradation can be viewed as any change or disturbance to the land perceived to be deleterious or undesirable (Eswaran, 2001). In the study area, the researcher observed loss of the biological and economic productivity and complexity of rain-fed cropland, irrigated cropland, range, forest and woodlands resulting from land uses or from a combination of processes arising from human activities and habitation patterns such as soil erosion caused by wind or water, deterioration of the physical, chemical, biological and economic properties of soil and long-term loss of natural vegetation. But there are also off-site effects, such as loss of watershed functions which is a major problem in Calabar South.

Natural hazards are excluded as a cause of land degradation in Calabar South, however human activities can indirectly affect phenomena such as floods and bush fires.

Research has shown that up to 60% of agricultural land in Calabar South is seriously degraded. Furthermore, the main outcome of land degradation is a substantial reduction in the productivity of the land as shown in figure 2

The major causes of land degradation include, land clearance poor farming practices, overgrazing, inappropriate irrigation, urban sprawl, and commercial development, land

pollution including industrial waste and quarrying of stone, sand and minerals. High population density is not necessarily related to land degradation within Calabar South, but it is what a population does to the land that determines the extent of degradation. In the study area, where a large proportion of human population depend almost entirely on land resources for their sustenance, this over dependency results in the increasing competing demand for land utilization such as grazing, fish pond construction, quarrying, crop farming amongst others.

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 23

The productivity of some land in Calabar South has declined by 60 percent as a result of soil erosion and nutrient loss (Bruinsma, 2003). Presently, reduction of land in Calabar due to past soil erosion range from 55-79% percent with a mean loss of 67%. If accelerated erosion continues unabated, yield reductions by 2020 may be 87%. Soil compaction is a general problem affecting some part of Calabar South especially in the adoption of mechanized agriculture. It has caused yield reduction of 35-60%. It is in the context of these global, economic and environmental impacts of land degradation on productivity in Calabar South

The study was done at 45 different farm lands to determine the present state of the soil or land, cause and effect relationship and the soil property that was highly degraded. Different varieties of crops planted at different locations were surveyed and their nutrient status measured. Soil auger was used in the collection of the soil samples between the depth of 0- 15cm for shallow and 15-30cm for sub-surface depths respectively. The physico-chemical parameters of the soil analyzed were ph, organic carbon, Nitrogen, Phosphorus, Exchangeable acidity, Cations exchange capacity and base saturation. The equipments listed

that resilience concepts are relevant, since land resources are exhaustible.

**2. Underlying reasons for land degradation in calabar south** 

below in table 1 were used in analyzing soil properties.

Exchangeable acidity Titration Method

**Table 1.** Soil Properties and Equipments

**SOIL PROPERTIES EQUIPMENTS FOR MEASUREMENT** PH Potentiometer using glass electrode(Bates, 1954)

Exchangeable Cations Atomic absorption spectrometer (AAs) Cation exchange capacity Titration using (Chapman, 1965)

Total Nitrogen Micro Kse/dahi Method(Bremer and Melvaney, 1982)

Base saturation Total exchangeable bases (Ca, Mg, K, Na) divided by their percentages. (Nssc 1995)

Organic Carbon Oxidation Method (Allison 1965)

**Figure 2.** Degraded agricultural land

**Figure 1.** Map of Calobar South Government Area showing

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 23

**Figure 2.** Degraded agricultural land

22 Environmental Land Use Planning

farming amongst others.

**Figure 1.** Map of Calobar South Government Area showing

pollution including industrial waste and quarrying of stone, sand and minerals. High population density is not necessarily related to land degradation within Calabar South, but it is what a population does to the land that determines the extent of degradation. In the study area, where a large proportion of human population depend almost entirely on land resources for their sustenance, this over dependency results in the increasing competing demand for land utilization such as grazing, fish pond construction, quarrying, crop

> The productivity of some land in Calabar South has declined by 60 percent as a result of soil erosion and nutrient loss (Bruinsma, 2003). Presently, reduction of land in Calabar due to past soil erosion range from 55-79% percent with a mean loss of 67%. If accelerated erosion continues unabated, yield reductions by 2020 may be 87%. Soil compaction is a general problem affecting some part of Calabar South especially in the adoption of mechanized agriculture. It has caused yield reduction of 35-60%. It is in the context of these global, economic and environmental impacts of land degradation on productivity in Calabar South that resilience concepts are relevant, since land resources are exhaustible.

## **2. Underlying reasons for land degradation in calabar south**

The study was done at 45 different farm lands to determine the present state of the soil or land, cause and effect relationship and the soil property that was highly degraded. Different varieties of crops planted at different locations were surveyed and their nutrient status measured. Soil auger was used in the collection of the soil samples between the depth of 0- 15cm for shallow and 15-30cm for sub-surface depths respectively. The physico-chemical parameters of the soil analyzed were ph, organic carbon, Nitrogen, Phosphorus, Exchangeable acidity, Cations exchange capacity and base saturation. The equipments listed below in table 1 were used in analyzing soil properties.


**Table 1.** Soil Properties and Equipments

Soil loss and runoff were measured at each study location and their respective cumulative yield calculated from the data obtained at the field. Runoff was calculated using the velocity area technique with the formula;

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 25

5.6 0.32

4.7 0.27

0.51

0.37

0.29 0.39

0.36 0.46

0.32

0.28 0.41

0.34 0.55

0.19 0.28

0.20 0.46

0.15 0.36

0.12 0.47

0.24 0.56

0.18 0.41

0.21 0.46

0.16 0.58

0.25 0.49

0.36 0.74 6.20 7.80

4.30 6.90

5.20 6.50

5.40 6.30

6.90 8.50

6.30 7.40

4.10 6.20

3.10 4.50

3.40 5.60

3.20 4.50

6.50 3.20

5.10 3.10

3.00 4.60

3.20 5.30

2.40 4.10

2.30 4.70

3.40 5.60 59 61

57 70

63 71

52 69

63 70

57 72

53 65

44 75

33 49

41 62

36 54

43 67

34 56

42 54

48 73

64 78

50 65

3.4

2.5

5.3

2.4 4.9

5.40 3.5 0.24

2.4 4.0

2.4 3.5

2.2 4.5

3.1 5.2

3.4 4.3

3.3 4.7

2.6 3.4

2.2 3.8

3.4 4.9

2.3 4.1

2.0 3.0

4.3 5.6

14. Okro 0-15

15. Vegetable 0-15

16. Spinach 0-15

18. Otazi 0-15

19. Afang 0-15

20. Etinkene 0-15

21. Garden Egg

22. Sugar cane

23. Scent leave

24. Curry leave

25. Ginger 0-15

26. Pineapple 0-15

27. Banana 0-15

29. Lettuce 0-15

30. Melon 0-15

28. Groundnu t

17. Bitter leave 15-30

15-30

15-30

0-15 15-30

15-30

15-30

15-30

0-15 15-30

0-15 15-30

0-15 15-30

0-15 15-30

15-30

15-30

15-30

0-15 15-30

15-30

15-30

2.6 4.5

6.9 7.5

4.3 5.0

5.2 6.2

4.9 7.6

6.5 8.7

3.5 5.3

4.3 6.9

4.6 6.9

3.4 6.4

3.2 4.9

0.6 6.8

3.9 6.7

4.9 8.3

6.3 7.2

5.2 6.9

4.4 5.7 0.49 0.60

0.39 0.65

0.51 0.63

0.42 0.54

0.34

0.26 0.50

0.41 0.59

0.38 076

0.42 0.66

0.34 0.84

0.34 0.76

0.42 0.69

0.34 0.75

0.41 0.83

0.36 0.74

0.41 0.98

0.31 0.52

0.59 2.30

5.60 7.50

2.50 4.40

6.70 8.40

2.70 4.70

4.50 6.50

3.40 4.90

5.20 8.20

3.50 5.40

4.60 6.40

3.40 7.40

4.40 5.80

6.30 7.80

3.20 4.80

2.80 5.90

4.10 5.20

**Table 2.** Soil Physico- Chemical Properties for Different Varieties of Crops Cultivated in Calabar South.

**Table 2** depicts that the selected physico-chemical properties of soil varies between the surface layer of (0-15cm) and subsurface of (15-30cm). The research further revealed that

3.20 3.9

#### Q=AV,

where

#### Q= Discharge

#### V= Water velocity

#### A= Cross sectional area of the soil

The result from the research findings is as presented in Table 2 and 3 respectively.



where

**Sampling point** 

**Crop cultivated**

2. Yam 0-15

3. Cowpea 0-15

4. Melon 0-15

5. Cassava 0-15

8. Maize 0-15

9. Rice 0-15

10. Tomatoes 0-15

11. Pepper 0-15

13. Waterleaf 0-15

12. Sweet potatoes

6. Water Yam

7. Cocoa Yam

1. Water yam

**Depth (CM) PH** 

0-15 15-30

15-30

15-30

15-30

15-30

0-15 15-30

0-15 15-30

15-30

15-30

15-30

15-30

0-15 15-30

15-30

4.8 5.7

5.8 6.3

3.9 5.4

5.3 6.7

6.8 5.6

7.2 4.3

4.3 6.5

51 63

4.2 51

4.0 5.0

3.2 4.9

4.2 63

4.9 7.3

area technique with the formula;

Soil loss and runoff were measured at each study location and their respective cumulative yield calculated from the data obtained at the field. Runoff was calculated using the velocity

Q=AV,

Q= Discharge

V= Water velocity

A= Cross sectional area of the soil

**Nitrogen (N) (kg)**

> 2.30 3.45

> 2.50 4.20

> 4.10 5.00

> 4.90 6.21

> 6.30 7.23

> 2.40 3.50

> 3.30 4.40

> 3.60 5.50

> 5.60 6.70

> 3.60 4.80

> 7.30 9.60

> 6.30 8.50

> 3.32 6.55

**Available phosphorus (p) (kg)** 

> 3.1 4.5

> 1.5 3.2

> 3.3 4.1

> 2.4 3.3

> 2.7 3.5

> 2.9 4.2

> 2.1 3.3

> 3.4 5.6

> 2.5 4.4

> 2.8 4.9

> 2.4 3.8

> 4.2 6.5

> 2.3 4.5

**Potassium K (kg)** 

> 0.18 0.34

> 0.32 0.45

> 0.29 0.36

> 0.15 0.21

> 0.26 0.30

> 0.42 0.51

> 0.19 0.28

> 0.22 0.31

> 0.22 0.44

> 0.15 0.30

> 0.26 0.32

> 0.24 0.41

> 0.32 0.41

**Cation Exchange Capacity (CEC) ( mol/mg)** 

> 6.30 8.45

> 5.9 7.20

> 4.50 6.30

> 3.20 5.40

> 6.50 7.35

> 5.20 6.50

> 7.30 8.20

> 5.3 6.00

> 3.20 5.40

> 4.70 5.80

> 4.70 6.90

> 5.20 6.50

> 6.20 7.40

**Base Saturation (%)** 

> 76 84

> 68 82

> 72 81

> 59 65

> 60 78

> 70 89

> 67 76

> 68 72

> 59 64

> 53 69

> 58 72

> 61 74

> 34 66

The result from the research findings is as presented in Table 2 and 3 respectively.

**Organic carbon ( C) %** 

> 0.49 0.65

> 0.31 0.45

> 0.32 0.54

> 0.57 0.49

> 0.67 0.65

> 0.72 0.69

> 0.69 0.98

> 0.61 0.82

> 0.69 0.85

> 0.43 0.80

> 0.43 0.71

> 0.60 0.75

> 0.56 0.70

**Table 2.** Soil Physico- Chemical Properties for Different Varieties of Crops Cultivated in Calabar South.

**Table 2** depicts that the selected physico-chemical properties of soil varies between the surface layer of (0-15cm) and subsurface of (15-30cm). The research further revealed that

due to land degradation, most of the nutrients were leached in to the sub-surface. The resultant effect was that plants restricted to shallow depth did not do well. At certain times some were seen to die because they were no more having nutrients from their roots, this affected their productivity negatively.

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 27

nutrient mining rates at more than 30kg nutrients (NPK)/hectare yearly and 40 percent had rates greater than 60kg/ha yearly. Partly as a consequence, cereal yields are the lowest in the study area, averaging about one tonne per hectare for the same ten years age. Within specific agro-ecological environments, experimental data from the field allow soil

Long term data obtained from the field indicates that intensive farming can cause yield reductions of 60% and more in some parts of Calabar South environments. Even under best variety selections and management practices, yields are stagnated and even fallen under

Patterns of degradation vary in Calabar South according to agro-ecological conditions, farming systems, levels of intensification, and resource endowments, but this also interact with social and economic systems. The areas of prime concern for this chapter are the Calabar South marginal lands, which have low physical resilience to land degradation, and are also associated with societies in which property rights are weakly defined, information

Assessing the effects of land degradation in the study area is not an easy task, a wide range of methods were used. Some authors examined the risk of degradation in climatic factors and land use rather than the present state of the land. The methodology utilized for this study is the cause-effect relationship between severity of degradation and productivity. Criteria for designating different classes of land degradation into Low, moderate, high are generally based on soil properties rather than their impact on productivity as presented in figures 4, 5, and 6.

Land degradation in the study area is treated as an open-access resource; it is then difficult to reclaim the value of soil improvements, so land users lack incentives to invest in maintaining long term soil productivity. In areas of low population density, land is

degradation processes to be observed with greater precision.

systems are weak and managerial capacity is low.

**3. Effects of land degradation in Calabar** 

**Figure 4.** Shows low degraded land

long-term intensive monoculture for irrigated cassava and rain fed corn.

The research further revealed that, severe land degradation has affected significant portion of Calabar South's arable land, decreasing the wealth and economic development of the study area. As land becomes less productive, food security is compromised and competition for dwindling resources increases, the seeds of famine and potential conflict are sown.

Recently in Calabar South, agricultural activities have increased vastly at the expense of natural forests, rangelands, wetlands and even deserts. Some of the expansion is compensated by farmer's investment in soils, such as fertilization, terracing, and tree planting. New soil formation also occurs through natural processes, but in general these proceed too slowly to compensate for human-induced degradation as shown in Figure 3 below.

**Figure 3.** Degraded Land Due to Poor Farming Practice in Calabar South

This research is based on consultation with experts, extrapolation from case studies, field experiments and other micro studies or inferences from landuse patterns, current land status, trends, and to what extent the degradation processes are human-induced.

Nutrient depletion as a form of land degradation has a severe economic impact on the study area where it represents a loss of long-run carrying power for farmers and negative externalities for the urban populations. Farmlands used for the cultivation of crops such as Maize, Okro, Water leaf, Pepper, Vegetables, Spinach and Afang had their N.P.K nutrients highly depleted because of their shallow root system which can no longer get nutrient from the leached soil. The economic impact of land degradation is extremely severe in Calabar South. On plot and field scales, erosion can cause yield reductions of 50-70% in some root restrictive shallow lands of Anantigha.

Eni et al, (2010), have estimated nutrient balances for some parts of the study in his findings; he estimated annual depletions of soil fertility at 32kg Nitrogen, 5kg phosphorus and 18kg potassium per hectare of land degraded. In 2002 about 85% of Calabar South farmland had nutrient mining rates at more than 30kg nutrients (NPK)/hectare yearly and 40 percent had rates greater than 60kg/ha yearly. Partly as a consequence, cereal yields are the lowest in the study area, averaging about one tonne per hectare for the same ten years age. Within specific agro-ecological environments, experimental data from the field allow soil degradation processes to be observed with greater precision.

Long term data obtained from the field indicates that intensive farming can cause yield reductions of 60% and more in some parts of Calabar South environments. Even under best variety selections and management practices, yields are stagnated and even fallen under long-term intensive monoculture for irrigated cassava and rain fed corn.

Patterns of degradation vary in Calabar South according to agro-ecological conditions, farming systems, levels of intensification, and resource endowments, but this also interact with social and economic systems. The areas of prime concern for this chapter are the Calabar South marginal lands, which have low physical resilience to land degradation, and are also associated with societies in which property rights are weakly defined, information systems are weak and managerial capacity is low.

## **3. Effects of land degradation in Calabar**

26 Environmental Land Use Planning

affected their productivity negatively.

due to land degradation, most of the nutrients were leached in to the sub-surface. The resultant effect was that plants restricted to shallow depth did not do well. At certain times some were seen to die because they were no more having nutrients from their roots, this

The research further revealed that, severe land degradation has affected significant portion of Calabar South's arable land, decreasing the wealth and economic development of the study area. As land becomes less productive, food security is compromised and competition for dwindling resources increases, the seeds of famine and potential conflict are sown.

Recently in Calabar South, agricultural activities have increased vastly at the expense of natural forests, rangelands, wetlands and even deserts. Some of the expansion is compensated by farmer's investment in soils, such as fertilization, terracing, and tree planting. New soil formation also occurs through natural processes, but in general these proceed too slowly to

This research is based on consultation with experts, extrapolation from case studies, field experiments and other micro studies or inferences from landuse patterns, current land

Nutrient depletion as a form of land degradation has a severe economic impact on the study area where it represents a loss of long-run carrying power for farmers and negative externalities for the urban populations. Farmlands used for the cultivation of crops such as Maize, Okro, Water leaf, Pepper, Vegetables, Spinach and Afang had their N.P.K nutrients highly depleted because of their shallow root system which can no longer get nutrient from the leached soil. The economic impact of land degradation is extremely severe in Calabar South. On plot and field scales, erosion can cause yield reductions of 50-70% in some root

Eni et al, (2010), have estimated nutrient balances for some parts of the study in his findings; he estimated annual depletions of soil fertility at 32kg Nitrogen, 5kg phosphorus and 18kg potassium per hectare of land degraded. In 2002 about 85% of Calabar South farmland had

status, trends, and to what extent the degradation processes are human-induced.

compensate for human-induced degradation as shown in Figure 3 below.

**Figure 3.** Degraded Land Due to Poor Farming Practice in Calabar South

restrictive shallow lands of Anantigha.

Assessing the effects of land degradation in the study area is not an easy task, a wide range of methods were used. Some authors examined the risk of degradation in climatic factors and land use rather than the present state of the land. The methodology utilized for this study is the cause-effect relationship between severity of degradation and productivity. Criteria for designating different classes of land degradation into Low, moderate, high are generally based on soil properties rather than their impact on productivity as presented in figures 4, 5, and 6.

**Figure 4.** Shows low degraded land

Land degradation in the study area is treated as an open-access resource; it is then difficult to reclaim the value of soil improvements, so land users lack incentives to invest in maintaining long term soil productivity. In areas of low population density, land is

abandoned when it has been degraded, and farmers move on to clear new land, leaving the degraded land as a negative externality.

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 29

**Runoff (mm)** 

**Cumulative Yield (Mg/ha)** 

**(Mgh-1)** 

Farmland 1. Water melon 41 12 10.5 2. Yam 63 18 8.3 3. Cowpea 20 6 25.6 4. Melon 35 8 18.7 5. Cassava 42 16 11.4 6. Water yam 45 14 10.3 7. Cocoa yam 43 15 12.1 8. Maize 56 22 10.7 9. Rice 49 20 9.6 10. Tomatoes 7 25 8.0 11. Pepper 63 46 4.5 12. Sweet potatoes 33 15 14.7 13. Water leaf 89 48 3.2 14. Okro 60 35 5.4 15. Vegetable 52 38 7.6 16. Spinach 56 32 8.9 17. Bitter leaf 42 26 10.8 18. Otazi 53 31 6.7 19. Afang 66 42 4.1 20. Etinkene 13 20 9.6 21. Garden Egg 38 17 8.5 22. Sugar cane 42 21 9.6 23. Scent leave 45 24 10.2 24. Curry leave 37 18 6.5 25. Ginger 44 20 7.9 26. Pineapple 39 22 8.6 27. Banana 41 20 6.8 28. Groundnut 34 12 10.3 29. Lettuce 31 16 9.2 30. Melon 23 8 5.3

**Table 3.** Cumulative Soil Loss and Runoff in Relation to Crop Yield in The Calabar South

crop thereby reducing the runoff rate at the soil surface.

Table 3 indicates that the greater the soil loss and runoff rates, the smaller the cumulative yield. Farmland number 13, in which water leaf was cultivated had a higher value for soil loss of 89mg/ha and runoff of 48mm, with a lower cumulative yield of 3.2 mg/ha. This means that the soil was severely eroded due to erosion which washed away all the available nutrients. Cowpea located in farmland 3 had the lowest soil loss and runoff rate of 30mg/ha and 6mm respectively with a higher value of 25.6mg/ha for cumulative yield. This was so because the cowpea had a symbiotic relationship with the soil, although it was getting its nutrient from the soil, the plant also played protective role to the soil by serving as a cover

 **Sampling** 

**points Crops cultivated Soil loss** 

**Figure 5.** Shows moderate degraded land

**Figure 6.** Shows high degraded land

Land degradation is a broad term that can be applied differently across wide range scenarios in the study area. The concept of land degradation was considered in four ways which includes, the effect on the soil productivity and the environment around, decline in the land usefulness, loss of bio-diversity, shifting ecological risk and a reduction on the land productive capacity.

Vulnerable lands are exposed to stresses such as accelerated soil erosion by water, soil acidification and the formation of acid sulphate resulting in barren soil, and reduced crop yields. Agricultural activities such as shifting cultivation, without adequate fallow periods, absence of soil conservation measures, fertilizer use and a host of possible problems arising from faulty planning or management of the land all lead to intense land degradation within the study area. Table showing cumulative soil loss and runoff in relation to crop yield in the study area is therefore presented overleaf.


degraded land as a negative externality.

**Figure 5.** Shows moderate degraded land

**Figure 6.** Shows high degraded land

study area is therefore presented overleaf.

productive capacity.

abandoned when it has been degraded, and farmers move on to clear new land, leaving the

Land degradation is a broad term that can be applied differently across wide range scenarios in the study area. The concept of land degradation was considered in four ways which includes, the effect on the soil productivity and the environment around, decline in the land usefulness, loss of bio-diversity, shifting ecological risk and a reduction on the land

Vulnerable lands are exposed to stresses such as accelerated soil erosion by water, soil acidification and the formation of acid sulphate resulting in barren soil, and reduced crop yields. Agricultural activities such as shifting cultivation, without adequate fallow periods, absence of soil conservation measures, fertilizer use and a host of possible problems arising from faulty planning or management of the land all lead to intense land degradation within the study area. Table showing cumulative soil loss and runoff in relation to crop yield in the

**Table 3.** Cumulative Soil Loss and Runoff in Relation to Crop Yield in The Calabar South

Table 3 indicates that the greater the soil loss and runoff rates, the smaller the cumulative yield. Farmland number 13, in which water leaf was cultivated had a higher value for soil loss of 89mg/ha and runoff of 48mm, with a lower cumulative yield of 3.2 mg/ha. This means that the soil was severely eroded due to erosion which washed away all the available nutrients. Cowpea located in farmland 3 had the lowest soil loss and runoff rate of 30mg/ha and 6mm respectively with a higher value of 25.6mg/ha for cumulative yield. This was so because the cowpea had a symbiotic relationship with the soil, although it was getting its nutrient from the soil, the plant also played protective role to the soil by serving as a cover crop thereby reducing the runoff rate at the soil surface.

This research have shown that soil erosion carries away a large volume of soil equivalent to one meter deep over 250,000 hectares every year. Some 194 million hectares of land are affected by water erosion. Recently, 6.1 million hectares of land have been lost to water erosion in the study area. Deforestation is also widespread, about 6 million hectares of forest are lost each year. The destruction of the forests is mainly a result of clearance for agriculture. The search for fuel wood, the growing frequency and severity of forest fires, are also taking their toll. As a result of this problem, Crop residues and animal manure, which were previously returned to the soil to add valuable nutrient have to be burnt for fuel.

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 31

benign crops. Reversal of these policies will have very high benefit-cost ratio, since their net cost is low, zero or even negative as long as political costs are disregarded. Increased intensity of cultivation in ecologically fragile upper water shed areas of Calabar have contributed to land expansion. Developing countries in particular have undertaken extensive reform of trade policies in manufacturing sectors, driven both by unilateral goals and by the need to conform with

Agricultural trade reform has lagged behind this process, with the result that average agricultural tariffs are now equal to or greater than those on non-agricultural goods in developing countries such as Nigeria and specifically in Calabar South(Anderson, 2006). Equilibrium simulation experiments, aimed at implementation of package of trade liberalization measures in Calabar South including a modest reduction in cereals prices, was found to exert a substantial effect on land use. The price of cassava the major annual crop grown in Calabar South falls in these experiments by about 0.75 percents. This fall, along with rises in wages and some input prices, causes a contraction of about 0.4 percent in demand for upland land for seasonal crops. If cassava land is primarily responsible for erosion, from upland fields and the base, annual soil loss from the upland farm will be 65-75 million trillion/year, the trade reforms permanent ground cover is re-established assuming

Research valued the nutrients lost to soil erosion in Calabar South at 30million/ton, adopting that as a very conservative indicator of the total value of soil lost, the experiment yields a direct, on-site gain of roughly 150million in addition to the other benefits that the trade liberalization brings to the economy. In these and similar tropical economies, substantive trade liberalization will result in major land use changes. Relaxing protectionist policies on crops which contribute to land degradation in Calabar south will shift their production to

In the case of subsidies, their relaxation creates fiscal savings that provide an opportunity to compensate farmers, who are often extremely poor. For environmental taxes, e.g. on activities that lead to downstream siltation, the challenge is to monitor and assess such widespread activities. Addressing policy-induced distortions that operate through markets to promote land-degrading activities is the most efficient single means to address land

The success of policy reforms, however, relies on the pervasiveness of markets and the feasibility of market-based instruments. Not as trade policy reforms on their own but a panacea for environmental damage, with comparative advantage in land degrading crops, greater trade openness without complementary environmental protection policies may lead

Finally the calabar south government had tried to set-aside programs, land use zoning policies and establishment of conservation areas, bans on degrading activities and public reforestation projects. Cross River State afforestation projects, is targeted at increasing forested areas in Calabar South by 50% and 15% decrease in cultural areas. The current program, however, lacks "Volunteerism" in participation, and therefore suffers from low cost effectiveness and high cost

countries and environments where they can be grown at lower environmental cost.

international obligations as signatories to regional and multilateral trade agreements.

that this is what happens after cassava production cases.

degradation in Calabar South.

to rapid worsening of land degradation.

Land degradation in Calabar south also exhibits hydrological conditions, where vegetal cover is removed, the soil surface is exposed to the impact of raindrops which causes a sealing of the soil surface, and less rain then infiltrates the soil. As runoff increases, stream flow fluctuates more than before, flooding becomes more frequent and extensive, and streams, springs become ephemeral. These conditions encourage erosion; as a result, sediment loads in rivers are increased, dams are filling with silt, hydro-electric schemes are damaged, navigable waterways are being blocked and water quality deteriorates.

## **4. Policy implications, individual efforts and institution**

Attempts to prevent land degradation in the Calabar South have been unsuccessful. One of the main reasons was that these attempts were centrally organized and it produced few short-term benefits for the farmers who had to execute them. The farmers had little motivation for the hard manual work involved in erecting mechanical barriers to control soil erosion. Government must spear head the formulation of policies, mobilize the people and initiate programs and projects that are needed for sustainability.

The key action required to combat land degradation in Calabar South is to develop a longterm land conservation plan which will provide the necessary continuity of the approach. These long-term plans need to be fashioned to suit the exact requirements of individuals in the study area. They should be based on three key principles; improving land use, obtaining the participation of the land users and developing the necessary institutional support.

However, agricultural policies can have a profound effect on land use. Subsidies, incentives and taxes can all have a big effect on what crops are grown where and whether or not the land is well managed. Governments attempting to achieve self-sufficiency in food crops frequently promotes policies which result in marginal land being misused, this, in turn leads to land degradation. On the other hand, the price of food crops is sometimes controlled and kept to such a low level that it becomes pointless for farmers to manage their crops or land well, this also results in land degradation. All government policies which affect the economics of land use should be carefully reviewed and where necessary, modified so that they encourage productive and sustainable land use rather than destructive practices.

Calabar South government explicitly subsidizes practices that increase land degradation, and tax activities that tend to reduce degradation. Examples are subsidies on cultivation of upland crops that drive expansion into the marginal lands; subsidies on water and energy in irrigation schemes; tariff protection for land-degrading crops, and export taxes on more environmentally benign crops. Reversal of these policies will have very high benefit-cost ratio, since their net cost is low, zero or even negative as long as political costs are disregarded. Increased intensity of cultivation in ecologically fragile upper water shed areas of Calabar have contributed to land expansion. Developing countries in particular have undertaken extensive reform of trade policies in manufacturing sectors, driven both by unilateral goals and by the need to conform with international obligations as signatories to regional and multilateral trade agreements.

30 Environmental Land Use Planning

This research have shown that soil erosion carries away a large volume of soil equivalent to one meter deep over 250,000 hectares every year. Some 194 million hectares of land are affected by water erosion. Recently, 6.1 million hectares of land have been lost to water erosion in the study area. Deforestation is also widespread, about 6 million hectares of forest are lost each year. The destruction of the forests is mainly a result of clearance for agriculture. The search for fuel wood, the growing frequency and severity of forest fires, are also taking their toll. As a result of this problem, Crop residues and animal manure, which were previously

Land degradation in Calabar south also exhibits hydrological conditions, where vegetal cover is removed, the soil surface is exposed to the impact of raindrops which causes a sealing of the soil surface, and less rain then infiltrates the soil. As runoff increases, stream flow fluctuates more than before, flooding becomes more frequent and extensive, and streams, springs become ephemeral. These conditions encourage erosion; as a result, sediment loads in rivers are increased, dams are filling with silt, hydro-electric schemes are

Attempts to prevent land degradation in the Calabar South have been unsuccessful. One of the main reasons was that these attempts were centrally organized and it produced few short-term benefits for the farmers who had to execute them. The farmers had little motivation for the hard manual work involved in erecting mechanical barriers to control soil erosion. Government must spear head the formulation of policies, mobilize the people and

The key action required to combat land degradation in Calabar South is to develop a longterm land conservation plan which will provide the necessary continuity of the approach. These long-term plans need to be fashioned to suit the exact requirements of individuals in the study area. They should be based on three key principles; improving land use, obtaining the participation of the land users and developing the necessary institutional support.

However, agricultural policies can have a profound effect on land use. Subsidies, incentives and taxes can all have a big effect on what crops are grown where and whether or not the land is well managed. Governments attempting to achieve self-sufficiency in food crops frequently promotes policies which result in marginal land being misused, this, in turn leads to land degradation. On the other hand, the price of food crops is sometimes controlled and kept to such a low level that it becomes pointless for farmers to manage their crops or land well, this also results in land degradation. All government policies which affect the economics of land use should be carefully reviewed and where necessary, modified so that they encourage productive and sustainable land use rather than destructive practices.

Calabar South government explicitly subsidizes practices that increase land degradation, and tax activities that tend to reduce degradation. Examples are subsidies on cultivation of upland crops that drive expansion into the marginal lands; subsidies on water and energy in irrigation schemes; tariff protection for land-degrading crops, and export taxes on more environmentally

damaged, navigable waterways are being blocked and water quality deteriorates.

**4. Policy implications, individual efforts and institution** 

initiate programs and projects that are needed for sustainability.

returned to the soil to add valuable nutrient have to be burnt for fuel.

Agricultural trade reform has lagged behind this process, with the result that average agricultural tariffs are now equal to or greater than those on non-agricultural goods in developing countries such as Nigeria and specifically in Calabar South(Anderson, 2006). Equilibrium simulation experiments, aimed at implementation of package of trade liberalization measures in Calabar South including a modest reduction in cereals prices, was found to exert a substantial effect on land use. The price of cassava the major annual crop grown in Calabar South falls in these experiments by about 0.75 percents. This fall, along with rises in wages and some input prices, causes a contraction of about 0.4 percent in demand for upland land for seasonal crops. If cassava land is primarily responsible for erosion, from upland fields and the base, annual soil loss from the upland farm will be 65-75 million trillion/year, the trade reforms permanent ground cover is re-established assuming that this is what happens after cassava production cases.

Research valued the nutrients lost to soil erosion in Calabar South at 30million/ton, adopting that as a very conservative indicator of the total value of soil lost, the experiment yields a direct, on-site gain of roughly 150million in addition to the other benefits that the trade liberalization brings to the economy. In these and similar tropical economies, substantive trade liberalization will result in major land use changes. Relaxing protectionist policies on crops which contribute to land degradation in Calabar south will shift their production to countries and environments where they can be grown at lower environmental cost.

In the case of subsidies, their relaxation creates fiscal savings that provide an opportunity to compensate farmers, who are often extremely poor. For environmental taxes, e.g. on activities that lead to downstream siltation, the challenge is to monitor and assess such widespread activities. Addressing policy-induced distortions that operate through markets to promote land-degrading activities is the most efficient single means to address land degradation in Calabar South.

The success of policy reforms, however, relies on the pervasiveness of markets and the feasibility of market-based instruments. Not as trade policy reforms on their own but a panacea for environmental damage, with comparative advantage in land degrading crops, greater trade openness without complementary environmental protection policies may lead to rapid worsening of land degradation.

Finally the calabar south government had tried to set-aside programs, land use zoning policies and establishment of conservation areas, bans on degrading activities and public reforestation projects. Cross River State afforestation projects, is targeted at increasing forested areas in Calabar South by 50% and 15% decrease in cultural areas. The current program, however, lacks "Volunteerism" in participation, and therefore suffers from low cost effectiveness and high cost

of performance monitoring and evaluation. In general, it is very difficult and costly to police and enforce bans against common and widely dispersed practices when these practices are profitable to land users or perhaps even necessary for survival. Project-based payment for environmental services schemes introduced in Calabar South is meant to provide a means of paying compensation to farmers who desist from environmentally undesirable activities. But since there is no internal mechanism for decreasing cost replication of payment for environmental services measures, in benefit cost terms these are expensive interventions if they are to be widely applied even before counting the cost of contract enforcement and monitoring.

Effects of Land Degradation on Soil Fertility: A Case Study of Calabar South, Nigeria 33

land degradation. A precautionary approach, must take into account the relative magnitude of the problem, the relative importance of land degradation to the poor and the relative weakness of existing institutional and market-based mechanisms to deal with on-site degradation and externalities this means that efforts to reduce land degradation should focus on sloping lands and forest margin areas in Calabar South and should depend mainly on market-based instruments, accompanied by efforts to ease and increase investment in the

Land resources are non renewable and it is necessary to adopt a positive approach to ensure sustainable management of these finite resources. Soil scientists have an obligation not only to show the spatial distribution of stressed systems but also to provide reasonable estimates of their rates of degradation. Many assessments in Calabar South have dealt with land degradation risks rather than dealing with degradation status, its socio-economic cause and its political driving force. Most estimates of soil erosion for instance, have been on erosion hazard not actual observed erosion. There are consequently large differences between

One of the most obvious direct causes and driving forces of land degradation in Calabar South is the mismatch between land potential and actual land use which is different from land cover and it includes information on land management and inputs. Some socioeconomic data have to be collected at farm level during rapid rural appraisal or other livelihood surveys to establish the general conditions leading to certain land use practices. It is important to realize that the socio-economic parameters collected should be simplified

This research can be summarized in two points. Firstly, it was observed that land degradation is proportionally and absolutely very severe in Calabar South, where it represents a loss of long-run earning power for farmers and negative externalities for larger rural populations. Monetary values aside, the problem of land degradation becomes more acute when the welfare of the poor is given higher priority. Secondly, we must note that the same policy instruments that we have advanced as the best means to alleviate land degradation are also components of reform packages with much broader economic development aims. In this sense our land degradation proposals are "bundled with"

development of technologies for sustainable agriculture

estimates of areas at risk and areas actually affected by land degradation

and classified according to their role in the assessment of land degradation.

measures that deliver gains that extend well beyond the environment.

Agricultural and Resources Economics. 44(2): P. 185-215.

agricultural R. & D Agricultural Economics 25(2-3): P. 141-152.

*Department of Geography and Regional Planning, University of Calabar, Calabar, Nigeria* 

Alston, J. M., M. C. Marra, P. G. Pardey, and T. J. Wyatt, (2000). Research returns redux: a meta-analysis of the returns of agriculture R. & D. the Australian Journal of

Alston, J. M. and P. G. Pardey (2001). Attribution and other problems in assessing returns to

**Author details** 

**7. References** 

Imoke Eni

## **5. Lessons and conclusion**

Over the years, there has been a progressive change in the approach to agricultural practices from crop substitution to integrated farming system. A concern for environmental aspects has been explicit till many other projects came up. However, the way this was undertaken, and the priority given to conservation, differs greatly. The use of erosion control structures such as Bench terraces, contour bank, contour ditches were localized.

Recently, based on research findingds, Calabar South farmers started using erosion control measures devoid of physical structures. This marked a major departure from the previous approach. The objective was not simply soil conservation, but sustainable farming systems. Among the key lessons learned are:


## **6. Conclusion**

There are six major causes of land degradation in Calabar South, they include; deforestation, shortage of land due to increased populations, poor land use, insecure land tenure, inappropriate land management practices and poverty, problems of valuation, and even of assigning causality, make it impossible to compute accurate benefit-cost ratios for reducing land degradation. A precautionary approach, must take into account the relative magnitude of the problem, the relative importance of land degradation to the poor and the relative weakness of existing institutional and market-based mechanisms to deal with on-site degradation and externalities this means that efforts to reduce land degradation should focus on sloping lands and forest margin areas in Calabar South and should depend mainly on market-based instruments, accompanied by efforts to ease and increase investment in the development of technologies for sustainable agriculture

Land resources are non renewable and it is necessary to adopt a positive approach to ensure sustainable management of these finite resources. Soil scientists have an obligation not only to show the spatial distribution of stressed systems but also to provide reasonable estimates of their rates of degradation. Many assessments in Calabar South have dealt with land degradation risks rather than dealing with degradation status, its socio-economic cause and its political driving force. Most estimates of soil erosion for instance, have been on erosion hazard not actual observed erosion. There are consequently large differences between estimates of areas at risk and areas actually affected by land degradation

One of the most obvious direct causes and driving forces of land degradation in Calabar South is the mismatch between land potential and actual land use which is different from land cover and it includes information on land management and inputs. Some socioeconomic data have to be collected at farm level during rapid rural appraisal or other livelihood surveys to establish the general conditions leading to certain land use practices. It is important to realize that the socio-economic parameters collected should be simplified and classified according to their role in the assessment of land degradation.

This research can be summarized in two points. Firstly, it was observed that land degradation is proportionally and absolutely very severe in Calabar South, where it represents a loss of long-run earning power for farmers and negative externalities for larger rural populations. Monetary values aside, the problem of land degradation becomes more acute when the welfare of the poor is given higher priority. Secondly, we must note that the same policy instruments that we have advanced as the best means to alleviate land degradation are also components of reform packages with much broader economic development aims. In this sense our land degradation proposals are "bundled with" measures that deliver gains that extend well beyond the environment.

## **Author details**

32 Environmental Land Use Planning

**5. Lessons and conclusion** 

Among the key lessons learned are:

measures in the area.

control land degradation.

to specific situations

**6. Conclusion** 

erosion.

of performance monitoring and evaluation. In general, it is very difficult and costly to police and enforce bans against common and widely dispersed practices when these practices are profitable to land users or perhaps even necessary for survival. Project-based payment for environmental services schemes introduced in Calabar South is meant to provide a means of paying compensation to farmers who desist from environmentally undesirable activities. But since there is no internal mechanism for decreasing cost replication of payment for environmental services measures, in benefit cost terms these are expensive interventions if they are to be widely applied

Over the years, there has been a progressive change in the approach to agricultural practices from crop substitution to integrated farming system. A concern for environmental aspects has been explicit till many other projects came up. However, the way this was undertaken, and the priority given to conservation, differs greatly. The use of erosion control structures

Recently, based on research findingds, Calabar South farmers started using erosion control measures devoid of physical structures. This marked a major departure from the previous approach. The objective was not simply soil conservation, but sustainable farming systems.

c. The use of vegetable barriers as the most pertinent and cost effective erosion control

d. The introduction of new technology in controlling land degradation was made use of in Calabar. These have led to a higher and more assured crop yield while controlling soil

e. The introduction of a mixture of leguminous creepers as cover crops on land that is planted with rubber and oil palms. Research has shown that desmodium ovalifolium, stylosanthes gracillis and clitoria ternetea provides useful ground cover, and help to

f. The provision of improve varieties and a large increase in the use of fertilizer

There are six major causes of land degradation in Calabar South, they include; deforestation, shortage of land due to increased populations, poor land use, insecure land tenure, inappropriate land management practices and poverty, problems of valuation, and even of assigning causality, make it impossible to compute accurate benefit-cost ratios for reducing

g. That the recommendations should be exceptionally comprehensive and user friendly. h. Finally, the farming system utilized must correctly identify a wide range of indicators and avoid the usual problem of selection of a limited number that can only be applied

even before counting the cost of contract enforcement and monitoring.

such as Bench terraces, contour bank, contour ditches were localized.

a. The importance of having a master plan for water shed development. b. The importance of the local people participating in all levels of conservation.

encourages high yield and provide good ground cover.

Imoke Eni *Department of Geography and Regional Planning, University of Calabar, Calabar, Nigeria* 

## **7. References**


Anderson, K. and W. Martin (2005). Scenarios for global trade reform, in T. W. Hertel and L. A. Winters, Editors Poverty and the WTO. Impacts of the Doha Development Agenda. Palgrave Macmillan and the World Bank; Hanisphire, UK, and Washington DC.

**Chapter 3** 

© 2012 Mejía and Hochschild, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Land Use and Land Cover (LULC) Change** 

**Andes, and Its Implications for** 

Joel Francisco Mejía and Volker Hochschild

Additional information is available at the end of the chapter

cultural landscapes worldwide [4] [5] [6].

http://dx.doi.org/10.5772/51259

**1. Introduction** 

**the Natural Resources Management** 

**in the Boconó River Basin, North Venezuelan** 

**Land Use** & **Land Cover (LULC)** have been historically permanently subject to biophysical and anthropogenic forces which induce changes in different structure-levels and space-time scales, and modify the energy and water exchange of the soil-vegetation-atmosphere system; such modifications become globally significant through their cumulative effects, so it would be particularly hazardous for food production and food security [1] [2] [3]. Thus, Land Use & Land Cover (LULC) changes are simply the most conspicuous changes in

Particularly the tropical regions have undergone dramatic Land Use changes in the last few decades, and these changes are the effect of an equally large number of local causes and factors, highlighting a complexity that tends to defy easy generalizations [7] [8] [9] [10] [11].

Many hydrological systems of the tropical regions are relatively densely populated, with relatively high rates of population growth, which has serious implications in the relationships between people and environmental services [12]. In mountainous regions, mostly poor people are settled in steep hillsides (slopes above 15%), usually practicing a smallholder farming system with agricultural production in small parcels for subsistence purposes, as well as shifting cultivation and slash & burn agriculture, which represent a pressure over natural resources in areas which are ecologically fragile and environmentally sensitive. About 25 to 30% of Central America and northern South America consist of mountainous areas where the conditions above mentioned are quite common [13]. Thus, the dynamics of natural resources use in river basins and watersheds across the mountain

and reproduction in any medium, provided the original work is properly cited.


Waggoner, P. E. (1996). How much land can ten billion people spare for nature? 125(3): p.73-93.

## **Land Use and Land Cover (LULC) Change in the Boconó River Basin, North Venezuelan Andes, and Its Implications for the Natural Resources Management**

Joel Francisco Mejía and Volker Hochschild

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51259

## **1. Introduction**

34 Environmental Land Use Planning

Elsevier: Ansterdam. P. 473-554.

University of wisconsinmadison: Madison, W.

European Journal of Social Sciences (18) 166-170

2000. science 300 (5620): 1 P.758-762.

Indonesia Cambridge, MA: the MIT Press.

and United Nations Environment programme.

Development Economics 65(2). P 267-290.

United Nations Convention to combats desertification, 1996.

Retrieved 2012-02-05.

resources: Manila.

Maryland, USA.

Earthscan: Rome; London.

Anderson, K. and W. Martin (2005). Scenarios for global trade reform, in T. W. Hertel and L. A. Winters, Editors Poverty and the WTO. Impacts of the Doha Development Agenda.

Bruinsma, J. Ed (2003) World agriculture: towards 2015/2030: an FAO perspective: FAO:

Cassman, K. G. and P. L. Pingali (1995). Intensification of irrigated rice systems: Learning

Conacher, Arthur; Conacher, Jeanette (1995). Rural land degradation in Australia South

Coxhead, I. and J. Plangpraphan (1978), Thailand's economic boom and agricultural bust.

Deininger, K. and J. S. Chamorro (2004). Investment and equity effects of land degradation:

Eni, D. Imoke, Upla,J. Ibu, Oko, C. Omonya, Obiefuna, J.N and Njar, G.N (2010). Effects of land degradation on soil productivity in Calabar south local government area, Nigeria.

Evenson, R. E. and D. Gollin (2003). Assessing the impact of the Green Revolution, 1930 to

Eswaran, H.; R. Lai and Reich, P. F. (2001). "Land degradation: proc. 2nd. International conference on land degradation and desertification New Delhi, India: Oxford press.

Forest management Bureau, (1998) the Philippines strategy for improved water shed resources management. Philippines Development of environmental and natural

Good, D. H. and Reureny, R. (2006). The fate of Easter Island: the limit of resources

Lindert, P. H. (2000). Shifting Ground. The changing Agricultural soils in China and

Johnson, Douglas; Lewis, Lawrence (2007). Land degradation; creation and destruction,

Olderman, L. R. hakkeling, R. T. A. and Sombrock, W. G. (1991). World Map of the status of human-induced soil degradation: An explanatory soil reference and information centre

Pagiola, S. Bishop, J. and Landell-mills, N. Eds. (2002). Selling forest environmental services market-based mechanism for conservation and development. Earthson: London. Scherr, S. J. (1999). Social degradation. A threat to developing-country food security by

Shively, G. E. (2001). Poverty, consumption risk, and soil conservation. Journal of

Waggoner, P. E. (1996). How much land can ten billion people spare for nature? 125(3): p.73-93.

2020? International Food Policy Research Institute: Wastington DC.

malbourne, Victoria: Oxford University Press Australia P. ISBN 0195534360

from the past to meet future challenges. Geo-Journal 35:p.299-305.

The case of Nicraragua. Agricultural Economics. 30 (2): P.101 – 116.

management Institutions. Ecological economics 58(3): P.473.

Palgrave Macmillan and the World Bank; Hanisphire, UK, and Washington DC. Banevjee, A. V. and E. Duflo (2005). Growth theory though the lens of development economics, in P. Aghion and S. N. Durlauf, Editors Handbook of economics growth.

> **Land Use** & **Land Cover (LULC)** have been historically permanently subject to biophysical and anthropogenic forces which induce changes in different structure-levels and space-time scales, and modify the energy and water exchange of the soil-vegetation-atmosphere system; such modifications become globally significant through their cumulative effects, so it would be particularly hazardous for food production and food security [1] [2] [3]. Thus, Land Use & Land Cover (LULC) changes are simply the most conspicuous changes in cultural landscapes worldwide [4] [5] [6].

> Particularly the tropical regions have undergone dramatic Land Use changes in the last few decades, and these changes are the effect of an equally large number of local causes and factors, highlighting a complexity that tends to defy easy generalizations [7] [8] [9] [10] [11].

> Many hydrological systems of the tropical regions are relatively densely populated, with relatively high rates of population growth, which has serious implications in the relationships between people and environmental services [12]. In mountainous regions, mostly poor people are settled in steep hillsides (slopes above 15%), usually practicing a smallholder farming system with agricultural production in small parcels for subsistence purposes, as well as shifting cultivation and slash & burn agriculture, which represent a pressure over natural resources in areas which are ecologically fragile and environmentally sensitive. About 25 to 30% of Central America and northern South America consist of mountainous areas where the conditions above mentioned are quite common [13]. Thus, the dynamics of natural resources use in river basins and watersheds across the mountain

© 2012 Mejía and Hochschild, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

regions in the tropics are determined by three factors: environmental, social and economical conditions [4] [9] [12] [14] [15].

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 37

Located in the northern part of that region, the Boconó River Basin can be considered as a representative case of the complex dynamics characterizing the Andean hydrological systems. Having a total surface area of 1580 km2 and a wide altitudinal range, the River Basin harbor many ecosystems ranging from the Sub-Andean Páramo in the upland areas, to the Savannah ecosystem downstream in the upper plains of the Llanos region. With an annual yield of 2,300 million m3 and a very acceptable chemical quality, the Boconó River was included into the regional planning policies in the Seventies, in order to develop the water resources in the lowlands region, so that the Boconó – Tucupido Dam systems were built in the Llanos region,

A very significant portion of the area is still under natural Land Cover types, like the **Tropical Montane Cloudy Forest (TMCF)**. This ecosystem has a paramount importance, not only in terms of their ecological richness, but also in terms of hydrological functioning, specifically for water yield. In such forests there is usually a net gain of water that comes from the "**horizontal precipitation**" or "**occult precipitation**" in form of wind-driven

On the other hand, there are also numerous sparse rural settlements representing a huge potential for agricultural production of some crops like coffee and vegetables (potatoes, carrots, onions, beans and others). The high accessibility through an intricate network of rural and local roads makes it easier to promote the sparse settlement in sloping hillsides across the area, being a crucial factor which determines the LULC changes contributing to

All these conditions acting together in a strongly integrated way, resulting in a complex situation in which the increasingly sparse population is making even more pressure on natural land cover types, particularly on the **Tropical Montane Cloudy Forest**, so that the conversion of LC into LU appears to be persistent and intense. The River Basin was declared in 1974 "Protected Area" in order to preserve the water resources [40], and the Guaramacal National Park was created in 1988, which cover the southeastern flank of the area. Both figures aimed to guarantee the conservation of the ecosystems, the biodiversity, and to

Nevertheless, the area continues to show a trend respect to the anthropogenic pressure, so the agricultural frontier is even more extended, meanwhile the forested land cover types tends to be decreased, and some land degradation processes like erosion and sediment yield seems to be even more intense. This has severe implications for the biodiversity, but also affects substantially the hydrological dynamic through changes in local microclimates, changes in moisture regimes, that eventually could lead changes in the hydrological regimes, especially the seasonal flows, peak flows, as well as changes in the water quality.

The main goal of this paper is to analyze the spatial dynamic of the Boconó River Basin during the Period 1988 – 2008, in terms of the main LULC changes and systematic transitions that have been occurring in the area under an ecosystem-oriented approach.

in order to generate energy, flooding control and for irrigated cropping also [37].

the intensification of some erosion and land degradation processes [39].

drizzle and fog [9] [38].

ensure the water production [41].

**2.1. Main goal** 

According to [8], the LULC changes have a notorious impact on climate, at local and regional levels, due to the modifications in the carbon cycle, the local evapotranspiration patterns, as well as precipitation regimes. This fact justifies many concerns about the implications that the LULC changes could have in the water resources, particularly in the hydrological regimes worldwide. These concerns have been motivating the analysis of the relationships between the LULC changes and the hydrological regimes (river flows, runoff dynamic, floodings, water depletion, etc) in a spatial-temporal perspective. Some examples of these includes: [16 - 31].

Certainly, these are valuable experiences to deal with such a complex task; however, there are still many gaps in this process to be solved, and many questions to be answered. Moreover, many of these experiences are all spatially confined to temperate regions, where biophysical as well as socioeconomic conditions are particular. Tropical ecosystems are very different from their counterparts in higher latitudes. They have different geological and evolutionary histories, and different climatic extremes and dynamics. The number of interacting species is typically much higher in tropical ecosystems, including streams networks also, and the interactions are often more complex [9]. Social, economical and political conditions in tropical rural areas are also very complex; thus, the poverty, depressive local economies, instability and lack of plans and investment programs are always current, and usually such complex realities and the collateral relationship has not been well studied so far. Thus, the knowledge remains still weak and the lack of information about local and regional environmental dynamic is remarkable [11] [14] [15] [32] [33] [34] [35].

The river basins are subject to constant processes of change, so the state and the structure of river landscapes and land resources are primarily determined by the type and intensity of the utilisation of the ecological, economic, social or cultural functions provided by the river systems. The new paradigm recognize the river basins as complex, ecological and interactive systems, which means that the integrated water resource management follows the central themes of the **ecosystem approach** and of adaptative management; in fact, the WFD (Water Framework Directive) of the European Union, has adopted the "**ecosystem-oriented river management** " as central approach to be followed by the institution [36].

## **2. Problem description**

The Andean region in Venezuela is considered the most important "**water resource-area**" in the western part of the country. The source streams of many important river systems in the country are located there, having a complex and intricate channel network, with the "**first order streams**" (the most important sources of fresh water in many regions worldwide) broadly dominating the landscape system. Due to the biophysical configuration and the attractiveness of the Andean landscapes, the region has been under anthropogenic impact from times before the arrival of the Spaniards. However, in recent decades that pressure has been gradually increasing, which eventually could have significant impacts on the natural resources basis, particularly water, forest and soils.

Located in the northern part of that region, the Boconó River Basin can be considered as a representative case of the complex dynamics characterizing the Andean hydrological systems. Having a total surface area of 1580 km2 and a wide altitudinal range, the River Basin harbor many ecosystems ranging from the Sub-Andean Páramo in the upland areas, to the Savannah ecosystem downstream in the upper plains of the Llanos region. With an annual yield of 2,300 million m3 and a very acceptable chemical quality, the Boconó River was included into the regional planning policies in the Seventies, in order to develop the water resources in the lowlands region, so that the Boconó – Tucupido Dam systems were built in the Llanos region, in order to generate energy, flooding control and for irrigated cropping also [37].

A very significant portion of the area is still under natural Land Cover types, like the **Tropical Montane Cloudy Forest (TMCF)**. This ecosystem has a paramount importance, not only in terms of their ecological richness, but also in terms of hydrological functioning, specifically for water yield. In such forests there is usually a net gain of water that comes from the "**horizontal precipitation**" or "**occult precipitation**" in form of wind-driven drizzle and fog [9] [38].

On the other hand, there are also numerous sparse rural settlements representing a huge potential for agricultural production of some crops like coffee and vegetables (potatoes, carrots, onions, beans and others). The high accessibility through an intricate network of rural and local roads makes it easier to promote the sparse settlement in sloping hillsides across the area, being a crucial factor which determines the LULC changes contributing to the intensification of some erosion and land degradation processes [39].

All these conditions acting together in a strongly integrated way, resulting in a complex situation in which the increasingly sparse population is making even more pressure on natural land cover types, particularly on the **Tropical Montane Cloudy Forest**, so that the conversion of LC into LU appears to be persistent and intense. The River Basin was declared in 1974 "Protected Area" in order to preserve the water resources [40], and the Guaramacal National Park was created in 1988, which cover the southeastern flank of the area. Both figures aimed to guarantee the conservation of the ecosystems, the biodiversity, and to ensure the water production [41].

Nevertheless, the area continues to show a trend respect to the anthropogenic pressure, so the agricultural frontier is even more extended, meanwhile the forested land cover types tends to be decreased, and some land degradation processes like erosion and sediment yield seems to be even more intense. This has severe implications for the biodiversity, but also affects substantially the hydrological dynamic through changes in local microclimates, changes in moisture regimes, that eventually could lead changes in the hydrological regimes, especially the seasonal flows, peak flows, as well as changes in the water quality.

## **2.1. Main goal**

36 Environmental Land Use Planning

conditions [4] [9] [12] [14] [15].

of these includes: [16 - 31].

**2. Problem description** 

resources basis, particularly water, forest and soils.

regions in the tropics are determined by three factors: environmental, social and economical

According to [8], the LULC changes have a notorious impact on climate, at local and regional levels, due to the modifications in the carbon cycle, the local evapotranspiration patterns, as well as precipitation regimes. This fact justifies many concerns about the implications that the LULC changes could have in the water resources, particularly in the hydrological regimes worldwide. These concerns have been motivating the analysis of the relationships between the LULC changes and the hydrological regimes (river flows, runoff dynamic, floodings, water depletion, etc) in a spatial-temporal perspective. Some examples

Certainly, these are valuable experiences to deal with such a complex task; however, there are still many gaps in this process to be solved, and many questions to be answered. Moreover, many of these experiences are all spatially confined to temperate regions, where biophysical as well as socioeconomic conditions are particular. Tropical ecosystems are very different from their counterparts in higher latitudes. They have different geological and evolutionary histories, and different climatic extremes and dynamics. The number of interacting species is typically much higher in tropical ecosystems, including streams networks also, and the interactions are often more complex [9]. Social, economical and political conditions in tropical rural areas are also very complex; thus, the poverty, depressive local economies, instability and lack of plans and investment programs are always current, and usually such complex realities and the collateral relationship has not been well studied so far. Thus, the knowledge remains still weak and the lack of information about local and regional

The river basins are subject to constant processes of change, so the state and the structure of river landscapes and land resources are primarily determined by the type and intensity of the utilisation of the ecological, economic, social or cultural functions provided by the river systems. The new paradigm recognize the river basins as complex, ecological and interactive systems, which means that the integrated water resource management follows the central themes of the **ecosystem approach** and of adaptative management; in fact, the WFD (Water Framework Directive) of the European Union, has adopted the "**ecosystem-oriented river** 

The Andean region in Venezuela is considered the most important "**water resource-area**" in the western part of the country. The source streams of many important river systems in the country are located there, having a complex and intricate channel network, with the "**first order streams**" (the most important sources of fresh water in many regions worldwide) broadly dominating the landscape system. Due to the biophysical configuration and the attractiveness of the Andean landscapes, the region has been under anthropogenic impact from times before the arrival of the Spaniards. However, in recent decades that pressure has been gradually increasing, which eventually could have significant impacts on the natural

environmental dynamic is remarkable [11] [14] [15] [32] [33] [34] [35].

**management** " as central approach to be followed by the institution [36].

The main goal of this paper is to analyze the spatial dynamic of the Boconó River Basin during the Period 1988 – 2008, in terms of the main LULC changes and systematic transitions that have been occurring in the area under an ecosystem-oriented approach.

They were discussed in terms of the implications that such changes and transitions have for the natural resources management at the river basin level (watershed management). The results showed here are only a partial output of the still ongoing PhD project: "Spatial changes and hydrological dynamic of the Boconó River Basin, north venezuelan Andes", which is actually developed at the Eberhard Karls University – Tübingen, Germany.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 39

The catchment is located within the tectonic axis formed by the Boconó Fault, which is the most important structural feature of the Venezuelan Andes [37]. The Fault cross longitudinally the river, separating the metamorphosed crystalline rocks in the north portion, from those less metamorphosed in the south part [33]. The basin has a massive and strongly dissected topography, so that the topographic conditions are quite complex and varied, determined by different landforms like: structural risks, erosion risks, structural escarpments, hillsides and alluvial accumulations, and a mean slope which range between

The lithological framework is generally highly jointed, due the tectonic dynamic, and the rocks basically correspond to the formations: Iglesias Group (gneisses and schist), Sierra Nevada (granites), Mucuchachí (Shale and phyllites) and Palmarito (shale and marl) [44]. Soils are in general relatively deep, with textural classes ranging from clayed to sandy loam, being Ultisols, Inceptisols and Alfisols the most important and representative taxonomic

The altitudinal gradient (2300 m.a.s.l) and the climatic conditions, particularly the intense rainfall regime, lead to the existence of the Tropical Montane Cloudy Forest, which cover the 44, 6 % of the total surface. Other important ecosystems in the area are: sub-montane forest, grass, sucessional shrubland, schrub and sub-alpine Páramo. These categories of land cover coexist also with specific land use types, which are very importance not only in economical terms, but in social and cultural perspectives also [46]. Shifting cultivation is located mostly in upland areas, where slash and burning are usual tasks. Conventional agriculture is also developed in lower parts and quaternary landforms, in some cases under irrigation. Coffee plantations are very usual between 800 and 2000 m above sea level, occupying an important portion of the Sub-montane forest. In a small proportion, the extensive grazing shows a moderate development, being usually spatially confined to the low parts and the quaternary landforms [42]. Finally, the 1, 6 % of total surface is occupied by urban use, being the Boconó

In order to achieve the purpose of this project, a methodological approach combining remote sensing methods with spatial and multi-temporal analysis in GIS in an interactive way was implemented. At first, the study area was delineated from the SRTM data set (90 m spatial resolution) using the open source GIS software SAGA (System for Automated Geoscientific Analysis), in order to build the Digital Elevation Model (DEM), and also to prepare the basic thematic maps (Topography, Slope, Aspect, Drainage Network). Based on the structure pointed out by [47], the LULC mapping process was done in three main

Three time-points were defined in order to analyse the LULC dynamic in the river basin: T0 (1988); T1 (1997); and T2 (2008). For each time-point a group of LANDSAT TM scenes

35 – 40% [43].

categories in the area [45].

city the most important urban system in the area.

**4. Methodological approach** 

straightforward steps, as follows:

**4.1. The pre - processing** 

## **3. The geographical context – study area**

The study was focused on the upland part of the Boconó River Basin, located in the southeast part of the State of Trujillo, between the coordinates 09°11'40" - 09°31'50" N and 70°04'08" – 70°22'53" W, with a surface area of 537.62 km2. The highest point in the Basin is 3400 m.a.s.l in the Páramo of Cendé, and the lowest point (outlet) is the confluence between the Boconó and Burate river (1100 m.a.s.l) (Fig. 1). The Boconó River drops from the northeast to the south-west, over a distance of approximately 57 km, having a mean runoff about 15, 55 m3/sec [33].

**Figure 1.** Location of the Study area

The area has a seasonally humid climate, having a wet period from April to October, and a dry period from November to March. Annual mean rainfall is about 1838 mm, and the annual mean temperature range from 19.7 °C to 21.5 °C [42]. The Basin has a relatively elongated form, and the drainage pattern is dendritic with a tendency to be rectangular, due to the intense tectonic activity [37].

The catchment is located within the tectonic axis formed by the Boconó Fault, which is the most important structural feature of the Venezuelan Andes [37]. The Fault cross longitudinally the river, separating the metamorphosed crystalline rocks in the north portion, from those less metamorphosed in the south part [33]. The basin has a massive and strongly dissected topography, so that the topographic conditions are quite complex and varied, determined by different landforms like: structural risks, erosion risks, structural escarpments, hillsides and alluvial accumulations, and a mean slope which range between 35 – 40% [43].

The lithological framework is generally highly jointed, due the tectonic dynamic, and the rocks basically correspond to the formations: Iglesias Group (gneisses and schist), Sierra Nevada (granites), Mucuchachí (Shale and phyllites) and Palmarito (shale and marl) [44]. Soils are in general relatively deep, with textural classes ranging from clayed to sandy loam, being Ultisols, Inceptisols and Alfisols the most important and representative taxonomic categories in the area [45].

