**Sea Level Rise**

[2] Baede APM editor. Annex I glossary. In Climate Change 2007: The Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, and Mille HL, editors. Contribution of working group I to the fourth assessment report of the inter governmental panel on climate change. Cambridge: Cambridge University Press;

[3] Pinn EH, Mitchell K, Corkill J. The assemblages of groynes in relation to substratum age, aspect and microhabitat. Estuarine Coastal and Shelf Science. 2005;**62**:271-282

[4] Chen JY, Chen SL. Estuarine and coastal challenges in China. Chinese Journal of

[5] Maglara M. Estimation of Exposure of Elafonisos Coast on Coastal Natural Hazards.

[6] Doukakis E. Coastal vulnerability and risk parameters. European Water. 2005;**11**(12):3-7 [7] Van der Werf JJ, Donoghue TO, Buijsrogge RH, Kranenburg WM. Practical sand transport formula for non-breaking waves & currents. Coastal Engineering. 2013;**76**:26-42 [8] Callaghan DP, Wainwright D. The impact of various methods of wave transfers from deep water to nearshore when determining extreme beach erosion. Coastal Engineering

Oceanology and Limnology. June 2002;**20**(2):174-181

Athens: Harokopion University, Department of Geography; 2011

2007. pp. 941-954

6 Sea Level Rise and Coastal Infrastructure

Journal. 2013;**74**:50-58

**Chapter 2**

Provisional chapter

**Constructing Local Sea Level Rise Scenarios for**

Constructing Local Sea Level Rise Scenarios for

**Insights from Coasts of India**

Insights from Coasts of India

Kandasami Palanivelu

Kandasami Palanivelu

Abstract

coastal inundation

Dhanya Praveen, Andimuthu Ramachandran and

Dhanya Praveen, Andimuthu Ramachandran and

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.74325

**Assessing Possible Impacts and Adaptation Needs:**

DOI: 10.5772/intechopen.74325

Rising seas are one of the crucial impacts of global warming. Rise in the mean sea level may impact coastal communities under an increasingly warming climate. The coastal zones are highly resourceful and dynamic. The coastal zones are facing many natural hazards such as erosion, storm surge, tsunami, coastal flooding and sea level rise. It is projected to have a three-time expansion of density of population in the coastal areas, and 50% of the world's population will be occupied within the vicinity of 100 km of coastal areas. India has a very long coastline of 7500 km and covers 16.7% of the world's population and has a very high population growth rate which itself make India highly sensitive to these environmental challenge. Projections of mean global sea level rise (GSLR) provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Therefore, in this chapter, the main endeavor is to identify and compare coastal vulnerability to projected future sea level rise. In order to project the sea level rise at local level, a climateand sea level rise simulator model output based on IPCC AR5 (Special Report on Emission Scenarios) has been employed under different scenarios. The results reveal that sea level for Visakhapatnam, Chennai, Cochin and Mumbai may increase by 1.16, 1.19, 1.34, 1.24 m, respectively, by 2100 under the high-emission business as usual carbon pollution scenario under IPCC AR5 Representative Concentration Pathway. The sea level of west coast tends to rise slightly more than the east coastal areas of India. These estimates have great potential for the coastal regulatory authority and other decision-makers to take precautions with regard to inundations of low-lying areas and to conserve India's eco-sensitive coastal resources.

Keywords: sea level rise, climate change, RCP, coast, India, LSLR, adaptation, IPCC AR5,

© 2016 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 eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. 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.

Assessing Possible Impacts and Adaptation Needs:

#### **Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights from Coasts of India** Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights from Coasts of India

DOI: 10.5772/intechopen.74325

Dhanya Praveen, Andimuthu Ramachandran and Kandasami Palanivelu Dhanya Praveen, Andimuthu Ramachandran and Kandasami Palanivelu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.74325

#### Abstract

Rising seas are one of the crucial impacts of global warming. Rise in the mean sea level may impact coastal communities under an increasingly warming climate. The coastal zones are highly resourceful and dynamic. The coastal zones are facing many natural hazards such as erosion, storm surge, tsunami, coastal flooding and sea level rise. It is projected to have a three-time expansion of density of population in the coastal areas, and 50% of the world's population will be occupied within the vicinity of 100 km of coastal areas. India has a very long coastline of 7500 km and covers 16.7% of the world's population and has a very high population growth rate which itself make India highly sensitive to these environmental challenge. Projections of mean global sea level rise (GSLR) provide insufficient information to plan adaptive responses; local decisions require local projections that accommodate different risk tolerances and time frames and that can be linked to storm surge projections. Therefore, in this chapter, the main endeavor is to identify and compare coastal vulnerability to projected future sea level rise. In order to project the sea level rise at local level, a climateand sea level rise simulator model output based on IPCC AR5 (Special Report on Emission Scenarios) has been employed under different scenarios. The results reveal that sea level for Visakhapatnam, Chennai, Cochin and Mumbai may increase by 1.16, 1.19, 1.34, 1.24 m, respectively, by 2100 under the high-emission business as usual carbon pollution scenario under IPCC AR5 Representative Concentration Pathway. The sea level of west coast tends to rise slightly more than the east coastal areas of India. These estimates have great potential for the coastal regulatory authority and other decision-makers to take precautions with regard to inundations of low-lying areas and to conserve India's eco-sensitive coastal resources.

Keywords: sea level rise, climate change, RCP, coast, India, LSLR, adaptation, IPCC AR5, coastal inundation

© 2016 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 eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. 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.

### 1. Introduction

The Earth's climate has changed throughout history; however, the current warming trend is of particular connotation because most of it is very likely human-induced and proceeding at a rate that is unprecedented in the past 1300 years [1]. Over the period 1951–2012, global mean surface temperature increased approximately by 0.12C per decade [2, 3]. The considerable coastal threats include shoreline erosion and inundation of coastal areas. Various global models and forecasts are warning of the loss of critical habitats along the coast due to this global issue. India's Coastal Regulation Zone (CRZ) act of 1991 was revised and reissued in 2011 to incorporate such threats. It has seriously taken into consideration the impact of future sea level changes with more of a management approach rather than a regulatory approach. Additionally, the Disaster Management (DM) act of 2005 and DM policy of 2009 stress that developing contemporary forecasting and early warning systems are prerogative of every State in India. These form part of ensuring efficient response and relief to the vulnerable sections of the society. It is reported that global sea level rose about 17 cm (6.7 inches) in the twentieth century. The rate in the last decade, however, is nearly double than that of the last century [4]. It has also recently been reported by Kopp that global sea levels have risen faster from the late twentieth century than in any of the previous 27 centuries [5]. It is certain that the mean sea level would continue to rise all over the globe in the years to come [2]. Gitay et al. [6] projected that 20% of the coastal wetlands could be lost due to sea level rise by the end of this century. Natural and cultural ecosystems along the coasts are highly susceptible to the consequences of sea level rise and resultant impacts [7–9]). According the IPCC fourth assessment report, SLR would be in the range of 18–59 cm from 1990 to 2090 [10].

provide hands-on information to facilitate coastal manager and adaptation planners to frame location-specific and time-based adaptation strategies to sea level rise. The state of Andhra Pradesh has long coastline of 930 km. A climate simulator model based sea level rise data based on IPCC Assessment Report 5 (Special Report on Emission Scenarios-AR5) has been used to project the sea level rise at local level under different scenarios. The global SLR data were downloaded separately for the selected study areas such as Mumbai, Visakhapatnam, Chennai and Cochin were processed (Source: https://tidesandcurrents.noaa.gov/sltrends/& http://www.psmsl.org/data) to understand the mean changes by the end of twenty-first century [15]. The sea level rise projection is employed based on GHG emission trajectories as per the below-mentioned Representative Concentration Pathways (RCPs) of IPCC AR5 (Table 1). Box and whisker plots were used to make visual regional comparisons of projected sea level

Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights…

http://dx.doi.org/10.5772/intechopen.74325

11

India owns 6100 km of mainland coastline and coastline of 1197 Indian islands constituting a total coastal length of 7516.6 km touching 13 States and Union Territories (UTs) (Figure 1). The eastern coastal area lies between the Eastern Ghats and the Bay of Bengal and extends from the Ganges delta to Kanyakumari. Chilika Lake and the Pulicat Lake (lagoon) are the important geographical features of east coast. The western coastal strip encompasses from the Gulf of Cambay (Gulf of Khambhat) in the north to Cape Comorin (Kanyakumari) in the south. It is divided into three parts: (1) the Konkan Coast, (2) the Karnataka Coast and (3) the Kerala

It is obvious from the results that the study area may experience sea level rise in future. The state of Andhra Pradesh, located at south-east coastal areas of India, has long coastline of 930 km. The outcomes from the climate simulator model based on IPCC AR5 (Special Report on Emission Scenarios) emission trajectories projects rise in sea level at local level under different scenarios.. The results reveal that sea level may increase by 1.16 m under the highemission carbon pollution scenario Representative Concentration Pathway 8.5. Under moderately strong emission reduction scenario, that is, RCP 4.5, the sea level is projected to be 0.89 m above MSL. It can be noted that with aggressive cuts in the carbon pollution, we can further

The state of Tamil Nadu has the second largest coastline of India with 1076 km. It is a part of Coromandel Coast of Bay of Bengal and Indian Ocean. In the northernmost part of the coast, Chennai is located, which is not only the capital of the state but it is an important commercial and industrial centre in the country, with Kanyakumari forming the southern tip where the Indian Ocean, Bay of Bengal and Arabian Sea meets. Pulicat Lake, which has a rich and fragile

Name Radiative forcing CO2 equiv (p.p.m)

RCP 8.5 8.5 wm2 in 2100 1370 RCP 4.5 4.5 Wm2 post in 2100 650 RCP2.6 (RCP3PD) 3 Wm<sup>2</sup> before 2100, declining to 2.6 Wm<sup>2</sup> by 2100 490

Table 1. Radiative forcing as per representative concentration pathways (RCPs) of IPCC AR5.

limit the undesirable rise in sea level to be around 0.77 m as per RCP 2.6 (Figure 2).

rise.

Coast.

Church and White revealed that there is a considerable variability in the rate of rise in sea level during the twentieth century, but there has been statistically significant acceleration of 0.009 0.003 and 0.009 0.004 mm year-2 since 1880 and 1900 [11].

Understanding the local changes with respect to climate and sea level rise warrants better adaptation and future management especially for the vulnerable low-lying coastal areas in the developing countries [11, 12]. Although it is highly exigent, downscaling high-resolution data at the local scale using the regional climate and sea level rise simulation models are the best available tools that provide huge potential for further impact assessments and adaptation planning [13].

### 2. Methodology and results

Projections on sea level rise are highly important especially from the point of view of Asia, as plenty of natural resources and ecosystems are already vulnerable to climate variations [14]. Noteworthy progress has been made during the last decade in estimating and understanding historical sea level rise. However, much work remains to be done in the future. Of particular importance is the maintenance and continuation of the observing network such as the permanent service for mean sea level (PSMSL) archive. Thus, the objective of this chapter is also to provide hands-on information to facilitate coastal manager and adaptation planners to frame location-specific and time-based adaptation strategies to sea level rise. The state of Andhra Pradesh has long coastline of 930 km. A climate simulator model based sea level rise data based on IPCC Assessment Report 5 (Special Report on Emission Scenarios-AR5) has been used to project the sea level rise at local level under different scenarios. The global SLR data were downloaded separately for the selected study areas such as Mumbai, Visakhapatnam, Chennai and Cochin were processed (Source: https://tidesandcurrents.noaa.gov/sltrends/& http://www.psmsl.org/data) to understand the mean changes by the end of twenty-first century [15]. The sea level rise projection is employed based on GHG emission trajectories as per the below-mentioned Representative Concentration Pathways (RCPs) of IPCC AR5 (Table 1). Box and whisker plots were used to make visual regional comparisons of projected sea level rise.

1. Introduction

10 Sea Level Rise and Coastal Infrastructure

planning [13].

2. Methodology and results

The Earth's climate has changed throughout history; however, the current warming trend is of particular connotation because most of it is very likely human-induced and proceeding at a rate that is unprecedented in the past 1300 years [1]. Over the period 1951–2012, global mean surface temperature increased approximately by 0.12C per decade [2, 3]. The considerable coastal threats include shoreline erosion and inundation of coastal areas. Various global models and forecasts are warning of the loss of critical habitats along the coast due to this global issue. India's Coastal Regulation Zone (CRZ) act of 1991 was revised and reissued in 2011 to incorporate such threats. It has seriously taken into consideration the impact of future sea level changes with more of a management approach rather than a regulatory approach. Additionally, the Disaster Management (DM) act of 2005 and DM policy of 2009 stress that developing contemporary forecasting and early warning systems are prerogative of every State in India. These form part of ensuring efficient response and relief to the vulnerable sections of the society. It is reported that global sea level rose about 17 cm (6.7 inches) in the twentieth century. The rate in the last decade, however, is nearly double than that of the last century [4]. It has also recently been reported by Kopp that global sea levels have risen faster from the late twentieth century than in any of the previous 27 centuries [5]. It is certain that the mean sea level would continue to rise all over the globe in the years to come [2]. Gitay et al. [6] projected that 20% of the coastal wetlands could be lost due to sea level rise by the end of this century. Natural and cultural ecosystems along the coasts are highly susceptible to the consequences of sea level rise and resultant impacts [7–9]). According the IPCC fourth assessment

report, SLR would be in the range of 18–59 cm from 1990 to 2090 [10].

0.009 0.003 and 0.009 0.004 mm year-2 since 1880 and 1900 [11].

Church and White revealed that there is a considerable variability in the rate of rise in sea level during the twentieth century, but there has been statistically significant acceleration of

Understanding the local changes with respect to climate and sea level rise warrants better adaptation and future management especially for the vulnerable low-lying coastal areas in the developing countries [11, 12]. Although it is highly exigent, downscaling high-resolution data at the local scale using the regional climate and sea level rise simulation models are the best available tools that provide huge potential for further impact assessments and adaptation

Projections on sea level rise are highly important especially from the point of view of Asia, as plenty of natural resources and ecosystems are already vulnerable to climate variations [14]. Noteworthy progress has been made during the last decade in estimating and understanding historical sea level rise. However, much work remains to be done in the future. Of particular importance is the maintenance and continuation of the observing network such as the permanent service for mean sea level (PSMSL) archive. Thus, the objective of this chapter is also to India owns 6100 km of mainland coastline and coastline of 1197 Indian islands constituting a total coastal length of 7516.6 km touching 13 States and Union Territories (UTs) (Figure 1). The eastern coastal area lies between the Eastern Ghats and the Bay of Bengal and extends from the Ganges delta to Kanyakumari. Chilika Lake and the Pulicat Lake (lagoon) are the important geographical features of east coast. The western coastal strip encompasses from the Gulf of Cambay (Gulf of Khambhat) in the north to Cape Comorin (Kanyakumari) in the south. It is divided into three parts: (1) the Konkan Coast, (2) the Karnataka Coast and (3) the Kerala Coast.

It is obvious from the results that the study area may experience sea level rise in future. The state of Andhra Pradesh, located at south-east coastal areas of India, has long coastline of 930 km. The outcomes from the climate simulator model based on IPCC AR5 (Special Report on Emission Scenarios) emission trajectories projects rise in sea level at local level under different scenarios.. The results reveal that sea level may increase by 1.16 m under the highemission carbon pollution scenario Representative Concentration Pathway 8.5. Under moderately strong emission reduction scenario, that is, RCP 4.5, the sea level is projected to be 0.89 m above MSL. It can be noted that with aggressive cuts in the carbon pollution, we can further limit the undesirable rise in sea level to be around 0.77 m as per RCP 2.6 (Figure 2).

The state of Tamil Nadu has the second largest coastline of India with 1076 km. It is a part of Coromandel Coast of Bay of Bengal and Indian Ocean. In the northernmost part of the coast, Chennai is located, which is not only the capital of the state but it is an important commercial and industrial centre in the country, with Kanyakumari forming the southern tip where the Indian Ocean, Bay of Bengal and Arabian Sea meets. Pulicat Lake, which has a rich and fragile


Table 1. Radiative forcing as per representative concentration pathways (RCPs) of IPCC AR5.

Figure 2. Sea level changes till the end of twenty-first century for Visakhapatnam, Andhra Pradesh coast.

Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights…

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13

Figure 3. Sea level changes till the end of twenty-first century for Chennai, Tamil Nadu coast.

Figure 1. Locations of the study area.

ecosystem, is the second largest brackish water lake in India and is located in the northern part of the coast. A climate simulator model based on IPCC AR4 (Special Report on Emission Scenarios) has been used to project the sea level rise at local level under different scenarios. The results from the simulation reveal that sea level of Chennai coasts may rise 1.19 m under the high-emission carbon pollution scenario Representative Concentration Pathway 8.5. Under moderately strong emission reduction scenario under RCP 4.5, the sea level is projected to be 0.92 m above MSL. It can be noted that under RCP 2.6 with aggressive cuts in the carbon pollution, we can further reduce/limit the undesirable rise in sea level to be around 0.79 m for Chennai (Figure 3).

Figure 2. Sea level changes till the end of twenty-first century for Visakhapatnam, Andhra Pradesh coast.

Figure 3. Sea level changes till the end of twenty-first century for Chennai, Tamil Nadu coast.

ecosystem, is the second largest brackish water lake in India and is located in the northern part of the coast. A climate simulator model based on IPCC AR4 (Special Report on Emission Scenarios) has been used to project the sea level rise at local level under different scenarios. The results from the simulation reveal that sea level of Chennai coasts may rise 1.19 m under the high-emission carbon pollution scenario Representative Concentration Pathway 8.5. Under moderately strong emission reduction scenario under RCP 4.5, the sea level is projected to be 0.92 m above MSL. It can be noted that under RCP 2.6 with aggressive cuts in the carbon pollution, we can further reduce/limit the undesirable rise in sea level to be around 0.79 m for

Chennai (Figure 3).

Figure 1. Locations of the study area.

12 Sea Level Rise and Coastal Infrastructure

The state of Kerala has long coastline of 569.7 km. The usage of the climate simulator model helped in projecting sea level rise at local level under different emission pathways. The results reveal that sea level of Cochin coasts may rise 1.34 m under the high-emission carbon pollution

scenario Representative Concentration Pathway 8.5. Under moderately strong emission reduction scenario under RCP 4.5, the sea level is projected to be 1.04 m above MSL. It can be noted that under RCP 2.6 with aggressive cuts in the carbon pollution, we can further reduce/limit the undesirable rise in sea level to be around 0.91 m for Cochin (Figure 4). It shows the highest rate of sea level rise among all other three locations chosen for study. As a next step forward, inundation impact study needs to be conducted as Cochin has a group of islands that form part of the city like Vypin which is not only a fishing harbor but an industrial hub, which may

Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights…

http://dx.doi.org/10.5772/intechopen.74325

15

The coastline of the state of Maharashtra is 652.6 km long. The climate simulator model has been employed to predict the sea level rise at local level for Mumbai coast under different scenarios. The results reveal that under the high-emission carbon pollution scenario Representative Concentration Pathway 8.5 is 1.24 m. Under moderately strong emission reduction scenario under RCP 4.5, the sea level is projected to be 0.94 m above MSL. It can be noted that under RCP 2.6 with aggressive cuts in the carbon pollution, we can further reduce/limit the

The CRZ 2011 notification has been a new addition to the list of policies using bottom-up approach as a good governance tool. A modelling study carried out by (Aggarwal & Lal SLR rise of 30–80 cm had been projected for Indian coast over the twenty-first century [16]. All these study areas Mumbai, Chennai, Cochin and Visakhapatnam are not only thickly populated but also intensively used zone with significant harbour activities such as travel and trade.

Natesan and Parthasarathy observed in their study that about 13 km2 of the land area would be inundated permanently due to SLR in Kanyakumari [17]. Khan et al. carried out sea level rise simulation study using SimCLIM for the coasts of Tamil Nadu, South India [18]. Researchers reported that studies involving SLR projections are highly useful to find susceptible areas to SLR and to minimize its potential impacts on coastal area [19–21]. Rising ocean heat content (and hence ocean thermal expansion) is an important element of climate change and sea level rise [22– 24, 11]. Sea levels are rising now and are expected to continue rising for centuries, even if greenhouse gas emissions are curbed and their atmospheric concentrations are stabilized. In this case, ecosystem- and community-based adaptation is the need of the hour. As mentioned by Adger et al., it is the citizen's responsibility to identify three mechanisms: altering (human) exposure to climate change-induced sea levels, reducing sensitivity (sometimes called 'climate proofing') and increasing the resilience of the coastal eco systems [25]. Coastal agriculture is also going to be hit. Apart from several biotic and abiotic factors, salt water intrusions into coastal wetlands would also negatively affect the growth and yield of cultivated plant species, especially in potential arable land in our coastal states, where food production is already considered a critical issue. Even though India has strengthened its potential in coastal management, disaster management and several community-based field projects to enhance the participation of stake-

undesirable rise in sea level to be around 0.81 m (Figure 5).

These coastal tracts do possess historical heritage sites as well.

holders, and much work needs to be done in the future [26].

3. Discussions and conclusions

be under high risk.

Figure 4. Sea level changes till the end of twenty-first century for cochin, Kerala coast.

Figure 5. Sea level changes till the end of twenty-first century for Mumbai, Maharashtra coast.

scenario Representative Concentration Pathway 8.5. Under moderately strong emission reduction scenario under RCP 4.5, the sea level is projected to be 1.04 m above MSL. It can be noted that under RCP 2.6 with aggressive cuts in the carbon pollution, we can further reduce/limit the undesirable rise in sea level to be around 0.91 m for Cochin (Figure 4). It shows the highest rate of sea level rise among all other three locations chosen for study. As a next step forward, inundation impact study needs to be conducted as Cochin has a group of islands that form part of the city like Vypin which is not only a fishing harbor but an industrial hub, which may be under high risk.

The coastline of the state of Maharashtra is 652.6 km long. The climate simulator model has been employed to predict the sea level rise at local level for Mumbai coast under different scenarios. The results reveal that under the high-emission carbon pollution scenario Representative Concentration Pathway 8.5 is 1.24 m. Under moderately strong emission reduction scenario under RCP 4.5, the sea level is projected to be 0.94 m above MSL. It can be noted that under RCP 2.6 with aggressive cuts in the carbon pollution, we can further reduce/limit the undesirable rise in sea level to be around 0.81 m (Figure 5).

### 3. Discussions and conclusions

The state of Kerala has long coastline of 569.7 km. The usage of the climate simulator model helped in projecting sea level rise at local level under different emission pathways. The results reveal that sea level of Cochin coasts may rise 1.34 m under the high-emission carbon pollution

14 Sea Level Rise and Coastal Infrastructure

Figure 4. Sea level changes till the end of twenty-first century for cochin, Kerala coast.

Figure 5. Sea level changes till the end of twenty-first century for Mumbai, Maharashtra coast.

The CRZ 2011 notification has been a new addition to the list of policies using bottom-up approach as a good governance tool. A modelling study carried out by (Aggarwal & Lal SLR rise of 30–80 cm had been projected for Indian coast over the twenty-first century [16]. All these study areas Mumbai, Chennai, Cochin and Visakhapatnam are not only thickly populated but also intensively used zone with significant harbour activities such as travel and trade. These coastal tracts do possess historical heritage sites as well.

Natesan and Parthasarathy observed in their study that about 13 km2 of the land area would be inundated permanently due to SLR in Kanyakumari [17]. Khan et al. carried out sea level rise simulation study using SimCLIM for the coasts of Tamil Nadu, South India [18]. Researchers reported that studies involving SLR projections are highly useful to find susceptible areas to SLR and to minimize its potential impacts on coastal area [19–21]. Rising ocean heat content (and hence ocean thermal expansion) is an important element of climate change and sea level rise [22– 24, 11]. Sea levels are rising now and are expected to continue rising for centuries, even if greenhouse gas emissions are curbed and their atmospheric concentrations are stabilized. In this case, ecosystem- and community-based adaptation is the need of the hour. As mentioned by Adger et al., it is the citizen's responsibility to identify three mechanisms: altering (human) exposure to climate change-induced sea levels, reducing sensitivity (sometimes called 'climate proofing') and increasing the resilience of the coastal eco systems [25]. Coastal agriculture is also going to be hit. Apart from several biotic and abiotic factors, salt water intrusions into coastal wetlands would also negatively affect the growth and yield of cultivated plant species, especially in potential arable land in our coastal states, where food production is already considered a critical issue. Even though India has strengthened its potential in coastal management, disaster management and several community-based field projects to enhance the participation of stakeholders, and much work needs to be done in the future [26].

Accurate sea level predictions are vital for planning coastal infrastructure development within the buffer zone in low-lying coastal areas anticipating predicted sea level rises of almost a meter by 2100. Hybrid approaches consisting of empirical and semi-empirical models and process-based models have also been undertaken to reduce uncertainties in the projections [27]. Projecting accurately the population growth per locations, especially in a rapidly growing Asian coastal city, adds to additional uncertainty [28]. Forecasting sea level rise does not just depend on how much sea water rises, but also how land levels change due to tectonics, natural compaction of soft soils as well as human influences [29]. Hence it is the responsibility of the research team to communicate and sensitize about integrated coastal zone management for minimizing the impacts of future sea level rise to all the stakeholders.