The altitudinal gradient (2300 m.a.s.l) and the climatic conditions, particularly the intense rainfall regime, lead to the existence of the Tropical Montane Cloudy Forest, which cover the 44, 6 % of the total surface. Other important ecosystems in the area are: sub-montane forest, grass, sucessional shrubland, schrub and sub-alpine Páramo. These categories of land cover coexist also with specific land use types, which are very importance not only in economical terms, but in social and cultural perspectives also [46]. Shifting cultivation is located mostly in upland areas, where slash and burning are usual tasks. Conventional agriculture is also developed in lower parts and quaternary landforms, in some cases under irrigation. Coffee plantations are very usual between 800 and 2000 m above sea level, occupying an important portion of the Sub-montane forest. In a small proportion, the extensive grazing shows a moderate development, being usually spatially confined to the low parts and the quaternary landforms [42]. Finally, the 1, 6 % of total surface is occupied by urban use, being the Boconó city the most important urban system in the area.

## **4. Methodological approach**

38 Environmental Land Use Planning

15, 55 m3/sec [33].

**Figure 1.** Location of the Study area

to the intense tectonic activity [37].

**3. The geographical context – study area** 

They were discussed in terms of the implications that such changes and transitions have for the natural resources management at the river basin level (watershed management). The results showed here are only a partial output of the still ongoing PhD project: "Spatial changes and hydrological dynamic of the Boconó River Basin, north venezuelan Andes",

The study was focused on the upland part of the Boconó River Basin, located in the southeast part of the State of Trujillo, between the coordinates 09°11'40" - 09°31'50" N and 70°04'08" – 70°22'53" W, with a surface area of 537.62 km2. The highest point in the Basin is 3400 m.a.s.l in the Páramo of Cendé, and the lowest point (outlet) is the confluence between the Boconó and Burate river (1100 m.a.s.l) (Fig. 1). The Boconó River drops from the northeast to the south-west, over a distance of approximately 57 km, having a mean runoff about

The area has a seasonally humid climate, having a wet period from April to October, and a dry period from November to March. Annual mean rainfall is about 1838 mm, and the annual mean temperature range from 19.7 °C to 21.5 °C [42]. The Basin has a relatively elongated form, and the drainage pattern is dendritic with a tendency to be rectangular, due

which is actually developed at the Eberhard Karls University – Tübingen, Germany.

In order to achieve the purpose of this project, a methodological approach combining remote sensing methods with spatial and multi-temporal analysis in GIS in an interactive way was implemented. At first, the study area was delineated from the SRTM data set (90 m spatial resolution) using the open source GIS software SAGA (System for Automated Geoscientific Analysis), in order to build the Digital Elevation Model (DEM), and also to prepare the basic thematic maps (Topography, Slope, Aspect, Drainage Network). Based on the structure pointed out by [47], the LULC mapping process was done in three main straightforward steps, as follows:

### **4.1. The pre - processing**

Three time-points were defined in order to analyse the LULC dynamic in the river basin: T0 (1988); T1 (1997); and T2 (2008). For each time-point a group of LANDSAT TM scenes

corresponding to missions 4, 5 and 7 were compiled from USGS LANDSAT Archive and the Institute of Geography (IGCRN) – ULA (Venezuela), which were considered suitable to the research requirements. The compilation process was quite difficult because the study area is frequently covered by dense clouds, especially during the rainy season. It means that the cloudiness and fog represented a challenge to deal with into the classification process, leading to compile additional scenes for special processing. Thus, the compiled scenes were classified in two groups: "**pilot**" scenes and "**control**" scenes. The first group included the main scenes to be classified for each time-point to be considered in the multi-temporal evaluation: 1988, 1997 and 2008, respectively. The second group were used as control images for the optimization of the classification for the first group, in order to improve the clustering processing in those areas covered by cloud, fog and shadows.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 41

supervised and unsupervised methods. Thus, the classes were correctly separated from the others. During the second – level classification, the clouds, fog and shadows were appropriately separated from other classes. They were used as mask scenes in order to cut the control images through spatial analysis, and finally they were processed like the "**mixed** 

All the clusters were merged to form twelve final classes using the grouping process. Additionally, a spatial modelling process was done in order to make the altitudinal differentiation of the LC in the river basin, defining the Land Cover categories in an ecological sense, following the ecosystem approach. For this purpose the DEM was combined with the classified images using the ecological criteria from Sarmiento & Ataroff in [50]. Thus, the Land cover categories delineated are virtually "ecosystems units". The classified scenes were finally filtered and exported to GIS software for the mapping creation

The classifications were validated using conventional methods, depending on the availability of the reference ancillary data. For the T0 classification, only a land use map for 1980 was available in a non digital format. This map was then used as a reference source for the validation. A total of 255 validation points corresponding to reference pixels were randomly selected using the "**stratified random**" sampling method. They were interactively compared with the digital reference map, and the results were stored in the Accuracy Assessment Cell Array (software ERDAS 9,3), which is simply a list of class values for the pixels in the classified image file and the class values for the corresponding reference pixels [51]. The tool finally calculated the error matrix and the corresponding basic statistics, including the Kappa Coefficient, which were listed in the Accuracy report. For T2, a field validation process was driven, combined with validation points defined using Quickbird high resolution scenes available on the "open source" software GOOGLE EARTH, through the same process described for the T0 scenes. Finally, the T1 Classification was validated using the maps for T0 and T2, defining validation points basically in areas considered

**4.4. Multi-temporal evaluation of LULC changes in the Boconó River Basin (Post** 

The multi-temporal evaluation process was conducted through spatial analysis in GIS. Hence, paired overlay was done in order to detect the changes occurred during the timeperiod considered. The Matrix operation used in this case allows two thematic images or vector files of different years to be compared [52]. This tool allowed to cross two different maps corresponding to the same area, in order to differentiate the changes occurred between the time-points. The resulting class values of a matrix operation are thus unique for each coincidence of two input class values described by rows (input layer 1) and columns (input layer 2) [53]; hence, the process produce two type of results: Maps which can

**clusters**", in the same way above described.

**4.3. The product generation process** 

and display processes.

persistent across the time-period.

**classification)** 

All the LANDSAT scenes compiled were pre-processed individually to make the geometric and radiometric correction, as well as the enhancement of some elements like brightness, contrast, haze reduction and equalization, in order to improve the image quality. All these processes were carried out interactively.

## **4.2. The LULC classification/analysis process**

The classification process was developed through a semi – supervised method, following a multi – level clustering for a multi – class segmentation of the scenes. The scenes were separately classified, a procedure considered highly flexible and extensively used in the past, with good results reported [47].

At the first level the scenes were classified through an unsupervised method using the "**hyperclustering approach**", a simple and relatively common approach to classify multiple LANDSAT scene mosaics. This classification approach generate many hyperclusters from the image data available by testing for within – cluster heterogeneity; then the hyperclusters can be merged into a smaller number of more reasonable groups which may resemble homogeneous classes, and finally label the resulting classes as spatial features of interest according to a pre-determined map legend or class hierarchy [48]. The process was done using the algorithm K-means available within the ISODATA decision-rule. In this case, the method was applied using 50 clusters to be classified after 24 iterations through the unsupervised approach (previous tests using 80 and 100 clusters, showed not many differences in the effective separation of the classes). The amount was then though reasonable to manage by the interpreter, and appropriate to differentiate the LULC classes in the study area.

Two groups of clusters were then identified: "**pure clusters**" representing categories with unique spectral signal; and "**mixed clusters**", having two or more categories with similar spectral signal, which is normal because LANDSAT imagery for tropical forest regions display minimal band separability among vegetation types, so that different types of categories can be usually difficult to separate [49]. The "**mixed clusters**" were prone to a second - level classification process. They were separated from the scene through masking process, and after that they were submitted into a second clustering process, using supervised and unsupervised methods. Thus, the classes were correctly separated from the others. During the second – level classification, the clouds, fog and shadows were appropriately separated from other classes. They were used as mask scenes in order to cut the control images through spatial analysis, and finally they were processed like the "**mixed clusters**", in the same way above described.

## **4.3. The product generation process**

40 Environmental Land Use Planning

processes were carried out interactively.

past, with good results reported [47].

in the study area.

**4.2. The LULC classification/analysis process** 

corresponding to missions 4, 5 and 7 were compiled from USGS LANDSAT Archive and the Institute of Geography (IGCRN) – ULA (Venezuela), which were considered suitable to the research requirements. The compilation process was quite difficult because the study area is frequently covered by dense clouds, especially during the rainy season. It means that the cloudiness and fog represented a challenge to deal with into the classification process, leading to compile additional scenes for special processing. Thus, the compiled scenes were classified in two groups: "**pilot**" scenes and "**control**" scenes. The first group included the main scenes to be classified for each time-point to be considered in the multi-temporal evaluation: 1988, 1997 and 2008, respectively. The second group were used as control images for the optimization of the classification for the first group, in order to improve the

All the LANDSAT scenes compiled were pre-processed individually to make the geometric and radiometric correction, as well as the enhancement of some elements like brightness, contrast, haze reduction and equalization, in order to improve the image quality. All these

The classification process was developed through a semi – supervised method, following a multi – level clustering for a multi – class segmentation of the scenes. The scenes were separately classified, a procedure considered highly flexible and extensively used in the

At the first level the scenes were classified through an unsupervised method using the "**hyperclustering approach**", a simple and relatively common approach to classify multiple LANDSAT scene mosaics. This classification approach generate many hyperclusters from the image data available by testing for within – cluster heterogeneity; then the hyperclusters can be merged into a smaller number of more reasonable groups which may resemble homogeneous classes, and finally label the resulting classes as spatial features of interest according to a pre-determined map legend or class hierarchy [48]. The process was done using the algorithm K-means available within the ISODATA decision-rule. In this case, the method was applied using 50 clusters to be classified after 24 iterations through the unsupervised approach (previous tests using 80 and 100 clusters, showed not many differences in the effective separation of the classes). The amount was then though reasonable to manage by the interpreter, and appropriate to differentiate the LULC classes

Two groups of clusters were then identified: "**pure clusters**" representing categories with unique spectral signal; and "**mixed clusters**", having two or more categories with similar spectral signal, which is normal because LANDSAT imagery for tropical forest regions display minimal band separability among vegetation types, so that different types of categories can be usually difficult to separate [49]. The "**mixed clusters**" were prone to a second - level classification process. They were separated from the scene through masking process, and after that they were submitted into a second clustering process, using

clustering processing in those areas covered by cloud, fog and shadows.

All the clusters were merged to form twelve final classes using the grouping process. Additionally, a spatial modelling process was done in order to make the altitudinal differentiation of the LC in the river basin, defining the Land Cover categories in an ecological sense, following the ecosystem approach. For this purpose the DEM was combined with the classified images using the ecological criteria from Sarmiento & Ataroff in [50]. Thus, the Land cover categories delineated are virtually "ecosystems units". The classified scenes were finally filtered and exported to GIS software for the mapping creation and display processes.

The classifications were validated using conventional methods, depending on the availability of the reference ancillary data. For the T0 classification, only a land use map for 1980 was available in a non digital format. This map was then used as a reference source for the validation. A total of 255 validation points corresponding to reference pixels were randomly selected using the "**stratified random**" sampling method. They were interactively compared with the digital reference map, and the results were stored in the Accuracy Assessment Cell Array (software ERDAS 9,3), which is simply a list of class values for the pixels in the classified image file and the class values for the corresponding reference pixels [51]. The tool finally calculated the error matrix and the corresponding basic statistics, including the Kappa Coefficient, which were listed in the Accuracy report. For T2, a field validation process was driven, combined with validation points defined using Quickbird high resolution scenes available on the "open source" software GOOGLE EARTH, through the same process described for the T0 scenes. Finally, the T1 Classification was validated using the maps for T0 and T2, defining validation points basically in areas considered persistent across the time-period.

## **4.4. Multi-temporal evaluation of LULC changes in the Boconó River Basin (Post classification)**

The multi-temporal evaluation process was conducted through spatial analysis in GIS. Hence, paired overlay was done in order to detect the changes occurred during the timeperiod considered. The Matrix operation used in this case allows two thematic images or vector files of different years to be compared [52]. This tool allowed to cross two different maps corresponding to the same area, in order to differentiate the changes occurred between the time-points. The resulting class values of a matrix operation are thus unique for each coincidence of two input class values described by rows (input layer 1) and columns (input layer 2) [53]; hence, the process produce two type of results: Maps which can

illustrate the changes in a spatial context (land cover change map); and a **cross-tabulation matrix** containing the differences in area for the different classes.

The **cross-tabulation matrix**, also denominated "**transition matrix**" follows the format displayed on Table 1. The rows display the categories of time 1, and the columns display the categories of time 2. Entries on the diagonal indicate persistence in the landscape between the time-period, meanwhile the entries off the diagonal indicate a transition from category "*i*" to a different category "*j*" [54].


**Table 1.** General cross-tabulation matrix for comparing two maps from different points in time

Starting from the matrix-values, the Gain (G*ij*) was calculated through the difference between the total value for time 2 (P*+j*) and the persistence (P*ij*), using the Eq 1:

$$\mathbf{G}\_{ij} = \mathbf{P}\_{+j} - \mathbf{P}\_{ji} \tag{1}$$

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

ଵିା (5)

ଵିା (6)

North Venezuelan Andes, and Its Implications for the Natural Resources Management 43

gain/loss in each category were to occur randomly [54]. The randomly expected gains for

ܩ݆݅ ൌ ሺାିሻ௫ା

In this case, the gain as well as the proportion for each category at time 2 is considered fixed, distributing the gain across the other categories according the relative proportion of the other categories in time 1. The procedure to calculate the randomly expected losses for each

ܮ݆݅ ൌ ሺାିሻ௫ା

As in the gain, the equation assumes that the loss of each category is fixed, and then distributes the loss across the other categories according to the relative proportion of the

Finally, the systematic transitions were identified trough a comparison between the

Twelve (12) LULC categories to be analyzed were identified in the Boconó River Basin for T0, T1 and T2 classifications. The Table 2 display the LULC categories, each with the corresponding identity-code, designation, as well as a brief description. The results showing the accuracy and the Kappa Coefficient for the three time-points are displayed on

1. - The Category Open-cleared Forest (Oc-F) correspond to the lower sectors of the Tropical Montane Cloudy Forest (Tmc-F), which are prone to a clearcutting process for logging and wood extraction, eliminating partly the canopy of the tallest forest species; the clearing alter greatly the phenological structure of the forest, resulting in a very specific and different spectral signal respect the climax or undisturbed forest. They were conveniently considered

2.- Coffee plantations constitute an important land use practice in the area; however, during the classification process the plantations (shade coffee) usually showed a very similar spectral signal as the Sub-montane Forest, which is the ecosystem where these plantations are usually located. They couldn't be effectively separated at this resolution level, and more detailed remote sensing material for the study area was no available. For that reason the coffee

The corresponding surface values for the time-points analyzed (T0, T1 and T2), are gently resumed on Table 4. An overview of the differences among the period, lead us to set up a

plantations were necessarily combined with the Category: Sub-montane Forest (Sm-F).

basic differentiation between the LULC categories in three main groups as follow:

each category were calculated using the Eq 5:

other categories in time 2.

**5. Results & discussion** 

category is quite similar to those explained above, using the Eq 6:

observed and expected values for gain and loss, for each category.

Table 3. Two important clarifications must be here pointed out:

**5.1. General quantification of the change** 

separated categories for practical purposes inherent to the research goals.

On the other hand, the Loss (L*ij*) was the difference between the total value for the time 1 file (P*j+*) and the persistence, using the Eq 2:

$$\mathbf{G}\_{ij} = \mathbf{P}\_{+j} - \mathbf{P}\_{ji} \tag{2}$$

The swapping (S*j*) between the categories was calculated as two times the minimum value of the gains and losses, through the Eq 3:

$$\mathbf{S}\_{j} = \mathbf{2} \times \text{MIN} \left( \mathbf{P}\_{j+} - \mathbf{P}\_{jj'} \; \mathbf{P}\_{+j} - \mathbf{P}\_{jj} \right) \tag{3}$$

The total change for each category (C*j*) was the sum of net change (D*j*) and the swapping (S*j*), or the sum of gain and loss (Eq 4):

$$\mathbf{C}\_{j} = \left(\mathbf{D}\_{j} + \mathbf{S}\_{j}\right) \tag{4}$$

In order to intend a more detailed analysis of the LULC changes, particularly the systematic inter-category transitions, the methodology proposed by [54] was applied, which analyze the off-diagonal entries to identify systematic transitions of land change for a given landscape´s degree of persistence. For that, the transitions must be interpreted relative to the sizes of the categories, leading to define the gain/loss that would be expected if the gain/loss in each category were to occur randomly [54]. The randomly expected gains for each category were calculated using the Eq 5:

$$Gilj = \frac{(p+j-Pj)\ge Pl+}{1-Pj+} \tag{5}$$

In this case, the gain as well as the proportion for each category at time 2 is considered fixed, distributing the gain across the other categories according the relative proportion of the other categories in time 1. The procedure to calculate the randomly expected losses for each category is quite similar to those explained above, using the Eq 6:

$$Llij = \frac{(Pl+-Pll)\ge P+j}{1-P+l} \tag{6}$$

As in the gain, the equation assumes that the loss of each category is fixed, and then distributes the loss across the other categories according to the relative proportion of the other categories in time 2.

Finally, the systematic transitions were identified trough a comparison between the observed and expected values for gain and loss, for each category.

## **5. Results & discussion**

42 Environmental Land Use Planning

"*i*" to a different category "*j*" [54].

(P*j+*) and the persistence, using the Eq 2:

the gains and losses, through the Eq 3:

or the sum of gain and loss (Eq 4):

illustrate the changes in a spatial context (land cover change map); and a **cross-tabulation** 

The **cross-tabulation matrix**, also denominated "**transition matrix**" follows the format displayed on Table 1. The rows display the categories of time 1, and the columns display the categories of time 2. Entries on the diagonal indicate persistence in the landscape between the time-period, meanwhile the entries off the diagonal indicate a transition from category

Category 1 *P11 P12 P13 P14 P1+ P1+ - P11* Category 2 *P21 P22 P23 P24 P2+ P2+ - P22* Category 3 *P31 P32 P33 P34 P3+ P3+ - P33* Category 4 *P41 P42 P43 P44 P4+ P4+ - P44*

**Table 1.** General cross-tabulation matrix for comparing two maps from different points in time

between the total value for time 2 (P*+j*) and the persistence (P*ij*), using the Eq 1:

Starting from the matrix-values, the Gain (G*ij*) was calculated through the difference

On the other hand, the Loss (L*ij*) was the difference between the total value for the time 1 file

The swapping (S*j*) between the categories was calculated as two times the minimum value of

The total change for each category (C*j*) was the sum of net change (D*j*) and the swapping (S*j*),

In order to intend a more detailed analysis of the LULC changes, particularly the systematic inter-category transitions, the methodology proposed by [54] was applied, which analyze the off-diagonal entries to identify systematic transitions of land change for a given landscape´s degree of persistence. For that, the transitions must be interpreted relative to the sizes of the categories, leading to define the gain/loss that would be expected if the

Time 2 Total Time 1 Loss

G P – P *ij j jj* (1)

G P – P *ij j jj* (2)

C D S *j jj* (4)

S 2 x MIN P P , P – P *j j jj <sup>j</sup> jj* (3)

**matrix** containing the differences in area for the different classes.

Time 1 Category 1 Category 2 Category 3 Category 4

Total Time 2 *P+1 P+2 P+3 P+4 1*  Gain *P+1 – P11 P+2 – P22 P+3 – P33 P+4 – P44* 

> Twelve (12) LULC categories to be analyzed were identified in the Boconó River Basin for T0, T1 and T2 classifications. The Table 2 display the LULC categories, each with the corresponding identity-code, designation, as well as a brief description. The results showing the accuracy and the Kappa Coefficient for the three time-points are displayed on Table 3. Two important clarifications must be here pointed out:

> 1. - The Category Open-cleared Forest (Oc-F) correspond to the lower sectors of the Tropical Montane Cloudy Forest (Tmc-F), which are prone to a clearcutting process for logging and wood extraction, eliminating partly the canopy of the tallest forest species; the clearing alter greatly the phenological structure of the forest, resulting in a very specific and different spectral signal respect the climax or undisturbed forest. They were conveniently considered separated categories for practical purposes inherent to the research goals.

> 2.- Coffee plantations constitute an important land use practice in the area; however, during the classification process the plantations (shade coffee) usually showed a very similar spectral signal as the Sub-montane Forest, which is the ecosystem where these plantations are usually located. They couldn't be effectively separated at this resolution level, and more detailed remote sensing material for the study area was no available. For that reason the coffee plantations were necessarily combined with the Category: Sub-montane Forest (Sm-F).

## **5.1. General quantification of the change**

The corresponding surface values for the time-points analyzed (T0, T1 and T2), are gently resumed on Table 4. An overview of the differences among the period, lead us to set up a basic differentiation between the LULC categories in three main groups as follow:

a. LULC categories losing surface: basically the natural LC like forest (Tmc-F, Oc-F, Sm-F) and Grass (Gr-L) were included here. All of them show a decreasing trend between T0 and T2 (except Gr-L, which experienced a light increase between T1 – T2). The Tmc-F and Oc-F had a reduction of 3530, 43 ha between T0 – T2, representing the 12, 8 % of the total for the two categories combined in 1988. The reduction of the Sm-F in the river basin was more dramatic, losing the 43, 1% of the surface area respect to 1988, that is, 3244, 59 ha. On the other hand, Gr-L loosed 412, 11 ha between T0-T1, and slightly recovered 85, 05 ha in the next period, losing a total of 327, 06 ha (9 % of the total in 1988).

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

Area (ha) Area (ha) Area (ha) T2-T0

T1-T0

Dif T2-T1 Dif total

North Venezuelan Andes, and Its Implications for the Natural Resources Management 45

LULC Categories 1988 (T0) 1997 (T1) 2008 (T2) Dif

**Table 4.** LULC evolution during the considered period

category gained 135, 36 ha (12%) respect to T0.

other categories.

Tropical Montane Cloudy Forest 24573,78 23676,12 22493,97 -897,66 -1182,15 -2079,81 Open-cleared Forest 2973,51 1648,8 1522,89 -1324,71 -125,91 -1450,62 Sub-Montane Forest 7523,1 6224,13 4278,51 -1298,97 -1945,62 -3244,59 Scrub 1142,37 1199,88 1277,73 57,51 77,85 135,36 Grasland 3662,82 3250,71 3335,76 -412,11 85,05 -327,06 Sub-andean Paramo 1114,2 1117,71 1114,47 3,51 -3,24 0,27 Grassland (Anthropogenic) 1280,34 1181,16 2832,03 -99,18 1650,87 1551,69 Cropping Area 2202,84 2330,46 2867,4 127,62 536,94 664,56 Eroded Land 28,62 27,27 39,87 -1,35 12,6 11,25 Urban Area 433,98 729,18 865,26 295,2 136,08 431,28 Flooding plain 234,9 391,95 293,76 157,05 -98,19 58,86 Sucessional Shrubland 8591,67 11984,76 12840,75 3393,09 855,99 4249,08 Total 53762,13 53762,13 53762,13 - - -

b. LULC categories gaining surface: they are basically the human-induced types of land cover categories (Gr-An, Cro-L and Ur-U), as well as the categories: Schr and S-Shr. They increased progressively during the period, except Gr-An, which experienced a decrease in T0 – T1; however, the evident increase experienced during T1-T2 justify the inclusion of the category in this group. Gr-An and Cro-L combined, gained 2216, 25 ha, representing an increase of 63, 6 % of the agriculture in the river basin respect 1988. The Urban use (Ur-U) experienced a dramatic increase during the whole period, gaining 99,36 % (431,28 ha) of the surface area that the category occupied in T0. Meanwhile, the LC category S-Shr experienced a big change, gaining almost 50% (49, 5%) of the surface area for T0; so it gained a total of 4249, 08 ha. respect 1988. During the period Schr

c. Relatively stable LULC categories: here are included the rest of the LC categories: Sa-P, Ero-L and Fl-P. These categories showed a similar pattern during the whole period, in which they loosed and gained surface, but maintaining its proportionality respect the rest of the LULC categories. The Fl-P gained 157, 05 ha (67%) because of the flooding events occurred during the T0-T1. But in the second time-period it loosed 98, 19 ha to

These basic groups illustrate the general trends for the recent evolution of the LULCC in the river basin. However, they are only the initial framework to understand the spatial dynamic in the study area, so they cannot reflect conveniently the spatial changes in a quantitative/qualitative way. The next section provides a more comprehensive and detailed


**Table 2.** Land Use / Land Cover (LULC) Categories identified in the Boconó River Basin.


**Table 3.** Main results obtained in the Accuracy assessment for the T0, T1 and T2 classifications.

LULC Categories 1988 (T0) 1997 (T1) 2008 (T2) Dif T1-T0 Dif T2-T1 Dif total Area (ha) Area (ha) Area (ha) T2-T0 Tropical Montane Cloudy Forest 24573,78 23676,12 22493,97 -897,66 -1182,15 -2079,81 Open-cleared Forest 2973,51 1648,8 1522,89 -1324,71 -125,91 -1450,62 Sub-Montane Forest 7523,1 6224,13 4278,51 -1298,97 -1945,62 -3244,59 Scrub 1142,37 1199,88 1277,73 57,51 77,85 135,36 Grasland 3662,82 3250,71 3335,76 -412,11 85,05 -327,06 Sub-andean Paramo 1114,2 1117,71 1114,47 3,51 -3,24 0,27 Grassland (Anthropogenic) 1280,34 1181,16 2832,03 -99,18 1650,87 1551,69 Cropping Area 2202,84 2330,46 2867,4 127,62 536,94 664,56 Eroded Land 28,62 27,27 39,87 -1,35 12,6 11,25 Urban Area 433,98 729,18 865,26 295,2 136,08 431,28 Flooding plain 234,9 391,95 293,76 157,05 -98,19 58,86 Sucessional Shrubland 8591,67 11984,76 12840,75 3393,09 855,99 4249,08 Total 53762,13 53762,13 53762,13 - - -

Land Use and Land Cover (LULC) Change in the Boconó River Basin, North Venezuelan Andes, and Its Implications for the Natural Resources Management 45

**Table 4.** LULC evolution during the considered period

44 Environmental Land Use Planning

1988).

a. LULC categories losing surface: basically the natural LC like forest (Tmc-F, Oc-F, Sm-F) and Grass (Gr-L) were included here. All of them show a decreasing trend between T0 and T2 (except Gr-L, which experienced a light increase between T1 – T2). The Tmc-F and Oc-F had a reduction of 3530, 43 ha between T0 – T2, representing the 12, 8 % of the total for the two categories combined in 1988. The reduction of the Sm-F in the river basin was more dramatic, losing the 43, 1% of the surface area respect to 1988, that is, 3244, 59 ha. On the other hand, Gr-L loosed 412, 11 ha between T0-T1, and slightly recovered 85, 05 ha in the next period, losing a total of 327, 06 ha (9 % of the total in

**Table 2.** Land Use / Land Cover (LULC) Categories identified in the Boconó River Basin.

Indicator T0 (1988) T1 (1997) T2 (2008)

Producers Accuracy 87,46 85,02 91,53 Users Accuracy 87,62 82,90 91,67 Total Accuracy 87,35 82,59 88,80 Kappa Coefficient 0,79 0,79 0,87

**Table 3.** Main results obtained in the Accuracy assessment for the T0, T1 and T2 classifications.


These basic groups illustrate the general trends for the recent evolution of the LULCC in the river basin. However, they are only the initial framework to understand the spatial dynamic in the study area, so they cannot reflect conveniently the spatial changes in a quantitative/qualitative way. The next section provides a more comprehensive and detailed

description of the LULC categorical changes for the two time-periods, in terms of quantification, net change, swapping as well as inter-category transitions.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 47

represent the 22,4 % (12037,6 ha), of the total surface. It showed also the highest values for gain and losses respect the rest of LULC. During the period, S-Shr gained 14, 35% of surface area, losing at the same time 8 % to other categories. This category has also the highest value for swapping (16,1 % of the surface area), which means that this LCC constantly experienced changes during the period, losing surface area to other categories and gaining at the same time area from other categories whose changed to this one. Thus, 72% of the change for this

The second more dynamic category in the area was Sm-F, which experienced a total change of 4485, 51 ha, representing the 8, 3% of the total surface area. In this period Sm-F gained 1593, 27 ha (third highest value), which in many cases could represent an expansion of the shade coffee plantations in the area (included in this category). However, it lost 2892, 24 ha (second highest value) to other categories, representing an important reduction of the forested cover in the area. The category has the third highest value of swapping (3186, 54 ha), which suggest that the Sub-montane Forest also experienced a swapping-change

The third category experiencing important changes in the period is the Oc-F, with a total change value of 3721, 41 ha, (7 % of the total area). The Open-cleared Forest gained the fifth biggest portion of surface: 1198, 35 ha, suggesting that the clearcutting and logging in the lowest part of Tmc-F were intense during the period. However, it lost 2523, 06 ha (third

category occurred as swapping-change dynamic.

**Figure 2.** Persistence and changing area in Boconó River Basin

dynamic.

The Figure 2 show the spatial distribution of the changes in the Boconó River Basin, which occurred within the both periods: T0 - T1 and T1 – T2. In the first period the River Basin experienced a total change of 30,34%, which means that 16309,89 ha were affected by a kind of spatial change processes, meanwhile the 69,66% of the surface area (37452,24 ha) was accounted as persistent landscape or simply persistence. Thus, persistence dominates widely the landscape system of the River Basin, which is considered normal, because the persistence usually dominates most landscapes, including those where authors claim that the change is important and / or large [54].

[55] accounted 92% of persistence for natural land covers in Mexico; in the Atlanta metropolitan area (one of the USA´s fastest growing metropolises), there have been 75% persistence over the last 3 decades (Yang & Lo, 2002) in [54]. [56] determined a persistence of 94, 2% in the community of Madrid – Spain. [57] accounted 93, 3 of landscape persistence in the State of Mexico – Mexico. Finally, [30] also detected a persistence of 80, 5% in the Catamayo-Chira Basin (Ecuador – Peru).

Although the persistence dominates the landscape, as usual, the persistence value of the Boconó River Basin can be considered slightly lower in comparison with those values above mentioned. This fact is important to highlight, considering that the whole river basin is defined as "**Protected Area**", with a portion of the surface area also belonging to the **Guaramacal National Park**.

In the second period the total change was slight higher, with 18464, 7 ha affected by a type of change, representing the 34, 35% of the total area, and the persistence value descended to 65, 65 % of the total surface (35297, 46 ha).

As seen on Figure 2, the change have been occurring in the middle – lower part of the river basin, basically across the sloping dissected areas, the river valley and some extensive quaternary landforms located in the lowest part; in this case, the LULC categories coexist in a very intricate way, showing a very complex and strong patching effect, which is typical of landscapes where the categories are highly fragmented, originating the so – called "**chessboard effect**" or "**chessboard landscape**" [58].

## **5.2. Landscape dynamic: A more detailed view of changes in the River Basin**

A more detailed analysis of the transition Matrix derived for the two combined time-periods (T0-T1 and T1 – T2), using the approach proposed by [54], lead to interpret the changes in a more detailed perspective, as follows:

## *5.2.1. Net change and swapping*

The Table 5 resume the landscape dynamic observed for the period T0 – T1. S-Shr was the most dynamic category in the river basin during this period, having a total change which represent the 22,4 % (12037,6 ha), of the total surface. It showed also the highest values for gain and losses respect the rest of LULC. During the period, S-Shr gained 14, 35% of surface area, losing at the same time 8 % to other categories. This category has also the highest value for swapping (16,1 % of the surface area), which means that this LCC constantly experienced changes during the period, losing surface area to other categories and gaining at the same time area from other categories whose changed to this one. Thus, 72% of the change for this category occurred as swapping-change dynamic.

**Figure 2.** Persistence and changing area in Boconó River Basin

46 Environmental Land Use Planning

the change is important and / or large [54].

Catamayo-Chira Basin (Ecuador – Peru).

65, 65 % of the total surface (35297, 46 ha).

more detailed perspective, as follows:

*5.2.1. Net change and swapping* 

"**chessboard effect**" or "**chessboard landscape**" [58].

**Guaramacal National Park**.

description of the LULC categorical changes for the two time-periods, in terms of

The Figure 2 show the spatial distribution of the changes in the Boconó River Basin, which occurred within the both periods: T0 - T1 and T1 – T2. In the first period the River Basin experienced a total change of 30,34%, which means that 16309,89 ha were affected by a kind of spatial change processes, meanwhile the 69,66% of the surface area (37452,24 ha) was accounted as persistent landscape or simply persistence. Thus, persistence dominates widely the landscape system of the River Basin, which is considered normal, because the persistence usually dominates most landscapes, including those where authors claim that

[55] accounted 92% of persistence for natural land covers in Mexico; in the Atlanta metropolitan area (one of the USA´s fastest growing metropolises), there have been 75% persistence over the last 3 decades (Yang & Lo, 2002) in [54]. [56] determined a persistence of 94, 2% in the community of Madrid – Spain. [57] accounted 93, 3 of landscape persistence in the State of Mexico – Mexico. Finally, [30] also detected a persistence of 80, 5% in the

Although the persistence dominates the landscape, as usual, the persistence value of the Boconó River Basin can be considered slightly lower in comparison with those values above mentioned. This fact is important to highlight, considering that the whole river basin is defined as "**Protected Area**", with a portion of the surface area also belonging to the

In the second period the total change was slight higher, with 18464, 7 ha affected by a type of change, representing the 34, 35% of the total area, and the persistence value descended to

As seen on Figure 2, the change have been occurring in the middle – lower part of the river basin, basically across the sloping dissected areas, the river valley and some extensive quaternary landforms located in the lowest part; in this case, the LULC categories coexist in a very intricate way, showing a very complex and strong patching effect, which is typical of landscapes where the categories are highly fragmented, originating the so – called

**5.2. Landscape dynamic: A more detailed view of changes in the River Basin** 

A more detailed analysis of the transition Matrix derived for the two combined time-periods (T0-T1 and T1 – T2), using the approach proposed by [54], lead to interpret the changes in a

The Table 5 resume the landscape dynamic observed for the period T0 – T1. S-Shr was the most dynamic category in the river basin during this period, having a total change which

quantification, net change, swapping as well as inter-category transitions.

The second more dynamic category in the area was Sm-F, which experienced a total change of 4485, 51 ha, representing the 8, 3% of the total surface area. In this period Sm-F gained 1593, 27 ha (third highest value), which in many cases could represent an expansion of the shade coffee plantations in the area (included in this category). However, it lost 2892, 24 ha (second highest value) to other categories, representing an important reduction of the forested cover in the area. The category has the third highest value of swapping (3186, 54 ha), which suggest that the Sub-montane Forest also experienced a swapping-change dynamic.

The third category experiencing important changes in the period is the Oc-F, with a total change value of 3721, 41 ha, (7 % of the total area). The Open-cleared Forest gained the fifth biggest portion of surface: 1198, 35 ha, suggesting that the clearcutting and logging in the lowest part of Tmc-F were intense during the period. However, it lost 2523, 06 ha (third

biggest amount) to other categories, showing that the clearcutting and logging was also intense within the category. A total of 2396, 7 ha (fifth highest value) were swappingchange dynamic for this category.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

of net change

North Venezuelan Andes, and Its Implications for the Natural Resources Management 49

tends to be cero. These values lead to affirm that the LCC and particularly the Forested LCC

The Table 6 resume the landscape dynamic for the second period T1 – T2. Some slight differences can be observed respect to the last period. S-Shr remains as the most dynamic category, with a total change value of 11750, 85 ha (22 % of the total area). It gained 6303, 42 ha and lost 5447, 43 ha. The 93% of the total value for this category (10894, 86 ha), occurred as swapping-change dynamic. Sm-F remains in the second position, with a total change of 3638, 88 ha (7 % of the total area). It gained less surface than in the last period (846, 99 ha), which is the seventh observed value for the period. Meanwhile, the losses remained high, having the second highest value for the period (2791, 89 ha). A total of 1693, 98 ha changed

Gain Loss Total Change Swap Absolute value

ha % ha % ha % ha % ha %

Tmc-F 877,5 1,632 2023,74 3,764 2901,24 5,396 1755,0 3,266 1146,24 2,132 Oc\_F 990,63 1,843 1113,21 2,071 2103,84 3,914 1981,26 3,686 122,58 0,228 Sm-F 846,99 1,575 2791,89 5,193 3638,88 6,768 1693,98 3,152 1944,9 3,618 Schr 29,25 0,054 1,35 0,003 30,6 0,057 2,7 0,006 27,9 0,051 Gr-L 1731,78 3,221 1646,73 3,063 3378,51 6,284 3293,46 6,126 85,05 0,158 Sa-P 7,11 0,013 0,63 0,001 7,74 0,014 1,26 0,002 6,48 0,012 Gr-An 2588,85 4,815 937,98 1,745 3526,83 6,560 1875,96 3,490 1650,87 3,070 Cro-L 2000,25 3,721 1463,31 2,722 3463,56 6,443 2926,62 5,444 536,94 0,999 Ero-L 17,46 0,032 4,86 0,009 22,32 0,041 9,72 0,018 12,6 0,023 Ur-U 138,24 0,257 2,16 0,004 140,4 0,261 4,32 0,008 136,08 0,253 Fl-P 36,54 0,068 134,73 0,251 171,27 0,319 73,08 0,136 98,19 0,183 S-Shr 6303,42 11,725 5447,43 10,132 11750,85 21,857 10894,86 20,264 855,99 1,593 Total 15568 28,956 15568 28,958 15568,02 28,957 12256,11 22,799 3311,91 6,16

**Table 6.** Landscape Dynamic in the Boconó River Basin for the Period T1 – T2 (1997-2008)

high value for swapping (2926, 62 ha) which is the third highest value for the period.

The Gr-L had a total change of 3378, 51 ha (6, 3% of total area), as the fifth changing category. It maintained the same trend as in the last period, gaining 1731, 78 ha, losing 1646, 73 ha, with 3293, 46 ha as swapping-change value. The sixth position in this period was for the Tmc-F, which showed a total change of 2901, 24 ha (5, 4% of the total area). It showed the

The third category experiencing changes in the period is Gr-An, with a value of 3526, 83 ha (6, 6% of total area) for total change. It had the second higher value for gains in the period (2588, 85 ha), meanwhile the losses (937, 98 ha), were lower in comparison to the last period. Of the total value, 1875, 96 ha changed in a swapping-change form. The fourth position in this period is for Cro-L, having a value of 3463, 56 ha (6, 4% of the total area). Cropland gained 2000, 25 ha (the 3rd highest value) during the period, losing 1463, 31 ha (5th value), which can be explained for the type of agriculture applied in the area (small/scale agriculture with shifting cultivation and slash and burn practices). This could explain the

experienced the most important net changes in the river basin during this period.

in a swapping-change form.

LULC Category


**Table 5.** Landscape Dynamic in the Boconó River Basin for the Period T0 – T1 (1988 – 1997).

The fourth position in terms of total change (3542, 31 ha), gains (1565, 1 ha), loses (1977, 21 ha) and swapping (3130, 2 ha), is for Grassland; the balance between gains and losses, as well the swapping value, suggest that this category has a strong interaction with other LULCC. The fifth changing category with a total change of 3450, 78 ha (6, 4 % of the total area) is Cro-L, suggesting that the cropping area also experienced important changes during the period. The category gained 1789, 2 ha, which is the second highest value for the period, losing also 1661, 58 ha (sixth value). With the second highest value (3323, 16 ha), Cro-L experienced also a swapping-change dynamic in the area.

Tcm-F is located in the sixth position of total changes, with a total value of 2482, 56 ha (4, 6 % of the total). The category gained 792, 45 ha (seventh value), but lost 1690, 11 ha; meanwhile, 1584, 9 ha were accounted as swapping-change. Finally, Gr-An showed the seventh highest change, with 2270, 16 ha (4, 2 % of the total area). It gained 1085, 49 ha and lost 1184, 67 ha, with a swapping value of 2170, 98 ha.

Despite of the dynamic above described the values for net change shows some differences among the positions between categories. Having the highest net value of 3393, 09 ha, the S-Shr remains as the most dynamic category for the period. The Oc-F had the second highest net change value (1324, 71 ha), and the third position was for Sm-F (1298, 97 ha). The Tropical Montane Cloudy Forest had the fourth highest net change value (947, 66 ha), followed by Gr-L (412, 11 ha), and the sixth position is for the category Ur-U, with a net change value of 295, 2 ha (most of the change in this category is net change, as usual), and a swapping value which tends to be cero. These values lead to affirm that the LCC and particularly the Forested LCC experienced the most important net changes in the river basin during this period.

48 Environmental Land Use Planning

LULC Categor y

change dynamic for this category.

biggest amount) to other categories, showing that the clearcutting and logging was also intense within the category. A total of 2396, 7 ha (fifth highest value) were swapping-

Tmc-F 792,45 1,474 1690,11 3,143 2482,56 4,617 1584,9 2,948 947,66 1,669 Oc-F 1198,35 2,229 2523,06 4,693 3721,41 6,922 2396,7 4,458 1324,71 2,464 Sm-F 1593,27 2,963 2892,24 5,380 4485,51 8,343 3186,54 5,926 1298,97 2,417 Schr 58,86 0,109 1,35 0,003 60,21 0,112 2,7 0,006 57,51 0,106 Gr-L 1565,1 2,911 1977,21 3,678 3542,31 6,589 3130,2 5,822 412,11 0,767 Sa-P 7,29 0,014 3,78 0,007 11,07 0,021 7,56 0,024 3,51 0,003 Gr-An 1085,49 2,019 1184,67 2,204 2270,16 4,223 2170,98 4,038 99,18 0,185 Cro-L 1789,2 3,328 1661,58 3,091 3450,78 6,419 3323,16 6,180 127,62 0,237 Ero-L 13,59 0,025 14,94 0,028 28,53 0,053 27,18 0,052 1,35 0,003 Ur-U 295,92 0,550 0,72 0,001 296,64 0,551 1,44 0,004 295,2 0,549 Fl-P 195,03 0,363 37,98 0,071 233,01 0,434 75,96 0,142 157,05 0,292 S-Shr 7715,34 14,350 4322,25 8,040 12037,6 22,390 8644,5 16,080 3393,09 6,310 Total 16309,89 30,335 16309,89 30,339 16309,89 30,337 12275,91 22,84 4058,98 7,501

**Table 5.** Landscape Dynamic in the Boconó River Basin for the Period T0 – T1 (1988 – 1997).

experienced also a swapping-change dynamic in the area.

lost 1184, 67 ha, with a swapping value of 2170, 98 ha.

The fourth position in terms of total change (3542, 31 ha), gains (1565, 1 ha), loses (1977, 21 ha) and swapping (3130, 2 ha), is for Grassland; the balance between gains and losses, as well the swapping value, suggest that this category has a strong interaction with other LULCC. The fifth changing category with a total change of 3450, 78 ha (6, 4 % of the total area) is Cro-L, suggesting that the cropping area also experienced important changes during the period. The category gained 1789, 2 ha, which is the second highest value for the period, losing also 1661, 58 ha (sixth value). With the second highest value (3323, 16 ha), Cro-L

Tcm-F is located in the sixth position of total changes, with a total value of 2482, 56 ha (4, 6 % of the total). The category gained 792, 45 ha (seventh value), but lost 1690, 11 ha; meanwhile, 1584, 9 ha were accounted as swapping-change. Finally, Gr-An showed the seventh highest change, with 2270, 16 ha (4, 2 % of the total area). It gained 1085, 49 ha and

Despite of the dynamic above described the values for net change shows some differences among the positions between categories. Having the highest net value of 3393, 09 ha, the S-Shr remains as the most dynamic category for the period. The Oc-F had the second highest net change value (1324, 71 ha), and the third position was for Sm-F (1298, 97 ha). The Tropical Montane Cloudy Forest had the fourth highest net change value (947, 66 ha), followed by Gr-L (412, 11 ha), and the sixth position is for the category Ur-U, with a net change value of 295, 2 ha (most of the change in this category is net change, as usual), and a swapping value which

Gain Loss Total Change Swap Absolute value

ha % ha % ha % ha % ha %

of net change

The Table 6 resume the landscape dynamic for the second period T1 – T2. Some slight differences can be observed respect to the last period. S-Shr remains as the most dynamic category, with a total change value of 11750, 85 ha (22 % of the total area). It gained 6303, 42 ha and lost 5447, 43 ha. The 93% of the total value for this category (10894, 86 ha), occurred as swapping-change dynamic. Sm-F remains in the second position, with a total change of 3638, 88 ha (7 % of the total area). It gained less surface than in the last period (846, 99 ha), which is the seventh observed value for the period. Meanwhile, the losses remained high, having the second highest value for the period (2791, 89 ha). A total of 1693, 98 ha changed in a swapping-change form.


**Table 6.** Landscape Dynamic in the Boconó River Basin for the Period T1 – T2 (1997-2008)

The third category experiencing changes in the period is Gr-An, with a value of 3526, 83 ha (6, 6% of total area) for total change. It had the second higher value for gains in the period (2588, 85 ha), meanwhile the losses (937, 98 ha), were lower in comparison to the last period. Of the total value, 1875, 96 ha changed in a swapping-change form. The fourth position in this period is for Cro-L, having a value of 3463, 56 ha (6, 4% of the total area). Cropland gained 2000, 25 ha (the 3rd highest value) during the period, losing 1463, 31 ha (5th value), which can be explained for the type of agriculture applied in the area (small/scale agriculture with shifting cultivation and slash and burn practices). This could explain the high value for swapping (2926, 62 ha) which is the third highest value for the period.

The Gr-L had a total change of 3378, 51 ha (6, 3% of total area), as the fifth changing category. It maintained the same trend as in the last period, gaining 1731, 78 ha, losing 1646, 73 ha, with 3293, 46 ha as swapping-change value. The sixth position in this period was for the Tmc-F, which showed a total change of 2901, 24 ha (5, 4% of the total area). It showed the

same trend for gain as in the last period (877, 5 ha), but the losses were quite higher (2023, 74 ha), with 1755, 0 ha as swapping-change dynamic value.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 51

contribute to explain the other change patterns occurring in the rest of categories,

particularly in the human-induced types of Land Cover.

Ov: Observed Value/ Ev: Expected Value

**Table 7.** The most systematic transitions occurred in T0-T1, in terms of Losses

Finally, the Oc-F descended to the seventh position in the period, showing a total change of 2103, 84 ha (3, 9% of the total area). It gained 990, 63 ha, and lost 1113,21 ha, with a swapping value of 1981, 26 ha for the period.

The dynamic showed by the net change values changed slightly respect the last period. The category with the highest net change value was Sm-F (1944, 9 ha), followed by Gr-An (1650, 87 ha); and the Tmc-F reached the third position, with a net change of 1146 ha. S-Shr descended to the fourth position with 855, 99 ha, followed by Cro-L (536, 94 ha) and Ur-U in the sixth position, with a net change value of 136, 08 ha (most of the change occurring as net change).

## *5.2.2. Systematic Inter-category transitions in the landscape system*

Now is possible to derive the categorical trajectory of the changes which have been occurring in the river basin during the considered period. The Table 7 accounts for the most important inter-category transitions for T0-T1 in terms of Losses. The magnitude of the ratio (fifth column) indicates in all cases the strength of the systematic transition between categories [54].

The first thirteen rows on Table 7 indicate spatial patterns or transitions affecting the Forested Land Covers in the River Basin: Tmc-F, Oc-F and Sm-F. These transitions indicate changes associated with deterioration, decrease or disappearance of the Forested areas, depending on the LULC category for which the forested categories have been migrating during the period. For example, the first transition process: Tmc-F – Oc-F indicate that the Tropical Montane Cloudy Forest changed to Open-cleared Forest in 3,764 times more than would be expected If the change were to occur randomly, losing 348,65 ha more than the expected value. This transition, together with the second one, indicate that the TMCF is changing systematically to an intermediate stage (Open-cleared Forest or Successional Shrubland), before it can finally change or migrate to any human–induced types of Land Use categories (Gr-An or Cro-L). No transitions from Tmc-F to Land Use categories were observed. Similar transitional trends were observed in the Highlands of Chiapas – Mexico by [13] and [59], being also described in two different regions in Chile [60][61].

The processes driving the transitions of the Tmc-F are basically associated with: clearcutting, logging, wood extraction and also plants and non-wood extraction. These processes could have been occurring in a successive way, and particularly the logging is probably occurring in a selective form, as observed during the field validation. The selective extraction or harvesting of non-wood products (like Orchids and Bromeliads), has been also reported as a critical problem occurring in this ecosystem [9].

Another example is the transition Sm-F – Ero-L, indicating that in this portion of the surface area, the clearcutting/ logging processes derived in severe land degradation processes like erosion in 6,428 times more than expected, affecting 12, 33 ha. The rest of transitions


contribute to explain the other change patterns occurring in the rest of categories, particularly in the human-induced types of Land Cover.

Ov: Observed Value/ Ev: Expected Value

50 Environmental Land Use Planning

change).

categories [54].

ha), with 1755, 0 ha as swapping-change dynamic value.

*5.2.2. Systematic Inter-category transitions in the landscape system* 

swapping value of 1981, 26 ha for the period.

same trend for gain as in the last period (877, 5 ha), but the losses were quite higher (2023, 74

Finally, the Oc-F descended to the seventh position in the period, showing a total change of 2103, 84 ha (3, 9% of the total area). It gained 990, 63 ha, and lost 1113,21 ha, with a

The dynamic showed by the net change values changed slightly respect the last period. The category with the highest net change value was Sm-F (1944, 9 ha), followed by Gr-An (1650, 87 ha); and the Tmc-F reached the third position, with a net change of 1146 ha. S-Shr descended to the fourth position with 855, 99 ha, followed by Cro-L (536, 94 ha) and Ur-U in the sixth position, with a net change value of 136, 08 ha (most of the change occurring as net

Now is possible to derive the categorical trajectory of the changes which have been occurring in the river basin during the considered period. The Table 7 accounts for the most important inter-category transitions for T0-T1 in terms of Losses. The magnitude of the ratio (fifth column) indicates in all cases the strength of the systematic transition between

The first thirteen rows on Table 7 indicate spatial patterns or transitions affecting the Forested Land Covers in the River Basin: Tmc-F, Oc-F and Sm-F. These transitions indicate changes associated with deterioration, decrease or disappearance of the Forested areas, depending on the LULC category for which the forested categories have been migrating during the period. For example, the first transition process: Tmc-F – Oc-F indicate that the Tropical Montane Cloudy Forest changed to Open-cleared Forest in 3,764 times more than would be expected If the change were to occur randomly, losing 348,65 ha more than the expected value. This transition, together with the second one, indicate that the TMCF is changing systematically to an intermediate stage (Open-cleared Forest or Successional Shrubland), before it can finally change or migrate to any human–induced types of Land Use categories (Gr-An or Cro-L). No transitions from Tmc-F to Land Use categories were observed. Similar transitional trends were observed in the Highlands of Chiapas – Mexico

by [13] and [59], being also described in two different regions in Chile [60][61].

critical problem occurring in this ecosystem [9].

The processes driving the transitions of the Tmc-F are basically associated with: clearcutting, logging, wood extraction and also plants and non-wood extraction. These processes could have been occurring in a successive way, and particularly the logging is probably occurring in a selective form, as observed during the field validation. The selective extraction or harvesting of non-wood products (like Orchids and Bromeliads), has been also reported as a

Another example is the transition Sm-F – Ero-L, indicating that in this portion of the surface area, the clearcutting/ logging processes derived in severe land degradation processes like erosion in 6,428 times more than expected, affecting 12, 33 ha. The rest of transitions

**Table 7.** The most systematic transitions occurred in T0-T1, in terms of Losses

As seen on Table 7, Gr-L is basically migrating to Gr-An (174, 33 ha), Cro-L (316, 53 ha) and S-Shr (1224, 45 ha), and with less importance, to Fl-P (31, 32 ha) and Ur-U (31, 32 ha), respectively. Gr-An is basically migrating to Gr-L in 2,146 times more than expected (230, 4 ha). This contributes to explain the high swapping value observed for Gr-L during the period. The category Cro-L migrated to Ur-U in 3,164 times more than expected (98, 1 ha); to Fl-P in 2,874 (49, 05 ha), and to S-Shr in 1,187 times more than expected (846, 63 ha). Particularly the transition Cro-L – Fl-P indicates that the hydrological dynamic of the river, especially the peak flows or flooding events, affected cropping areas. The transition Ero-L – Fl-P suggests an intense hydrological dynamic during the period, which augmented the sediments emission of the river. [62] determined that the yield of sediments in the whole catchment area have increased by 914 % with respect of the estimated value in order to build the Boconó-Tucupido Dam System, located dowmstreams in the lowland region.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 53

Ov: Observed Value/ Ev: Expected Value

**Table 8.** The most systematic transitions occurred in T0-T1, in terms of Gains

The fact that the urban areas have been growing at the expense of croplands is corroborated again with the transition Cro-L – Ur-U, which indicates that the urban areas grew up from

The transition Ur-U – Fl-P also suggest that the hydrological events occurred during the period, affected the urban area of Boconó city, which had been expanding across the fluvial plain of the River; it can be corroborated some rows below, with the transition Fl-P – Ur-U, in which the urban area grew up across the Flooding Plain 11,625 times more than expected (6,57 ha). Important flooding events occurred in 1988, 1989, 1991 and 1995 were analyzed by [63]; unfortunately, the historical data for the River Basin is quite deficient and no more reference data exist since 1997.

Finally, the transitions for the category S-Shr suggest a trend for the category to migrate to the human-induced types of Land Cover categories Gr-An (3,070 times more than expected); Cro-L (2,179 times more than expected) and Ur-U (0,114 times more than expected). The rest of the transitions suggest a regeneration process. Shrubland was also observed as a highly dynamic category in the Kalu District-Ethiopia by [64], and also in Central Chile by [60], which can be explained by the forms of cultivation above mentioned, mostly typical in these regions.

The Table 8 shows the most systematic inter-category transitions occurred in the period T0 – T1 in terms of gain. The first twelve transitions are associated to changes in the Forested Land Covers. Particularly the transition Sm-F – Ero-L indicate erosion processes occurring after the clearcutting of the Sub-montane Forest, in 5,489 times more than expected, affecting a total of 12,33 ha. On the other hand, the transitions Gr-An – Gr-l (4,760); Gr-An – S-Shr (2,231), and Gr-An - Sm-F (0,232) suggest a regeneration/revegetation process.

As seen on Table 8, the cropland area in the river basin is growing at the expense of the categories: Oc-F (176, 76 ha), Sm-F (339, 48 ha), Gr-L (316, 53 ha), Gr-An (100, 89 ha), and S-Shr (766, 53 ha). On the other hand, the Gr-An gained surface area migrating basically from: Oc-F (168, 93 ha), Gr-L (174, 33 ha), Cro-L (70, 11 ha), and from S-Shr (497, 34 ha).

The transition Cro-L – Sm-F could to indicate regeneration, or perhaps a change to coffee plantation, or a combination of both scenarios. The transition Cro-L – Gr-L could be explained by the type of cultivation usually practiced in the area, above mentioned.

#### Land Use and Land Cover (LULC) Change in the Boconó River Basin, North Venezuelan Andes, and Its Implications for the Natural Resources Management 53


Ov: Observed Value/ Ev: Expected Value

52 Environmental Land Use Planning

reference data exist since 1997.

regions.

As seen on Table 7, Gr-L is basically migrating to Gr-An (174, 33 ha), Cro-L (316, 53 ha) and S-Shr (1224, 45 ha), and with less importance, to Fl-P (31, 32 ha) and Ur-U (31, 32 ha), respectively. Gr-An is basically migrating to Gr-L in 2,146 times more than expected (230, 4 ha). This contributes to explain the high swapping value observed for Gr-L during the period. The category Cro-L migrated to Ur-U in 3,164 times more than expected (98, 1 ha); to Fl-P in 2,874 (49, 05 ha), and to S-Shr in 1,187 times more than expected (846, 63 ha). Particularly the transition Cro-L – Fl-P indicates that the hydrological dynamic of the river, especially the peak flows or flooding events, affected cropping areas. The transition Ero-L – Fl-P suggests an intense hydrological dynamic during the period, which augmented the sediments emission of the river. [62] determined that the yield of sediments in the whole catchment area have increased by 914 % with respect of the estimated value in order to build

the Boconó-Tucupido Dam System, located dowmstreams in the lowland region.

The transition Ur-U – Fl-P also suggest that the hydrological events occurred during the period, affected the urban area of Boconó city, which had been expanding across the fluvial plain of the River; it can be corroborated some rows below, with the transition Fl-P – Ur-U, in which the urban area grew up across the Flooding Plain 11,625 times more than expected (6,57 ha). Important flooding events occurred in 1988, 1989, 1991 and 1995 were analyzed by [63]; unfortunately, the historical data for the River Basin is quite deficient and no more

Finally, the transitions for the category S-Shr suggest a trend for the category to migrate to the human-induced types of Land Cover categories Gr-An (3,070 times more than expected); Cro-L (2,179 times more than expected) and Ur-U (0,114 times more than expected). The rest of the transitions suggest a regeneration process. Shrubland was also observed as a highly dynamic category in the Kalu District-Ethiopia by [64], and also in Central Chile by [60], which can be explained by the forms of cultivation above mentioned, mostly typical in these

The Table 8 shows the most systematic inter-category transitions occurred in the period T0 – T1 in terms of gain. The first twelve transitions are associated to changes in the Forested Land Covers. Particularly the transition Sm-F – Ero-L indicate erosion processes occurring after the clearcutting of the Sub-montane Forest, in 5,489 times more than expected, affecting a total of 12,33 ha. On the other hand, the transitions Gr-An – Gr-l (4,760); Gr-An – S-Shr

As seen on Table 8, the cropland area in the river basin is growing at the expense of the categories: Oc-F (176, 76 ha), Sm-F (339, 48 ha), Gr-L (316, 53 ha), Gr-An (100, 89 ha), and S-Shr (766, 53 ha). On the other hand, the Gr-An gained surface area migrating basically from:

The transition Cro-L – Sm-F could to indicate regeneration, or perhaps a change to coffee plantation, or a combination of both scenarios. The transition Cro-L – Gr-L could be

(2,231), and Gr-An - Sm-F (0,232) suggest a regeneration/revegetation process.

Oc-F (168, 93 ha), Gr-L (174, 33 ha), Cro-L (70, 11 ha), and from S-Shr (497, 34 ha).

explained by the type of cultivation usually practiced in the area, above mentioned.

**Table 8.** The most systematic transitions occurred in T0-T1, in terms of Gains

The fact that the urban areas have been growing at the expense of croplands is corroborated again with the transition Cro-L – Ur-U, which indicates that the urban areas grew up from

Cropland in 7,028 times more than expected (98,1 ha). The urban areas also grew up at the expense of other categories: Sm-F (61, 92 ha), Gr-L (31, 31 ha), S-Shr (84, 06 ha) and Gr-An (9, 72 ha). On the other hand, the Fl-P grew up at the expense of Cro-L in 5,108 times more than expected, affecting 49, 05 ha.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 55

Ov: Observed Value/ Ev: Expected Value

**Table 9.** The most systematic transitions occurred in T1-T2, in terms of Losses

The transition Ero-L – Tmc-F suggest a regeneration/revegetation process, showing a high level of resilience for the TMCF to be regenerated after such disturbances like landslides, as in this case. The transition Ero-L - Fl-P focuses a source of sediments which were transported by the river during the period. On the other hand, the transition Fl-P – Ur-U confirms the fact that the urban areas (in this case, the urban area of Boconó city) is expanding through the Flooding plain. The last transitions help to confirm the higher swapping-change dynamic associated to the category S-Shr.

The Tables 9 and 10 resume the most systematic transitions occurred in the second period (T1 – T2) in terms of losses and gains, respectively.

As seen on Table 9, the number of rows accounting for changes in the Forested LC was reduced to 9, because of a slight reduction in the transitions of Sm-F, which explains the reduction in the swapping value observed in the category for this period.

The same trend in the transitions for the Tmc-F can be observed in this period, but additionally 5,31 ha of the area covered by the category was affected by erosion processes, particularly landslides. An incipient transition process for the Sa-P occurred during the period, suggesting that some changes derived by anthropogenic pressure have been occurring in the Páramo ecosystems of the river basin. The growing anthropogenic pressure over the Sub-Andean Páramo in the study area was already reported by [65].

The categories Gr-L, Gr-An and Cro-L show the same transitional trends as in the last period. The Urban use continued to growing up at the expense of croplands and the flooding plain, and at the same time, the urban area continued being affected by peak flows or flooding processes. Finally, the S-Shr showed migrating trends to Gr-An (3,168), Cro-L (1,719), to Gr-L (1,507) and to Oc-F (0,910) also.

Respect to the gains in this period, the Table 10 illustrates the trend, where the first ten rows show the changes affecting the Forested LCC. In general, the trends and patterns for the transitions observed on last period remain during the second period.

The category Gr-L showed less intensity in the swapping, meanwhile Cro-L gained surface at the expense of Oc-F (66, 78 ha), Sm-F (269, 37 ha), Gr-L (361, 17 ha), Gr-An (172, 44 ha), Fl-P (36, 9 ha) and S-Shr (1037, 97 ha). Gr-An gained surface migrating from Gr-L in 2,142 times more than expected (502, 83 ha), and also from Cro-L (213, 39 ha), and from S-Shr (1571, 40 ha).

The urban areas continued to growing up in the period, gaining surface area basically from Sm-F (43, 38 ha), Gr-An (3, 78 ha), Cro-L (46, 08 ha), Fl-P (5, 85 ha) and S-Shr (31, 95 ha). Particularly the urban area growing close or into the category Fl-P is vulnerable to the river dynamic. During this period the category S-Shr reported systematic transitions with all the rest of the categories, which explain the high value for swapping-change for the category in this period.


Ov: Observed Value/ Ev: Expected Value

54 Environmental Land Use Planning

expected, affecting 49, 05 ha.

Cropland in 7,028 times more than expected (98,1 ha). The urban areas also grew up at the expense of other categories: Sm-F (61, 92 ha), Gr-L (31, 31 ha), S-Shr (84, 06 ha) and Gr-An (9, 72 ha). On the other hand, the Fl-P grew up at the expense of Cro-L in 5,108 times more than

The transition Ero-L – Tmc-F suggest a regeneration/revegetation process, showing a high level of resilience for the TMCF to be regenerated after such disturbances like landslides, as in this case. The transition Ero-L - Fl-P focuses a source of sediments which were transported by the river during the period. On the other hand, the transition Fl-P – Ur-U confirms the fact that the urban areas (in this case, the urban area of Boconó city) is expanding through the Flooding plain. The last transitions help to confirm the higher

The Tables 9 and 10 resume the most systematic transitions occurred in the second period

As seen on Table 9, the number of rows accounting for changes in the Forested LC was reduced to 9, because of a slight reduction in the transitions of Sm-F, which explains the

The same trend in the transitions for the Tmc-F can be observed in this period, but additionally 5,31 ha of the area covered by the category was affected by erosion processes, particularly landslides. An incipient transition process for the Sa-P occurred during the period, suggesting that some changes derived by anthropogenic pressure have been occurring in the Páramo ecosystems of the river basin. The growing anthropogenic pressure

The categories Gr-L, Gr-An and Cro-L show the same transitional trends as in the last period. The Urban use continued to growing up at the expense of croplands and the flooding plain, and at the same time, the urban area continued being affected by peak flows or flooding processes. Finally, the S-Shr showed migrating trends to Gr-An (3,168), Cro-L

Respect to the gains in this period, the Table 10 illustrates the trend, where the first ten rows show the changes affecting the Forested LCC. In general, the trends and patterns for the

The category Gr-L showed less intensity in the swapping, meanwhile Cro-L gained surface at the expense of Oc-F (66, 78 ha), Sm-F (269, 37 ha), Gr-L (361, 17 ha), Gr-An (172, 44 ha), Fl-P (36, 9 ha) and S-Shr (1037, 97 ha). Gr-An gained surface migrating from Gr-L in 2,142 times more than expected (502, 83 ha), and also from Cro-L (213, 39 ha), and from S-Shr (1571, 40 ha). The urban areas continued to growing up in the period, gaining surface area basically from Sm-F (43, 38 ha), Gr-An (3, 78 ha), Cro-L (46, 08 ha), Fl-P (5, 85 ha) and S-Shr (31, 95 ha). Particularly the urban area growing close or into the category Fl-P is vulnerable to the river dynamic. During this period the category S-Shr reported systematic transitions with all the rest of the categories, which explain the high value for swapping-change for the category in

swapping-change dynamic associated to the category S-Shr.

reduction in the swapping value observed in the category for this period.

over the Sub-Andean Páramo in the study area was already reported by [65].

transitions observed on last period remain during the second period.

(T1 – T2) in terms of losses and gains, respectively.

(1,719), to Gr-L (1,507) and to Oc-F (0,910) also.

this period.

**Table 9.** The most systematic transitions occurred in T1-T2, in terms of Losses


Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 57

level also. Considering that the Boconó River Basin constitute a double "**Protected Area**", which has a paramount importance for the development of the water resources in the lowlands, the evaluation of LULC change under the ecosystem approach represent a innovative variation respect the traditional LULC evaluations, in which the LULC are usually considered categories in an abstract sense. In this case, the Land Cover categories are essentially valuable ecosystems which have an ecological richness as well as complementary environmental attributes, being very important to the conservation and

The systematic transitions show the trajectory or directionality of the changes in a categorical sense, leading to identify not only the categories which are more dynamic in a spatial-temporal perspective, but also the possible biophysical and anthropogenic processes driving the transitions. When both interpretations are correctly established, they simply lead

a. the way how the land resources have been used in the river basin during the last twenty

c. the trends existing for the different Land Use/Land Cover categories, in a

Particularly the spatial visualization (geographical visualization) results in a undoubtedly helpful tool for the planning process, allowing to perceive how these trends are spatially occurring, where are occurring specific processes accounted for problems to be solved, and

As an example, the Figure 3 show the geographic visualization of the transitions for the three main forested Land Cover Categories (LCC) (Tmc-F, Oc-F and Sm-F), for the period T0-T1. The transitions occurred during the Period T1-T2 are displayed on Figure 4. A simple observation of the maps, based on the systematic transitions above described, can lead to the

1.- The changes affecting the forested land covers, particularly the Tcm-F and the Sm-F tends to be produced in the boundary area between categories. The same trend was observed by [30] in Ecuador. This lead to define belts of clearcutting / logging, which are also called "**hot fronts**" of deforestation [9], being more evident for the categories: Tcm-F and Oc-F. In the Sub-montane Forest, the belts or "**hot fronts**" are not clearly defined, because this land cover is highly fragmented among the area. The "Río Negro" Sector located at the upper Boconó River (Figure 4) was severely affected by the changes on the three types of LC, indicating that the processes: clearcutting, logging, wood extraction and non wood & plant extractions were more intense in this sector, during the period. The sector could be defined as "**hot spot**" or "**red flag area**", considering that the deforestation and the LC change is occurring in the sector where the most important streams-sources of the river are located. This sector covers almost the 40% of the stream network area, having therefore the greatest water yield

sustainability of the three basic land resources: water, soils and biodiversity.

to define the key elements to be considered in the land planning processes:

b. the form how the land cover categories as ecosystems have been affected

where these problems are more diverse or intense (hot spots).

years

following statements:

[42].

spatial/temporal perspective.

Ov: Observed Value/ Ev: Expected Value

**Table 10.** The most systematic transitions occurred in T1-T2, in terms of Gains

## **6. Implications of the observed LULC changes for the watershed management and land use planning**

The dynamic of the LULC in the Boconó River Basin for the considered period and through the approach used in this project, lead to establish key elements and a support basis to be considered in the planning processes at the watershed level or even at regional planning level also. Considering that the Boconó River Basin constitute a double "**Protected Area**", which has a paramount importance for the development of the water resources in the lowlands, the evaluation of LULC change under the ecosystem approach represent a innovative variation respect the traditional LULC evaluations, in which the LULC are usually considered categories in an abstract sense. In this case, the Land Cover categories are essentially valuable ecosystems which have an ecological richness as well as complementary environmental attributes, being very important to the conservation and sustainability of the three basic land resources: water, soils and biodiversity.

The systematic transitions show the trajectory or directionality of the changes in a categorical sense, leading to identify not only the categories which are more dynamic in a spatial-temporal perspective, but also the possible biophysical and anthropogenic processes driving the transitions. When both interpretations are correctly established, they simply lead to define the key elements to be considered in the land planning processes:


56 Environmental Land Use Planning

Ov: Observed Value/ Ev: Expected Value

**management and land use planning** 

**Table 10.** The most systematic transitions occurred in T1-T2, in terms of Gains

**6. Implications of the observed LULC changes for the watershed** 

The dynamic of the LULC in the Boconó River Basin for the considered period and through the approach used in this project, lead to establish key elements and a support basis to be considered in the planning processes at the watershed level or even at regional planning c. the trends existing for the different Land Use/Land Cover categories, in a spatial/temporal perspective.

Particularly the spatial visualization (geographical visualization) results in a undoubtedly helpful tool for the planning process, allowing to perceive how these trends are spatially occurring, where are occurring specific processes accounted for problems to be solved, and where these problems are more diverse or intense (hot spots).

As an example, the Figure 3 show the geographic visualization of the transitions for the three main forested Land Cover Categories (LCC) (Tmc-F, Oc-F and Sm-F), for the period T0-T1. The transitions occurred during the Period T1-T2 are displayed on Figure 4. A simple observation of the maps, based on the systematic transitions above described, can lead to the following statements:

1.- The changes affecting the forested land covers, particularly the Tcm-F and the Sm-F tends to be produced in the boundary area between categories. The same trend was observed by [30] in Ecuador. This lead to define belts of clearcutting / logging, which are also called "**hot fronts**" of deforestation [9], being more evident for the categories: Tcm-F and Oc-F. In the Sub-montane Forest, the belts or "**hot fronts**" are not clearly defined, because this land cover is highly fragmented among the area. The "Río Negro" Sector located at the upper Boconó River (Figure 4) was severely affected by the changes on the three types of LC, indicating that the processes: clearcutting, logging, wood extraction and non wood & plant extractions were more intense in this sector, during the period. The sector could be defined as "**hot spot**" or "**red flag area**", considering that the deforestation and the LC change is occurring in the sector where the most important streams-sources of the river are located. This sector covers almost the 40% of the stream network area, having therefore the greatest water yield [42].

2.- Observing the two maps, is evident that in the first period, the Open-cleared Forest was systematically reduced among the river basin, meanwhile in the second period, the transition of the Tropical Montane Cloudy Forest was clearly spatially intensified. This lead to corroborate the fact that the dynamic of the TMCF is characterized by a systematic and progressive change, in which the category is migrating to an "intermediate" stage or LCC like Open-cleared Forest or Successional Shrubland, and in other successive stage it can to migrate to another LC or LU categories.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 59

events. The ecological conditions, and particularly the type and density of the land cover play a very important role in the hydrological behaviour and the hydrological response of the landscape. Many authors like: [9] [36] [38] [66] [67] [68] and [69] have been highlighting the importance of the forest ecosystems in the hydrological patterns. Particularly the TMCF is considered as "**producer-water forest**", playing a paramount role in the rainfall dynamic, as well as the transpiration, interception, water budget and streamflows [9] [38]. Thus, the systematic reduction of this kind of forest may significantly reduce the rainfall interception,

probably leading to an even higher streamflow in the area.

**Figure 4.** Transition area for the Forested Categories in the period T1-T2

jointed bedrocks.

5.- The transitions Sm-F – Ero-L; S-Shr – Ero-L; and Cro-L – Ero-L, indicate that the area is highly susceptible to soil degradation processes like sheet erosion, rill erosion, landslides and so on, processes which have been activating through the migration of Forested LCC to other categories like Cro-L. Only intense erosive processes like landslides were observed in the classification. However, [39] identified severe erosion processes, especially sheet erosion, in the San Miguel and San Rafael Watersheds (within the study area), which are spatially extended due the high accessibility (intricate road network), the fragile soils and the highly

The accessibility (roads network) has been considered as one of the most important and critical drivers facilitating the LULC change in many regions worldwide [1] [39] [57] [67]

3.- Although the "Guaramacal National Park" was created on 1988, covering the flank south-east of the river basin [41], a "**hot front**" of deforestation can be observed in the inferior border of this protected area (Figure 3), which clearly increased during the second period (Figure 4). This fact reveals that the creation of the Park has not been completely effective in the protection of the ecosystems included in the protected area.

**Figure 3.** Transition area for the Forested Categories in the period T0-T1

4.- The transitions Sm-F – Fl-P; Cro-L – Fl-P and Ur-U – Fl-P suggests a relevant hydrological dynamic occurring during the period studied. The LC Flooding Plain changed actively on last 20 years, accounting for important events like peak flows or even flash-floodings, which expanded the limits of the category among the area, affecting other categories like Sm-F, Cro-L and Ur-U. The dynamic accounted for the Forested LC and the increase of cultivated soils and grass could have been playing a role in the intensification of the hydrological events. The ecological conditions, and particularly the type and density of the land cover play a very important role in the hydrological behaviour and the hydrological response of the landscape. Many authors like: [9] [36] [38] [66] [67] [68] and [69] have been highlighting the importance of the forest ecosystems in the hydrological patterns. Particularly the TMCF is considered as "**producer-water forest**", playing a paramount role in the rainfall dynamic, as well as the transpiration, interception, water budget and streamflows [9] [38]. Thus, the systematic reduction of this kind of forest may significantly reduce the rainfall interception, probably leading to an even higher streamflow in the area.

58 Environmental Land Use Planning

migrate to another LC or LU categories.

2.- Observing the two maps, is evident that in the first period, the Open-cleared Forest was systematically reduced among the river basin, meanwhile in the second period, the transition of the Tropical Montane Cloudy Forest was clearly spatially intensified. This lead to corroborate the fact that the dynamic of the TMCF is characterized by a systematic and progressive change, in which the category is migrating to an "intermediate" stage or LCC like Open-cleared Forest or Successional Shrubland, and in other successive stage it can to

3.- Although the "Guaramacal National Park" was created on 1988, covering the flank south-east of the river basin [41], a "**hot front**" of deforestation can be observed in the inferior border of this protected area (Figure 3), which clearly increased during the second period (Figure 4). This fact reveals that the creation of the Park has not been completely

effective in the protection of the ecosystems included in the protected area.

**Figure 3.** Transition area for the Forested Categories in the period T0-T1

4.- The transitions Sm-F – Fl-P; Cro-L – Fl-P and Ur-U – Fl-P suggests a relevant hydrological dynamic occurring during the period studied. The LC Flooding Plain changed actively on last 20 years, accounting for important events like peak flows or even flash-floodings, which expanded the limits of the category among the area, affecting other categories like Sm-F, Cro-L and Ur-U. The dynamic accounted for the Forested LC and the increase of cultivated soils and grass could have been playing a role in the intensification of the hydrological

**Figure 4.** Transition area for the Forested Categories in the period T1-T2

5.- The transitions Sm-F – Ero-L; S-Shr – Ero-L; and Cro-L – Ero-L, indicate that the area is highly susceptible to soil degradation processes like sheet erosion, rill erosion, landslides and so on, processes which have been activating through the migration of Forested LCC to other categories like Cro-L. Only intense erosive processes like landslides were observed in the classification. However, [39] identified severe erosion processes, especially sheet erosion, in the San Miguel and San Rafael Watersheds (within the study area), which are spatially extended due the high accessibility (intricate road network), the fragile soils and the highly jointed bedrocks.

The accessibility (roads network) has been considered as one of the most important and critical drivers facilitating the LULC change in many regions worldwide [1] [39] [57] [67] [70] [71] [72]. With the exception of the "Río Negro" Sector (See Figure 3), the Boconó River Basin presents a moderately high accessibility [43] [45] [63]. The results obtained by [39] in San Miguel / San Rafael watersheds through a regression tree analysis, revealed that the accessibility had the greatest level of contribution in the occurrence of soil erosion in the area, being the sectors where the cropland have been progressively expanding during the last decades. The occurrence of erosion processes was directly associated to the distance to the road network. This suggest that the accessibility could play a determinant role explaining the intensity and spatiality of the changes that the LULC have been experiencing in the River Basin, as demonstrated by studies conducted in other regions [1] [57] [70-72]. Due the nature and complexity of the variables usually involved, a rigorous analysis of the drivers of LULC change in the area was out of scope of this project, so that further research in this subject is strictly necessary in the near future, in order to comprehensively determine the causal relationship of the factors influencing the changes that affect the River Basin.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 61

show the expanding process that the Boconó city experienced during the two periods, showing how the city has been expanding among the flooding plain, in areas susceptible to be flooded. The transition Ur-U – Fl-P clearly indicates that some urban sectors have been

All these interpretations constitutes important tools having practical importance for the institutions or stakeholders involved with the environmental and land planning at local/regional level, being a rational basis to design new plans, or even to improve those which already exists, in order to guaranty the optimization of the natural resource uses in the river basin. This is very important to encourage the effectiveness of the protective figures defined for the whole river basin, accounting for a more sustainable evolution of the

damaged during the two periods analyzed.

LULC in this important "**water resource area**".

**Figure 6.** Transition area for the Land Use categories in the period T1-T2

The methodological approach combining the multitemporal LULC evaluation, together with the ecosystem approach and the inter-category transitional method, represented a very useful tool to define, to describe an to analyze the LULC system in the Boconó River Basin and the changes occurred in the last 20 years. The study demonstrated that the categories: Successional Srhubland (S-Shr), Sub-montane Forest (Sm-F), Open-cleared Forest (Oc-F) and

**7. Conclusions** 

The Figures 5 and 6 show the transitions occurred in the Land Use Categories during the first and the second period, respectively. It can be clearly observed where the LUC grow up more intensively in the two periods. The superior window show the San Miguel – San Rafael Watersheds, the sectors where the croplands and the grass anthropogenic grew up more intensively for both periods. These are the sectors which have the most relevant problems related with land degradation in the area, as studied by [39]. The inferior window

**Figure 5.** Transition area for the Land Use categories in the period T0-T1

show the expanding process that the Boconó city experienced during the two periods, showing how the city has been expanding among the flooding plain, in areas susceptible to be flooded. The transition Ur-U – Fl-P clearly indicates that some urban sectors have been damaged during the two periods analyzed.

All these interpretations constitutes important tools having practical importance for the institutions or stakeholders involved with the environmental and land planning at local/regional level, being a rational basis to design new plans, or even to improve those which already exists, in order to guaranty the optimization of the natural resource uses in the river basin. This is very important to encourage the effectiveness of the protective figures defined for the whole river basin, accounting for a more sustainable evolution of the LULC in this important "**water resource area**".

**Figure 6.** Transition area for the Land Use categories in the period T1-T2

## **7. Conclusions**

60 Environmental Land Use Planning

[70] [71] [72]. With the exception of the "Río Negro" Sector (See Figure 3), the Boconó River Basin presents a moderately high accessibility [43] [45] [63]. The results obtained by [39] in San Miguel / San Rafael watersheds through a regression tree analysis, revealed that the accessibility had the greatest level of contribution in the occurrence of soil erosion in the area, being the sectors where the cropland have been progressively expanding during the last decades. The occurrence of erosion processes was directly associated to the distance to the road network. This suggest that the accessibility could play a determinant role explaining the intensity and spatiality of the changes that the LULC have been experiencing in the River Basin, as demonstrated by studies conducted in other regions [1] [57] [70-72]. Due the nature and complexity of the variables usually involved, a rigorous analysis of the drivers of LULC change in the area was out of scope of this project, so that further research in this subject is strictly necessary in the near future, in order to comprehensively determine the causal relationship of the factors influencing the changes that affect the River Basin.

The Figures 5 and 6 show the transitions occurred in the Land Use Categories during the first and the second period, respectively. It can be clearly observed where the LUC grow up more intensively in the two periods. The superior window show the San Miguel – San Rafael Watersheds, the sectors where the croplands and the grass anthropogenic grew up more intensively for both periods. These are the sectors which have the most relevant problems related with land degradation in the area, as studied by [39]. The inferior window

**Figure 5.** Transition area for the Land Use categories in the period T0-T1

The methodological approach combining the multitemporal LULC evaluation, together with the ecosystem approach and the inter-category transitional method, represented a very useful tool to define, to describe an to analyze the LULC system in the Boconó River Basin and the changes occurred in the last 20 years. The study demonstrated that the categories: Successional Srhubland (S-Shr), Sub-montane Forest (Sm-F), Open-cleared Forest (Oc-F) and

Cropland (Cro-L) were the most dynamic among the two considered periods, accounting for the highest total change value, as well as gains, losses, swapping and net change.

Land Use and Land Cover (LULC) Change in the Boconó River Basin,

North Venezuelan Andes, and Its Implications for the Natural Resources Management 63

Volker Hochschild

PhD program.

**8. References** 

– 255.

Geographical Journal, 170 (1): 51 – 63.

dynamics. Landscape Ecology, 25 (2): 163 – 167.

regions. Progress in Physical Geography, 21 (3): 375 – 393.

Hodder Headline Group.

**Acknowledgement** 

*Geographisches Institut, Eberhard Karls Universität, Tübingen, Germany* 

We thank to the following institutions which helped in the development of this project: The United States Geological Survey (USGS) and the Institute of Geography (IGCRN) – Universidad de Los Andes, Venezuela, which were the provider of the LANDSAT scenes used in the classification process. The Centre of Geoinformatic and GIS (GIS Zentrum) of the Eberhard Karls University – Tübingen - Germany, which provided the technical support, software and personal who helped during the development of the project. Finally, thanks to DAAD (German Academic Exchange) and FUNDAYACUCHO (Fundación Gran Mariscal de Ayacucho), which have been providing the financial support for the development of the

[1] Verburg P, Overmars K, Witte N (2004) Accessibility and land-use patterns at the forest fringe in the northeastern part of the Philippines. The Geographical Journal, 170 (3): 238

[2] Krishna V, Badarinth K, (2004) Land use changes and trends in Human Appropriation of Above Ground Net Primary Production (HANPP) in India (1961-98). The

[3] Turner B, Lambin E, Reenberg A (2007) The emergence of land change science for global environmental change and sustainability. PNAS, 104 (52): 20666 – 20671. [4] Mannion A, (2002) Dynamic World. Land cover and Land Use Change. London.

[5] Bormann H, Breuer L, Gräff T., Huisman J, Croke B (2009) Assessing the impact of land use change on hydrology by ensemble modelling: IV. Model sensitivity to data aggregation and spatial (re-) distribution. Advances in Water Resources, 32: 171 – 192. [6] Houet Th, Verburg P, Loveland Th (2010) Monitoring and modelling landscape

[7] Lambin E, (1997) Modelling and monitoring land-cover change processes in tropical

[8] Lambin E, Geist H, Lepers E, (2003) Dynamics of Land-Use and Land-Cover Change in

[9] Bonell M, Brujinzeel L (Edit.) (2005) Forest, water and people in the humid Tropics. Past, present and future hydrological research for integrated land and water

[10] Armenteras D, Rudas G, Rodriguez N, Sua, S, Romero M (2006) Patterns and causes of

Tropical Regions. Annual Review of Environmental Resources, 28: 205-241.

management. Cambridge. Cambridge University Press. and UNESCO.

deforestation in the Colombian Amazon. Ecological Indicators, 6: 353-368.

The study also demonstrated that the changes and the reduction showed by the Tropical Montane Cloudy Forest in the area, cannot be directly associated to the expansion of land use categories like Cropland or Grass Anthropogenic. At least on the last 20 years, the TMCF have been changing to an intermediate condition for LC, basically to Open-cleared Forest (Oc-F) and Sucessional Srhubland (S-Shr). Even when the TMCF is under anthropogenic pressure, it can be only associated with logging, wood and timber extraction, as well as the extraction of non wood products and plants.

The systematic transitions that have been occurring in the LULC categories reveal that the land uses Cropland (Cro-L) and Grass Anthropogenic (Gr-An) have been growing, gaining surface basically from Sucessional Shrubland (S-Shr), Sub-montane Forest (Sm-F), and Grassland (Gr-L).This justify the higher values for swapping-change, observed in these categories. On the other hand, the urban areas (Ur-U) have been growing basically at the expense of Cro-L, Gr-L and Fl-P.

The systematic transitions Sm-F – Fl-P; Cro-L – Fl-P and Ur-U – Fl-P, as well as the variation of the category Fl-P during the period, suggest an intense dynamic of the river, and the occurrence of high peak flows and important flooding events during the period, which have been affecting the urban expanding area, as well as croplands. Probably, the decrease of the forested areas, and particularly the TMCF, as well as the increase of the croplands and the grass-anthropogenic, could be directly affecting the hydrological dynamic in the river basin, particularly the behavior of the seasonal flows.

Finally, the systematic transitions helped to focus specific processes that suggest the existence of problems which need to be solved into the land use planning or the watershed management processes. The "**hot fronts**" of deforestation could be considered as critical areas or priority areas in order to promote the conservation/preservation of the valuable ecosystems as the TMCF, helping to define "**area-oriented policies**" to ensure the water resources management in the river basin.

Further rigorous research about the associated drivers for LULC change in the area is strictly necessary, in order to reach a comprehensive understanding of the dynamic and transitions of the LULC categories identified and characterized in this project, seeking to encourage the future decisions for land use planning within the watershed management at regional and local level.

## **Author details**

Joel Francisco Mejía\* *Instituto de Geografía y Conservación de Recursos Naturales, Universidad de Los Andes, Mérida, Venezuela Geographisches Institut, Eberhard Karls Universität, Tübingen, Germany* 

<sup>\*</sup> Corresponding Author

Volker Hochschild *Geographisches Institut, Eberhard Karls Universität, Tübingen, Germany* 

## **Acknowledgement**

62 Environmental Land Use Planning

extraction of non wood products and plants.

particularly the behavior of the seasonal flows.

resources management in the river basin.

local level.

*Venezuela* 

 \*

**Author details** 

Joel Francisco Mejía\*

Corresponding Author

expense of Cro-L, Gr-L and Fl-P.

Cropland (Cro-L) were the most dynamic among the two considered periods, accounting for

The study also demonstrated that the changes and the reduction showed by the Tropical Montane Cloudy Forest in the area, cannot be directly associated to the expansion of land use categories like Cropland or Grass Anthropogenic. At least on the last 20 years, the TMCF have been changing to an intermediate condition for LC, basically to Open-cleared Forest (Oc-F) and Sucessional Srhubland (S-Shr). Even when the TMCF is under anthropogenic pressure, it can be only associated with logging, wood and timber extraction, as well as the

The systematic transitions that have been occurring in the LULC categories reveal that the land uses Cropland (Cro-L) and Grass Anthropogenic (Gr-An) have been growing, gaining surface basically from Sucessional Shrubland (S-Shr), Sub-montane Forest (Sm-F), and Grassland (Gr-L).This justify the higher values for swapping-change, observed in these categories. On the other hand, the urban areas (Ur-U) have been growing basically at the

The systematic transitions Sm-F – Fl-P; Cro-L – Fl-P and Ur-U – Fl-P, as well as the variation of the category Fl-P during the period, suggest an intense dynamic of the river, and the occurrence of high peak flows and important flooding events during the period, which have been affecting the urban expanding area, as well as croplands. Probably, the decrease of the forested areas, and particularly the TMCF, as well as the increase of the croplands and the grass-anthropogenic, could be directly affecting the hydrological dynamic in the river basin,

Finally, the systematic transitions helped to focus specific processes that suggest the existence of problems which need to be solved into the land use planning or the watershed management processes. The "**hot fronts**" of deforestation could be considered as critical areas or priority areas in order to promote the conservation/preservation of the valuable ecosystems as the TMCF, helping to define "**area-oriented policies**" to ensure the water

Further rigorous research about the associated drivers for LULC change in the area is strictly necessary, in order to reach a comprehensive understanding of the dynamic and transitions of the LULC categories identified and characterized in this project, seeking to encourage the future decisions for land use planning within the watershed management at regional and

*Instituto de Geografía y Conservación de Recursos Naturales, Universidad de Los Andes, Mérida,* 

*Geographisches Institut, Eberhard Karls Universität, Tübingen, Germany* 

the highest total change value, as well as gains, losses, swapping and net change.

We thank to the following institutions which helped in the development of this project: The United States Geological Survey (USGS) and the Institute of Geography (IGCRN) – Universidad de Los Andes, Venezuela, which were the provider of the LANDSAT scenes used in the classification process. The Centre of Geoinformatic and GIS (GIS Zentrum) of the Eberhard Karls University – Tübingen - Germany, which provided the technical support, software and personal who helped during the development of the project. Finally, thanks to DAAD (German Academic Exchange) and FUNDAYACUCHO (Fundación Gran Mariscal de Ayacucho), which have been providing the financial support for the development of the PhD program.

## **8. References**


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**Analytical Methods/Tools for Environmental** 

**Land Use Planning** 


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**Chapter 4** 

© 2012 Shen and Wang licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Predicting Changes in Regional Land Use** 

Jing Shen and Hao Wang

http://dx.doi.org/10.5772/50678

**1. Introduction** 

Additional information is available at the end of the chapter

**1.1. Predict changes in regional landscape pattern** 

**Pattern: The Case of Jiangsu Province, China** 

Space-time simulation of regional landscape change is important to regional ecological management. Effective policies and measures according to the study on regional landscape could guarantee the regional sustainable development. The simulation of land use change (LUC) is a frequently required but difficult process. To make informed planning decisions must be able to predict land use change. Many land use change models use remotely-sensed images to make predictions based on historical trends. Accurate land use change information is needed for land use policy making and scientific research. Therefore, scientists realized the need to assess the land use change dynamics and related the special situation of the regional pattern. Recently a large number of studies on future land use change have been conducted at regional scale (Andrew Gilg, 2009; Andy, 1997; C. Ma, 2012)

The Marcov mothod of land use change can provide useful result. This research predicts the landscape pattern with the quantity model. Only do the numerical prediction of research

Land use models are core subject of LUCC. In recent years, the LUCC community has produced a large set ofoperational models that can be used to predict or explore possible land use change trajectories (Verburg et al., 2006). The landscape pattern development model, and simulate different situations of the land using change pattern in the future. Investigate and evaluate the system of land using changes in reality and the potential ecological environment influence and feedback process. It have been considered by many researchers that it's revealed with land use system terrestrial ecosystem interaction mechanism. Optimize land using pattern. It's one of the effective ways to reduce the level of

and reproduction in any medium, provided the original work is properly cited.

regional, without considering the change of landscape form (2D and 3D).

risk potential ecological process in the land using process.

## **Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China**

Jing Shen and Hao Wang

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50678

## **1. Introduction**

## **1.1. Predict changes in regional landscape pattern**

Space-time simulation of regional landscape change is important to regional ecological management. Effective policies and measures according to the study on regional landscape could guarantee the regional sustainable development. The simulation of land use change (LUC) is a frequently required but difficult process. To make informed planning decisions must be able to predict land use change. Many land use change models use remotely-sensed images to make predictions based on historical trends. Accurate land use change information is needed for land use policy making and scientific research. Therefore, scientists realized the need to assess the land use change dynamics and related the special situation of the regional pattern. Recently a large number of studies on future land use change have been conducted at regional scale (Andrew Gilg, 2009; Andy, 1997; C. Ma, 2012)

The Marcov mothod of land use change can provide useful result. This research predicts the landscape pattern with the quantity model. Only do the numerical prediction of research regional, without considering the change of landscape form (2D and 3D).

Land use models are core subject of LUCC. In recent years, the LUCC community has produced a large set ofoperational models that can be used to predict or explore possible land use change trajectories (Verburg et al., 2006). The landscape pattern development model, and simulate different situations of the land using change pattern in the future. Investigate and evaluate the system of land using changes in reality and the potential ecological environment influence and feedback process. It have been considered by many researchers that it's revealed with land use system terrestrial ecosystem interaction mechanism. Optimize land using pattern. It's one of the effective ways to reduce the level of risk potential ecological process in the land using process.

© 2012 Shen and Wang licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The model's biggest significance can predict the future changes and have very good guidance function for scientific decision. On the one hand, according to the former development trend and direction, decide to the driving factors and the weight and make model operate to the different time in the future. Analyze each scene to the change of land using produce situation. This kind of model can build-up mixed related model. The advantage is that it considers the driving factors in model forecasting, Because of much relationship and the resistance in establishing model, it is not easy to cause the scientific results.

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 73

quantitative (Han Wenquan, 2005). If many in the period of transformation probability are compared, and further explains the change in ecological meaning, it can make the landscape

The data of land use and land cover (LULC) were obtained from Chinese Academy of Science. Landsat Thematic Mapper (TM) images for the 1985,1995,2000 and 2005 years after being geometrically registered. They were classified analysis and aggregated into six major land types, they are build-up-up, forest, water ,farm, grass and other land. There are main three types for studying. There are main reasons accounted for selecting this scheme of LULC classification. First, three LULC types represented the dominant ecosystems and reflected the land use in the study area. Second, the selection keeps in accordance with the local official standards for land use classification and at the same time considers the ability of TM images to interpret LULC patterns. The local official standards for land use

Jiangsu Province is located in the lower reaches of the Yangtze River, east of the Yellow Sea, at latitude 30 ° 46'N-35 ° 02'N, longitude 116 ° 22'E-121 ° 55'E. The province's total area of

plaques dynamic quantitative research more valuable.

classification divided the LULC into two hierarchica levels.

**2. Materials and methods** 

**2.1. Study area** 

**Figure 1.** The Study area location map

About the prediction model of the landscape pattern, including the concept model and mechanism model. it's classified into dynamic model, CA model, system dynamic model (SD model) that is based on cybernetics, System theory and information theory and it's characterized by studying feedback System structure, function and the dynamic behavior dynamic model. Its outstanding characteristic is to reflect the complex System structure, function and the dynamic behavior of the interaction between the relations. So as to study complex System change behavior and trend in different situations, and provide decision supporting (Chen Shupeng, 1999). The existing research shows that system dynamics model can reflect land using system of complex behavior on macroscopic and it is the good simulation tools in land using (Zhang Hanxiong, 1997; van et al., 1999; Li and Simonovic, 2002). Li, such as using the SD model for North America grasslands waters increased temperatures and ice melt water flooding caused by area of various ways simulation, obtained a good simulation results (Li and Simonovic, 2002). ZhangHanXiong applies system dynamics theory to establish Jin Shan loess hilly-gully region soil erosion dynamic SD model. Markov chain models basically does not consider the landscape pattern changes affecting the driving force, only using the past changes of the landscape pattern and the degree of change. According to the mathematical functions, speculated that the future of the landscape pattern utilization condition. It is based on two period of the landscape pattern and use data to calculate the types of land using change as the transition probability ratio. Markov process on matrix calculate to speculate that different stages of the land utilization condition. For example, Turner and Ruscher rules to mesh the study area and use Markov chain model to calculate the change of landscape plaques type probability, plus eight image elements and the effect of land using category, to determine each box type change the landscape of probability matrix (Turner, 1987, 1989; Ruscher, 1988). In order to reduce the general error that using Markov chain prediction model of the landscape pattern condition. Aoki, etc, introduce Hopfield neural network model. First the research in the area of the landscape pattern changes similar degree is divided into several sub-regional (subarea).again with Markov chain respectively model to calculate the transition probability matrix. Using this method in the central business district in Tokyo, forecast to 2014 years of urban landscape pattern condition (Aoki et al, 1996).

The feasibility of the model is also an important aspect of the model, at present in the big, mesoscale landscape pattern prediction area using model is Markov chain models. This kind of dynamic simulation model using Markov model can forecast a quantitative description of dynamic landscape plaques. Markov transfer matrix make dynamic of landscape patches quantitative (Han Wenquan, 2005). If many in the period of transformation probability are compared, and further explains the change in ecological meaning, it can make the landscape plaques dynamic quantitative research more valuable.

## **2. Materials and methods**

72 Environmental Land Use Planning

results.

The model's biggest significance can predict the future changes and have very good guidance function for scientific decision. On the one hand, according to the former development trend and direction, decide to the driving factors and the weight and make model operate to the different time in the future. Analyze each scene to the change of land using produce situation. This kind of model can build-up mixed related model. The advantage is that it considers the driving factors in model forecasting, Because of much relationship and the resistance in establishing model, it is not easy to cause the scientific

About the prediction model of the landscape pattern, including the concept model and mechanism model. it's classified into dynamic model, CA model, system dynamic model (SD model) that is based on cybernetics, System theory and information theory and it's characterized by studying feedback System structure, function and the dynamic behavior dynamic model. Its outstanding characteristic is to reflect the complex System structure, function and the dynamic behavior of the interaction between the relations. So as to study complex System change behavior and trend in different situations, and provide decision supporting (Chen Shupeng, 1999). The existing research shows that system dynamics model can reflect land using system of complex behavior on macroscopic and it is the good simulation tools in land using (Zhang Hanxiong, 1997; van et al., 1999; Li and Simonovic, 2002). Li, such as using the SD model for North America grasslands waters increased temperatures and ice melt water flooding caused by area of various ways simulation, obtained a good simulation results (Li and Simonovic, 2002). ZhangHanXiong applies system dynamics theory to establish Jin Shan loess hilly-gully region soil erosion dynamic SD model. Markov chain models basically does not consider the landscape pattern changes affecting the driving force, only using the past changes of the landscape pattern and the degree of change. According to the mathematical functions, speculated that the future of the landscape pattern utilization condition. It is based on two period of the landscape pattern and use data to calculate the types of land using change as the transition probability ratio. Markov process on matrix calculate to speculate that different stages of the land utilization condition. For example, Turner and Ruscher rules to mesh the study area and use Markov chain model to calculate the change of landscape plaques type probability, plus eight image elements and the effect of land using category, to determine each box type change the landscape of probability matrix (Turner, 1987, 1989; Ruscher, 1988). In order to reduce the general error that using Markov chain prediction model of the landscape pattern condition. Aoki, etc, introduce Hopfield neural network model. First the research in the area of the landscape pattern changes similar degree is divided into several sub-regional (subarea).again with Markov chain respectively model to calculate the transition probability matrix. Using this method in the central business district in Tokyo, forecast to 2014 years of

The feasibility of the model is also an important aspect of the model, at present in the big, mesoscale landscape pattern prediction area using model is Markov chain models. This kind of dynamic simulation model using Markov model can forecast a quantitative description of dynamic landscape plaques. Markov transfer matrix make dynamic of landscape patches

urban landscape pattern condition (Aoki et al, 1996).

The data of land use and land cover (LULC) were obtained from Chinese Academy of Science. Landsat Thematic Mapper (TM) images for the 1985,1995,2000 and 2005 years after being geometrically registered. They were classified analysis and aggregated into six major land types, they are build-up-up, forest, water ,farm, grass and other land. There are main three types for studying. There are main reasons accounted for selecting this scheme of LULC classification. First, three LULC types represented the dominant ecosystems and reflected the land use in the study area. Second, the selection keeps in accordance with the local official standards for land use classification and at the same time considers the ability of TM images to interpret LULC patterns. The local official standards for land use classification divided the LULC into two hierarchica levels.

### **2.1. Study area**

Jiangsu Province is located in the lower reaches of the Yangtze River, east of the Yellow Sea, at latitude 30 ° 46'N-35 ° 02'N, longitude 116 ° 22'E-121 ° 55'E. The province's total area of

**Figure 1.** The Study area location map

approximately 102,600 square kilometers, accounting for 1.06% of the total area of 954 km long coastline.

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 75

(2)

*<sup>p</sup>*( ) ( 1) *t pt P* (3)

*k k*

11 12 1 21 22 2

*pp p*

*k k kk*

*pp p*

The state probabilities pi(t) at time t are estimated from the relative frequencies of the k

Denoting the ν-th observed state with iν, a stochastic chain fulfilling is a first-order Markov

( | , ,..., ) ( | ) 1 1 1 1 00 1 1 *PX i X i X i X i PX i X i t v t vt v t v tv* 2, , ,..., 1... 01 1 *v ii i k <sup>v</sup>*

Predictions of future state probabilities can be calculated by solving the matrix

Agriculture land demand data are stochastic time series data, so Markov chain model can be employed to forecast the future data according to historical data. Generally time series data can be divided into a continuous real number zone. In order to use Markov chain model, the continuous real number zone should be divided into finite number unambiguous

Markov transfer matrix simulate the dynamic landscape pattern not only need to understand a landscape status changes to another landscape the present situation of the process, the more important is clear and the reason for the variation of the landscape pattern. The landscape pattern evolution is the result from a combined effect of natural, economic, social and cultural factors , from the change of landscape pattern driving factors, different driving factors in the landscape pattern change have different functions, establish landscape pattern evolution simulation model of driving mechanism. perhaps is the landscape pattern evolution trend of the development of simulation. The current limit landscape pattern of dynamic simulation of a major reason is the lack of landscape processes and the landscape pattern of the interaction of the understanding and how to integrate this knowledge in the model. The mutual transformation of the landscape, patch and gallery. As the study area of Jiangsu province, the landscape pattern has significantly characteristics. From 1980 to 2005 yr., while the agriculture land area from 1980 in 7.23 km2 reduced to 2005 years of 6.85 km2. But in the whole study, regional landscape in the proportion of minimum is 69% (2005 yr.) . So its landscape's substrate position is unshakable. The basal characteristics, the single transfer matrix will not be reflected in the study period. But the whole state as a kind of "information" is retained, how to use this

... ... ... ... ... ... ...

1 2

At time 0 the initial distribution of states is ( ) (0) <sup>0</sup> *PX i pi i k* 1,...,

states, resulting in the vector ( ) ( ( ), ( ).... ( )) 1 2 *Pt p t p t p t <sup>k</sup>*

*2.2.2. Computation of transition potential* 

chain:

equation

state sets.

*pp p <sup>P</sup>*

## **2.2. Method**

Markov model has been widely applied in the prediction of urban landscape change, however, it can be amended though the regional socio-economic indicators to improve its forecast accuracy. Based on TM satellite images in different years (1995, 2001, 2005 and 2008), urban land-use change maps were created and analyzed in Taicang County of Jiangsu Province, then a weighed Markov model was established based on the driving force of urban land-use change to predict the urban landscape structure ( agricultural land, constructive land, etc. ) in 2013. Based on the analysis of driving forces of land-use change, the periods of driving forces were divided into 1995 - 2001 and 2001 - 2005 two stages. The transfer matrixes were used as the weighted factors of Markov model whose weights were calculated to constitute the model in order to build-up a transfer matrix more in line with the urban landscape change in the stage from 2008 to 2013, then the structure of the urban landscape in 2013 was predict. On the basis of status value (2008) of urban landscape, the weighted Markov model was more reasonable than the non-weighted Markov model.

## *2.2.1. Using Markov model*

The Russian mathematician Andrei Andreyevich Markov (1856–1922) developed the theory of Markov chains in his paper "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain" (Markov, 1907). A Markov chain is defined as a stochastic process fulfilling the Markov property (Eq. (3) with a discrete state space and a discrete or continuous parameter space. In this paper, the parameter space represents time, and is considered to be discrete. In this process, the outcomes of a given experiment can the out come of the next experiment. This type of process is called a Markov chain. Accordingly, a Markov chain represents a system of elements making transitions from one state to another over time. The order of the chain gives the number of time steps in the past influencing the probability distribution of the present state, and can be greater than one.

$$P(\mathbf{X}\_t = j \Big| \mathbf{x}\_s = i) = P\_{ij}(\mathbf{s}, t) \tag{1}$$

The conditional probabilities are called transition probabilities of order r=t−s from state i to state j .

They are denoted as the transition matrix P. For k states P has the form,The purpose of this section is to introduce the concept of a stochastic complement in an irreducible stochastic matrix and to develop some of the basic properties of stochastic complementation. These ideas will be the cornerstone for all subsequent discussions. It is a non-negative matrix (such a matrix is called stochastic). The transition probabilities matrix can be described as following:

$$P = \begin{bmatrix} p\_{11} & p\_{12} & \dots & p\_{1k} \\ p\_{21} & p\_{22} & \dots & p\_{2k} \\ \dots & \dots & \dots & \dots \\ p\_{k1} & p\_{k2} & \dots & p\_{kk} \end{bmatrix} \tag{2}$$

At time 0 the initial distribution of states is ( ) (0) <sup>0</sup> *PX i pi i k* 1,...,

74 Environmental Land Use Planning

*2.2.1. Using Markov model* 

be greater than one.

state j .

following:

long coastline.

**2.2. Method** 

approximately 102,600 square kilometers, accounting for 1.06% of the total area of 954 km

Markov model has been widely applied in the prediction of urban landscape change, however, it can be amended though the regional socio-economic indicators to improve its forecast accuracy. Based on TM satellite images in different years (1995, 2001, 2005 and 2008), urban land-use change maps were created and analyzed in Taicang County of Jiangsu Province, then a weighed Markov model was established based on the driving force of urban land-use change to predict the urban landscape structure ( agricultural land, constructive land, etc. ) in 2013. Based on the analysis of driving forces of land-use change, the periods of driving forces were divided into 1995 - 2001 and 2001 - 2005 two stages. The transfer matrixes were used as the weighted factors of Markov model whose weights were calculated to constitute the model in order to build-up a transfer matrix more in line with the urban landscape change in the stage from 2008 to 2013, then the structure of the urban landscape in 2013 was predict. On the basis of status value (2008) of urban landscape, the weighted Markov model was more reasonable than the non-weighted Markov model.

The Russian mathematician Andrei Andreyevich Markov (1856–1922) developed the theory of Markov chains in his paper "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain" (Markov, 1907). A Markov chain is defined as a stochastic process fulfilling the Markov property (Eq. (3) with a discrete state space and a discrete or continuous parameter space. In this paper, the parameter space represents time, and is considered to be discrete. In this process, the outcomes of a given experiment can the out come of the next experiment. This type of process is called a Markov chain. Accordingly, a Markov chain represents a system of elements making transitions from one state to another over time. The order of the chain gives the number of time steps in the past influencing the probability distribution of the present state, and can

The conditional probabilities are called transition probabilities of order r=t−s from state i to

They are denoted as the transition matrix P. For k states P has the form,The purpose of this section is to introduce the concept of a stochastic complement in an irreducible stochastic matrix and to develop some of the basic properties of stochastic complementation. These ideas will be the cornerstone for all subsequent discussions. It is a non-negative matrix (such a matrix is called stochastic). The transition probabilities matrix can be described as

t s (X x ) ( , ) *ij P j i P st* (1)

The state probabilities pi(t) at time t are estimated from the relative frequencies of the k states, resulting in the vector ( ) ( ( ), ( ).... ( )) 1 2 *Pt p t p t p t <sup>k</sup>*

Denoting the ν-th observed state with iν, a stochastic chain fulfilling is a first-order Markov chain:

$$P(X\_{t+1} = i\_{v+1} \mid X\_t = i\_v, X\_{t-1} = i\_{v-1}, \dots, X\_0 = i\_0) = P(X\_{t+1} = i\_{v+1} \mid X\_t = i\_v) \quad \forall v \ge 2, \forall i\_0, i\_1, \dots, i\_{v+1} \in \{1 \dots k\}$$

Predictions of future state probabilities can be calculated by solving the matrix equation

$$p(t) = p(t-1) \cdot P \tag{3}$$

Agriculture land demand data are stochastic time series data, so Markov chain model can be employed to forecast the future data according to historical data. Generally time series data can be divided into a continuous real number zone. In order to use Markov chain model, the continuous real number zone should be divided into finite number unambiguous state sets.

#### *2.2.2. Computation of transition potential*

Markov transfer matrix simulate the dynamic landscape pattern not only need to understand a landscape status changes to another landscape the present situation of the process, the more important is clear and the reason for the variation of the landscape pattern. The landscape pattern evolution is the result from a combined effect of natural, economic, social and cultural factors , from the change of landscape pattern driving factors, different driving factors in the landscape pattern change have different functions, establish landscape pattern evolution simulation model of driving mechanism. perhaps is the landscape pattern evolution trend of the development of simulation. The current limit landscape pattern of dynamic simulation of a major reason is the lack of landscape processes and the landscape pattern of the interaction of the understanding and how to integrate this knowledge in the model. The mutual transformation of the landscape, patch and gallery. As the study area of Jiangsu province, the landscape pattern has significantly characteristics. From 1980 to 2005 yr., while the agriculture land area from 1980 in 7.23 km2 reduced to 2005 years of 6.85 km2. But in the whole study, regional landscape in the proportion of minimum is 69% (2005 yr.) . So its landscape's substrate position is unshakable. The basal characteristics, the single transfer matrix will not be reflected in the study period. But the whole state as a kind of "information" is retained, how to use this information? Researching needs a model that can have absorbing function for the global information.

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 77

**Figure 2.** Fig2. Types of land change trends use SM model

**2.3. Combination process of Marcov model and Baysian fomula** 

changes and consequences of land using situation in the future.

Bayesian-Markov model for research,

The change of the landscape pattern is one of the changes of the elements. This change including numbers and forms, Form the landscape pattern change is one of the important research directions in the future. In the stable number of elements, it's better to grasp the landscape pattern. This research predicts the landscape pattern with the quantity model. Only do the numerical prediction, without considering the change of landscape form (2D and 3D). Landscape elements quantity change over time, because of the area of restrictions, the original plaques elements (image element) covered area of change, this state will eventually reach a stable state. The researchers hope to understand the changes among this process . Through the analysis of the development of different opinions predicted results, to explore in this situation, the land using the changes will tend to which direction, what effects. Maybe considering the different degrees of land management policy, predicting the

The changes and the trend of the landscape pattern of Jiangsu province can be used

There are reasons 1. in research area, different landscape types with mutual transformation can be sex each other 2. Landscape types of mutual transformation between process contains a multiple function relation which can accurate description of events 3. This research data is all kinds of data of land use which is got from TM satellite image data analysis in 1980, 1985, 1995, 2000 and 2005 , largely reflects the changes of landscape in jiangsu province, and to represent the future trend of the development in a certain period 4. GIS technology can provide technical

Markov process according to system development, time discrete into *k n* 1, 2, , , each state with X*k* to say, Take *n* discrete values X 1,2,... *<sup>k</sup> n* to introduce the state vector and transition probability vector respectively for

$$\begin{pmatrix} A\_1(k)\_\prime A\_2(k)\_\prime ...\_\prime A\_n(k) \end{pmatrix}\_\prime P = \begin{pmatrix} p\_{ij} \end{pmatrix}\_{nm}$$
 
$$A(k+1) = A(k)P$$

Every step of the transition probability can usually through the statistical data to determine, according to all kinds of random factors, the system the whole process of change can usually expressed as

$$X\_0 \xrightarrow{P\_1} X\_1 \xrightarrow{P\_2} X\_2 \xrightarrow{P\_3} \cdots \xrightarrow{P\_n} X\_{m-1} \xrightarrow{P\_m} X\_m$$

In the past the study, application Markov process research the process, main is both the math model:

1. Xt1 2 3 1 2 ,among *p p X X P PP t t* 2. 1 2 X t1 2 3 *p p X X t t* Use local linearization stages measuring method.

#### *2.2.3. Present problems*

The above two models of science has been confirmed. In the actual landscape forecast research, the former method is currently using in the widest range, the last only study in the succession in the forest landscape (XiongLiMin, 1991). The shortage of the Method lies in: ( 1)In many cases, it is an idealized model by the impact of the initial value ( Forman, 1986; Zhao Yi , 2001; ShenJing , 2006). Model sometimes does not reflect the authenticity of the system, the transfer matrix of the simple Markov model with the increase of the transfer step is only related to the most recent time and it's also different with the facts.(2) Segmentation processing methods often require transient from *Xm*1 to *Xm* or mutant, Rather than a gradual process. If this process is the time span, data integrity, it can be used to describe as *<sup>t</sup>* <sup>1</sup> *P* or *<sup>t</sup> P* the status changes.

Otherwise it will cause a large deviation. But the larger regional landscape pattern changes in a gradual process, and began to study the time is not long, such a model can not reflect the actual situation of the regional landscape pattern changes.

Need to construct state transition information on the entire process of absorption of the Bayes method, in order to solve this regional landscape pattern sub- Markov process.

**Figure 2.** Fig2. Types of land change trends use SM model

transition probability vector respectively for

information.

expressed as

math model:

2. 1 2 X t1 2 3 *p p*

1. Xt1 2 3 1 2 ,among

*X X P PP t t*

*p p*

*2.2.3. Present problems* 

*<sup>t</sup>* <sup>1</sup> *P* or *<sup>t</sup> P* the status changes.

process.

information? Researching needs a model that can have absorbing function for the global

Markov process according to system development, time discrete into *k n* 1, 2, , , each state with X*k* to say, Take *n* discrete values X 1,2,... *<sup>k</sup> n* to introduce the state vector and

1 2 ( ), ( ),..., ( ) , *AkAk Ak <sup>n</sup> ijnn P p*

*A*( 1) ( ) *k AkP*

Every step of the transition probability can usually through the statistical data to determine, according to all kinds of random factors, the system the whole process of change can usually

012 1 *<sup>m</sup> P P P P XXX X X m m*

In the past the study, application Markov process research the process, main is both the

The above two models of science has been confirmed. In the actual landscape forecast research, the former method is currently using in the widest range, the last only study in the succession in the forest landscape (XiongLiMin, 1991). The shortage of the Method lies in: ( 1)In many cases, it is an idealized model by the impact of the initial value ( Forman, 1986; Zhao Yi , 2001; ShenJing , 2006). Model sometimes does not reflect the authenticity of the system, the transfer matrix of the simple Markov model with the increase of the transfer step is only related to the most recent time and it's also different with the facts.(2) Segmentation processing methods often require transient from *Xm*1 to *Xm* or mutant, Rather than a gradual process. If this process is the time span, data integrity, it can be used to describe as

Otherwise it will cause a large deviation. But the larger regional landscape pattern changes in a gradual process, and began to study the time is not long, such a model can not reflect

Need to construct state transition information on the entire process of absorption of the Bayes method, in order to solve this regional landscape pattern sub- Markov

the actual situation of the regional landscape pattern changes.

1 2 3

*X X t t* Use local linearization stages measuring method.

## **2.3. Combination process of Marcov model and Baysian fomula**

The change of the landscape pattern is one of the changes of the elements. This change including numbers and forms, Form the landscape pattern change is one of the important research directions in the future. In the stable number of elements, it's better to grasp the landscape pattern. This research predicts the landscape pattern with the quantity model. Only do the numerical prediction, without considering the change of landscape form (2D and 3D). Landscape elements quantity change over time, because of the area of restrictions, the original plaques elements (image element) covered area of change, this state will eventually reach a stable state. The researchers hope to understand the changes among this process . Through the analysis of the development of different opinions predicted results, to explore in this situation, the land using the changes will tend to which direction, what effects. Maybe considering the different degrees of land management policy, predicting the changes and consequences of land using situation in the future.

The changes and the trend of the landscape pattern of Jiangsu province can be used Bayesian-Markov model for research,

There are reasons 1. in research area, different landscape types with mutual transformation can be sex each other 2. Landscape types of mutual transformation between process contains a multiple function relation which can accurate description of events 3. This research data is all kinds of data of land use which is got from TM satellite image data analysis in 1980, 1985, 1995, 2000 and 2005 , largely reflects the changes of landscape in jiangsu province, and to represent the future trend of the development in a certain period 4. GIS technology can provide technical support for establishing a realistic probability transfer matrix 5. This is a practical method which predict short-term changes of landscape structure trend using Markov chain quantitatively. We can consider the effect in the build-uping of model of Markov and proposed the Bayes transition probability model. We will forecast regional changes of landscape structure based on this area.

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 79

*P PX X P P*

2 12 2

1 *n j*

(2)

*P PX X P P*

2 1 1

*m m <sup>m</sup> m mm <sup>m</sup> <sup>j</sup> <sup>j</sup>*

( ) ( 1) 1 ( 2) ( 1) ( 2) ( 1) ( ( ) ( 1)

( 2) ( 1) ( 2) ( 1) ( )

*<sup>n</sup> m m ij ij <sup>j</sup>*

( ) ( 1)

 

*m m m ij ij ij m m a a mmm*

*a a*

*a b aaa*

 

> 1 ( ) ( 1) 1

 

*<sup>n</sup> m m mmm <sup>n</sup> m m ij ij ij ij ij <sup>m</sup> ij ij <sup>j</sup> ij <sup>n</sup> <sup>j</sup> m m ij ij <sup>j</sup>*

2 22

1 2 (2) 1 21 1

*j j*

*PP X X PP*

*a b aaa a a <sup>a</sup> a a*

 

( 2) 2 12 2

( 2) ( 2) <sup>1</sup> ( ) *m m <sup>i</sup> ij n P C* 

*m ij ij ij ij ij*

( 2)

*a*

321 *PPP m mm XXXX mmmm* 

(1) 1 21 1

( ) (1) <sup>1</sup> 1, 2, , ( ) <sup>1</sup> <sup>1</sup> <sup>1</sup>

*ij <sup>j</sup> <sup>k</sup> <sup>n</sup>*

This Bayesian - Markov transition probability formula , this model is called as the landscape

From the results of the study in chapter 3,we can see the deceleration of the agriculture land area in our province experiences from increase than sharp decrease than slow decrease and decrease again ,during the process of rapid urbanization, industrialization, on the one hand,scale the construction land use has expanded, valuable agriculture land resources are invaded,on the other hand,in the amount of land in city or development zone, there are scattered layout, land intensive degree lower outstanding problems.At present the notable

*a*

*<sup>m</sup> <sup>k</sup> ij*

*a*

( ) *<sup>i</sup>*

*<sup>k</sup> i n <sup>n</sup> <sup>m</sup> <sup>k</sup>*

*P PX X P P*

(/) (/) (/)

In accordance with the above steps, the final transition probability can be revised to

1 ( 2)

*m m mm m*

*P PX X*

( 2)

The state transition probability once again amended to next

*P PX X*

*P*

patch change forecast Bayesian - Markov model (BM model).

*2.3.2. Computation of transition by B-M model* 

*ij*

*C*

(1) *P* in *i* row element (1) *Pi*

set to as follows:

(/) (/) (/)

1

*m*

)

### *2.3.1. B-M model*

For know initial state *X*<sup>0</sup> , in the system, we set up its state transition of the prior distribution as <sup>1</sup> *P X* . After *PX X k k*<sup>1</sup> to *Xk*1 occurred under the conditions of a priori probability distribution. Replacement of the Bayes formula, the a priori probability distribution is amended to get the posterior probability distribution.

First set the state to change the whole process as follows,

$$X\_0 \xrightarrow{P\_1 = P(X\_1/X\_0)} X\_1 \xrightarrow{P\_2 = P(X\_2/X\_1)} X\_2 \cdots X\_{m-1} \xrightarrow{P\_{P\_1 = P(X\_m/X\_{m-1})}} X\_m$$

Among <sup>1</sup> 2, ( / )( 1, 2, ) *m P PX X k m k kk* is the state transition probability.

$$\begin{aligned} \,^i\_k = \begin{pmatrix} a\_{11}^{(k)} & a\_{12}^{(k)} & \cdots & a\_{1m}^{(k)} \\ a\_{21}^{(k)} & a\_{22}^{(k)} & \cdots & a\_{2n}^{(k)} \\ \cdots & \cdots & \cdots & \cdots \\ a\_{n1}^{(k)} & a\_{n1}^{(k)} & \cdots & a\_{nm}^{(k)} \end{pmatrix} \\\\ \,^\pi\_j d\_{ij}^{(k)} = 1 & (i = 1, 2, \cdots, n), k = 1, 2, \cdots, m \end{aligned}$$

That the *k* transition probability matrix, first remove the last three state as follows.

$$\begin{aligned} \mathbf{X\_{m-1}} \xrightarrow{P\_{m-1}} \mathbf{X\_{m}} \xrightarrow{P\_{m}} \mathbf{X\_{m}} \xrightarrow{P\_{m}} \mathbf{X\_{m+1}} \\\\ P^{m-1} = P(\mathbf{X\_{m-1}} / \mathbf{X\_{m}}) = \frac{P\_{m-1} \cdot P(\mathbf{X\_{m}} / \mathbf{X\_{m-1}})}{P(\mathbf{X\_{m}})} = \frac{P\_{m-1} \cdot P(\mathbf{X\_{m}} / \mathbf{X\_{m-1}})}{\sum\_{j} P\_{m-1} \cdot P(\mathbf{X\_{m}} / \mathbf{X\_{m-1}})} = \frac{P\_{m-1} \cdot P\_{m}}{\sum\_{j} P\_{m-1} \cdot P\_{m}} \end{aligned}$$

1 ( 1) <sup>1</sup> ( ) *ij m m <sup>n</sup> P b* Among ( 1) *<sup>m</sup> <sup>P</sup>* is *n n* matrix, defined by

$$b\_{ij}^{\{m-1\}} = \frac{a\_{ij}^{\{m\}} a\_{ij}^{\{m-1\}}}{\sum\_{j=1}^n a\_{ij}^{\{m\}} a\_{ij}^{\{m-1\}}} \to \qquad i = 1, 2, \dots, m \qquad \text{then} \qquad \sum\_{j=1}^n b\_{ij}^{\{m-1\}}$$

This state transition <sup>1</sup> 2 1 *m m P P X XX m mm* is revised to <sup>1</sup> 2 1 *P P m m X XX m mm* . Then revised to 1 1 <sup>2</sup> 32 1 *m m mP P P XX XX mm mm* 

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 79

$$P^{(m-2)} = P(\mathbf{X}\_{m-2} \mid \mathbf{X}\_{m-1}) = \frac{P\_{m-2}P(\mathbf{X}\_{m-1} \mid \mathbf{X}\_{m-2})}{\sum\_{j} P\_{m-2}P(\mathbf{X}\_{m-1} \mid \mathbf{X}\_{m-2})} = \frac{P\_{m-2}P^{m-1}}{\sum\_{j} P\_{m-2}P^{m-1}}$$

set to as follows:

78 Environmental Land Use Planning

*2.3.1. B-M model* 

amended to get the posterior probability distribution.

First set the state to change the whole process as follows,

support for establishing a realistic probability transfer matrix 5. This is a practical method which predict short-term changes of landscape structure trend using Markov chain quantitatively. We can consider the effect in the build-uping of model of Markov and proposed the Bayes transition probability model. We will forecast regional changes of landscape structure based on this area.

For know initial state *X*<sup>0</sup> , in the system, we set up its state transition of the prior distribution as <sup>1</sup> *P X* . After *PX X k k*<sup>1</sup> to *Xk*1 occurred under the conditions of a priori probability distribution. Replacement of the Bayes formula, the a priori probability distribution is

(/ ) 1 10 2 21 1 1 (/) (/)

() () () 11 12 1 () () () 21 22 2

 

*aa a aa a*

*kk k*

 

*kk k <sup>n</sup> <sup>P</sup>*

*m*

1 11

2 1 *P P m m X XX m mm* 

.

*m m mm m m j j*

*P X P PX X P P*

*P PX X P PX X P P*

() () ()

*aa a*

*kk k n n nm*

1( 1, 2, , ), 1, 2, ,

*a i nk m*

m-1 <sup>1</sup> *<sup>m</sup> <sup>P</sup> <sup>P</sup> X XX m m*

1 1 11 1 1

( 1) ( 1) ( ) ( 1) 1

is revised to <sup>1</sup>

*m m <sup>n</sup> <sup>m</sup> ij ij <sup>m</sup> ij n ij m m j*

*b in b*

1,2, , then

(/ ) (/ ) ( /) ( ) (/ ) *m m mm m mm m m*

1 1

*P PX X m m P PX X P PX X <sup>P</sup> <sup>X</sup> <sup>X</sup> X Xm m <sup>X</sup>*

0 1 2 1

Among <sup>1</sup> 2, ( / )( 1, 2, ) *m P PX X k m k kk* is the state transition probability.

*k*

( ) 1

That the *k* transition probability matrix, first remove the last three state as follows.

m-1

*<sup>n</sup> <sup>k</sup> ij <sup>j</sup>*

Among ( 1) *<sup>m</sup> <sup>P</sup>* is *n n* matrix, defined by

( ) ( 1)

2 1 *m m P P X XX m mm*

32 1 *m m mP P P XX XX mm mm* 

*a a*

*ij ij*

*a a*

1

*j*

Then revised to 1 1 <sup>2</sup>

1

*P PX X*

This state transition <sup>1</sup>

1 ( 1) <sup>1</sup> ( ) *ij*

*m m <sup>n</sup> P b* *m m*

$$P\_i^{(m-2)} = \{\mathbf{C}\_{ij}^{(m-2)}\}\_{1 \times n}$$

$$\mathbf{C}\_{ij}^{(m-2)} = \frac{a\_{ij}^{(m-2)}b\_{ij}^{(m-1)}}{\sum\_{j=1}^n a\_{ij}^{(m-2)}b\_{ij}^{(m-1)}} = \frac{a\_{ij}^{(m-2)}\left[\frac{a\_{ij}^{(m)}a\_{ij}^{(m-1)}}{\sum\_{j=1}^n a\_{ij}^{(m)}a\_{ij}^{(m-1)}}\right]}{\sum\_{j=1}^n a\_{ij}^{(m-2)}\left[\frac{a\_{ij}^{(m)}a\_{ij}^{(m-1)}}{\sum\_{j=1}^n a\_{ij}^{(m)}a\_{ij}^{(m-1)}}\right]} = \frac{a\_{ij}^{(m-2)}a\_{ij}^{(m-1)}a\_{ij}^{(m)}}{\sum\_{j=1}^n a\_{ij}^{(m-2)}a\_{ij}^{(m-1)}a\_{ij}^{(m)}} \to 0$$

The state transition probability once again amended to next

$$X\_{m-3} \xrightarrow{P^{m-2}} X\_{m-2} \xrightarrow{P^{m-2}} X\_{m-1} \xrightarrow{P^{m-2}} X\_{m-1}$$

In accordance with the above steps, the final transition probability can be revised to

$$P^{(1)} = P(X\_1 \mid X\_2) = \frac{P\_1 P(X\_2 \mid X\_1)}{\sum\_j P\_1 \cdot P(X\_2 \mid X\_1)} = \frac{P\_1 P^{(2)}}{\sum\_j P\_1 \cdot P^{(2)}}$$

(1) *P* in *i* row element (1) *Pi*

$$P\_i^{(1)} = \left[ \frac{\prod\_{k=1}^m a\_{ij}^{(k)}}{\sum\_{j=1}^n \left[ \prod\_{k=1}^m a\_{ij}^{(k)} \right]} \right]\_{1 \times n} \{i = 1, 2, \dots, n\}$$

This Bayesian - Markov transition probability formula , this model is called as the landscape patch change forecast Bayesian - Markov model (BM model).