Mastrandrea MD, Mach KJ, Plattner G.-K, Allen SK, Tignor M, Midgley PM, editors.

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eration and Development, Paris; 2003

Geophysics. 2011;32:585-602

Site-to-site differences in LSL projections may be attributed to varying non-climatic background uplift or subsidence, oceanographic effects and spatially variable responses of the geoid and the lithosphere to shrinking land ice. Thus, the objective of this chapter is to provide sea level projections to facilitate coastal manager and adaptation planners to frame locationspecific and time-based adaptation strategies to sea level rise. This chapter also recommends Chennai Metropolis, Greater Visakhapatnam Smart City Corporation, Mumbai Corporation and Cochin development Corporation to conceptualize the future sea level rise scenario while preparing the city developmental plans to incorporate adaptation and enhanced disaster risk reduction. Metro City Development Corporation may include this matter while preparing plans for developing smart city master plans to deal with enhanced disaster risk reduction proactively. Futuristic coastal zone land use planning, construction of dykes, afforestation programmes using bamboo, mangrove, bamboo fencing, adapting coastal agriculture to salt tolerant species, strengthening the existing coastal regulation zone (CRZ) policies, and so on would help in conserving critical ecosystem services and infrastructure.

### Author details

Dhanya Praveen\*, Andimuthu Ramachandran and Kandasami Palanivelu

\*Address all correspondence to: dhanyapraveen.cc@gmail.com

Centre for Climate Change and Adaptation Research, Anna University, Chennai, India

### References


Mastrandrea MD, Mach KJ, Plattner G.-K, Allen SK, Tignor M, Midgley PM, editors. Cambridge, UK and New York, NY, USA: Cambridge University Press; 582 pp

Accurate sea level predictions are vital for planning coastal infrastructure development within the buffer zone in low-lying coastal areas anticipating predicted sea level rises of almost a meter by 2100. Hybrid approaches consisting of empirical and semi-empirical models and process-based models have also been undertaken to reduce uncertainties in the projections [27]. Projecting accurately the population growth per locations, especially in a rapidly growing Asian coastal city, adds to additional uncertainty [28]. Forecasting sea level rise does not just depend on how much sea water rises, but also how land levels change due to tectonics, natural compaction of soft soils as well as human influences [29]. Hence it is the responsibility of the research team to communicate and sensitize about integrated coastal zone management for

Site-to-site differences in LSL projections may be attributed to varying non-climatic background uplift or subsidence, oceanographic effects and spatially variable responses of the geoid and the lithosphere to shrinking land ice. Thus, the objective of this chapter is to provide sea level projections to facilitate coastal manager and adaptation planners to frame locationspecific and time-based adaptation strategies to sea level rise. This chapter also recommends Chennai Metropolis, Greater Visakhapatnam Smart City Corporation, Mumbai Corporation and Cochin development Corporation to conceptualize the future sea level rise scenario while preparing the city developmental plans to incorporate adaptation and enhanced disaster risk reduction. Metro City Development Corporation may include this matter while preparing plans for developing smart city master plans to deal with enhanced disaster risk reduction proactively. Futuristic coastal zone land use planning, construction of dykes, afforestation programmes using bamboo, mangrove, bamboo fencing, adapting coastal agriculture to salt tolerant species, strengthening the existing coastal regulation zone (CRZ) policies, and so on

minimizing the impacts of future sea level rise to all the stakeholders.

would help in conserving critical ecosystem services and infrastructure.

Dhanya Praveen\*, Andimuthu Ramachandran and Kandasami Palanivelu

Centre for Climate Change and Adaptation Research, Anna University, Chennai, India

[1] IPCC 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on

[2] IPCC 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL,

\*Address all correspondence to: dhanyapraveen.cc@gmail.com

Author details

16 Sea Level Rise and Coastal Infrastructure

References

Climate Change


Bonn; 2007. pp. 33. http:/unfccc.int/files/cooperation and support/financial\_Mechanism/appli cation/pdf/nicholls.pdf

[26] Adger WN, Arnell NW, Tompkins EL. Successful adaptation to climate change across

Constructing Local Sea Level Rise Scenarios for Assessing Possible Impacts and Adaptation Needs: Insights…

http://dx.doi.org/10.5772/intechopen.74325

19

[27] Krishnamurthy RR, DasGupta R, Chatterjee R, Shaw R. Managing the Indian coast in the face of disasters & climate change: a review and analysis of India's coastal zone manage-

[28] Mengel M, Levermann A, Frieler K, Robinison A, Marzeion B, Winkelmann R.. Future sea level rise constrained by observations and long-term commitment. Proceedings of the

[29] Neumann B, Vafeidis AT, Zimmermann J, Nicholls RJ. Future coastal population growth and exposure to sea-level rise and coastal flooding – A global assessment. PLoS ONE.

National Academy of Sciences of the United States of America; 2016

scales. Global Environmental Change. 2005a;15:77-86

ment policies. Journal of coastal conservation. 2014

2015;10(3):e0118571. DOI: 10.1371/journal.pone.0118571


[26] Adger WN, Arnell NW, Tompkins EL. Successful adaptation to climate change across scales. Global Environmental Change. 2005a;15:77-86

Bonn; 2007. pp. 33. http:/unfccc.int/files/cooperation and support/financial\_Mechanism/appli

[14] FAO 2011. Climate Change and Bioenergy Glossary. Food and Agriculture Organisation

[15] https://tidesandcurrents.noaa.gov/sltrends/ & http://www.psmsl.org/data, Accessed on

[16] Aggarwal D, Lal M. Vulnerability of Indian coastal line to sea level rise. New Delhi:

[17] Natesan U, Parthasarathy A. The Potential impact of sea level rise along the coastal zone of Kanyakumari District in Tamil Nadu, India. Journal of Coastal Conservator. 2010;14(3):

[18] Khan AS, Ramachandran A, Usha N, Punitha P, Selvam V. Predicted impact of sea-level rise at Vellar-Coleroon estuarine region of Tamil Nadu Coast, in India: Mainstreaming adaptation as a coastal zone management option. Ocean and Coastal Management. 2012;

[19] Titus JG, Richman C. Maps of Lands Vulnerable to Sea Level Rise: Modeled Elevations

[20] Dasgupta D, Laplante B, Meisner C, Wheeler D, Yan J. The impact of sea level rise on developing countries: a comparative analysis. Word Bank Policy Research Working Paper. 2007;4136:51. Available at http://wwwwds.worldbank.org/servlet/WDSContentSer ver/WDSP/IB/2007/02/09/00001640620070209161430/Rendered/PDF/wps4136.pdf [Accessed

[21] Li X, Rowley RJ, Kostelnick JC, Braaten D, Miesel J, Hulbutta K. GIS analysis of global impact from sea level rise. Photogrammetric Engineering and Remote Sensing. 2009;75(7):

[22] Cooper MJP, Beavers MD, Oppenheimer M. Future sea level rise and New Jersey coast. Woodrow Wilson School of Public and International Affairs, Princeton University,

[23] IPCC 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. In: Field C, Barros V, Mach K, Mastrandrea M, editors. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge:

[24] Woodworth PL, White NJ, Jevrejeva S, Holgate SJ, Church JA, Gehrels WR. Evidence for the accelerations of sea level on multi-decade and century timescales. International Jour-

[25] FAO 2014. The State of Food Insecurity in the World 2014. Strengthening the enabling

Princeton, New Jercy: Science Technology and Policy Program; 2005. p. 36

University Press, Cambridge, United Kingdom and New York, NY

nal of Climatology. 2009;29:777-789. DOI: 10.1002/joc.1771

environment for food security and nutrition. FAO: Rome

along the U.S. Atlantic and Gulf Coasts. Climatic Research. 2001;18:205-228

of United Nation. http:// www.fao.org/climatechange/65923/en/#i

Center for Atmospheric Science, Indian Institute of Technology; 2001

cation/pdf/nicholls.pdf

18 Sea Level Rise and Coastal Infrastructure

May 18, 2016

207-214

69:327-339

Jun 25, 2012]

807-818


**Chapter 3**

**Provisional chapter**

**Analysis of Dynamic Effects on the Brazilian Vertical**

This chapter presents a methodology of analyzing the dynamic effect from mean sea level variations, based on Global Navigation Satellite System (GNSS) data, velocity models, tide gauge observations, and satellite altimetry data. GNSS observations were processed in order to obtain the variation of up coordinate required to identify the possible crust movements. Velocity model served as a comparative basis to verify the obtained results from the GNSS data processing and served as a basis for analyzing the time periods without GNSS information. Tide gauge data were used to evaluate the sea level temporal evolution in the Imbituba Brazilian Vertical Datum (I-BVD). Satellite altimetry data were used for checking the results from the GNSS and the tide gauge time series. The analyses were based on time series of observations by GNSS from 2007 until 2016, tide gauge from 1948 until 1968 and 2001 until 2016, and satellite altimetry data from 1991 until 2015 from different missions. As basis for the analysis, it used GNSS SIRGAS-CON stations, the SIRGAS velocity model (VEMOS), and NUVEL velocity model. Considering the discrimination of the crust vertical movement (GNSS processing) from the results obtained with the tide gauge observations, it was observed that there is an evidence of mean sea

**Analysis of Dynamic Effects on the Brazilian Vertical** 

DOI: 10.5772/intechopen.71546

© 2016 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,

© 2018 The Author(s). Licensee IntechOpen. 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.

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

Nowadays, Geodesy provides fundamental reference basis and quantities for using in modern earth observation systems. The main contributions involve the basis for understanding geokinematics and control of mass redistribution. Several complementary sources of information

**Keywords:** mean sea level rising, crustal movement, GNSS time series and data

Luciana M. Da Silva, Sílvio R.C. De Freitas and

Luciana M. Da Silva, Sílvio R.C. De Freitas and

Additional information is available at the end of the chapter

level (MSL) rising approximately +2.24 ± 0.4 mm/year.

processing, satellite altimetry, velocity model, SIRGAS-CON

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71546

**Datum**

**Datum**

Regiane Dalazoana

**Abstract**

**1. Introduction**

Regiane Dalazoana

**Provisional chapter**

### **Analysis of Dynamic Effects on the Brazilian Vertical Datum Datum**

**Analysis of Dynamic Effects on the Brazilian Vertical** 

DOI: 10.5772/intechopen.71546

Luciana M. Da Silva, Sílvio R.C. De Freitas and Regiane Dalazoana Regiane Dalazoana Additional information is available at the end of the chapter

Luciana M. Da Silva, Sílvio R.C. De Freitas and

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71546

#### **Abstract**

This chapter presents a methodology of analyzing the dynamic effect from mean sea level variations, based on Global Navigation Satellite System (GNSS) data, velocity models, tide gauge observations, and satellite altimetry data. GNSS observations were processed in order to obtain the variation of up coordinate required to identify the possible crust movements. Velocity model served as a comparative basis to verify the obtained results from the GNSS data processing and served as a basis for analyzing the time periods without GNSS information. Tide gauge data were used to evaluate the sea level temporal evolution in the Imbituba Brazilian Vertical Datum (I-BVD). Satellite altimetry data were used for checking the results from the GNSS and the tide gauge time series. The analyses were based on time series of observations by GNSS from 2007 until 2016, tide gauge from 1948 until 1968 and 2001 until 2016, and satellite altimetry data from 1991 until 2015 from different missions. As basis for the analysis, it used GNSS SIRGAS-CON stations, the SIRGAS velocity model (VEMOS), and NUVEL velocity model. Considering the discrimination of the crust vertical movement (GNSS processing) from the results obtained with the tide gauge observations, it was observed that there is an evidence of mean sea level (MSL) rising approximately +2.24 ± 0.4 mm/year.

**Keywords:** mean sea level rising, crustal movement, GNSS time series and data processing, satellite altimetry, velocity model, SIRGAS-CON

### **1. Introduction**

Nowadays, Geodesy provides fundamental reference basis and quantities for using in modern earth observation systems. The main contributions involve the basis for understanding geokinematics and control of mass redistribution. Several complementary sources of information

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. © 2018 The Author(s). Licensee IntechOpen. 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.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

coming from geodetic observations associated with Earth's rotation control of variations in time of positions and gravity field, which reflects mainly in the geokinematic aspects, in the gravity field variations, and in the Earth's rotation.

Datum (I-BVD), the first aspect was faced by [11] by determining geopotential numbers from GPS satellite surveying and disturbing potential modeling as well as [12] by fixed solution of the geodetic boundary value problem (GBVP) in the Brazilian vertical datum. The second aspect was partially studied by [13] considering short series of satellite altimetry, tide gauge observations, and I-BVD geocentric positioning. This chapter concentrates more in the second aspect because more than 90% of the Brazilian Fundamental Vertical Network (B-FVN) with about 65,000 benchmarks and about 180,000 km of leveling lines is referenced to the I-BVD. Therefore, in this work, we used time series of Global Navigation Satellite System (GNSS) positioning in relation to I-BVD. These geocentric coordinates series are then associated with tide gauge and satellite altimetry time series, aiming to determine the temporal evolution on I-BVD in the IHRS, from the effects related to the crust movement and to local MSL evolution. The discrimination of these movements in the sea level trends by tide gauges can be realized by determining the tide gauge geocentric position with GNSS continuous positioning.

Analysis of Dynamic Effects on the Brazilian Vertical Datum

http://dx.doi.org/10.5772/intechopen.71546

23

For this, the continuous GNSS station must be installed close to the tide gauge [14–16].

According to [17, 18], Brazilian vertical network was deployed in early 1945, in Santa Catarina, through spirit leveling. Torres Datum, in Rio Grande do Sul, south Brazil, was taken as a reference, with a provisional character, because it was only defined with 1 year of sea level observations (1919, 1920). In 1949, Inter-American geodetic survey (IAGS) began the deployment

In 1958, Torres datum was replaced by Imbituba datum, for having a longer time series. Imbituba datum was defined by the MSL annual mean value from 1949 to 1957 [19], in the Imbituba Harbor, based on the assumption that the MSL materialized the geoid. The monthly and annual averages of the IAGS tide gauge network are stored in the Permanent Service for Mean Sea Level (PSMSL). The sea level observations from Imbituba, after 1969, have not been recovered yet. The existing observations at PSMSL database are related to the period from 1949 to 1969.

From the 1980s, with the advent of modern space techniques, it has been shown that there are differences between the MSL and the geoid, called the Sea Surface Topography (SSTop). In 1997, the Federal University of Paraná, together with the Brazilian Institute of Geography and Statistics (in Portuguese IBGE), started with different multiparametric campaigns in the datum area, that was carried out over the years. These include GPS positioning, atmospheric pressure monitoring, earth and ocean tides, as well as the recovery of historical altimetric references, tide gauge station reactivation, gravimetry and geodetic observations densification in the contiguous region involving the Imaruí Lagoon system and the search for the link

After I-BVD establishment, no changes were made in its definition, although there are observations in Imbituba and other stations, for much longer periods [26]. In order to correct the

**2. Study area and data sets**

**2.1. Imbituba Brazilian Vertical Datum**

between B-FVN and IHRS [8, 17, 18, 20–25].

of a tide gauge stations network along the Brazilian coast.

Global geodetic observing system (GGOS), established by the International Association of Geodesy (IAG), is constituted by the scientific basis and by the geodetic infrastructure needed for global changes monitoring. This, in the present, consists in the main line in the global interaction of Geodesy with several human knowledge fields, coordinated by the IAG [1]. The three central themes of GGOS related to global changes are [1]: Theme 1 – unified global height system (IHRS); Theme 2 – geohazards monitoring; Theme 3 – sea level changes, variability, and forecasting.

In 2012, the United Nations (UN) in its regional conference in Bangkok recommended the adoption of GGOS. The UN Resolution (A/RES/69/266) in February 26, 2015 by considering the coordinated approach of IAG fixed the basic elements of the Global Geodetic Reference Frame (GGRF) as realization of the Global Geodetic Reference System (GGRS) inside the UN-GGIM context. The IAG established the International Height Reference System (IHRS) by its Resolution No. 1 of IAG in July 2015 [2] and its global realization is being discussed inside the GGOS [3]. With basis in the specifications given by IAG in April, 2016 about the GGRS, the coordinates of a point P in the Earth' s surface must be given by its geometric coordinates specified by the vector *X* <sup>→</sup> (*X*, *<sup>Y</sup>*, *<sup>Z</sup>*) in agreement with ITRS/ITRF and by a physical part linked to the geopotential space by considering the geopotential value in the point, *<sup>W</sup> <sup>P</sup>* (*X* <sup>→</sup> ). It is intended that the specification in the geopotential space is to be contained by the International Height Reference Frame (IHRF), realization of the IHRS. It is expected that GGRF as having an overall consistency of at least one centimeter in its realization and space/temporal control in the order of millimeter per year [4].

One of the most important aspects in the global changes discussions is associated to mean sea level (MSL) evolution, especially when observing the evolution in shore areas, in view of the direct impacts on the coastal areas that usually present highest concentration of human occupation. This is emphasized in [5].

From the geodetic point of view and considering mainly GGOS themes 1 and 3, it is necessary to discuss the geokinematic aspects of the ocean-continent interaction, fundamental for the vertical reference system (VRS) definition and realization within a global consistency [6]. The continents interactions with the oceans and the atmosphere must be analyzed in relation to Earth's dynamic response, in order to allow secular and periodic movements discrimination and sporadic loading effects such as those associated with the meteorological fronts passage [7, 8]. In a modern view, the vertical datum (VD) should be related to a unique global reference [2] based on a univocal value of geopotential and that the primary vertical coordinates in the vertical reference networks (VRNs) are the geopotential numbers. The issue of univocal value was discussed in [9]. The current requirements regarding VRSs and VRNs have their foundation expressed mainly in the assumptions of the GGOS. To this end, a new working group was established in the context of GGOS in 2016: strategy for the realization of the IHRS (Chair L. Sanchez) [10].

Two fundamental steps for linking each national vertical network to IHRS/IHRF exist. They are the understanding of the relationship of each national vertical datum with the specified *W0* value and the temporal variations, respectively. Considering the Imbituba Brazilian Vertical

Datum (I-BVD), the first aspect was faced by [11] by determining geopotential numbers from GPS satellite surveying and disturbing potential modeling as well as [12] by fixed solution of the geodetic boundary value problem (GBVP) in the Brazilian vertical datum. The second aspect was partially studied by [13] considering short series of satellite altimetry, tide gauge observations, and I-BVD geocentric positioning. This chapter concentrates more in the second aspect because more than 90% of the Brazilian Fundamental Vertical Network (B-FVN) with about 65,000 benchmarks and about 180,000 km of leveling lines is referenced to the I-BVD. Therefore, in this work, we used time series of Global Navigation Satellite System (GNSS) positioning in relation to I-BVD. These geocentric coordinates series are then associated with tide gauge and satellite altimetry time series, aiming to determine the temporal evolution on I-BVD in the IHRS, from the effects related to the crust movement and to local MSL evolution. The discrimination of these movements in the sea level trends by tide gauges can be realized by determining the tide gauge geocentric position with GNSS continuous positioning. For this, the continuous GNSS station must be installed close to the tide gauge [14–16].

### **2. Study area and data sets**

coming from geodetic observations associated with Earth's rotation control of variations in time of positions and gravity field, which reflects mainly in the geokinematic aspects, in the

Global geodetic observing system (GGOS), established by the International Association of Geodesy (IAG), is constituted by the scientific basis and by the geodetic infrastructure needed for global changes monitoring. This, in the present, consists in the main line in the global interaction of Geodesy with several human knowledge fields, coordinated by the IAG [1]. The three central themes of GGOS related to global changes are [1]: Theme 1 – unified global height system (IHRS); Theme 2 – geohazards monitoring; Theme 3 – sea level changes, variability, and forecasting.

In 2012, the United Nations (UN) in its regional conference in Bangkok recommended the adoption of GGOS. The UN Resolution (A/RES/69/266) in February 26, 2015 by considering the coordinated approach of IAG fixed the basic elements of the Global Geodetic Reference Frame (GGRF) as realization of the Global Geodetic Reference System (GGRS) inside the UN-GGIM context. The IAG established the International Height Reference System (IHRS) by its Resolution No. 1 of IAG in July 2015 [2] and its global realization is being discussed inside the GGOS [3]. With basis in the specifications given by IAG in April, 2016 about the GGRS, the coordinates of a point P in the Earth' s surface must be given by its geometric coordinates specified by the vector

<sup>→</sup> (*X*, *<sup>Y</sup>*, *<sup>Z</sup>*) in agreement with ITRS/ITRF and by a physical part linked to the geopotential space

the geopotential space is to be contained by the International Height Reference Frame (IHRF), realization of the IHRS. It is expected that GGRF as having an overall consistency of at least one centimeter in its realization and space/temporal control in the order of millimeter per year [4].

One of the most important aspects in the global changes discussions is associated to mean sea level (MSL) evolution, especially when observing the evolution in shore areas, in view of the direct impacts on the coastal areas that usually present highest concentration of human

From the geodetic point of view and considering mainly GGOS themes 1 and 3, it is necessary to discuss the geokinematic aspects of the ocean-continent interaction, fundamental for the vertical reference system (VRS) definition and realization within a global consistency [6]. The continents interactions with the oceans and the atmosphere must be analyzed in relation to Earth's dynamic response, in order to allow secular and periodic movements discrimination and sporadic loading effects such as those associated with the meteorological fronts passage [7, 8]. In a modern view, the vertical datum (VD) should be related to a unique global reference [2] based on a univocal value of geopotential and that the primary vertical coordinates in the vertical reference networks (VRNs) are the geopotential numbers. The issue of univocal value was discussed in [9]. The current requirements regarding VRSs and VRNs have their foundation expressed mainly in the assumptions of the GGOS. To this end, a new working group was established in the context of

Two fundamental steps for linking each national vertical network to IHRS/IHRF exist. They are the understanding of the relationship of each national vertical datum with the specified *W0* value and the temporal variations, respectively. Considering the Imbituba Brazilian Vertical

GGOS in 2016: strategy for the realization of the IHRS (Chair L. Sanchez) [10].

(*X*

<sup>→</sup> ). It is intended that the specification in

gravity field variations, and in the Earth's rotation.

22 Sea Level Rise and Coastal Infrastructure

by considering the geopotential value in the point, *<sup>W</sup> <sup>P</sup>*

occupation. This is emphasized in [5].

*X*

#### **2.1. Imbituba Brazilian Vertical Datum**

According to [17, 18], Brazilian vertical network was deployed in early 1945, in Santa Catarina, through spirit leveling. Torres Datum, in Rio Grande do Sul, south Brazil, was taken as a reference, with a provisional character, because it was only defined with 1 year of sea level observations (1919, 1920). In 1949, Inter-American geodetic survey (IAGS) began the deployment of a tide gauge stations network along the Brazilian coast.

In 1958, Torres datum was replaced by Imbituba datum, for having a longer time series. Imbituba datum was defined by the MSL annual mean value from 1949 to 1957 [19], in the Imbituba Harbor, based on the assumption that the MSL materialized the geoid. The monthly and annual averages of the IAGS tide gauge network are stored in the Permanent Service for Mean Sea Level (PSMSL). The sea level observations from Imbituba, after 1969, have not been recovered yet. The existing observations at PSMSL database are related to the period from 1949 to 1969.

From the 1980s, with the advent of modern space techniques, it has been shown that there are differences between the MSL and the geoid, called the Sea Surface Topography (SSTop). In 1997, the Federal University of Paraná, together with the Brazilian Institute of Geography and Statistics (in Portuguese IBGE), started with different multiparametric campaigns in the datum area, that was carried out over the years. These include GPS positioning, atmospheric pressure monitoring, earth and ocean tides, as well as the recovery of historical altimetric references, tide gauge station reactivation, gravimetry and geodetic observations densification in the contiguous region involving the Imaruí Lagoon system and the search for the link between B-FVN and IHRS [8, 17, 18, 20–25].

After I-BVD establishment, no changes were made in its definition, although there are observations in Imbituba and other stations, for much longer periods [26]. In order to correct the problems between the geodetic surveys and the sea level observation, in 1994 the Brazilian Institute of Geography and Statistics (in Portuguese IBGE) started the operation of tide gauge stations with geodetic characteristics. In 1999, the harbor authorities in Imbituba resumed sea level conventional observation, and in 2001, IBGE installed tide gauge and meteorological digital equipment to accompaniment I-BVD [27]. At the time of I-BVD definition, the inclusion of gravity observations and SSTop were not considered, the same happened in the majority of VDs in other countries [17]. **Figure 1** shows the location of I-BVD, where it stands out: an image that defines the city of Imbituba and the location of the meteorological, tide gauge, and GNSS stations; location of Imbituba in the State of Santa Catarina (SC); location of Santa Catarina in the country and the continent.

The compatibility study of different data sources, such as tide gauge observations and satellite altimetry data, which reflect the oceans dynamic surface should also be considered. Dalazoana highlighted that the existence of discrepancies cannot be fully explained by the Earth's dynamic

Analysis of Dynamic Effects on the Brazilian Vertical Datum

http://dx.doi.org/10.5772/intechopen.71546

25

GNSS monitoring in the I-BVD was established aiming to evaluate the crust dynamic behavior in the region. In Imbituba, near the tide gauge, the IMBT station of SIRGAS-CON (SAT–94,024–International Code, IBGE database) was materialized in 2007. This station was established as successor to the IMBI station (SAT–91,854) where GPS positioning campaigns were carried out. The campaigns at the IMBI station were sporadic, processed with the Bernese 5.0 software, carried out in at least 10 days of continuous observations (1997, 2000, and 2005) [30]. The processing strategy involved the use of precise orbits and antenna calibration parameters, application of tidal correction models, and ocean loading for positions

The two stations (IMBI and IMBT) were connected with 17 days of GNSS observations, along with geodetic and topographical methods and cross leveling. During this campaign, it also performed the Van de Casteele test [31]. It is noteworthy that the RLs vertical control and the tide gauge geocentric position determination were essential for the connection of these stations.

Sea level registrations in the form of monthly mean values of the Permanent Service for Mean Sea Level (PSMSL) are available from September 1948 to December 1968, and daily mean values of the University of Hawaii Sea Level Center (UHSLC) are available from August 2001 to December 2007, and hourly mean values of the RMPG (Permanent Tide Gauge Network for Geodesy) are available from November 2006 to January 2016, these registers were considered to MSL analysis. However, during this period, some tide gauges registrations were irregular

Data from 35 Brazilian stations of RBMC (Brazilian Network for Continuous Monitoring) and belonging to GNSS SIRGAS-CON network were selected to support the data processing for obtaining the IMBT GNSS position time series comprising the period from 2007, GPS week 1443, when the IMBT GNSS station was established, to 2016, GPS week 1877. The observations for these stations are available for approximately 10 years. For three of these stations, coordinates and velocities are used in the IGb08 reference frame, an IGS-specific realization of the ITRF2008.

For the present analysis T/P, JASON-1 and JASON-2 missions data for cycles 001-364, 001-259, and 001-262, respectively, as provided by [32] were used. The cycles cover the period from September 1992 to February 2015. This period includes approximately 23 years. According to [33], to the measurements of the satellite altimetry missions were applied real geophysical

corrections (e.g. for tides, atmospheric delays, and for the inverse barometer effect).

effects [17], but these are also associated with the different reference systems used.

and velocities estimated.

**2.2. Tide gauge observations**

with significant interruptions

**2.4. Satellite altimetry data**

**2.3. GNSS data**

I-BVD involves several fundamental reference levels for the study of its evolution and historical links. Dalazoana carried out an extensive work of recovering ties between reference levels in I-BVD [17], allowing the integration of new observations with modern sensors to the historical reference levels. This study was fundamental for the development of new research in Imbituba. Ferreira performed a SSTop estimation in the I-BVD based on the Imaruí lagoons system average surface adjustment to the Earth gravitational model 1996 (EGM1996) [22]. Palmeiro followed the studies, implementing the integration of free and fixed solutions of the GBVP based on terrestrial and marine gravimetry [23], and derived from satellite altimetry as well as global geopotential model (GGM) [3, 28, 29], which allowed an estimation of SSTop aiming the connection of the B-FVN to IHRS according to GGOS current directives. Palmeiro et al. showed that the association of local gravimetric geoids with the intended global geoid involves a series of associated problems [12]. These are the consequences of the different reference levels (RLs) involved in addition to the resolutions of the various used databases, as can be seen in [25].

**Figure 1.** Location of I-BVD in SC where meteorological, tide gauge, and GNSS stations are installed.

The compatibility study of different data sources, such as tide gauge observations and satellite altimetry data, which reflect the oceans dynamic surface should also be considered. Dalazoana highlighted that the existence of discrepancies cannot be fully explained by the Earth's dynamic effects [17], but these are also associated with the different reference systems used.

GNSS monitoring in the I-BVD was established aiming to evaluate the crust dynamic behavior in the region. In Imbituba, near the tide gauge, the IMBT station of SIRGAS-CON (SAT–94,024–International Code, IBGE database) was materialized in 2007. This station was established as successor to the IMBI station (SAT–91,854) where GPS positioning campaigns were carried out. The campaigns at the IMBI station were sporadic, processed with the Bernese 5.0 software, carried out in at least 10 days of continuous observations (1997, 2000, and 2005) [30]. The processing strategy involved the use of precise orbits and antenna calibration parameters, application of tidal correction models, and ocean loading for positions and velocities estimated.

The two stations (IMBI and IMBT) were connected with 17 days of GNSS observations, along with geodetic and topographical methods and cross leveling. During this campaign, it also performed the Van de Casteele test [31]. It is noteworthy that the RLs vertical control and the tide gauge geocentric position determination were essential for the connection of these stations.

### **2.2. Tide gauge observations**

Sea level registrations in the form of monthly mean values of the Permanent Service for Mean Sea Level (PSMSL) are available from September 1948 to December 1968, and daily mean values of the University of Hawaii Sea Level Center (UHSLC) are available from August 2001 to December 2007, and hourly mean values of the RMPG (Permanent Tide Gauge Network for Geodesy) are available from November 2006 to January 2016, these registers were considered to MSL analysis. However, during this period, some tide gauges registrations were irregular with significant interruptions

### **2.3. GNSS data**

problems between the geodetic surveys and the sea level observation, in 1994 the Brazilian Institute of Geography and Statistics (in Portuguese IBGE) started the operation of tide gauge stations with geodetic characteristics. In 1999, the harbor authorities in Imbituba resumed sea level conventional observation, and in 2001, IBGE installed tide gauge and meteorological digital equipment to accompaniment I-BVD [27]. At the time of I-BVD definition, the inclusion of gravity observations and SSTop were not considered, the same happened in the majority of VDs in other countries [17]. **Figure 1** shows the location of I-BVD, where it stands out: an image that defines the city of Imbituba and the location of the meteorological, tide gauge, and GNSS stations; location of Imbituba in the State of Santa Catarina (SC); location of Santa

I-BVD involves several fundamental reference levels for the study of its evolution and historical links. Dalazoana carried out an extensive work of recovering ties between reference levels in I-BVD [17], allowing the integration of new observations with modern sensors to the historical reference levels. This study was fundamental for the development of new research in Imbituba. Ferreira performed a SSTop estimation in the I-BVD based on the Imaruí lagoons system average surface adjustment to the Earth gravitational model 1996 (EGM1996) [22]. Palmeiro followed the studies, implementing the integration of free and fixed solutions of the GBVP based on terrestrial and marine gravimetry [23], and derived from satellite altimetry as well as global geopotential model (GGM) [3, 28, 29], which allowed an estimation of SSTop aiming the connection of the B-FVN to IHRS according to GGOS current directives. Palmeiro et al. showed that the association of local gravimetric geoids with the intended global geoid involves a series of associated problems [12]. These are the consequences of the different reference levels (RLs) involved in addition to the resolutions of the various used databases, as can be seen in [25].

**Figure 1.** Location of I-BVD in SC where meteorological, tide gauge, and GNSS stations are installed.

Catarina in the country and the continent.

24 Sea Level Rise and Coastal Infrastructure

Data from 35 Brazilian stations of RBMC (Brazilian Network for Continuous Monitoring) and belonging to GNSS SIRGAS-CON network were selected to support the data processing for obtaining the IMBT GNSS position time series comprising the period from 2007, GPS week 1443, when the IMBT GNSS station was established, to 2016, GPS week 1877. The observations for these stations are available for approximately 10 years. For three of these stations, coordinates and velocities are used in the IGb08 reference frame, an IGS-specific realization of the ITRF2008.

#### **2.4. Satellite altimetry data**

For the present analysis T/P, JASON-1 and JASON-2 missions data for cycles 001-364, 001-259, and 001-262, respectively, as provided by [32] were used. The cycles cover the period from September 1992 to February 2015. This period includes approximately 23 years. According to [33], to the measurements of the satellite altimetry missions were applied real geophysical corrections (e.g. for tides, atmospheric delays, and for the inverse barometer effect).

## **3. Methodology**

### **3.1. Geocentric position**

To meet the global demands, [34] presented the Enterprise for Verification of Anomalies in Mean Sea Level by Satellite Altimetry and Tide Gauge Records in the North Atlantic (EVAMARIA) in order to identify and verify sea level anomalies. The authors used 8 years of TOPEX/POSEIDON (T/P) data for comparison with GPS and associated tide gauge data. Häfele et al. addressed the issues [35] related to the EVAMARIA project, in addition to the tide gauge and GPS time series, to verify the crust movement and sea level variations.

In a current view, the location of a point in Earth's surface is defined by its position in a geocentric reference system for a given epoch. This implies that the observation time must be taken into account; the definition time of the Geodetic Reference Systems (GRSs) and the temporal variations of the coordinates for their reductions to the GRS realization time. The lack of knowledge of one of these aspects implies associated problems with the observation techniques combination that are usually at different times and GRSs [36, 37].

**Figure 2.** Imbituba tide gauge geocentric position monitoring via GNSS observations and spirit leveling. Source: Da

Analysis of Dynamic Effects on the Brazilian Vertical Datum

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27

**Figure 3.** Schematic of required leveling between various benchmarks at a tide gauge station to compare with satellite

Silva [25].

altimetry.

In Brazil, studies have been developed over time for VD geocentric position monitoring, within these, we can cite [8, 17, 18, 20–25, 38–44]. They addressed issues related to the use of GPS tracking near the tide gauge to determine the datum geocentric position in the context of the SIRGAS project. The MSL evolution stems from two distinct phenomena: the MSL eustatic movement concerning the geocenter that is largely associated with oceans thermal expansion, and crust tectonic movements along the shoreline, especially the vertical elevation (plate deformation) or crustal subsidence [42].

The MSL in a given location presents deviations from the global average due to the winds, ocean currents, among other factors. The MSL spatial variation is difficult to compare between the referred heights to the VDs. It is also necessary to consider the relative movements due to the periodic differential loading of the tide effects on the crust [45].

With a long period of oceanic tide observations and the tide gauge geocentric position fixation, defined at a certain epoch, secular effects determination can be made by comparison with reference historical levels and the association of crust velocity models and MSL evolution. These procedures allow discrimination between epyrogenic and crustal movements, and between eustatic movements and MSL variations.

According to [17], the GNSS positioning of an RL, associated with spirit leveling allows the zero reference of a tide gauge to a geocentric GRS, as shown in **Figure 2**.

**Figure 3** shows a scheme of a local leveling network within a system for MSL measurement without the possible effects of crustal vertical movement. In **Figure 3**, it is observed that for possible detection of crust movements, a GNSS station and an absolute gravimeter are considered next to the tide gauge, besides LRs with overlapping targets for leveling and the tide staff next to the

**3. Methodology**

**3.1. Geocentric position**

26 Sea Level Rise and Coastal Infrastructure

to verify the crust movement and sea level variations.

deformation) or crustal subsidence [42].

eustatic movements and MSL variations.

To meet the global demands, [34] presented the Enterprise for Verification of Anomalies in Mean Sea Level by Satellite Altimetry and Tide Gauge Records in the North Atlantic (EVAMARIA) in order to identify and verify sea level anomalies. The authors used 8 years of TOPEX/POSEIDON (T/P) data for comparison with GPS and associated tide gauge data. Häfele et al. addressed the issues [35] related to the EVAMARIA project, in addition to the tide gauge and GPS time series,

In a current view, the location of a point in Earth's surface is defined by its position in a geocentric reference system for a given epoch. This implies that the observation time must be taken into account; the definition time of the Geodetic Reference Systems (GRSs) and the temporal variations of the coordinates for their reductions to the GRS realization time. The lack of knowledge of one of these aspects implies associated problems with the observation

In Brazil, studies have been developed over time for VD geocentric position monitoring, within these, we can cite [8, 17, 18, 20–25, 38–44]. They addressed issues related to the use of GPS tracking near the tide gauge to determine the datum geocentric position in the context of the SIRGAS project. The MSL evolution stems from two distinct phenomena: the MSL eustatic movement concerning the geocenter that is largely associated with oceans thermal expansion, and crust tectonic movements along the shoreline, especially the vertical elevation (plate

The MSL in a given location presents deviations from the global average due to the winds, ocean currents, among other factors. The MSL spatial variation is difficult to compare between the referred heights to the VDs. It is also necessary to consider the relative movements due to

With a long period of oceanic tide observations and the tide gauge geocentric position fixation, defined at a certain epoch, secular effects determination can be made by comparison with reference historical levels and the association of crust velocity models and MSL evolution. These procedures allow discrimination between epyrogenic and crustal movements, and between

According to [17], the GNSS positioning of an RL, associated with spirit leveling allows the

**Figure 3** shows a scheme of a local leveling network within a system for MSL measurement without the possible effects of crustal vertical movement. In **Figure 3**, it is observed that for possible detection of crust movements, a GNSS station and an absolute gravimeter are considered next to the tide gauge, besides LRs with overlapping targets for leveling and the tide staff next to the

techniques combination that are usually at different times and GRSs [36, 37].

the periodic differential loading of the tide effects on the crust [45].

zero reference of a tide gauge to a geocentric GRS, as shown in **Figure 2**.

**Figure 2.** Imbituba tide gauge geocentric position monitoring via GNSS observations and spirit leveling. Source: Da Silva [25].

**Figure 3.** Schematic of required leveling between various benchmarks at a tide gauge station to compare with satellite altimetry.

tide gauge. Another point presented is the question of satellite altimeters, which do not suffer influence of the crust movement. This scheme shown in **Figure 3** is important for linking different sensors, and it is the main support of methodology.

**3.3. Detection of crust movement using GNSS data**

are described in [25, 53].

domain.

database.

January 1, 2000.

and JASON-2 missions to the GRS80 ellipsoid.

As a result of these investigations, a dedicated processing of periods from 2007 to 2016 was performed. The GNSS processing strategy is characterized by Bernese software version 5.2; ionosphere free double difference observations; CODE combined orbits, satellite clock offsets and Earth orientation parameters used; elevation cutoff angle set to 10°; tropospheric delay predicted using the global mapping function (GMF), the Dry\_GMF, and Wet\_GMF, both available as standard options in the Bernese; practically unconstrained estimates of residual zenith delays for 2 hours intervals; ocean tide loading, and atmospheric loading model according to [53]. There are different criteria to select the baselines to be processed in one session; the adopted in this chapter were using the stations that have maximum number of common observations, weekly processing of coordinate of each session is reduced to the average day of each processed GPS week. Normal equations of all epochs were combined using the ADDNEQ program. More details of the GNSS processing

Analysis of Dynamic Effects on the Brazilian Vertical Datum

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29

In order to compare the tide gauge observations with the ocean sea level variability, a dedicated procedure for the altimetric sea surface height residuals extrapolation toward the tide

**1.** In order to elaborate the time series, the tide gauge observations were concatenated using Python, since RMPG observations are taken every 5 minutes; the tide gauge data sets were filtered with a low-pass filter, symmetrical and centered on the hour integer value, with the objective of separating noises or interferences in the observations within the time

**2.** The differences among in the zero reference of the digital sensor, tide staff, and the ana-

**3.** For the sea level series resampling in Imbituba at the same time of the satellite altimetry SSH series, it was fulfilled with cubic splines. So, it was necessary to develop scripts for readings and concatenation of the data. Firstly, a Python script was developed to read the data. After this process, a consistency check is performed and it is imported into the developed MySQL

**4.** The SSH values come with Julian day, so the tide gauge series were been put in the same time reference. In this processing, the Julian day 0 was established as at 12:00 TU (noon) on

The SSH data used for analysis were considered within the ±3σ range. Values SSH > 3.0 m were eliminated. Transformation of the SSH data defined on the ellipsoid of the T/P, JASON-1,

logue sensor in Imbituba were verified for the integration of the tidal series.

**3.4. Comparison between tide gauge and satellite altimetry observations**

gauge position was developed and applied [25], for this it was realized:

According to [46], the basic idea of combining results from several different techniques is to avoid systematic errors from a specific technique, the combination being the only way to achieve reliability along with accuracy.

### **3.2. Vertical displacements**

Currently, geodetic structures have three components defining the point position in space, in addition of the temporal definition component. SIRGAS is an example based on ITRF2000 [47]. The SIRGAS coordinates can be used as constrain in GNSS processing, but rather it must be reduced to the time of the satellite observations from the geophysical or geodetic models of plate movements [46].

Based on stations' velocities, it is possible to update the station coordinates, from the reference epoch to any other, or knowing the observation epoch, it is possible to determine the coordinates for the reference epoch. It is worth mentioning some details regarding the coordinates update process:


A method to estimate the horizontal velocities is presented in the SIRGAS Velocity Model [VElocity MOdel of Sirgas (VEMOS)]. It is worth noting that from VEMOS2009 [48], vertical velocities are not obtained because they cannot be interpolated with regional models due to local deformations, tectonic movements, and hydrological, glacial, and meteorological effects. This model was updated for VEMOS2015 [49], with it is possible to obtain the vertical velocities, since the calculation is based on GNSS measurements.

In the South American (SOAM) plate, particularly in Brazil, intraplate crustal movements are small compared to regions with intense tectonic activities. In the peripheral zones of the plate, there are relative movements with different directions and magnitudes, generating several types of geological structures [50]. For the combination of these geological structures is indicated the Geophysical model No Net Rotation–Northwestern University VELocity model 1A (NNR-NUVEL-1A) [51, 52].

### **3.3. Detection of crust movement using GNSS data**

tide gauge. Another point presented is the question of satellite altimeters, which do not suffer influence of the crust movement. This scheme shown in **Figure 3** is important for linking different

According to [46], the basic idea of combining results from several different techniques is to avoid systematic errors from a specific technique, the combination being the only way to achieve reli-

Currently, geodetic structures have three components defining the point position in space, in addition of the temporal definition component. SIRGAS is an example based on ITRF2000 [47]. The SIRGAS coordinates can be used as constrain in GNSS processing, but rather it must be reduced to the time of the satellite observations from the geophysical or geodetic models

Based on stations' velocities, it is possible to update the station coordinates, from the reference epoch to any other, or knowing the observation epoch, it is possible to determine the coordinates for the reference epoch. It is worth mentioning some details regarding the coordinates

• Differences between the various achievements of ITRF as of the Helmert transformation

• The fiducial stations coordinates at the GRS definition epoch can be transformed to the

• The new stations coordinates must be reduced to the ITRF yyyy (year), the velocities of the new stations are unknown, being necessary to interpolate as of some velocity

A method to estimate the horizontal velocities is presented in the SIRGAS Velocity Model [VElocity MOdel of Sirgas (VEMOS)]. It is worth noting that from VEMOS2009 [48], vertical velocities are not obtained because they cannot be interpolated with regional models due to local deformations, tectonic movements, and hydrological, glacial, and meteorological effects. This model was updated for VEMOS2015 [49], with it is possible to obtain the vertical veloci-

In the South American (SOAM) plate, particularly in Brazil, intraplate crustal movements are small compared to regions with intense tectonic activities. In the peripheral zones of the plate, there are relative movements with different directions and magnitudes, generating several types of geological structures [50]. For the combination of these geological structures is indicated the Geophysical model No Net Rotation–Northwestern University VELocity model 1A

sensors, and it is the main support of methodology.

ability along with accuracy.

28 Sea Level Rise and Coastal Infrastructure

**3.2. Vertical displacements**

of plate movements [46].

parameters (7 or 14);

(NNR-NUVEL-1A) [51, 52].

observation epoch with known velocities;

ties, since the calculation is based on GNSS measurements.

update process:

model.

As a result of these investigations, a dedicated processing of periods from 2007 to 2016 was performed. The GNSS processing strategy is characterized by Bernese software version 5.2; ionosphere free double difference observations; CODE combined orbits, satellite clock offsets and Earth orientation parameters used; elevation cutoff angle set to 10°; tropospheric delay predicted using the global mapping function (GMF), the Dry\_GMF, and Wet\_GMF, both available as standard options in the Bernese; practically unconstrained estimates of residual zenith delays for 2 hours intervals; ocean tide loading, and atmospheric loading model according to [53]. There are different criteria to select the baselines to be processed in one session; the adopted in this chapter were using the stations that have maximum number of common observations, weekly processing of coordinate of each session is reduced to the average day of each processed GPS week. Normal equations of all epochs were combined using the ADDNEQ program. More details of the GNSS processing are described in [25, 53].

#### **3.4. Comparison between tide gauge and satellite altimetry observations**

In order to compare the tide gauge observations with the ocean sea level variability, a dedicated procedure for the altimetric sea surface height residuals extrapolation toward the tide gauge position was developed and applied [25], for this it was realized:


The SSH data used for analysis were considered within the ±3σ range. Values SSH > 3.0 m were eliminated. Transformation of the SSH data defined on the ellipsoid of the T/P, JASON-1, and JASON-2 missions to the GRS80 ellipsoid.

### **4. Results and discussion**

The IMBT station is the object of study of this research. Thus, **Table 1** shows the velocity results the VEMOS2009 and VEMOS2015 models and estimation via GNSS processing in the Bernese 5.2. Using the geophysical and geological models, it was possible to obtain the SOAM plate rotation vectors, as well as the plate spherical coordinates **Table 2**.

It should be noted that the calculated values in **Tables 1** and **2** derived of the stations velocities used in the processing represent the Brazilian stations movement in a more realistic way.

In order to compare tide gauge observations with SSH series, the satellite trail closest to the tide gauge was considered. Comparison was made considering 71 cells closest to the tide gauge. It should be noted that the cells comprises located up data (SSH) to approximately 500 km from the coast. Therefore, the most important is to consider the nearest cell where the observations of standard deviation are still within acceptable values because they have not been affected by coastal effects, and application of the differential tidal correction [56].

In particular in shore areas, where the comparisons with the tide gauges are performed, these corrections can improve considerable errors. **Table 3** presents the comparison of characteristics before and after the differential tidal correction. The comparative results of the cell with the best results are highlighted to JASON-2 mission. **Figure 4** shows two comparisons of tide gauge daily registrations with the JASON-2 altimeter data. It should be that the altimeter data was applied the tide correction and the extrapolation to nearest area of tide gauge.

Considering the GNSS processing to detect crust movements possible, the result was used to remove these movements from the tide gauge observations and thus to carry out the absolute comparison of the tide gauge data with the satellite altimetry data.

It should be noted that the employed data and methods were allowed to distinguish the crust movement from the MSL relative variation, as well as to estimate the MSL absolute increase. These were used to determinate the I-BVD geocentric position.

Analyzing the 2007–2016 period, we obtained 5.26 ± 0.11 mm/year for the tide gauge series, −3.02 ± 0.39 mm/year for the GNSS processing and 2.23 ± 0.42 mm/year for the satellite altimetry data processing. These results were essential for the crust movement and MSL relative variations distinction, enabling the obtained time series integration of tide gauge observations and satellite altimetry data. **Figure 5** shows the time series integration of the tide gauge observations


and the altimetric mission's data, in Julian Day. In order to concatenate the averages that were

**) Δy (s/MA) Δz (s/MA) Φ<sup>o</sup> Λ<sup>o</sup> Ω<sup>o</sup>**

Analysis of Dynamic Effects on the Brazilian Vertical Datum

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NNR-NUVEL-1ª SOAM −0.060 −0.087 −0.050 −25.241 235.571 0.11641 APKIM2008 SOAM −0.231 −0.367 −0.153 −19.402 237.802 0.46002 Calculated SOAM −0.157 −0.112 −0.089 −24.80 215.64 0.2126

**Analyses Before correction After correction**

Cell 478 478 Correlation coefficient 0.93 0.96 Standard deviation (mm) 69 50 Distance from the Coast 77 77

**/MA**

31

The MSL time series presented in **Figure 5** has already made the correction of the crust movements, as well as a tide gauge observations filtering, the considered averages as outliers were eliminated and replaced by satellite altimetry data. **Figure 6** shows MSL estimate from 1948 to 2016.

in Julian days for monthly averages, a script in Python was developed.

**Figure 4.** Extrapolated series of the JASON-2 altimeter data with differential tidal correction.

**Model Plate Δx (s/MA\***

Refs. [51–52, 54–55]; 2

Degree**; \***MA - Millions of years**.**

Ref. [49].

**Table 2.** SOAM plate rotation vectors and spherical coordinates.

**Table 3.** Comparison of before and after tide differential correction.

Source: 1

ªA; <sup>o</sup>

**Table 1.** Derived velocities, the VEMOS2009 and VEMOS2015 models and the Bernese processing for IMBT station.


Source: 1 Refs. [51–52, 54–55]; 2 Ref. [49].

ªA; <sup>o</sup> Degree**; \***MA - Millions of years**.**

**4. Results and discussion**

30 Sea Level Rise and Coastal Infrastructure

The IMBT station is the object of study of this research. Thus, **Table 1** shows the velocity results the VEMOS2009 and VEMOS2015 models and estimation via GNSS processing in the Bernese 5.2. Using the geophysical and geological models, it was possible to obtain the SOAM

It should be noted that the calculated values in **Tables 1** and **2** derived of the stations velocities used in the processing represent the Brazilian stations movement in a more realistic way. In order to compare tide gauge observations with SSH series, the satellite trail closest to the tide gauge was considered. Comparison was made considering 71 cells closest to the tide gauge. It should be noted that the cells comprises located up data (SSH) to approximately 500 km from the coast. Therefore, the most important is to consider the nearest cell where the observations of standard deviation are still within acceptable values because they have not

been affected by coastal effects, and application of the differential tidal correction [56].

was applied the tide correction and the extrapolation to nearest area of tide gauge.

comparison of the tide gauge data with the satellite altimetry data.