#### *2.3.2. Computation of transition by B-M model*

From the results of the study in chapter 3,we can see the deceleration of the agriculture land area in our province experiences from increase than sharp decrease than slow decrease and decrease again ,during the process of rapid urbanization, industrialization, on the one hand,scale the construction land use has expanded, valuable agriculture land resources are invaded,on the other hand,in the amount of land in city or development zone, there are scattered layout, land intensive degree lower outstanding problems.At present the notable

features is the rapid expansion of the built-up urban scale in the change of land use in Jiangsu province(Zhao YaoYang, 2006),research predict that the population of Jiangsu Province will reach 76.9519 million in 2010 and increase average 0.3086 million every year from 2005 to 2010,but the population will reach 79.192 million by 2020,and increase average 0.2240 million every year from 2010 to 2020,which explain that population growth has a stable state (Yang LiXia, 2006),the development of traffic promotes the Regional towns nearby,for 30 years, jiangsu's traffic will enter the period of big development of network, balanced development situation is clear (han jia, 2008),through the results of this study,we can preliminary judge, the fastest change in regional landscape pattern is in the build-up land in the next period,while the share of agriculture land will be stability have fall.

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 81

land(FL) grass waters Build-up

land grass waters Build-up

land

Other land

land Other land

land Other land

From this result,we can generate the transition probability matrix of regional landscape type

Remote sensing (RS) and geographic information systems (GIS) are essential tools for monitoring land distribution area, and spatial and temporal analysis of wetland dynamic

AL 9.86E-01 2.07E-04 0.00E+00 1.11E-03 1.30E-02 3.66E-05 FL 1.89E-03 9.95E-01 0.00E+00 0.00E+00 3.47E-03 0.00E+00 grass 1.75E-02 0.00E+00 9.76E-01 4.12E-03 2.06E-03 0.00E+00 waters 1.76E-03 0.00E+00 0.00E+00 9.98E-01 6.49E-04 0.00E+00

land 9.85E-04 0.00E+00 0.00E+00 7.39E-04 9.98E-01 0.00E+00 Other land 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1.00E+00

land 9.92E-01 3.09E-04 4.94E-05 1.85E-03 6.09E-03 0.00E+00 Forest land 4.73E-03 9.85E-01 0.00E+00 1.58E-03 9.15E-03 0.00E+00 grass 9.60E-02 0.00E+00 7.65E-01 6.43E-02 7.49E-02 0.00E+00 waters 1.66E-03 0.00E+00 4.61E-04 9.97E-01 6.45E-04 0.00E+00

land 3.30E-03 0.00E+00 0.00E+00 3.89E-04 9.96E-01 0.00E+00 Other land 7.69E-02 0.00E+00 0.00E+00 1.54E-01 0.00E+00 7.69E-01

land Forest land grass waters Build-up

land 8.69E-01 8.55E-03 2.42E-03 2.14E-02 9.89E-02 3.74E-05 Forest land 1.20E-01 8.29E-01 8.36E-03 1.16E-02 2.93E-02 1.93E-03 grass 4.45E-02 1.67E-02 8.57E-01 5.84E-02 2.09E-02 2.78E-03 waters 3.98E-02 4.83E-03 1.12E-02 9.38E-01 5.92E-03 9.11E-05

land 2.88E-01 1.36E-02 1.68E-02 2.39E-02 6.57E-01 1.75E-04 Other land 0.00E+00 3.00E-01 0.00E+00 1.00E-01 1.00E-01 5.00E-01

Forest

**Table 3.** In 1980-1985 six kinds of transition probability matrix of plaques types

**Table 4.** In 1985-1995 six kinds of transition probability matrix of plaques types

**Table 5.** In 1995-2000 six kinds of transition probability matrix of plaques types

Forest

plaques of the jiangsu province , respectively table3 –table 6,

**3. Result** 

change.

Build-up

Agriculture

Build-up

Agriculture

Build-up

**3.1. Model validation** 

<sup>p</sup>Agriculture

<sup>p</sup>Agriculture

<sup>p</sup>Agriculture

land

land(AL)

## *2.3.3. Summary of the data and the transfer matrix of the generation of landscape*

Because this data is made up by two different format in different period, and data of different precision ,at first,they should be unified into the grid of image formats,what's more,we should united them to 1000km \* 1000km. Like size of pixel , we can get plaque kind of pixel conversion matrix in Arc Gis spatial analysis.The following were listed four stages patches type number transfer matrix and plaques between the type of transition probability matrix.Due to the limitation of length,we make two transfer number matrix in our four stages, the following table 1 shows for 1980-1985,the following table 2 shows the period of 2000-2005 yr..


**Table 1.** In 1980-1985, six kinds of number transfer matrix of the landscape types


**Table 2.** In 2000-2005, six kinds of number transfer matrix of the landscape types

From this result,we can generate the transition probability matrix of regional landscape type plaques of the jiangsu province , respectively table3 –table 6,

## **3. Result**

80 Environmental Land Use Planning

features is the rapid expansion of the built-up urban scale in the change of land use in Jiangsu province(Zhao YaoYang, 2006),research predict that the population of Jiangsu Province will reach 76.9519 million in 2010 and increase average 0.3086 million every year from 2005 to 2010,but the population will reach 79.192 million by 2020,and increase average 0.2240 million every year from 2010 to 2020,which explain that population growth has a stable state (Yang LiXia, 2006),the development of traffic promotes the Regional towns nearby,for 30 years, jiangsu's traffic will enter the period of big development of network, balanced development situation is clear (han jia, 2008),through the results of this study,we can preliminary judge, the fastest change in regional landscape pattern is in the build-up

land in the next period,while the share of agriculture land will be stability have fall.

*2.3.3. Summary of the data and the transfer matrix of the generation of landscape* 

1 shows for 1980-1985,the following table 2 shows the period of 2000-2005 yr..

**Table 1.** In 1980-1985, six kinds of number transfer matrix of the landscape types

**Table 2.** In 2000-2005, six kinds of number transfer matrix of the landscape types

Unit 1km2 Agriculture Forest

Agriculture

Because this data is made up by two different format in different period, and data of different precision ,at first,they should be unified into the grid of image formats,what's more,we should united them to 1000km \* 1000km. Like size of pixel , we can get plaque kind of pixel conversion matrix in Arc Gis spatial analysis.The following were listed four stages patches type number transfer matrix and plaques between the type of transition probability matrix.Due to the limitation of length,we make two transfer number matrix in our four stages, the following table

land 80861 17 0 91 1069 3 Forest land 6 3154 0 0 11 0 grass 17 0 948 4 2 0 waters 19 0 0 10756 7 0 Build-up land 4 0 0 3 4055 0 Other land 0 0 0 0 0 10

Unit 1km2 Agriculture land Forest land grass waters Build-up land Other land

Agriculture land 60821 673 85 2159 8359 5 Forest land 638 2490 20 77 177 6 grass 172 22 593 196 121 0 waters 1341 77 79 10447 2890 2 Build-up land 5311 119 40 187 6342 2 Other land 2 6 2 2 0 6

land grass waters Build-up

land

Other land

### **3.1. Model validation**

Remote sensing (RS) and geographic information systems (GIS) are essential tools for monitoring land distribution area, and spatial and temporal analysis of wetland dynamic change.


**Table 3.** In 1980-1985 six kinds of transition probability matrix of plaques types




**Table 5.** In 1995-2000 six kinds of transition probability matrix of plaques types


Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 83

land Other land

land other land

<sup>3</sup>21.845609 0.009731691

points out that when we use Markov models to make prediction, Recent effect of prediction is better than long-term that of prediction (ShenJing, 2006). In order to the reliability prediction, we should choose the data of 2005 to do initial matrix and predict future years.

ure land Forest land grass waters Build-up

land 0.926304 0.000417 2.3257E-05 0.001705937 0.071577 1.3454E-08 Forest land 0.094777 0.851918 0.00278079 0.000916227 0.04962 1.0811E-05 grass 0.060054 0.002472 0.84407206 0.038840138 0.012852 1.9104E-08 waters 0.010645 7.16E-05 0.00016138 0.98 0.009923 1.1433E-08

land 0.093941 0.00144 0.00029571 0.001649559 0.901701 8.3021E-08 Other land 0.057783 0.722013 0.00013623 0.023523454 0.003758 0.12960613

land Forest land grassland water Build-up

2010 64.60235 2.956314 0.711314685 12.9051 18.78414 0.012721832

2020 59.96652 2.262479 0.587888366 12.73299 24.40454 0.001291957 2025 58.22519 1.990883 0.513177977 12.64582 26.54399 0.000194895 2030 56.78195 1.760878 0.449939727 12.55778 28.33319 0.00013 **Table 8.** Bayesian-Markov model to simulate the prediction of proportion of the land type (unit %)

If we analysis research index of landscape types simply,we can have some knowledge of the landscape pattern.if we want to have further study of the change trend of landscape pattern,we need to do research on the change of type of the landscape or inside structure (Yue TianXiang, 2000). From transfer matrix of the four stages of the landscape plaques, we can see the transfer of the patches of landscape, for example, from the transfer matrix of the plaque-sin 1980-1985,there are 17 zero accounting for nearly half of the total conversion type, in1985-1995 yr. there are 12 zero, in 1995-2000 there are 2 zero,in 2000-2005 yr., in patches transfer matrix, there is only about 1 zero(did not use the land to build-up land type). From this change we can draw a conclusion that is conversion activities in the increase in the regional landscape pattern plaques,change of type of regional landscape plaques enter into the active period around 2000 yr. or so. We can get table 9 through comparing the plaques number of urban construction land. From the table we can see that during agriculture land into land for construction, it experiences that the change of agriculture turning into building-up increased in process of the first and then decreased, rapid increase again. Agriculture land is the main "origin" for the build-uping land

**Table 7.** Bayesian transition probability matrix during study period

2015 62.06647 2.5771644 0.676184411 12.8185887

Get table8.

Agriculture

Build-up

year Agriculture

**3.3. Analysis result** 

P(1) Agricult

**Table 6.** In 2000-2005 six kinds of transition probability matrix of plaques types

## **3.2. Analysis of transition matrix**

According to 2.1 section,the derivation of process,and the transition probability matrix we get in 2.3 section ,after four times the iterative calculation we can get that in the 1980-2005 study period,considering effect of global information, the leaf Markov transition probability matrix,based on this,we can take 1980, 1985, 1995, 2000 and 2005 years of plaque in the share of landscape type respectively for the initial P0 and the product of the transfer matrix, the landscape pattern change forecast, here to solve the lack of data in 1990, to 1985 years for the initial vector, multiplied by the Bayesian transition probability matrix P (1), (see table 7).

We can get the vector of the occupies plaques of virtual landscape types in 1990, (farmland, forest land, meadow, waters, construction land ,unused land)T= (78.76588, 3.133267, 0.901077, 10.77607, 5.46728, 0.007758)T

So we can have a data set of six flags,than we can take the Bayes transition probability matrix P (1) as a step (five years) transfer matrix,because we must consider global effective forecasting methods,the study of data,in this way we can make the data complete,because in the landscape pattern, the basic data of integrity is very common, Markov provides reference method for the missing data of The landscape pattern,at the same time we also must see that when we use this method,we should keep the main character information preserved of the characteristics oflandscape,but this method can't handle mutations,through the raw data we can be see,the area of the build-uping land plaques is nearly doubled during from 1995 to 2000 yr.. The thing which is increasing of the landscape pattern plaques will be weaken because of the global of fusion ways of Bayesian-Markov,we can think that mutations are not resolved, and Bayes framework provides a very good idea. We can add mutations factor in the global transfer matrix. In order to make the prediction based on the stable landscape plaques in this study, the environment of forecast period must be the same with that of 1980-2005.we can take the data of 1980,2000,2005 yr. as the initial state to forecast something,agriculture land, for example,starting share is81.199%in 1980,while he forecast result is 75% if we take the initial state of agriculture land area of 2005. While by the actual measurement data of the gods we can know that in 2000,proportion of agriculture land has less than 70%.But this just shows, feature that the changing proportion in the growth of acceleration in regional landscape pattern plaques. Existing research conclusion points out that when we use Markov models to make prediction, Recent effect of prediction is better than long-term that of prediction (ShenJing, 2006). In order to the reliability prediction, we should choose the data of 2005 to do initial matrix and predict future years. Get table8.


**Table 7.** Bayesian transition probability matrix during study period


**Table 8.** Bayesian-Markov model to simulate the prediction of proportion of the land type (unit %)

### **3.3. Analysis result**

82 Environmental Land Use Planning

Agriculture

Build-up

<sup>p</sup>Agriculture

**3.2. Analysis of transition matrix** 

0.901077, 10.77607, 5.46728, 0.007758)T

land Forest land grass waters Build-up

land 8.44E-01 9.33E-03 1.18E-03 2.99E-02 1.16E-01 6.93E-05 Forest land 1.87E-01 7.31E-01 5.87E-03 2.26E-02 5.19E-02 1.76E-03 grass 1.56E-01 1.99E-02 5.37E-01 1.78E-01 1.10E-01 0.00E+00 waters 1.10E-01 6.29E-03 6.46E-03 8.54E-01 2.36E-02 1.63E-04

land 4.43E-01 9.92E-03 3.33E-03 1.56E-02 5.28E-01 1.67E-04 Other land 1.11E-01 3.33E-01 1.11E-01 1.11E-01 0.00E+00 3.33E-01

According to 2.1 section,the derivation of process,and the transition probability matrix we get in 2.3 section ,after four times the iterative calculation we can get that in the 1980-2005 study period,considering effect of global information, the leaf Markov transition probability matrix,based on this,we can take 1980, 1985, 1995, 2000 and 2005 years of plaque in the share of landscape type respectively for the initial P0 and the product of the transfer matrix, the landscape pattern change forecast, here to solve the lack of data in 1990, to 1985 years for the initial vector, multiplied by the Bayesian transition probability matrix P (1), (see table 7).

We can get the vector of the occupies plaques of virtual landscape types in 1990, (farmland, forest land, meadow, waters, construction land ,unused land)T= (78.76588, 3.133267,

So we can have a data set of six flags,than we can take the Bayes transition probability matrix P (1) as a step (five years) transfer matrix,because we must consider global effective forecasting methods,the study of data,in this way we can make the data complete,because in the landscape pattern, the basic data of integrity is very common, Markov provides reference method for the missing data of The landscape pattern,at the same time we also must see that when we use this method,we should keep the main character information preserved of the characteristics oflandscape,but this method can't handle mutations,through the raw data we can be see,the area of the build-uping land plaques is nearly doubled during from 1995 to 2000 yr.. The thing which is increasing of the landscape pattern plaques will be weaken because of the global of fusion ways of Bayesian-Markov,we can think that mutations are not resolved, and Bayes framework provides a very good idea. We can add mutations factor in the global transfer matrix. In order to make the prediction based on the stable landscape plaques in this study, the environment of forecast period must be the same with that of 1980-2005.we can take the data of 1980,2000,2005 yr. as the initial state to forecast something,agriculture land, for example,starting share is81.199%in 1980,while he forecast result is 75% if we take the initial state of agriculture land area of 2005. While by the actual measurement data of the gods we can know that in 2000,proportion of agriculture land has less than 70%.But this just shows, feature that the changing proportion in the growth of acceleration in regional landscape pattern plaques. Existing research conclusion

**Table 6.** In 2000-2005 six kinds of transition probability matrix of plaques types

land Other land

If we analysis research index of landscape types simply,we can have some knowledge of the landscape pattern.if we want to have further study of the change trend of landscape pattern,we need to do research on the change of type of the landscape or inside structure (Yue TianXiang, 2000). From transfer matrix of the four stages of the landscape plaques, we can see the transfer of the patches of landscape, for example, from the transfer matrix of the plaque-sin 1980-1985,there are 17 zero accounting for nearly half of the total conversion type, in1985-1995 yr. there are 12 zero, in 1995-2000 there are 2 zero,in 2000-2005 yr., in patches transfer matrix, there is only about 1 zero(did not use the land to build-up land type). From this change we can draw a conclusion that is conversion activities in the increase in the regional landscape pattern plaques,change of type of regional landscape plaques enter into the active period around 2000 yr. or so. We can get table 9 through comparing the plaques number of urban construction land. From the table we can see that during agriculture land into land for construction, it experiences that the change of agriculture turning into building-up increased in process of the first and then decreased, rapid increase again. Agriculture land is the main "origin" for the build-uping land

plaques.for their own growth of urban construction land use, we can understand their own patch expansion , also can be used as a proof of urbanization. Have to, there are also turn out, the advantage of the transfer matrix is that it can let researchers from two aspects to see a problem.when we take 2000-2005 years of plaques in the land transfer condition built turn out as an example,we will see table 10 for 2005 years to build-up land moved on to turn out and balance in this time. We can see clearly that the water is the second source of the land for construction,in the process of build-uping land expansion. In the study period, the occupation of arable land is very obvious.

Predicting Changes in Regional Land Use Pattern: The Case of Jiangsu Province, China 85

**Figure 3.** Change difference figure of forecast model of landscape types in different period (%)

Bayesian - Markov model landscape pattern of the number of patch types the proportion of change in the whole Jiangsu Province Forecast (2010-2030). Results show that the type of landscape pattern in the region of plaque volume changeson will be leveled off, and arable land remains the matrix characteristics of the region, construction sites still show a growth trend, but not doubling phenomenon around 1995-2000 yr.. Economic, population growth, the region of the plaque type unit bearing capacity increase is the main form of Jiangsu Province regional landscape pattern change, and the layout of the landscape patch types in

It also studied the main driving factors of landscape changes and established a systematic landscape model and made empirical research by using Jaingsu as a subject. The paper's conclusions may offer some essential refernces to the decision making for the regional sustainable development. At last this predict model will use B-M spatial model . This is

*College of Landscape Architecture Nanjing Forest University, Nanjing, China* 

**4. Conclusion and direction for the future** 

space is worthy of further study.

quantitative model to location model.

**Author details** 

Jing Shen and Hao Wang


**Table 9.** the plaques number of the transfer of the land to build-up in each phase ( number of pixel)


**Table 10.** Interconverting table of build-up land and other types (2005)

From the number prediction of landscape plaques , we can see that after 2005 years land use type change trend for the decrease in the number of agriculture land, forest land reduce more slow, grassland area to reduce slow, build-up the area of land to increase, and after a certain time period of slowing rate increase, unused land is constantly slowly decrease. We can see that at present the landscape pattern is steady change,agriculture land, build-uping land is the biggest patch types of area change. It indicates the landscape pattern in jiangsu province is affected from human activities. Natural landscape in the research area gradually blurred. It is form of urban and rural economic integration of the landscape pattern,if agriculture land diminishing, it is a challenge for the landscape structure based on agriculture land. But at present the implementation of land management policy and considering the effect of global Bayesian-Markov forecast model results show the agriculture land area of landscape plaques , and build-up land types and the rate of change in patches type slow (figure 3). As the economy and population growth,it will be the main form of regional patches type unit bearing capacity of the change of regional landscape pattern, and there are changes in jiangsu province in the future.

**Figure 3.** Change difference figure of forecast model of landscape types in different period (%)

## **4. Conclusion and direction for the future**

Bayesian - Markov model landscape pattern of the number of patch types the proportion of change in the whole Jiangsu Province Forecast (2010-2030). Results show that the type of landscape pattern in the region of plaque volume changeson will be leveled off, and arable land remains the matrix characteristics of the region, construction sites still show a growth trend, but not doubling phenomenon around 1995-2000 yr.. Economic, population growth, the region of the plaque type unit bearing capacity increase is the main form of Jiangsu Province regional landscape pattern change, and the layout of the landscape patch types in space is worthy of further study.

It also studied the main driving factors of landscape changes and established a systematic landscape model and made empirical research by using Jaingsu as a subject. The paper's conclusions may offer some essential refernces to the decision making for the regional sustainable development. At last this predict model will use B-M spatial model . This is quantitative model to location model.

### **Author details**

84 Environmental Land Use Planning

occupation of arable land is very obvious.

plaques.for their own growth of urban construction land use, we can understand their own patch expansion , also can be used as a proof of urbanization. Have to, there are also turn out, the advantage of the transfer matrix is that it can let researchers from two aspects to see a problem.when we take 2000-2005 years of plaques in the land transfer condition built turn out as an example,we will see table 10 for 2005 years to build-up land moved on to turn out and balance in this time. We can see clearly that the water is the second source of the land for construction,in the process of build-uping land expansion. In the study period, the

 1980-1985 1985-1995 1995-2000 2000-2005 P15 1069 493 7929 8359 P25 11 29 91 177 P35 2 71 15 121 P45 7 7 65 289 P55 4055 5125 5762 6342 P65 0 0 1 0 **Table 9.** the plaques number of the transfer of the land to build-up in each phase ( number of pixel)

Agriculture land 8359 5311 3048 Forest land 177 119 58 grass 121 40 81 waters 289 187 102 Build-up land 6342 6342 0 Other land 0 2 -2

From the number prediction of landscape plaques , we can see that after 2005 years land use type change trend for the decrease in the number of agriculture land, forest land reduce more slow, grassland area to reduce slow, build-up the area of land to increase, and after a certain time period of slowing rate increase, unused land is constantly slowly decrease. We can see that at present the landscape pattern is steady change,agriculture land, build-uping land is the biggest patch types of area change. It indicates the landscape pattern in jiangsu province is affected from human activities. Natural landscape in the research area gradually blurred. It is form of urban and rural economic integration of the landscape pattern,if agriculture land diminishing, it is a challenge for the landscape structure based on agriculture land. But at present the implementation of land management policy and considering the effect of global Bayesian-Markov forecast model results show the agriculture land area of landscape plaques , and build-up land types and the rate of change in patches type slow (figure 3). As the economy and population growth,it will be the main form of regional patches type unit bearing capacity of the change of regional landscape

**Table 10.** Interconverting table of build-up land and other types (2005)

pattern, and there are changes in jiangsu province in the future.

Turn to build-up Build-up turn to other types D-value

Jing Shen and Hao Wang *College of Landscape Architecture Nanjing Forest University, Nanjing, China* 

## **Acknowledgement**

The authors are grateful to Prof. Yannan Xu for simulating discussions and thank china natural science fund (No.30972414 /c161202 and No.30971609) .

**Chapter 0**

**Chapter 5**

**An Integrated Land-Use System Model**

Jennifer Koch, Florian Wimmer, Rüdiger Schaldach and Janina Onigkeit

The Jordan River region (Israel, Jordan, and the Palestinian National Authority (PA)) is one of the most water scarce regions of the world. The total renewable water resource values in the Jordan River region are 52 to 535 m3 per capita and year [15], which is far below the threshold value of 1000 m3 per capita and year indicating chronic water scarcity [14]. On average, water resources withdrawn for agricultural activities, such as irrigated crop production, amount to two thirds of the total actual renewable water resources in the Jordan River region [17]. This makes the agricultural sector the region's major water user and shows the strong regional impact of agricultural land-use activities on water resources. Besides the effect on water resources, land-use activities also have a considerable effect on other natural resources [20]. Examples are desertification caused by maladjusted land management policies [1, 4], biodiversity loss due to habitat destruction and fragmentation [41, 64], and salinization of land induced by irrigation [22]. Current pressures on natural resources in the Jordan River region are likely to aggravate in the future due to high projected population growth rates, economic development, and changing climate conditions. This may cause a further degradation of the region's ecosystems and reduce their capacity to provide ecosystem services in the long run. Hence, there is an urgent need for a better understanding of the complex relationships in these human-environmental systems, in order to develop sustainable management strategies for the

Water resources in the Jordan River region are largely transboundary and their distribution between Israel, Jordan, and PA is a potential source of conflicts. Hence, strategies for sustainable natural resource management in this region have to capture regulations at the state level and have to be based on consistent assessment methods and collaboration between the parties involved. This makes modeling approaches operating at the small scale or approaches focusing solely on natural systems unsuitable. However, existing integrated modeling approaches that cover the entire Jordan River region, such as presented in the Global Environmental Outlook 4 [58], apply spatial resolutions that are too coarse to capture the biophysical heterogeneity in the region, which is governed by a steep precipitation gradient

and reproduction in any medium, provided the original work is properly cited.

©2012 Koch et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**for the Jordan River Region**

Additional information is available at the end of the chapter

use of natural resources in the Jordan River region.

cited.

http://dx.doi.org/10.5772/51247

**1. Introduction**

[13].

## **5. References**

AndrewGilg. Perceptions about land use Review Article. Land Use Policy, 2009(26): 76-82.


**Chapter 0 Chapter 5**

## **An Integrated Land-Use System Model for the Jordan River Region**

Jennifer Koch, Florian Wimmer, Rüdiger Schaldach and Janina Onigkeit

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51247

## **1. Introduction**

86 Environmental Land Use Planning

**Acknowledgement** 

Science, 1997(277): 515-525.

2006,114(1): 39–56.

21-30.

(414-415):413-424

Evolution, 2010,25, (7):410-418

**5. References** 

The authors are grateful to Prof. Yannan Xu for simulating discussions and thank china

AndrewGilg. Perceptions about land use Review Article. Land Use Policy, 2009(26): 76-82. Andy P .Dobson et al. Hope for the future: restoration ecology and conservation biology.

Bolker, B.M..Approximate Bayesian Computation (ABC) in practice. Trends in Ecology &

C. Ma, G.Y. Zhang, X.C. Zhang, et al. Application of Markov model in wetland change dynamics in Tianjin Coastal Area, China OProcedia Environmental Sciences, 2012(13): 252-262 Peter H. Verburg, C.J.E. Schulp, N. Witte, et al. Downscaling of land use change scenarios to assess the dynamics of European landscapes Agriculture, Ecosystems & Environment.

Chen Shupeng, Urbanization and Urban Geographic Information System,1999 Environmental Protection Agence (EPA). Landscape monitoring and assessment research plan. Office of Research and Development. Washington, D.C, 1994:221-238 Gert Jan Reinds, Marcel van Oijen, Gerard B.M et al. Bayesian calibration of the VSD soil acidification model using European forest monitoring data Geoderma, 146( 3–4): 475-488.

Abraham Tamir . Applications of Markov Chains in Chemical Engineering, 1998 :11-185 He chunyang, Li Jinggang, Shi peijun etal. Modelling scenarios of land use change in northern China in the next 50 years. Journal of Geographical Science, 2005,15(2):177-186 Jamal Jokar Arsanjani, Marco Helbich, Wolfgang Kainz, et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion,

International Journal of Applied Earth Observation and Geoinformation, 2012 Liu Yansui, Ren Zhiyuan. Analysis of the land landscapechanges and its driving mechanism

Massimiliano Kaucic . Investment using evolutionary learning methods and technical rules.

MG Turner. Spatial and temporal analysis of landscape pattern.landscape ecology,1990,4(1):

Michael Strauch, Christian Bernhofer, Sérgio Koide et al. Using precipitation data ensemble for uncertainty analysis in SWAT stream flow simulation. Journal of Hydrology, 2012,

S.Q. Wang, X.Q. Zheng, X.B. Zang. Accuracy assessments of land use change simulation based on Markov-cellular automata model Procedia Environmental Sciences, 2012(13): 1238-1245

in vulnerable ecological area. Resources Science, 2005, 27 (2): 128-133.

European Journal of Operational Research, 2010,207(3) : 1717-1727.

natural science fund (No.30972414 /c161202 and No.30971609) .

The Jordan River region (Israel, Jordan, and the Palestinian National Authority (PA)) is one of the most water scarce regions of the world. The total renewable water resource values in the Jordan River region are 52 to 535 m3 per capita and year [15], which is far below the threshold value of 1000 m3 per capita and year indicating chronic water scarcity [14]. On average, water resources withdrawn for agricultural activities, such as irrigated crop production, amount to two thirds of the total actual renewable water resources in the Jordan River region [17]. This makes the agricultural sector the region's major water user and shows the strong regional impact of agricultural land-use activities on water resources. Besides the effect on water resources, land-use activities also have a considerable effect on other natural resources [20]. Examples are desertification caused by maladjusted land management policies [1, 4], biodiversity loss due to habitat destruction and fragmentation [41, 64], and salinization of land induced by irrigation [22]. Current pressures on natural resources in the Jordan River region are likely to aggravate in the future due to high projected population growth rates, economic development, and changing climate conditions. This may cause a further degradation of the region's ecosystems and reduce their capacity to provide ecosystem services in the long run. Hence, there is an urgent need for a better understanding of the complex relationships in these human-environmental systems, in order to develop sustainable management strategies for the use of natural resources in the Jordan River region.

Water resources in the Jordan River region are largely transboundary and their distribution between Israel, Jordan, and PA is a potential source of conflicts. Hence, strategies for sustainable natural resource management in this region have to capture regulations at the state level and have to be based on consistent assessment methods and collaboration between the parties involved. This makes modeling approaches operating at the small scale or approaches focusing solely on natural systems unsuitable. However, existing integrated modeling approaches that cover the entire Jordan River region, such as presented in the Global Environmental Outlook 4 [58], apply spatial resolutions that are too coarse to capture the biophysical heterogeneity in the region, which is governed by a steep precipitation gradient [13].

©2012 Koch et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 2 Will-be-set-by-IN-TECH 88 Environmental Land Use Planning An Integrated Land-Use System Model for the Jordan River Region <sup>3</sup>

In order to gain a better scientific understanding of the linkages between natural resources, land management, and ecosystem functioning in the Jordan River region, we developed the integrated modeling system LandSHIFT.JR (Land Simulation to Harmonize and Integrate Freshwater Availability and the Terrestrial Environment - Jordan River). LandSHIFT.JR is based on the spatially explicit land-use model LandSHIFT [49] and covers Israel, Jordan, and PA. It applies a cellular automata approach to calculate land-use changes and corresponding irrigation water requirements under current and future climate conditions. LandSHIFT.JR operates on a regular grid with a spatial resolution of 30 arc seconds. It was tailored specifically to the environmental and socio-economic conditions in the Jordan River region [28, 29]. Since scarce water resources, vegetation degradation due to overgrazing, detrimental effects of climate change on crop yields and irrigation water requirements, and increased soil salinity caused by irrigation are the major environmental issues in the Jordan River region, LandSHIFT.JR explicitly addresses these issues. This distinguishes LandSHIFT.JR from other integrated land-use modeling systems operating at a similar spatial resolution and scale, e.g. the CLUE(-S) model [61, 62]. LandSHIFT.JR simulates the spatial and temporal dynamics of land-use systems in the Jordan River region and allows exploring the impact of alterations in socio-economic and biophysical conditions on the spatial distribution and intensity of land-use activities and the feedback of land-use changes on socio-economic conditions. The modeling system's main field of application is the simulation of spatially explicit, midto long-term scenarios of land-use change. These scenarios show trends in land use and support the identification of hot spots of change and competition for land. Thus, spatially explicit land-use change scenarios generated with LandSHIFT.JR provide scientific support for evaluation and formulation of sustainable land-use planning and promote informed decision making.

Southeast and a mountainous region in the East and Northeast. In the North, the mountains force the coastal air masses to rise and, as a result, induce relatively high precipitation amounts [13]. A key physiographic feature of the Jordan River region is the Great Rift Valley in which the Jordan River, Lake Tiberias, and the Dead Sea are located. With 407 m below sea level, the Dead Sea marks the lowest point in the region and on the Earth's surface. The highland area in the Western part of Jordan, located along the Great Rift Valley, rises to elevations of 1200 m above sea level and drops gradually in elevation towards the East, where it develops into the Jordan desert plateau [13]. The point with the highest elevation in the Jordan River region is the Jabal Umm ad Dami, located in the South Jordan desert, with about 1854 m above sea

± **Elevation (m)**

**0 50 100 25**

**Figure 1.** The study region covers Israel, Jordan, and the Palestinian National Authority.

**(km)**

The climate in the Northern, Central, and Western part of the Jordan River region is Mediterranean, characterized by hot, dry summers and cool winters [13]. In the residual part of the Jordan River region a semi-arid to arid climate predominates. A dominant feature of the regional climatic conditions is the steep precipitation gradient, ranging from 900 mm mean annual precipitation in the Northern tip of Israel to less than 50 mm in the desert areas in the South of Israel and the South and Southeast of Jordan. Temperatures also exhibit a high spatial variability across the Jordan River region with cold winters and hot summers in the mountainous regions and more moderate extremes in the Rift Valley and the Coastal Plain

The Jordan River region covers about 116 thousand km2 of land area and 1 thousand km<sup>2</sup> of inland water area. Approximately 76.1% of the land area in the region is located in Jordan, 18.7% in Israel, and 5.2% in PA [18]. About 2600 km2 in the region are forest area. Arable land and permanent cropland sum up to about 9200 km2. Approximately 3000 km2 in the Jordan River region are equipped for irrigation. Thereof about two thirds are located in Israel. About 14 million people live in the Jordan River region [18]. The largest cities in the study region are

An Integrated Land-Use System Model for the Jordan River Region 89

level.

[13].

Amman, Jerusalem, Tel Aviv, and Gaza.

The objective of this chapter is to provide a comprehensive description of the integrated modeling system LandSHIFT.JR and of its validation. Moreover, we present an example of an application of LandSHIFT.JR - a scenario-based assessment of land-use changes in the Jordan River region. In section 2, a short description of the biophysical conditions and the most important land characteristics of the Jordan River region is provided. In sections 3 to 5, a detailed description of LandSHIFT.JR is given. The description focuses on the underlying concepts of the modeling system as well as on the distinctive features of LandSHIFT.JR. The structure of these sections follows the "Overview, Design concepts, Details" protocol for model descriptions as proposed by [23] and is based on a description of an earlier version of LandSHIFT.JR [30]. Sections 6 and 7 provide overviews of the validation of LandSHIFT.JR and the results of an application example, respectively. The chapter closes with a discussion of and conclusions on the integrated modeling system, its validation, and simulation results in section 8.

## **2. The Jordan River region**

LandSHIFT.JR was developed for Israel, Jordan, and PA (Fig. 1). The Jordan River region is bordered by Lebanon and Syria in the North, by Iraq and Saudi Arabia in the East, by Egypt in the Southwest, and by the Mediterranean Sea in the West. The region ranges from 34.22◦E, 29.19◦N to 39.30◦E, 33.38◦N. The terrain in the Jordan River region is very heterogeneous. The Coastal Plain, stretching along the Mediterranean Sea, is flanked by the Negev desert in the Southeast and a mountainous region in the East and Northeast. In the North, the mountains force the coastal air masses to rise and, as a result, induce relatively high precipitation amounts [13]. A key physiographic feature of the Jordan River region is the Great Rift Valley in which the Jordan River, Lake Tiberias, and the Dead Sea are located. With 407 m below sea level, the Dead Sea marks the lowest point in the region and on the Earth's surface. The highland area in the Western part of Jordan, located along the Great Rift Valley, rises to elevations of 1200 m above sea level and drops gradually in elevation towards the East, where it develops into the Jordan desert plateau [13]. The point with the highest elevation in the Jordan River region is the Jabal Umm ad Dami, located in the South Jordan desert, with about 1854 m above sea level.

2 Will-be-set-by-IN-TECH

In order to gain a better scientific understanding of the linkages between natural resources, land management, and ecosystem functioning in the Jordan River region, we developed the integrated modeling system LandSHIFT.JR (Land Simulation to Harmonize and Integrate Freshwater Availability and the Terrestrial Environment - Jordan River). LandSHIFT.JR is based on the spatially explicit land-use model LandSHIFT [49] and covers Israel, Jordan, and PA. It applies a cellular automata approach to calculate land-use changes and corresponding irrigation water requirements under current and future climate conditions. LandSHIFT.JR operates on a regular grid with a spatial resolution of 30 arc seconds. It was tailored specifically to the environmental and socio-economic conditions in the Jordan River region [28, 29]. Since scarce water resources, vegetation degradation due to overgrazing, detrimental effects of climate change on crop yields and irrigation water requirements, and increased soil salinity caused by irrigation are the major environmental issues in the Jordan River region, LandSHIFT.JR explicitly addresses these issues. This distinguishes LandSHIFT.JR from other integrated land-use modeling systems operating at a similar spatial resolution and scale, e.g. the CLUE(-S) model [61, 62]. LandSHIFT.JR simulates the spatial and temporal dynamics of land-use systems in the Jordan River region and allows exploring the impact of alterations in socio-economic and biophysical conditions on the spatial distribution and intensity of land-use activities and the feedback of land-use changes on socio-economic conditions. The modeling system's main field of application is the simulation of spatially explicit, midto long-term scenarios of land-use change. These scenarios show trends in land use and support the identification of hot spots of change and competition for land. Thus, spatially explicit land-use change scenarios generated with LandSHIFT.JR provide scientific support for evaluation and formulation of sustainable land-use planning and promote informed

The objective of this chapter is to provide a comprehensive description of the integrated modeling system LandSHIFT.JR and of its validation. Moreover, we present an example of an application of LandSHIFT.JR - a scenario-based assessment of land-use changes in the Jordan River region. In section 2, a short description of the biophysical conditions and the most important land characteristics of the Jordan River region is provided. In sections 3 to 5, a detailed description of LandSHIFT.JR is given. The description focuses on the underlying concepts of the modeling system as well as on the distinctive features of LandSHIFT.JR. The structure of these sections follows the "Overview, Design concepts, Details" protocol for model descriptions as proposed by [23] and is based on a description of an earlier version of LandSHIFT.JR [30]. Sections 6 and 7 provide overviews of the validation of LandSHIFT.JR and the results of an application example, respectively. The chapter closes with a discussion of and conclusions on the integrated modeling system, its validation, and simulation results

LandSHIFT.JR was developed for Israel, Jordan, and PA (Fig. 1). The Jordan River region is bordered by Lebanon and Syria in the North, by Iraq and Saudi Arabia in the East, by Egypt in the Southwest, and by the Mediterranean Sea in the West. The region ranges from 34.22◦E, 29.19◦N to 39.30◦E, 33.38◦N. The terrain in the Jordan River region is very heterogeneous. The Coastal Plain, stretching along the Mediterranean Sea, is flanked by the Negev desert in the

decision making.

in section 8.

**2. The Jordan River region**

**Figure 1.** The study region covers Israel, Jordan, and the Palestinian National Authority.

The climate in the Northern, Central, and Western part of the Jordan River region is Mediterranean, characterized by hot, dry summers and cool winters [13]. In the residual part of the Jordan River region a semi-arid to arid climate predominates. A dominant feature of the regional climatic conditions is the steep precipitation gradient, ranging from 900 mm mean annual precipitation in the Northern tip of Israel to less than 50 mm in the desert areas in the South of Israel and the South and Southeast of Jordan. Temperatures also exhibit a high spatial variability across the Jordan River region with cold winters and hot summers in the mountainous regions and more moderate extremes in the Rift Valley and the Coastal Plain [13].

The Jordan River region covers about 116 thousand km2 of land area and 1 thousand km<sup>2</sup> of inland water area. Approximately 76.1% of the land area in the region is located in Jordan, 18.7% in Israel, and 5.2% in PA [18]. About 2600 km2 in the region are forest area. Arable land and permanent cropland sum up to about 9200 km2. Approximately 3000 km2 in the Jordan River region are equipped for irrigation. Thereof about two thirds are located in Israel. About 14 million people live in the Jordan River region [18]. The largest cities in the study region are Amman, Jerusalem, Tel Aviv, and Gaza.

#### 4 Will-be-set-by-IN-TECH 90 Environmental Land Use Planning An Integrated Land-Use System Model for the Jordan River Region <sup>5</sup>

## **3. Overview**

## **3.1. Purpose**

LandSHIFT.JR is a regional version of the integrated modeling system LandSHIFT [49]. It was adjusted and further developed to specifically simulate the spatial and temporal dynamics of land-use systems in the Jordan River region. LandSHIFT.JR was designed for exploring the effects of changes in socio-economic, climatic, and biophysical conditions on the spatial distribution and intensity of land-use activities. In addition, LandSHIFT.JR serves as a tool to formalize knowledge on and gain new insights into the functioning of land-use systems in the Jordan River region. It can be used to test hypotheses about processes and interlinkages within land-use systems, promote the understanding of these systems by identifying key processes and their interlinkages, and, as a result, reveal demands for future research activities.

approximately 2.2 km at the equator. The state variables defined on this level include potential *irrigated wheat yields*, potential *rainfed wheat yields*, and *net irrigation water requirements*. Changes in potential yields are considered to be drivers of land-use change. • **Micro level:** The geographic area of each state is specified by the micro level-aregular grid with a uniform cell size of 30 arc seconds, which equals about 0.00833 dd or 1 km at the equator. The state of a micro-level grid cell is specified by the state variables dominant *land-use type*, *settlement area*, *population density*, *stocking density* for sheep and goats, *net primary productivity* (NPP) of rangeland and natural vegetation, *relative human appropriation of net primary production* (rel. HANPP [25, 29]), and *crop production*. The latter is defined separately for each crop category. Furthermore, a set of quasi-static landscape characteristics (e.g. slope) and land-use constraints (e.g. conservation areas) are defined

An Integrated Land-Use System Model for the Jordan River Region 91

LandSHIFT.JR applies a 5-year time step. The length of the simulation period depends on the research question the respective simulation experiment is supposed to answer and typically ranges between 20 and 50 years. After each simulation step, LandSHIFT.JR writes the simulation results to files. This output comprises micro-level maps displaying the dominant land-use and land-cover type, population density, net irrigation water requirements, stocking density, and rel. HANPP. Moreover, the output includes a set of indicators and area statistics

The processes implemented in LandSHIFT.JR are organized in three modules (Fig. 2), which operate on the different spatial scale levels by modifying the scale-specific state variables. The **Biophysics module**, which describes the environmental subsystem, comprises process representations for the calculation of potential irrigated and rainfed wheat yields, net irrigation water requirements, and NPP of rangeland and natural vegetation. All the variables provided by the Biophysics module are climate dependent and, hence, differ between climate scenarios. This module operates on the intermediate level II and on the micro level. The **Socio-economy module** and the **Land Use Change module** (LUC module) represent the human subsystem. The Socio-economy module provides information on population growth, agricultural production and trade (implemented via the state variables crop demand and livestock numbers), and yield change due to technological progress. The processes of this module operate on the macro level and, in case crop demands are specified with a higher spatial resolution, also on the intermediate level I. For each simulation step, the Biophysics module and the Socio-economy module are executed and the corresponding state variables are updated. Subsequently, the updated information is used by the LUC module to simulate changes in land use and land cover. The processes of the LUC module operate on the micro level. Bidirectional information exchange between the modules is implemented via

The LUC module calculates the extent and location of land-use and land-cover changes. Therefore, it implements four land-use activities: housing and infrastructure, irrigated crop production, rainfed crop production, and livestock grazing. The processes representing the different land-use activities are organized in submodules: **METRO** for housing and

on the micro level.

aggregated to the macro level.

the exchange of the state variables.

**3.3. Process overview and scheduling**

LandSHIFT.JR's main field of application is the simulation of spatially explicit, mid- to long-term future scenarios of land-use and land-cover change. These scenarios explore possible trends in land use and visualize alternative land-use configurations. Main model output are maps displaying changes in land-use patterns. These maps help to reveal hot spots of land-use change and allow for the identification of priority areas for further research or focus areas for alternative management strategies. By these means, spatially explicit land-use change scenarios generated with LandSHIFT.JR provide scientific support for the evaluation and formulation of sustainable land-use planning and promote informed decision making.

## **3.2. State variables and scales**

The representation of land-use systems in LandSHIFT.JR is operationalized on interacting spatial scale levels. On these scale levels, the state variables of the modeled land-use systems are defined. In total, there are four different spatial scale levels:


approximately 2.2 km at the equator. The state variables defined on this level include potential *irrigated wheat yields*, potential *rainfed wheat yields*, and *net irrigation water requirements*. Changes in potential yields are considered to be drivers of land-use change.

• **Micro level:** The geographic area of each state is specified by the micro level-aregular grid with a uniform cell size of 30 arc seconds, which equals about 0.00833 dd or 1 km at the equator. The state of a micro-level grid cell is specified by the state variables dominant *land-use type*, *settlement area*, *population density*, *stocking density* for sheep and goats, *net primary productivity* (NPP) of rangeland and natural vegetation, *relative human appropriation of net primary production* (rel. HANPP [25, 29]), and *crop production*. The latter is defined separately for each crop category. Furthermore, a set of quasi-static landscape characteristics (e.g. slope) and land-use constraints (e.g. conservation areas) are defined on the micro level.

LandSHIFT.JR applies a 5-year time step. The length of the simulation period depends on the research question the respective simulation experiment is supposed to answer and typically ranges between 20 and 50 years. After each simulation step, LandSHIFT.JR writes the simulation results to files. This output comprises micro-level maps displaying the dominant land-use and land-cover type, population density, net irrigation water requirements, stocking density, and rel. HANPP. Moreover, the output includes a set of indicators and area statistics aggregated to the macro level.

## **3.3. Process overview and scheduling**

4 Will-be-set-by-IN-TECH

LandSHIFT.JR is a regional version of the integrated modeling system LandSHIFT [49]. It was adjusted and further developed to specifically simulate the spatial and temporal dynamics of land-use systems in the Jordan River region. LandSHIFT.JR was designed for exploring the effects of changes in socio-economic, climatic, and biophysical conditions on the spatial distribution and intensity of land-use activities. In addition, LandSHIFT.JR serves as a tool to formalize knowledge on and gain new insights into the functioning of land-use systems in the Jordan River region. It can be used to test hypotheses about processes and interlinkages within land-use systems, promote the understanding of these systems by identifying key processes

and their interlinkages, and, as a result, reveal demands for future research activities.

LandSHIFT.JR's main field of application is the simulation of spatially explicit, mid- to long-term future scenarios of land-use and land-cover change. These scenarios explore possible trends in land use and visualize alternative land-use configurations. Main model output are maps displaying changes in land-use patterns. These maps help to reveal hot spots of land-use change and allow for the identification of priority areas for further research or focus areas for alternative management strategies. By these means, spatially explicit land-use change scenarios generated with LandSHIFT.JR provide scientific support for the evaluation and formulation of sustainable land-use planning and promote informed decision making.

The representation of land-use systems in LandSHIFT.JR is operationalized on interacting spatial scale levels. On these scale levels, the state variables of the modeled land-use systems

• **Macro level:** The spatial definition of the macro level is based on states. The state of a macro-level entity (i.e. a state) is specified by the state variables *population*, *crop demand*, *livestock number* (goats and sheep), *yield change* driven by technological progress, and *fraction of irrigated crop production* in total crop production. The state variables *crop demand*, *yield change*, and *fraction of irrigation area* are specified separately for each crop category. Changes in macro-level state variables constitute driving forces of land-use change in

• **Intermediate level I:** The spatial scale hierarchy of LandSHIFT.JR includes a level based on natural regions. This scale level allows including information on crop demands with a higher spatial resolution such as the output from economic land-use models [26]. The only state variable specified on this level is *crop demand*. This state variable is specified separately for each crop category; a change in *crop demand* constitutes a driving force of land-use change in LandSHIFT.JR. *Crop demand* can only be specified on one spatial scale level. In case it is specified on the macro level, it cannot be specified on the intermediate

• **Intermediate level II:** The spatial configuration of the intermediate level II is specified by a regular grid with a spatial resolution of 0.02 decimal degrees (dd), which equals

are defined. In total, there are four different spatial scale levels:

**3. Overview**

**3.1. Purpose**

**3.2. State variables and scales**

LandSHIFT.JR.

level I and vice versa.

The processes implemented in LandSHIFT.JR are organized in three modules (Fig. 2), which operate on the different spatial scale levels by modifying the scale-specific state variables. The **Biophysics module**, which describes the environmental subsystem, comprises process representations for the calculation of potential irrigated and rainfed wheat yields, net irrigation water requirements, and NPP of rangeland and natural vegetation. All the variables provided by the Biophysics module are climate dependent and, hence, differ between climate scenarios. This module operates on the intermediate level II and on the micro level. The **Socio-economy module** and the **Land Use Change module** (LUC module) represent the human subsystem. The Socio-economy module provides information on population growth, agricultural production and trade (implemented via the state variables crop demand and livestock numbers), and yield change due to technological progress. The processes of this module operate on the macro level and, in case crop demands are specified with a higher spatial resolution, also on the intermediate level I. For each simulation step, the Biophysics module and the Socio-economy module are executed and the corresponding state variables are updated. Subsequently, the updated information is used by the LUC module to simulate changes in land use and land cover. The processes of the LUC module operate on the micro level. Bidirectional information exchange between the modules is implemented via the exchange of the state variables.

The LUC module calculates the extent and location of land-use and land-cover changes. Therefore, it implements four land-use activities: housing and infrastructure, irrigated crop production, rainfed crop production, and livestock grazing. The processes representing the different land-use activities are organized in submodules: **METRO** for housing and

#### 6 Will-be-set-by-IN-TECH 92 Environmental Land Use Planning An Integrated Land-Use System Model for the Jordan River Region <sup>7</sup>

**4. Design concepts**

quantity of land use.

**5.1. Initialization**

**5. Details**

The choice of design concepts was guided by the purpose of the modeling system as described in section 3.1. LandSHIFT.JR combines a set of different design concepts that can be specified

An Integrated Land-Use System Model for the Jordan River Region 93

Research questions that land-use modeling typically addresses are related to the timing and rate of land-use changes [35]. A prerequisite for the representation of the temporal behavior of land-use systems is a dynamic modeling approach [63]. LandSHIFT.JR applies such a dynamic modeling approach. It subdivides the simulation period into several time steps and, hence, fulfills the basic requirements for the simulation of land-use change trajectories, feedbacks,

According to [2], integrated modeling systems have to include information from more than one discipline, organize information in a modularized program structure, and link scientific findings with policy analysis. LandSHIFT.JR was developed to bring together information from different disciplines to support decision making and it is typically applied in the context of scenario analyses with strong relevance for land-use planning and policy [29, 31]. Furthermore, it provides a framework for the combination of biophysical and socio-economic information with geographic information in form of a modularized program structure.

LandSHIFT.JR applies a process-based modeling approach in order to describe the land-use systems of the Jordan River region as human-environmental systems and to explore the interlinkages between their subsystems. The modeling system includes representations of the key processes resulting in changes in human-environment systems. As pivotal process, LandSHIFT.JR implements human decision making with regard to the extent, location, and intensity of land-use activities. The process-based approach allows analyzing trajectories and

Spatially explicit land-use models simulate changes in land use for individual spatial entities [63]. In case of LandSHIFT.JR these spatial entities are cells of a regular grid. Spatially explicit models, such as LandSHIFT.JR, are able to simulate the location and spatial variability of land-use and land-cover changes and, as a result, enable the analysis of the interlinkages between socio-economic and biophysical environments as well as variations in location and

Since LandSHIFT.JR integrates data from different fields and sources, considerable effort is required to synchronize the different datasets in an initial simulation step. In order to harmonize the information on population density and the land-use/land-cover map information on urban areas, LandSHIFT.JR initially reads the basic land-use/land-cover map (derived from the MODIS global land cover dataset [21]). This information is then combined with the micro-level information on population density [8]. On micro-level grid cells, at which the land-use type is not "urban", but where the population density exceeds the upper limit for population density in rural regions, the land-use type is changed to "urban". Furthermore,

as a dynamic, integrated, process based, and spatially explicit.

and path dependencies in the evolution of land-use systems.

intermediate states of land-use and land-cover change [63].

**Figure 2.** Conceptual structure of the integrated modeling system LandSHIFT.JR, adapted from [48].

infrastructure, **AGRO IR** for irrigated crop production, **AGRO RF** for rainfed crop production, and **GRAZE** for livestock grazing. The competition between these activities for land is addressed by a ranking of the four activities, which defines the sequence of execution. The ranking can be defined flexibly based on the research question; a straightforward way of ranking land-use activities is to follow their economic importance: METRO AGRO IR AGRO RF GRAZE. In one simulation step, cells occupied by a superordinate land-use activity are unavailable for a subordinate land-use activity.

In every simulation step, each land-use activity submodule executes the functional parts **demand processing**, **preference ranking**, and **demand allocation**. This complies with the generalized structure of spatially explicit land-use change models as presented by [63]. First, within the demand processing part, driving forces of land-use change are converted to macro-level/intermediate level I demands for services (e.g. housing) and agricultural commodities. Second, within the preference ranking part, the suitability of the micro-level grid cells for the different land-use activities is assessed, resulting in suitability maps. The grid cells are then ranked based on their suitability. Third, within the demand allocation part, each land-use activity manipulates the dominant land-use type as well as the corresponding state variable (population density for METRO, irrigated crop production for AGRO IR, rainfed crop production for AGRO RF, stocking density for GRAZE) of the best-suited micro-level grid cells, in order to meet the demand for the service or agricultural commodity under consideration. The range and magnitude of change is constrained by the demand for the service or agricultural commodity on the one hand and by the supply, i.e., the productivity on the particular micro-level grid cells on the other hand.

## **4. Design concepts**

6 Will-be-set-by-IN-TECH

**Figure 2.** Conceptual structure of the integrated modeling system LandSHIFT.JR, adapted from [48].

activity are unavailable for a subordinate land-use activity.

the particular micro-level grid cells on the other hand.

infrastructure, **AGRO IR** for irrigated crop production, **AGRO RF** for rainfed crop production, and **GRAZE** for livestock grazing. The competition between these activities for land is addressed by a ranking of the four activities, which defines the sequence of execution. The ranking can be defined flexibly based on the research question; a straightforward way of ranking land-use activities is to follow their economic importance: METRO AGRO IR AGRO RF GRAZE. In one simulation step, cells occupied by a superordinate land-use

In every simulation step, each land-use activity submodule executes the functional parts **demand processing**, **preference ranking**, and **demand allocation**. This complies with the generalized structure of spatially explicit land-use change models as presented by [63]. First, within the demand processing part, driving forces of land-use change are converted to macro-level/intermediate level I demands for services (e.g. housing) and agricultural commodities. Second, within the preference ranking part, the suitability of the micro-level grid cells for the different land-use activities is assessed, resulting in suitability maps. The grid cells are then ranked based on their suitability. Third, within the demand allocation part, each land-use activity manipulates the dominant land-use type as well as the corresponding state variable (population density for METRO, irrigated crop production for AGRO IR, rainfed crop production for AGRO RF, stocking density for GRAZE) of the best-suited micro-level grid cells, in order to meet the demand for the service or agricultural commodity under consideration. The range and magnitude of change is constrained by the demand for the service or agricultural commodity on the one hand and by the supply, i.e., the productivity on

The choice of design concepts was guided by the purpose of the modeling system as described in section 3.1. LandSHIFT.JR combines a set of different design concepts that can be specified as a dynamic, integrated, process based, and spatially explicit.

Research questions that land-use modeling typically addresses are related to the timing and rate of land-use changes [35]. A prerequisite for the representation of the temporal behavior of land-use systems is a dynamic modeling approach [63]. LandSHIFT.JR applies such a dynamic modeling approach. It subdivides the simulation period into several time steps and, hence, fulfills the basic requirements for the simulation of land-use change trajectories, feedbacks, and path dependencies in the evolution of land-use systems.

According to [2], integrated modeling systems have to include information from more than one discipline, organize information in a modularized program structure, and link scientific findings with policy analysis. LandSHIFT.JR was developed to bring together information from different disciplines to support decision making and it is typically applied in the context of scenario analyses with strong relevance for land-use planning and policy [29, 31]. Furthermore, it provides a framework for the combination of biophysical and socio-economic information with geographic information in form of a modularized program structure.

LandSHIFT.JR applies a process-based modeling approach in order to describe the land-use systems of the Jordan River region as human-environmental systems and to explore the interlinkages between their subsystems. The modeling system includes representations of the key processes resulting in changes in human-environment systems. As pivotal process, LandSHIFT.JR implements human decision making with regard to the extent, location, and intensity of land-use activities. The process-based approach allows analyzing trajectories and intermediate states of land-use and land-cover change [63].

Spatially explicit land-use models simulate changes in land use for individual spatial entities [63]. In case of LandSHIFT.JR these spatial entities are cells of a regular grid. Spatially explicit models, such as LandSHIFT.JR, are able to simulate the location and spatial variability of land-use and land-cover changes and, as a result, enable the analysis of the interlinkages between socio-economic and biophysical environments as well as variations in location and quantity of land use.

## **5. Details**

## **5.1. Initialization**

Since LandSHIFT.JR integrates data from different fields and sources, considerable effort is required to synchronize the different datasets in an initial simulation step. In order to harmonize the information on population density and the land-use/land-cover map information on urban areas, LandSHIFT.JR initially reads the basic land-use/land-cover map (derived from the MODIS global land cover dataset [21]). This information is then combined with the micro-level information on population density [8]. On micro-level grid cells, at which the land-use type is not "urban", but where the population density exceeds the upper limit for population density in rural regions, the land-use type is changed to "urban". Furthermore, spatial information on population density is combined with information on per capita area demands [12] in order to calculate the settlement area on non-urban grid cells; this area is not available for land-use activities such as crop production or livestock grazing.

tonnes, Jordan: 0.044 million tonnes, PA: 0.041 million tonnes). The crop specific management parameter is evaluated for initial conditions and is applied in the following simulation steps in order to adjust the yield values and, as a result, transfer potential crop yields into actual yields. Based on irrigated area under crop, rainfed area under crop, and the adjusted crop yields, the fraction of irrigated crop production in total crop production is calculated. This

An Integrated Land-Use System Model for the Jordan River Region 95

There are two different modes available in LandSHIFT.JR for calculating the initial distribution of rangeland and the related stocking densities for small ruminants: a production-driven approach and an area-driven approach. For the production-driven approach, the forage demand is allocated to the best suited micro-level grid cells and the land-use type of these cells is converted to "rangeland". The forage demand is calculated from livestock numbers derived from statistical data and the forage demand per animal [40]. Livestock numbers used in this context were derived from FAOSTAT [18] and amount to 0.4 million sheep and goats in Israel, 2.2 million sheep and goats in Jordan, and 0.9 million sheep and goats in PA. For the area-driven approach, rangeland area for the year 2000 (also derived from FAOSTAT, Table 1) is allocated to the best suited micro-level grid cells. The land-use type of these grid cells is set to "rangeland". The stocking densities on the rangeland cells are then calculated from the local NPP of rangeland and natural vegetation and the forage demand per sheep or goat.

LandSHIFT.JR input comprises time series on population, crop demands, yield change due to technological progress, livestock numbers as well as socio-economic information, e.g. on environmental policies or regional planning. For the application example presented in this chapter, this information is derived from the participatory scenario exercise of the GLOWA Jordan River project5. An overview of the scenarios and the corresponding values for the drivers of land-use change is given in [6]. Besides the above mentioned input specified on the macro level and/or intermediate level I, LandSHIFT.JR requires data on landscape and land-use characteristics specified on intermediate level II and on the micro level. This category of input includes potential crop yields and NPP under current and future climate conditions or landscape attributes such as slope or river network density. A detailed description of the

The processes in LandSHIFT.JR are organized in the three submodels Biophysics module, Socio-economy module, and LUC module. The details of these submodels are described in

In each simulation step, the Biophysics module updates the state variables potential irrigated wheat yields, potential rainfed wheat yields, net irrigation water requirements, and NPP of rangeland and natural vegetation. The updated information is then provided to the LUC

data input on landscape and land-use characteristics is given in section 5.3.4.

parameter is also invariant.

**5.2. Input**

**5.3. Submodels**

*5.3.1. Biophysics module*

<sup>5</sup> http://www.glowa-jordan-river.de

this section.

Available land-use/land-cover map products for the Jordan River region do not distinguish between area under crop for different crops. Furthermore, grazing areas are not assigned separately. Hence, an initial distribution of rangeland as well as area under crop for the considered crop categories has to be generated artificially. In order to derive the initial distribution of area under crop, LandSHIFT.JR distributes areas for the different crop categories to the best suited micro-level grid cells (see section 5.3.3). These areas under crop are derived from national statistics for Israel1, Jordan2, and PA3 for the year 2000. Based on the MIRCA2000 dataset [44], double cropping is assumed for rainfed and irrigated vegetables in all three states. The applied area values are displayed in Table 1. The best suited micro-level grid cells, on which these areas are distributed, are preferably those cells that are categorized as "cropland" in the underlying land-use/land-cover map. The distribution of areas under crop is carried out separately for irrigated and rainfed crop production. In case grid cells categorized as "cropland" in the underlying map are not categorized as one of the considered crop categories during initialization, their land-use type is set to "other crops" and kept static for the rest of the simulation run. This is based on the assumption that "cropland" in the original land-use/land-cover map also includes areas covered with crops that are not contained in one of the considered categories and that for these crops no drivers are specified as model input.


**Table 1.** Cropland and rangeland areas derived from national statistics and FAOSTAT [18] used to initialize cropland and rangeland area distribution in LandSHIFT.JR.

Based on the resulting land-use type distribution, LandSHIFT.JR relates the macro-level production *pcensc* for each of the irrigated and rainfed crop categories *c* (derived from census data) to the sum of the local production on grid cells with that crop category in the newly generated map *pcalcc*. This is done in order to calculate a separate management parameter *basec* for each of these categories. The management parameter is defined as *basec* = *pcensc*/*pcalcc*. It accounts for inconsistencies between different data sources and uncertainties due to agricultural management strategies (e.g. multiple cropping, fertilization) that affect the total production of a crop but are not explicitly considered in LandSHIFT.JR. Crop production values applied in this context are4: about 1.78 million tonnes of fruits (Israel: 1.304 million tonnes, Jordan: 0.232 million tonnes, PA: 0.246 million tonnes), about 3.01 million tonnes of vegetables and melons (Israel: 1.643 million tonnes, Jordan: 0.825 million tonnes, PA: 0.541 million tonnes), and about 0.27 million tonnes of cereals (Israel: 0.183 million

<sup>1</sup> http://www1.cbs.gov.il/reader/cw\_usr\_view\_Folder?ID=141

<sup>2</sup> http://www.dos.gov.jo/dos\_home\_e/main/index.htm

<sup>3</sup> http://www.pcbs.gov.ps/Default.aspx?tabID=1&lang=en

<sup>4</sup> All values derived from FAOSTAT are given as 3-year average for the years 1999-2001.

tonnes, Jordan: 0.044 million tonnes, PA: 0.041 million tonnes). The crop specific management parameter is evaluated for initial conditions and is applied in the following simulation steps in order to adjust the yield values and, as a result, transfer potential crop yields into actual yields. Based on irrigated area under crop, rainfed area under crop, and the adjusted crop yields, the fraction of irrigated crop production in total crop production is calculated. This parameter is also invariant.