These were used to determinate the I-BVD geocentric position.

In particular in shore areas, where the comparisons with the tide gauges are performed, these corrections can improve considerable errors. **Table 3** presents the comparison of characteristics before and after the differential tidal correction. The comparative results of the cell with the best results are highlighted to JASON-2 mission. **Figure 4** shows two comparisons of tide gauge daily registrations with the JASON-2 altimeter data. It should be that the altimeter data

Considering the GNSS processing to detect crust movements possible, the result was used to remove these movements from the tide gauge observations and thus to carry out the absolute

It should be noted that the employed data and methods were allowed to distinguish the crust movement from the MSL relative variation, as well as to estimate the MSL absolute increase.

Analyzing the 2007–2016 period, we obtained 5.26 ± 0.11 mm/year for the tide gauge series, −3.02 ± 0.39 mm/year for the GNSS processing and 2.23 ± 0.42 mm/year for the satellite altimetry data processing. These results were essential for the crust movement and MSL relative variations distinction, enabling the obtained time series integration of tide gauge observations and satellite altimetry data. **Figure 5** shows the time series integration of the tide gauge observations

**Model VLat (mm/y) VLong (mm/y) h (mm/y) VX (mm/y) VY (mm/y) VZ (mm/y)** VEMOS2009 12.00 −2.60 — 1.80 −6.00 10.60 VEMOS2015 14.20 −3.80 −3.40 −0.37 −5.30 14.14 Processing 16.18 −3.87 −3.02 −0.39 −5.69 12.56

**Table 1.** Derived velocities, the VEMOS2009 and VEMOS2015 models and the Bernese processing for IMBT station.

plate rotation vectors, as well as the plate spherical coordinates **Table 2**.

**Table 2.** SOAM plate rotation vectors and spherical coordinates.


**Table 3.** Comparison of before and after tide differential correction.

**Figure 4.** Extrapolated series of the JASON-2 altimeter data with differential tidal correction.

and the altimetric mission's data, in Julian Day. In order to concatenate the averages that were in Julian days for monthly averages, a script in Python was developed.

The MSL time series presented in **Figure 5** has already made the correction of the crust movements, as well as a tide gauge observations filtering, the considered averages as outliers were eliminated and replaced by satellite altimetry data. **Figure 6** shows MSL estimate from 1948 to 2016.

For a better analysis of the mean sea level, we analyzed data from satellite altimetry of different missions with tide gauge observations. These allowed a better I-BVD evolution analysis comparing the SSH values time series related to the located cells along the satellite track and the tide

Analysis of Dynamic Effects on the Brazilian Vertical Datum

http://dx.doi.org/10.5772/intechopen.71546

33

I-BVD temporal evolution can be modeled from long time series (over 5 years) of satellite altimetry data, GNSS, and tide gauge observations. This assertive is in accordance with the integration vision to the IHRS. Therefore, the based results on the I-BVD geocentric position analysis showed an elevation rate of +2.24 ± 0.4 mm/year. This value is in agreement with global information of mean sea level elevation, stands out that evidenced the MSL evolution in the I-BVD region determining the tide gauge geocentric position temporal variation associated the time

The authors would like to thank CNPq (Research and Development National Council) for the financial support for the development of the project under grant process number 160309/2013-1 and 306936/2015-1. Thanks to Post Graduate Program in Geodetic Science of UFPR (Federal University of Paraná) for providing support and structure. As like as we thank to the IBGE, DGFI,

[1] GGOS–The Global Geodetic Observing System. Introducing GGOS – Additional Information: GGOS [Internet]. Available from: http://www.ggos.org/ [Accessed: June 6, 2017]

[2] IAG – International Association of Geodesy. Home: IAG and IUGG Resolutions – IAG Resolutions Prague, Czech Republic 2015. Resolution n°1 for the Definition and Realization of an International Height Reference System (IHRS) [Internet]. Available from: http://iag. dgfi.tum.de/fileadmin/IAG-docs/IAG\_Resolutions\_2015.pdf [Accessed: July 06, 2017] [3] IAG – International Association of Geodesy. IAG Office – IAG GGGOS [Internet]. Available from: http://iag.dgfi.tum.de/index.php?id=253 [Accessed: June 10, 2017]

gauge sea level observations integrated with GNSS positioning time series.

series analysis of tide gauge observations and satellite altimetry data.

Luciana M. Da Silva\*, Sílvio R.C. De Freitas and Regiane Dalazoana

\*Address all correspondence to: lumasilva15@gmail.com

Federal University of Parana, Curitiba, Brazil

**Acknowledgements**

**Author details**

**References**

PSMSL, and UHSLC by data provided.

**Figure 5.** Tide gauge observations of temporal series and satellite altimetry data at the same instant.

**Figure 6.** Mean sea level time series on I-BVD.

From the obtained results, it was evidenced that there is an MSL evolution in the I-BVD region by the determination of temporal variation resulting of approximately +2.24 ± 0.4 mm/year. There was also agreement with based studies on tide gauge observations and satellite altimetry data. These studies were already mentioned in this research.

### **5. Conclusion**

Imbituba sea level shows remarkable changes within the time analyzed period. The GNSS observations time series processing made it possible to generate its own velocity model as well as to compare with the proposed models by SIRGAS, and geophysical and geological velocity models.

For a better analysis of the mean sea level, we analyzed data from satellite altimetry of different missions with tide gauge observations. These allowed a better I-BVD evolution analysis comparing the SSH values time series related to the located cells along the satellite track and the tide gauge sea level observations integrated with GNSS positioning time series.

I-BVD temporal evolution can be modeled from long time series (over 5 years) of satellite altimetry data, GNSS, and tide gauge observations. This assertive is in accordance with the integration vision to the IHRS. Therefore, the based results on the I-BVD geocentric position analysis showed an elevation rate of +2.24 ± 0.4 mm/year. This value is in agreement with global information of mean sea level elevation, stands out that evidenced the MSL evolution in the I-BVD region determining the tide gauge geocentric position temporal variation associated the time series analysis of tide gauge observations and satellite altimetry data.

### **Acknowledgements**

The authors would like to thank CNPq (Research and Development National Council) for the financial support for the development of the project under grant process number 160309/2013-1 and 306936/2015-1. Thanks to Post Graduate Program in Geodetic Science of UFPR (Federal University of Paraná) for providing support and structure. As like as we thank to the IBGE, DGFI, PSMSL, and UHSLC by data provided.

### **Author details**

Luciana M. Da Silva\*, Sílvio R.C. De Freitas and Regiane Dalazoana

\*Address all correspondence to: lumasilva15@gmail.com

Federal University of Parana, Curitiba, Brazil

### **References**

From the obtained results, it was evidenced that there is an MSL evolution in the I-BVD region by the determination of temporal variation resulting of approximately +2.24 ± 0.4 mm/year. There was also agreement with based studies on tide gauge observations and satellite altim-

**Figure 5.** Tide gauge observations of temporal series and satellite altimetry data at the same instant.

Imbituba sea level shows remarkable changes within the time analyzed period. The GNSS observations time series processing made it possible to generate its own velocity model as well as to compare with the proposed models by SIRGAS, and geophysical and geological velocity models.

etry data. These studies were already mentioned in this research.

**5. Conclusion**

**Figure 6.** Mean sea level time series on I-BVD.

32 Sea Level Rise and Coastal Infrastructure


[4] Sanchez L, Sideris MG. Vertical datum unification for the International Height Reference System (IHRS). Geophysical Journal International. 2017:570-586

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**Coastal Urban Environments**


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38 Sea Level Rise and Coastal Infrastructure

**Chapter 4**

**Provisional chapter**

**Alterations within the Coastal Urban Environments:**

Two-thirds of the megacities of the world are standing on the coastal areas. Today, coastal megacities are under the impact of varying factors like human-induced changes such as urbanization and mega projects and the natural ones as global climate change and natural disasters. Many European coastal cities are examining the impacts of the sea level change due to the global climate change. Regarding its long history, interplay with the sea and the drastic population, Istanbul captures a significant place both in Turkey and in the world. It is standing as a city, which is phase by phase losing its interaction with the sea due to the mega projects generated within the last decades. Although their limited number; public squares and parks attached with the promenades are the only openings to the sea and they contribute maintaining the continuity and sustainability of coastal identity. This chapter handles five significant historical squares and interrogates their interplay with the natural and physical challenges of the twenty-first century. Regarding this aim, case areas are evaluated by parameters of morphological attributes, formation of squares, qualification of the surfaces and coastal-based natural disaster impacts such as sea level rise and tsunami through literature-based studies and spatio-temporal dia-

**Alterations within the Coastal Urban Environments:** 

DOI: 10.5772/intechopen.73508

© 2016 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,

© 2018 The Author(s). Licensee IntechOpen. 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.

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

Megacities are the world's most populated areas that are subjected to a sustained growth both in terms of physical and in terms of demographic parameters. According to the world population ranking of megacities, 6 out of 10 most populated ones are "coastal," and they are altering under the impact of many dynamics. The main criteria for being a "coastal" megacity

**Keywords:** coastal megacities, coastal squares, spatial alterations, Istanbul

**Case of the Coastal Squares of Istanbul Megacity**

**Case of the Coastal Squares of Istanbul Megacity**

Hatice Ayatac, Fatma Aycim Turer Baskaya,

Hatice Ayatac, Fatma Aycim Turer Baskaya, Eren Kurkcuoglu, Ozge Celik and Sinem Becerik

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73508

**Abstract**

grammatic maps.

**1. Introduction**

Eren Kurkcuoglu, Ozge Celik and Sinem Becerik

**Provisional chapter**

### **Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul Megacity Case of the Coastal Squares of Istanbul Megacity**

**Alterations within the Coastal Urban Environments:** 

DOI: 10.5772/intechopen.73508

Hatice Ayatac, Fatma Aycim Turer Baskaya, Eren Kurkcuoglu, Ozge Celik and Sinem Becerik Eren Kurkcuoglu, Ozge Celik and Sinem Becerik Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Hatice Ayatac, Fatma Aycim Turer Baskaya,

http://dx.doi.org/10.5772/intechopen.73508

#### **Abstract**

Two-thirds of the megacities of the world are standing on the coastal areas. Today, coastal megacities are under the impact of varying factors like human-induced changes such as urbanization and mega projects and the natural ones as global climate change and natural disasters. Many European coastal cities are examining the impacts of the sea level change due to the global climate change. Regarding its long history, interplay with the sea and the drastic population, Istanbul captures a significant place both in Turkey and in the world. It is standing as a city, which is phase by phase losing its interaction with the sea due to the mega projects generated within the last decades. Although their limited number; public squares and parks attached with the promenades are the only openings to the sea and they contribute maintaining the continuity and sustainability of coastal identity. This chapter handles five significant historical squares and interrogates their interplay with the natural and physical challenges of the twenty-first century. Regarding this aim, case areas are evaluated by parameters of morphological attributes, formation of squares, qualification of the surfaces and coastal-based natural disaster impacts such as sea level rise and tsunami through literature-based studies and spatio-temporal diagrammatic maps.

**Keywords:** coastal megacities, coastal squares, spatial alterations, Istanbul

### **1. Introduction**

Megacities are the world's most populated areas that are subjected to a sustained growth both in terms of physical and in terms of demographic parameters. According to the world population ranking of megacities, 6 out of 10 most populated ones are "coastal," and they are altering under the impact of many dynamics. The main criteria for being a "coastal" megacity

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. © 2018 The Author(s). Licensee IntechOpen. 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.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

are simply related with the connection and integration with coastlines both with positive and negative aspects, while coastal areas provide access to rich and diversified usages. On the other hand, even a slight increase in sea level would have significant physical impacts within the city boundaries [1]. Megacities are defined with at least 10 million population, but coastal megacities need further physical parameters like 100 km distance from the coastline and 100 m elevation from the sea level in all city limits [2].

city, and it had a strategic location as being on the intersection point of the Sea of Marmara, Bosphorus and Golden Horn to maintain control in terms of defence and maritime trade [8].

Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul…

http://dx.doi.org/10.5772/intechopen.73508

43

Istanbul is a unique city that connects two continents with a total coastline of 647 km. [9]. Within the development of the city, not only the historical and cultural values but also the internationally important coasts have been influential. In the pre-republic period, coastal areas of the city had served as trade and port. Together with the establishment of the republic, the capital function of the city came to an end. Hence, the seaside mansions emerged along the coastal edges along the strait of Bosphorus. Following the 1930s, Istanbul has gained its importance back by its unique historical sites and the attractive public places. However, the coastal areas have examined the most significant alterations again in the coastal areas due to

Coastal landfill areas had been constructed till the end of the 1990s with the aim of establishing transportation lines and nodes besides the coastal recreation areas [12]. Deindustrialization decisions in the 1980s brought about the initial steps of the coastal urban transformation projects. Starting in the first half of the 2000s, the coastal urban environments of Istanbul have started to examine drastic spatial changes by welcoming mega projects. "Urban Transformation Projects," which are a series of mega projects regarding many planning and implementation studies, became more popular after the mid-2000s and attract many star architects and planners to propose various contemporary and sometimes contradictory design projects for coastal areas such as Kartal-Pendik Regeneration Master Plan, Galataport and Haydarpaşa [11]. They were also controversial in terms of ignoring the historical pattern, destructing physical and social traces, leading to social

dissociation by responding to high-income groups' requests and limiting public access.

the Sea of Marmara [13].

Coastal areas of the Istanbul megacity are open to several disasters like earthquake, tsunami and sea level rise. Turer Baskaya revealed the city as one of the most hazard-prone coastal megacities in the world due to the existence of the active North Anatolian Fault laying under

As the citizens are gradually losing their contact with the sea, due to the dynamics effective on the coastal areas, historical coastal squares appear to be more important. Historical coastal squares of the Istanbul megacity which is experiencing drastic coastal alterations have always been the "gates to the sea" and should be regarded as the unique cultural elements of urban memory. As a city representing a synthesis of western and eastern cultures, coastal squares stand significantly even expressing limited social similarities with the Mediterranean countries examining cultural diversity. This chapter highlights the importance of assuring the sustainability of the coastal cultural spaces as in the case of squares even with their associated meanings.

In this context, this chapter aims to interrogate the dynamics, physical and spatial alterations/transformations pertinent with the square and identifies five historically specific coastal squares for the studies. Regarding this aim, the case areas are evaluated by the parameters of morphological attributes, the formation of squares, qualification of the surfaces and coastalbased natural disaster impacts such as sea level rise and tsunami through literature-based

studies and spatio-temporal diagrammatic mappings.

the rapid urbanization and industrialization starting in the 1950s [10, 11].

Coastal megacities are mainly compositions of concrete buildings, skyscrapers, complex traffic roads and so forth standing next to the coast, but they also have significant influences on the formation of the coastline, ecological balance, air pollution, sea habitat and weather systems. Thus, a dynamic and integrated bond can be found between the city and the coast regarding human factors and environmental issues. As a result, the current coastal megacities are facing several facts like "urbanization and pertinent mega-projects," "global climate change and the pertinent sea level rise," "earthquake and the secondary hazards" and "environmental pollution."

Regarding their location-based factors, European cities have no risks on hurricane storms, and they do not face with tropical cyclones; however, tidal and nontropical storm floods are still effective such that some cities were built under sea level height and sheltered by walls/ overflow sets [3].

Squares are the most important public spaces of a city and usually compensate different functions [4]. Within the context of urban space organization, public squares are either formed spontaneously or designed through several determinative factors (physical or sociocultural). The most important physical determinants/reasons for the formation of squares are the intersection of main roads, gateway for urban coastal areas and association with coastal components like bridges, ports and harbors and a scene for monumental buildings/landmarks [5]. In the historical development of public squares, the basic definitions are classified referring to their locations, main functions and surrounding buildings such as ceremonial squares, market squares, church squares, political squares and so forth. Today, this classification is diversified with new concepts like historical squares, transfer squares, urban interior squares and port/coastal squares. Coastal squares are semi-enclosed and usually amorphous urban gaps that constitute an entrance for people who use sea transportation and also respond to their actions like meeting, waiting, welcoming or watching seascape [6]. They also provide significant contributions to the identity of the city and the perception of coastal landscape. In most of the existing coastal cities, there are many privileged coastal squares which generate an interface between the city and water, become a focal point and enrich the urban identity like Piazza San Marco in Venice.

In the case of Istanbul, we are dealing with an ever-expanding city with an area of 5313 square kilometres and a population of 14.8 million [7]. Standing as a historical city, Istanbul has a long interplay with its surrounding seas, which reaches back to the seventh century BC. Its location in between Europe and Asia generates its unique natural and cultural coastal formations. As a capital of two empires, the coastal location brings about both advantages and disadvantages. The north-eastern hill of the historical peninsula was the first nucleus of the city, and it had a strategic location as being on the intersection point of the Sea of Marmara, Bosphorus and Golden Horn to maintain control in terms of defence and maritime trade [8].

are simply related with the connection and integration with coastlines both with positive and negative aspects, while coastal areas provide access to rich and diversified usages. On the other hand, even a slight increase in sea level would have significant physical impacts within the city boundaries [1]. Megacities are defined with at least 10 million population, but coastal megacities need further physical parameters like 100 km distance from the coastline and 100 m

Coastal megacities are mainly compositions of concrete buildings, skyscrapers, complex traffic roads and so forth standing next to the coast, but they also have significant influences on the formation of the coastline, ecological balance, air pollution, sea habitat and weather systems. Thus, a dynamic and integrated bond can be found between the city and the coast regarding human factors and environmental issues. As a result, the current coastal megacities are facing several facts like "urbanization and pertinent mega-projects," "global climate change and the pertinent sea level rise," "earthquake and the secondary hazards" and "envi-

Regarding their location-based factors, European cities have no risks on hurricane storms, and they do not face with tropical cyclones; however, tidal and nontropical storm floods are still effective such that some cities were built under sea level height and sheltered by walls/

Squares are the most important public spaces of a city and usually compensate different functions [4]. Within the context of urban space organization, public squares are either formed spontaneously or designed through several determinative factors (physical or sociocultural). The most important physical determinants/reasons for the formation of squares are the intersection of main roads, gateway for urban coastal areas and association with coastal components like bridges, ports and harbors and a scene for monumental buildings/landmarks [5]. In the historical development of public squares, the basic definitions are classified referring to their locations, main functions and surrounding buildings such as ceremonial squares, market squares, church squares, political squares and so forth. Today, this classification is diversified with new concepts like historical squares, transfer squares, urban interior squares and port/coastal squares. Coastal squares are semi-enclosed and usually amorphous urban gaps that constitute an entrance for people who use sea transportation and also respond to their actions like meeting, waiting, welcoming or watching seascape [6]. They also provide significant contributions to the identity of the city and the perception of coastal landscape. In most of the existing coastal cities, there are many privileged coastal squares which generate an interface between the city and water, become a focal point and enrich the urban identity

In the case of Istanbul, we are dealing with an ever-expanding city with an area of 5313 square kilometres and a population of 14.8 million [7]. Standing as a historical city, Istanbul has a long interplay with its surrounding seas, which reaches back to the seventh century BC. Its location in between Europe and Asia generates its unique natural and cultural coastal formations. As a capital of two empires, the coastal location brings about both advantages and disadvantages. The north-eastern hill of the historical peninsula was the first nucleus of the

elevation from the sea level in all city limits [2].

ronmental pollution."

42 Sea Level Rise and Coastal Infrastructure

overflow sets [3].

like Piazza San Marco in Venice.

Istanbul is a unique city that connects two continents with a total coastline of 647 km. [9]. Within the development of the city, not only the historical and cultural values but also the internationally important coasts have been influential. In the pre-republic period, coastal areas of the city had served as trade and port. Together with the establishment of the republic, the capital function of the city came to an end. Hence, the seaside mansions emerged along the coastal edges along the strait of Bosphorus. Following the 1930s, Istanbul has gained its importance back by its unique historical sites and the attractive public places. However, the coastal areas have examined the most significant alterations again in the coastal areas due to the rapid urbanization and industrialization starting in the 1950s [10, 11].

Coastal landfill areas had been constructed till the end of the 1990s with the aim of establishing transportation lines and nodes besides the coastal recreation areas [12]. Deindustrialization decisions in the 1980s brought about the initial steps of the coastal urban transformation projects. Starting in the first half of the 2000s, the coastal urban environments of Istanbul have started to examine drastic spatial changes by welcoming mega projects. "Urban Transformation Projects," which are a series of mega projects regarding many planning and implementation studies, became more popular after the mid-2000s and attract many star architects and planners to propose various contemporary and sometimes contradictory design projects for coastal areas such as Kartal-Pendik Regeneration Master Plan, Galataport and Haydarpaşa [11]. They were also controversial in terms of ignoring the historical pattern, destructing physical and social traces, leading to social dissociation by responding to high-income groups' requests and limiting public access.

Coastal areas of the Istanbul megacity are open to several disasters like earthquake, tsunami and sea level rise. Turer Baskaya revealed the city as one of the most hazard-prone coastal megacities in the world due to the existence of the active North Anatolian Fault laying under the Sea of Marmara [13].

As the citizens are gradually losing their contact with the sea, due to the dynamics effective on the coastal areas, historical coastal squares appear to be more important. Historical coastal squares of the Istanbul megacity which is experiencing drastic coastal alterations have always been the "gates to the sea" and should be regarded as the unique cultural elements of urban memory. As a city representing a synthesis of western and eastern cultures, coastal squares stand significantly even expressing limited social similarities with the Mediterranean countries examining cultural diversity. This chapter highlights the importance of assuring the sustainability of the coastal cultural spaces as in the case of squares even with their associated meanings.

In this context, this chapter aims to interrogate the dynamics, physical and spatial alterations/transformations pertinent with the square and identifies five historically specific coastal squares for the studies. Regarding this aim, the case areas are evaluated by the parameters of morphological attributes, the formation of squares, qualification of the surfaces and coastalbased natural disaster impacts such as sea level rise and tsunami through literature-based studies and spatio-temporal diagrammatic mappings.

### **2. Materials and methods**

Two basic methods have been used in the research to evaluate the temporal and spatial changes of selected squares in the city of Istanbul over the changes on the coast: (1) literature-based studies to understand and explain the historical evolution and changing spatial dynamics of the selected squares and (2) diagrammatic mapping with the data obtained from maps, satellite images and other visualized analyses.

the use of architectural structures and monumental objects in Islamic culture. Thus, the large mosques were responsible for the gathering people, while fountains and other architectural structures were created as a square. According to Kuban, those singular elements also empha-

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In the historical period, from 1680, expansion of city borders of Istanbul was started through Bosphorus seashore, from inside the historical walls. New settlements extended from Bosphorus to Beykoz (Black Sea) to the whole Golden Horn shores and to Kadikoy districts (Marmara Sea). There has been a horizontal development along the coastal axis since the trade was prioritized in these settlements; the coastal side has been developed due to the importance of water transportation [19]. The disconnected coastal settlements were usually built towards the foothills or into the valleys. In the meantime, the hillsides were covered by plantation. These features of Istanbul lasted until the twentieth century [18]. When unpretending coastal settlements significantly developed in direct proportion to population growth, Istanbul began to lose its landscape characteristics. Therefore, rapid planned/unplanned developments had led to the disappearance of natural values. On the contrary, the expansion of the physical environment through the city walls was regarded as the beginning of the westernization process in the context of urban form and scale [18]. The inner city of Istanbul, restricted by the walls, had given place to the coastal city, which concentrated on the coastal line. Further, coastal settlements have continued to develop rapidly with the ignorance of the topography; in the meantime, the functional division of the city and its historical continuity has been ensured by port trade.

Especially in design perspective of squares, in the late nineteenth century with the construction of the Galata Bridge and The New Mosque (Yeni Camii), Eminonu Square was designed as a transportation hub inside the city walls and by the coastal area of Golden Horn. Even though The New Mosque and Spice Bazaar were significant architectural structures which increased common use of the square, the square was, and still is, a connection point in the city. However, it has been observed that tourism has developed along with the existence of historical monuments in Eminonu, which is a central area throughout history [20]. Because of safety and security reasons, city borders were limited to the city walls until the Ottoman period. Therefore, the square within the city walls has the characteristics of being separated from the other coastal squares by its historical infrastructure. Observing the historical background, the square was defined as a square which serves the entire city of Istanbul, while other coastal squares, outside the city walls, occurred with the establishment of new neighborhoods in the Golden Horn, Bosphorus and Uskudar in the fifteenth century without defining as a square [18]. Uskudar, on the other hand, was developed to transfer the commercial axis from Anatolia to Europe due to the significance of water transportation system [19]. With the construction of the first bridge and its connection vehicle roads, Uskudar's development has started to move towards higher hills/areas beside the coast. In the 1930s, urban planning strategies had changed according to vehicle traffic and road system in the city. In consequences of those changes, Uskudar square became a nodal point of Anatolian side [21]. Population growth and urbanization cause air pollution and reinforcement, and the rapid increase in density also led to unfavorable developments of topographical features of the area. It was observed that the forests in the district were replaced with agricultural land and then with housing areas in time [22]. The effects/pressure

size permanency in the urban texture [18].

Literature-based studies are mainly focused on coastal megacities, transformation of coastal areas and historical development of Istanbul. Throughout the mapping study, historical maps (Jacques Pervititch's insurance maps, drafted over 25 years between 1921 and 1946) and satellite images (Istanbul Metropolitan Municipality City Maps) are used to evaluate morphological transformation process in each of the five squares [14]. Pervititch's historical maps showed the first formations of squares, coastline and coastal areas and their relationship with other functional subdistricts and satellite maps (1970) and (2016) to emphasize the rapid change with coastal plan applications. In this context, physical transformation processes (especially alteration of coastlines) of selected areas are integrated to final maps [15].

Spatio-temporal diagrammatic maps have been produced on the 1/1000 scale current maps from Istanbul Metropolitan Municipality (IMM) [14]. Main parameters and components of these diagrams can be classified into two subcategories: morphological attributes (including physical environment features such as buildings, street networks and other open spaces, location and formation of coastal squares and permeability of surfaces) and coastal-based natural disaster impacts (sea level rise and tsunami). Firstly, current maps, satellite images, on-site analyses and historical-actual photographs are used to decode the morphological key elements and all superposed on the diagrams. Secondly, coastal-based disaster impacts are illustrated as layers where related information is obtained from literature-based studies or visualized data analyses: sea level rise data are received from Flood Map: Water Level Elevation Map (Beta) application and tsunami run-up height data are received from a geological-geotechnical study report (2007), which is prepared for Istanbul Metropolitan Municipality by OYO International Corporation [16, 17]. Finally, all the morphological and disaster-related parameters are comparatively evaluated to understand the dynamics and alteration processes of coastal squares. In this context, causality relations between spatio-temporal changes of coastal structure and coastal-based disasters are revealed within a multidisciplinary investigation.

### **3. Spatial development of Istanbul and its effects on coastal squares**

The old city plan of Istanbul is like an irregular network in which there are nodes in various dimensions. While small nodes express fountains and small-scale mosques, large nodes express Islamic-social complexes where mosques, tombs, fountains and madrasah [18]. Although the urban texture has been continuously changed by the emergence of new building complexes, in other words, functional nodes, it has preserved its general fabric. Unlike other cultures, the formation of public space and the emergence of the square have arisen with the use of architectural structures and monumental objects in Islamic culture. Thus, the large mosques were responsible for the gathering people, while fountains and other architectural structures were created as a square. According to Kuban, those singular elements also emphasize permanency in the urban texture [18].

**2. Materials and methods**

44 Sea Level Rise and Coastal Infrastructure

maps, satellite images and other visualized analyses.

Two basic methods have been used in the research to evaluate the temporal and spatial changes of selected squares in the city of Istanbul over the changes on the coast: (1) literature-based studies to understand and explain the historical evolution and changing spatial dynamics of the selected squares and (2) diagrammatic mapping with the data obtained from

Literature-based studies are mainly focused on coastal megacities, transformation of coastal areas and historical development of Istanbul. Throughout the mapping study, historical maps (Jacques Pervititch's insurance maps, drafted over 25 years between 1921 and 1946) and satellite images (Istanbul Metropolitan Municipality City Maps) are used to evaluate morphological transformation process in each of the five squares [14]. Pervititch's historical maps showed the first formations of squares, coastline and coastal areas and their relationship with other functional subdistricts and satellite maps (1970) and (2016) to emphasize the rapid change with coastal plan applications. In this context, physical transformation processes (especially

Spatio-temporal diagrammatic maps have been produced on the 1/1000 scale current maps from Istanbul Metropolitan Municipality (IMM) [14]. Main parameters and components of these diagrams can be classified into two subcategories: morphological attributes (including physical environment features such as buildings, street networks and other open spaces, location and formation of coastal squares and permeability of surfaces) and coastal-based natural disaster impacts (sea level rise and tsunami). Firstly, current maps, satellite images, on-site analyses and historical-actual photographs are used to decode the morphological key elements and all superposed on the diagrams. Secondly, coastal-based disaster impacts are illustrated as layers where related information is obtained from literature-based studies or visualized data analyses: sea level rise data are received from Flood Map: Water Level Elevation Map (Beta) application and tsunami run-up height data are received from a geological-geotechnical study report (2007), which is prepared for Istanbul Metropolitan Municipality by OYO International Corporation [16, 17]. Finally, all the morphological and disaster-related parameters are comparatively evaluated to understand the dynamics and alteration processes of coastal squares. In this context, causality relations between spatio-temporal changes of coastal structure and coastal-based disasters are revealed within a multidisciplinary investigation.

**3. Spatial development of Istanbul and its effects on coastal squares**

The old city plan of Istanbul is like an irregular network in which there are nodes in various dimensions. While small nodes express fountains and small-scale mosques, large nodes express Islamic-social complexes where mosques, tombs, fountains and madrasah [18]. Although the urban texture has been continuously changed by the emergence of new building complexes, in other words, functional nodes, it has preserved its general fabric. Unlike other cultures, the formation of public space and the emergence of the square have arisen with

alteration of coastlines) of selected areas are integrated to final maps [15].

In the historical period, from 1680, expansion of city borders of Istanbul was started through Bosphorus seashore, from inside the historical walls. New settlements extended from Bosphorus to Beykoz (Black Sea) to the whole Golden Horn shores and to Kadikoy districts (Marmara Sea). There has been a horizontal development along the coastal axis since the trade was prioritized in these settlements; the coastal side has been developed due to the importance of water transportation [19]. The disconnected coastal settlements were usually built towards the foothills or into the valleys. In the meantime, the hillsides were covered by plantation. These features of Istanbul lasted until the twentieth century [18]. When unpretending coastal settlements significantly developed in direct proportion to population growth, Istanbul began to lose its landscape characteristics. Therefore, rapid planned/unplanned developments had led to the disappearance of natural values. On the contrary, the expansion of the physical environment through the city walls was regarded as the beginning of the westernization process in the context of urban form and scale [18]. The inner city of Istanbul, restricted by the walls, had given place to the coastal city, which concentrated on the coastal line. Further, coastal settlements have continued to develop rapidly with the ignorance of the topography; in the meantime, the functional division of the city and its historical continuity has been ensured by port trade.

Especially in design perspective of squares, in the late nineteenth century with the construction of the Galata Bridge and The New Mosque (Yeni Camii), Eminonu Square was designed as a transportation hub inside the city walls and by the coastal area of Golden Horn. Even though The New Mosque and Spice Bazaar were significant architectural structures which increased common use of the square, the square was, and still is, a connection point in the city. However, it has been observed that tourism has developed along with the existence of historical monuments in Eminonu, which is a central area throughout history [20]. Because of safety and security reasons, city borders were limited to the city walls until the Ottoman period. Therefore, the square within the city walls has the characteristics of being separated from the other coastal squares by its historical infrastructure. Observing the historical background, the square was defined as a square which serves the entire city of Istanbul, while other coastal squares, outside the city walls, occurred with the establishment of new neighborhoods in the Golden Horn, Bosphorus and Uskudar in the fifteenth century without defining as a square [18]. Uskudar, on the other hand, was developed to transfer the commercial axis from Anatolia to Europe due to the significance of water transportation system [19]. With the construction of the first bridge and its connection vehicle roads, Uskudar's development has started to move towards higher hills/areas beside the coast. In the 1930s, urban planning strategies had changed according to vehicle traffic and road system in the city. In consequences of those changes, Uskudar square became a nodal point of Anatolian side [21]. Population growth and urbanization cause air pollution and reinforcement, and the rapid increase in density also led to unfavorable developments of topographical features of the area. It was observed that the forests in the district were replaced with agricultural land and then with housing areas in time [22]. The effects/pressure of construction can also be seen in Ortakoy, which has a strategic location because of having connection roads. Combining the two sides of Istanbul and being a transit zone along the coastal route, settlements/residential areas are located on valley slopes and alluvial plain by the sea. Ortakoy Mosque, where the stream reaches the sea with reclamation, was constructed in 1854–1855 by Sultan Abdulmecit, and Fountain of Damat Ibrahim Pasa was constructed in 1973. Both architectural monuments were used to form the Ortakoy square. With the construction of Bosphorus Bridge in 1970, Ortakoy stream was completely covered and the route of stream planned as the main street. It shows the intervention to streams because of the consequences of land use decisions related to changing transportation models after the 1950s. Similarly, Stream of Bulbul in Uskudar was affected by rapid urbanization and transportation policies. The unbalanced development between nature and human communities has also an impact on local climates.

There has been an acceleration of filling of coastal areas after the 1980s due to population increase and inadequacy of the infrastructure. It shows that coastal squares were affected by contrasts between the development of transportation networks and the change of function in coastal areas. With demolishing residential areas, Uskudar square was enlarged the same way as other parts of Istanbul [23]. Meanwhile, the filling areas in Kadikoy were designed for both vehicle traffic and recreational areas. Today, however, Kadikoy square is still a transportation hub and transfer centre. One of the most important reasons for that is the division of the square by roads, and due to the structures with different functions and the loss of boundaries, the square is perceived as an amorphous layout.

Besides, the first settlement in Buyukada, which has a different development process, was established as a fishing village where the garden of Aya Nikola Monastery is located. The fire in 1850 had destroyed the architecture of the island, and the settlement on the coastal area, located to the north of the island, began to develop hereafter [24]. The area, which was composed of summer houses scattered in the eighteenth century, is today the centre of the sea transportation. On the other hand, the inner square where the clock tower is located is today the centre of commerce. It is used for recreational purposes along with the pier and its surroundings. Even though the district is the densest part of the island, the height above the water level of the dock allows only visual contact with the sea. The most significant feature that distinguishes the square from other squares is the lack of vehicle traffic, the fact that it is a pedestrian-oriented district. But the situation did not prevent the island geography from being influenced by the rapid urbanization. The increase in population and prioritization of tourism in the preliminary plan caused sprawl and diffusion on the physical pattern.

Under the pressure of urbanization on Istanbul's coastal line, the ecological balance has been ruined by the settlement areas of the valleys and the destruction of the forests and the decrease of the green areas. This situation has created the basis for the change of the climatic conditions of Istanbul. At the same time, the physical characteristics of the topographical structure of the coastal settlements began to disappear. The reason to examine these five squares is related to their location and their historical background while the city is facing those problems. Methodologically, five key squares are chosen by underlining changes in physical environment and on natural values (**Figure 1**).

**3.1. Morphological and climatic alterations within coastal squares**

(developed from [25], the sources of the images, respectively [26–30]).

and Buyukada Squares are classified as medium sized (5000–15,000 m<sup>2</sup>

By the historical development process of Istanbul, all the selected coastal squares have become important focal points within their urban environment. Except for Buyukada, each square has historical roots that preceded the Republican Period and a dominant influence in the formation of the physical environment. Buyukada Square was acquired by filling the coastal area after the 1970s, and the impact of the inner bazaar square was extended to the coastal zone both to be associated with the pier. In terms of size, Eminonu, Kadikoy, Ortakoy

**Figure 1.** Location of the squares and classification of Istanbul coastline due to natural and cultural characteristics

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Square, which was previously considered as medium sized but later included in the very

), except Uskudar

Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul… http://dx.doi.org/10.5772/intechopen.73508 47

**Figure 1.** Location of the squares and classification of Istanbul coastline due to natural and cultural characteristics (developed from [25], the sources of the images, respectively [26–30]).

#### **3.1. Morphological and climatic alterations within coastal squares**

of construction can also be seen in Ortakoy, which has a strategic location because of having connection roads. Combining the two sides of Istanbul and being a transit zone along the coastal route, settlements/residential areas are located on valley slopes and alluvial plain by the sea. Ortakoy Mosque, where the stream reaches the sea with reclamation, was constructed in 1854–1855 by Sultan Abdulmecit, and Fountain of Damat Ibrahim Pasa was constructed in 1973. Both architectural monuments were used to form the Ortakoy square. With the construction of Bosphorus Bridge in 1970, Ortakoy stream was completely covered and the route of stream planned as the main street. It shows the intervention to streams because of the consequences of land use decisions related to changing transportation models after the 1950s. Similarly, Stream of Bulbul in Uskudar was affected by rapid urbanization and transportation policies. The unbalanced development between nature

There has been an acceleration of filling of coastal areas after the 1980s due to population increase and inadequacy of the infrastructure. It shows that coastal squares were affected by contrasts between the development of transportation networks and the change of function in coastal areas. With demolishing residential areas, Uskudar square was enlarged the same way as other parts of Istanbul [23]. Meanwhile, the filling areas in Kadikoy were designed for both vehicle traffic and recreational areas. Today, however, Kadikoy square is still a transportation hub and transfer centre. One of the most important reasons for that is the division of the square by roads, and due to the structures with different functions and the loss of boundaries,

Besides, the first settlement in Buyukada, which has a different development process, was established as a fishing village where the garden of Aya Nikola Monastery is located. The fire in 1850 had destroyed the architecture of the island, and the settlement on the coastal area, located to the north of the island, began to develop hereafter [24]. The area, which was composed of summer houses scattered in the eighteenth century, is today the centre of the sea transportation. On the other hand, the inner square where the clock tower is located is today the centre of commerce. It is used for recreational purposes along with the pier and its surroundings. Even though the district is the densest part of the island, the height above the water level of the dock allows only visual contact with the sea. The most significant feature that distinguishes the square from other squares is the lack of vehicle traffic, the fact that it is a pedestrian-oriented district. But the situation did not prevent the island geography from being influenced by the rapid urbanization. The increase in population and prioritization of

tourism in the preliminary plan caused sprawl and diffusion on the physical pattern.

Under the pressure of urbanization on Istanbul's coastal line, the ecological balance has been ruined by the settlement areas of the valleys and the destruction of the forests and the decrease of the green areas. This situation has created the basis for the change of the climatic conditions of Istanbul. At the same time, the physical characteristics of the topographical structure of the coastal settlements began to disappear. The reason to examine these five squares is related to their location and their historical background while the city is facing those problems. Methodologically, five key squares are chosen by underlining changes in physical envi-

and human communities has also an impact on local climates.

the square is perceived as an amorphous layout.

46 Sea Level Rise and Coastal Infrastructure

ronment and on natural values (**Figure 1**).

By the historical development process of Istanbul, all the selected coastal squares have become important focal points within their urban environment. Except for Buyukada, each square has historical roots that preceded the Republican Period and a dominant influence in the formation of the physical environment. Buyukada Square was acquired by filling the coastal area after the 1970s, and the impact of the inner bazaar square was extended to the coastal zone both to be associated with the pier. In terms of size, Eminonu, Kadikoy, Ortakoy and Buyukada Squares are classified as medium sized (5000–15,000 m<sup>2</sup> ), except Uskudar Square, which was previously considered as medium sized but later included in the very large (25,000 m2 and above) classification because of new transportation policies and urban design-regulation interventions developed from the beginning of the 2000s. In addition, each coastal square has richness and considerable similarities in terms of functional qualities and morphological characteristics (**Table 1**).

public space. Similarly, the coastal area in Uskudar that which gained its local identity with its small-sized piers and beaches and an important interaction surface between sea and land has undergone a major change in form with landfills associated with new "transfer point" identity and further transportation policies. The situation is much different in Kadikoy coastal area; due to the port and square were placed within a sheltered bay, large landfills were constructed on the west coast that oriented to the Marmara Sea. In comparison, it is possible to see the least coastal change in Ortakoy, which has a more rigid and compact structure in terms of morphology. Although the Bosphorus Bridge (as an essential transportation project) was built right next to the district, coastal form and local identity have been highly conserved (**Table 2**). Interventions such as filling-splitting on coastal areas and the alteration of coastline not only lead to striking differences in the identity-perception qualities of coastal areas but also bring important macro- and microclimatic changes both in terms of sea and in terms of land ecology. Therewithal, these interventions considerably increase the risk level in terms of natural disasters such as earthquakes, floods and tsunami. In this context, locational and coastal characteristics of selected squares also bring different risk factors: as a result of sea level rise (1 m), floods or submersions cover lands in different proportions. In a similar way, tsunami run-up heights and impact areas also vary for each coastal area: 0–1 m for Ortakoy, 1–2 m for

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Eminonu Square, as one of the oldest squares of Istanbul, has links to the coastal area in close proximity through historical periods but later detached from the coast due to the altered land use and transportation policies, and currently, the connection is provided with underground pedestrian crossings. The interventions on the coastal line also affected functional areas, and the coastal belt became a complex transfer hub. Thus, the square itself remained in the inner part and is acting as the entrance to the bazaar area. While the former square was located in front of Yeni Camii (Mosque) with an elliptical form, it is now about three times larger and has

Due to the destruction of the historical structures on the coastline and the construction of wide transportation axes in different elevations (some below sea level), the coastal belt and the eastern part of the square are under risk for disasters such as floods and tsunami. After a possible 1 m rise at sea level, approximately 70% of the square and the entrance of the bazaar

**Detached by vehicle road Detached by pedestrian road Adjacent**

Eminonu and Uskudar and 2–3 m for Kadikoy and Buyukada [17].

**Coastal square • Former + Current**

been converted into an amorphous-shaped sectional/jointed layout (**Figure 2**).

**Transfer hub Contact with coastline**

**Uskudar** • + + • **Kadikoy** + • + **Ortakoy** • +

**Eminonu** • + + •

**Buyukada** +

**Table 2.** (Former and current) Relevance of the squares to the coastline.

Morphologically, Eminonu, Uskudar, Ortakoy and Buyukada have "organic pattern" attributes with the incorporation of various urban fabric formations throughout the historical development process. On the other hand, Kadikoy has a typical "grid pattern" formation since it was founded as a Greek colonial settlement in ancient times and its properties (urban blocks, street networks, etc.) have survived so far on a large scale. These morphological attributes also affect the formation of the squares along with other transportation issues directly: Eminonu, Uskudar and Ortakoy Squares have amorphous forms (Uskudar later "amorphous-linear" with further extensions), while Kadikoy Square has a linear formation. Although located within an organic pattern, Buyukada Square has an exceptional situation with its linear form due to the regular construction of coastal filling. Street networks are also irregular and formed with different cross-sections in organic patterns in contrast with the organized and hierarchical system in grid-shaped Kadikoy. Considering that the squares as "portals/entrance gates" on the coastal belt, it can be argued that they also constitute "joints " or "intersection hubs" of land and sea transportation networks (both in terms of vehicle and in terms of pedestrian usage).

Undoubtedly, one of the most influential factors on the morphological properties of coastal squares is the transformation/alteration process of coastlines. While coastal areas have been formed in more organic forms with beaches, rocky cliffs, small-sized piers and waterside residential areas in history, the coastline has been reshaped by landfills, beaches have lost their qualities and more linear and impermeable areas have begun to emerge due to the megaurbanization process of Istanbul in the second half of the twentieth century. In this context, one of the most striking examples for the new formation of coastal squares after the radical change of coastline is the Buyukada Square: the coastal area, which defined with waterfront mansions and beach areas until the 1970s, was filled up to the level of the port resulting in a brand new


**Table 1.** Morphological comparison of selected coastal squares.

public space. Similarly, the coastal area in Uskudar that which gained its local identity with its small-sized piers and beaches and an important interaction surface between sea and land has undergone a major change in form with landfills associated with new "transfer point" identity and further transportation policies. The situation is much different in Kadikoy coastal area; due to the port and square were placed within a sheltered bay, large landfills were constructed on the west coast that oriented to the Marmara Sea. In comparison, it is possible to see the least coastal change in Ortakoy, which has a more rigid and compact structure in terms of morphology. Although the Bosphorus Bridge (as an essential transportation project) was built right next to the district, coastal form and local identity have been highly conserved (**Table 2**).

large (25,000 m2

48 Sea Level Rise and Coastal Infrastructure

morphological characteristics (**Table 1**).

**Table 1.** Morphological comparison of selected coastal squares.

and above) classification because of new transportation policies and urban

design-regulation interventions developed from the beginning of the 2000s. In addition, each coastal square has richness and considerable similarities in terms of functional qualities and

Morphologically, Eminonu, Uskudar, Ortakoy and Buyukada have "organic pattern" attributes with the incorporation of various urban fabric formations throughout the historical development process. On the other hand, Kadikoy has a typical "grid pattern" formation since it was founded as a Greek colonial settlement in ancient times and its properties (urban blocks, street networks, etc.) have survived so far on a large scale. These morphological attributes also affect the formation of the squares along with other transportation issues directly: Eminonu, Uskudar and Ortakoy Squares have amorphous forms (Uskudar later "amorphous-linear" with further extensions), while Kadikoy Square has a linear formation. Although located within an organic pattern, Buyukada Square has an exceptional situation with its linear form due to the regular construction of coastal filling. Street networks are also irregular and formed with different cross-sections in organic patterns in contrast with the organized and hierarchical system in grid-shaped Kadikoy. Considering that the squares as "portals/entrance gates" on the coastal belt, it can be argued that they also constitute "joints " or "intersection hubs" of land and sea

transportation networks (both in terms of vehicle and in terms of pedestrian usage).

Undoubtedly, one of the most influential factors on the morphological properties of coastal squares is the transformation/alteration process of coastlines. While coastal areas have been formed in more organic forms with beaches, rocky cliffs, small-sized piers and waterside residential areas in history, the coastline has been reshaped by landfills, beaches have lost their qualities and more linear and impermeable areas have begun to emerge due to the megaurbanization process of Istanbul in the second half of the twentieth century. In this context, one of the most striking examples for the new formation of coastal squares after the radical change of coastline is the Buyukada Square: the coastal area, which defined with waterfront mansions and beach areas until the 1970s, was filled up to the level of the port resulting in a brand new Interventions such as filling-splitting on coastal areas and the alteration of coastline not only lead to striking differences in the identity-perception qualities of coastal areas but also bring important macro- and microclimatic changes both in terms of sea and in terms of land ecology. Therewithal, these interventions considerably increase the risk level in terms of natural disasters such as earthquakes, floods and tsunami. In this context, locational and coastal characteristics of selected squares also bring different risk factors: as a result of sea level rise (1 m), floods or submersions cover lands in different proportions. In a similar way, tsunami run-up heights and impact areas also vary for each coastal area: 0–1 m for Ortakoy, 1–2 m for Eminonu and Uskudar and 2–3 m for Kadikoy and Buyukada [17].

Eminonu Square, as one of the oldest squares of Istanbul, has links to the coastal area in close proximity through historical periods but later detached from the coast due to the altered land use and transportation policies, and currently, the connection is provided with underground pedestrian crossings. The interventions on the coastal line also affected functional areas, and the coastal belt became a complex transfer hub. Thus, the square itself remained in the inner part and is acting as the entrance to the bazaar area. While the former square was located in front of Yeni Camii (Mosque) with an elliptical form, it is now about three times larger and has been converted into an amorphous-shaped sectional/jointed layout (**Figure 2**).

Due to the destruction of the historical structures on the coastline and the construction of wide transportation axes in different elevations (some below sea level), the coastal belt and the eastern part of the square are under risk for disasters such as floods and tsunami. After a possible 1 m rise at sea level, approximately 70% of the square and the entrance of the bazaar


**Table 2.** (Former and current) Relevance of the squares to the coastline.

**Figure 2.** Morphological structure of Eminönü; location of the public square, alteration process of the coastline and sea level rise/tsunami impact areas.

area will be flooded. Likewise, a potential tsunami will also affect especially the eastern part up to about 2 m and probably spread towards the inner parts (**Figure 2**). It can be argued that the location factors (intersection point of Bosphorus, Golden Horn and Sea of Marmara) below sea level transportation regulations and the lack of impermeable surfaces (undefined open spaces) should be the main reasons for expected intensive disaster effect.

Like Eminonu Square, it is critically located at the intersection point of Bosphorus and the Sea of Marmara, so disaster-based risks still have a certain level of influence: In case of a 1 m rise in sea level, landfills, port area and about 50% of the extended part of the square are facing the threat of submersion or flood. Or in case of a tsunami, historical parts of the square (on the eastern side) are under the first degree of flood risk; for the rest, the flood may spread towards the other parts of the square and inner parts of the bazaar (**Figure 3**). Besides, the coastal area already faces floods, especially during heavy rain falls. In this context, it is strikingly seen in Uskudar that the radical changes on the coastline increase the risks of floods due to sea level rise or tsunami as well as rainfalls. It should also be underlined that there is a risk of collapse

**Figure 3.** Morphological structure of Uskudar; location of the public square, alteration process of the coastline and sea

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51

The coastal area in Kadikoy was rather characterized by open green spaces and separated from the coastline by vehicle road at the beginning of the Republican Period, but later, the northwestern part was transformed into a linear square as intended to include ports and the cultural centre. Currently, it is one of the significant coastal squares, which has a direct contact with the sea with its adjacent spatial organization as well as an impressive vista platform on looking Haydarpasa Train Station (a monumental landmark) and the historical peninsula. Interaction

during a severe earthquake.

level rise/tsunami impact areas.