There are two different modes available in LandSHIFT.JR for calculating the initial distribution of rangeland and the related stocking densities for small ruminants: a production-driven approach and an area-driven approach. For the production-driven approach, the forage demand is allocated to the best suited micro-level grid cells and the land-use type of these cells is converted to "rangeland". The forage demand is calculated from livestock numbers derived from statistical data and the forage demand per animal [40]. Livestock numbers used in this context were derived from FAOSTAT [18] and amount to 0.4 million sheep and goats in Israel, 2.2 million sheep and goats in Jordan, and 0.9 million sheep and goats in PA. For the area-driven approach, rangeland area for the year 2000 (also derived from FAOSTAT, Table 1) is allocated to the best suited micro-level grid cells. The land-use type of these grid cells is set to "rangeland". The stocking densities on the rangeland cells are then calculated from the local NPP of rangeland and natural vegetation and the forage demand per sheep or goat.

## **5.2. Input**

8 Will-be-set-by-IN-TECH

spatial information on population density is combined with information on per capita area demands [12] in order to calculate the settlement area on non-urban grid cells; this area is not

Available land-use/land-cover map products for the Jordan River region do not distinguish between area under crop for different crops. Furthermore, grazing areas are not assigned separately. Hence, an initial distribution of rangeland as well as area under crop for the considered crop categories has to be generated artificially. In order to derive the initial distribution of area under crop, LandSHIFT.JR distributes areas for the different crop categories to the best suited micro-level grid cells (see section 5.3.3). These areas under crop are derived from national statistics for Israel1, Jordan2, and PA3 for the year 2000. Based on the MIRCA2000 dataset [44], double cropping is assumed for rainfed and irrigated vegetables in all three states. The applied area values are displayed in Table 1. The best suited micro-level grid cells, on which these areas are distributed, are preferably those cells that are categorized as "cropland" in the underlying land-use/land-cover map. The distribution of areas under crop is carried out separately for irrigated and rainfed crop production. In case grid cells categorized as "cropland" in the underlying map are not categorized as one of the considered crop categories during initialization, their land-use type is set to "other crops" and kept static for the rest of the simulation run. This is based on the assumption that "cropland" in the original land-use/land-cover map also includes areas covered with crops that are not contained in one of the considered categories and that for these crops no drivers are specified

State IR fruits IR vegetables IR cereals RF fruits RF vegetables RF cereals Rangeland [km2] [km2] [km2] [km2] [km2] [km2] [km2]

Israel 661.4 253.6 643.0 162.6 22.1 1206.9 1480 Jordan 348.2 155.3 110.3 521.3 9.1 1045.5 7910 PA 81.6 67.1 26.8 1092.9 19.9 440.4 1500 **Table 1.** Cropland and rangeland areas derived from national statistics and FAOSTAT [18] used to

Based on the resulting land-use type distribution, LandSHIFT.JR relates the macro-level production *pcensc* for each of the irrigated and rainfed crop categories *c* (derived from census data) to the sum of the local production on grid cells with that crop category in the newly generated map *pcalcc*. This is done in order to calculate a separate management parameter *basec* for each of these categories. The management parameter is defined as *basec* = *pcensc*/*pcalcc*. It accounts for inconsistencies between different data sources and uncertainties due to agricultural management strategies (e.g. multiple cropping, fertilization) that affect the total production of a crop but are not explicitly considered in LandSHIFT.JR. Crop production values applied in this context are4: about 1.78 million tonnes of fruits (Israel: 1.304 million tonnes, Jordan: 0.232 million tonnes, PA: 0.246 million tonnes), about 3.01 million tonnes of vegetables and melons (Israel: 1.643 million tonnes, Jordan: 0.825 million tonnes, PA: 0.541 million tonnes), and about 0.27 million tonnes of cereals (Israel: 0.183 million

initialize cropland and rangeland area distribution in LandSHIFT.JR.

<sup>1</sup> http://www1.cbs.gov.il/reader/cw\_usr\_view\_Folder?ID=141 <sup>2</sup> http://www.dos.gov.jo/dos\_home\_e/main/index.htm <sup>3</sup> http://www.pcbs.gov.ps/Default.aspx?tabID=1&lang=en

<sup>4</sup> All values derived from FAOSTAT are given as 3-year average for the years 1999-2001.

available for land-use activities such as crop production or livestock grazing.

as model input.

LandSHIFT.JR input comprises time series on population, crop demands, yield change due to technological progress, livestock numbers as well as socio-economic information, e.g. on environmental policies or regional planning. For the application example presented in this chapter, this information is derived from the participatory scenario exercise of the GLOWA Jordan River project5. An overview of the scenarios and the corresponding values for the drivers of land-use change is given in [6]. Besides the above mentioned input specified on the macro level and/or intermediate level I, LandSHIFT.JR requires data on landscape and land-use characteristics specified on intermediate level II and on the micro level. This category of input includes potential crop yields and NPP under current and future climate conditions or landscape attributes such as slope or river network density. A detailed description of the data input on landscape and land-use characteristics is given in section 5.3.4.

## **5.3. Submodels**

The processes in LandSHIFT.JR are organized in the three submodels Biophysics module, Socio-economy module, and LUC module. The details of these submodels are described in this section.

### *5.3.1. Biophysics module*

In each simulation step, the Biophysics module updates the state variables potential irrigated wheat yields, potential rainfed wheat yields, net irrigation water requirements, and NPP of rangeland and natural vegetation. The updated information is then provided to the LUC

<sup>5</sup> http://www.glowa-jordan-river.de

module. The calculation of wheat yields and irrigation water requirements is based on the output of the dynamic, process-based crop growth model EPIC [67, 68]. In order to include future progress in the agricultural sector such as new management methods or fertilizers into the crop yield calculations, yields are corrected with a state-specific factor for yield change due to technological progress. The calculation of the state variable NPP of rangeland and natural vegetation is based on output of WADISCAPE [34]. In contrast to the wheat yield calculations, no effect of technological change on productivity is taken into account. This is based on the assumption that small ruminant grazing in the Jordan River region usually takes place on largely unmanaged marginal lands. The impact of changing climate conditions is considered for wheat yields, irrigation water requirements, and NPP. This is realized by a correction for climate change based on a linear interpolation between the productivities or water requirements calculated for current climate conditions and the respective productivities or water requirements calculated for future climate conditions given by regional climate simulations for the Jordan River region [53].

30◦). In order to determine the stocking capacity of the vegetation, these simulations were

An Integrated Land-Use System Model for the Jordan River Region 97

The Socio-economy module operates on the macro level and, if crop demand information with a higher spatial resolution is included, additionally on the intermediate level I. The module accounts for the organization and processing of the state variables population, crop demand, livestock number, and changes in crop yield due to technological progress. For historical periods, information on these state variables is derived from statistical databases (e.g. FAOSTAT [18]). For future periods, this information is typically generated with participatory scenario development, following the SAS approach [3] and the economic model IMPACT [45]. An update of the state variables is carried out by the Socio-economy module

**IMPACT.** The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), a representation of a competitive global market for agricultural commodities [45], was designed for the analysis of current conditions and possible future developments in food demand, supply, trade, prices, and malnutrition outcomes. The model covers 32 commodities and 36 countries/regions linked through trade and, hence, accounts for almost all of the world's food production and consumption. IMPACT is based on a system of supply and demand elasticities implemented via linear and nonlinear equations. It incorporates demand as a function of price, income and population growth, and changes in crop production. The changes in crop production are determined on the basis of crop prices and productivity

**SAS.** The SAS (Story And Simulation) approach to scenario analysis [3] combines both, quantitative and qualitative aspects of scenarios. The combination of those two aspects makes the resulting scenarios on the one hand generally understandable and on the other hand suitable for planning purposes. Distinctive features of SAS are the iterative structure and the intensive participation of experts and stakeholders. A detailed description of SAS is provided by [3]; a description of the SAS application in the context of a scenario analysis for the Jordan River region is given in [6]. Results from this scenario analysis were used to derive information on future development of population and livestock numbers in the Jordan

The LUC module is the central component of LandSHIFT.JR. The module accomplishes the simulation of the location and quantity of land-use and land-cover changes. This is realized by a regionalization of the macro-level/intermediate level I demands for area intensive services and agricultural commodities to the micro level. The basic principle is to allocate the demands to the most suitable micro-level grid cells by changing the land-use type, population density, crop production or livestock density of as many cells as required to meet the demand. Each service or commodity is linked to a land-use type. The LUC module implements the submodules METRO (housing and infrastructure), AGRO IR (irrigated crop production), AGRO RF (rainfed crop production), and GRAZE (livestock grazing). In every simulation

River region for the application example (see section 7).

conducted for stocking densities ranging from 0 to 10 animals per hectare.

*5.3.2. Socio-economy module*

within each simulation step.

growth rates [45].

*5.3.3. Land Use Change module*

**GEPIC.** We applied GIS-based EPIC (GEPIC) [37], a combination of the crop growth model EPIC [67, 68] with a GIS, to simulate wheat yields and crop water requirements under current and future climate. EPIC has been used successfully to simulate crop yields under a wide range of weather conditions, soil properties, and management schemes [37]. EPIC works on a daily time step and considers the major processes in the soil-crop-atmosphere-management system [67]. We used simulated potential yield under rainfed and optimal irrigated conditions for wheat as a proxy crop type. In order to derive irrigated/rainfed yields for the crop categories fruits, vegetables, and cereals from irrigated/rainfed wheat yield, an additional processing step was required. We multiplied the grid cell values of potential wheat yield by the ratio of mean actual yield for an irrigated/rainfed crop category to the mean potential yield on irrigated/rainfed areas covered by the crop category. This step was based on values for the year 2000. The actual yields stem from IMPACT model [45] calculations that were also used to provide input on future crop production. By this means, we ensure the consistency of yield values between the various model drivers and inputs. At the same time, we are able to include spatial and temporal variability of the crop yield simulations with GEPIC in our analysis.

**WADISCAPE.** The WADISCAPE model [34] provides information on stocking capacities<sup>6</sup> as well as information on the relationship between stocking density with small ruminants (goats and sheep) and productivity of green biomass7 under current and future climate conditions. WADISCAPE simulates the growth and dispersal of herbaceous plants and dwarf shrubs in artificial, fractal wadi landscapes (wadiscapes) of 1.5 km × 1.5 km. The main exogenous driver of vegetation dynamics in WADISCAPE is water availability, which is calculated from precipitation under consideration of topography. The simulation of vegetation dynamics is based on validated small-scale models of annual plants [33, 34] and dwarf shrubs [38]. WADISCAPE simulations were conducted for five climatic regions (arid to mesic Mediterranean) and, in factorial combination, five varying slope categories (0◦ to

<sup>6</sup> Stocking capacity is defined as the number of sheep and goats per hectare for which the green biomass production provides enough food in nine of ten years in year-round grazing [34].

<sup>7</sup> In this context, green biomass is defined as the aboveground biomass of herbaceous plants and leaf mass of dwarf shrubs.

30◦). In order to determine the stocking capacity of the vegetation, these simulations were conducted for stocking densities ranging from 0 to 10 animals per hectare.

#### *5.3.2. Socio-economy module*

10 Will-be-set-by-IN-TECH

module. The calculation of wheat yields and irrigation water requirements is based on the output of the dynamic, process-based crop growth model EPIC [67, 68]. In order to include future progress in the agricultural sector such as new management methods or fertilizers into the crop yield calculations, yields are corrected with a state-specific factor for yield change due to technological progress. The calculation of the state variable NPP of rangeland and natural vegetation is based on output of WADISCAPE [34]. In contrast to the wheat yield calculations, no effect of technological change on productivity is taken into account. This is based on the assumption that small ruminant grazing in the Jordan River region usually takes place on largely unmanaged marginal lands. The impact of changing climate conditions is considered for wheat yields, irrigation water requirements, and NPP. This is realized by a correction for climate change based on a linear interpolation between the productivities or water requirements calculated for current climate conditions and the respective productivities or water requirements calculated for future climate conditions given by regional climate

**GEPIC.** We applied GIS-based EPIC (GEPIC) [37], a combination of the crop growth model EPIC [67, 68] with a GIS, to simulate wheat yields and crop water requirements under current and future climate. EPIC has been used successfully to simulate crop yields under a wide range of weather conditions, soil properties, and management schemes [37]. EPIC works on a daily time step and considers the major processes in the soil-crop-atmosphere-management system [67]. We used simulated potential yield under rainfed and optimal irrigated conditions for wheat as a proxy crop type. In order to derive irrigated/rainfed yields for the crop categories fruits, vegetables, and cereals from irrigated/rainfed wheat yield, an additional processing step was required. We multiplied the grid cell values of potential wheat yield by the ratio of mean actual yield for an irrigated/rainfed crop category to the mean potential yield on irrigated/rainfed areas covered by the crop category. This step was based on values for the year 2000. The actual yields stem from IMPACT model [45] calculations that were also used to provide input on future crop production. By this means, we ensure the consistency of yield values between the various model drivers and inputs. At the same time, we are able to include spatial and temporal variability of the crop yield simulations with GEPIC in our

**WADISCAPE.** The WADISCAPE model [34] provides information on stocking capacities<sup>6</sup> as well as information on the relationship between stocking density with small ruminants (goats and sheep) and productivity of green biomass7 under current and future climate conditions. WADISCAPE simulates the growth and dispersal of herbaceous plants and dwarf shrubs in artificial, fractal wadi landscapes (wadiscapes) of 1.5 km × 1.5 km. The main exogenous driver of vegetation dynamics in WADISCAPE is water availability, which is calculated from precipitation under consideration of topography. The simulation of vegetation dynamics is based on validated small-scale models of annual plants [33, 34] and dwarf shrubs [38]. WADISCAPE simulations were conducted for five climatic regions (arid to mesic Mediterranean) and, in factorial combination, five varying slope categories (0◦ to

<sup>6</sup> Stocking capacity is defined as the number of sheep and goats per hectare for which the green biomass production

<sup>7</sup> In this context, green biomass is defined as the aboveground biomass of herbaceous plants and leaf mass of dwarf

provides enough food in nine of ten years in year-round grazing [34].

simulations for the Jordan River region [53].

analysis.

shrubs.

The Socio-economy module operates on the macro level and, if crop demand information with a higher spatial resolution is included, additionally on the intermediate level I. The module accounts for the organization and processing of the state variables population, crop demand, livestock number, and changes in crop yield due to technological progress. For historical periods, information on these state variables is derived from statistical databases (e.g. FAOSTAT [18]). For future periods, this information is typically generated with participatory scenario development, following the SAS approach [3] and the economic model IMPACT [45]. An update of the state variables is carried out by the Socio-economy module within each simulation step.

**IMPACT.** The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), a representation of a competitive global market for agricultural commodities [45], was designed for the analysis of current conditions and possible future developments in food demand, supply, trade, prices, and malnutrition outcomes. The model covers 32 commodities and 36 countries/regions linked through trade and, hence, accounts for almost all of the world's food production and consumption. IMPACT is based on a system of supply and demand elasticities implemented via linear and nonlinear equations. It incorporates demand as a function of price, income and population growth, and changes in crop production. The changes in crop production are determined on the basis of crop prices and productivity growth rates [45].

**SAS.** The SAS (Story And Simulation) approach to scenario analysis [3] combines both, quantitative and qualitative aspects of scenarios. The combination of those two aspects makes the resulting scenarios on the one hand generally understandable and on the other hand suitable for planning purposes. Distinctive features of SAS are the iterative structure and the intensive participation of experts and stakeholders. A detailed description of SAS is provided by [3]; a description of the SAS application in the context of a scenario analysis for the Jordan River region is given in [6]. Results from this scenario analysis were used to derive information on future development of population and livestock numbers in the Jordan River region for the application example (see section 7).

#### *5.3.3. Land Use Change module*

The LUC module is the central component of LandSHIFT.JR. The module accomplishes the simulation of the location and quantity of land-use and land-cover changes. This is realized by a regionalization of the macro-level/intermediate level I demands for area intensive services and agricultural commodities to the micro level. The basic principle is to allocate the demands to the most suitable micro-level grid cells by changing the land-use type, population density, crop production or livestock density of as many cells as required to meet the demand. Each service or commodity is linked to a land-use type. The LUC module implements the submodules METRO (housing and infrastructure), AGRO IR (irrigated crop production), AGRO RF (rainfed crop production), and GRAZE (livestock grazing). In every simulation step, these four submodules are executed subsequently and each of this submodules executes the three functional parts demand processing, preference ranking, and demand allocation. In the following, the general operating mode of the functional parts is described.

**Demand processing.** The functional part demand processing is responsible for the transformation of the drivers of land-use change to macro-level/intermediate level I demands for the implemented services and commodities.

**Preference ranking.** The functional part preference ranking operates on the micro level and serves for the identification and ranking of the preferred grid cells for the different land-use activities and the corresponding land-use types. A method from the field of Multi Criteria Analysis [11] is applied in order to calculate the preference values of the grid cells for the different land-use types. The preference value *ψ<sup>k</sup>* of a grid cell *k* is calculated as

$$\psi\_k = \underbrace{\sum\_{i=1}^n w\_i f\_i(p\_{i,k})}\_{\text{suitability}} \times \underbrace{\prod\_{j=1}^m g\_j(c\_{j,k})}\_{\text{constraints}} \tag{1}$$

relationships and are scaled by the range of the respective factor within a state in order to

An Integrated Land-Use System Model for the Jordan River Region 99

Suitability factors and constraints can be state variables, landscape attributes, zoning regulations, or spatial neighborhood characteristics. The neighborhood of the micro-level grid cells is analyzed in each simulation step in order to generate information about the land-use/land-cover type of the adjacent cells. The neighborhood of a cell can be defined by type and order, e.g. von Neumann or Moore neighborhood. Additionally, a (geographic) search radius can be specified. The set of relevant suitability factors and land-use constraints, the types of value functions, and the factor weights can be derived either by data driven procedures (e.g. geostatistical analysis) or by expert knowledge (e.g. by means of the

**Demand allocation.** The functional part demand allocation assigns the macro-level and intermediate-level I demands for the implemented services and commodities to the micro-level grid cells with the highest preference for the associated land-use type. For this

• **METRO.** The submodule METRO simulates the spatial and temporal dynamics of area for housing and infrastructure. Changes in quantity and location of this area are driven by alterations in population numbers, specified on the macro-level. The demand allocation procedure for METRO distinguishes between municipal regions and rural regions. Depending on the category, a different kind of growth process is applied. Therefore, the micro-level grid cells are grouped into these two categories. A municipal cell is defined as a cell that features the land-use type "urban" or has at least one grid cell with the land-use type "urban" in its direct neighborhood. All other cells are defined as rural cells. The growth of urban areas is implemented as urban encroachment process [69], i.e., new area for housing and infrastructure is located at the edges of existing urban area

In order to allocate additional population, a three-step procedure is applied. First, a parameter defines the fractions of the additional population that is assigned to municipal and rural regions, respectively. Second, depending on the grid cell's actual population density and suitability values, additional population is allocated. On cells with the land-use type "urban", an upper threshold for population density is defined, which limits the population amount that can be allocated to these grid cells. Third, based on the recalculated population densities, land-use conversions are calculated: rural cells feature a threshold value for population density. In case, this population density value is exceeded,

In rural regions, each cell has a fraction of settlement area that is occupied by housing and infrastructure. The amount of settlement area on a rural grid cell is calculated based on population density and the per capita area demand [12]. The area not required for housing and infrastructure is available for other land use or land cover that specifies the dominant land-use type on rural grid cells. On grid cells with the dominant land-use type "urban",

• **AGRO IR and AGRO RF.** The two AGRO submodules AGRO IR and AGRO RF are separate submodules that are executed one after another (see section 3.3). AGRO IR is

functional part, each land-use activity implements its own allocation strategy:

the land-use type of the grid cells is changed to "urban".

all area is required for housing and infrastructure.

account for the spatial heterogeneity.

Analytical Hierarchy Process [46]).

[54].

with ∑*i*(*wi*) = 1 and *fi*(*pi*,*k*), *gj*(*cj*,*k*) ∈ [0, 1]. The first part of the equation is the sum of the different weighted suitability factors *p*, contributing to the suitability of a grid cell *k* for a particular land-use type. The weights *w* determine the importance of a suitability factor in the analysis. The factor weights were determined according to the CRITIC method [10]. This method allows the calculation of "objective weights" on the basis of the contrast intensity between the evaluation criteria, i.e., the standard deviation of normalized criteria values and the inter-criteria correlation. The second term of the equation is the product of the land-use constraints *c*. These constraints reflect important aspects of human decision making, e.g. land-use restrictions in conservation areas. One constraint implemented in LandSHIFT.JR is the transition between the different land-use types: not all land-use and land-cover types can be converted into each other. These conversion elasticities are a frequently used method in the field of land-use modeling [62]. A summary of all possible conversions is given in Table 2.


**Table 2.** Land-use transition matrix. Possible conversions are indicated by "+", impossible conversions are indicated by "-".

The suitability factors, their weights, and the land-use constraints are specified on the macro level and implemented as time-dependent variables. This enables the representation of changing policies and environmental boundary conditions. Suitability factors and constraints are normalized by factor-specific value functions *f* and constraint specific value functions *g*. Value functions, based on logistic regression analysis, can be defined as positive or negative relationships and are scaled by the range of the respective factor within a state in order to account for the spatial heterogeneity.

12 Will-be-set-by-IN-TECH

step, these four submodules are executed subsequently and each of this submodules executes the three functional parts demand processing, preference ranking, and demand allocation. In

**Demand processing.** The functional part demand processing is responsible for the transformation of the drivers of land-use change to macro-level/intermediate level I demands

**Preference ranking.** The functional part preference ranking operates on the micro level and serves for the identification and ranking of the preferred grid cells for the different land-use activities and the corresponding land-use types. A method from the field of Multi Criteria Analysis [11] is applied in order to calculate the preference values of the grid cells for the

*wi fi*(*pi*,*k*)

with ∑*i*(*wi*) = 1 and *fi*(*pi*,*k*), *gj*(*cj*,*k*) ∈ [0, 1]. The first part of the equation is the sum of the different weighted suitability factors *p*, contributing to the suitability of a grid cell *k* for a particular land-use type. The weights *w* determine the importance of a suitability factor in the analysis. The factor weights were determined according to the CRITIC method [10]. This method allows the calculation of "objective weights" on the basis of the contrast intensity between the evaluation criteria, i.e., the standard deviation of normalized criteria values and the inter-criteria correlation. The second term of the equation is the product of the land-use constraints *c*. These constraints reflect important aspects of human decision making, e.g. land-use restrictions in conservation areas. One constraint implemented in LandSHIFT.JR is the transition between the different land-use types: not all land-use and land-cover types can be converted into each other. These conversion elasticities are a frequently used method in the field of land-use modeling [62]. A summary of all possible conversions is given in Table

**From / To** Urban IR cropland RF cropland Rangeland Set aside Natural veg. Urban + - - -- - IR cropland + + - -+ - RF cropland + + + -+ - Rangeland + + + +- - Set aside + + + ++ - Natural veg. + + + +- +

**Table 2.** Land-use transition matrix. Possible conversions are indicated by "+", impossible conversions

The suitability factors, their weights, and the land-use constraints are specified on the macro level and implemented as time-dependent variables. This enables the representation of changing policies and environmental boundary conditions. Suitability factors and constraints are normalized by factor-specific value functions *f* and constraint specific value functions *g*. Value functions, based on logistic regression analysis, can be defined as positive or negative

× *m* ∏ *j*=1

*gj*(*cj*,*k*)

(1)

 constraints

 suitability

the following, the general operating mode of the functional parts is described.

different land-use types. The preference value *ψ<sup>k</sup>* of a grid cell *k* is calculated as

*n* ∑ *i*=1

*ψ<sup>k</sup>* =

for the implemented services and commodities.

2.

are indicated by "-".

Suitability factors and constraints can be state variables, landscape attributes, zoning regulations, or spatial neighborhood characteristics. The neighborhood of the micro-level grid cells is analyzed in each simulation step in order to generate information about the land-use/land-cover type of the adjacent cells. The neighborhood of a cell can be defined by type and order, e.g. von Neumann or Moore neighborhood. Additionally, a (geographic) search radius can be specified. The set of relevant suitability factors and land-use constraints, the types of value functions, and the factor weights can be derived either by data driven procedures (e.g. geostatistical analysis) or by expert knowledge (e.g. by means of the Analytical Hierarchy Process [46]).

**Demand allocation.** The functional part demand allocation assigns the macro-level and intermediate-level I demands for the implemented services and commodities to the micro-level grid cells with the highest preference for the associated land-use type. For this functional part, each land-use activity implements its own allocation strategy:

• **METRO.** The submodule METRO simulates the spatial and temporal dynamics of area for housing and infrastructure. Changes in quantity and location of this area are driven by alterations in population numbers, specified on the macro-level. The demand allocation procedure for METRO distinguishes between municipal regions and rural regions. Depending on the category, a different kind of growth process is applied. Therefore, the micro-level grid cells are grouped into these two categories. A municipal cell is defined as a cell that features the land-use type "urban" or has at least one grid cell with the land-use type "urban" in its direct neighborhood. All other cells are defined as rural cells. The growth of urban areas is implemented as urban encroachment process [69], i.e., new area for housing and infrastructure is located at the edges of existing urban area [54].

In order to allocate additional population, a three-step procedure is applied. First, a parameter defines the fractions of the additional population that is assigned to municipal and rural regions, respectively. Second, depending on the grid cell's actual population density and suitability values, additional population is allocated. On cells with the land-use type "urban", an upper threshold for population density is defined, which limits the population amount that can be allocated to these grid cells. Third, based on the recalculated population densities, land-use conversions are calculated: rural cells feature a threshold value for population density. In case, this population density value is exceeded, the land-use type of the grid cells is changed to "urban".

In rural regions, each cell has a fraction of settlement area that is occupied by housing and infrastructure. The amount of settlement area on a rural grid cell is calculated based on population density and the per capita area demand [12]. The area not required for housing and infrastructure is available for other land use or land cover that specifies the dominant land-use type on rural grid cells. On grid cells with the dominant land-use type "urban", all area is required for housing and infrastructure.

• **AGRO IR and AGRO RF.** The two AGRO submodules AGRO IR and AGRO RF are separate submodules that are executed one after another (see section 3.3). AGRO IR is responsible for the allocation of the crop categories irrigated fruits (excluding melons), irrigated vegetables and melons, and irrigated cereals. AGRO RF allocates the crop categories rainfed fruits (excluding melons), rainfed vegetables and melons, and rainfed cereals. The crop category definition is based on the crop type aggregation of the FAOSTAT database [18]. Both, AGRO IR and AGRO RF, follow the same general approach regarding demand allocation, and are hence described jointly.

The basic principle of the demand allocation part in AGRO is to formulate a "compromise-solution"-problem for the calculation of a quasi-optimum crop allocation, in order to deal with the competition for land resources between the different crop categories. This is implemented as a modified version of the Multi-Objective Land Allocation (MOLA) heuristic [11]. This heuristic resolves emerging conflicts by a pair-wise comparison; cells claimed by more than one crop category are allocated to the category with the higher preference value. In LandSHIFT, the heuristic was modified in two ways [49]. First, the modified version allocates crop demands instead of a given area. Second, pattern stability is considered in the conflict resolution step, i.e., the land-use patterns remain constant if no changes in crop demands occur.

The amount of crop production on a micro-level grid cell is based on the local production *P*. The local production *P* for a crop category *c* at simulation step *t* for a particular grid cell, is defined as:

$$P\_{\mathcal{C}}(t) = base\_{\mathcal{C}} \times y\_{\mathcal{C}}(t) \times (1 + teeth\_{\mathcal{C}}(t)) \times a\_{\mathcal{C}}(t) \tag{2}$$

vegetation). This relationship is specified by non-linear correlation functions between stocking density (number of small ruminants per hectare) and green biomass productivity (tonnes per hectare), calculated with WADISCAPE [34]. The correlation functions were generated for all combinations of five slope categories (0◦ to <5◦, ≥5◦ to <12.5◦, ≥12.5◦ to <17.5◦, ≥17.5◦ to <25◦, ≥25◦) with five categories of mean annual precipitation (Table 3). Areas with mean annual precipitation values, that are not covered by the WADISCAPE simulations (values below 80 mm mean annual precipitation) are not suitable for livestock grazing. Except for micro-level grid cells located in these areas, each micro-level grid cell is attributed to one of the correlation functions depending on the grid-cell value for slope

Category Mean annual precipitation

Two different allocation routines are available for calculating the initial distribution of

1. Demand-driven approach: The forage demand is allocated to the preferred micro-level grid cells and the land-use type of these cells is converted to "rangeland". The local biomass productivity is calculated from the non-linear correlation function that is valid for the respective grid cells, assuming no former grazing activity on these grid cells. Based on the available biomass productivity, the local stocking density is calculated

2. Area-driven approach: Instead of a forage demand, a certain amount of rangeland area (Table 1) is allocated to the micro-level grid cells. The land-use type of these grid cells is converted to "rangeland". The potential total biomass production on these grid cells is calculated from the non-linear correlation functions, assuming no former use of these cells as rangeland. Based on the potential biomass production on the resulting area, the stocking density is adjusted and assigned to the grid cells, in order to meet the forage

In order to calculate the local biomass productivity in the following simulation steps, the cell's correlation function is chosen and combined with the stocking density set in the initial simulation step. The actual stocking density is then calculated from this productivity via the forage demand and assigned to the grid cell. In the subsequent simulation step, this stocking density is then used to derive the new local productivity from the cell specific

An important effect of this feedback between stocking density and biomass productivity is the resulting self-regulation of the grazing system: the allocation of high stocking densities in one simulation step results in reduced biomass productivity in the following simulation step and, hence, lower stocking densities. In addition to the dynamic calculation of local

correlation function. This procedure is repeated for each simulation step [29].

Arid ≥ 80 to < 200 Semiarid ≥ 200 to < 400 Dry Mediterranean ≥ 400 to < 500 Typical Mediterranean ≥ 500 to < 700 Mesic Mediterranean ≥ 700 to < 960

**Table 3.** Mean annual precipitation categories in WADISCAPE [34].

rangeland and the corresponding stocking densities:

under consideration of the forage demand per animal.

[mm]

An Integrated Land-Use System Model for the Jordan River Region 101

and mean annual precipitation.

demand.

*Pc*(*t*) micro-level grid cell production of crop category *c* for simulation step *t* [Mg], *basec* management parameter for crop category category *c* [-],

*yc*(*t*) micro-level grid cell yield for crop category *c* in simulation step *t* [Mg km−2], *techc*(*t*) technology-induced yield change for crop category *c* in simulation step *t* [-], *ac*(*t*) available cell area for production of crop category *c* in simulation step *t* [km2]. The crop production *P* is computed by combining state variables from different spatial scale levels (crop yield and yield change) and the cell area *a* that is not used as settlement area. The local crop yield is updated in each simulation step by the Biophysics module in order to include changes due to alterations in climatic conditions. The management factor *base* is a proxy for agricultural management characteristics (see section 5.1), which are not directly taken into account by LandSHIFT.JR. If not enough suitable land resources are available to allocate the crop demands, unmet demands are documented in a text file. In case more cropland was allocation in a previous simulation step as required in the following simulation step, the land-use type of dispensable cells is converted to "set aside" (fallow).

• **GRAZE.** The GRAZE submodule accounts for the spatial and temporal dynamics of livestock grazing. Changes in quantity and location of grazing area, which has the land-use type "rangeland", are driven by alterations in livestock numbers (sheep and goats) given in livestock units (LU), specified on the macro-level. Based on the livestock number, the amount of required forage, which has to be provided by grazing land, is calculated. This is done under consideration of the daily forage demand per LU and the share of grazing in feed composition. The residual share in feed composition is assumed to be covered by crops and crop residues and is considered indirectly in LandSHIFT.JR.

The demand allocation part of GRAZE is based on a relationship between grazing intensity (stocking density) and local biomass productivity (NPP of rangeland and natural vegetation). This relationship is specified by non-linear correlation functions between stocking density (number of small ruminants per hectare) and green biomass productivity (tonnes per hectare), calculated with WADISCAPE [34]. The correlation functions were generated for all combinations of five slope categories (0◦ to <5◦, ≥5◦ to <12.5◦, ≥12.5◦ to <17.5◦, ≥17.5◦ to <25◦, ≥25◦) with five categories of mean annual precipitation (Table 3). Areas with mean annual precipitation values, that are not covered by the WADISCAPE simulations (values below 80 mm mean annual precipitation) are not suitable for livestock grazing. Except for micro-level grid cells located in these areas, each micro-level grid cell is attributed to one of the correlation functions depending on the grid-cell value for slope and mean annual precipitation.


**Table 3.** Mean annual precipitation categories in WADISCAPE [34].

14 Will-be-set-by-IN-TECH

demand allocation, and are hence described jointly.

no changes in crop demands occur.

cell, is defined as:

aside" (fallow).

responsible for the allocation of the crop categories irrigated fruits (excluding melons), irrigated vegetables and melons, and irrigated cereals. AGRO RF allocates the crop categories rainfed fruits (excluding melons), rainfed vegetables and melons, and rainfed cereals. The crop category definition is based on the crop type aggregation of the FAOSTAT database [18]. Both, AGRO IR and AGRO RF, follow the same general approach regarding

The basic principle of the demand allocation part in AGRO is to formulate a "compromise-solution"-problem for the calculation of a quasi-optimum crop allocation, in order to deal with the competition for land resources between the different crop categories. This is implemented as a modified version of the Multi-Objective Land Allocation (MOLA) heuristic [11]. This heuristic resolves emerging conflicts by a pair-wise comparison; cells claimed by more than one crop category are allocated to the category with the higher preference value. In LandSHIFT, the heuristic was modified in two ways [49]. First, the modified version allocates crop demands instead of a given area. Second, pattern stability is considered in the conflict resolution step, i.e., the land-use patterns remain constant if

The amount of crop production on a micro-level grid cell is based on the local production *P*. The local production *P* for a crop category *c* at simulation step *t* for a particular grid

*Pc*(*t*) micro-level grid cell production of crop category *c* for simulation step *t* [Mg],

*yc*(*t*) micro-level grid cell yield for crop category *c* in simulation step *t* [Mg km−2], *techc*(*t*) technology-induced yield change for crop category *c* in simulation step *t* [-], *ac*(*t*) available cell area for production of crop category *c* in simulation step *t* [km2]. The crop production *P* is computed by combining state variables from different spatial scale levels (crop yield and yield change) and the cell area *a* that is not used as settlement area. The local crop yield is updated in each simulation step by the Biophysics module in order to include changes due to alterations in climatic conditions. The management factor *base* is a proxy for agricultural management characteristics (see section 5.1), which are not directly taken into account by LandSHIFT.JR. If not enough suitable land resources are available to allocate the crop demands, unmet demands are documented in a text file. In case more cropland was allocation in a previous simulation step as required in the following simulation step, the land-use type of dispensable cells is converted to "set

• **GRAZE.** The GRAZE submodule accounts for the spatial and temporal dynamics of livestock grazing. Changes in quantity and location of grazing area, which has the land-use type "rangeland", are driven by alterations in livestock numbers (sheep and goats) given in livestock units (LU), specified on the macro-level. Based on the livestock number, the amount of required forage, which has to be provided by grazing land, is calculated. This is done under consideration of the daily forage demand per LU and the share of grazing in feed composition. The residual share in feed composition is assumed to be covered by

The demand allocation part of GRAZE is based on a relationship between grazing intensity (stocking density) and local biomass productivity (NPP of rangeland and natural

crops and crop residues and is considered indirectly in LandSHIFT.JR.

*basec* management parameter for crop category category *c* [-],

*Pc*(*t*) = *basec* × *yc*(*t*) × (1 + *techc*(*t*)) × *ac*(*t*) (2)

Two different allocation routines are available for calculating the initial distribution of rangeland and the corresponding stocking densities:


In order to calculate the local biomass productivity in the following simulation steps, the cell's correlation function is chosen and combined with the stocking density set in the initial simulation step. The actual stocking density is then calculated from this productivity via the forage demand and assigned to the grid cell. In the subsequent simulation step, this stocking density is then used to derive the new local productivity from the cell specific correlation function. This procedure is repeated for each simulation step [29].

An important effect of this feedback between stocking density and biomass productivity is the resulting self-regulation of the grazing system: the allocation of high stocking densities in one simulation step results in reduced biomass productivity in the following simulation step and, hence, lower stocking densities. In addition to the dynamic calculation of local biomass productivity, change in biomass productivity due to climate change, also derived from WADISCAPE calculations driven by regional climate simulations [53], is considered. The GRAZE demand allocation part features two different methods for rangeland management: (1) sustainable rangeland management and (2) intensive rangeland management [29]. These allocation modes use micro-level grid cell specific information on stocking capacities calculated by WADISCAPE. The allocation modes apply different procedures in case the local stocking density exceeds the stocking capacity (overgrazing). In case of sustainable management, the local sustainable stocking capacity defines the maximum possible stocking density at a grid cell. The sustainable stocking capacity is a user defined fraction of the maximum stocking capacity. Each time the stocking density, assessed from local biomass productivity, exceeds the sustainable stocking capacity of the grid cell, the stocking density is set back to the sustainable stocking capacity, i.e., no overgrazing is allowed. For intensive management, this limitation is not applied and the stocking density is exclusively limited by the local biomass productivity. For both managements, the upper limit for stocking density is 10 animals per hectare, given by the range of the WADISCAPE simulations [34].

Activity Suitability factor Factor weight METRO Terrain slope 0.366

AGRO IR Area equipped for irrigation 0.233

AGRO RF Population density 0.067

GRAZE Population density 0.044

LandSHIFT.JR over the course of the simulation. The river network density is calculated as the line density of rivers per grid cell, based on the RWDB2 river-surface water body network dataset [19]. As land-use constraints, conservation areas are considered. Furthermore, a risk map on soil sensitivity to adverse effects of irrigation with treated wastewater [47] was

**AGRO RF.** For AGRO RF, four suitability factors were considered. These are rainfed crop yields, slope, travel time to major cities, and population density. Rainfed crop yields were calculated with GEPIC and vary with time based on changes in climate conditions. The only

**GRAZE.** For GRAZE, the four considered suitability factors are NPP on rangeland and natural vegetation, slope, river network density, and population density. In conservation areas, the use as rangeland is constrained. The information on NPP is derived from WADISCAPE calculations. To derive the forage demand from the livestock number, we assume one sheep or goat to equal 0.125 LU [51]. In addition, we apply a regional factor for Israel (0.8) and Jordan/PA (0.42) that considers the geographical variability in animal body size [51]. The daily forage demand per goat or sheep is 1.35 kg dry matter [40] of which we assume 30 % to be covered by grazing [4]. The consumable part of the aboveground green biomass is 75 %.

We applied three different methods to validate our modeling system. First, we validated the underlying assumptions of the suitability assessment with the Relative Operating Characteristics (ROC) method [43]. Second, we used the MODIS land cover dataset for the

**Table 4.** Suitability factors and corresponding weights for the different land-use activities.

included and can be used for future studies.

**6. Model validation**

land-use constraint for this activity is conservation area.

Travel time to major cities 0.634

An Integrated Land-Use System Model for the Jordan River Region 103

Irrigated crop yield 0.145 Population density 0.049 River network density 0.262 Terrain slope 0.147 Travel time to major cities 0.164

Rainfed crop yield 0.311 Terrain slope 0.258 Travel time to major cities 0.364

NPP of rangeland/nat. veg. 0.529 River network density 0.256 Terrain slope 0.170

Besides the above mentioned land-use/land-cover types urban, irrigated fruits (excl. melons), irrigated vegetables (incl. melons), irrigated cereals, rainfed fruits (excl. melons), rainfed vegetables (incl. melons), rainfed cereals, other crops, set aside, and rangeland, a set of other types exist. These are: forests, cropland/natural vegetation mosaic, grassland, shrub land, woody savannah, barren land, water, and wetlands. Changes in those are not directly simulated by LandSHIFT.JR but result from land-use conversions of the land-use types that area covered by METRO, AGRO, and GRAZE.

#### *5.3.4. Submodel parameterization*

**METRO.** For METRO, two suitability factors were considered: terrain slope [59] and travel time to major cities [57]. In Table 4, all suitability factors and their weights for the different land-use activities are displayed. As land-use constraint, conservation areas were implemented. As a result, no new urban area can be allocated in conservation areas. Spatially explicit information on national and international nature conservation area was derived from the World Database on Protected Areas [66]. The basic principle of METRO is to convert the population to a cell specific population density value. For this purpose, one part of the population is allocated to urban areas; the residual part is allocated to rural areas. The fraction of population allocation to urban areas is 65 % [58]. In case that the rural population density exceeds 5000 people/km2, or the area demand for housing and infrastructure on a grid cell exceeds 80 % of the grid cell size, the land-cover type of the grid cell is changed to "urban". The maximum population density per grid cell is 26098 people/km2, derived from the population density map for the study region for the year 2000 [8].

**AGRO IR.** For AGRO IR, six different suitability factors were considered. Besides terrain slope and travel time to major cities, additionally area equipped for irrigation [52], irrigated crop yields, population density, and river network density were considered. Irrigated crop yields were calculated with GEPIC and vary with time based on changes in climate conditions. Population density for the year 2000 is derived from the CIESIN dataset [8] and is updated by


**Table 4.** Suitability factors and corresponding weights for the different land-use activities.

LandSHIFT.JR over the course of the simulation. The river network density is calculated as the line density of rivers per grid cell, based on the RWDB2 river-surface water body network dataset [19]. As land-use constraints, conservation areas are considered. Furthermore, a risk map on soil sensitivity to adverse effects of irrigation with treated wastewater [47] was included and can be used for future studies.

**AGRO RF.** For AGRO RF, four suitability factors were considered. These are rainfed crop yields, slope, travel time to major cities, and population density. Rainfed crop yields were calculated with GEPIC and vary with time based on changes in climate conditions. The only land-use constraint for this activity is conservation area.

**GRAZE.** For GRAZE, the four considered suitability factors are NPP on rangeland and natural vegetation, slope, river network density, and population density. In conservation areas, the use as rangeland is constrained. The information on NPP is derived from WADISCAPE calculations. To derive the forage demand from the livestock number, we assume one sheep or goat to equal 0.125 LU [51]. In addition, we apply a regional factor for Israel (0.8) and Jordan/PA (0.42) that considers the geographical variability in animal body size [51]. The daily forage demand per goat or sheep is 1.35 kg dry matter [40] of which we assume 30 % to be covered by grazing [4]. The consumable part of the aboveground green biomass is 75 %.

## **6. Model validation**

16 Will-be-set-by-IN-TECH

Besides the above mentioned land-use/land-cover types urban, irrigated fruits (excl. melons), irrigated vegetables (incl. melons), irrigated cereals, rainfed fruits (excl. melons), rainfed vegetables (incl. melons), rainfed cereals, other crops, set aside, and rangeland, a set of other types exist. These are: forests, cropland/natural vegetation mosaic, grassland, shrub land, woody savannah, barren land, water, and wetlands. Changes in those are not directly simulated by LandSHIFT.JR but result from land-use conversions of the land-use types that

**METRO.** For METRO, two suitability factors were considered: terrain slope [59] and travel time to major cities [57]. In Table 4, all suitability factors and their weights for the different land-use activities are displayed. As land-use constraint, conservation areas were implemented. As a result, no new urban area can be allocated in conservation areas. Spatially explicit information on national and international nature conservation area was derived from the World Database on Protected Areas [66]. The basic principle of METRO is to convert the population to a cell specific population density value. For this purpose, one part of the population is allocated to urban areas; the residual part is allocated to rural areas. The fraction of population allocation to urban areas is 65 % [58]. In case that the rural population density exceeds 5000 people/km2, or the area demand for housing and infrastructure on a grid cell exceeds 80 % of the grid cell size, the land-cover type of the grid cell is changed to "urban". The maximum population density per grid cell is 26098 people/km2, derived from

**AGRO IR.** For AGRO IR, six different suitability factors were considered. Besides terrain slope and travel time to major cities, additionally area equipped for irrigation [52], irrigated crop yields, population density, and river network density were considered. Irrigated crop yields were calculated with GEPIC and vary with time based on changes in climate conditions. Population density for the year 2000 is derived from the CIESIN dataset [8] and is updated by

the population density map for the study region for the year 2000 [8].

range of the WADISCAPE simulations [34].

area covered by METRO, AGRO, and GRAZE.

*5.3.4. Submodel parameterization*

biomass productivity, change in biomass productivity due to climate change, also derived from WADISCAPE calculations driven by regional climate simulations [53], is considered. The GRAZE demand allocation part features two different methods for rangeland management: (1) sustainable rangeland management and (2) intensive rangeland management [29]. These allocation modes use micro-level grid cell specific information on stocking capacities calculated by WADISCAPE. The allocation modes apply different procedures in case the local stocking density exceeds the stocking capacity (overgrazing). In case of sustainable management, the local sustainable stocking capacity defines the maximum possible stocking density at a grid cell. The sustainable stocking capacity is a user defined fraction of the maximum stocking capacity. Each time the stocking density, assessed from local biomass productivity, exceeds the sustainable stocking capacity of the grid cell, the stocking density is set back to the sustainable stocking capacity, i.e., no overgrazing is allowed. For intensive management, this limitation is not applied and the stocking density is exclusively limited by the local biomass productivity. For both managements, the upper limit for stocking density is 10 animals per hectare, given by the

> We applied three different methods to validate our modeling system. First, we validated the underlying assumptions of the suitability assessment with the Relative Operating Characteristics (ROC) method [43]. Second, we used the MODIS land cover dataset for the

years 2001 and 2005 to perform a map comparison analysis using version 2.0 of the Map Comparison Kit8. Third, we compared macro-level simulation results on area under crop for the different irrigated and rainfed crop categories for the year 2005 with the corresponding values from statistical databases.

assessment results in a value of AUC = 0.5. In contrast, a suitability map that assigns the *n* highest values to the *n* cells where real change occurs (the perfect suitability map) yields AUC = 1. Hence, an AUC-value between 0.5 and 1 indicates that the suitability assessment explains

An Integrated Land-Use System Model for the Jordan River Region 105

We performed three separate ROC analyses for the land-use activities METRO, AGRO, and GRAZE. Therefore, we compiled three different real-change maps. For METRO and AGRO, we used the MODIS land cover dataset for the years 2001 and 2005. All cells that were "urban" ("cropland") in the 2005 map but not in the 2001 map are categorized as change for METRO (AGRO). For GRAZE, the real change map was derived from the small ruminant density (SRD) maps adjusted to match FAO totals for the years 2000 and 2005 [16]. We defined real change from non-grazing to grazing if the small ruminant density increases by 25% and by a minimum of 25 animals per km2 over the five year period. The ROC curves resulting from the

**Figure 3.** Relative Operating Characteristics (ROC) curves for the three land-use activities METRO, AGRO, and GRAZE. The 45◦ line indicates the ROC curve for randomly distributed suitability values.

We carried out a map comparison analysis to validate the resulting land-use maps. For this purpose, we compared the simulated land-use map *S* for the year 2005 with the MODIS land cover map for the same year, which we considered the actual or reference land-use map *A*, by calculating the kappa coefficient of agreement (*κ*) [9, 42] and kappa simulation (*κsim*)[60].

We applied *κ* because it is commonly used for validation of simulated land-use maps. The coefficient takes into account that the proportion of cells that are classified correctly by chance, denoted as the expected proportion correct *pe*, can be very large. The *pe* depends on the

The area under the curve (AUC) is the performance measure of ROC.

**6.2. Map comparison analysis**

the location of change better than a random process.

analyses are shown in Fig. 3.

## **6.1. Relative Operating Characteristics**

The agreement of simulated and observed land-use change depends on the agreement of both the quantity and location of change. Only if the simulated quantity of change equals the observed quantity of change, the simulated land-use changes can agree perfectly with the real land-use changes. On contrary, if the simulated quantity of change equals the observed quantity of change the location of simulated change can still lead to disagreement of modeled and real land-use change.

The ROC method [43] allows assessing to what degree the model is capable to assess the right location of change independently of the simulated quantity of change. Hence, the ROC analysis can be used to validate the underlying preference ranking processes that guide the location of changes in land use and land cover, represented by a suitability map. For this purpose, an independent real-change map indicating observed land-use or land-cover changes is necessary. Since the ROC method is only meaningful for testing the suitability map for the conversion of any land-use/land-cover type to one single land-use/land-cover type at a time, we applied the method to validate the suitability maps for each land-use activity separately. Whereas the suitability map for a specific land-use activity is a direct model output of LandSHIFT.JR, the categorical real-change map had to be constructed. For this purpose, observed raster maps for two points in time were compared to each other. Grid cells that feature a land-use or land-cover change between these two points in time were categorized as *change* cells whereas all other cells are categorized as *non-change* cells.

The ROC method compares the real-change map to a sequence of virtual simulated land-use change maps that result from a successively increasing quantity of change. The maps are derived by assuming that land-use change occurs on cells where the suitability value exceeds a certain threshold. Typically, the minimum, the deciles, and the maximum of the distribution of the suitability values are used as thresholds to prepare a sequence of maps assuming land-use change on 0 % to 100 % of all cells in 10 %-steps. In order to compare each of these maps to the real-change map, the rates of true positives (TP) and false positives (FP) are calculated. A cell is counted as a TP if real land-use change is modeled correctly. In contrast, if simulated land-use change coincides with non-change in reality, the cell is counted as a FP. The rates of TP and FP are computed as the ratio of the number of TPs and the number of possible TPs and the ratio of the number of FPs and the number of possible FPs, respectively. Based on the results of each comparison in the sequence, the ROC-diagram is constructed by plotting a curve in a coordinate system with the FP-rate on the x-axis and the TP-rate on the y-axis. The ROC curve starts at the point (FP = 0, TP = 0), resulting from the assumption of zero simulated land-use change, and ends at the point (FP = 1, TP = 1), resulting from the assumption that land-use change is simulated on all cells. The performance metric of ROC, the area under the curve (AUC), is calculated by trapezoidal approximation. On average, a random suitability

<sup>8</sup> http://www.riks.nl/products/Map\_Comparison\_Kit

assessment results in a value of AUC = 0.5. In contrast, a suitability map that assigns the *n* highest values to the *n* cells where real change occurs (the perfect suitability map) yields AUC = 1. Hence, an AUC-value between 0.5 and 1 indicates that the suitability assessment explains the location of change better than a random process.

We performed three separate ROC analyses for the land-use activities METRO, AGRO, and GRAZE. Therefore, we compiled three different real-change maps. For METRO and AGRO, we used the MODIS land cover dataset for the years 2001 and 2005. All cells that were "urban" ("cropland") in the 2005 map but not in the 2001 map are categorized as change for METRO (AGRO). For GRAZE, the real change map was derived from the small ruminant density (SRD) maps adjusted to match FAO totals for the years 2000 and 2005 [16]. We defined real change from non-grazing to grazing if the small ruminant density increases by 25% and by a minimum of 25 animals per km2 over the five year period. The ROC curves resulting from the analyses are shown in Fig. 3.

**Figure 3.** Relative Operating Characteristics (ROC) curves for the three land-use activities METRO, AGRO, and GRAZE. The 45◦ line indicates the ROC curve for randomly distributed suitability values. The area under the curve (AUC) is the performance measure of ROC.

#### **6.2. Map comparison analysis**

18 Will-be-set-by-IN-TECH

years 2001 and 2005 to perform a map comparison analysis using version 2.0 of the Map Comparison Kit8. Third, we compared macro-level simulation results on area under crop for the different irrigated and rainfed crop categories for the year 2005 with the corresponding

The agreement of simulated and observed land-use change depends on the agreement of both the quantity and location of change. Only if the simulated quantity of change equals the observed quantity of change, the simulated land-use changes can agree perfectly with the real land-use changes. On contrary, if the simulated quantity of change equals the observed quantity of change the location of simulated change can still lead to disagreement of modeled

The ROC method [43] allows assessing to what degree the model is capable to assess the right location of change independently of the simulated quantity of change. Hence, the ROC analysis can be used to validate the underlying preference ranking processes that guide the location of changes in land use and land cover, represented by a suitability map. For this purpose, an independent real-change map indicating observed land-use or land-cover changes is necessary. Since the ROC method is only meaningful for testing the suitability map for the conversion of any land-use/land-cover type to one single land-use/land-cover type at a time, we applied the method to validate the suitability maps for each land-use activity separately. Whereas the suitability map for a specific land-use activity is a direct model output of LandSHIFT.JR, the categorical real-change map had to be constructed. For this purpose, observed raster maps for two points in time were compared to each other. Grid cells that feature a land-use or land-cover change between these two points in time were categorized as

The ROC method compares the real-change map to a sequence of virtual simulated land-use change maps that result from a successively increasing quantity of change. The maps are derived by assuming that land-use change occurs on cells where the suitability value exceeds a certain threshold. Typically, the minimum, the deciles, and the maximum of the distribution of the suitability values are used as thresholds to prepare a sequence of maps assuming land-use change on 0 % to 100 % of all cells in 10 %-steps. In order to compare each of these maps to the real-change map, the rates of true positives (TP) and false positives (FP) are calculated. A cell is counted as a TP if real land-use change is modeled correctly. In contrast, if simulated land-use change coincides with non-change in reality, the cell is counted as a FP. The rates of TP and FP are computed as the ratio of the number of TPs and the number of possible TPs and the ratio of the number of FPs and the number of possible FPs, respectively. Based on the results of each comparison in the sequence, the ROC-diagram is constructed by plotting a curve in a coordinate system with the FP-rate on the x-axis and the TP-rate on the y-axis. The ROC curve starts at the point (FP = 0, TP = 0), resulting from the assumption of zero simulated land-use change, and ends at the point (FP = 1, TP = 1), resulting from the assumption that land-use change is simulated on all cells. The performance metric of ROC, the area under the curve (AUC), is calculated by trapezoidal approximation. On average, a random suitability

*change* cells whereas all other cells are categorized as *non-change* cells.

<sup>8</sup> http://www.riks.nl/products/Map\_Comparison\_Kit

values from statistical databases.

and real land-use change.

**6.1. Relative Operating Characteristics**

We carried out a map comparison analysis to validate the resulting land-use maps. For this purpose, we compared the simulated land-use map *S* for the year 2005 with the MODIS land cover map for the same year, which we considered the actual or reference land-use map *A*, by calculating the kappa coefficient of agreement (*κ*) [9, 42] and kappa simulation (*κsim*)[60].

We applied *κ* because it is commonly used for validation of simulated land-use maps. The coefficient takes into account that the proportion of cells that are classified correctly by chance, denoted as the expected proportion correct *pe*, can be very large. The *pe* depends on the number of categories and the number of cells in each category in *S* and *A*. Based on the observed proportion correct *po* and *pe*, *κ* is defined as [60]:

$$\kappa = \frac{p\_{\mathcal{o}} - p\_{\mathcal{e}}}{1 - p\_{\mathcal{e}}} \tag{3}$$

The *κ* coefficient can be interpreted as the gain in agreement of the model as compared to a baseline assumption. For standard *κ* the baseline is a process that randomly allocates the proportion of categories given be the model. For *κsim*, the baseline is an improved random process using the additional information that possible changes in land use are limited to a certain, potentially very small, proportion of the cells, which is derived from the simulation results and the reference map. Therefore, the expected proportion correct increases for *κsim* and the values are generally lower. Hence, a *κsim* of 0.12 still indicates that LandSHIFT.JR explains the land-use changes in the study region significantly better than the improved

An Integrated Land-Use System Model for the Jordan River Region 107

When we used only the information originally given by the MODIS dataset (i.e. omitting the land-use type rangeland and using the set "UC") *κ* increased to 0.72 and *κsim* increased to 0.22. This can partly be attributed to the inaccuracies induced by the simple approach to derive the reference distribution of rangeland. Furthermore, it is important to consider that the reference map is derived from a remote sensing product (MODIS) and the small ruminant density dataset, which both are subject to classification and measurement errors. Additional sources of error may by introduced by data preparation, e.g. spatial aggregation (MODIS) and

We compared the simulated area for rainfed and irrigated cropland for the year 2005 to estimates of the national statistical agencies of Israel, Jordan, and PA (Table 5). Although the model results for area under crops were in very good agreement for PA, the model simulated considerably higher area demands in Israel and Jordan (Table 5). For Israel, the simulated area demand for irrigated and rainfed cropland in 2005 was 48% and 66% higher than reported by the statistics, respectively. According to the Central Bureau of Statistics in Israel, the method to estimate the area under crops has changed starting in 2003. For that reason, a comparison to earlier years is not possible. However, LandSHIFT.JR uses the estimates of area under crops for the base year 2000 as an initial condition. Hence, the simulated area and the area reported by the statistics cannot be compared directly. For Jordan, the simulated area demand was overestimated by 41% and 57% for irrigated and rain-fed crops, respectively. This discrepancy can partly be explained by the fact that, according to the state statistics, the area under crops increased by only 4% while the production of agricultural products, which is the main driver of LandSHIFT.JR, increased by 46% [18]. Assuming that high-quality land resources are already in use for crop production, this is only possible if crop productivity increases considerably due to massive changes in agricultural management, e.g., fertilizer application or irrigation techniques. Currently, LandSHIFT.JR cannot simulate such effects because of missing input data. According to the MODIS land cover dataset for 2005 the area of cropland increased by about 63 %, which is more consistent with the relative increase in

In order to give an application example of LandSHIFT.JR, we set up a modeling exercise. As drivers for the model, we use the assumptions on the dynamics of population number,

baseline process.

disaggregation (SRD).

**6.3. Comparison with statistics**

crop production simulated with LandSHIFT.JR.

**7. Application example**

Values for *κ* range from -1 (indicating no agreement for any of the cells) to 1 (indicating perfect agreement of *S* and *A*). If *po* is equal to *pe*, i.e., if the land-use types are allocated randomly, *κ* is equal to 0. The *κ* coefficient tends to overestimate the performance of land-use change models, which use an initial land-use map as a starting point, if the number of actually changing cells is small compared to the number of cells with persistent land-use. In this case, a model that randomly allocates a small quantity of change, or simulates no change at all, can reach *κ* values close to 1. Hence, we also calculated the *κsim* coefficient, which considers the number of actual and simulated land-use transitions for the calculation of the expected proportion correct *pe*(*transition*). In order to calculate *pe*(*transition*), additionally the initial land-use map was considered. The value range for *κsim* is similar to that of *κ* and can be interpreted in the same way. Similarly to the standard *κ*, *κsim* is then defined as [60]:

$$\kappa\_{sim} = \frac{p\_o - p\_{\varepsilon(transition)}}{1 - p\_{\varepsilon(transition)}} \tag{4}$$

In order to calculate *κ* and *κsim*, the land-use categories in the simulated land-use map and the MODIS dataset were harmonized. For this purpose, the land-use categories that LandSHIFT.JR simulates explicitly, i.e., "urban land", "cropland", and "rangeland", were coded similarly in both maps. The remaining land-use types, e.g. "barren land", were lumped together in the categories "natural land-cover" or "water". Rangeland is not classified as a separate land-use type in the MODIS dataset. Therefore, we used the SRD map to derive the extent of rangeland. We defined a cell as rangeland if the density of small ruminants was 87 animals per km<sup>2</sup> or higher and at the same time the land-use/land-cover type assigned in the MODIS map was different from urban, cropland, and water. The threshold value of small ruminant density was adjusted in order to maximize *κ*. Since SRD is provided on a different spatial resolution (0.05 dd) and the conversion of SRD to "real" grazing land is very straightforward we consider the classification of rangeland to be rather inaccurate. Therefore, we tested the model performance based on two different sets of land-use maps. In set "UCR" urban, cropland, and rangeland were considered; in set "UC" only urban and cropland were considered as separate land-use categories.

For the "UCR" set, the validation results for the map comparison were 0.6 and 0.12 for *κ* and *κsim*, respectively. A value of *κ*=0.6 indicates that the agreement of the simulated and observed land-use map was significantly better than it can be expected for a random model. Compared to other studies, which report *κ* values from 0.6 to above 0.9 for land-use change modeling [36, 65], the agreement of LandSHIFT.JR results and the reference map was relatively low. However, it is important to bear in mind that we did not calibrate LandSHIFT.JR in order to maximize the agreement to observed datasets. The results are entirely based on parsimonious assumptions and objective methods to derive model parameters, e.g. the suitability factor weights. Hence, lower *κ*-values are to be expected.

The *κ* coefficient can be interpreted as the gain in agreement of the model as compared to a baseline assumption. For standard *κ* the baseline is a process that randomly allocates the proportion of categories given be the model. For *κsim*, the baseline is an improved random process using the additional information that possible changes in land use are limited to a certain, potentially very small, proportion of the cells, which is derived from the simulation results and the reference map. Therefore, the expected proportion correct increases for *κsim* and the values are generally lower. Hence, a *κsim* of 0.12 still indicates that LandSHIFT.JR explains the land-use changes in the study region significantly better than the improved baseline process.

When we used only the information originally given by the MODIS dataset (i.e. omitting the land-use type rangeland and using the set "UC") *κ* increased to 0.72 and *κsim* increased to 0.22. This can partly be attributed to the inaccuracies induced by the simple approach to derive the reference distribution of rangeland. Furthermore, it is important to consider that the reference map is derived from a remote sensing product (MODIS) and the small ruminant density dataset, which both are subject to classification and measurement errors. Additional sources of error may by introduced by data preparation, e.g. spatial aggregation (MODIS) and disaggregation (SRD).