Uskudar Square, which is another historical place that became an important transportation hub on the urban scale, has a similar process like Eminonu as a result of altered land use decisions and detached from the coastal area by vehicle roads. The first formation of the square was quadrangular-shaped, small-sized public space (Bosphorus village port square) defined by the coastline and the surrounding buildings but later grew with the identity of transportation hub over time and transformed into a large-scale amorphous-linear layout as a result of the most recent interventions. The square still maintains the characteristics of being the entrance of the bazaar and interregional transfer point, but it has lost its identical attributes (**Figure 3**).

The former coastline, which defined by historical buildings, small-scale ports and restricted beaches-rocky areas, is transformed into a sharp and linear formation, and all the mentioned functional areas have demolished. The new shape of the coastline also affected the square directly, such that last extensions were conducted parallel to the coastline towards the western part of the area (**Figure 3**).

Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul… http://dx.doi.org/10.5772/intechopen.73508 51

**Figure 3.** Morphological structure of Uskudar; location of the public square, alteration process of the coastline and sea level rise/tsunami impact areas.

area will be flooded. Likewise, a potential tsunami will also affect especially the eastern part up to about 2 m and probably spread towards the inner parts (**Figure 2**). It can be argued that the location factors (intersection point of Bosphorus, Golden Horn and Sea of Marmara) below sea level transportation regulations and the lack of impermeable surfaces (undefined

**Figure 2.** Morphological structure of Eminönü; location of the public square, alteration process of the coastline and sea

Uskudar Square, which is another historical place that became an important transportation hub on the urban scale, has a similar process like Eminonu as a result of altered land use decisions and detached from the coastal area by vehicle roads. The first formation of the square was quadrangular-shaped, small-sized public space (Bosphorus village port square) defined by the coastline and the surrounding buildings but later grew with the identity of transportation hub over time and transformed into a large-scale amorphous-linear layout as a result of the most recent interventions. The square still maintains the characteristics of being the entrance of the

bazaar and interregional transfer point, but it has lost its identical attributes (**Figure 3**).

ern part of the area (**Figure 3**).

level rise/tsunami impact areas.

50 Sea Level Rise and Coastal Infrastructure

The former coastline, which defined by historical buildings, small-scale ports and restricted beaches-rocky areas, is transformed into a sharp and linear formation, and all the mentioned functional areas have demolished. The new shape of the coastline also affected the square directly, such that last extensions were conducted parallel to the coastline towards the west-

open spaces) should be the main reasons for expected intensive disaster effect.

Like Eminonu Square, it is critically located at the intersection point of Bosphorus and the Sea of Marmara, so disaster-based risks still have a certain level of influence: In case of a 1 m rise in sea level, landfills, port area and about 50% of the extended part of the square are facing the threat of submersion or flood. Or in case of a tsunami, historical parts of the square (on the eastern side) are under the first degree of flood risk; for the rest, the flood may spread towards the other parts of the square and inner parts of the bazaar (**Figure 3**). Besides, the coastal area already faces floods, especially during heavy rain falls. In this context, it is strikingly seen in Uskudar that the radical changes on the coastline increase the risks of floods due to sea level rise or tsunami as well as rainfalls. It should also be underlined that there is a risk of collapse during a severe earthquake.

The coastal area in Kadikoy was rather characterized by open green spaces and separated from the coastline by vehicle road at the beginning of the Republican Period, but later, the northwestern part was transformed into a linear square as intended to include ports and the cultural centre. Currently, it is one of the significant coastal squares, which has a direct contact with the sea with its adjacent spatial organization as well as an impressive vista platform on looking Haydarpasa Train Station (a monumental landmark) and the historical peninsula. Interaction with the residential area is disjointed as in the original formation; the gap is again used as public green space and the connections with the bazaar are provided by radial pedestrian arteries.

the landfill areas in the south-west and the inner parts of the bazaar will also be affected. Therefore, it can be argued that the location- and orientation-based risk factors are prior and higher in Kadikoy coastal area, and the alterations in the coastline are also influential as inter-

Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul…

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53

As a typical Bosphorus Village settlement, Ortakoy has substantially succeeded in protecting its morphological attributes and functional relations from the past. On the other hand, Ortakoy Square was one of the essential interfaces between the land and sea and also a landmark since the time it was first built. Although the square has an amorphous and jointed structure, it is highly defined by other urban components (coastal line, ports, mosque, fountains, commercial buildings and other historical structures), both terms of boundaries and subregions and also entirely in human scale (**Figure 5**). Unlike Eminonu, Uskudar and Kadikoy, the coastal

Ortakoy is the area that has faced the least coastal alterations compared to the other squares. Although most of the port squares in many Bosphorus Village settlements have several transformation processes, Ortakoy Square retained its morphological structure due to its identity and characteristic features. Only some small-scale landfills were constructed during the renovation of ports; nevertheless, the most prominent change is observed in the south-eastern part of the square. In accordance with the location and coastal formation attributes, the least impacts from coastal-based disasters are also seen in Ortakoy Square and its vicinity: a possible 1 m rise in sea level will not affect the entire district. Also in case of a tsunami, only a very limited part of the coastline (up to 1 m) may face a possible flood (**Figure 5**). The sheltered and limited structure of Bosphorus does not permit the construction of large-scale landfills, and the dynamic movement of the water reduces the risk of coastal-based disasters. When considered with the minor alteration process due to its local identity, Ortakoy Square is the most advantageous

**Figure 5.** Morphological structure of Ortakoy; location of the public square, alteration process of the coastline and

area is completely dissociated from vehicular traffic that also diversifies its identity.

ventions were large scale and in radical formations.

one as compared to other selected coastal squares.

tsunami impact areas.

The coastline on the side of the square is largely protected; however, large-scale landfills were constructed in the south-western and north-eastern parts of the coast. By historical chronology, it can be observed that the coastal formation of the north-eastern port has undergone at least three radical changes (**Figure 4**); likewise, about 120,000 m2 landfills were added to the south-western part between 1985 and 1993 resulting in a prominent alteration of coastal identity and usages.

Coastal-based disaster risk levels of the square and landfill areas are quite high due to the area formed as a jetty/an extension towards the Sea of Marmara westwards. In case of a 1 m rise in sea level, approximately 80% of the coastal square and most of the gaps between the bazaars will be flooded or submerged. However, the expected disaster level of a possible tsunami will have much more impact than sea level rise: the water will run-up to 3 m height and completely cover the entire coastal area up to the entrance of the bazaar (**Figure 4**). Secondly,

**Figure 4.** Morphological structure of Kadikoy; location of the public square, alteration process of the coastline and sea level rise/tsunami impact areas.

the landfill areas in the south-west and the inner parts of the bazaar will also be affected. Therefore, it can be argued that the location- and orientation-based risk factors are prior and higher in Kadikoy coastal area, and the alterations in the coastline are also influential as interventions were large scale and in radical formations.

with the residential area is disjointed as in the original formation; the gap is again used as public green space and the connections with the bazaar are provided by radial pedestrian arteries. The coastline on the side of the square is largely protected; however, large-scale landfills were constructed in the south-western and north-eastern parts of the coast. By historical chronology, it can be observed that the coastal formation of the north-eastern port has undergone at least three

part between 1985 and 1993 resulting in a prominent alteration of coastal identity and usages.

Coastal-based disaster risk levels of the square and landfill areas are quite high due to the area formed as a jetty/an extension towards the Sea of Marmara westwards. In case of a 1 m rise in sea level, approximately 80% of the coastal square and most of the gaps between the bazaars will be flooded or submerged. However, the expected disaster level of a possible tsunami will have much more impact than sea level rise: the water will run-up to 3 m height and completely cover the entire coastal area up to the entrance of the bazaar (**Figure 4**). Secondly,

**Figure 4.** Morphological structure of Kadikoy; location of the public square, alteration process of the coastline and sea

level rise/tsunami impact areas.

landfills were added to the south-western

radical changes (**Figure 4**); likewise, about 120,000 m2

52 Sea Level Rise and Coastal Infrastructure

As a typical Bosphorus Village settlement, Ortakoy has substantially succeeded in protecting its morphological attributes and functional relations from the past. On the other hand, Ortakoy Square was one of the essential interfaces between the land and sea and also a landmark since the time it was first built. Although the square has an amorphous and jointed structure, it is highly defined by other urban components (coastal line, ports, mosque, fountains, commercial buildings and other historical structures), both terms of boundaries and subregions and also entirely in human scale (**Figure 5**). Unlike Eminonu, Uskudar and Kadikoy, the coastal area is completely dissociated from vehicular traffic that also diversifies its identity.

Ortakoy is the area that has faced the least coastal alterations compared to the other squares. Although most of the port squares in many Bosphorus Village settlements have several transformation processes, Ortakoy Square retained its morphological structure due to its identity and characteristic features. Only some small-scale landfills were constructed during the renovation of ports; nevertheless, the most prominent change is observed in the south-eastern part of the square.

In accordance with the location and coastal formation attributes, the least impacts from coastal-based disasters are also seen in Ortakoy Square and its vicinity: a possible 1 m rise in sea level will not affect the entire district. Also in case of a tsunami, only a very limited part of the coastline (up to 1 m) may face a possible flood (**Figure 5**). The sheltered and limited structure of Bosphorus does not permit the construction of large-scale landfills, and the dynamic movement of the water reduces the risk of coastal-based disasters. When considered with the minor alteration process due to its local identity, Ortakoy Square is the most advantageous one as compared to other selected coastal squares.

**Figure 5.** Morphological structure of Ortakoy; location of the public square, alteration process of the coastline and tsunami impact areas.

Buyukada, as a typical island settlement, is one of the most critical areas regarding the earthquake risk due to its proximity to fault lines. The settlement has emerged as a fisher village in the historical period and later gained a special identity with the gathering of different ethnic groups. The physical formation of the area constituted a unique mosaic, which has conserved since today. There is no physical connection with the land, and the entire island is completely closed to vehicular traffic, thus providing positive qualities both for scale perception and for spatial order as in Ortakoy. The coast is privileged as the only access is made by sea transportation, and the coastline, ports and functional areas are significant components (**Figure 6**).

Buyukada Square is different from the other examples since the overall square was obtained by landfills after the 1970s due to lack of capacity—the limited spatial organization of port and its vicinity. The buildings formerly located near the coastline now constitute the southern border of the square. Other small piers, recreational areas and commercial open spaces are also linked with the linearly shaped square.

In this context, it can be argued that the alterations on the coastline constitute specific threads regarding sea level rise, but locational factors and geological structure compose of higher

**Change in coastal line**

● **Change/impact level** (1-very low, 2-low, 3-medium, 4- high, 5-very high); ○ **No change/impact.**

**Table 3.** Comparison of coastal squares in terms of physical- and disaster-based issues.

**Eminonu** ●●●●○ ●●●●○ ●●●○○ ●●●●● ●●●●○ **Uskudar** ●●●●○ ●●●●● ●●●○○ ●●●●○ ●●●○○ **Kadikoy** ●●●○○ ●●●●○ ●●●●○ ●●●●● ●●●●● **Ortakoy** ●○○○○ ●○○○○ ●○○○○ ○○○○○ ●○○○○ **Buyukada** ●●○○○ ●●●○○ ●●●●● ●●●●○ ●●●●○

**Location-based** 

Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul…

**Sea level rise impact**

http://dx.doi.org/10.5772/intechopen.73508

**Tsunami impact**

55

**risks**

Comparison of the squares according to the alterations within morphological features, coastal land uses and durability to disaster-based risks reveals the Ortakoy square standing on the Bosphorus as the best one (**Table 3**). Squares standing on the coasts of the Marmara Sea and at the intersection area in between the Marmara Sea, Golden Horn and Bosphorus have been spatially and sub-functionally altered much due to the coastal landfills. Besides, they have got weak strength towards the disasters like sea level rise and tsunami due to their locations. The most extreme morphological alterations occur at the transportation-oriented squares such as Eminonu, Uskudar and Kadikoy. These three squares and the one in Buyukada stand as the

In this context, it has been examined, comparatively on different squares, how the transportation and land use-based decisions of mega-urbanization affected the coastal usage and how the man-made alterations towards the coastal line and the locational characteristics triggered

By handling the rapid changing coastal megacity of Istanbul and focusing on its historical coastal squares, this chapter aims to figure out the dynamics effective on these cultural open spaces as it is the initial stage of developing sustainable development strategies. Thus, this chapter handles five significant historical squares and interrogates their interplay with the natural and physical challenges of the twenty-first century. They are evaluated by five major parameters such as morphological attributes, the formation of squares, qualification of the

surfaces and coastal-based natural disaster impacts such as sea level rise and tsunami.

Coastal squares, which became important focal points due to their morphological and sociocultural values in the historical process, have a fragile relationship with the global phenomenon of "sustainability" through first-degree dependent, constant or variant parameters. The

priority risks related to the tsunami.

**Change in morphological** 

**structure**

highest vulnerable one to coastal hazards.

the disaster risk.

**4. Conclusion**

Although the physical attributes are mostly conserved except this landfill square, locational factors (open to Sea of Marmara, first-degree earthquake zone, etc.) expose many major threats for coastal-based disasters: 90% of the coastal area, buildings on the south-western border and the port area will be flooded after a 1 m rise in sea level. Similarly, the entire coastal band will be affected after a possible tsunami, and the run-up will reach inner parts (**Figure 6**).

**Figure 6.** Morphological structure of Buyukada; location of the public square, alteration process of the coastline and sea level rise/tsunami impact areas.

Alterations within the Coastal Urban Environments: Case of the Coastal Squares of Istanbul… http://dx.doi.org/10.5772/intechopen.73508 55


**Table 3.** Comparison of coastal squares in terms of physical- and disaster-based issues.

In this context, it can be argued that the alterations on the coastline constitute specific threads regarding sea level rise, but locational factors and geological structure compose of higher priority risks related to the tsunami.

Comparison of the squares according to the alterations within morphological features, coastal land uses and durability to disaster-based risks reveals the Ortakoy square standing on the Bosphorus as the best one (**Table 3**). Squares standing on the coasts of the Marmara Sea and at the intersection area in between the Marmara Sea, Golden Horn and Bosphorus have been spatially and sub-functionally altered much due to the coastal landfills. Besides, they have got weak strength towards the disasters like sea level rise and tsunami due to their locations. The most extreme morphological alterations occur at the transportation-oriented squares such as Eminonu, Uskudar and Kadikoy. These three squares and the one in Buyukada stand as the highest vulnerable one to coastal hazards.

In this context, it has been examined, comparatively on different squares, how the transportation and land use-based decisions of mega-urbanization affected the coastal usage and how the man-made alterations towards the coastal line and the locational characteristics triggered the disaster risk.

### **4. Conclusion**

Buyukada, as a typical island settlement, is one of the most critical areas regarding the earthquake risk due to its proximity to fault lines. The settlement has emerged as a fisher village in the historical period and later gained a special identity with the gathering of different ethnic groups. The physical formation of the area constituted a unique mosaic, which has conserved since today. There is no physical connection with the land, and the entire island is completely closed to vehicular traffic, thus providing positive qualities both for scale perception and for spatial order as in Ortakoy. The coast is privileged as the only access is made by sea transportation, and the coastline, ports and functional areas are significant components (**Figure 6**). Buyukada Square is different from the other examples since the overall square was obtained by landfills after the 1970s due to lack of capacity—the limited spatial organization of port and its vicinity. The buildings formerly located near the coastline now constitute the southern border of the square. Other small piers, recreational areas and commercial open spaces are

Although the physical attributes are mostly conserved except this landfill square, locational factors (open to Sea of Marmara, first-degree earthquake zone, etc.) expose many major threats for coastal-based disasters: 90% of the coastal area, buildings on the south-western border and the port area will be flooded after a 1 m rise in sea level. Similarly, the entire coastal band will be affected after a possible tsunami, and the run-up will reach inner parts (**Figure 6**).

**Figure 6.** Morphological structure of Buyukada; location of the public square, alteration process of the coastline and sea

also linked with the linearly shaped square.

54 Sea Level Rise and Coastal Infrastructure

level rise/tsunami impact areas.

By handling the rapid changing coastal megacity of Istanbul and focusing on its historical coastal squares, this chapter aims to figure out the dynamics effective on these cultural open spaces as it is the initial stage of developing sustainable development strategies. Thus, this chapter handles five significant historical squares and interrogates their interplay with the natural and physical challenges of the twenty-first century. They are evaluated by five major parameters such as morphological attributes, the formation of squares, qualification of the surfaces and coastal-based natural disaster impacts such as sea level rise and tsunami.

Coastal squares, which became important focal points due to their morphological and sociocultural values in the historical process, have a fragile relationship with the global phenomenon of "sustainability" through first-degree dependent, constant or variant parameters. The most important constant parameter is the location factor: coastal areas that already have sustainable qualities/formations are gradually losing their endurance due to the changing climate conditions on a global scale. In this context, it is possible to say that another variant parameter (climate) directly influences the core attributes of coastal squares. On the other hand, variant essential parameters of coastal squares such as morphological alterations through physical interventions and transformation of square forms/usages through the changes in coastlines increase the risk factor even at higher levels in terms of coastal-based disasters.

**References**

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[3] Thead E. Sea level rise: Risk and resilence in coastal cities. A Publication of the Climate

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[11] Ayatac H. The International Diffusion of Planning Ideas–Influence on Istanbul's Urban

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[16] Flood Map: Water Level Elevation Map (Beta) [Internet]. 2017. Available from: www.

As seen in the results through evaluation of five selected coastal squares in Istanbul, Ortakoy Square stands out as a less risky one against coastal-based natural hazards due to its sheltered structure regarding location-based characteristics and the limited morphological change on both coastline and square layout. Morphological transformations in other selected squares and the increasing location-based risks either with climate change or geological metamorphoses reduce the level of durability of coastal squares and make them more vulnerable. Currently, coastal squares of Eminonu, Uskudar and Kadikoy are adversely affected by excessive rains and floods: the structural enclosure of transportation axes, high amount of impermeable surfaces and the lack of water evacuation areas can be identified as critical factors which also constitute a hazardous foundation for future risks.

Today, it is required to provide urban coastal conservation strategies capable of diminishing the emerging natural and physical challenges of the twenty-first century. Reducing the influence of urbanization pressure on the squares should be the right approach especially for conservation and sustainability of coastal squares. In addition to historical values and spatial significances of coastal squares, their presence in the coastal skyline/silhouette should also be maintained. Strategies should also consider that coastal squares are key components that provide critical benefits such as functional continuity on the coastal areas, interface connections of the interior parts with coastline and urgent public usages to save the city/citizens against natural disasters.

### **Acknowledgements**

This manuscript is supported by the Istanbul Technical University Scientific Research Support Project through the "Development of Planning and Design Strategies for Urban Squares; Istanbul Example" with the project number 39974.

### **Author details**

Hatice Ayatac1 \*, Fatma Aycim Turer Baskaya2 , Eren Kurkcuoglu1 , Ozge Celik<sup>1</sup> and Sinem Becerik1

\*Address all correspondence to: ayatachatice@gmail.com

1 Urban and Regional Planning Department, Istanbul Technical University, Taskisla, Beyoglu, Istanbul, Turkey

2 Department of Landscape Architecture, Istanbul Technical University, Taskisla, Beyoglu, Istanbul, Turkey

### **References**

most important constant parameter is the location factor: coastal areas that already have sustainable qualities/formations are gradually losing their endurance due to the changing climate conditions on a global scale. In this context, it is possible to say that another variant parameter (climate) directly influences the core attributes of coastal squares. On the other hand, variant essential parameters of coastal squares such as morphological alterations through physical interventions and transformation of square forms/usages through the changes in coastlines

As seen in the results through evaluation of five selected coastal squares in Istanbul, Ortakoy Square stands out as a less risky one against coastal-based natural hazards due to its sheltered structure regarding location-based characteristics and the limited morphological change on both coastline and square layout. Morphological transformations in other selected squares and the increasing location-based risks either with climate change or geological metamorphoses reduce the level of durability of coastal squares and make them more vulnerable. Currently, coastal squares of Eminonu, Uskudar and Kadikoy are adversely affected by excessive rains and floods: the structural enclosure of transportation axes, high amount of impermeable surfaces and the lack of water evacuation areas can be identified as critical factors

Today, it is required to provide urban coastal conservation strategies capable of diminishing the emerging natural and physical challenges of the twenty-first century. Reducing the influence of urbanization pressure on the squares should be the right approach especially for conservation and sustainability of coastal squares. In addition to historical values and spatial significances of coastal squares, their presence in the coastal skyline/silhouette should also be maintained. Strategies should also consider that coastal squares are key components that provide critical benefits such as functional continuity on the coastal areas, interface connections of the interior parts with coastline and urgent public usages to save the city/citizens against natural disasters.

This manuscript is supported by the Istanbul Technical University Scientific Research Support Project through the "Development of Planning and Design Strategies for Urban Squares;

1 Urban and Regional Planning Department, Istanbul Technical University, Taskisla,

2 Department of Landscape Architecture, Istanbul Technical University, Taskisla, Beyoglu,

, Eren Kurkcuoglu1

, Ozge Celik<sup>1</sup>

and

increase the risk factor even at higher levels in terms of coastal-based disasters.

which also constitute a hazardous foundation for future risks.

**Acknowledgements**

56 Sea Level Rise and Coastal Infrastructure

**Author details**

Hatice Ayatac1

Sinem Becerik1

Istanbul, Turkey

Beyoglu, Istanbul, Turkey

Istanbul Example" with the project number 39974.

\*, Fatma Aycim Turer Baskaya2

\*Address all correspondence to: ayatachatice@gmail.com


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**Section 4**

**Coastal Vegetations**


**Section 4**

**Coastal Vegetations**

[17] Geological–geotechnical study report according to the construction plans as a result of settlement purposed microzonationworks-production of microzonation report and maps European side (south) to Istanbul. OYO International Corporation [Internet]. 2007. Available from: http://www.preventionweb.net/files/43040\_paulanu.pdf [Accessed:

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[20] Tekeli I, Eyice S. Dünden Bugüne Istanbul Ansiklopedisi. Kultur Bakanlıgı ve Tarih

[21] Tapan M. Istanbul'un Kentsel Planlamasının Tarihsel Gelisimi ve Planlama Eylemleri. In: Sey Y, editor. 75 Yılda Degisen Kent ve Mimarlık. İstanbul: Türkiye Ekonomik ve

[22] Cecen K. Istanbul'ın Vakıf Sularından Üsküdar Suları. T.C. Istanbul Büyükşehir Belediyesi, Istanbul Su ve Kanalizasyon Idaresi Genel Müdürlüğü: Istanbul; 1991

[23] Ozbek İ. Üsküdar Meydanı'nın Geçirdiği Mekansal Dönüşüm. In: Üsküdar Sempozyumu II; 12-13 March 2004; Istanbul. Üsküdar Araştırma Merkezleri; 2005. pp. 371-385

[24] Akpinar S. Adaların Tarihi ve Arkeolojisi. In: Adaların Türk Turizmindeki Yeri ve Önemi Semineri; 3 May 1984; Istanbul. Burgazada Lioness Kulübü Derneği; 1984. pp. 3-17 [25] Turer Baskaya FA, Tekeli E. Coastline Changes and Istanbul Coastal Landscape. In: The Twelfth International Conference on the Mediterranean Coastal Environment

(MEDCOAST '15); 06-10 October 2015; Varna. MEDCOAST; 2015. pp. 171-182 [26] http://www.istanbeautiful.com/tr/istanbul-meydanlar-caddeler/eminonu-meydani/

[30] https://m.tripinview.com/en/places/anchorage/50888/turkey-istanbul-adalar-

[27] http://www.antikaistanbul.com/fotoalbum/5/uskudar-renkli

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Türkiye Ekonomik ve Toplumsal Tarih Vakfı; 1996

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58 Sea Level Rise and Coastal Infrastructure

Istanbul; 2013

buyukada-port

Vakfi: Istanbul; 1994

Toplumsal Tarih Vakfı. 1998. pp. 75-88

**Chapter 5**

**Provisional chapter**

**Coastal Wetland Vegetation in Response to Global**

**Coastal Wetland Vegetation in Response to Global** 

Under the background of global warming, rising sea level, extreme weather and other global climate changes, vegetation has played a targeted and irreplaceable role. The characteristics of individual plant, community landscape and vegetation succession in response to the major driving factor (mainly includes habitat relative elevation, net loss of coastal habitat, salinity, etc.) were analyzed. An obvious development of vegetation landscape fragmentation has results from the competitive advantages of salt-tolerant species or invasive species, which eventually results in the regressive succession and unreasonable secondary succession of vegetation. Compared with the botanical community statistics method, the method of combined of GIS-mapping and remote sensing data provide a more effective way to extract the individual plant stress information, vegetation community structure and dynamic change of vegetation landscape pattern, which can reflect the spatial differentiation of the vegetation at a macro-scale. In addition, in view of the high-efficiency carbon sequestration capability of coastal wetland vegetation, the spatial distribution, temporal dynamic and extraction method of vegetation and soil sequestration were discussed. Synthesize above analysis result, further studies in vegetation response to global climate change were proposed, which need to

**Keywords:** coastal wetland vegetation, climate change, vegetation succession, remote

sensing, vegetation carbon sequestration, vulnerability assessment

DOI: 10.5772/intechopen.73509

© 2016 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.

© 2018 The Author(s). Licensee IntechOpen. 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.

**Warming and Climate Change**

**Warming and Climate Change**

Chao Zhou, Kapo Wong and Jianhua Zhao

Chao Zhou, Kapo Wong and Jianhua Zhao

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73509

be improved or expanded.

**Abstract**

#### **Coastal Wetland Vegetation in Response to Global Warming and Climate Change Coastal Wetland Vegetation in Response to Global Warming and Climate Change**

DOI: 10.5772/intechopen.73509

Chao Zhou, Kapo Wong and Jianhua Zhao Chao Zhou, Kapo Wong and Jianhua Zhao

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73509

#### **Abstract**

Under the background of global warming, rising sea level, extreme weather and other global climate changes, vegetation has played a targeted and irreplaceable role. The characteristics of individual plant, community landscape and vegetation succession in response to the major driving factor (mainly includes habitat relative elevation, net loss of coastal habitat, salinity, etc.) were analyzed. An obvious development of vegetation landscape fragmentation has results from the competitive advantages of salt-tolerant species or invasive species, which eventually results in the regressive succession and unreasonable secondary succession of vegetation. Compared with the botanical community statistics method, the method of combined of GIS-mapping and remote sensing data provide a more effective way to extract the individual plant stress information, vegetation community structure and dynamic change of vegetation landscape pattern, which can reflect the spatial differentiation of the vegetation at a macro-scale. In addition, in view of the high-efficiency carbon sequestration capability of coastal wetland vegetation, the spatial distribution, temporal dynamic and extraction method of vegetation and soil sequestration were discussed. Synthesize above analysis result, further studies in vegetation response to global climate change were proposed, which need to be improved or expanded.

**Keywords:** coastal wetland vegetation, climate change, vegetation succession, remote sensing, vegetation carbon sequestration, vulnerability assessment

© 2016 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. © 2018 The Author(s). Licensee IntechOpen. 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.

### **1. Introduction**

#### **1.1. Climate change and sea level rise**

It is certain that global mean surface temperature has increased since the late nineteenth century, which increasingly received attention of the governments and academia. Intergovernmental Panel on Climate Change (IPCC) assessed the effect of the global climate change on the natural ecosystems and human socioeconomic system five times from 1990. According to the latest IPCC Fifth Assessment Report (AR5) [1], the global combined land and ocean temperature data showed an increase of about 0.89°C over the period 1901–2012. The global mean sea level has increased by 0.19 m over the period 1901–2010, the mean rate of sea level rise was 1.7 mm year−1 between 1901 and 2010. Three-quarters of the contributions to rise in the sea level are the expansion of the ocean water as it warms and the transfer to ocean water from glaciers and ice sheets. The atmospheric abundances of CO<sup>2</sup> , CH4 , N<sup>2</sup> O were 390.5 ppm, 1803.2 ppb, 390.5 ppb in 2012, respectively, and were highest than experienced on earth for at least the last 800,000 years, which has increased by 40, 150, 20% since pre-industrial times. The observed changes in the frequency and intensity of extreme weather-climate events are increasing on the global scale since the mid-twentieth century. These are clearly showed by the observed increased intensity of extreme precipitation events and frequency of extremely warm.

most remarkable examples is mangrove forest known as the "Chlory The Ocean Guard" [4]. As shown in the **Figure 1**, mangrove roots cover the upper banks of the Daly Estuary, Australia, providing a protective barrier against erosion of the upper banks, although not

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**3. Marine habitat:** communities of submerged aquatic vegetation found in marine, estuarine and coastal freshwater environments provide critical habitat for fish, shrimp, wintering

**4. Plant purification:** the heavy metal concentration in the tissue of the submerged aquatic vegetation is about 100,000 times higher in the surrounding water. Some typical plants such as bulrush, water hyacinth, etc., have been successfully used to degrade sewage [7].

China is a country with a long mainland coastline of about 18,000 km, across territory north and south of three climatic zones. There are many types of vegetation growing along coastlines, which have typical growth process and research value. Thus, the coastal wetland vegetation classification system of China probably has a guide role for the world, which was classified into three levels [8]. First level is vegetation type groups, which was named by the difference of habitat physiognomy in the constructive species. Second level is vegetation types, which was named by the life forms of the dominant species. Third level is vegetation formations, which was gathered from the same community that includes constructive or

In this chapter, the global change research of coastal wetland vegetation seeks to (1) identify the influence factors and consequences of climate change to vegetation, (2) develop more efficient methods which extraction the environmental stress information of vegetation and (3) understand the current carbon fixation capacity of various coastal wetland vegetation.

waterfowl and endangered species such as sea turtles and manatees [6].

protecting against undercutting in the lower banks [5].

**1.3. Major classes of the coastal wetland vegetation**

dominant species. The detailed classification is shown in **Table 1**.

**Figure 1.** Mangrove roots cover the upper banks of the Daly Estuary, Australia.

#### **1.2. Major role of vegetation in the coastal wetland ecosystems**

The role of vegetation in the global ecosystem is self-evident, in particular, coastal wetland vegetation plays an even more critical role under the influence of global climate change and human activities, which can be summarized as follows.


most remarkable examples is mangrove forest known as the "Chlory The Ocean Guard" [4]. As shown in the **Figure 1**, mangrove roots cover the upper banks of the Daly Estuary, Australia, providing a protective barrier against erosion of the upper banks, although not protecting against undercutting in the lower banks [5].


#### **1.3. Major classes of the coastal wetland vegetation**

**1. Introduction**

62 Sea Level Rise and Coastal Infrastructure

**1.1. Climate change and sea level rise**

and ice sheets. The atmospheric abundances of CO<sup>2</sup>

**1.2. Major role of vegetation in the coastal wetland ecosystems**

higher than forest, which were captured and stored 862–1650 Tg CO2

human activities, which can be summarized as follows.

It is certain that global mean surface temperature has increased since the late nineteenth century, which increasingly received attention of the governments and academia. Intergovernmental Panel on Climate Change (IPCC) assessed the effect of the global climate change on the natural ecosystems and human socioeconomic system five times from 1990. According to the latest IPCC Fifth Assessment Report (AR5) [1], the global combined land and ocean temperature data showed an increase of about 0.89°C over the period 1901–2012. The global mean sea level has increased by 0.19 m over the period 1901–2010, the mean rate of sea level rise was 1.7 mm year−1 between 1901 and 2010. Three-quarters of the contributions to rise in the sea level are the expansion of the ocean water as it warms and the transfer to ocean water from glaciers

390.5 ppb in 2012, respectively, and were highest than experienced on earth for at least the last 800,000 years, which has increased by 40, 150, 20% since pre-industrial times. The observed changes in the frequency and intensity of extreme weather-climate events are increasing on the global scale since the mid-twentieth century. These are clearly showed by the observed

The role of vegetation in the global ecosystem is self-evident, in particular, coastal wetland vegetation plays an even more critical role under the influence of global climate change and

**1. Carbon storage, carbon fixation**: coastal wetlands are the important "source" and "sink" of greenhouse gases. Because vegetation have higher rate of carbon sequestration and lower rate of methane emission, which are the most important part of "sink" [2]. In 2009, the United Nations Environment Program (UNEP), Food and Agriculture Organization (FAO) and four departments have jointly issued a report about the ocean carbon sinks – "Blue carbon. A UNEP rapid response assessment". More than half of global biomedical carbon was captured by the ocean's vegetated habitats, in particular seagrasses, mangroves and salt marshes, and this carbon was called 'Blue carbon'. The biomass of coastal wetland vegetation is 0.05% times than terrestrial vegetation, but the carbon fixation rate of blue carbon ecosystem is 10–50 times

and this amount is equivalent to the total carbon emissions of the global transportation [3]. **2. Disaster mitigation**: as the buffer zone between land and oceans, the coastal wetland vegetation can store excess water in the rainy season, and relieve the pressure of the flood disasters. The vegetation also adsorbed the intertidal sediments with its root system, quickens the progress of the promoting deposition and creating land, which plays a great role in mitigating the erosion action of waves on the coastline. In addition, the vegetation protects the building and crops from the damage of strong and salty winds, and one of the

increased intensity of extreme precipitation events and frequency of extremely warm.

, CH4 , N<sup>2</sup>

O were 390.5 ppm, 1803.2 ppb,

(Tg = 1012 g) per year

China is a country with a long mainland coastline of about 18,000 km, across territory north and south of three climatic zones. There are many types of vegetation growing along coastlines, which have typical growth process and research value. Thus, the coastal wetland vegetation classification system of China probably has a guide role for the world, which was classified into three levels [8]. First level is vegetation type groups, which was named by the difference of habitat physiognomy in the constructive species. Second level is vegetation types, which was named by the life forms of the dominant species. Third level is vegetation formations, which was gathered from the same community that includes constructive or dominant species. The detailed classification is shown in **Table 1**.

In this chapter, the global change research of coastal wetland vegetation seeks to (1) identify the influence factors and consequences of climate change to vegetation, (2) develop more efficient methods which extraction the environmental stress information of vegetation and (3) understand the current carbon fixation capacity of various coastal wetland vegetation.

**Figure 1.** Mangrove roots cover the upper banks of the Daly Estuary, Australia.


surface, the medium-low compound vegetation community appears such as Reed-Alkali [9]. Due to the increase in topography and reduction in groundwater level, the non-zonal top community is eventually formed, such as Tamarix Chinensis. **(2) Tidal flat wetland:** the vegetation has a horizontal zonal distribution. Succession starts from the salt-tolerant vegetation, along with the uplift of the coastal beach, soil salinity decreased and perennial wet plants invaded, and vegetation litter accelerated the soil desalination process, resulting in the moist woody plants gradually appeared, such as Tamarix. The soil is further biochemical, and the medium vegetation becomes the dominant community [10]. **(3) Mangrove wetlands:** mangrove forests often form along the estuary or gulf coastline that is a strip distribution. Pioneer communities are often composed of non-mangrove plants, which have stronger adaptability to wind waves and leanness. With the development of the demineralization, the later and

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To sum up, no matter what type of coastal wetlands, the vegetation succession starts from the salt resistance, waterlogging resistance and barren species, after the pioneer community formation, soil salinity reduction. Then the environment became gradually stable, which provides the conditions for medium vegetation growth. Finally, a complete and stable coastal wetland vegetation ecosystem are formed. Wetland vegetation community development

**Figure 2.** Wetland vegetation community development along water table continuum (adapted from Mortsch et al. [12]).

typical mangrove communities have developed the dominant positions [11].

along water table continuum is shown in **Figure 2**.

**Table 1.** Vegetation classification system of coastal wetlands in China.

### **2. Response analysis and driving factors of vegetation succession under climate change**

The coastal wetland vegetation occurs positive succession under the influence of the acceptable natural conditions. However, with the global climate changes, the effect of some environmental factors is beyond the carrying capacity of coastal wetland vegetation, which will lead to the fragmentation of vegetation landscape, regressive succession of vegetation and other consequences.

#### **2.1. Positive succession of vegetation under natural conditions**

In this section, three kinds of typical habitats are taken as examples to analyze the normal succession law of coastal wetland vegetation. **(1) Estuary delta:** due to the difference of soil salinity in spatial distribution, the vegetation distribution in estuarine delta is zonal. The vegetation community succession starts from the bare flat, and the highly salt-tolerant community appears first, such as Wing-Alkali. With the increase of vegetation and litter in the surface, the medium-low compound vegetation community appears such as Reed-Alkali [9]. Due to the increase in topography and reduction in groundwater level, the non-zonal top community is eventually formed, such as Tamarix Chinensis. **(2) Tidal flat wetland:** the vegetation has a horizontal zonal distribution. Succession starts from the salt-tolerant vegetation, along with the uplift of the coastal beach, soil salinity decreased and perennial wet plants invaded, and vegetation litter accelerated the soil desalination process, resulting in the moist woody plants gradually appeared, such as Tamarix. The soil is further biochemical, and the medium vegetation becomes the dominant community [10]. **(3) Mangrove wetlands:** mangrove forests often form along the estuary or gulf coastline that is a strip distribution. Pioneer communities are often composed of non-mangrove plants, which have stronger adaptability to wind waves and leanness. With the development of the demineralization, the later and typical mangrove communities have developed the dominant positions [11].

To sum up, no matter what type of coastal wetlands, the vegetation succession starts from the salt resistance, waterlogging resistance and barren species, after the pioneer community formation, soil salinity reduction. Then the environment became gradually stable, which provides the conditions for medium vegetation growth. Finally, a complete and stable coastal wetland vegetation ecosystem are formed. Wetland vegetation community development along water table continuum is shown in **Figure 2**.

**2. Response analysis and driving factors of vegetation succession** 

**Vegetation types Vegetation formations Habitat characteristics**

*Miscanthus sacchariflorus*, etc.

community coverage Brush marsh *Tamarix chinensis*, *Vitex* 

period in the surface Floating leaf

*Ceratophyllum demersum*, etc.

*Avicennia marina*, *Rhizophora* 

*Heritiera littoralis*, *Barringtonia* 

*Nymphoides peltatum*, *Nelumbo nucifera*, etc.

Mainly distributed in the low-lying areas, grow in the coastal saline soil with high salinity

Mainly distributed in the river deltas

Mainly distributed in the perennial water or seasonal waterlogged marsh, with higher

Mainly distributed in the middle-upper part of the slanting flat or constructed wetlands, there are standing water for long time or longer

Distributed in the tropical or subtropical

intertidal zone or the estuary

Grow in the intertidal zone

*mariqueter*, etc.

*sibirica* Pall, etc.

*rotundifolia*, etc.

*equisetifolia*, etc.

*polyrhiza*, etc.

*apiculata*, etc.

*racemosa*, etc.

*isoetifolium*, etc.

Brush salt marsh *Tamarix chinensis*, *Nitraria* 

Herbal marsh *Phragmites australis*,

Forest marsh *Pinus elliottii*, *Casuarina* 

Floating wetland *Salvinia natans*, *Spirodela* 

Submerged wetland *Myriophyllum verticillatum*,

Salt marsh Herbal salt marsh *Spartina anglica*, *Scirpus* 

**2.1. Positive succession of vegetation under natural conditions**

The coastal wetland vegetation occurs positive succession under the influence of the acceptable natural conditions. However, with the global climate changes, the effect of some environmental factors is beyond the carrying capacity of coastal wetland vegetation, which will lead to the fragmentation of vegetation landscape, regressive succession of vegetation and other

In this section, three kinds of typical habitats are taken as examples to analyze the normal succession law of coastal wetland vegetation. **(1) Estuary delta:** due to the difference of soil salinity in spatial distribution, the vegetation distribution in estuarine delta is zonal. The vegetation community succession starts from the bare flat, and the highly salt-tolerant community appears first, such as Wing-Alkali. With the increase of vegetation and litter in the

**under climate change**

wetland

Mangrove vegetation type

Semi-mangrove vegetation type

Seaweed wetland *Halophila ovalis*, *Syringodium* 

**Table 1.** Vegetation classification system of coastal wetlands in China.

consequences.

**Vegetation type groups**

64 Sea Level Rise and Coastal Infrastructure

Coastal marsh wetland

Shallow vegetation wetland

Mangrove swamp

**Figure 2.** Wetland vegetation community development along water table continuum (adapted from Mortsch et al. [12]).

#### **2.2. Driving factors of vegetation succession under climate change**

In addition to the influence of geographical location and elevation, the succession characteristics of vegetation also depend on the factors such as water content, soil nutrient and human activities. However, some of these factors will be magnified and become the dominant factors under climate change.

of physical-biological processes occurring in the top few meters of the soil are faster than accumulation rate [15]. In addition, in the new shallow strata of coastal wetland such as the estuary delta, the soil surface under the action of artificial coastal engineering is compressed, which is

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Coastal wetland can be persisted by extending inland and occupying formerly upland sites under the influence of sea level rise [17]. However, the ability to landward movement of coastal wetland depends on the relationship to topography. As lower elevation sites become submerged, marsh build-up or expansion may occur up the slope of the landward marsh boundary. The slope will present an effective barrier to the growth of some plant communities, effectively squeezing area available for coastal vegetation [18]. Duke University and USGS scientists modeled the movement of the marsh edge in a few typical coastal wetland, which implies that inland marsh movement is controlled not only by sea level rise but also by human activities [15]. The rate of human reclamation is much higher than that of the sediment accumulation, in the early period of reclamation and the inland evolution of coastal wetlands may be speeded up. As shown in the **Figure 4(1)**, subsistence agricultural plantations take place within the wettest zones of the wetlands on the Maputaland Coastal Plain of South Africa. However in the long run, because the purpose of human reclamation is different from the natural evolution of coastal wetland, it will become an obstacle to the inland evolution of the vegetation habitats, and further aggravate the loss of the wetland vegetation habitat [19], for example, a drained

and destructed wetland caused by the human reclamation, as shown in **Figure 4(2)**.

As sea level continues to rise, salt water will move farther inland, subjecting vegetation communities to salinity stress. The change of habitat area and relative elevation mainly controlled the direction of vegetation succession by the soil salinity. The high salt-tolerant plants are mostly

**Figure 4.** (1) An example of the numerous informal economic plantations that has sprung up on the Maputaland coastal

plain over the past 20 years; (2) an example of a drained and destructed wetland.

 *and other factors*

very easy to induce soil subsidence [16].

*2.2.2. Net loss of coastal habitat*

*2.2.3. Salinity, CO<sup>2</sup>*

#### *2.2.1. Changes of habitat relative elevation*

The evaluation of coastal wetland will increase with tidal flat sediment accumulation, which can slow down or even offset the influence of sea level rise [13]. Firstly, as shown in **Figure 3a**, if the sea level rise rate equals the sediment accumulation rate, the relative elevation of coastal wetland is constant, the flooding degree of plants remain stable, and their growth are not affected by sea level rise. Secondly, as shown in **Figure 3b**, if the sea level rise rate is smaller than the sediment accumulation rate, the relative elevation is increased, the coastal wetland gradually siltation to the seaward direction and the habitat area for the plant growth is enlarged. Thirdly, as shown in **Figure 3c**, if the sea level rise rate is higher than the sediment accumulation rate, the relative elevation is decreased, the flooding frequency and depth are increased, which will affect the survival and growth of plant [14]. However, other research suggests that time-effect

**Figure 3.** The sketch map of coastal wetland vegetation response to sea level rise. (a) Relative sea level unchanged; (b) relative sea level dropped; (c) relative sea level raised.

of physical-biological processes occurring in the top few meters of the soil are faster than accumulation rate [15]. In addition, in the new shallow strata of coastal wetland such as the estuary delta, the soil surface under the action of artificial coastal engineering is compressed, which is very easy to induce soil subsidence [16].

### *2.2.2. Net loss of coastal habitat*

**2.2. Driving factors of vegetation succession under climate change**

under climate change.

66 Sea Level Rise and Coastal Infrastructure

*2.2.1. Changes of habitat relative elevation*

In addition to the influence of geographical location and elevation, the succession characteristics of vegetation also depend on the factors such as water content, soil nutrient and human activities. However, some of these factors will be magnified and become the dominant factors

The evaluation of coastal wetland will increase with tidal flat sediment accumulation, which can slow down or even offset the influence of sea level rise [13]. Firstly, as shown in **Figure 3a**, if the sea level rise rate equals the sediment accumulation rate, the relative elevation of coastal wetland is constant, the flooding degree of plants remain stable, and their growth are not affected by sea level rise. Secondly, as shown in **Figure 3b**, if the sea level rise rate is smaller than the sediment accumulation rate, the relative elevation is increased, the coastal wetland gradually siltation to the seaward direction and the habitat area for the plant growth is enlarged. Thirdly, as shown in **Figure 3c**, if the sea level rise rate is higher than the sediment accumulation rate, the relative elevation is decreased, the flooding frequency and depth are increased, which will affect the survival and growth of plant [14]. However, other research suggests that time-effect

**Figure 3.** The sketch map of coastal wetland vegetation response to sea level rise. (a) Relative sea level unchanged;

(b) relative sea level dropped; (c) relative sea level raised.

Coastal wetland can be persisted by extending inland and occupying formerly upland sites under the influence of sea level rise [17]. However, the ability to landward movement of coastal wetland depends on the relationship to topography. As lower elevation sites become submerged, marsh build-up or expansion may occur up the slope of the landward marsh boundary. The slope will present an effective barrier to the growth of some plant communities, effectively squeezing area available for coastal vegetation [18]. Duke University and USGS scientists modeled the movement of the marsh edge in a few typical coastal wetland, which implies that inland marsh movement is controlled not only by sea level rise but also by human activities [15]. The rate of human reclamation is much higher than that of the sediment accumulation, in the early period of reclamation and the inland evolution of coastal wetlands may be speeded up. As shown in the **Figure 4(1)**, subsistence agricultural plantations take place within the wettest zones of the wetlands on the Maputaland Coastal Plain of South Africa. However in the long run, because the purpose of human reclamation is different from the natural evolution of coastal wetland, it will become an obstacle to the inland evolution of the vegetation habitats, and further aggravate the loss of the wetland vegetation habitat [19], for example, a drained and destructed wetland caused by the human reclamation, as shown in **Figure 4(2)**.

#### *2.2.3. Salinity, CO<sup>2</sup> and other factors*

As sea level continues to rise, salt water will move farther inland, subjecting vegetation communities to salinity stress. The change of habitat area and relative elevation mainly controlled the direction of vegetation succession by the soil salinity. The high salt-tolerant plants are mostly

**Figure 4.** (1) An example of the numerous informal economic plantations that has sprung up on the Maputaland coastal plain over the past 20 years; (2) an example of a drained and destructed wetland.

distributed in the sea near or low-lying where susceptible to tidal erosion. From low to high-tidal flat, soil salinity decreased, plant species tended to diversify and low salt-tolerant. The groundwater depth increases gradually as the elevation from high to low, which is directly related to whether the soil capillary water can reach the surface, and then affected the soil salinity.

*2.3.2. Changes of vegetation community structure*

of invasion into natural stands.

**Figure 5.** The sketch map of change of vegetation succession direction.

Various factors induce the change in the internal structure of the community, which caused by climate change such as coastal erosion, storm surge and salinity stress. Vegetation landscape patches showed a discrete distribution and their numbers increased [24, 25]. For example, due to insufficient freshwater, in the process of vegetation transformation from wet-unripe vegetation to saline-marsh vegetation, two kinds of vegetation types were distributed in disorder and the landscape pattern was mottled. In the comparatively macroscopic level between difference types of vegetation community, the change trend of community structure is not fragmentation, but tends to be concentrated distribution, forming a relatively large plaque [26, 27]. For example, in the area where the natural wetland vegetation is connected with the artificial economic crop, the natural wetland vegetation is gradually eroded by artificially killing other interfering plants except cash crops. Considerable variation in salt tolerance existed among natural vegetation populations, and the new salt-tolerant varieties can be developed and used in reforestation efforts where existing populations have been killed by saltwater intrusion. Thus, salt-tolerant, drought-resistant and other artificial cultivated plants with strong environmental tolerance expanded rapidly. In addition, the increased disruptions to the vegetation community will provide recruitment opportunities for exotic species, enhancing their rate

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Underlying the predicted climatic changes is an overall increase in carbon dioxide concentrations in the atmosphere. Increased atmospheric carbon dioxide concentrations should also result in an increase of dissolved inorganic carbon concentrations in water, such as a change may affect the submerged plant communities [20]. An increase in the severity of tropical storms associated with climate change can also have acute impacts on vegetation communities [21]. Flooding was more important than small increase in salinity in the growth and survival of most tree species, whereas chronic or large increases in salinity were very harmful to all of the species tested regardless of the flooding extent [22].

### **2.3. Analysis of vegetation in response to the various factors**

A large area of land will be quickly converted from coastal salt and freshwater marsh to open water over the next several decades if current trends in sea level rise continue [17]. Large-scale movement of vegetation community and change of vegetation community structure are likely to occur. Field and laboratory experiments analyzed the succession characteristic of vegetation in response to the various factors.

#### *2.3.1. Analysis of individual plants in response to the various factors*

Experiments in a greenhouse showed that plants known to be strong competitors for light and nutrients dominated at low salinities but did not grow well at higher salinities because of a physiological intolerance to high salinity [23]. Species tolerant of high salinities proved to be weak competitors at low salinities, however high salt-tolerant species will occupy a leading position of the vegetation community at moderate to high salinities environment. Long-term monitoring help quantify the dynamics of forest structure and response to changes in climate, which suggest that (1) increases in drought associated with changing climate may significantly alter understory seedling populations in bottomland forests and recruitment into the sapling layers, and ultimately influence over story canopy structure [13], (2) increased disturbance associated with flooding and storms may form early successional, shade-intolerant species at the expense of shade tolerant species [21] and (3) damage associated with hurricane disturbance or strong storms also plays major role in the structural composition of mangrove forests, which will likely result in future mangrove forests of smaller stature [11].