#### **6.3. Comparison with statistics**

20 Will-be-set-by-IN-TECH

number of categories and the number of cells in each category in *S* and *A*. Based on the

*<sup>κ</sup>* <sup>=</sup> *po* <sup>−</sup> *pe* 1 − *pe*

Values for *κ* range from -1 (indicating no agreement for any of the cells) to 1 (indicating perfect agreement of *S* and *A*). If *po* is equal to *pe*, i.e., if the land-use types are allocated randomly, *κ* is equal to 0. The *κ* coefficient tends to overestimate the performance of land-use change models, which use an initial land-use map as a starting point, if the number of actually changing cells is small compared to the number of cells with persistent land-use. In this case, a model that randomly allocates a small quantity of change, or simulates no change at all, can reach *κ* values close to 1. Hence, we also calculated the *κsim* coefficient, which considers the number of actual and simulated land-use transitions for the calculation of the expected proportion correct *pe*(*transition*). In order to calculate *pe*(*transition*), additionally the initial land-use map was considered. The value range for *κsim* is similar to that of *κ* and can be interpreted in the

> *<sup>κ</sup>sim* <sup>=</sup> *po* <sup>−</sup> *pe*(*transition*) 1 − *pe*(*transition*)

In order to calculate *κ* and *κsim*, the land-use categories in the simulated land-use map and the MODIS dataset were harmonized. For this purpose, the land-use categories that LandSHIFT.JR simulates explicitly, i.e., "urban land", "cropland", and "rangeland", were coded similarly in both maps. The remaining land-use types, e.g. "barren land", were lumped together in the categories "natural land-cover" or "water". Rangeland is not classified as a separate land-use type in the MODIS dataset. Therefore, we used the SRD map to derive the extent of rangeland. We defined a cell as rangeland if the density of small ruminants was 87 animals per km<sup>2</sup> or higher and at the same time the land-use/land-cover type assigned in the MODIS map was different from urban, cropland, and water. The threshold value of small ruminant density was adjusted in order to maximize *κ*. Since SRD is provided on a different spatial resolution (0.05 dd) and the conversion of SRD to "real" grazing land is very straightforward we consider the classification of rangeland to be rather inaccurate. Therefore, we tested the model performance based on two different sets of land-use maps. In set "UCR" urban, cropland, and rangeland were considered; in set "UC" only urban and cropland were

For the "UCR" set, the validation results for the map comparison were 0.6 and 0.12 for *κ* and *κsim*, respectively. A value of *κ*=0.6 indicates that the agreement of the simulated and observed land-use map was significantly better than it can be expected for a random model. Compared to other studies, which report *κ* values from 0.6 to above 0.9 for land-use change modeling [36, 65], the agreement of LandSHIFT.JR results and the reference map was relatively low. However, it is important to bear in mind that we did not calibrate LandSHIFT.JR in order to maximize the agreement to observed datasets. The results are entirely based on parsimonious assumptions and objective methods to derive model parameters, e.g. the suitability factor

(3)

(4)

observed proportion correct *po* and *pe*, *κ* is defined as [60]:

same way. Similarly to the standard *κ*, *κsim* is then defined as [60]:

considered as separate land-use categories.

weights. Hence, lower *κ*-values are to be expected.

We compared the simulated area for rainfed and irrigated cropland for the year 2005 to estimates of the national statistical agencies of Israel, Jordan, and PA (Table 5). Although the model results for area under crops were in very good agreement for PA, the model simulated considerably higher area demands in Israel and Jordan (Table 5). For Israel, the simulated area demand for irrigated and rainfed cropland in 2005 was 48% and 66% higher than reported by the statistics, respectively. According to the Central Bureau of Statistics in Israel, the method to estimate the area under crops has changed starting in 2003. For that reason, a comparison to earlier years is not possible. However, LandSHIFT.JR uses the estimates of area under crops for the base year 2000 as an initial condition. Hence, the simulated area and the area reported by the statistics cannot be compared directly. For Jordan, the simulated area demand was overestimated by 41% and 57% for irrigated and rain-fed crops, respectively. This discrepancy can partly be explained by the fact that, according to the state statistics, the area under crops increased by only 4% while the production of agricultural products, which is the main driver of LandSHIFT.JR, increased by 46% [18]. Assuming that high-quality land resources are already in use for crop production, this is only possible if crop productivity increases considerably due to massive changes in agricultural management, e.g., fertilizer application or irrigation techniques. Currently, LandSHIFT.JR cannot simulate such effects because of missing input data. According to the MODIS land cover dataset for 2005 the area of cropland increased by about 63 %, which is more consistent with the relative increase in crop production simulated with LandSHIFT.JR.

#### **7. Application example**

In order to give an application example of LandSHIFT.JR, we set up a modeling exercise. As drivers for the model, we use the assumptions on the dynamics of population number,


A comparison of Fig. 4 (a) and (b) shows considerable increases in the area demands for the main land-use activities. By 2050, the extent of urban land increases by about 56 %, while irrigated cropland expands to more than twice, rainfed cropland to more than three times, and grazing land to more than four times the area as compared to 2000. The figures for agricultural area reflect the ranking of the four activities: the lower the priority of a land-use activity is the lower is the productivity on the areas it is allocated to and, consequently, the larger is the area expansion needed to fulfill the demands. The increasing population density between 2000 and 2050 is shown in Fig. 4 (c) and (d). The maps show the typical differences between the modeling approaches for rural and urban population growth. On the one hand, the urban encroachment approach leads to relative fast growth of urban land (population density above 5000 people/km2) at the edges of existing cities or urban centers. On the other hand, rural population density increases uniformly and proportional to the initial population density, which is distributed homogeneously over administrative units. Hence, the outlines of these districts can partly be recognized in the maps. The land-use activity with the lowest priority is grazing. Therefore, rangeland is more and more displaced from areas with relatively high productivity, where it is predominantly allocated in 2000 (Fig. 4 (e)), and shifted to less productive areas (Fig. 4 (f)). This leads to a vast extent of rangeland with low stocking densities in 2050. The expansion of irrigated cropland (Fig. 4 (a) and (b)) causes irrigation water requirements to rise. Table 6 presents the simulated total irrigation water demand and area specific irrigation water demand on state level. According to these figures, the projected irrigation water demand almost doubles in PA and Jordan and is about threefold in Israel in

An Integrated Land-Use System Model for the Jordan River Region 109

State I R water demand [106 m3] A vg. IR water demand [mm] (2000) (2050) (2000) (2050)

In this chapter, we introduce the integrated modeling system LandSHIFT.JR for the Jordan River region. We give a detailed description of the modeling system, its parameterization, and validation. We furthermore present a sample application of LandSHIFT.JR for the *Modest Hopes* scenario, developed in the context of the GLOWA Jordan River scenario exercise [6]. Since vegetation degradation due to overgrazing is a major problem in the Jordan River region [1] and since the intensity levels of grazing management strongly affect the environment via different pathways (e.g. woody encroachment [7], biodiversity loss [1] or erosion [27]) we developed a separate module for livestock grazing, that not only implements indicators for grazing intensity, but also includes different rangeland management strategies [28, 29]. This allows to consider the effect of rangeland management strategies in environmental impact

Israel 638 1477 (+132%) 31 72 Jordan 322 772 (+140%) 4 9 PA 86 162 ( +88%) 14 26 **Table 6.** Total simulated (change between 2000 and 2050 in parenthesis) and average area specific irrigation water demand in 2000 and 2050 for Israel, Jordan, and the Palestinian National Authority (PA).

2050 as compared to the year 2000.

**8. Discussion and conclusions**

assessments.

**Table 5.** Area under rainfed and irrigated crops in 2005 for Israel, Jordan, and the Palestinian National Authority (PA) as simulated with LandSHIFT.JR and estimated by the national statistical agencies.

agricultural production, livestock production, and yield change due to technological progress as given by the GLOWA Jordan River *Modest Hopes* scenario [6]. Figure 4 shows the LandSHIFT.JR results for land-use/land-cover distribution, population density, and livestock density for the base year (2000) and the corresponding projections for the year 2050.

**Figure 4.** Maps of land-use and land-cover distribution, population density, and livestock density for the years 2000 and 2050 simulated with LandSHIFT.JR for the *Modest Hopes* scenario [6].

A comparison of Fig. 4 (a) and (b) shows considerable increases in the area demands for the main land-use activities. By 2050, the extent of urban land increases by about 56 %, while irrigated cropland expands to more than twice, rainfed cropland to more than three times, and grazing land to more than four times the area as compared to 2000. The figures for agricultural area reflect the ranking of the four activities: the lower the priority of a land-use activity is the lower is the productivity on the areas it is allocated to and, consequently, the larger is the area expansion needed to fulfill the demands. The increasing population density between 2000 and 2050 is shown in Fig. 4 (c) and (d). The maps show the typical differences between the modeling approaches for rural and urban population growth. On the one hand, the urban encroachment approach leads to relative fast growth of urban land (population density above 5000 people/km2) at the edges of existing cities or urban centers. On the other hand, rural population density increases uniformly and proportional to the initial population density, which is distributed homogeneously over administrative units. Hence, the outlines of these districts can partly be recognized in the maps. The land-use activity with the lowest priority is grazing. Therefore, rangeland is more and more displaced from areas with relatively high productivity, where it is predominantly allocated in 2000 (Fig. 4 (e)), and shifted to less productive areas (Fig. 4 (f)). This leads to a vast extent of rangeland with low stocking densities in 2050. The expansion of irrigated cropland (Fig. 4 (a) and (b)) causes irrigation water requirements to rise. Table 6 presents the simulated total irrigation water demand and area specific irrigation water demand on state level. According to these figures, the projected irrigation water demand almost doubles in PA and Jordan and is about threefold in Israel in 2050 as compared to the year 2000.


**Table 6.** Total simulated (change between 2000 and 2050 in parenthesis) and average area specific irrigation water demand in 2000 and 2050 for Israel, Jordan, and the Palestinian National Authority (PA).

### **8. Discussion and conclusions**

22 Will-be-set-by-IN-TECH

agricultural production, livestock production, and yield change due to technological progress as given by the GLOWA Jordan River *Modest Hopes* scenario [6]. Figure 4 shows the LandSHIFT.JR results for land-use/land-cover distribution, population density, and livestock

**Figure 4.** Maps of land-use and land-cover distribution, population density, and livestock density for the

years 2000 and 2050 simulated with LandSHIFT.JR for the *Modest Hopes* scenario [6].

density for the base year (2000) and the corresponding projections for the year 2050.

Rainfed cropland Irrigated cropland Total cropland Statistics LandSHIFT.JR Statistics LandSHIFT.JR Statistics LandSHIFT.JR [km2] [km2] [km2] [km2] [km2] [km2] Israel 1283 2129 (+66%) 1298 1926 (+48%) 2581 4055 (+57%) Jordan 1663 2613 (+57%) 610 858 (+41%) 2273 3471 (+53%) PA 1545 1561 ( +1%) 184 184 ( 0%) 1729 1745 ( +1%) **Table 5.** Area under rainfed and irrigated crops in 2005 for Israel, Jordan, and the Palestinian National Authority (PA) as simulated with LandSHIFT.JR and estimated by the national statistical agencies.

> In this chapter, we introduce the integrated modeling system LandSHIFT.JR for the Jordan River region. We give a detailed description of the modeling system, its parameterization, and validation. We furthermore present a sample application of LandSHIFT.JR for the *Modest Hopes* scenario, developed in the context of the GLOWA Jordan River scenario exercise [6].

> Since vegetation degradation due to overgrazing is a major problem in the Jordan River region [1] and since the intensity levels of grazing management strongly affect the environment via different pathways (e.g. woody encroachment [7], biodiversity loss [1] or erosion [27]) we developed a separate module for livestock grazing, that not only implements indicators for grazing intensity, but also includes different rangeland management strategies [28, 29]. This allows to consider the effect of rangeland management strategies in environmental impact assessments.

#### 24 Will-be-set-by-IN-TECH 110 Environmental Land Use Planning An Integrated Land-Use System Model for the Jordan River Region <sup>25</sup>

In contrast to earlier versions of LandSHIFT.JR, the current version includes the effect of changing climate conditions on crop yields and productivity of natural vegetation, which was shown to have a strong effect on land demand in the Jordan River region [32]. The indirect effect of productivity on area demand for the different agricultural activities is included indirectly by spatially explicit simulation models (WADISCAPE and GEPIC), driven by high-resolution climate change simulations for the Jordan River region [53]. This allows the inclusion of a high level of spatial detail into the simulations of land-use and land-cover change, which is carried out on a grid with a spatial resolution of 30 arc seconds. This is of high importance in a region with such high variability in biogeographic conditions as the Jordan River region. Furthermore, it applies a consistent assessment method to the entire Jordan River region and allows the combined assessment of socio-economic and climate impact on the food production systems in the Jordan River region which is considered to be mandatory [55, 56].

As shown for the application example, LandSHIFT.JR implements modules for the four land-use activities infrastructure and housing, irrigated crop production, rainfed crop production, and livestock grazing. For each land-use activity, besides the dominant land-use types also an indicator of land-use intensity is allocated (population density, irrigated or rainfed crop production amount, stocking density). Hence, the model concept implemented in LandSHIFT.JR considers not only land-use patterns, but also the corresponding land-use intensities. This makes LandSHIFT.JR land-use simulation results suitable for applications focusing on natural resource management and environmental impact assessment [24, 39].

An Integrated Land-Use System Model for the Jordan River Region 111

We see a potential for improvement regarding the validation process. The spatially explicit validation of rangeland, net irrigation water requirements, and the separate validation of irrigated and rainfed cropland was limited by insufficient data availability. This will be caught up for, once suitable datasets are available. We encounter this validation issues by choosing a straightforward modeling approach, based on logical assumptions and renunciation of model

In addition to extensive sensitivity and uncertainty analyses to improve the scientific knowledge and understanding of land-use systems in the Jordan River region, we see a strong potential for future studies on the relationship between irrigation water supply (including treated wastewater), net irrigation water requirements, and soil sensitivity towards the irrigation with treated wastewater [47]. For this purpose, additional GEPIC simulations for other crop types besides wheat would be required in order to be able to assess the irrigation water requirements more accurately. This would allow for interesting analyses regarding the potential of using treated wastewater for irrigation purposes, under consideration of possible environmental problems associated with the use of treated wastewater for irrigation [5].

This study is part of the GLOWA Jordan River project financed by the German Federal Ministry of Education and Research (FKZ 01LW0502). We thank Katja Geissler (Potsdam University, Research Group Plant Ecology and Nature Conservation) and Martin Köchy (Johann Heinrich von Thünen Insitut, Braunschweig) for the provision of WADISCAPE model output. Furthermore, we thank Gerhard Smiatek (IMK-IFU, Institute for Meteorology and Climate Research-Atmospheric Environmental Research) for the provision of regional climate

[1] Abahussain, A.A., Abdu, A.S., Al-Zubari, W.K., El-Deen, N.A. & Abdul-Raheem, M. (2002). Desertification in the Arab region: analysis of current status and trends, *Journal*

calibration and consider this approach as second best to data.

Jennifer Koch, Florian Wimmer, Rüdiger Schaldach, Janina Onigkeit *Center for Environmental Systems Research, University of Kassel, Germany*

*of Arid Environments* 51(4):521–545.

**Acknowledgments**

simulation results.

**Author details**

**9. References**

Another striking feature of the presented modeling system is the separate module for irrigated crop production. This module allows to simulate spatial and temporal dynamics of irrigated crop production and the resulting land-use patterns and intensities [32]. The model also enables an assessment of climate dependent net irrigation water requirements simulated with the GEPIC model. Hence, the modeling system can now be used the evaluate the effect of changes in cropland extent (induced by changing climate conditions and/or demands for agricultural commodities) on the net irrigation water requirements. However, it has to be mentioned that the current LandSHIFT.JR version only evaluates the demand and no connection to water supply is implemented so far. LandSHIFT.JR considers only crop categories and does not differentiate between crop types. The net irrigation water requirements for the different crop categories were inferred from GEPIC simulations for wheat yields using a crop-specific adjustment parameter. This approach introduces some inaccuracy into the simulation and, as a result, makes the simulation results more suitable for the evaluation of changes in water requirements as compared to the absolute amounts. Furthermore, no information on conveyance efficiencies or irrigation efficiencies (e.g. drip irrigation versus sprinkler irrigation) is included, which would be required to derive the gross irrigation water requirements.

In order to validate LandSHIFT.JR, three different validation methods were applied: (1) ROC analysis [43], (2) map comparison using *κ* and *κsim* as performance measures [42, 60], and (3) a comparison of the quantity of simulated land-use changes with observed land-use changes. The results for the ROC analysis (AUC = 0.81 for METRO, AUC = 0.84 for AGRO, and AUC = 0.83 for GRAZE) indicate that the suitability assessment in LandSHIFT.JR explains the location of change to a high degree. The validation results for the map comparison are at the lower range of values reported for land-use models, with 0.6 and 0.12 for *κ* and *κsim* (0.72 and 0.22 without rangeland), respectively. Bearing in mind that the modeling approach of LandSHIFT.JR does not include a calibration step (e.g. [50, 65]), but is entirely based on parsimonious assumptions and objective methods, we consider these values as acceptable. The comparison of observed and simulated land-use changes shows an almost perfect agreement for PA. Discrepancies resulting for Israel and Jordan might partly be induced by inconsistencies in the reported values. Based on the validation results, we consider LandSHIFT.JR suitable for the simulation of the location and quantity of land-use changes in the Jordan River region.

As shown for the application example, LandSHIFT.JR implements modules for the four land-use activities infrastructure and housing, irrigated crop production, rainfed crop production, and livestock grazing. For each land-use activity, besides the dominant land-use types also an indicator of land-use intensity is allocated (population density, irrigated or rainfed crop production amount, stocking density). Hence, the model concept implemented in LandSHIFT.JR considers not only land-use patterns, but also the corresponding land-use intensities. This makes LandSHIFT.JR land-use simulation results suitable for applications focusing on natural resource management and environmental impact assessment [24, 39].

We see a potential for improvement regarding the validation process. The spatially explicit validation of rangeland, net irrigation water requirements, and the separate validation of irrigated and rainfed cropland was limited by insufficient data availability. This will be caught up for, once suitable datasets are available. We encounter this validation issues by choosing a straightforward modeling approach, based on logical assumptions and renunciation of model calibration and consider this approach as second best to data.

In addition to extensive sensitivity and uncertainty analyses to improve the scientific knowledge and understanding of land-use systems in the Jordan River region, we see a strong potential for future studies on the relationship between irrigation water supply (including treated wastewater), net irrigation water requirements, and soil sensitivity towards the irrigation with treated wastewater [47]. For this purpose, additional GEPIC simulations for other crop types besides wheat would be required in order to be able to assess the irrigation water requirements more accurately. This would allow for interesting analyses regarding the potential of using treated wastewater for irrigation purposes, under consideration of possible environmental problems associated with the use of treated wastewater for irrigation [5].

## **Acknowledgments**

24 Will-be-set-by-IN-TECH

In contrast to earlier versions of LandSHIFT.JR, the current version includes the effect of changing climate conditions on crop yields and productivity of natural vegetation, which was shown to have a strong effect on land demand in the Jordan River region [32]. The indirect effect of productivity on area demand for the different agricultural activities is included indirectly by spatially explicit simulation models (WADISCAPE and GEPIC), driven by high-resolution climate change simulations for the Jordan River region [53]. This allows the inclusion of a high level of spatial detail into the simulations of land-use and land-cover change, which is carried out on a grid with a spatial resolution of 30 arc seconds. This is of high importance in a region with such high variability in biogeographic conditions as the Jordan River region. Furthermore, it applies a consistent assessment method to the entire Jordan River region and allows the combined assessment of socio-economic and climate impact on the food production systems in the Jordan River region which is considered to be mandatory

Another striking feature of the presented modeling system is the separate module for irrigated crop production. This module allows to simulate spatial and temporal dynamics of irrigated crop production and the resulting land-use patterns and intensities [32]. The model also enables an assessment of climate dependent net irrigation water requirements simulated with the GEPIC model. Hence, the modeling system can now be used the evaluate the effect of changes in cropland extent (induced by changing climate conditions and/or demands for agricultural commodities) on the net irrigation water requirements. However, it has to be mentioned that the current LandSHIFT.JR version only evaluates the demand and no connection to water supply is implemented so far. LandSHIFT.JR considers only crop categories and does not differentiate between crop types. The net irrigation water requirements for the different crop categories were inferred from GEPIC simulations for wheat yields using a crop-specific adjustment parameter. This approach introduces some inaccuracy into the simulation and, as a result, makes the simulation results more suitable for the evaluation of changes in water requirements as compared to the absolute amounts. Furthermore, no information on conveyance efficiencies or irrigation efficiencies (e.g. drip irrigation versus sprinkler irrigation) is included, which would be required to derive the gross

In order to validate LandSHIFT.JR, three different validation methods were applied: (1) ROC analysis [43], (2) map comparison using *κ* and *κsim* as performance measures [42, 60], and (3) a comparison of the quantity of simulated land-use changes with observed land-use changes. The results for the ROC analysis (AUC = 0.81 for METRO, AUC = 0.84 for AGRO, and AUC = 0.83 for GRAZE) indicate that the suitability assessment in LandSHIFT.JR explains the location of change to a high degree. The validation results for the map comparison are at the lower range of values reported for land-use models, with 0.6 and 0.12 for *κ* and *κsim* (0.72 and 0.22 without rangeland), respectively. Bearing in mind that the modeling approach of LandSHIFT.JR does not include a calibration step (e.g. [50, 65]), but is entirely based on parsimonious assumptions and objective methods, we consider these values as acceptable. The comparison of observed and simulated land-use changes shows an almost perfect agreement for PA. Discrepancies resulting for Israel and Jordan might partly be induced by inconsistencies in the reported values. Based on the validation results, we consider LandSHIFT.JR suitable for the simulation of the location and quantity of land-use changes in

[55, 56].

irrigation water requirements.

the Jordan River region.

This study is part of the GLOWA Jordan River project financed by the German Federal Ministry of Education and Research (FKZ 01LW0502). We thank Katja Geissler (Potsdam University, Research Group Plant Ecology and Nature Conservation) and Martin Köchy (Johann Heinrich von Thünen Insitut, Braunschweig) for the provision of WADISCAPE model output. Furthermore, we thank Gerhard Smiatek (IMK-IFU, Institute for Meteorology and Climate Research-Atmospheric Environmental Research) for the provision of regional climate simulation results.

## **Author details**

Jennifer Koch, Florian Wimmer, Rüdiger Schaldach, Janina Onigkeit *Center for Environmental Systems Research, University of Kassel, Germany*

## **9. References**

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© 2012 Appiah-Opoku and Taylor, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

properly cited.

**Environmental Land Use** 

Seth Appiah-Opoku and Crystal Taylor

http://dx.doi.org/10.5772/48191

**1. Introduction** 

a less sustainable society.

Additional information is available at the end of the chapter

**and the Ecological Footprint of Higher Learning** 

The lifestyles of individuals, groups, or nations can be measured by utilizing an accounting tool known as ecological footprint. Ecological footprint refers to the productive land needed to support a given population. As discussed by Wackernagel and Rees (1996), "The ecological footprint concept is simple, yet potentially comprehensive: it accounts for the flows of energy and matter to and from any defined economy and converts these into the corresponding land/water are required from nature to support these flows" (p. 3). A concept known as "overshoot" occurs if demands by humans exceed the supply of a given biologically productive area (Turner et al., 2006). Thus, a larger ecological footprint indicates

Research on ecological footprint literature links together the concepts of footprint size and economic development. In other words, footprints represent population size and consumption levels (Wackernagel & Rees, 1996). Furthermore, more-developed countries contain market economies that consume greater levels of natural resources, and environmental degradation is largely driven by the growth and intensification of market economies (Jorgenson, as cited in Jorgenson & Burns, 2006). For example, Americans when compared to the rest of the world exhibit a large ecological footprint due to an intensely consumption-oriented lifestyle. The average ecological footprint for an American is 23.68 acres as compared to the world's average of 5.53 acres (Global Footprint Network, 2003). Further research suggests an economical discrepancy between those who possess large ecological footprints and those who possess small ecological footprints. Wackernagel et al. (2003) found that those contributing most to climate change through their energy intensive lifestyles will most likely be less affected by, and better shielded from, the outfalls of climate

change than poor people living on marginal land or in underserved urban conditions.

and reproduction in any medium, provided the original work is properly cited.

## **Environmental Land Use and the Ecological Footprint of Higher Learning**

Seth Appiah-Opoku and Crystal Taylor

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/48191

## **1. Introduction**

30 Will-be-set-by-IN-TECH

[62] Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V. & Mastura, S.S.A. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model,

[63] Verburg, P.H., Kok, K., Pontius, R.G. & Veldkamp, A. (2006). Modeling land-use and land-cover change, *in* Lambin, E.F. & Geist, H.J. (eds.), *Land-use and land-cover change - local processes and global impacts*, Springer Verlag, Berlin, Heidelberg, Germany, pp.

[64] Vitousek, P.M. (1994). Beyond global warming: ecology and global change, *Ecology*

[65] Wang, F., Hasbani, J.-G., Wang, X. & Marceau, D.J. (2011). Identifying dominant factors for the calibration of a land-use cellular automata model using Rough Set Theory,

[66] WDPA Consortium (2004) *Word Database on Protected Areas.* URL: http://www.wdpa.

[67] Williams, J.R., Jones, C.A., Kiniry, J.R. & Spanel, D.A. (1989). The EPIC crop growth model, *Transactions of the American Society of Agricultural Engineers* 32(2):497–511. [68] Williams, J.R. (1995). The EPIC model, *in* Singh, V.P. (ed.), *Computer Models of Watershed Hydrology*, Water Resources Publications, Colorado, United States, pp. 909–1000. [69] Wu, F. (1999). GIS-based simulation as an exploratory analysis for space-time processes,

*Environmental Management* 30(3):391–405.

*Journal of Geographical Systems* 1(3):199–218.

*Computers, Environment and Urban Systems* 35(2):116–125.

117–135.

org/

75(7):1861–1876.

The lifestyles of individuals, groups, or nations can be measured by utilizing an accounting tool known as ecological footprint. Ecological footprint refers to the productive land needed to support a given population. As discussed by Wackernagel and Rees (1996), "The ecological footprint concept is simple, yet potentially comprehensive: it accounts for the flows of energy and matter to and from any defined economy and converts these into the corresponding land/water are required from nature to support these flows" (p. 3). A concept known as "overshoot" occurs if demands by humans exceed the supply of a given biologically productive area (Turner et al., 2006). Thus, a larger ecological footprint indicates a less sustainable society.

Research on ecological footprint literature links together the concepts of footprint size and economic development. In other words, footprints represent population size and consumption levels (Wackernagel & Rees, 1996). Furthermore, more-developed countries contain market economies that consume greater levels of natural resources, and environmental degradation is largely driven by the growth and intensification of market economies (Jorgenson, as cited in Jorgenson & Burns, 2006). For example, Americans when compared to the rest of the world exhibit a large ecological footprint due to an intensely consumption-oriented lifestyle. The average ecological footprint for an American is 23.68 acres as compared to the world's average of 5.53 acres (Global Footprint Network, 2003). Further research suggests an economical discrepancy between those who possess large ecological footprints and those who possess small ecological footprints. Wackernagel et al. (2003) found that those contributing most to climate change through their energy intensive lifestyles will most likely be less affected by, and better shielded from, the outfalls of climate change than poor people living on marginal land or in underserved urban conditions.

© 2012 Appiah-Opoku and Taylor, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Though ecological footprint can be used as a useful tool to help measure sustainability, some scientists have criticized ecological footprint calculations for oversimplifying ecosystem processes to numerical values. Assumptions may not be valid as the ecological footprint arbitrarily assumes both zero greenhouse gas emissions, which may not be optimal, and national boundaries, which makes extrapolating from the average ecological footprint problematic (Fiala, 2008). Despite these criticisms, the ecological footprint calculation can serve as a heuristic tool for designing and implementing plans for today as well as for tomorrow. Moreover, plans that take environmental calculations into consideration will have a far greater potential of keeping the Earth as a stakeholder in the planning process than those plans without such calculations.

Environmental Land Use and the Ecological Footprint of Higher Learning 119

Although all the above-listed universities have displayed an extraordinary commitment to green initiatives, Harvard University located in Cambridge, MA; Emory University located near Atlanta, GA; and Bates College in Lewiston, ME, were chosen for closer examination in part due to the accessibility of online information concerning green programs as well as in respect to their diverse financial strategies for integrating sustainable principles. An inventory was performed encompassing a list of similarities and differences concerning green initiatives and strategies. Moreover, this inventory can serve as a framework for other colleges to follow in the future. It is in this context that we discuss the current consumption and environmental awareness levels associated with the use of water and energy resources

The University of Alabama is in the preliminary stages of moving toward a more sustainable campus. Currently, it is difficult to track environmentally friendly progress on campus, as no study has been previously performed to establish where The University of Alabama is concerning environmental initiatives. Thus, if a snapshot of the University were established to include both consumption and environmental awareness levels, then those findings would serve as a benchmark from which the implementation of green strategies may be evaluated in terms of effectiveness. Accordingly, this research documented the environmental awareness and consumption levels of dormitory students concerning energy and water resources on The University of Alabama's campus. Moreover, findings were gathered from the dormitories Ridgecrest East and Lakeside East. The goal was to measure the current ecological footprint of dormitory students on The University of Alabama campus. Specific research objectives were to (a) determine the current state of students' environmental awareness, and (b) determine the current consumption levels in terms of

A case study approach was utilized during this research. According to Theodorson and Theodorson (as cited in Punch, 1998) a case study is defined as "a method of studying social phenomena through the thorough analysis of an individual case. Described simply, a case study provides a snapshot of particular social phenomena (Hakim, 1987, p. 61). Thus, the case study approach allows for in-depth research on specific populations, such as the dormitory students that will serve as the focus for this research. This approach also permits the researcher to evaluate subjects in a naturalistic setting as well as conduct research from a wide array of methods such as interviews, observations, numerical data, and questionnaires

Georgia Institute of Technology

University of New Hampshire

State University of New York at Binghamton

for dormitory students on The University of Alabama's campus.

electricity and water usage for specific dormitories on campus.

Harvard University

 University of Oregon University of Washington

**2. Research methods** 

Yale University

Colleges and Universities across the world serve as incubators for tomorrow's leaders. In essence, they leave an educational imprint on individuals in an effort to educate and facilitate the development of tomorrow's leaders. These institutions serve as the setting where ideas can take form and this is where ideas can be implemented in a semi contained setting as part of the larger community. Though it is well established that educational institutions leave their imprints on innovative minds, this chapter introduces the idea that institutions of higher learning also leave ecological footprints on the landscape. Universities provide support to environmental issues through policies, programs, and research. The idea of greening campuses has become so popular that the Princeton Review has posted a Green Rating Honor Roll to document the top schools that provide a healthy and sustainable quality of life for the students, environmentally-minded and educational preparations for the future workforce, and environmentally responsible school policies for all to follow (Princeton Review, 2008).

Thinking green has been a hot topic among US Colleges in recent years. To think green is to incorporate environmental impacts into decision-making activities that affect daily lifestyles. The impact that a society imposes on the environment holds importance, as it is a key issue of sustainability. Sustainability refers to the dilemma of how to "meet the needs of the present without compromising the ability of future generations to meet their own needs" (Wackernagel & Rees, 1996, p. 33). Fortunately, one place where sustainable initiatives have spread is on campuses throughout America. Universities have provided support to environmental issues through policies, programs, and research.

The Princeton Review has posted a Green Rating Honor Roll to document the top schools that provide a healthy and sustainable quality of life for the students, environmentallyminded and educational preparations for the future workforce, and environmentally responsible school policies for all to follow (Princeton Review, 2008). The Princeton Review ranked the following eleven colleges throughout the United States as receiving a green rating of ninety-nine points.


(Princeton Review, 2008).

rating of ninety-nine points.

 College of the Atlantic Emory University

Bates College

Arizona State University, Tempe

Though ecological footprint can be used as a useful tool to help measure sustainability, some scientists have criticized ecological footprint calculations for oversimplifying ecosystem processes to numerical values. Assumptions may not be valid as the ecological footprint arbitrarily assumes both zero greenhouse gas emissions, which may not be optimal, and national boundaries, which makes extrapolating from the average ecological footprint problematic (Fiala, 2008). Despite these criticisms, the ecological footprint calculation can serve as a heuristic tool for designing and implementing plans for today as well as for tomorrow. Moreover, plans that take environmental calculations into consideration will have a far greater potential of keeping the Earth as a stakeholder in the

Colleges and Universities across the world serve as incubators for tomorrow's leaders. In essence, they leave an educational imprint on individuals in an effort to educate and facilitate the development of tomorrow's leaders. These institutions serve as the setting where ideas can take form and this is where ideas can be implemented in a semi contained setting as part of the larger community. Though it is well established that educational institutions leave their imprints on innovative minds, this chapter introduces the idea that institutions of higher learning also leave ecological footprints on the landscape. Universities provide support to environmental issues through policies, programs, and research. The idea of greening campuses has become so popular that the Princeton Review has posted a Green Rating Honor Roll to document the top schools that provide a healthy and sustainable quality of life for the students, environmentally-minded and educational preparations for the future workforce, and environmentally responsible school policies for all to follow

Thinking green has been a hot topic among US Colleges in recent years. To think green is to incorporate environmental impacts into decision-making activities that affect daily lifestyles. The impact that a society imposes on the environment holds importance, as it is a key issue of sustainability. Sustainability refers to the dilemma of how to "meet the needs of the present without compromising the ability of future generations to meet their own needs" (Wackernagel & Rees, 1996, p. 33). Fortunately, one place where sustainable initiatives have spread is on campuses throughout America. Universities have provided support to

The Princeton Review has posted a Green Rating Honor Roll to document the top schools that provide a healthy and sustainable quality of life for the students, environmentallyminded and educational preparations for the future workforce, and environmentally responsible school policies for all to follow (Princeton Review, 2008). The Princeton Review ranked the following eleven colleges throughout the United States as receiving a green

planning process than those plans without such calculations.

environmental issues through policies, programs, and research.


Although all the above-listed universities have displayed an extraordinary commitment to green initiatives, Harvard University located in Cambridge, MA; Emory University located near Atlanta, GA; and Bates College in Lewiston, ME, were chosen for closer examination in part due to the accessibility of online information concerning green programs as well as in respect to their diverse financial strategies for integrating sustainable principles. An inventory was performed encompassing a list of similarities and differences concerning green initiatives and strategies. Moreover, this inventory can serve as a framework for other colleges to follow in the future. It is in this context that we discuss the current consumption and environmental awareness levels associated with the use of water and energy resources for dormitory students on The University of Alabama's campus.

The University of Alabama is in the preliminary stages of moving toward a more sustainable campus. Currently, it is difficult to track environmentally friendly progress on campus, as no study has been previously performed to establish where The University of Alabama is concerning environmental initiatives. Thus, if a snapshot of the University were established to include both consumption and environmental awareness levels, then those findings would serve as a benchmark from which the implementation of green strategies may be evaluated in terms of effectiveness. Accordingly, this research documented the environmental awareness and consumption levels of dormitory students concerning energy and water resources on The University of Alabama's campus. Moreover, findings were gathered from the dormitories Ridgecrest East and Lakeside East. The goal was to measure the current ecological footprint of dormitory students on The University of Alabama campus. Specific research objectives were to (a) determine the current state of students' environmental awareness, and (b) determine the current consumption levels in terms of electricity and water usage for specific dormitories on campus.

## **2. Research methods**

A case study approach was utilized during this research. According to Theodorson and Theodorson (as cited in Punch, 1998) a case study is defined as "a method of studying social phenomena through the thorough analysis of an individual case. Described simply, a case study provides a snapshot of particular social phenomena (Hakim, 1987, p. 61). Thus, the case study approach allows for in-depth research on specific populations, such as the dormitory students that will serve as the focus for this research. This approach also permits the researcher to evaluate subjects in a naturalistic setting as well as conduct research from a wide array of methods such as interviews, observations, numerical data, and questionnaires (Punch, 1998, p. 153). Suitably, interviews, observations, surveys, and data analysis are the primary methods utilized in this research. Even as the case study approach proves to be a viable research tool, a limitation is the inability of the researcher to derive generalizations from specific instances (Punch, 1998, p. 155). In light of this accusation, it is of importance to note that the case study approach warrants merit as this research requires an in-depth inquiry into a particular situation that has yet to be documented.

Environmental Land Use and the Ecological Footprint of Higher Learning 121

**Figure 2.** Lakeside East Residential Hall.

**Figure 3.** Ridgecrest East Residential Hall.

themes and patterns. Additionally, the records assisted with the calculation of the ecological footprint analysis of energy and water usage in dormitories on campus. The energy records acquired reported monthly electrical and natural gas usage figures for the two dormitories from 2007 and 2008. Due to some technical problems with the water meters, only the last five months of 2008 were available for analysis. However, water usage assumptions were derived for the entire year of 2008. Ecological footprint calculations were projected from estimates of the average water usage in 2008 and from the actual natural gas and electricity usage figures from 2007 and 2008. Even from water approximations, the derived ecological footprint has the ability to serve as a benchmark that can be utilized in future research. During the analysis of water and energy records, the data concerning the population rates for Ridgecrest East and Lakeside East during 2007 were unfortunately unattainable; consequently, the 2008 population numbers were substituted. In addition to the analysis of energy records, an interview with the Director of Energy Management was conducted in an

effort to get a proper vision of the campus in terms of resource management.

As mentioned previously, the focus of this research is centered around dormitory students residing on The University of Alabama campus located in Tuscaloosa, Alabama. In the fall of 2008, The University of Alabama reached a record enrollment of 27,052 students (Andreen, 2008). Of the 27,052 students approximately 7,000 students are housed on campus (E. Russell, e-mail, February 24, 2009).1 Therefore, on-campus residency accounts for approximately 26% of the student population as illustrated by Figure 1.

## **UA Student Housing**

**Figure 1.** UA student housing.

For this study two dormitories were chosen for sampling. The selection was done by methods of random sampling. Random sampling allowed every dormitory to have an equal opportunity of being selected. The process entailed writing down the names of all the possible dormitories on campus on individual slips of paper. The dormitory names were mixed up and then drawn out of a hat. The dormitories Lakeside East and Ridgecrest East were selected for an analysis of energy and water usage records. The coed student populations housed within Lakeside East and Ridgecrest East are 238 and 316 students, respectively.

## **3. Survey and data analysis**

The University of Alabama's Department of Energy Management aided in providing energy and water consumption records concerning the Ridgecrest East and Lakeside East dormitories. A content analysis of the records was performed to determine applicable

<sup>1</sup> An e-mail was received from Russell, E. on February 24, 2009. This e-mail is not traceable by the reader and is therefore not found in the references per APA style.

Environmental Land Use and the Ecological Footprint of Higher Learning 121

**Figure 2.** Lakeside East Residential Hall.

120 Environmental Land Use Planning

**Figure 1.** UA student housing.

74%

**3. Survey and data analysis** 

therefore not found in the references per APA style.

(Punch, 1998, p. 153). Suitably, interviews, observations, surveys, and data analysis are the primary methods utilized in this research. Even as the case study approach proves to be a viable research tool, a limitation is the inability of the researcher to derive generalizations from specific instances (Punch, 1998, p. 155). In light of this accusation, it is of importance to note that the case study approach warrants merit as this research requires an in-depth

As mentioned previously, the focus of this research is centered around dormitory students residing on The University of Alabama campus located in Tuscaloosa, Alabama. In the fall of 2008, The University of Alabama reached a record enrollment of 27,052 students (Andreen, 2008). Of the 27,052 students approximately 7,000 students are housed on campus (E. Russell, e-mail, February 24, 2009).1 Therefore, on-campus residency accounts for

26%

**UA Student Housing**

For this study two dormitories were chosen for sampling. The selection was done by methods of random sampling. Random sampling allowed every dormitory to have an equal opportunity of being selected. The process entailed writing down the names of all the possible dormitories on campus on individual slips of paper. The dormitory names were mixed up and then drawn out of a hat. The dormitories Lakeside East and Ridgecrest East were selected for an analysis of energy and water usage records. The coed student populations housed within Lakeside East and Ridgecrest East are 238 and 316 students, respectively.

On-Campus Housing

Off-Campus Housing

The University of Alabama's Department of Energy Management aided in providing energy and water consumption records concerning the Ridgecrest East and Lakeside East dormitories. A content analysis of the records was performed to determine applicable

1 An e-mail was received from Russell, E. on February 24, 2009. This e-mail is not traceable by the reader and is

inquiry into a particular situation that has yet to be documented.

approximately 26% of the student population as illustrated by Figure 1.

**Figure 3.** Ridgecrest East Residential Hall.

themes and patterns. Additionally, the records assisted with the calculation of the ecological footprint analysis of energy and water usage in dormitories on campus. The energy records acquired reported monthly electrical and natural gas usage figures for the two dormitories from 2007 and 2008. Due to some technical problems with the water meters, only the last five months of 2008 were available for analysis. However, water usage assumptions were derived for the entire year of 2008. Ecological footprint calculations were projected from estimates of the average water usage in 2008 and from the actual natural gas and electricity usage figures from 2007 and 2008. Even from water approximations, the derived ecological footprint has the ability to serve as a benchmark that can be utilized in future research. During the analysis of water and energy records, the data concerning the population rates for Ridgecrest East and Lakeside East during 2007 were unfortunately unattainable; consequently, the 2008 population numbers were substituted. In addition to the analysis of energy records, an interview with the Director of Energy Management was conducted in an effort to get a proper vision of the campus in terms of resource management.

## **4. Calculating the ecological footprint**

Data from the Department of Energy Management were utilized in the ecological footprint calculation. The following identifies the process for calculating ecological footprints:

Environmental Land Use and the Ecological Footprint of Higher Learning 123

**Step 4** Estimate the Ecological Footprint for the average person

Ridgecrest East Lakeside East

**Step 5** Multiply the per capita footprint by the total population on campus

Source: University of Alabama Department of Energy Management

**Step 2** Obtain water records

**Watter Usage (CF)**

**Step 1** Identify the population size of the dormitories

**Figure 5.** Ecological footprint procedure for water.

dormitory by the subsequent student populations residing in each residential hall. Consequently, the average amount of water consumed per student for Lakeside East was 3,830 cubic feet (28,649 gallons) and 2,363 cubic feet (17,674 gallons) for Ridgecrest East.

Aug-08 Sep-08 Oct-08 Nov-08 Dec-08

**2008 Water Usage for Ridgecrest East & Lakeside East**

**Months**

**Step 3** Determine land area requirements for water resources

To obtain a real-world comparison, consumption figures of the individual dormitory student are listed in gallons as well as cubic feet. The individual usage levels can further be broken down into daily usage figures by dividing by 365 to represent the approximate number of days in a year. As a result the daily consumption level for an individual residing in Lakeside East was 10.49 cubic feet or 78.49 gallons and 6.47 cubic feet or 48.42 gallons for those in Ridgecrest East. Daily usage figures are useful as they can be easily compared to the national average of the average American. According to the Environmental Protection Agency (2003), the average American consumes 90 gallons of water daily in the home, as

**Figure 4.** 2008 Water Usage for Ridgecrest East and Lakeside East.


The ecological footprint calculation was utilized to determine land use requirements associated with the consumption of resources. The calculation was performed utilizing water, electric, and natural gas records. All the records used in this study were obtained from the Department of Energy Management.

Water

Water usage records were acquired pertaining to Ridgecrest East and Lakeside East Residential Halls from August to December 2008. Due to some technical problems with the water meters, accurate water usage readings prior to August 2008 were unattainable. The trend for water usage at Ridgecrest East showed little variation during the months of August, September, and October as consumption ranged from approximately 72,000 to 80,000 cubic feet or approximately 538,000 gallons to 599,000 gallons. Usage dropped slightly during November followed by a dramatic decrease in December. Lakeside East Residential Hall demonstrated more drastic trends than Ridgecrest East as usage in August peaked at nearly 140,000 cubic feet followed by a marked decline in September as Lakeside levels dropped around 40%. A slight increase occurred during the month of October. In November and December consumption decreased drastically as water usage dipped below Ridgecrest levels.

Figure 11 details water usage in cubic feet consumed. Figure 12 depicts the steps we were utilized to calculate the ecological footprint of water resources consumed in the dormitories Lakeside East and Ridgecrest East. First, the populations of Lakeside East and Ridgecrest East were established. As mentioned previously, 238 students reside within Lakeside East, whereas 316 students live in Ridgecrest East. A full twelve months of records were unavailable, so estimations were used to approximate the yearly water consumption levels within the dormitories. The 2008 yearly estimations for each building were derived from taking the average amount of water used during the five months and then multiplying that average by twelve months. For Ridgecrest East the figure 746,616 cubic feet was used as the 2008 water usage estimate, while the figure 911,496 cubic feet was used for Lakeside East.

Thus, an ecological footprint calculation concerning water resources can be derived by utilizing the water consumption estimates for the two residential halls as indicated above. Initially, the amount of water consumed in cubic feet per dormitory student must be established. The number was calculated by dividing the total water estimates for each

## **2008 Water Usage for Ridgecrest East & Lakeside East**

Source: University of Alabama Department of Energy Management

122 Environmental Land Use Planning

Water

Ridgecrest levels.

**4. Calculating the ecological footprint** 

1. Estimate the average population size.

from the Department of Energy Management.

Data from the Department of Energy Management were utilized in the ecological footprint

3. Estimate the land area appropriated per capita for the production of items consumed. 4. Estimate the ecological footprint of the average person for all items consumed.

The ecological footprint calculation was utilized to determine land use requirements associated with the consumption of resources. The calculation was performed utilizing water, electric, and natural gas records. All the records used in this study were obtained

Water usage records were acquired pertaining to Ridgecrest East and Lakeside East Residential Halls from August to December 2008. Due to some technical problems with the water meters, accurate water usage readings prior to August 2008 were unattainable. The trend for water usage at Ridgecrest East showed little variation during the months of August, September, and October as consumption ranged from approximately 72,000 to 80,000 cubic feet or approximately 538,000 gallons to 599,000 gallons. Usage dropped slightly during November followed by a dramatic decrease in December. Lakeside East Residential Hall demonstrated more drastic trends than Ridgecrest East as usage in August peaked at nearly 140,000 cubic feet followed by a marked decline in September as Lakeside levels dropped around 40%. A slight increase occurred during the month of October. In November and December consumption decreased drastically as water usage dipped below

Figure 11 details water usage in cubic feet consumed. Figure 12 depicts the steps we were utilized to calculate the ecological footprint of water resources consumed in the dormitories Lakeside East and Ridgecrest East. First, the populations of Lakeside East and Ridgecrest East were established. As mentioned previously, 238 students reside within Lakeside East, whereas 316 students live in Ridgecrest East. A full twelve months of records were unavailable, so estimations were used to approximate the yearly water consumption levels within the dormitories. The 2008 yearly estimations for each building were derived from taking the average amount of water used during the five months and then multiplying that average by twelve months. For Ridgecrest East the figure 746,616 cubic feet was used as the 2008 water usage estimate, while the figure 911,496 cubic feet was used for Lakeside East.

Thus, an ecological footprint calculation concerning water resources can be derived by utilizing the water consumption estimates for the two residential halls as indicated above. Initially, the amount of water consumed in cubic feet per dormitory student must be established. The number was calculated by dividing the total water estimates for each

calculation. The following identifies the process for calculating ecological footprints:

2. Estimate the average annual consumption for a particular item.

5. Multiply the population by the per capita footprint.

**Figure 4.** 2008 Water Usage for Ridgecrest East and Lakeside East.

**Figure 5.** Ecological footprint procedure for water.

dormitory by the subsequent student populations residing in each residential hall. Consequently, the average amount of water consumed per student for Lakeside East was 3,830 cubic feet (28,649 gallons) and 2,363 cubic feet (17,674 gallons) for Ridgecrest East.

To obtain a real-world comparison, consumption figures of the individual dormitory student are listed in gallons as well as cubic feet. The individual usage levels can further be broken down into daily usage figures by dividing by 365 to represent the approximate number of days in a year. As a result the daily consumption level for an individual residing in Lakeside East was 10.49 cubic feet or 78.49 gallons and 6.47 cubic feet or 48.42 gallons for those in Ridgecrest East. Daily usage figures are useful as they can be easily compared to the national average of the average American. According to the Environmental Protection Agency (2003), the average American consumes 90 gallons of water daily in the home, as

compared to the average European consuming 53 gallons daily, and the typical Sub-Saharan African citizen consuming only 3-5 gallons per day.

Environmental Land Use and the Ecological Footprint of Higher Learning 125

Despite the September peak for Lakeside East, electricity usage throughout the 2007 year remained somewhat consistent as January through March accounted for a range of approximately 50,000 to 65,000 kWh. April to May experienced a slight increase with consumption hovering near 80,000 kWh. June to July numbers were barely below 70,000 kWh, while August numbers increased back up to nearly 80,000 kWh. October boasted the second highest usage for 2007 at 87,151 kWh. Finally, during the months of November and December consumption ranged from 65,000 to 55,000 kWh. Interestingly, even as Lakeside East consistently consumed less power per month during 2007 with the exception of the September spike, the total 2007 energy consumption figures for Lakeside East (1,067,609

> **2007 Electricity Usage for Lakeside East & Ridgecrest East**

kWh) were slightly higher than Ridgecrest East (1,066,400 kWh).

Source: University of Alabama Department of Energy Management

Feb-07

Mar-07

Apr-07

May-07

Jun-07

**Months**

Jul-07

Aug-07

Sep-07

Oct-07

Nov-07

Dec-07

Lakeside East

Ridgecrest East

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

**Kwh**

Jan-07

**Figure 6.** 2007 Electricity Usage for Lakeside East and Ridgecrest East.

As mentioned previously, Ridgecrest East has in general consumed a higher amount of electricity in terms of kilowatt hours per month during 2007 when compared to Lakeside East. Those higher consumption rates for Ridgecrest East are indicated as the following approximated percentages above Lakeside East's usage levels: January was 11% higher, February displayed an 8% increase, March had an 11% increase, April saw a 16% increase, May's increase jumped up 22%, June displayed a 24% increase, July had a 35% increase, October displayed a 27% rise, November increased to 35%, and finally December had a 36% increase over Lakeside East's consumption levels. Electricity consumption for Ridgecrest

East during September 2007 was only about 44% of what Lakeside East consumed.

During 2008, Lakeside East consumed less total electricity each month than Ridgecrest East. Moreover, when the total consumption figures of 2008 for both dormitories are compared to the 2007 fiscal year, together the buildings show an overall decrease in electrical usage. Lakeside East displayed the following monthly consumption during 2008 recorded in

After establishing the consumption levels for water resources, it was necessary to determine the amount of land required for the utilization of water resources. Thus, water resources were converted to cubic meters by multiplying by 0.0283 and then divided by 1,500 m3/ha/yr to accommodate the amount of forested land needed to accommodate the water consumed (Anundson et al., 2001, p. 26). The result was equivalent to

0.0723 hectares (0.1785 acres) per dormitory student in Lakeside East and 0.0446 hectares (0.1101 acres) per dormitory student in Ridgecrest East.


**Table 1.** Ecological Footprint for Water 2008

It is germane to keep in mind that all of these figures, concerning hectares/acreage required, only apply to the land required concerning water resources utilized during the consumption of housing. Accordingly, "the ecological footprint concept is based on the idea that for every item of material or energy consumption, a certain amount of land in one or more ecosystem categories is required to provide the consumption-related resource flows and waste sinks" (Wackernagel & Rees, 1996, p. 63). Thus, a complete ecological footprint calculation encompasses many different goods and services as this study looks specifically at water and energy resources associated with housing needs of dormitory students on The University of Alabama's campus.

#### a. Electricity

In addition to supplying the water records, as indicated in the findings in the previous section, the Department of Energy Management also provided electric and natural gas records for use in this research. To assist with the analysis of Lakeside East and Ridgecrest East Residential Communities, complete electrical and natural gas records were gathered from January 2007 to December 2008. Energy consumptions records from both 2007 and 2008 show a general trend of Lakeside East utilizing slightly less electricity per month with the exception of a peak on September 2007. During September 2007, Lakeside East Residential Hall experienced a spike in usage as

315,007 kilowatt hours (kWh) were consumed. This consumption stands-out on the electrical records as neither Lakeside East nor Ridgecrest East demonstrated another usage level over 140,000 kilowatt hours during the two-year span.

Despite the September peak for Lakeside East, electricity usage throughout the 2007 year remained somewhat consistent as January through March accounted for a range of approximately 50,000 to 65,000 kWh. April to May experienced a slight increase with consumption hovering near 80,000 kWh. June to July numbers were barely below 70,000 kWh, while August numbers increased back up to nearly 80,000 kWh. October boasted the second highest usage for 2007 at 87,151 kWh. Finally, during the months of November and December consumption ranged from 65,000 to 55,000 kWh. Interestingly, even as Lakeside East consistently consumed less power per month during 2007 with the exception of the September spike, the total 2007 energy consumption figures for Lakeside East (1,067,609 kWh) were slightly higher than Ridgecrest East (1,066,400 kWh).

Source: University of Alabama Department of Energy Management

124 Environmental Land Use Planning

African citizen consuming only 3-5 gallons per day.

(Anundson et al., 2001, p. 26). The result was equivalent to

(0.1101 acres) per dormitory student in Ridgecrest East.

**Table 1.** Ecological Footprint for Water 2008

Residential Hall experienced a spike in usage as

140,000 kilowatt hours during the two-year span.

Alabama's campus.

a. Electricity

compared to the average European consuming 53 gallons daily, and the typical Sub-Saharan

After establishing the consumption levels for water resources, it was necessary to determine the amount of land required for the utilization of water resources. Thus, water resources were converted to cubic meters by multiplying by 0.0283 and then divided by 1,500 m3/ha/yr to accommodate the amount of forested land needed to accommodate the water consumed

0.0723 hectares (0.1785 acres) per dormitory student in Lakeside East and 0.0446 hectares

**Ecological Footprint for Water 2008 Lakeside East Ridgecrest East**  Total Water Usage 2008 (cubic ft) 911,496 746,616 Water Usage per Month (cubic ft) 75,958 62,218 Water per Student in 2008 (cubic ft) 3,830 2,363 Total Land Area in Hectares per Dormitory Student 0.0723 0.0446 Total Land Area in Acres per Dormitory Student 0.1785 0.1101

It is germane to keep in mind that all of these figures, concerning hectares/acreage required, only apply to the land required concerning water resources utilized during the consumption of housing. Accordingly, "the ecological footprint concept is based on the idea that for every item of material or energy consumption, a certain amount of land in one or more ecosystem categories is required to provide the consumption-related resource flows and waste sinks" (Wackernagel & Rees, 1996, p. 63). Thus, a complete ecological footprint calculation encompasses many different goods and services as this study looks specifically at water and energy resources associated with housing needs of dormitory students on The University of

In addition to supplying the water records, as indicated in the findings in the previous section, the Department of Energy Management also provided electric and natural gas records for use in this research. To assist with the analysis of Lakeside East and Ridgecrest East Residential Communities, complete electrical and natural gas records were gathered from January 2007 to December 2008. Energy consumptions records from both 2007 and 2008 show a general trend of Lakeside East utilizing slightly less electricity per month with the exception of a peak on September 2007. During September 2007, Lakeside East

315,007 kilowatt hours (kWh) were consumed. This consumption stands-out on the electrical records as neither Lakeside East nor Ridgecrest East demonstrated another usage level over **Figure 6.** 2007 Electricity Usage for Lakeside East and Ridgecrest East.

As mentioned previously, Ridgecrest East has in general consumed a higher amount of electricity in terms of kilowatt hours per month during 2007 when compared to Lakeside East. Those higher consumption rates for Ridgecrest East are indicated as the following approximated percentages above Lakeside East's usage levels: January was 11% higher, February displayed an 8% increase, March had an 11% increase, April saw a 16% increase, May's increase jumped up 22%, June displayed a 24% increase, July had a 35% increase, October displayed a 27% rise, November increased to 35%, and finally December had a 36% increase over Lakeside East's consumption levels. Electricity consumption for Ridgecrest East during September 2007 was only about 44% of what Lakeside East consumed.

During 2008, Lakeside East consumed less total electricity each month than Ridgecrest East. Moreover, when the total consumption figures of 2008 for both dormitories are compared to the 2007 fiscal year, together the buildings show an overall decrease in electrical usage. Lakeside East displayed the following monthly consumption during 2008 recorded in

**2008 Electricity Usage for Lakeside East & Ridgecrest East**

Environmental Land Use and the Ecological Footprint of Higher Learning 127

was considerably less than those found at Lakeside East. Just as the 2007 electricity records were broken down for analysis, the 2008 electricity records were evaluated for individual

To acquire the electricity consumed per dormitory student during 2008, the electrical totals were divided by the amount of the respective residential populations. Thus, the average student consumed 3,343 kWh within Lakeside East and 3,177 kWh for Ridgecrest East. To relate student electricity consumption rates to a real-world example the 2007 and 2008 figures were broken into monthly averages. The 2007 monthly rates per dormitory student were calculated to be approximately 374 kWh for Lakeside East and approximately 281 kWh for Ridgecrest East. For 2008 the monthly averages were approximately 279 kWh for Lakeside East and approximately 265 kWh for Ridgecrest East. According to the Energy Information Administration (2007), the average Alabama household consumes 1,305 kWh per month.

After the consumption levels were successfully calculated for electrical resources, the amount of land could be determined for the usage of electrical resources. To accommodate the carbon emissions from the utilization of electricity the rate of 169 m2 of forest for every 100 kWh of electricity was used for the following ecological footprint calculations (Anundson et al., 2001, p.11). Thus, the individual amount of electricity per dormitory student was first divided by 100 kWh and then multiplied by 169 m2. Accordingly during 2007 for Lakeside East, the amount of land needed per dormitory student was 7,581 m2 (0.758 hectares or 1.873 acres) and for Ridgecrest East 5,703 m2 (0.570 hectares or 1.409 acres). During 2008, the amount of forested land area necessary per student amounted to 5,650 m2 (0.565 hectares or 1.396 acres) for Lakeside East and 5,369 m2 (0.537 or 1.327 acres) for Ridgecrest East. In Table 7, meters squared were converted to hectares by dividing by

**Ecological Footprint for Electricity 2007 Lakeside East Ridgecrest East**  Total Electricity 2007 (kWh) 1,067,609 1,066,400 Electricity per Student in 2007 (kWh) 4,486 3,375 2007 Total Land (m)2 per dormitory student 7,581 5,703 2007 Total Land in Hectares per dormitory student 0.758 0.570 2007 Total Land in Acres per dormitory student 1.873 1.409 **Ecological Footprint for Electricity 2008 Lakeside East Ridgecrest East**  Total Electricity 2008 (kWh) 795,636 1,004,000 Electricity per Student in 2008 (kWh) 3,343 3,177 2008 Total Land (m)2 per dormitory student 5,650 5,369 2008 Total Land in Hectares per dormitory student 0.565 0.537 2008 Total Land in Acres per dormitory student 1.396 1.327

10,000. Additionally, hectares were converted by multiplying by 2.471.

**Table 2.** Ecological Footprint for Electricity 2007 and 2008

usage levels.

Source: University of Alabama Department of Energy Management

**Figure 7.** 2008 Electricity Usage for Lakeside East and Ridgecrest East.

kilowatt hours: January was 39,628 kWh; followed by February with 62,320 kWh; March consumed 58,206 kWh; April used 65,469 kWh; May was 68,613 kWh; June was recorded at 59,222 kWh; July had 62,597 kWh; August consumed 72,264 kWh; September was recorded at 108,040 kWh; October used 81,022 kWh; November had 60,623 kWh of usage; and finally during December 57,632 kWh were utilized. Similar to the methodology utilized to calculate the ecological footprint concerning water resources, Figure 15 depicts the ecological footprint procedure from which the electrical impact of students was derived.

**Figure 8.** Ecological footprint procedure for electricity

For a more in-depth analysis of electrical usage for the two dormitories, the amount of energy utilized by each dormitory student for the year was calculated as the total electricity consumption numbers were divided by the amount of students residing within each dormitory. This accounted for the amount of electricity utilized per student to be 4,486 kWh at Lakeside East and 3,375 kWh at Ridgecrest East. It is important to note that even though the energy consumption numbers showed little variation during the 2007 fiscal year, the higher population numbers within Ridgecrest East resulted in energy usage per student that was considerably less than those found at Lakeside East. Just as the 2007 electricity records were broken down for analysis, the 2008 electricity records were evaluated for individual usage levels.

126 Environmental Land Use Planning

0

50,000

**Step 1** Identify the population size of the dormitories

100,000

**Kwh**

150,000

Source: University of Alabama Department of Energy Management

**Figure 8.** Ecological footprint procedure for electricity

**Step 2** Obtain electricity records

**Figure 7.** 2008 Electricity Usage for Lakeside East and Ridgecrest East.

kilowatt hours: January was 39,628 kWh; followed by February with 62,320 kWh; March consumed 58,206 kWh; April used 65,469 kWh; May was 68,613 kWh; June was recorded at 59,222 kWh; July had 62,597 kWh; August consumed 72,264 kWh; September was recorded at 108,040 kWh; October used 81,022 kWh; November had 60,623 kWh of usage; and finally during December 57,632 kWh were utilized. Similar to the methodology utilized to calculate the ecological footprint concerning water resources, Figure 15 depicts the ecological

> **Step 3** Determine land area requirements for electrical resources

**Step 4** Estimate the Ecological Footprint for the average person

**Step 5** Multiply the per capita footprint by the total population on campus

Lakeside East Ridgecrest East

For a more in-depth analysis of electrical usage for the two dormitories, the amount of energy utilized by each dormitory student for the year was calculated as the total electricity consumption numbers were divided by the amount of students residing within each dormitory. This accounted for the amount of electricity utilized per student to be 4,486 kWh at Lakeside East and 3,375 kWh at Ridgecrest East. It is important to note that even though the energy consumption numbers showed little variation during the 2007 fiscal year, the higher population numbers within Ridgecrest East resulted in energy usage per student that

footprint procedure from which the electrical impact of students was derived.

**Months**

**2008 Electricity Usage for Lakeside East & Ridgecrest East**

> To acquire the electricity consumed per dormitory student during 2008, the electrical totals were divided by the amount of the respective residential populations. Thus, the average student consumed 3,343 kWh within Lakeside East and 3,177 kWh for Ridgecrest East. To relate student electricity consumption rates to a real-world example the 2007 and 2008 figures were broken into monthly averages. The 2007 monthly rates per dormitory student were calculated to be approximately 374 kWh for Lakeside East and approximately 281 kWh for Ridgecrest East. For 2008 the monthly averages were approximately 279 kWh for Lakeside East and approximately 265 kWh for Ridgecrest East. According to the Energy Information Administration (2007), the average Alabama household consumes 1,305 kWh per month.

> After the consumption levels were successfully calculated for electrical resources, the amount of land could be determined for the usage of electrical resources. To accommodate the carbon emissions from the utilization of electricity the rate of 169 m2 of forest for every 100 kWh of electricity was used for the following ecological footprint calculations (Anundson et al., 2001, p.11). Thus, the individual amount of electricity per dormitory student was first divided by 100 kWh and then multiplied by 169 m2. Accordingly during 2007 for Lakeside East, the amount of land needed per dormitory student was 7,581 m2 (0.758 hectares or 1.873 acres) and for Ridgecrest East 5,703 m2 (0.570 hectares or 1.409 acres). During 2008, the amount of forested land area necessary per student amounted to 5,650 m2 (0.565 hectares or 1.396 acres) for Lakeside East and 5,369 m2 (0.537 or 1.327 acres) for Ridgecrest East. In Table 7, meters squared were converted to hectares by dividing by 10,000. Additionally, hectares were converted by multiplying by 2.471.


**Table 2.** Ecological Footprint for Electricity 2007 and 2008

As a reminder, it is important to note that all the ecological footprint analysis that has been mentioned in this section pertains only to the electrical energy consumption as related to housing concerns. In reality electricity consumed for housing is only one area of a person's life where electricity is utilized. Therefore, the electrical usage and subsequent land area may in fact be larger than the estimates listed above. In general, ecological footprint calculations encompass a variety of goods and services associated with a person's lifestyle. This research looked specifically at water and energy usage of the footprint equation as related to housing needs.

Environmental Land Use and the Ecological Footprint of Higher Learning 129

Although much progress has been made in recent years there is more that The University of Alabama can do in support of sustainable practices, as exemplified by green universities across the country. The first step toward becoming a green campus merely entails setting the goal of wanting to be more sustainable. The President of University of Alabama's message to the student body during fall of 2008 was the initial step required to set the tone for the campus. Now that a goal has been set, a subsequent plan will need to be developed. Objectives will need to be established in order to facilitate progress toward

Before any other steps of the plan can be formulated lest carried out, it is essential to stop and take an inventory. The inventory determines where the campus is now so that progress may be more accurately measured. Thus, this research has served as a snapshot of where the campus currently is, during the academic semesters of fall of 2008 to early spring of 2009 in terms of sustainability. The snapshot is a useful tool as it was used to compare The University of Alabama to the top green schools. These prestigious universities were utilized in this analysis to serve as the pinnacle of where The University of Alabama may strive to be

Taking the other schools analyzed in this research into consideration, our first recommendation is to formulate an official environmental plan that involves a variety of stakeholders in the planning process. This initiative needs the involvement of students, faculty, staff, alumni, investors, and the community as a whole. During the planning process, objectives must be set that are measurable as well as quantifiable to the overall goal of the plan. If these objectives are to serve as serve as milestones towards the goal of sustainability. Ecological footprint calculations as used in this study will be beneficial for

Our second recommendation is to strive to establish a recognizable environmental office on campus supported by a full-time staff. This ensures availability of knowledgeable staff to assist with inquiries from environmentally-aware students and community members as well as to address sustainability issues in accordance with the campus's environmental plan. According to data gathered on sustainable universities by the Sustainable Endowments Institute (2009), a considerable number of schools have recognized the need for full-time campus sustainability administrators. Currently, 56 percent report having dedicated

We also recommend the incorporation of green building elements within residential student housing just. Generally speaking, universities are long-term owners of institutions. Hence, looking at the cost of operation over the period of a product's life cycle will help them accept some of the additional costs associated with green building methods. According to Moskow (2008), "Sustainable developments are more cost-effective in the long term and, therefore,

**5. Conclusion and policy implications** 

concerning environmental initiatives.

monitoring progress towards this goal.

sustainability staff.

the end goal.

In addition, each student's consumption of natural gas was calculated in the same way. Thereafter, each students total land area requirement at Lakeside East was calculated as follows: 0.179 acres for water resources in 2008, 1.873 acres for electricity in 2007, 1.396 acres for electricity in 2008, 0.170 acres for natural gas in 2007, and 0.177 acres for natural gas in 2008. Furthermore, Ridgecrest East's numbers were 0.110 acres for water in 2008, 1.409 acres for electricity in 2007, 1.327 acres for electricity in 2008, 0.142 acres for natural gas in 2007, and 0.140 acres for natural gas in 2008. Thus, if the entire student population that resides oncampus of approximately 7,000 individuals adopted the consumption habits of either Lakesides East or Ridgecrest East residents, then the land acreage as illustrated in Table 7 would have been needed.

When evaluating these figures it is important to understand that Lakeside East and Ridgecrest East are both relatively new buildings found on The University of Alabama's campus. As this study represents a sample of consumption levels taken from the new and therefore more efficiently constructed dormitories, the land requirement estimations for the students living on-campus are likely to be a best-case scenario. Overall, from the ecological footprint calculations utilized, Ridgecrest East displayed a lower environmental impact or land requirement than Lakeside East for water, electricity, and natural gas.

Additionally, land requirements decreased for electricity needs for both dormitories from 2007 to 2008. On the other hand, during the two year-span the land requirements for natural gas showed only a slight decrease for Ridgecrest East while Lakeside East showed an increase in demand. Acreage for water resources were not compared from 2007 to 2008 as the required data were unattainable.


**Table 3.** Ecological Footprint for the On-Campus Population

## **5. Conclusion and policy implications**

128 Environmental Land Use Planning

related to housing needs.

would have been needed.

the required data were unattainable.

**Ecological Footprint: Land Requirements in Acres** 

**Table 3.** Ecological Footprint for the On-Campus Population

As a reminder, it is important to note that all the ecological footprint analysis that has been mentioned in this section pertains only to the electrical energy consumption as related to housing concerns. In reality electricity consumed for housing is only one area of a person's life where electricity is utilized. Therefore, the electrical usage and subsequent land area may in fact be larger than the estimates listed above. In general, ecological footprint calculations encompass a variety of goods and services associated with a person's lifestyle. This research looked specifically at water and energy usage of the footprint equation as

In addition, each student's consumption of natural gas was calculated in the same way. Thereafter, each students total land area requirement at Lakeside East was calculated as follows: 0.179 acres for water resources in 2008, 1.873 acres for electricity in 2007, 1.396 acres for electricity in 2008, 0.170 acres for natural gas in 2007, and 0.177 acres for natural gas in 2008. Furthermore, Ridgecrest East's numbers were 0.110 acres for water in 2008, 1.409 acres for electricity in 2007, 1.327 acres for electricity in 2008, 0.142 acres for natural gas in 2007, and 0.140 acres for natural gas in 2008. Thus, if the entire student population that resides oncampus of approximately 7,000 individuals adopted the consumption habits of either Lakesides East or Ridgecrest East residents, then the land acreage as illustrated in Table 7

When evaluating these figures it is important to understand that Lakeside East and Ridgecrest East are both relatively new buildings found on The University of Alabama's campus. As this study represents a sample of consumption levels taken from the new and therefore more efficiently constructed dormitories, the land requirement estimations for the students living on-campus are likely to be a best-case scenario. Overall, from the ecological footprint calculations utilized, Ridgecrest East displayed a lower environmental impact or

Additionally, land requirements decreased for electricity needs for both dormitories from 2007 to 2008. On the other hand, during the two year-span the land requirements for natural gas showed only a slight decrease for Ridgecrest East while Lakeside East showed an increase in demand. Acreage for water resources were not compared from 2007 to 2008 as

**for the Dormitory Student Population Lakeside East Ridgecrest East**  From 2008 Water Consumed 1,253 770 From 2007 Electricity Consumed 13,111 9,863 From 2008 Electricity Consumed 9,772 9,289 From 2007 Natural Gas Consumed 1,190 994 From 2008 Natural Gas Consumed 1,239 980

land requirement than Lakeside East for water, electricity, and natural gas.

Although much progress has been made in recent years there is more that The University of Alabama can do in support of sustainable practices, as exemplified by green universities across the country. The first step toward becoming a green campus merely entails setting the goal of wanting to be more sustainable. The President of University of Alabama's message to the student body during fall of 2008 was the initial step required to set the tone for the campus. Now that a goal has been set, a subsequent plan will need to be developed. Objectives will need to be established in order to facilitate progress toward the end goal.

Before any other steps of the plan can be formulated lest carried out, it is essential to stop and take an inventory. The inventory determines where the campus is now so that progress may be more accurately measured. Thus, this research has served as a snapshot of where the campus currently is, during the academic semesters of fall of 2008 to early spring of 2009 in terms of sustainability. The snapshot is a useful tool as it was used to compare The University of Alabama to the top green schools. These prestigious universities were utilized in this analysis to serve as the pinnacle of where The University of Alabama may strive to be concerning environmental initiatives.

Taking the other schools analyzed in this research into consideration, our first recommendation is to formulate an official environmental plan that involves a variety of stakeholders in the planning process. This initiative needs the involvement of students, faculty, staff, alumni, investors, and the community as a whole. During the planning process, objectives must be set that are measurable as well as quantifiable to the overall goal of the plan. If these objectives are to serve as serve as milestones towards the goal of sustainability. Ecological footprint calculations as used in this study will be beneficial for monitoring progress towards this goal.

Our second recommendation is to strive to establish a recognizable environmental office on campus supported by a full-time staff. This ensures availability of knowledgeable staff to assist with inquiries from environmentally-aware students and community members as well as to address sustainability issues in accordance with the campus's environmental plan. According to data gathered on sustainable universities by the Sustainable Endowments Institute (2009), a considerable number of schools have recognized the need for full-time campus sustainability administrators. Currently, 56 percent report having dedicated sustainability staff.

We also recommend the incorporation of green building elements within residential student housing just. Generally speaking, universities are long-term owners of institutions. Hence, looking at the cost of operation over the period of a product's life cycle will help them accept some of the additional costs associated with green building methods. According to Moskow (2008), "Sustainable developments are more cost-effective in the long term and, therefore,

ultimately, more valuable" (p.xv). This is especially true as the price of resources such as electricity and natural gas continue to rise. Additionally, green buildings have been noted to promote a healthy, productive work environment that would benefit the welfare and academic status of The University of Alabama.

Environmental Land Use and the Ecological Footprint of Higher Learning 131

American College & University Presidents Climate Commitment. (2008). *About the American College & University Presidents Climate Commitment.*Retrieved March 10, 2009, from

Andreen, C. (2008). *UA Enrollment Reaches Record 27,052 Students; Freshman Class Tops* 

Anundson, B., Crooks, J., Fletcher, A., Frank, M., et al. (2001). *A Study of the Ecological* 

Barrella, N. (2008, November 20). *In competition, Harvard seeks to "green up" dorms*. Harvard

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Bates College. (2008). *Student Housing at 280 College Street.* Retrieved December 2, 2008, from

Bralley, B. (2008, September 10). *UA Construction Goes Green*. Crimson White. Retrieved

Bursch, K. (2009, January 7). *UA Starts New Green Campaign.*Crimson White. Retrieved January 15, 2009, from http://www.cw.ua.edu/ua\_starts\_new \_green\_campaign Emory University. (2008a). *Emory Sustainable Initiative: History.* Retrieved December 11, 2008,

Emory University. (2008b). *Emory Sustainable Initiative: Sustainable Food.* Retrieved December

Enck, J., & Turner, S. (2003). *ASHRAE Green Guide: An ASHRAE Publication Addressing Matters of Interest to Those Involved in Green or Sustainable Design of Buildings.* Atlanta:

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March 25, 2009, from http://www.epa.gov/safewater/wot/pdfs/

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book\_waterontap\_full.pdf

*5,000.*Retrieved February 26, 2009, from

http://www.bates.edu/x175547.xml

**6. References** 

Fortunately, The University of Alabama has already begun incorporating some green features in buildings such as low flow toilets, low flow faucets, low flow showerheads as well as plans for lighting controls and high efficiency hoods for new projects. Though those efforts are commendable, our recommendation is to use Bates College as an example to strive toward concerning green buildings. Due to cost restrictions, Bates College has not filed for the proper LEED certification for their structures. Despite not having filed, Bates College has used the LEED criteria as a standard in which to construct LEED equivalent buildings. Furthermore, green is marketable and green building designs are a good way to promote The University of Alabama's image.

Our final recommendation is education. Additional educational opportunities may in fact reduce the environmental impact of the University. Due to the fact that the role of academic institutions is to educate and facilitate in the development of tomorrow's leaders, this is a prime environment within which to integrate green technologies. Leaders that are unable to recognize the mismanagement of resources will be incapable of solving environmental problems. If environmentally friendly strategies are to be incorporated into future policies, then exposure to sustainable education is essential.

An expansion of research concerning ecological footprint analysis would be beneficial in an effort to determine the environmental impact of the campus. Though food and recycling strategies were only briefly discussed in this study, a more in-depth analysis may be needed to evaluate whether or not the University should try to promote locally or organically supplied food in the cafeterias and whether or not to participate in the *RecycleMania*  competition. Additionally as only dormitory students were analyzed in this study, more sample groups could be evaluated and include both on-campus and off-campus students. Studies on climate change, transportation issues, student led initiatives, and a plethora of other opportunities exist for exploration.

In conclusion, we wish to emphasize that if places of higher learning are able to lessen their ecological footprints, they would ultimately have a greater positive impact on humanity and the dwindling resources of the World.

## **Author details**

Seth Appiah-Opoku *Geography Department, University of Alabama, Tuscaloosa, USA* 

Crystal Taylor *Florida State University, USA* 

#### **6. References**

130 Environmental Land Use Planning

academic status of The University of Alabama.

promote The University of Alabama's image.

then exposure to sustainable education is essential.

other opportunities exist for exploration.

the dwindling resources of the World.

*Geography Department, University of Alabama, Tuscaloosa, USA* 

**Author details** 

Seth Appiah-Opoku

*Florida State University, USA* 

Crystal Taylor

ultimately, more valuable" (p.xv). This is especially true as the price of resources such as electricity and natural gas continue to rise. Additionally, green buildings have been noted to promote a healthy, productive work environment that would benefit the welfare and

Fortunately, The University of Alabama has already begun incorporating some green features in buildings such as low flow toilets, low flow faucets, low flow showerheads as well as plans for lighting controls and high efficiency hoods for new projects. Though those efforts are commendable, our recommendation is to use Bates College as an example to strive toward concerning green buildings. Due to cost restrictions, Bates College has not filed for the proper LEED certification for their structures. Despite not having filed, Bates College has used the LEED criteria as a standard in which to construct LEED equivalent buildings. Furthermore, green is marketable and green building designs are a good way to

Our final recommendation is education. Additional educational opportunities may in fact reduce the environmental impact of the University. Due to the fact that the role of academic institutions is to educate and facilitate in the development of tomorrow's leaders, this is a prime environment within which to integrate green technologies. Leaders that are unable to recognize the mismanagement of resources will be incapable of solving environmental problems. If environmentally friendly strategies are to be incorporated into future policies,

An expansion of research concerning ecological footprint analysis would be beneficial in an effort to determine the environmental impact of the campus. Though food and recycling strategies were only briefly discussed in this study, a more in-depth analysis may be needed to evaluate whether or not the University should try to promote locally or organically supplied food in the cafeterias and whether or not to participate in the *RecycleMania*  competition. Additionally as only dormitory students were analyzed in this study, more sample groups could be evaluated and include both on-campus and off-campus students. Studies on climate change, transportation issues, student led initiatives, and a plethora of

In conclusion, we wish to emphasize that if places of higher learning are able to lessen their ecological footprints, they would ultimately have a greater positive impact on humanity and


http://uanews.ua.edu/anews 2008/sep08/enrollment091608.htm


http://www.eia.doe.gov/cneaf/electricity/esr/table5.html

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http://www.princetonreview.com/green-honor-roll.aspx?uidbadge=%07

RecycleMania (2009). *General Overview.* Retrieved January 05, 2009, from

*Specification*.(2nd ed.). New Jersey: John Wiley & Sons, Inc.

http://news.nationalgeographic.com/news/2002/01/0110\_020110worldwatch.html

http://www.emory.edu/EMORY\_MAGAZINE/2007/autumn/halls.html

University of Alabama.

York: McGraw-Hill

Sage Publications.

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2009/categories/administration.

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Retrieved October 11, 2008, from

*Earth.*Gabriola Island: New Society Publishers.

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Retrieved December 3, 2008, from

*Geographic*. Retrieved on October 11, 2008, from

http://www.epa.gov/osw/conserve/rrr/reduce.htm


http://www.footprintnetwork.org/gfn\_sub.php?content=national\_footprints


http://www.emory.edu/home/news/releases/2008/09/green-dorms-open.html


http://news.nationalgeographic.com/news/2005/03/0331\_050330\_unenvironment.html


Jorgenson, A., & Burns, T. (2006). The political-economic causes of change in the ecological footprints of nations, 1991-2001: A quantitative investigation. *Social Science Research*. Retrieved October 11, 2008, from Science Direct database.


http://www.emory.edu/EMORY\_MAGAZINE/2007/autumn/halls.html

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Inc.

ScienceDirect database.

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Retrieved December 3, 2008, from

London: Allen & Unwin.

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http://www.epa.gov/osw/conserve/rrr/reduce.htm

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Fiala, N. (2008). Measuring sustainability: Why the ecological footprint is bad economic and bad environmental science. *Ecological Economics*. Retrieved October 11, 2008, from

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"Global Warming". (n.d.).*New York Times*. Retrieved October 11, 2008, from http://topics.nytimes.com/top/news/science/topics/globalwarming/index.html "Graduate Green Living Program Enters Its Second Year". (2007, Spring). *Harvard Green* 

 http://www.greencampus.harvard.edu/newsletter/archives/2007/05/graduate\_green.php Gray, K. (2008, September 19). *Emory Freshman Live 'Green' in New Housing.* [News Release].

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Jorgenson, A., & Burns, T. (2006). The political-economic causes of change in the ecological footprints of nations, 1991-2001: A quantitative investigation. *Social Science Research*.

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Kobet, B., Lee, S., & Mondor, C. (1999). *Green Buildings: Guidelines for Creating High-Performance Green Buildings*. Pennsylvania Department of Environmental

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Wymer, M. (2008) *UA Experts Offer 'Going Green' Advice.* Retrieved February 16, 2009, from http://uanews.edu/anews2008/apr08/earthday040808.htm

**Chapter 7** 

© 2012 Carneiro and Miguez, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**A Flood Control Approach Integrated** 

**with a Sustainable Land Use Planning** 

Paulo Roberto Ferreira Carneiro and Marcelo Gomes Miguez

The Brazilian National Water Resources Policy, instituted by Law no. 9.433 in 1997, is based on six fundamental principles that structure the whole National Water Resource Management System: 1) water is a commodity in the public domain; 2) water is a limited natural resource, endowed with economic value; 3) in situations of scarcity, the priority water resources use is for human consumption and watering animals; 4) the management of water resources must always provide multiple water uses; 5) the hydrographical basin is the territorial unit for the implementation of the National Water Resources Policy and the activities of the entities belonging to the of National Water Resources Management System; 6) the water resource management must be decentralized and have the participation of

This Law and its regulatory texts incorporate municipalities, along with users and civil organisations, into the management system, ensuring a greater balance of power on water resource committees and boards. However, no legal text has clearly defined the relation between water management, which is a state or federal attribution, and land use planning, which is responsibility of the municipalities. In this sense, there remains a lack of definition regarding the fundamental role of municipal administrations as formulators and implementers of urban policies with impacts on water resources, whether through direct

Besides the gap pointed out above, the occurrence of conflicts of competency is also observed in the hydrographical basins related to metropolitan areas, given that the 1988 Brazilian Constitution did not establish clear management rules for these territories. The definition of the needed and related administrative organisation for the metropolitan

and reproduction in any medium, provided the original work is properly cited.

**in Metropolitan Regions** 

Additional information is available at the end of the chapter

public authorities, water users, civil society and communities.

investment, or by means of actions of regulatory nature.

http://dx.doi.org/10.5772/50573

**1. Introduction** 

## **Chapter 7**

## **A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions**

Paulo Roberto Ferreira Carneiro and Marcelo Gomes Miguez

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50573

## **1. Introduction**

134 Environmental Land Use Planning

Wymer, M. (2008) *UA Experts Offer 'Going Green' Advice.* Retrieved February 16, 2009, from

http://uanews.edu/anews2008/apr08/earthday040808.htm

The Brazilian National Water Resources Policy, instituted by Law no. 9.433 in 1997, is based on six fundamental principles that structure the whole National Water Resource Management System: 1) water is a commodity in the public domain; 2) water is a limited natural resource, endowed with economic value; 3) in situations of scarcity, the priority water resources use is for human consumption and watering animals; 4) the management of water resources must always provide multiple water uses; 5) the hydrographical basin is the territorial unit for the implementation of the National Water Resources Policy and the activities of the entities belonging to the of National Water Resources Management System; 6) the water resource management must be decentralized and have the participation of public authorities, water users, civil society and communities.

This Law and its regulatory texts incorporate municipalities, along with users and civil organisations, into the management system, ensuring a greater balance of power on water resource committees and boards. However, no legal text has clearly defined the relation between water management, which is a state or federal attribution, and land use planning, which is responsibility of the municipalities. In this sense, there remains a lack of definition regarding the fundamental role of municipal administrations as formulators and implementers of urban policies with impacts on water resources, whether through direct investment, or by means of actions of regulatory nature.

Besides the gap pointed out above, the occurrence of conflicts of competency is also observed in the hydrographical basins related to metropolitan areas, given that the 1988 Brazilian Constitution did not establish clear management rules for these territories. The definition of the needed and related administrative organisation for the metropolitan

© 2012 Carneiro and Miguez, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

areas is left to the federative states. On the other hand, overlaps is observed in the attributions of the local, state, or even federal administrations, and various undefined roles are identified, which make the task of coordination and sharing of the responsibilities even more complex.

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 137

Although local administrations are closer to local populations, their politico-administrative role does not allow a systemic vision of the territory in which they lie. More effective participation of local governments in water management is hindered, or even made unviable, also by the absence of clear definitions about its nature and functions, and by the fact that the majority of municipalities have limited budgetary autonomy, bearing in mind that they depend heavily on fund transfers from the other levels of government

Regarding the financial restrictions [2], it is alarming that most of the multilateral financial agencies, except the Global Environment Facility – GEF, still have not included, in their agenda, projects of integrated natural resources management articulated to land use planning, particularly in urban areas. There are few planning experiments implemented articulating water conservation and/or preservation measures and land use regulation,

Another aspect is that the sectoral nature of local government interests makes them act more as users than as "impartial" managers of water resources [3]. The debility and lack of institutional hierarchy of local governments confronted by actors wielding greater power would lead to greater vulnerability and to the possibility of capture and politicisation in water management [3]. These aspects are aggravated in metropolitan areas, where municipal administrations often express antagonistic interests and priorities among themselves, creating atmospheres of dissension with little space for

Although there are restrictions on the participation of municipalities as direct managers of water resources, there is no doubt related to the importance of local governments in territorial planning, as well as in its consequences to water resources conservation. It is the attribution of municipalities to devise, approve and inspect instruments related with territorial order, such as master plans, zonings, development of housing programs, delimitation of industrial, urban and environmental preservation areas, among other activities with impacts on water resources, mainly in the case of predominantly urban

These attributions have recently been strengthened upon approval of the Brazilian Statute of the City. This is a Federal Act, established in 2001, which proposes standards of public and social interest to govern the use of the urban property in favour of the collectivity safety and welfare, as well as the environmental balance. The urban policy established aims to organise the fulfilment of the social functions of the city and of the urban property by the application of a set of general guidelines, from which the following topics are

 the guarantee of the right to sustainable cities, meaning the right to urban land, housing, environmental sanitation, urban infrastructure, transport and public services,

work and leisure for present and future generations;

administration.

cooperation.

hydrographical basins.

detached:

despite the dysfunctions of urban growth.

Based on these elements, and departing from Brazilian reality, the proposed chapter deals with the need of integration of land use planning with water resource management, seeking to establish relations between the types of land use, urban settlements and the problems involving urban flooding.

A case study was developed for the Iguaçu-Sarapuí River Basin, located in the western portion of the Guanabara Bay Basin, which lies at the Rio de Janeiro State Metropolitan Region, in Brazil, and is one of the most critical areas in the state in relation to urban flooding. In this region, urban expansion dynamics is, in general, marked by irregular occupation of risk areas, without the appropriated infrastructure in terms of land tenure.

The significant investments in infrastructure in progress in the region, mainly the construction of the Metropolitan Ring Road1 will bring substantial transformation to the region current urban configuration. The scenarios built with the aid of mathematical modelling demonstrate that the disorderly urban expansion, induced by the accessibility to the rural areas in the interior of the region, may be degrading for the medium and long term urban flooding control in this basin.

## **2. The role of the municipality in water resource management in Brazil**

The competence of municipalities in federated countries is concentrated on functions that, in general, are related with the allocation or rendering of local public services and with the functions of planning, incentive and inspection of the territorial order, environmental protection and also with some level of regulation of economic activities [1]. In the case of Brazil, recently, municipalities with greater capacity of investment have begun to incorporate functions related with the provision of more comprehensive social services, which, traditionally, were restricted to the state and federal spheres.

In the specific case of water resource management, however, municipal participation in basin committees has been the main form, if not the sole, of interaction with other public and private actors related with water. Many factors hinder the municipality action in the water management sphere, the main one being the legal impossibility, by Constitutional definition, of the municipalities directly managing water resources, even in the case of basins entirely contained by their territories. The exceptions may be associated to the transfer of some specific attributions through cooperation agreements with the states or the Federal Government.

<sup>1</sup> The Metropolitan Ring Road is a Federal Government work, whose estimated cost is approximately US\$ 1.6 billion. It will have an intersection with five federal highways, a railroad and a link with various large scale industrial poles being set up in the Rio de Janeiro Metropolitan Region.

Although local administrations are closer to local populations, their politico-administrative role does not allow a systemic vision of the territory in which they lie. More effective participation of local governments in water management is hindered, or even made unviable, also by the absence of clear definitions about its nature and functions, and by the fact that the majority of municipalities have limited budgetary autonomy, bearing in mind that they depend heavily on fund transfers from the other levels of government administration.

136 Environmental Land Use Planning

involving urban flooding.

responsibilities even more complex.

urban flooding control in this basin.

Federal Government.

being set up in the Rio de Janeiro Metropolitan Region.

areas is left to the federative states. On the other hand, overlaps is observed in the attributions of the local, state, or even federal administrations, and various undefined roles are identified, which make the task of coordination and sharing of the

Based on these elements, and departing from Brazilian reality, the proposed chapter deals with the need of integration of land use planning with water resource management, seeking to establish relations between the types of land use, urban settlements and the problems

A case study was developed for the Iguaçu-Sarapuí River Basin, located in the western portion of the Guanabara Bay Basin, which lies at the Rio de Janeiro State Metropolitan Region, in Brazil, and is one of the most critical areas in the state in relation to urban flooding. In this region, urban expansion dynamics is, in general, marked by irregular occupation of risk areas, without the appropriated infrastructure in terms of land tenure.

The significant investments in infrastructure in progress in the region, mainly the construction of the Metropolitan Ring Road1 will bring substantial transformation to the region current urban configuration. The scenarios built with the aid of mathematical modelling demonstrate that the disorderly urban expansion, induced by the accessibility to the rural areas in the interior of the region, may be degrading for the medium and long term

**2. The role of the municipality in water resource management in Brazil** 

which, traditionally, were restricted to the state and federal spheres.

The competence of municipalities in federated countries is concentrated on functions that, in general, are related with the allocation or rendering of local public services and with the functions of planning, incentive and inspection of the territorial order, environmental protection and also with some level of regulation of economic activities [1]. In the case of Brazil, recently, municipalities with greater capacity of investment have begun to incorporate functions related with the provision of more comprehensive social services,

In the specific case of water resource management, however, municipal participation in basin committees has been the main form, if not the sole, of interaction with other public and private actors related with water. Many factors hinder the municipality action in the water management sphere, the main one being the legal impossibility, by Constitutional definition, of the municipalities directly managing water resources, even in the case of basins entirely contained by their territories. The exceptions may be associated to the transfer of some specific attributions through cooperation agreements with the states or the

1 The Metropolitan Ring Road is a Federal Government work, whose estimated cost is approximately US\$ 1.6 billion. It will have an intersection with five federal highways, a railroad and a link with various large scale industrial poles Regarding the financial restrictions [2], it is alarming that most of the multilateral financial agencies, except the Global Environment Facility – GEF, still have not included, in their agenda, projects of integrated natural resources management articulated to land use planning, particularly in urban areas. There are few planning experiments implemented articulating water conservation and/or preservation measures and land use regulation, despite the dysfunctions of urban growth.

Another aspect is that the sectoral nature of local government interests makes them act more as users than as "impartial" managers of water resources [3]. The debility and lack of institutional hierarchy of local governments confronted by actors wielding greater power would lead to greater vulnerability and to the possibility of capture and politicisation in water management [3]. These aspects are aggravated in metropolitan areas, where municipal administrations often express antagonistic interests and priorities among themselves, creating atmospheres of dissension with little space for cooperation.

Although there are restrictions on the participation of municipalities as direct managers of water resources, there is no doubt related to the importance of local governments in territorial planning, as well as in its consequences to water resources conservation. It is the attribution of municipalities to devise, approve and inspect instruments related with territorial order, such as master plans, zonings, development of housing programs, delimitation of industrial, urban and environmental preservation areas, among other activities with impacts on water resources, mainly in the case of predominantly urban hydrographical basins.

These attributions have recently been strengthened upon approval of the Brazilian Statute of the City. This is a Federal Act, established in 2001, which proposes standards of public and social interest to govern the use of the urban property in favour of the collectivity safety and welfare, as well as the environmental balance. The urban policy established aims to organise the fulfilment of the social functions of the city and of the urban property by the application of a set of general guidelines, from which the following topics are detached:

 the guarantee of the right to sustainable cities, meaning the right to urban land, housing, environmental sanitation, urban infrastructure, transport and public services, work and leisure for present and future generations;

 the democratic management through people's participation representing segments of the community in the formulation, implementation and monitoring of plans, programs and projects for urban development;

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 139

several areas present land use patterns that do not ensure minimal standards of living,

consequently, several serious flooding problems occur in the watershed plain

water sources found in the basin area are used for complementing the Metropolitan

Tinguá Biological Reserve, the main remnant of the Atlantic Forest in Rio de Janeiro

 organised social movements, congregating federations of residents associations and entities involved in matters of environment, sanitation, housing, among others, are present in the basin, what demonstrates the great organisation capacity of its population vis-à-vis the questions related to citizenship and quality of

local administrations are becoming more committed to efficiency in public affairs,

the presence of major private and public investments in infrastructure will lead to

The Iguaçu-Sarapuí River basin is situated in Baixada Fluminense lowlands. Its drainage area covers around 727 km2, all of which is situated in the Rio de Janeiro Metropolitan Region. Iguaçu River springs in Serra do Tinguá massif, at an altitude of 1,600m. Its course runs southeast for approximately 43 km, until it reaches the outfall at Guanabara Bay. Its main tributaries from the left margins are Tinguá, Pati and Capivari Rivers, and, from the

The physiography of Iguaçu-Sarapuí river basin is characterized by two main elements: the Serra do Mar Mountains and Baixada Fluminense lowlands, with a marked difference in altitude. The climate in the basin is hot and humid with a rainy season in the summer, the average annual precipitation being around 1,700mm, and the mean annual temperature approximately 22o C. The rivers run down the mountains in torrents with great erosive force, losing speed after reaching the plains, often overflowing their banks

The basin fully encompasses the municipalities of Belford Roxo and Mesquita, also hosting part of the municipalities of Rio de Janeiro (covering the neighbourhoods of Bangu, Padre Miguel and Senador Câmara), Nilópolis, São João de Meriti, Nova Iguaçu and Duque de Caxias (Figure 1). According to the 2010 Brazilian census, the population of these municipalities reached 9,225,557 habitants (Table 1). However, just two of these

significant transformations in the present urban configuration of the region.

**3.1. Physical and socio-economic characteristics of the basin** 

there rural areas in a process of urban development

especially those of poor drainage;

Region drinking water supply;

State, is situated in this territory;

although in a still timid process;

right margins, Botas and Sarapuí Rivers.

municipalities are totally inserted in the basin.

into large wetlands.

areas;

life;

the basin also contains rural areas still protected from urbanisation;


Several important urban management tools were made available in the context of the Statute of the City and the Urban Master Plan is considered to be the basic instrument for the urban developing policy.

The possibility of achieving a sustainable water resource management must necessarily pass through a clear articulation with land use plans. What is observed in Brazil, however, is the disarticulation between instruments of water resource management and land use planning, reflecting, perhaps, the lack of legitimacy of planning and urban legislation in Brazilian cities, marked by a high degree of informality, and even illegality, in land use occupation. According to Tucci [4], the greatest difficulty for the implementation of integrated planning arises from the limited institutional capacity of municipalities in facing complex interdisciplinary problems, and in the sectoral ways in which local administrations are organized.

Here, however, it is worth stressing the differences among municipalities: while in large cities, mainly metropolitan cores, it is possible to find efficient administrations, with good capacity to access information and with relatively modern legislation, in other minor cities, like peripheral municipalities in metropolitan areas, a total obsolescence in the legislation is verified. This is aggravated by the absence of reliable general data and information about the processes of urban structuring and also by the small number and low qualification of the technical staff [5].

This inequality in the municipal scale presents a great obstacle for a greater effectiveness of water resource management structures and for the cooperation among the different hierarchical levels of government.

## **3. Flood control in the Baixada Fluminense lowland**

Baixada Fluminense lowland is located in the western portion of the Guanabara Bay basin, in one of the most critical regions of Rio de Janeiro State, in terms of urban flooding. It is particularly interesting as an empirical study, considering the following aspects:


there rural areas in a process of urban development

138 Environmental Land Use Planning

developing policy.

organized.

technical staff [5].

hierarchical levels of government.

and projects for urban development;

and its negative effects on the environment;

services to serve the interests and needs of the population;

besides cultural, historical, artistic and landscape heritages.

 the democratic management through people's participation representing segments of the community in the formulation, implementation and monitoring of plans, programs

the planning of city development to prevent and correct the distortions of urban growth

the supply of urban infrastructure and community equipments, transport and public

the protection, preservation and restoration of the natural and built environment,

Several important urban management tools were made available in the context of the Statute of the City and the Urban Master Plan is considered to be the basic instrument for the urban

The possibility of achieving a sustainable water resource management must necessarily pass through a clear articulation with land use plans. What is observed in Brazil, however, is the disarticulation between instruments of water resource management and land use planning, reflecting, perhaps, the lack of legitimacy of planning and urban legislation in Brazilian cities, marked by a high degree of informality, and even illegality, in land use occupation. According to Tucci [4], the greatest difficulty for the implementation of integrated planning arises from the limited institutional capacity of municipalities in facing complex interdisciplinary problems, and in the sectoral ways in which local administrations are

Here, however, it is worth stressing the differences among municipalities: while in large cities, mainly metropolitan cores, it is possible to find efficient administrations, with good capacity to access information and with relatively modern legislation, in other minor cities, like peripheral municipalities in metropolitan areas, a total obsolescence in the legislation is verified. This is aggravated by the absence of reliable general data and information about the processes of urban structuring and also by the small number and low qualification of the

This inequality in the municipal scale presents a great obstacle for a greater effectiveness of water resource management structures and for the cooperation among the different

Baixada Fluminense lowland is located in the western portion of the Guanabara Bay basin, in one of the most critical regions of Rio de Janeiro State, in terms of urban flooding. It is

particularly interesting as an empirical study, considering the following aspects:

**3. Flood control in the Baixada Fluminense lowland** 

there are areas with consolidated urban and industrial growth;

its location is in the metropolitan periphery;


## **3.1. Physical and socio-economic characteristics of the basin**

The Iguaçu-Sarapuí River basin is situated in Baixada Fluminense lowlands. Its drainage area covers around 727 km2, all of which is situated in the Rio de Janeiro Metropolitan Region. Iguaçu River springs in Serra do Tinguá massif, at an altitude of 1,600m. Its course runs southeast for approximately 43 km, until it reaches the outfall at Guanabara Bay. Its main tributaries from the left margins are Tinguá, Pati and Capivari Rivers, and, from the right margins, Botas and Sarapuí Rivers.

The physiography of Iguaçu-Sarapuí river basin is characterized by two main elements: the Serra do Mar Mountains and Baixada Fluminense lowlands, with a marked difference in altitude. The climate in the basin is hot and humid with a rainy season in the summer, the average annual precipitation being around 1,700mm, and the mean annual temperature approximately 22o C. The rivers run down the mountains in torrents with great erosive force, losing speed after reaching the plains, often overflowing their banks into large wetlands.

The basin fully encompasses the municipalities of Belford Roxo and Mesquita, also hosting part of the municipalities of Rio de Janeiro (covering the neighbourhoods of Bangu, Padre Miguel and Senador Câmara), Nilópolis, São João de Meriti, Nova Iguaçu and Duque de Caxias (Figure 1). According to the 2010 Brazilian census, the population of these municipalities reached 9,225,557 habitants (Table 1). However, just two of these municipalities are totally inserted in the basin.


A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 141

It is in the lower parts of the basin, with elevations near the medium sea level, where it is concentrated mostly of the urban area, with something about 1.5 million people living there. Calculations from IBGE, the Brazilian Institute of Geography and Statistics, show that the incidence of poverty in these municipalities is quite significant, especially in Belford Roxo, Nova Iguaçu and Duque de Caxias, affecting more than half of their populations (Table 2).

> **Municipality %** Belford Roxo 60,06 Duque de Caxias 53,53 Mesquita - Nilópolis 32,48 Nova Iguaçu 54,15 Rio de Janeiro 23,85 São João de Meriti 47,00

Source: IBGE, Demographic Census of 2000 and Household Budget Survey - POF 2002/2003.

promote social and economic development for the region.

migration flows and housing 195,000 migrants, i.e. 62% of the total [6].

Fluminense Lowlands

**Table 2.** Poverty and inequality map – Brazilian Municipalities, 2003- Poverty incidence in Baixada

The structural analysis of per capita income and the capability to finance investments by municipalities in the region, according to the Observatory of the Metropolis [6], demonstrate the strong differences between the municipalities belonging to the Metropolitan Region of Rio de Janeiro. Such differences constitute obstacles to cooperation in solving common problems. Moreover, the fragile financial structure, coupled with the shortage of technical capacity, particularly in the areas of planning and budget, strengthen the uncertainty, discouraging long-term partnerships in infrastructure projects that could be used to

After a century of intense population growth, Brazil has entered the new millennium with quite modest rates of population growth. As shown by the data of the last Census, the Brazilian population grew at an average rate of 1.6% per year in the 1990s, following a decline trend after the strong growth happened from the 1950 to 1970. Projections developed recently estimated that the Brazilian population is growing at rates below 1.3% per year.

The city of Rio de Janeiro has been the centre of services for the Metropolitan region, although this characteristic has not reflected in a high degree of attractiveness for population in recent times. The region remained with the lowest population growth rate among large Brazilian cities. It should be noted, however, that in absolute terms, there was a warming of migration in the last decade towards Rio de Janeiro. Between 1980 and 1991 the total number of migrants towards the metropolitan area of Rio de Janeiro was around 570,000 people, while between 1995 and 2000 (just in five years) the total migration reached 330,000 people. The capital of the state remained the main pole centre, receiving these

Source: (1) demographic census of 2010, with the territorial division of 2001, (2) Adapted from the Iguaçu Project; (\*) percentage of the municipal area in relation to basin area.

**Figure 1.** Iguaçu-Sarapuí River Basin

It is in the lower parts of the basin, with elevations near the medium sea level, where it is concentrated mostly of the urban area, with something about 1.5 million people living there. Calculations from IBGE, the Brazilian Institute of Geography and Statistics, show that the incidence of poverty in these municipalities is quite significant, especially in Belford Roxo, Nova Iguaçu and Duque de Caxias, affecting more than half of their populations (Table 2).


Source: IBGE, Demographic Census of 2000 and Household Budget Survey - POF 2002/2003.

140 Environmental Land Use Planning

**Municipal Population Total** 

Belford Roxo 469.332 - 469.332 7.350 7.350 10

Caxias 852.138 2.910 855.048 46.570 27.359 38

**Total 9.213.953 8.694 9.225.557 242.410 72.705 100** 

Source: (1) demographic census of 2010, with the territorial division of 2001, (2) Adapted from the Iguaçu Project; (\*)

**Table 1.** Municipal population, total municipal area, and insertion in Iguaçu-Sarapuí River Basin

Nilópolis 157.425 - 157.425 1.920 1.042 1 Mesquita 168.376 - 168.376 3.477 3.477 5 Nova Iguaçu 787.563 8.694 796.257 53.183 27.894 38 Rio de Janeiro 6.320.446 - 6.320.446 126.420 3.290 5

**Area1) (ha)** 

**% (\*) Urban Rural Total** 

458.673 - 458.673 3.490 2.293 3

**Area inside the basin(2) (ha)** 

**City** 

Duque de

São João de Meriti

percentage of the municipal area in relation to basin area.

**Figure 1.** Iguaçu-Sarapuí River Basin

**Table 2.** Poverty and inequality map – Brazilian Municipalities, 2003- Poverty incidence in Baixada Fluminense Lowlands

The structural analysis of per capita income and the capability to finance investments by municipalities in the region, according to the Observatory of the Metropolis [6], demonstrate the strong differences between the municipalities belonging to the Metropolitan Region of Rio de Janeiro. Such differences constitute obstacles to cooperation in solving common problems. Moreover, the fragile financial structure, coupled with the shortage of technical capacity, particularly in the areas of planning and budget, strengthen the uncertainty, discouraging long-term partnerships in infrastructure projects that could be used to promote social and economic development for the region.

After a century of intense population growth, Brazil has entered the new millennium with quite modest rates of population growth. As shown by the data of the last Census, the Brazilian population grew at an average rate of 1.6% per year in the 1990s, following a decline trend after the strong growth happened from the 1950 to 1970. Projections developed recently estimated that the Brazilian population is growing at rates below 1.3% per year.

The city of Rio de Janeiro has been the centre of services for the Metropolitan region, although this characteristic has not reflected in a high degree of attractiveness for population in recent times. The region remained with the lowest population growth rate among large Brazilian cities. It should be noted, however, that in absolute terms, there was a warming of migration in the last decade towards Rio de Janeiro. Between 1980 and 1991 the total number of migrants towards the metropolitan area of Rio de Janeiro was around 570,000 people, while between 1995 and 2000 (just in five years) the total migration reached 330,000 people. The capital of the state remained the main pole centre, receiving these migration flows and housing 195,000 migrants, i.e. 62% of the total [6].

According to Britto and Bessa [7], historical investments were made in the region by different state governors, like the one of the 1980s, with an amount up to R\$ 3 billion, without, however, effectively guaranteeing universal access to environmental sanitation, housing and a better quality of life. Explanations for this are related with: (i) the lack of a profound diagnosis of the dimension of the problem in the region to correctly orient the profile of the interventions; (ii) the discontinuity and non-integration among the programs and projects implemented throughout these years; (iii) the political disputes in the region often decharacterised the projects, again lacking continuity; (iv) the fragility of social control in the process, once the format of the implemented programs have not provided an effective participation of the population (although this component existed in various of these projects); (v) the lack of institutional capacity, allied to the centralizing culture of the state governors in relation to sanitation management; (vi) the strong clientelistic culture in the municipal administrations; (vii) the growing demobilisation of organized social movements, which need members qualification for following up the policies implementation.

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 143

process and considering the construction of Metropolitan Ring Road, which is being taken as an urban development inductor factor. Another objective of the modelling consisted of evaluating the impact of an average rise in mean sea level, regarding the drainage system conditions, according to forecasts made by the Intergovernmental Panel on Climate Change (IPCC) [8]. In both situations, which may critically combine effects, planning actions are required in order to control future negative effects, otherwise the human and material losses

In order to proceed with the proposed analysis, it was necessary to choose a mathematical model to support the simulations. With this aim, a hydrodynamic model for representing

The construction of MODCEL, based on the concept of flow cells [12] intended to provide an alternative tool for integrated flood solution design and research. MODCEL is a model that integrates a hydrologic model, applied to each cell in the modelled area, with a hydrodynamic looped model, in a spatial representation that links surface flow, channel flow and underground pipe flow, This arrangement can be interpreted as a hydrologichydrodynamic pseudo 3D-model, although all mathematical relations written are onedimensional. Pseudo 3D representation may be materialised by a vertical hydraulic link used to communicate two different layers of flow: a superficial one, corresponding to free surface channels and flooded areas; and a subterranean one, related to free surface or

The representation of urban surfaces by cells, acting as homogeneous compartments, in which rainfall run-off transformations are performed, allows the integration of all the basin area. The cells interact through hydraulic laws, represented by cell links capable to model different possible flow patterns. Different types of cells and links give versatility to the model. The cells, considered individually as units or taken in pre-arranged sets, are capable to represent the watershed landscape, composing more complex structures. Therefore, the task related to the topographic and hydraulic modelling is an important phase of the process. In large floodable areas, when leaving the drainage network, the water can follow any path, dictated by the topography and by the urban built patterns. Marginal sidewalks may become weirs for the spilling waters from the rivers, the streets may act as canals and the buildings, parks or squares may act like reservoirs. In this situation, it is perceived that overflowed waters may have an independent behaviour from the drainage network, generating their own flow patterns. These characteristics are adequately represented in

The modelled area of Iguaçu-Sarapuí River basin extended from Guanabara Bay to Botas River confluence. The upstream reaches of the basin, which were not divided in cells, had

their flows determined through a hydrological model called HIDRO-FLU [13].

could become irremediable.

*3.2.1. Brief Description of MODCEL* 

surcharged flow in storm drains.

MODCEL.

rural and urban floods – MODCEL [9, 10 e 11] was used.

## **3.2. Flood control in the Baixada Fluminense lowlands**

Floods in the Iguaçu-Sarapuí River Basin are aggravated basically by the f inadequate land use occupation, in the particular conditions of the lowlands of Baixada Fluminense. In this process, the most important factors are: lack of adequate urban infrastructure; deficiency of the sewage services and solid waste collection; uncontrolled exploitation of mineral deposits, mainly sand for construction purposes; disorderly, illegal occupation of river banks and floodplains; lack of adequate treatment for public roadways pavements; obstruction or strangulation of drainage due to structures built without the proper concerns (railway and road bridges, and water pipelines interferences), as well as walls and even buildings that partially obstruct river channels. At the heart of these problems one always finds either inadequate legislation regarding land use, or, in the great majority of cases, noncompliance with the existing legislation.

It is estimated that floods in the basin directly affect 189,000 people. However, the damage caused and the total number of people indirectly affected by floods are both difficult to estimate. Included in this latter category there are, for example, employees who cannot reach their workplaces and the interruption of traffic and commerce along the flooded roadways or nearby areas that become inaccessible.

In this context, in order to properly discuss the adequate possible planning actions for mitigating these problems, and to figure out the cause-effect process related to future scenarios, a mathematical model will be applied as an aiding tool. The case study alternatives are then introduced in order to allow the development of the discussions in practical terms, using examples of what may happen in the future without the proper concerns. The aim of hydrodynamic modelling was to evaluate the possible impacts of the expansion of urbanisation towards the interior of the basin without the adequate planning process and considering the construction of Metropolitan Ring Road, which is being taken as an urban development inductor factor. Another objective of the modelling consisted of evaluating the impact of an average rise in mean sea level, regarding the drainage system conditions, according to forecasts made by the Intergovernmental Panel on Climate Change (IPCC) [8]. In both situations, which may critically combine effects, planning actions are required in order to control future negative effects, otherwise the human and material losses could become irremediable.

## *3.2.1. Brief Description of MODCEL*

142 Environmental Land Use Planning

policies implementation.

compliance with the existing legislation.

roadways or nearby areas that become inaccessible.

**3.2. Flood control in the Baixada Fluminense lowlands** 

According to Britto and Bessa [7], historical investments were made in the region by different state governors, like the one of the 1980s, with an amount up to R\$ 3 billion, without, however, effectively guaranteeing universal access to environmental sanitation, housing and a better quality of life. Explanations for this are related with: (i) the lack of a profound diagnosis of the dimension of the problem in the region to correctly orient the profile of the interventions; (ii) the discontinuity and non-integration among the programs and projects implemented throughout these years; (iii) the political disputes in the region often decharacterised the projects, again lacking continuity; (iv) the fragility of social control in the process, once the format of the implemented programs have not provided an effective participation of the population (although this component existed in various of these projects); (v) the lack of institutional capacity, allied to the centralizing culture of the state governors in relation to sanitation management; (vi) the strong clientelistic culture in the municipal administrations; (vii) the growing demobilisation of organized social movements, which need members qualification for following up the

Floods in the Iguaçu-Sarapuí River Basin are aggravated basically by the f inadequate land use occupation, in the particular conditions of the lowlands of Baixada Fluminense. In this process, the most important factors are: lack of adequate urban infrastructure; deficiency of the sewage services and solid waste collection; uncontrolled exploitation of mineral deposits, mainly sand for construction purposes; disorderly, illegal occupation of river banks and floodplains; lack of adequate treatment for public roadways pavements; obstruction or strangulation of drainage due to structures built without the proper concerns (railway and road bridges, and water pipelines interferences), as well as walls and even buildings that partially obstruct river channels. At the heart of these problems one always finds either inadequate legislation regarding land use, or, in the great majority of cases, non-

It is estimated that floods in the basin directly affect 189,000 people. However, the damage caused and the total number of people indirectly affected by floods are both difficult to estimate. Included in this latter category there are, for example, employees who cannot reach their workplaces and the interruption of traffic and commerce along the flooded

In this context, in order to properly discuss the adequate possible planning actions for mitigating these problems, and to figure out the cause-effect process related to future scenarios, a mathematical model will be applied as an aiding tool. The case study alternatives are then introduced in order to allow the development of the discussions in practical terms, using examples of what may happen in the future without the proper concerns. The aim of hydrodynamic modelling was to evaluate the possible impacts of the expansion of urbanisation towards the interior of the basin without the adequate planning In order to proceed with the proposed analysis, it was necessary to choose a mathematical model to support the simulations. With this aim, a hydrodynamic model for representing rural and urban floods – MODCEL [9, 10 e 11] was used.

The construction of MODCEL, based on the concept of flow cells [12] intended to provide an alternative tool for integrated flood solution design and research. MODCEL is a model that integrates a hydrologic model, applied to each cell in the modelled area, with a hydrodynamic looped model, in a spatial representation that links surface flow, channel flow and underground pipe flow, This arrangement can be interpreted as a hydrologichydrodynamic pseudo 3D-model, although all mathematical relations written are onedimensional. Pseudo 3D representation may be materialised by a vertical hydraulic link used to communicate two different layers of flow: a superficial one, corresponding to free surface channels and flooded areas; and a subterranean one, related to free surface or surcharged flow in storm drains.

The representation of urban surfaces by cells, acting as homogeneous compartments, in which rainfall run-off transformations are performed, allows the integration of all the basin area. The cells interact through hydraulic laws, represented by cell links capable to model different possible flow patterns. Different types of cells and links give versatility to the model. The cells, considered individually as units or taken in pre-arranged sets, are capable to represent the watershed landscape, composing more complex structures. Therefore, the task related to the topographic and hydraulic modelling is an important phase of the process. In large floodable areas, when leaving the drainage network, the water can follow any path, dictated by the topography and by the urban built patterns. Marginal sidewalks may become weirs for the spilling waters from the rivers, the streets may act as canals and the buildings, parks or squares may act like reservoirs. In this situation, it is perceived that overflowed waters may have an independent behaviour from the drainage network, generating their own flow patterns. These characteristics are adequately represented in MODCEL.

The modelled area of Iguaçu-Sarapuí River basin extended from Guanabara Bay to Botas River confluence. The upstream reaches of the basin, which were not divided in cells, had their flows determined through a hydrological model called HIDRO-FLU [13].

## *3.2.2. Simulation criteria*

The main objective of the modelling of the lower and middle reaches of the Iguaçu River was to evaluate impacts caused by the expansion of uncontrolled urbanisation towards the middle/upper basin, arising from the development expected from the construction of the Metropolitan Ring Road, an important axial roadway.

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 145

Iguaçu 65 66 77 72 Botas 81 81 82 81 Capivari 67.5 65 77.9 72 Outeiro 72 84 84 84 Pilar 75 76 78.2 76 Calombé 68 79 79.8 79

The return period considered for the design rainfall was 20 years. The hydrologic parameters and rainfall information adopted were based on the Iguaçu Project [15] calculations. Regarding to the impacts caused by alterations in mean sea level, a local tide table was used as the base information. This table was produced by the Diretoria de Hidrografia e Navegação da Marinha do Brasil (Hydrography and Shipping Directorate of the Brazilian Navy), with values ranging from 0.09 to 0.90m, representing the tidal variation on the Rio de Janeiro coast. The meteorological tides were considered to influence this value with a majoring of 0.80m. Besides, a possible increment of 0.60m in the mean sea level was also considered (IPCC forecast), due to climate change expectative. With the values mentioned, the proposed scenarios were simulated, considering the tidal variations, the dynamics of urbanisation, the rise in the mean sea level, and combinations among these

Figure 2 represents the areas susceptible to flooding for the former conditions of urbanisation (at the time of Iguaçu Project [15]), in the 90's, without taking into account the meteorological tides and the effects of climate change. It is, therefore, a condition of reference for the current and future scenarios comparison, referring to flooding conditions of more than 15 years ago. It is observed that there are significant differences in floods in past conditions from those in the present scenario. The alteration already occurred in the land occupation in the upper reaches of the basin in the period justifies

The flood maps presented in Figures 3 and 4, respectively, were obtained through the following conditions: current situation of urbanisation in the basin, without considering meteorological tides and the effects of climate change (Scenario 1); and future condition of the basin urbanisation, considering disorderly urban expansion, typical tides and without

**Future CN** 

**without control with control** 

**Basin Past CN Current CN** 

**Table 3.** Curve Number (CN) used in each simulated scenario

**4. Results obtained in the modeling** 

the effects of climate change (Scenario 2).

variables.

this result.

The effective rainfall calculation method used was that of the SCS [Soil Conservation Service] of the Department of Agriculture of the USA - USDA. The *Curve Number* (CN), the main hydrological parameter of this method, varied for each of the simulated scenarios in accordance with different stages of urbanisation, as described below:


Each modelled cell in the basin representation had an individualised CN, depending on its particular characteristics.

Another objective of the modelling consisted of evaluating the impact of the mean sea level rise, as forecasted by the IPCC, on the drainage conditions of the hydrographical basin. The proposed scenarios tested the isolated and/or associated effect of the following variables:


It is important to stress that this paper does not intend to look for final solutions in order to minimise present flood conditions (although this discussion will be considered conceptually in the context of this study, in a nest topic). The main aim refers to the possibility of discussing future conditions worsening due to the inadequate planning process that take place today.


**Table 3.** Curve Number (CN) used in each simulated scenario

144 Environmental Land Use Planning

*3.2.2. Simulation criteria* 

particular characteristics.

place today.

effect of meteorological tide;

Metropolitan Ring Road, an important axial roadway.

from 1994 (LANDSAT satellite images) [14].

horizon of approximately 20 years (2030).

basis of images from the 2006 Aster sensor [14].

accordance with different stages of urbanisation, as described below:

The main objective of the modelling of the lower and middle reaches of the Iguaçu River was to evaluate impacts caused by the expansion of uncontrolled urbanisation towards the middle/upper basin, arising from the development expected from the construction of the

The effective rainfall calculation method used was that of the SCS [Soil Conservation Service] of the Department of Agriculture of the USA - USDA. The *Curve Number* (CN), the main hydrological parameter of this method, varied for each of the simulated scenarios in

1. Past situation: the CN values were defined based on soil types and land use mapping

2. Present situation: the CN values were determined by land use mapping, made on the

3. Future situation: assumed that the flat, still rural areas of the sub-basins of the Rivers Iguaçu (upper reach), Botas, Capivari, Pilar and Calombé, and the Outeiro canal will suffer a disorderly process of urbanisation, following the trend of peripherisation in progress in Baixada Fluminense lowlands. This future scenario corresponds to a

4. Controlled future situation: assumed an alteration in the current pattern of urbanisation of these areas, with the introduction of land use control by means of urban planning

Each modelled cell in the basin representation had an individualised CN, depending on its

Another objective of the modelling consisted of evaluating the impact of the mean sea level rise, as forecasted by the IPCC, on the drainage conditions of the hydrographical basin. The proposed scenarios tested the isolated and/or associated effect of the following variables:

a. different hydro-meteorological conditions, alternating typical tidal situations and the

b. variation in the soil impervious rates arising from the behaviour of future urbanisation, considering the maintenance of the current rates (without any increase in new urban areas); an increase in the impervious rates due to unplanned urban expansion; and a moderate increase in the rates due to planned control of urban expansion. For each of

It is important to stress that this paper does not intend to look for final solutions in order to minimise present flood conditions (although this discussion will be considered conceptually in the context of this study, in a nest topic). The main aim refers to the possibility of discussing future conditions worsening due to the inadequate planning process that take

the simulated scenarios, CN values were adopted as presented in Table 3.

actions and adoption of more sustainable urban drainage techniques.

The return period considered for the design rainfall was 20 years. The hydrologic parameters and rainfall information adopted were based on the Iguaçu Project [15] calculations. Regarding to the impacts caused by alterations in mean sea level, a local tide table was used as the base information. This table was produced by the Diretoria de Hidrografia e Navegação da Marinha do Brasil (Hydrography and Shipping Directorate of the Brazilian Navy), with values ranging from 0.09 to 0.90m, representing the tidal variation on the Rio de Janeiro coast. The meteorological tides were considered to influence this value with a majoring of 0.80m. Besides, a possible increment of 0.60m in the mean sea level was also considered (IPCC forecast), due to climate change expectative. With the values mentioned, the proposed scenarios were simulated, considering the tidal variations, the dynamics of urbanisation, the rise in the mean sea level, and combinations among these variables.

## **4. Results obtained in the modeling**

Figure 2 represents the areas susceptible to flooding for the former conditions of urbanisation (at the time of Iguaçu Project [15]), in the 90's, without taking into account the meteorological tides and the effects of climate change. It is, therefore, a condition of reference for the current and future scenarios comparison, referring to flooding conditions of more than 15 years ago. It is observed that there are significant differences in floods in past conditions from those in the present scenario. The alteration already occurred in the land occupation in the upper reaches of the basin in the period justifies this result.

The flood maps presented in Figures 3 and 4, respectively, were obtained through the following conditions: current situation of urbanisation in the basin, without considering meteorological tides and the effects of climate change (Scenario 1); and future condition of the basin urbanisation, considering disorderly urban expansion, typical tides and without the effects of climate change (Scenario 2).

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 147

**Figure 4.** Flood map obtained for future condition - Scenario 2

seen in the comparison of Figures 3 and 4

occupation.

The comparison among these three scenarios allows the assessment of the isolated effect of the urban expansion in the flooding aggravation. When the CN is altered for the upper reaches of the drainage area, in the simulation corresponding to Scenario 2, a significant worsening is noticed in flood conditions, even without any other worsening factor acting, as

If effective measures were implemented for land use development control, in order to prevent disorderly occupation in the middle and upper reaches of the basin, it can be seen, in Figure 5 (Scenario 3), that it is possible to avoid the worsening of floods in the referred sub-basins. It is perceived a reduction in the water levels in the densely urbanized areas, when compared with the previous development situation, without any control over land

 Figure 6: Flood map obtained for the future conditions of basin urbanisation with urban expansion without control over land use; meteorological tide of 80 cm and a 60 cm rise

The figures 6 and 7, presented in sequence, correspond to the following scenarios:

in the mean sea level due to climate changes (Scenario 4);

**Figure 2.** Reference flood map for former urban condition

**Figure 3.** Flood map obtained for the present condition - Scenario 1

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 147

**Figure 4.** Flood map obtained for future condition - Scenario 2

**Figure 2.** Reference flood map for former urban condition

**Figure 3.** Flood map obtained for the present condition - Scenario 1

The comparison among these three scenarios allows the assessment of the isolated effect of the urban expansion in the flooding aggravation. When the CN is altered for the upper reaches of the drainage area, in the simulation corresponding to Scenario 2, a significant worsening is noticed in flood conditions, even without any other worsening factor acting, as seen in the comparison of Figures 3 and 4

If effective measures were implemented for land use development control, in order to prevent disorderly occupation in the middle and upper reaches of the basin, it can be seen, in Figure 5 (Scenario 3), that it is possible to avoid the worsening of floods in the referred sub-basins. It is perceived a reduction in the water levels in the densely urbanized areas, when compared with the previous development situation, without any control over land occupation.

The figures 6 and 7, presented in sequence, correspond to the following scenarios:

 Figure 6: Flood map obtained for the future conditions of basin urbanisation with urban expansion without control over land use; meteorological tide of 80 cm and a 60 cm rise in the mean sea level due to climate changes (Scenario 4);

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 149

 Figure 7: Flood map obtained for the future conditions of basin urbanisation, with control over the land use development; meteorological tide of 80cm and climate change

**Figure 7.** Flood map obtained for controlled future condition, in the context of climate changes-

at low elevations, near the Iguaçu River estuary.

These two scenarios test the conjugated effect of the three variables considered in the simulations: urbanisation of the upper basin, presence of meteorological tide and mean sea level rise. Based on these scenarios, it is possible to conclude that the disorderly urbanisation of the upper basin causes flooding aggravation in the downstream urban areas already consolidated, while the tidal variations cause even greater floods in the lower reaches (under tidal influence). The sea level rise will worsen the floods in the urban areas situated

Both the urban expansion and the sea level rise are going to cause great impacts on the urban areas of the basin. Despite having their causes explained by independent variables, these factors, if combined, would lead to serious impacts on the population resident in the basin. If planning measures are not taken in advance, it will be very difficult to mitigate

Scenario 5

their impacts later.

effects, with a 60 cm rise in mean sea level (Scenario 5).

**Figure 5.** Flood map obtained for controlled future condition - Scenario 3

**Figure 6.** Flood map obtained for future condition, in the context of climate changes - Scenario 4

 Figure 7: Flood map obtained for the future conditions of basin urbanisation, with control over the land use development; meteorological tide of 80cm and climate change effects, with a 60 cm rise in mean sea level (Scenario 5).

148 Environmental Land Use Planning

**Figure 5.** Flood map obtained for controlled future condition - Scenario 3

**Figure 6.** Flood map obtained for future condition, in the context of climate changes - Scenario 4

**Figure 7.** Flood map obtained for controlled future condition, in the context of climate changes-Scenario 5

These two scenarios test the conjugated effect of the three variables considered in the simulations: urbanisation of the upper basin, presence of meteorological tide and mean sea level rise. Based on these scenarios, it is possible to conclude that the disorderly urbanisation of the upper basin causes flooding aggravation in the downstream urban areas already consolidated, while the tidal variations cause even greater floods in the lower reaches (under tidal influence). The sea level rise will worsen the floods in the urban areas situated at low elevations, near the Iguaçu River estuary.

Both the urban expansion and the sea level rise are going to cause great impacts on the urban areas of the basin. Despite having their causes explained by independent variables, these factors, if combined, would lead to serious impacts on the population resident in the basin. If planning measures are not taken in advance, it will be very difficult to mitigate their impacts later.

## **5. Conceptual discussion**

The urban drainage system includes two major subsystems: micro-drainage and macrodrainage. The micro-drainage system consists of the paving of streets, gutters, gullies, stormdrains and channels of small dimensions, intending to collect the runoff and conduct it to the macro-drainage net. Macro-drainage generally consists of natural or built channels of larger dimensions, receiving the input from micro-drainage, concentrating flows and discharging in the receiving water body. A complementary set of structures also take part in drainage systems, among which is possible to mention: reservoirs, protective dikes, and pumping stations. All these structures are arranged and designed to work in an n integrated way, intercepting, conveying, possibly infiltrating or temporarily storing and discharging the generated runoff. Ultimately, the receiving water body is the sea and this is the case of Iguaçu-Sarapuí Rivers.

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 151

This trend, though not motivated by the possibility of climate change, also goes toward this theme, with the possibility of reaching effective results, in opposition to the traditional approach that basically considers propositions of rectifying and canalising water courses. In this perspective, the traditional approach treats the consequence of the problem, related to the generation of exceeding superficial flows. The possibility of the mean sea level rising, however, limits the discharge capacity of the system and makes the traditional approach to fail. Thus, in this context, it is necessary to treat the problem of flow generation, acting in the causes of flooding, while trying to introduce infiltration and storage measures spread over the urban basin landscape in order to reduce and delay flood peaks, allow groundwater recharge and seek to restore the approximate natural flow conditions. This approach introduces the sustainable urbanization concept, proposing that the flood should not be transferred in space or time. This way, storage and infiltration measures may be important measures for sustaining adequate drainage conditions. Storage measures should consider detention or retention reservoirs, acting in-line with rivers or in the base of hill slopes, or combined in multifunctional landscapes in parks and public squares, or even in the plot level, as an on-source control option. By its turn, infiltration measures may involve reforestation actions, the use of pervious paving, or infiltration trenches, among others. All these measures, properly designed in an integrated manner, might be able to work preventively or correctively, if necessary, modifying the spatial and temporal distribution of

The storage measures, because of their applicability and diversity of use, in different combinations with the drainage net configuration, are highlighted in this conceptual discussion. The reservoirs are able to attack the problem of flooding worsening, both from the point of view of the uncontrolled urban growth, as well as from the point of view of possible climate changes. The storage capacity of these reservoirs allows facing the larger volumes and to control surface runoff released to the network, minimising chances of system failure, with a time of response that matches the velocity of the critical superficial processes that generates floods. Infiltration measures are very important, because they are able to reduce flow volumes, but infiltration process takes more time and, in this case, time may be a critical factor when trying to control floods. So, infiltration measures are desirable, but may usually they do not prescind from storage

The Iguaçu Project, related to the first Water Resources Management Master Plan, was the reference scenario used in this study. After more than one decade, the revision of the Water Resources Management Master Plan for Iguaçu-Sarapuí River Basin started in 2007 and finished in 2009. Lack of an adequate urban land use control and unplanned city growth led to several problems, as discussed previously. In the newer version of The Master Plan, the original set of proposed measures was reviewed. Part of these measures was maintained,

**6. Proposed solutions for Iguaçu-Sarapuí River Basin** 

flows, to face the new challenges.

measures.

The urban flooding process, by its turn, is directly associated with the failure of these subsystems, due to lack of maintenance, obsolescence, disordered urban growth or, as stated in recent discussions, due to the possibility of climate changes worsening flow conditions. Specifically to the drainage systems, the negative effects that may arise from the situation of climate changes refer to the increase of extreme rainfall events intensity, and to the restriction imposed by the expected sea level rise at the basin outfall. Evaluating this context, the increase in the mean sea level causes a reduction in the discharge capacity of the system, causing the drainage net to lose efficiency. The worsening of the extreme rainfall events intensity works in the other part of the problem, generating greater volumes to be drained by a system whose discharge capability diminished because of the new outfall restrictions. In this situation, in a context of already serious urban flooding problems, the effects generated by the possibility of climate changes can dramatically increase flooding areas, causing them to reach locations not previously affected by floods, increasing inundation depths and residence times, making the situation even worse.

Understanding how urbanisation affects floods is very important for urban flood control design. In general, it is possible to say that the urban flood control conjugates the adoption of structural measures that change the landscape of the basin, introducing interventions inside and outside the drainage network, to act directly in minimising the problem, and non-structural measures, associated with land use planning, environmental education and several possible other measures that allow a more harmonious coexistence with the phenomenon of flooding. The combination of structural and non-structural measures, in a context of planning integrated with urban growth, allows a composition capable of solving the problem of flooding in a harmonious and sustainable way. This approach, which is relatively recent, is being considered more appropriate to treat the urban flooding problem, by treating the problem in a systemic way and proposing actions that seek to minimize the impacts of urbanization.

Iguaçu-Sarapuí Rivers.

the situation even worse.

impacts of urbanization.

**5. Conceptual discussion** 

The urban drainage system includes two major subsystems: micro-drainage and macrodrainage. The micro-drainage system consists of the paving of streets, gutters, gullies, stormdrains and channels of small dimensions, intending to collect the runoff and conduct it to the macro-drainage net. Macro-drainage generally consists of natural or built channels of larger dimensions, receiving the input from micro-drainage, concentrating flows and discharging in the receiving water body. A complementary set of structures also take part in drainage systems, among which is possible to mention: reservoirs, protective dikes, and pumping stations. All these structures are arranged and designed to work in an n integrated way, intercepting, conveying, possibly infiltrating or temporarily storing and discharging the generated runoff. Ultimately, the receiving water body is the sea and this is the case of

The urban flooding process, by its turn, is directly associated with the failure of these subsystems, due to lack of maintenance, obsolescence, disordered urban growth or, as stated in recent discussions, due to the possibility of climate changes worsening flow conditions. Specifically to the drainage systems, the negative effects that may arise from the situation of climate changes refer to the increase of extreme rainfall events intensity, and to the restriction imposed by the expected sea level rise at the basin outfall. Evaluating this context, the increase in the mean sea level causes a reduction in the discharge capacity of the system, causing the drainage net to lose efficiency. The worsening of the extreme rainfall events intensity works in the other part of the problem, generating greater volumes to be drained by a system whose discharge capability diminished because of the new outfall restrictions. In this situation, in a context of already serious urban flooding problems, the effects generated by the possibility of climate changes can dramatically increase flooding areas, causing them to reach locations not previously affected by floods, increasing inundation depths and residence times, making

Understanding how urbanisation affects floods is very important for urban flood control design. In general, it is possible to say that the urban flood control conjugates the adoption of structural measures that change the landscape of the basin, introducing interventions inside and outside the drainage network, to act directly in minimising the problem, and non-structural measures, associated with land use planning, environmental education and several possible other measures that allow a more harmonious coexistence with the phenomenon of flooding. The combination of structural and non-structural measures, in a context of planning integrated with urban growth, allows a composition capable of solving the problem of flooding in a harmonious and sustainable way. This approach, which is relatively recent, is being considered more appropriate to treat the urban flooding problem, by treating the problem in a systemic way and proposing actions that seek to minimize the This trend, though not motivated by the possibility of climate change, also goes toward this theme, with the possibility of reaching effective results, in opposition to the traditional approach that basically considers propositions of rectifying and canalising water courses. In this perspective, the traditional approach treats the consequence of the problem, related to the generation of exceeding superficial flows. The possibility of the mean sea level rising, however, limits the discharge capacity of the system and makes the traditional approach to fail. Thus, in this context, it is necessary to treat the problem of flow generation, acting in the causes of flooding, while trying to introduce infiltration and storage measures spread over the urban basin landscape in order to reduce and delay flood peaks, allow groundwater recharge and seek to restore the approximate natural flow conditions. This approach introduces the sustainable urbanization concept, proposing that the flood should not be transferred in space or time. This way, storage and infiltration measures may be important measures for sustaining adequate drainage conditions. Storage measures should consider detention or retention reservoirs, acting in-line with rivers or in the base of hill slopes, or combined in multifunctional landscapes in parks and public squares, or even in the plot level, as an on-source control option. By its turn, infiltration measures may involve reforestation actions, the use of pervious paving, or infiltration trenches, among others. All these measures, properly designed in an integrated manner, might be able to work preventively or correctively, if necessary, modifying the spatial and temporal distribution of flows, to face the new challenges.

The storage measures, because of their applicability and diversity of use, in different combinations with the drainage net configuration, are highlighted in this conceptual discussion. The reservoirs are able to attack the problem of flooding worsening, both from the point of view of the uncontrolled urban growth, as well as from the point of view of possible climate changes. The storage capacity of these reservoirs allows facing the larger volumes and to control surface runoff released to the network, minimising chances of system failure, with a time of response that matches the velocity of the critical superficial processes that generates floods. Infiltration measures are very important, because they are able to reduce flow volumes, but infiltration process takes more time and, in this case, time may be a critical factor when trying to control floods. So, infiltration measures are desirable, but may usually they do not prescind from storage measures.

#### **6. Proposed solutions for Iguaçu-Sarapuí River Basin**

The Iguaçu Project, related to the first Water Resources Management Master Plan, was the reference scenario used in this study. After more than one decade, the revision of the Water Resources Management Master Plan for Iguaçu-Sarapuí River Basin started in 2007 and finished in 2009. Lack of an adequate urban land use control and unplanned city growth led to several problems, as discussed previously. In the newer version of The Master Plan, the original set of proposed measures was reviewed. Part of these measures was maintained,

especially in consolidated areas; however, whenever possible, new concepts on sustainable urban drainage were introduced. The basin was considered in an integrated way and environmental recovery concerns were added to the new plan. Irregular occupations of risky areas, subjected to frequent flooding, and especially riverbanks occupations, were considered not appropriated and people living in these houses without proper safe conditions needed to be relocated.

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 153

Fluvial Urban Park – riverbanks protection

Environmental Urban Park – green areas with minimal interventions, for pervious conditions maintenance

**Figure 8.** Fluvial parks typology – proposed distributed measures for flood control and environmental

recovery

Flooding Urban Park – playing flood plain functions (lower areas connected to the river, with or without formal

structures)

Both structural and non-structural measures were proposed for flood control purposes, ranging from short to long-term actions. Some of the proposed actions aiming to give more sustainable solutions considered:


In terms of flood control, riverbanks protection and natural vegetation preservation, three types of parks were proposed, as basic measures to be reproduced in a distributed way over the basin, encompassing the following functions (figure 8):


The figures 9 and 10 show two more detailed examples of the proposed parks, in practical conditions, being one for Sarapuí River, and another for Iguaçu River.

Complementary actions held by the State include the articulation with every Municipality in the basin, in order to implement the proposed measures, create local conditions for urban land use control and develop environmental education campaigns, with the financing of the Federal Government, through a specific Program of Developing Acceleration (PAC, in Portuguese). Besides, a habitation program is also being conducted in the basin, in order to support and allow people relocation from risky areas to safer near areas.

conditions needed to be relocated.

sustainable solutions considered:

peaks.

near areas.

the recovery of lost vegetated areas;

Environmental Preservation Areas; the implementation of urban parks;

especially in consolidated areas; however, whenever possible, new concepts on sustainable urban drainage were introduced. The basin was considered in an integrated way and environmental recovery concerns were added to the new plan. Irregular occupations of risky areas, subjected to frequent flooding, and especially riverbanks occupations, were considered not appropriated and people living in these houses without proper safe

Both structural and non-structural measures were proposed for flood control purposes, ranging from short to long-term actions. Some of the proposed actions aiming to give more

the maintenance of natural spaces free from urbanisation, preventing vegetation

a land use regulation and control, by means of the establishment of formal

the creation of public consortiums for integrated planning of policies for multi-counties

In terms of flood control, riverbanks protection and natural vegetation preservation, three types of parks were proposed, as basic measures to be reproduced in a distributed way over

1. Fluvial Urban Park – longitudinal parks along rivers, with the purpose of protecting

2. Flooding Urban Park – longitudinal parks implemented in low elevation areas to allow frequent inundations, with a storage function intending to help in damping flood

3. Environmental Urban Park – parks with greater dimensions, flat or not, with the purpose of environmental preservation and land use valuing, aiming to minimise

The figures 9 and 10 show two more detailed examples of the proposed parks, in practical

Complementary actions held by the State include the articulation with every Municipality in the basin, in order to implement the proposed measures, create local conditions for urban land use control and develop environmental education campaigns, with the financing of the Federal Government, through a specific Program of Developing Acceleration (PAC, in Portuguese). Besides, a habitation program is also being conducted in the basin, in order to support and allow people relocation from risky areas to safer

removal and the aggravation of flooding at the consolidated urban areas;

interests (recognising the importance of the metropolitan planning); the revision and adaptation of the municipalities urban planning instruments.

river banks from irregular occupation by low income population.

runoff generation and maintaining a buffer of pervious surfaces.

conditions, being one for Sarapuí River, and another for Iguaçu River.

the basin, encompassing the following functions (figure 8):

**Figure 8.** Fluvial parks typology – proposed distributed measures for flood control and environmental recovery

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 155

**Flooding Urban Park** 

**of Pilar Polder** 

**Figure 10.** Flooding Urban Park examples – Iguaçu River

**Figure 9.** Flooding Urban Park examples – Sarapuí River

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 155

**Figure 10.** Flooding Urban Park examples – Iguaçu River

154 Environmental Land Use Planning

**Figure 9.** Flooding Urban Park examples – Sarapuí River

**Flooding Urban Park of Gomes Freire polder** 

## **7. Conclusion**

a. Promoting integration of public policies that interact with the water resources is probably the most urgent and complex task on the agenda of public administrators who are really committed to a sustainable future for the metropolitan areas.

A Flood Control Approach Integrated with a Sustainable Land Use Planning in Metropolitan Regions 157

*Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE/UFRJ), Laboratório de Hidrologia e Estudos do Meio Ambiente, Ilha do* 

*Universidade Federal do Rio de Janeiro, Escola Politécnica (POLI/UFRJ), Rio de Janeiro/RJ-Brazil* 

The first author is grateful to the Support Program Postdoctoral CAPES/FAPERJ. The

[1] Dourojeanni, Axel, & Jouravlev, Andrei. Gestión de cuencas y ríos vinculados con centros urbanos. C E P A L - Comisión Económica para América Latina y el Caribe,

[2] Low-Beer, Jacqueline Doris, Cornejo, Ione Koseki. Instrumento de gestão integrada da água em áreas urbanas. Subsídios ao Programa Nacional de Despoluição das Bacias Hidrográficas e estudo exploratório de um programa nacional de apoio à gestão integrada. Relatório de Andamento. Extrato de resultados preliminares de pesquisa (módulo Institucional). Convênio FINEP CT-HIDRO 23.01.0547.00. Universidade de São

[3] Jouravlev, Andrei. Los municipios y la gestión de los recursos hídricos. Serie Recursos Naturales e Infraestructura. CEPAL - Comisión Económica para América Latina y el

[4] Tucci, Carlos E. M (2004). Gerenciamento integrado das inundações urbanas no Brasil. Rega/Global Water Partnership South América. Vol. 1, nº 1 Santiago: GWP/South

[5] Ibge. Pesquisa de informações básicas municipais – suplemento de meio ambiente, 2002. [6] Observatório Das Metrópoles. Como andam as metrópoles. Relatório Final – 21 de dezembro de 2005. Disponível em www.ippur.ufrj.br/. Acesso em 8/08/2008. ABRH,

[7] Britto, Ana Lucia Nogueira de Paiva, & BESSA, Eliane da Silva. Possibilidades de Mudanças no Ambiente Construído: o saneamento nos novos planos diretores da Baixada Fluminense. ANAIS do IV Encontro Nacional da ANPPAS. Brasília, DF, 2008. [8] Parry, M.L., Canziani, O.F., Palutikof, J.P., Van der Linden P.J. and Hanson, C.E. (eds). Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge,

second author acknowledges CNPq for his research support.

Paulo, Núcleo de Pesquisa em Informações Urbanas, 2002.

United Kingdom and New York, NY, USA, 2007.

**Author details** 

Paulo Roberto Ferreira Carneiro

*Fundão, Rio de Janeiro/RJ-Brazil* 

Marcelo Gomes Miguez

**Acknowledgement** 

**8. References** 

1999.

Caribe, nº 66, 2003.

João Pessoa, 2005.

América, jan./jun.,2004.


## **Author details**

156 Environmental Land Use Planning

a. Promoting integration of public policies that interact with the water resources is probably the most urgent and complex task on the agenda of public administrators who

b. There are reasons to believe that the new institutional arrangements in place in the country offer alternatives for the shared responsibilities involving states and municipalities, mainly in the large urban agglomerations. Specifically, in relation to municipalities, there is a vast spectrum of possibilities to be pursued within the Statute of the City. The new Master Plans can and must incorporate more effective mechanisms for land use management, using a greater range of legal, economic and fiscal instruments focused on urban development on a sustainable basis. However, master plans for urban development still lack mechanisms of inter-municipal coordination and regional agreements orientations that may prevent eventual unintended consequences

c. The Iguaçu-Sarapuí River basin still embodies conditions favourable to planning for urban flooding, albeit devised to apply for the long term. A significant part of its territory remains in the form of areas still not incorporated into the urban fabric – notably the areas situated between the mountains that rise abruptly and the lowland itself. This enables the maintenance of areas with high soil pervious rates, provided that

d. The disorderly occupation in Baixada Fluminense lowlands is going to increase the frequency and intensity of the urban floods, causing major damage to the already urbanized areas. The main limiting factor for the expansion of the urban perimeter is the lack of highway connection and regular mass transport lines in the upper parts of the basin, maintaining low occupation rates and rural activities in these areas. It is also worth highlighting the lack of preparation of local administrations to deal with the probable resulting impacts of climate change, above all in urban areas situated at low

maintenance of spaces free from urbanisation, preventing the aggravation of flooding at

land use regulation and control, by means of the establishment of formal

f. implementation of urban parks, mainly for storage purposes, minimising flooding

creation of public consortiums for integrated planning of policies for multi-counties

 revision and adaptation of the urban planning instruments for the municipalities. g. Complementary actions of state responsibility include articulation with every Municipality in the basin, in order to implement the proposed measures, create local conditions for urban land use control and develop environmental education campaigns

impacts and preparing the basin for future worse climatic conditions;

interests (recognizing the importance of the metropolitan planning);

are really committed to a sustainable future for the metropolitan areas.

of land use regulations, from one municipality to another.

the urban fabric does not expand to those areas.

elevations in relation to the sea level.

Environmental Preservation Areas;

about the risks of worsening the floods.

the consolidated urban areas;

e. Some of the actions proposed by this study were:

**7. Conclusion** 

#### Paulo Roberto Ferreira Carneiro

*Universidade Federal do Rio de Janeiro, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia (COPPE/UFRJ), Laboratório de Hidrologia e Estudos do Meio Ambiente, Ilha do Fundão, Rio de Janeiro/RJ-Brazil* 

Marcelo Gomes Miguez *Universidade Federal do Rio de Janeiro, Escola Politécnica (POLI/UFRJ), Rio de Janeiro/RJ-Brazil* 

## **Acknowledgement**

The first author is grateful to the Support Program Postdoctoral CAPES/FAPERJ. The second author acknowledges CNPq for his research support.

## **8. References**


[9] Miguez, M. G. Modelo Matemático de Células de Escoamento para Bacias Urbanas. Tese (Doutorado em Engenharia Civil), COPPE / UFRJ, Rio de Janeiro, 2001.

**Chapter 8** 

© 2012 Bingham and Kinnell, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**The Role of Socioeconomic and** 

Matthew F. Bingham and Jason C. Kinnell

Additional information is available at the end of the chapter

remove three of the dams and install fish passage on the fourth.

mathematically using the over-arching structure of Figure 1.

http://dx.doi.org/10.5772/50672

**1. Introduction** 

**Behavioral Modeling in an Integrated,** 

**Multidisciplinary Dam-Management Study:** 

The Boardman River flows through Grand Traverse and Kalkaska Counties in Northwest Michigan before flowing into Grand Traverse Bay at Traverse City. Approximately two million recreation user days are estimated to occur on the Boardman River each year. Many of these recreators come to fish the river, others enjoy scenic trails, camping, and paddling. Beginning in 1867, four small dams were constructed on the Boardman. The dams were constructed primarily to generate hydropower. However, as the dams have aged their commercial viability as hydroelectric stations diminished. As a result, Traverse City Light and Power did not seek to renew the leases of those dams. Because of this, the dams' owners (Grand Traverse County and the City of Traverse City) sought a cost-effective, environmentally and socially responsible dam-management outcome. The resulting process is considered one of the most comprehensive studies of its type ever undertaken in the United States. This process created the Boardman River Dams Committee (BRDC), an inclusive and diverse group of property owners, private citizens, agencies, nonprofits, businesses, scientific experts, and students. The BRDC involved over 1,000 people in 180 public meetings and the assessment of 91 options for the future of the dams. In April 2009, the Traverse City Commission and Grand Traverse County Board of Commissioners reviewed the scientific data and recommendations provided by the BRDC, and voted to

Simulation modeling was an important tool used to aid transparency and decision-making. The implications of physical changes in conditions on the river were integrated

and reproduction in any medium, provided the original work is properly cited.

**Case Study of the Boardman River Dams** 


## **The Role of Socioeconomic and Behavioral Modeling in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams**

Matthew F. Bingham and Jason C. Kinnell

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50672

## **1. Introduction**

158 Environmental Land Use Planning

WW2, pp. 181-199, 1970.

Brasileiro de Recursos Hídricos.

(COPPE/UFRJ), Rio de Janeiro.

de Inundações. Rio de Janeiro: SERLA, 1996.

[9] Miguez, M. G. Modelo Matemático de Células de Escoamento para Bacias Urbanas. Tese

[10] Mascarenhas, F.C.B.. & Miguez, M.G. Urban Flood Control through a Mathematical Cell

[11] Mascarenhas, F.C.B., & Miguez, M.G. Mathematical Modelling of Rural and Urban Floods: a hydraulic approach. In: Flood Risk Simulation. Wit Press, Gateshead, 2005. [12] Zanobetti, D.; Lorgeré, H.; Preissman, A.; Cunge, J. A. , Mekong Delta Mathematical Program Construction. Journal of the Waterways and Harbours Division, ASCE, v.96, n.

[13] Magalhães, L. P. C., Magalhães, P. C., Mascarenhas, F. C. B., Miguez, M. G., Colonese, B. L., & Bastos, E. T. Sistema Hidro-Flu para Apoio a Projetos de Drenagem. XVI Simpósio

[14] Carneiro, Paulo Roberto Ferreira. Controle de Inundações em Bacias Metropolitanas, Considerando a Integração do Planejamento do Uso Solo à Gestão dos Recursos Hídricos. Estudo de caso: bacia dos rios Iguaçu/Sarapuí na Região Metropolitana do Rio de Janeiro. 2008. IX, 296 p. (Doutorado em Engenharia Civil) Coordenação dos Programas de Pós-Graduação de Engenharia da Universidade Federal do Rio de Janeiro

[15] Laboratório De Hidrologia E Estudo Do Meio Ambiente Coppe/UFRJ - PNUD. Plano Diretor de Recursos Hídricos da Bacia dos Rios Iguaçu/Sarapuí, com Ênfase no Controle

(Doutorado em Engenharia Civil), COPPE / UFRJ, Rio de Janeiro, 2001.

Model. In: Water International. Vol. 27, nº 2, p. 208-218, 2002.

The Boardman River flows through Grand Traverse and Kalkaska Counties in Northwest Michigan before flowing into Grand Traverse Bay at Traverse City. Approximately two million recreation user days are estimated to occur on the Boardman River each year. Many of these recreators come to fish the river, others enjoy scenic trails, camping, and paddling.

Beginning in 1867, four small dams were constructed on the Boardman. The dams were constructed primarily to generate hydropower. However, as the dams have aged their commercial viability as hydroelectric stations diminished. As a result, Traverse City Light and Power did not seek to renew the leases of those dams. Because of this, the dams' owners (Grand Traverse County and the City of Traverse City) sought a cost-effective, environmentally and socially responsible dam-management outcome. The resulting process is considered one of the most comprehensive studies of its type ever undertaken in the United States. This process created the Boardman River Dams Committee (BRDC), an inclusive and diverse group of property owners, private citizens, agencies, nonprofits, businesses, scientific experts, and students. The BRDC involved over 1,000 people in 180 public meetings and the assessment of 91 options for the future of the dams. In April 2009, the Traverse City Commission and Grand Traverse County Board of Commissioners reviewed the scientific data and recommendations provided by the BRDC, and voted to remove three of the dams and install fish passage on the fourth.

Simulation modeling was an important tool used to aid transparency and decision-making. The implications of physical changes in conditions on the river were integrated mathematically using the over-arching structure of Figure 1.

© 2012 Bingham and Kinnell, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The Role of Socioeconomic and Behavioral Modeling

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 161

To ensure that they are both mathematically tractable and useful for policy analysis, we

4. Structural simulation modeling allows conducting policy experiments by comparing

5. Measures of changes in economic welfare are available from models that simulate

Recreational pressure provides an example. Recreational pressure estimates meet requirements 1 and 2 because the number of trips taken to the Boardman River over a particular time period is a measurable quantity that is generated through a socioeconomic process. With respect to requirement 3, recreational pressure does provide an indication of system performance. For example, an estimate of average recreational pressure that is "high" combined with an estimate of variation in pressure that is "low" could indicate "good" performance. As for 4 and 5, behavioral models of recreation site choice are specifically designed to predict both trips and economic welfare under baseline and

With this structure, required information for socioeconomic modeling of the system

Because alternatives are evaluated through the identification of changes in

and simulation of changes in β, identifying expected changes in *β* requires

and *β* to allow simulating outcomes under various dam-management alternatives.

in Baseline and counterfactual experiments as a mathematical simulation. This requires identifying Baseline conditions and the mathematical structures that link policies to

2. relevant site characteristics for both the site being evaluated and potential substitute

Following [2], policy implications are identified by evaluating differences across

and *β* in Baseline and mathematically modeling the relationship between

and *β*

2. Recreation site and residential property attributes—

3. Evaluating their statistical properties conveys a sense of system performance.

require that indicators have the following qualities:

2. They are real numbers that can be measured.

baseline and counterfactual outcomes.

changes in the indicators.

counterfactual conditions.

includes the following:

4. Property values—*β*

characterizing

outcomes.

sites

1. Dam operation characteristics—

5. Dam costs and revenues—*β*

Information requirements include

1. the population of affected recreators

3. travel costs from recreator origins to sites.

3. Recreational use patterns and values—*β*

1. They are generated through socioeconomic activities.

**Figure 1.** Mathematical notation for the integrated assessment

Mathematically, the Boardman River system was characterized as (S,). In this framework S represents the integrated physical, hydrologic, ecological, environmental, and socioeconomic relationships that link dam management alternatives with socioeconomic outcomes.

Dam-management alternatives that are relevant to local socioeconomic conditions are represented by . Prime notation is used to represent level of control. Factors that can be directly controlled are typically closely coupled to alternative-related physical characteristics such as the existence and operational status of the dams, and presence or absence of fish passage technology.1 Relevant, indirectly controllable hydrologic, ecologic, and environmental characteristics are represented by ' and ".2 Consequently, the specification of a resource characteristic as means that it is both relevant to socioeconomic processes and either directly or indirectly related to the physical status of the Boardman River dams.

Economic benefit estimates were based on the simulation of observable socioeconomic processes following the structure detailed in [1]. Socioeconomic processes that are impacted by changes to are represented by . These are specific, continually occurring collections of events. A particular person choosing how to spend a day off is an example of a socioeconomic process as is a real estate transaction.3 Because the complete properties of socioeconomic processes are rarely observed, quantitatively assessing the system performance requires using indicators. In the mathematical structure, these indicators are identified as *β*. 4

<sup>1</sup> By "closely coupled" we refer to changes that can be known with certainty. For example, the removal of a dam also eliminates a portage.

<sup>2</sup> The use of prime notation to represent degree of control (and thus degree certainty) recognizes that expert judgment and reduced form modeling (as opposed to detailed structural modeling) may be used to identify changes to the Θ.

<sup>3</sup> Mathematically this is represented with , subscripting by i for time periods and j for individuals and superscripting by R for recreation.

<sup>4</sup> These properties are developed as part of the public policy model of [1].

To ensure that they are both mathematically tractable and useful for policy analysis, we require that indicators have the following qualities:


160 Environmental Land Use Planning

outcomes.

represented by

River dams.

by changes to

identified as *β*.

eliminates a portage.

by R for recreation.

4

specification of a resource characteristic as

**Figure 1.** Mathematical notation for the integrated assessment

and environmental characteristics are represented by

4 These properties are developed as part of the public policy model of [1].

Mathematically, the Boardman River system was characterized as (S,

represents the integrated physical, hydrologic, ecological, environmental, and socioeconomic relationships that link dam management alternatives with socioeconomic

Dam-management alternatives that are relevant to local socioeconomic conditions are

directly controlled are typically closely coupled to alternative-related physical characteristics such as the existence and operational status of the dams, and presence or absence of fish passage technology.1 Relevant, indirectly controllable hydrologic, ecologic,

processes and either directly or indirectly related to the physical status of the Boardman

Economic benefit estimates were based on the simulation of observable socioeconomic processes following the structure detailed in [1]. Socioeconomic processes that are impacted

events. A particular person choosing how to spend a day off is an example of a socioeconomic process as is a real estate transaction.3 Because the complete properties of socioeconomic processes are rarely observed, quantitatively assessing the system performance requires using indicators. In the mathematical structure, these indicators are

1 By "closely coupled" we refer to changes that can be known with certainty. For example, the removal of a dam also

2 The use of prime notation to represent degree of control (and thus degree certainty) recognizes that expert judgment and reduced form modeling (as opposed to detailed structural modeling) may be used to identify changes to the Θ. 3 Mathematically this is represented with , subscripting by i for time periods and j for individuals and superscripting

. Prime notation is used to represent level of control. Factors that can be

are represented by . These are specific, continually occurring collections of

' and means that it is both relevant to socioeconomic

). In this framework S

".2 Consequently, the


Recreational pressure provides an example. Recreational pressure estimates meet requirements 1 and 2 because the number of trips taken to the Boardman River over a particular time period is a measurable quantity that is generated through a socioeconomic process. With respect to requirement 3, recreational pressure does provide an indication of system performance. For example, an estimate of average recreational pressure that is "high" combined with an estimate of variation in pressure that is "low" could indicate "good" performance. As for 4 and 5, behavioral models of recreation site choice are specifically designed to predict both trips and economic welfare under baseline and counterfactual conditions.

With this structure, required information for socioeconomic modeling of the system includes the following:


Because alternatives are evaluated through the identification of changes in and simulation of changes in β, identifying expected changes in *β* requires characterizing and *β* in Baseline and mathematically modeling the relationship between and *β* to allow simulating outcomes under various dam-management alternatives. Following [2], policy implications are identified by evaluating differences across and *β* in Baseline and counterfactual experiments as a mathematical simulation. This requires identifying Baseline conditions and the mathematical structures that link policies to outcomes.

Information requirements include


### **1.1. The mathematical models**

Mathematical models were applied for recreation (fishing, paddling, trails, and camping), economic impacts, hydroelectricity value, and property value.

The mathematical structure applied for recreation is the probabilistic site choice model. This modeling structure, based on choice theory, has the advantages of being professionally accepted, useful for policy-simulation predictions, consistent with economic theory, and capable of identifying resource values.5

These models identify the probability of a specific outcome (in this case, the selection of a recreation site), conditioned on the site characteristics of all relevant choices for recreators (e.g., distance from the site to the angler's home, expected catch rates, etc.). In the site choice framework, a recreator chooses a site by comparing characteristics across all sites. The mathematical structure is presented in Equation 1 below.

$$P\_i(j) = \frac{\exp(V\_{ij})}{\sum\_{j=1}^{l} \exp(V\_{ik})} \tag{1}$$

The Role of Socioeconomic and Behavioral Modeling

(3)

*<sup>i</sup> P V* (4)

, *Vf S <sup>i</sup>* (5)

*Pf S* , (6)

*Value S S* , , (7)

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 163

1 1

ln ln *J J i V V*

Aggregating over individuals identifies changes in trips for each site due to the policy that

Estimates of changes in economic value improve the ability to assess resource performance. The distance from an individual's home to a site is a critical variable in a site-choice model

When distance is converted to travel cost, the site-choice framework supports the calculation of monetary changes in value associated with changes in site characteristics. The mathematical form used to identify dollar-based changes in value associated with a policy that changes to is the difference between the utility levels scaled by the relative impact of travel costs. Equation 3 presents the mathematical structure used to evaluate the change

> *i ij ij i j j*

CVi refers to the compensating variation or dollar valued willingness-to-pay that recreator *i* has for the change from to . This is the amount of money that would make him

Mathematical structure (S) for property value is the hedonic price approach as developed by [5]. In this structure, property value, identified as market price, is determined according to

Properties are those with characteristics influenced by the Boardman River dam system

meaning that the expected market price relates to the state of the Boardman River system,

It is apparent that the change in property value stems directly from the difference in states

7 This information is useful for evaluating changes via a utilitarian perspective, such as benefit-cost analysis [6].

*AnnualTrips CV e e*

because it represents the fuel cost and travel time required to visit each site.

in annual value that a recreator attributes to the policy that changes to .

7

of the system between current conditions and an alternative.

where ( ,) ( ,) *ij V SV S ij*

where ( ,) ( ,) *ij V SV S ij*

indifferent between and .

property characteristics.

changes to .

where *Vij* = *f* (Θ, S)

This equation represents the probability that on any particular recreation choice occasion, a recreator (identified by i) will choose to visit a particular site (identified by j). Note that this likelihood, identified by *Pi(j)*, is determined on the basis of both site characteristics (Θ) and parameters representing the values recreators hold for those site characteristics (S).

This mathematical construct identifies visitation likelihood. However the probability that a recreator will visit a site is not an observable β that can be used to evaluate the performance of the system. Pressure is a closely related and commonly employed β. To estimate pressure for any given site *j, Pi(j)* is summed over all recreators' choice occasions.6

The hedonic decomposition of recreation sites into site characteristics and the representation of these site characteristics in the site-choice framework allow an evaluation of important information including changes in visitation probability, changes in site pressure, and changes in resource value. This is accomplished by developing an equivalent mathematical structure with appropriately altered Θ for policy alternatives and finding the difference in trips between this policy simulation model and the base case. Equation 2 presents the mathematics for an individual.

$$\text{AnnualChoiceOccations}\_{i}\left[\frac{\exp(V\_{ij})}{\sum\_{j=1}^{I}\exp(V\_{ik})} - \frac{\exp(\overline{V}\_{ij})}{\sum\_{j=2}^{I}\exp(\overline{V}\_{ik})}\right] \tag{2}$$

<sup>5</sup> The statistical basis for choice theory is the standard conditional logit model [3, 4].

<sup>6</sup> In the simulation context, this is accomplished by multiplying the likelihood of selecting each site (equation 1) by the total number of trips.

where ( ,) ( ,) *ij V SV S ij*

162 Environmental Land Use Planning

where *Vij* = *f* (Θ, S)

mathematics for an individual.

total number of trips.

**1.1. The mathematical models** 

capable of identifying resource values.5

economic impacts, hydroelectricity value, and property value.

mathematical structure is presented in Equation 1 below.

Mathematical models were applied for recreation (fishing, paddling, trails, and camping),

The mathematical structure applied for recreation is the probabilistic site choice model. This modeling structure, based on choice theory, has the advantages of being professionally accepted, useful for policy-simulation predictions, consistent with economic theory, and

These models identify the probability of a specific outcome (in this case, the selection of a recreation site), conditioned on the site characteristics of all relevant choices for recreators (e.g., distance from the site to the angler's home, expected catch rates, etc.). In the site choice framework, a recreator chooses a site by comparing characteristics across all sites. The

1

This equation represents the probability that on any particular recreation choice occasion, a recreator (identified by i) will choose to visit a particular site (identified by j). Note that this likelihood, identified by *Pi(j)*, is determined on the basis of both site characteristics (Θ) and

This mathematical construct identifies visitation likelihood. However the probability that a recreator will visit a site is not an observable β that can be used to evaluate the performance of the system. Pressure is a closely related and commonly employed β. To estimate pressure

The hedonic decomposition of recreation sites into site characteristics and the representation of these site characteristics in the site-choice framework allow an evaluation of important information including changes in visitation probability, changes in site pressure, and changes in resource value. This is accomplished by developing an equivalent mathematical structure with appropriately altered Θ for policy alternatives and finding the difference in trips between this policy simulation model and the base case. Equation 2 presents the

*<sup>V</sup> <sup>V</sup> AnnualChoiceOccasions*

6 In the simulation context, this is accomplished by multiplying the likelihood of selecting each site (equation 1) by the

1 2

*i J J*

*j j*

exp( ) exp( )

exp( ) exp( )

*V V*

*ij ij*

*ik ik*

(2)

*j*

exp( )

*V*

*ij*

*V*

*ik*

(1)

exp( ) ( )

*i J*

parameters representing the values recreators hold for those site characteristics (S).

for any given site *j, Pi(j)* is summed over all recreators' choice occasions.6

5 The statistical basis for choice theory is the standard conditional logit model [3, 4].

 

*P j*

Aggregating over individuals identifies changes in trips for each site due to the policy that changes to .

Estimates of changes in economic value improve the ability to assess resource performance. The distance from an individual's home to a site is a critical variable in a site-choice model because it represents the fuel cost and travel time required to visit each site.

When distance is converted to travel cost, the site-choice framework supports the calculation of monetary changes in value associated with changes in site characteristics. The mathematical form used to identify dollar-based changes in value associated with a policy that changes to is the difference between the utility levels scaled by the relative impact of travel costs. Equation 3 presents the mathematical structure used to evaluate the change in annual value that a recreator attributes to the policy that changes to .

$$\text{CV}\_{i} = \frac{\text{AnnualTrips}\_{i}}{\phi\_{i}} \left[ \ln \left( \sum\_{j=1}^{l} e\_{ij}^{V} \right) - \ln \left( \sum\_{j=1}^{l} e\_{ij}^{\overline{V}} \right) \right] \tag{3}$$

where ( ,) ( ,) *ij V SV S ij*

CVi refers to the compensating variation or dollar valued willingness-to-pay that recreator *i* has for the change from to . This is the amount of money that would make him indifferent between and . 7

Mathematical structure (S) for property value is the hedonic price approach as developed by [5]. In this structure, property value, identified as market price, is determined according to property characteristics.

$$P = V\_i \tag{4}$$

Properties are those with characteristics influenced by the Boardman River dam system

$$V\_i = f\left(\Theta, \mathcal{S}\right) \tag{5}$$

meaning that the expected market price relates to the state of the Boardman River system,

$$P = f\begin{pmatrix} \Theta \ \end{pmatrix} \tag{6}$$

It is apparent that the change in property value stems directly from the difference in states of the system between current conditions and an alternative.

$$
\Delta Value = \left[ \left( \Theta, S \right) - \left\| \left( \overline{\Theta}, S \right) \right\| \right] \tag{7}
$$

<sup>7</sup> This information is useful for evaluating changes via a utilitarian perspective, such as benefit-cost analysis [6].

Under the assumption that identification of partial effects is sufficient, the expected change in value can be determined by identifying the shadow values of any changing property attributes.8

$$
\Delta\text{ Value}\,\,=\,\text{d}\,\text{P}\,/\,\text{d}\Theta\,\,\tag{8}
$$

The Role of Socioeconomic and Behavioral Modeling

*NPV TR TC r* – (9)

*Q aV hour hour* (11)

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 165

Valuing an asset in financial terms is accomplished by valuing the (net) stream of income resulting from ownership. Because this income occurs at different point in time, values are

In this equation, total value is the net present value of total annual revenues minus total

Total revenues are composed of hourly price and quantity information by service. The various electrical services could include energy, renewable energy, and ancillary services as

*Annual Energy Energy Energy*

*MWAncillaryServices HoursAncillaryServices*

Revenues are composed of hourly price and quantity information. Hourly generation

where Qhour is hourly electricity production, Vhour is the volume of hourly flow and *a* is a positive constant that converts flow to electricity that is specific to technology and hydraulic

*Annual Overhead Government MDNR FERC TC Annual Cost Annual Cost Annual Cost Annual Cost* (12)

Information requirements include hourly electricity quantities and prices going out into the future. Quantities are identified via a combination of river flow and dam-specific

The analysis divides the river into 11 segments. The segments were chosen for their distinct characteristics. For example, each impoundment is physically different than the free-flowing sections in between. Each impoundment is represented by its own segment, and each river section between the inlet of one impoundment and the next upstream dam is represented by

*MWrenewableEnergy renewableEnergy renewableEnergy Hours <sup>P</sup>* (10)

*TR MW Hours P*

discounted to present values as indicated in the equation below:

8760

*Hours*

8760

1

8760

1

information, including head and turbine efficiency.

**1.2. Baseline data and transfer studies** 

1

annual costs.

indicated below.

quantity is identified as:

head.

Annual costs are:

These values are identified as model coefficients in empirical studies that use hedonic analysis to evaluate the relationship between market prices and house characteristics. Given studies that evaluate relevant characteristics, results from these studies can be calibrated and applied in mathematical simulation.

The evaluation of local economic impacts is typically accomplished via a mathematical economic technique called input/output (I/O) analysis [7]. I/O analysis was developed to address policy issues with respect to income, sales, demand, local infrastructure, and plant closing.

In I/O models, changes in final demand for one industry affect other industries within a local economic area.


Multipliers measure total changes in output, income, employment, or value added. Parameters required to specify I/O models include the following:


Data requirements include outputs and inputs from other sectors, value added, employment, wages and business taxes paid, imports and exports, final demand by households and government, capital investment, business inventories, marketing margins, and inflations factors (deflators). These data are available both for the 528 producing sectors at the national level and for the corresponding sectors at the county level. Data on the technological mix of inputs and levels of transactions between producing sectors are available from detailed input-output tables of the national economy.

<sup>8</sup> The identification of partial effects is most appropriate when expected changes are not dramatic and widespread.

Valuing an asset in financial terms is accomplished by valuing the (net) stream of income resulting from ownership. Because this income occurs at different point in time, values are discounted to present values as indicated in the equation below:

$$NPV = \begin{pmatrix} TR \ -TC \end{pmatrix} r \tag{9}$$

In this equation, total value is the net present value of total annual revenues minus total annual costs.

Total revenues are composed of hourly price and quantity information by service. The various electrical services could include energy, renewable energy, and ancillary services as indicated below.

$$\begin{aligned} \text{TR}\_{Annual} &= \sum\_{Hours = 1}^{8760} \text{MW}\_{Energy} \bullet \text{Hours}\_{Energy} \bullet \text{P}\_{Energy} \\ &+ \sum\_{1}^{8760} \text{MW}\_{remwakeEnergy} \bullet \text{Hours}\_{remwakeEnergy} \bullet \text{P}\_{remwakeHEargy} \\ &+ \sum\_{1}^{8760} \text{MW}\_{AniliaryService} \bullet \text{Hours}\_{AnilingService} \end{aligned} \tag{10}$$

Revenues are composed of hourly price and quantity information. Hourly generation quantity is identified as:

$$Q\_{hour} = aV\_{hour} \tag{11}$$

where Qhour is hourly electricity production, Vhour is the volume of hourly flow and *a* is a positive constant that converts flow to electricity that is specific to technology and hydraulic head.

Annual costs are:

164 Environmental Land Use Planning

and applied in mathematical simulation.

attributes.8

closing.

economy.

local economic area.

Under the assumption that identification of partial effects is sufficient, the expected change in value can be determined by identifying the shadow values of any changing property

These values are identified as model coefficients in empirical studies that use hedonic analysis to evaluate the relationship between market prices and house characteristics. Given studies that evaluate relevant characteristics, results from these studies can be calibrated

The evaluation of local economic impacts is typically accomplished via a mathematical economic technique called input/output (I/O) analysis [7]. I/O analysis was developed to address policy issues with respect to income, sales, demand, local infrastructure, and plant

In I/O models, changes in final demand for one industry affect other industries within a

*Indirect effects* are changes in inter-industry transactions as supplying industries

Induced effects reflect changes in local spending that result from income changes in the

Multipliers measure total changes in output, income, employment, or value added.

*Output multipliers* relate the changes in sales to final demand by one industry to total

*Income and employment multipliers* relate the change in direct income to changes in total

 *Value added multipliers* are interpreted the same as income and employment multipliers. They relate changes in value added in the industry experiencing the direct effect to total

Data requirements include outputs and inputs from other sectors, value added, employment, wages and business taxes paid, imports and exports, final demand by households and government, capital investment, business inventories, marketing margins, and inflations factors (deflators). These data are available both for the 528 producing sectors at the national level and for the corresponding sectors at the county level. Data on the technological mix of inputs and levels of transactions between producing sectors are available from detailed input-output tables of the national

8 The identification of partial effects is most appropriate when expected changes are not dramatic and widespread.

*Direct effects* represent the initial change in the industry in question.

directly and indirectly affected industry sectors.

changes in value added for the local economy.

income within the local economy.

Parameters required to specify I/O models include the following:

respond to increased demands from the directly affected industries.

changes in output (gross sales) by all industries within the local area.

*Value dP d* / (8)

$$\text{TC}\_{\text{Annual}} = \text{Annual Cost}\_{\text{Coverland}} + \text{Annual Cost}\_{\text{Covernment}} + \text{Annual Cost}\_{\text{MZNR}} + \text{Annual Cost}\_{\text{FERC}} \text{(12)}$$

Information requirements include hourly electricity quantities and prices going out into the future. Quantities are identified via a combination of river flow and dam-specific information, including head and turbine efficiency.

#### **1.2. Baseline data and transfer studies**

The analysis divides the river into 11 segments. The segments were chosen for their distinct characteristics. For example, each impoundment is physically different than the free-flowing sections in between. Each impoundment is represented by its own segment, and each river section between the inlet of one impoundment and the next upstream dam is represented by

a distinct segment. Table 1 contains segments used in this assessment. Figure 2 provides a map.

The Role of Socioeconomic and Behavioral Modeling

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 167

this division. Although Segments 9 and 10 are also relatively large when compared to the others, they are less likely to be affected by the dam management alternatives, given their

Pressure estimates for Boardman River anglers come from an on-site creel study that the Michigan Department of Natural Resources (MDNR) conducted during the 2005 season for Segments 1–8 of the Boardman River. The MDNR data collection included angler counts, as well as the number of fish caught by fish species. From these data, the MDNR developed statistically based seasonal estimates for the Boardman River in terms of the number of

Based on the MDNR results, we developed pressure estimates, by segment, for this

Allocate the total number of MDNR days across Segments 1–8 of the Boardman River.

Separate the number of angler days for each segment into resident days and visitor

Table 2 contains the resulting allocation of the fishing days across the various segments and

Segment Resident days Visitor days

Extrapolate angler day estimates from Segment 8 to Segments 9 and 10.

1 880 to 1 440 220 to 360 2 720 to 1 120 180 to 280 3 800 to 1 200 200 to 300 4 160 to 240 40 to 60 5 320 to 560 80 to 140 6a 1 680 to 2 720 420 to 680 6b 1 680 to 2 720 420 to 680 7 960 to 1 520 240 to 380 8 1 760 to 2 720 440 to 680 9 5 840 to 8 960 1 460 to 2 240 10 2 000 to 3 200 500 to 800 Total 16 800 to 26 400 4 200 to 6 600

**Table 2.** Annual number of resident and visitor angling days on the Boardman River

To simulate the implication of changes in site characteristics, we employ a recreational fishing study conducted by [11], which covers fishing sites across the state of Michigan and explicitly covers varied fishing experience, including inland rivers and inland lakes, as well as anadromous fishing opportunities, all of which are relevant to the Boardman River analysis. Because this statistical model studies the same activity on the same population, we can use both the site characteristics and the estimated parameters presented in the [11] study

locations well above the Brown Bridge Dam.

angler trips and hourly catch rates by species.

assessment by undertaking the following steps:

days.

residents or visitors.

and reproduced in Table 3 below.


Sources: [8–10]

**Table 1.** Boardman River segments

**Figure 2.** Location of segments 1–10 along the Boardman River

As Table 1 shows, the segments are numbered 1–10, with Segment 6 split into 6a and 6b. Segment 6 is physically homogeneous under our definition of a free-flowing river between impoundments. However, given its location between two impoundments, it has the potential to be affected by one or more of the dam management alternatives. By dividing it, the potentially affected recreation sites are closer in size to each than they would be without this division. Although Segments 9 and 10 are also relatively large when compared to the others, they are less likely to be affected by the dam management alternatives, given their locations well above the Brown Bridge Dam.

Pressure estimates for Boardman River anglers come from an on-site creel study that the Michigan Department of Natural Resources (MDNR) conducted during the 2005 season for Segments 1–8 of the Boardman River. The MDNR data collection included angler counts, as well as the number of fish caught by fish species. From these data, the MDNR developed statistically based seasonal estimates for the Boardman River in terms of the number of angler trips and hourly catch rates by species.

Based on the MDNR results, we developed pressure estimates, by segment, for this assessment by undertaking the following steps:


166 Environmental Land Use Planning

map.

Sources: [8–10]

**Table 1.** Boardman River segments

**Figure 2.** Location of segments 1–10 along the Boardman River

As Table 1 shows, the segments are numbered 1–10, with Segment 6 split into 6a and 6b. Segment 6 is physically homogeneous under our definition of a free-flowing river between impoundments. However, given its location between two impoundments, it has the potential to be affected by one or more of the dam management alternatives. By dividing it, the potentially affected recreation sites are closer in size to each than they would be without

a distinct segment. Table 1 contains segments used in this assessment. Figure 2 provides a

Site number Location Size 1 From mouth of Boardman River to Union Street Dam 1,2 miles 2 Boardman Lake 339,0 acres 3 From inlet of Boardman Lake to Sabin Dam 2,2 miles 4 Sabin Pond 40,0 acres 5 Keystone Pond 103,0 acres 6a From inlet of Keystone Pond to midpoint 6,9 miles 6b From midpoint to Brown Bridge Dam 6,9 miles 7 Brown Bridge Pond 191,0 acres 8 From inlet of Brown Bridge Pond to Forks 6,0 miles 9 North Branch of the Boardman River 23,5 miles 10 South Branch of the Boardman River 10,0 miles

 Separate the number of angler days for each segment into resident days and visitor days.


Table 2 contains the resulting allocation of the fishing days across the various segments and residents or visitors.

**Table 2.** Annual number of resident and visitor angling days on the Boardman River

To simulate the implication of changes in site characteristics, we employ a recreational fishing study conducted by [11], which covers fishing sites across the state of Michigan and explicitly covers varied fishing experience, including inland rivers and inland lakes, as well as anadromous fishing opportunities, all of which are relevant to the Boardman River analysis. Because this statistical model studies the same activity on the same population, we can use both the site characteristics and the estimated parameters presented in the [11] study and reproduced in Table 3 below.


The Role of Socioeconomic and Behavioral Modeling

top quality

1,2 0,0

0,0 2,2

Miles of second quality

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 169

Table 4 contains the lake segments, or impoundments, for the Boardman River. The table indicates the types of species in the various segments, based on conversations with representatives from the Grand Traverse Conservation District (GTCD) [13], the MDNR [14],

Segment Species type Acres

Table 5 contains the current conditions for the river segments of the Boardman, Segments 1, 3, 6, 8, 9, and 10. The number of stream miles that are "top quality" and "second quality" refers to the MDNR designation of fishery conditions previously described. Additionally, because Segments 1 and 3 support anadromous runs, the current conditions for these sites includes catch rates (number of fish caught per hour) for Coho Salmon, Chinook Salmon, and Rainbow Trout, based on information from the 2005 Boardman

> 0,001 to 0,014 0,006 to 0,042 0,084 to 0,237

> 0,000 to 0,001 0,002 to 0,006 0,067 to 0,084

The last type of information that completes the picture of current conditions for recreational fishing is a description of the substitute sites. Substitute sites play a key role in the

6a Cold N/A 6,9 0,0 6b Cold N/A 6,9 0,0 8 Cold N/A 6,0 0,0 9 Cold N/A 23,5 0,0 10 Cold N/A 10,0 0,0

and the fisheries reports prepared for this project [8,15].

**Table 4.** Current conditions of the impoundment fishing sites

River Creel Survey [16].

1 Anadromous

3 Anadromous

Sources: [13–14,16]

Warm Cold

Cold

determination of angler satisfaction.

2 Warm 339 4 Warm 40 5 Warm 103 7 Warm 191

Segment Species type Anadromous catch rates Miles of

Coho Chinook Rainbow

Coho Chinook Rainbow

**Table 5.** Current conditions of the Boardman River fishing sites

**Table 3.** Parameters for fishing site choice

Site characteristics to populate the model are based on physical site characteristics and MDNR quality and catch rate designations. MDNR [12] sets out its quality designations in the Manual of Fisheries Survey Methods II. The stream miles are categorized based on their quality. Those of high quality are rated either "top quality" or "second quality." Top quality stream miles are characterized by containing good self-sustaining fish populations. Second quality streams are characterized by containing significant fish populations, which are appreciably limited by such factors as inadequate natural reproduction, competition, siltation, or pollution.

Table 4 contains the lake segments, or impoundments, for the Boardman River. The table indicates the types of species in the various segments, based on conversations with representatives from the Grand Traverse Conservation District (GTCD) [13], the MDNR [14], and the fisheries reports prepared for this project [8,15].


**Table 4.** Current conditions of the impoundment fishing sites

Table 5 contains the current conditions for the river segments of the Boardman, Segments 1, 3, 6, 8, 9, and 10. The number of stream miles that are "top quality" and "second quality" refers to the MDNR designation of fishery conditions previously described. Additionally, because Segments 1 and 3 support anadromous runs, the current conditions for these sites includes catch rates (number of fish caught per hour) for Coho Salmon, Chinook Salmon, and Rainbow Trout, based on information from the 2005 Boardman River Creel Survey [16].


Sources: [13–14,16]

168 Environmental Land Use Planning

**Table 3.** Parameters for fishing site choice

siltation, or pollution.

Characteristic Mean

Trip cost -15 Great Lakes warm, walleye catch rate 6,63 Great Lakes warm, bass catch rate 1,45 Great Lakes warm, pike catch rate 0,36 Great Lakes warm, perch catch rate -2,75 Great Lakes warm, carp catch rate 0,87 Great Lakes cold, constant -14,75 Great Lakes cold, Chinook catch rate 5,14 Great Lakes cold, Coho catch rate 5,45 Great Lakes cold, lake trout catch rate 3,23 Great Lakes cold, rainbow catch rate 2,19 Inland lakes warm, shore constant -14,06 Inland lakes warm, interior constant -7,8 Inland lakes warm, warm lake acres/1,000 21,58 Inland lakes cold, shore constant -11,43 Inland lakes cold, interior constant -18,48 Inland lakes cold, cold lake acres/1,000 3,73 Rivers/streams warm, shore constant -10,09 Rivers/streams warm, interior constant -11,21 Rivers/streams warm, top quality miles/100 5,35 Rivers/streams warm, second quality miles/100 -3,58 Rivers/streams cold, shore constant -15,23 Rivers/streams cold, interior constant -19,24 Rivers/streams cold, top quality miles/100 5,09 Rivers/streams cold, second quality miles/100 0,05 Anadromous runs, shore constant -10,57 Anadromous runs, interior constant -7,78 Anadromous runs, Chinook catch rate 3,37 Anadromous runs, coho catch rate -0,3 Anadromous runs, rainbow catch rate 8,04

Site characteristics to populate the model are based on physical site characteristics and MDNR quality and catch rate designations. MDNR [12] sets out its quality designations in the Manual of Fisheries Survey Methods II. The stream miles are categorized based on their quality. Those of high quality are rated either "top quality" or "second quality." Top quality stream miles are characterized by containing good self-sustaining fish populations. Second quality streams are characterized by containing significant fish populations, which are appreciably limited by such factors as inadequate natural reproduction, competition,

**Table 5.** Current conditions of the Boardman River fishing sites

The last type of information that completes the picture of current conditions for recreational fishing is a description of the substitute sites. Substitute sites play a key role in the determination of angler satisfaction.

We used three criteria in the selection of substitute sites. The first criterion was that the substitute site be within 150 miles of some portion of the Boardman River. We selected this distance criterion to be consistent with the [11] study. The second criterion was to incorporate a variety of potential fishing opportunities consistent with the real world. Thus, the selected substitute sites include inland lakes, rivers, and Lake Michigan sites. Finally, when possible substitute sites met the first two criteria, we selected those with the most recent data available in terms of the site features identified in the [11] model.

The Role of Socioeconomic and Behavioral Modeling

Traverse Bay Leland Manistee

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 171

W. Grand

Elk Rapids E. Grand

**Table 8.** Current conditions of the Lake Michigan substitute sites

**Table 9.** Annual number of paddling days on the Boardman River

Catch rate:

Source: [21]

frequencies.

Sources: [22–23]

Traverse Bay

Angler days 9 930 9 974 21 977 6 294 63 815

Walleye 0,0010 0,0000 0,0000 0,0000 0,0001 Bass 0,0038 0,0037 0,0011 0,0031 0,0001 Pike 0,0000 0,0000 0,0000 0,0000 0,0002 Perch 0,4967 0,0465 0,1097 0,0005 0,0153 Carp 0,0000 0,0000 0,0000 0,0000 0,0000 Chinook 0,0141 0,0652 0,0558 0,0897 0,1757 Coho 0,0018 0,0006 0,0014 0,0004 0,0047 Lake trout 0,0087 0,0409 0,0267 0,0083 0,0051 Rainbow 0,0308 0,0014 0,0007 0,0448 0,0061

In addition to fishing, the Boardman provides canoeing and kayaking opportunities. Sitespecific data on the current number of paddling days along the Boardman River are not readily available. For this reason, we rely on estimates from local individuals with first-hand knowledge of paddling use of the Boardman. These estimates from knowledgeable locals are validated using publicly available data on paddling participation rates and trip-taking

The relative proportion of resident to visitor days was used to allocate the total days by

segment across residents and visitors. These results appear in Table 9 below.

Segment Resident days Visitor days 1 120 to 180 80 to 120 2 50 to 60 About 40 3 300 to 1 140 200 to 760 4 50 to 60 About 40 5 30 to 300 15 to 200 6a 600 to 2 400 400 to 1 600 6b 600 to 1 800 400 to 1 200 7 50 to 600 40 to 400 8 1 500 to 3 600 1 000 to 2 400 9 0 to 10 — 10 0 to 10 — Total 3 300 to 10 160 2 215 to 6 760

Table 6 contains the number of angler days to the inland lake substitute sites, as well as the current conditions. Table 7 describes the number of days and the current conditions for the substitute sites that are rivers. Table 8 contains information related to the number of days and the current conditions of the Lake Michigan substitute sites. The items in this table correspond to the features used in the [11] study for Great Lake sites. Unlike the inland lake or river sites, the features of these sites that affect angler satisfaction are the catch rates for the various cold and warm water species.


Sources: [17–18]

**Table 6.** Current conditions of the inland lake substitute sites


Sources: [18–20]

**Table 7.** Current conditions of the inland river substitute sites

The Role of Socioeconomic and Behavioral Modeling in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 171


Source: [21]

170 Environmental Land Use Planning

the various cold and warm water species.

Lake Leelanau 31 000 Warm

Higgins Lake 26 000 Warm

**Table 6.** Current conditions of the inland lake substitute sites

 Cold Warm

**Table 7.** Current conditions of the inland river substitute sites

Site Number

model.

Sources: [17–18]

Rogue River (Kent County)

Manistee River (from Hodenpyl Dam to Red Bridge)

Sources: [18–20]

We used three criteria in the selection of substitute sites. The first criterion was that the substitute site be within 150 miles of some portion of the Boardman River. We selected this distance criterion to be consistent with the [11] study. The second criterion was to incorporate a variety of potential fishing opportunities consistent with the real world. Thus, the selected substitute sites include inland lakes, rivers, and Lake Michigan sites. Finally, when possible substitute sites met the first two criteria, we selected those with the most recent data available in terms of the site features identified in the [11]

Table 6 contains the number of angler days to the inland lake substitute sites, as well as the current conditions. Table 7 describes the number of days and the current conditions for the substitute sites that are rivers. Table 8 contains information related to the number of days and the current conditions of the Lake Michigan substitute sites. The items in this table correspond to the features used in the [11] study for Great Lake sites. Unlike the inland lake or river sites, the features of these sites that affect angler satisfaction are the catch rates for

Site Number of days Species type Acres

catch rates

20 000 Anadromous Coho 0,001 7,5 5,0

0,095

cold 8 607

cold 9 600

Miles of top quality

0,112 12,9 0,0

Miles of second quality

Houghton Lake 107 000 Warm 20 075

Long Lake (Alpena County) 17 000 Warm 5 341 Green Lake 8 000 Warm 1 994

of days Species types Anadromous

Cold Chinook 0,043

Rainbow

8 000 Anadromous Rainbow

**Table 8.** Current conditions of the Lake Michigan substitute sites

In addition to fishing, the Boardman provides canoeing and kayaking opportunities. Sitespecific data on the current number of paddling days along the Boardman River are not readily available. For this reason, we rely on estimates from local individuals with first-hand knowledge of paddling use of the Boardman. These estimates from knowledgeable locals are validated using publicly available data on paddling participation rates and trip-taking frequencies.

The relative proportion of resident to visitor days was used to allocate the total days by segment across residents and visitors. These results appear in Table 9 below.


Sources: [22–23]

**Table 9.** Annual number of paddling days on the Boardman River


The Role of Socioeconomic and Behavioral Modeling

Scenic rating Predictability of water level

Water quality (pollution)

in an Integrated, Multidisciplinary Dam-Management Study: Case Study of the Boardman River Dams 173

Parking Crowding

Union Street Dam 0,0 3,8 3,8 3,0 1,8 4,6 2 Boardman Lake 0,0 4,0 3,9 2,9 3,0 4,8

Lake to Sabin Dam 0,0 3,1 4,0 4,3 4,8 4,6 4 Sabin Pond 0,0 3,3 4,4 4,3 4,4 4,6

Boardman Dam 1,0 3,5 4,1 4,3 3,6 3,5

Brown Bridge Dam 1,6 4,0 3,4 4,0 4,6 4,2 7 Brown Bridge Pond 0,0 4,3 4,3 4,4 5,0 4,4

9 North branch 0,0 2,7 4,7 3,7 5,0 4,3 10 South branch 0,0 2,7 4,7 3,7 5,0 4,3

We used information from Trails.com and the Michigan Atlas and Gazetteer [26] to compile a list of substitute sites. The list included the Au Sable, the Betsie, the Pine, and the Platte Rivers. As part of the questionnaire described above, we included a question about these substitutes and asked respondents to rate them in the same way that they rated the Boardman. Additionally, we provided the respondents with an opportunity to name other substitute sites and rate them. The responses to the questionnaire identified four potential

Table 12 below provides the information on the perceived site characteristics for the substitute sites. The second column of Table 12 contains an estimate of the total number of paddling days for the substitute sites. This number is a necessary input for modeling. We used a similar methodology to the top-down approach using verifiable data. The MDNR provides an estimate of the number of paddling days statewide [27]. Based on an estimate of the miles of navigable river statewide [28], we estimated the average number of days that a typical river mile supports. We applied that number to the number of river miles for the

The Boardman River enhances the recreation experience for a variety of trail activities, including hiking, walking, biking, and horseback riding. Several segments of the Boardman River support designated trails, particularly around the impoundments. In the segments farther upstream, portions of the Michigan Shore-to-Shore Riding Trail and the North

0,4 4,2 3,2 4,2 4,8 4,2

Seg.

8