The photosynthetic activity of three freshwater submerged plant species such as Wild celery, Coontail and Hydrilla as well as a seagrass species, shoal grass, exposed to higher concentrations of dissolved carbon dioxide were measured in the laboratory. All four species showed an increase in photosynthetic activity in response to higher carbon dioxide concentrations, and exhibited changes in biomass allocation and an increased ratio of carbon to nitrogen in certain plant tissues but did not respond with increased growth. Higher ratios of carbon to nitrogen in plant tissue tend to provide poorer quality forage for wintering waterfowl that rely on aquatic plant species for their food supply [17].

#### *2.3.2. Changes of vegetation community structure*

distributed in the sea near or low-lying where susceptible to tidal erosion. From low to high-tidal flat, soil salinity decreased, plant species tended to diversify and low salt-tolerant. The groundwater depth increases gradually as the elevation from high to low, which is directly related to

Underlying the predicted climatic changes is an overall increase in carbon dioxide concentrations in the atmosphere. Increased atmospheric carbon dioxide concentrations should also result in an increase of dissolved inorganic carbon concentrations in water, such as a change may affect the submerged plant communities [20]. An increase in the severity of tropical storms associated with climate change can also have acute impacts on vegetation communities [21]. Flooding was more important than small increase in salinity in the growth and survival of most tree species, whereas chronic or large increases in salinity were very harmful to

A large area of land will be quickly converted from coastal salt and freshwater marsh to open water over the next several decades if current trends in sea level rise continue [17]. Large-scale movement of vegetation community and change of vegetation community structure are likely to occur. Field and laboratory experiments analyzed the succession characteristic of vegeta-

Experiments in a greenhouse showed that plants known to be strong competitors for light and nutrients dominated at low salinities but did not grow well at higher salinities because of a physiological intolerance to high salinity [23]. Species tolerant of high salinities proved to be weak competitors at low salinities, however high salt-tolerant species will occupy a leading position of the vegetation community at moderate to high salinities environment. Long-term monitoring help quantify the dynamics of forest structure and response to changes in climate, which suggest that (1) increases in drought associated with changing climate may significantly alter understory seedling populations in bottomland forests and recruitment into the sapling layers, and ultimately influence over story canopy structure [13], (2) increased disturbance associated with flooding and storms may form early successional, shade-intolerant species at the expense of shade tolerant species [21] and (3) damage associated with hurricane disturbance or strong storms also plays major role in the structural composition of mangrove

forests, which will likely result in future mangrove forests of smaller stature [11].

The photosynthetic activity of three freshwater submerged plant species such as Wild celery, Coontail and Hydrilla as well as a seagrass species, shoal grass, exposed to higher concentrations of dissolved carbon dioxide were measured in the laboratory. All four species showed an increase in photosynthetic activity in response to higher carbon dioxide concentrations, and exhibited changes in biomass allocation and an increased ratio of carbon to nitrogen in certain plant tissues but did not respond with increased growth. Higher ratios of carbon to nitrogen in plant tissue tend to provide poorer quality forage for wintering waterfowl that

whether the soil capillary water can reach the surface, and then affected the soil salinity.

all of the species tested regardless of the flooding extent [22].

**2.3. Analysis of vegetation in response to the various factors**

*2.3.1. Analysis of individual plants in response to the various factors*

rely on aquatic plant species for their food supply [17].

tion in response to the various factors.

68 Sea Level Rise and Coastal Infrastructure

Various factors induce the change in the internal structure of the community, which caused by climate change such as coastal erosion, storm surge and salinity stress. Vegetation landscape patches showed a discrete distribution and their numbers increased [24, 25]. For example, due to insufficient freshwater, in the process of vegetation transformation from wet-unripe vegetation to saline-marsh vegetation, two kinds of vegetation types were distributed in disorder and the landscape pattern was mottled. In the comparatively macroscopic level between difference types of vegetation community, the change trend of community structure is not fragmentation, but tends to be concentrated distribution, forming a relatively large plaque [26, 27]. For example, in the area where the natural wetland vegetation is connected with the artificial economic crop, the natural wetland vegetation is gradually eroded by artificially killing other interfering plants except cash crops. Considerable variation in salt tolerance existed among natural vegetation populations, and the new salt-tolerant varieties can be developed and used in reforestation efforts where existing populations have been killed by saltwater intrusion. Thus, salt-tolerant, drought-resistant and other artificial cultivated plants with strong environmental tolerance expanded rapidly. In addition, the increased disruptions to the vegetation community will provide recruitment opportunities for exotic species, enhancing their rate of invasion into natural stands.

**Figure 5.** The sketch map of change of vegetation succession direction.

#### *2.3.3. Changes of vegetation succession direction*

Perhaps more importantly, climate change has disrupted the natural development process of coastal wetlands, resulting in the reverse or unreasonable secondary succession of wetland vegetation (show in **Figure 5**), which accelerates the function degradation of coastal wetlands [28]. The wet-unripe wetland vegetation is degraded to saline-marsh wetland vegetation caused by the lack of fresh water, when the sea level rise rate is higher than the accumulation rate, which makes the decrease of surface relative elevation. The sparse vegetation community in the intertidal zone is retreated by shoreline erosion, reverse succession into bare-light beach wetland. The economic crops planted by artificial reclamation have blocked the land movement of wetland vegetation caused by sea level rise. However, due to unreasonable tillage, the content of soil organic matter and ammonia nitrogen decreased, which result in secondary succession of vegetation occurs, or is degraded to bare-light beach, or to facilitate the invasion of harmful species [29].

**3.1. Methods for information extraction of individual plant under various stress** 

and quantitative extraction of stress information were carried out.

plants, such as heavy metal [32, 33], salinity [23], hydrocarbon [34], etc.

purpose of monitoring plant stress information [37].

**3.2. Methods for discrimination of plant species**

Coastal vegetation was subjected to salt stress under the influence of sea water encroachment. The increased frequency of extreme drought put forward a challenge to the drought-resistance ability of vegetation. Greenhouse gas dissolved in the water to form excessive amounts of carbon ions, which became the new stress factor of submerged plants. Thus, by studying the response of plants to salt, drought or carbon stress, qualitative identification of stress types

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Plant leaf spectrum is the result of the interaction between the incident electromagnetic wave, biochemical parameters and intercellular space. Plant physiologists have conducted on the response analysis of plant physiology-ecology to various stress factors. It is concluded that stress factors lead to the variation of biochemical parameters or internal structure, thus forming significant differences of leaf spectrum, which is the theoretical basis for extracting vegetation stress information using leaf spectrum. The waveform differences and diagnostic indices were derived from visible to near-infrared bands to extraction the environmental stress of

In the 1990s, with the emergence of hyperspectral technology, the quantitative extraction of vegetation biochemical parameters has been developed rapidly, which are mainly attributed to (1) empirical statistical approach: the regression equation between the content of stress substances and band value was established, and the band values are usually the original reflectance or its transformation form (such as derivative, logarithmic, etc. [35]) which strongly correlated with stress factors. Based on the regression equations, the stress substance contents of unknown samples are predicted. In recent years, the transformation form of vegetation reflectance has improved to extraction the stress information. For example, the plant spectrum after the continuum-removal can suppress the environmental background information, enhance the absorption characteristics [33]. Wavelet transform can extract the detailed energy information by separating the plant spectrum into high and low frequency [36]; (2) semiempirical statistical approach: also known as vegetation index method, with the deepening of the research on the formation mechanism of vegetation spectrum, a more vegetation indices were developed for estimating the biochemical parameters. Vegetation index is a linear or non-linear combination of two or several band values, resulting in an index that is highly correlated with the certain biochemical substance. Researchers mostly use the existing vegetation indices that can characterize chlorophyll, water and cell structure, etc. (shown in **Table 2**), and test the indication ability of vegetation indices to stress substances, so as to achieve the

A variety of remote sensing methods have been developed for the discrimination of plant species, which can be divided into three types. The first is the discrimination method based on vegetation index, which mainly uses the typical steep slope effect of vegetation spectrum

**factors**

### **3. Extraction methods of vegetation information in response to climate change**

Scholars mainly explore the response mechanism of coastal wetland vegetation under climate change from three scales of 'individual – community – landscape'. Botanical community statistics (BCS) method was widely used for the information extraction of vegetation communities, which mainly consists of three steps [30, 31]. Firstly, according to the characteristics of vegetation community derived from site survey, the reasonable length and width of belt transect are selected and in which the appropriate number and area of the quadrat are set up. Secondly, the plant parameters such as name, height and coverage degree were estimated by counting and visual estimation. Thirdly, using typical statistical methods such as cluster analysis and correlation analysis, to calculate the important value, occurrence frequency, species diversity and other vegetation community parameters, determine the dominant species. Aimed at stress information extraction of individual plant, except sampling in the quadrat is indispensable step, still need to test the content of biochemical substances (pigment, water, etc.) and stress substances in laboratory, and then using the statistical methods to analyze the relationship between two substances, finally the influence mechanism of stress factor to the physiological structure of plants was clarified.

The BCS method has obtained a comparatively ideal result in a much smaller area, but it depends on the quadrat with sufficient density, and a large number of samples testing data are needed. Therefore, the cost of BCS method will proliferate as the range of research area get larger. However, at present, we pay more attention to analyze the dynamic change of coastal wetland vegetation on a larger scale, and BCS method is difficult to obtain real-time update the data. In addition, the implementing prerequisite of BCS method is that the sampling is not limited by topography and climate, which is particularly difficult in coastal wetlands of complex terrain. Thus, in this section, the BCS method is not discussed in detail and we will focus on the remote sensing method.

### **3.1. Methods for information extraction of individual plant under various stress factors**

*2.3.3. Changes of vegetation succession direction*

70 Sea Level Rise and Coastal Infrastructure

physiological structure of plants was clarified.

focus on the remote sensing method.

**climate change**

Perhaps more importantly, climate change has disrupted the natural development process of coastal wetlands, resulting in the reverse or unreasonable secondary succession of wetland vegetation (show in **Figure 5**), which accelerates the function degradation of coastal wetlands [28]. The wet-unripe wetland vegetation is degraded to saline-marsh wetland vegetation caused by the lack of fresh water, when the sea level rise rate is higher than the accumulation rate, which makes the decrease of surface relative elevation. The sparse vegetation community in the intertidal zone is retreated by shoreline erosion, reverse succession into bare-light beach wetland. The economic crops planted by artificial reclamation have blocked the land movement of wetland vegetation caused by sea level rise. However, due to unreasonable tillage, the content of soil organic matter and ammonia nitrogen decreased, which result in secondary succession of vegetation occurs, or

is degraded to bare-light beach, or to facilitate the invasion of harmful species [29].

**3. Extraction methods of vegetation information in response to** 

Scholars mainly explore the response mechanism of coastal wetland vegetation under climate change from three scales of 'individual – community – landscape'. Botanical community statistics (BCS) method was widely used for the information extraction of vegetation communities, which mainly consists of three steps [30, 31]. Firstly, according to the characteristics of vegetation community derived from site survey, the reasonable length and width of belt transect are selected and in which the appropriate number and area of the quadrat are set up. Secondly, the plant parameters such as name, height and coverage degree were estimated by counting and visual estimation. Thirdly, using typical statistical methods such as cluster analysis and correlation analysis, to calculate the important value, occurrence frequency, species diversity and other vegetation community parameters, determine the dominant species. Aimed at stress information extraction of individual plant, except sampling in the quadrat is indispensable step, still need to test the content of biochemical substances (pigment, water, etc.) and stress substances in laboratory, and then using the statistical methods to analyze the relationship between two substances, finally the influence mechanism of stress factor to the

The BCS method has obtained a comparatively ideal result in a much smaller area, but it depends on the quadrat with sufficient density, and a large number of samples testing data are needed. Therefore, the cost of BCS method will proliferate as the range of research area get larger. However, at present, we pay more attention to analyze the dynamic change of coastal wetland vegetation on a larger scale, and BCS method is difficult to obtain real-time update the data. In addition, the implementing prerequisite of BCS method is that the sampling is not limited by topography and climate, which is particularly difficult in coastal wetlands of complex terrain. Thus, in this section, the BCS method is not discussed in detail and we will Coastal vegetation was subjected to salt stress under the influence of sea water encroachment. The increased frequency of extreme drought put forward a challenge to the drought-resistance ability of vegetation. Greenhouse gas dissolved in the water to form excessive amounts of carbon ions, which became the new stress factor of submerged plants. Thus, by studying the response of plants to salt, drought or carbon stress, qualitative identification of stress types and quantitative extraction of stress information were carried out.

Plant leaf spectrum is the result of the interaction between the incident electromagnetic wave, biochemical parameters and intercellular space. Plant physiologists have conducted on the response analysis of plant physiology-ecology to various stress factors. It is concluded that stress factors lead to the variation of biochemical parameters or internal structure, thus forming significant differences of leaf spectrum, which is the theoretical basis for extracting vegetation stress information using leaf spectrum. The waveform differences and diagnostic indices were derived from visible to near-infrared bands to extraction the environmental stress of plants, such as heavy metal [32, 33], salinity [23], hydrocarbon [34], etc.

In the 1990s, with the emergence of hyperspectral technology, the quantitative extraction of vegetation biochemical parameters has been developed rapidly, which are mainly attributed to (1) empirical statistical approach: the regression equation between the content of stress substances and band value was established, and the band values are usually the original reflectance or its transformation form (such as derivative, logarithmic, etc. [35]) which strongly correlated with stress factors. Based on the regression equations, the stress substance contents of unknown samples are predicted. In recent years, the transformation form of vegetation reflectance has improved to extraction the stress information. For example, the plant spectrum after the continuum-removal can suppress the environmental background information, enhance the absorption characteristics [33]. Wavelet transform can extract the detailed energy information by separating the plant spectrum into high and low frequency [36]; (2) semiempirical statistical approach: also known as vegetation index method, with the deepening of the research on the formation mechanism of vegetation spectrum, a more vegetation indices were developed for estimating the biochemical parameters. Vegetation index is a linear or non-linear combination of two or several band values, resulting in an index that is highly correlated with the certain biochemical substance. Researchers mostly use the existing vegetation indices that can characterize chlorophyll, water and cell structure, etc. (shown in **Table 2**), and test the indication ability of vegetation indices to stress substances, so as to achieve the purpose of monitoring plant stress information [37].

#### **3.2. Methods for discrimination of plant species**

A variety of remote sensing methods have been developed for the discrimination of plant species, which can be divided into three types. The first is the discrimination method based on vegetation index, which mainly uses the typical steep slope effect of vegetation spectrum


methods of remote sensing include visual interpretation, supervised and unsupervised classification, expert decision classification, neural network, vegetation index and so on. Some literatures compare the ability of different classification methods to extract vegetation community information, and results indicate that (1) the maximum likelihood classification in supervised classification have high efficiency and strong robustness [46], (2) band combination method and multi-temporal linear transformation method can effectively improve classification accuracy [47], (3) the classification accuracy of intelligent learning algorithms is more robust for the complicated geomorphic features [42]. In addition, the object-oriented classification is a newly arising method which is more widely used to vegetation distribution mapping, and the classification accuracy is generally higher than the traditional image-element classification method [4]. However, it is necessary to carry out the classification accuracy evaluation and robustness test in the multi landform environment. The dynamic characteristics of landscape pattern can reflect the interaction of various contradictions and external forces of vegetation, which was mainly analyzed by using the vegetation landscape index. At present, the relevant study in the dynamic changes is mainly concentrated in mangrove wetland landscape. For example, on the basis of identifying the dynamic changes of mangrove in Vietnam, Seto and Fragkias selected the maximum plaque index, patch number, patch size, fractal dimension, landscape shape index, etc., to reveal the changes of mangrove health and landscape heterogeneity [48].

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**3.4. Methods for vulnerability assessment of coastal wetland vegetation**

**Figure 6.** The SPEC model for vulnerability assessment on the coastal wetland vegetation.

The studies on the vulnerability assessment of coastal ecosystem in respect to sea level rise have been carried out since the 1980s, which to form several models. The latest and relatively perfect SPRC model was developed by European Union, which can reflect the effect process of '*Consequence*' of '*Source*' with '*Pathway*' on the '*Receptor*' [49]. '*Source*' (*S*) represents the affect factors of coastal wetland ecosystem. '*Pathway*' (*P*) is the tie between source and receptor. '*Receptor*' (*R*) represents the coastal wetland ecosystem. '*Consequence*' (*C*) represents the results of receptor under the influence of source. However, there is no definite method to evaluate the vulnerability of coastal wetland vegetation. Thus, in this section, according the conceptual framework of SPRC model, we analyze the factors with respect to vegetation in four steps (*S*, *P*, *R*, *C*), and then build the vulnerability assessment model of coastal wetland vegetation (shown in **Figure 6**).

**Table 2.** The calculation formula of existing vegetation indices.

to distinguish vegetation and non-vegetation, but the identification effect of different plant species needs to be verified [38, 39]. The second is the discrimination method based on the multi-temporal information, which uses to distinguish vegetation species with significant differences on the growth cycle [40, 41]. The third is the discrimination method based on machine learning algorithms such as neural network [42], expert decision classification [43] and so on. The knowledge representation and establishment of reasoning mechanism are the problems that need to be solved in the application.

Hyperspectral remote sensing provides more data sources for the discrimination of plant species. Previous studies have showed that the unsmooth spectral resolution of multi-spectrum images is a bottleneck to improve the recognition precision of plant species. The detailed diagnotic features of hyperspectral make up for the deficiency of multispectral and gradually form two typical discrimination method of plant species. One is mathematical statistics method based on the dimensionality reduction of hyperspectral image, such as principal component analysis (PCA), MNF transform, wavelet transform and so on. However, this method only uses limited spectral information that does not reflect the physical formation mechanism of vegetation spectra. The other one is, by comparing the spectral waveform difference of various plant species, extracting the spectral characteristic parameters to quantify the difference, such as spectral feature fitting (SFF) [44], spectral angle mapper (SAM) [45] and so on.

#### **3.3. Methods for dynamic change analysis of vegetation landscape pattern**

The precondition of change analysis of vegetation landscape pattern is to determine the spatial parameters of vegetation community such as area and position. The traditional classification methods of remote sensing include visual interpretation, supervised and unsupervised classification, expert decision classification, neural network, vegetation index and so on. Some literatures compare the ability of different classification methods to extract vegetation community information, and results indicate that (1) the maximum likelihood classification in supervised classification have high efficiency and strong robustness [46], (2) band combination method and multi-temporal linear transformation method can effectively improve classification accuracy [47], (3) the classification accuracy of intelligent learning algorithms is more robust for the complicated geomorphic features [42]. In addition, the object-oriented classification is a newly arising method which is more widely used to vegetation distribution mapping, and the classification accuracy is generally higher than the traditional image-element classification method [4]. However, it is necessary to carry out the classification accuracy evaluation and robustness test in the multi landform environment. The dynamic characteristics of landscape pattern can reflect the interaction of various contradictions and external forces of vegetation, which was mainly analyzed by using the vegetation landscape index. At present, the relevant study in the dynamic changes is mainly concentrated in mangrove wetland landscape. For example, on the basis of identifying the dynamic changes of mangrove in Vietnam, Seto and Fragkias selected the maximum plaque index, patch number, patch size, fractal dimension, landscape shape index, etc., to reveal the changes of mangrove health and landscape heterogeneity [48].

#### **3.4. Methods for vulnerability assessment of coastal wetland vegetation**

to distinguish vegetation and non-vegetation, but the identification effect of different plant species needs to be verified [38, 39]. The second is the discrimination method based on the multi-temporal information, which uses to distinguish vegetation species with significant differences on the growth cycle [40, 41]. The third is the discrimination method based on machine learning algorithms such as neural network [42], expert decision classification [43] and so on. The knowledge representation and establishment of reasoning mechanism are the

**Vegetation index Calculation formula**

Normalized differential water index (NDWI) (*R*<sup>860</sup> − *R*1240)/(*R*<sup>860</sup> + *R*1240)

Photochemical reflectance index (PRI) (*R*<sup>570</sup> − *R*531)/(*R*<sup>570</sup> + *R*531) Red-edge vegetation stress index (RVSI) ((*R*<sup>712</sup> + *R*752)/2) − *R*<sup>732</sup>

(*R*<sup>864</sup> − *R*671)/(*R*<sup>864</sup> + *R*671)

(*R*<sup>680</sup> − *R*460)/(*R*<sup>680</sup> + *R*460)

Absorption depth at 671 nm (Depth671) Removing continuum of spectrum from 569 to 763 nm

Absorption depth at 983 nm (Depth983) Removing continuum of spectrum from 933 to 1094 nm

Red edge position (REP) Band corresponding to the maximum value of a

first derivative

[(*R*<sup>700</sup> − *R*670) − 0.2(*R*<sup>700</sup> − *R*550)](*R*<sup>700</sup> /*R*670)

Hyperspectral remote sensing provides more data sources for the discrimination of plant species. Previous studies have showed that the unsmooth spectral resolution of multi-spectrum images is a bottleneck to improve the recognition precision of plant species. The detailed diagnotic features of hyperspectral make up for the deficiency of multispectral and gradually form two typical discrimination method of plant species. One is mathematical statistics method based on the dimensionality reduction of hyperspectral image, such as principal component analysis (PCA), MNF transform, wavelet transform and so on. However, this method only uses limited spectral information that does not reflect the physical formation mechanism of vegetation spectra. The other one is, by comparing the spectral waveform difference of various plant species, extracting the spectral characteristic parameters to quantify the difference,

such as spectral feature fitting (SFF) [44], spectral angle mapper (SAM) [45] and so on.

The precondition of change analysis of vegetation landscape pattern is to determine the spatial parameters of vegetation community such as area and position. The traditional classification

**3.3. Methods for dynamic change analysis of vegetation landscape pattern**

problems that need to be solved in the application.

**Table 2.** The calculation formula of existing vegetation indices.

Chlorophyll Normalized differential vegetation index

Modified chlorophyll absorption ratio index

Normalized differential chlorophyll index

Water Water index (WI) *R*<sup>870</sup> /*R*<sup>950</sup>

Cellular structure Structural independent pigment index (SIPI) (*R*<sup>800</sup> − *R*450)/(*R*<sup>800</sup> − *R*680)

(NDVI)

72 Sea Level Rise and Coastal Infrastructure

(MCARI)

(NDCI)

**Biochemical Parameter**

Plant healthy status

> The studies on the vulnerability assessment of coastal ecosystem in respect to sea level rise have been carried out since the 1980s, which to form several models. The latest and relatively perfect SPRC model was developed by European Union, which can reflect the effect process of '*Consequence*' of '*Source*' with '*Pathway*' on the '*Receptor*' [49]. '*Source*' (*S*) represents the affect factors of coastal wetland ecosystem. '*Pathway*' (*P*) is the tie between source and receptor. '*Receptor*' (*R*) represents the coastal wetland ecosystem. '*Consequence*' (*C*) represents the results of receptor under the influence of source. However, there is no definite method to evaluate the vulnerability of coastal wetland vegetation. Thus, in this section, according the conceptual framework of SPRC model, we analyze the factors with respect to vegetation in four steps (*S*, *P*, *R*, *C*), and then build the vulnerability assessment model of coastal wetland vegetation (shown in **Figure 6**).

**Figure 6.** The SPEC model for vulnerability assessment on the coastal wetland vegetation.

**(1) Analysis of '***Source***':** sea level rise will change the water level of the intertidal zone and depositional dynamic condition and affect the submerge time of vegetation. In addition, extreme climate will also directly change the succession process of vegetation. **(2) Analysis of '***Pathway***':** the silt by the river to the sea, sediment moved by the waves, decaying organic matter from the dead branches and fallen leaves, soil subsidence induced by the artificial coastal engineering are the key factors affecting the rate of sea level rise. **(3) Analysis of '***Receptor***':** in this step, the landscape pattern indices will be analyzed, such as community structure, community area and distribution position of vegetation. **(4) Analysis of '***Consequence***':** vegetation will produce various response under the influence of climate change, such as the variation of community structure, movement of the distribution position, changes in the vegetation succession. However, the dynamic analysis of vegetation based on the multi-temporal image deserves more attention.

growth cycle, and the rapid accumulation of annual carbon sequestration occurs when the gradual enhancement of plant photosynthesis during April–July [51]. Aboveground biomass of vegetation is highest in summer and autumn, and then gradually falls down, which is allocated for breeding and root storage, so that the underground biomass is the highest in winter.

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The soil carbon stocks in the bare beach is lowest, which is because there is no higher plants distribution that organic carbon sources are limited [52]. In the middle-high-tidal flat, the carbon stocks of soil depend on the capture capacity of vegetation communities. Studies have shown that in the growth progress of plants, 10–40% of photosynthetic products migrate into the soil through root exudates, and most of the rest transform the organic carbon into soil through litter form, resulting in increased soil carbon stocks [53]. Therefore, soil carbon stocks reached the higher value in the dense area of plant root, which with the underground biomass of vegetation has a significant positive relationship. The soil carbon stocks in the wetland are the lowest in spring, and the plant residues in soil are decomposed rapidly with the increase of temperature, which forms the peak of carbon sequestration. In autumn and winter, the soil mineralization rate slows down with the temperature gradually decreases, the carbon accumulation rate is reduced, and the soil carbon stocks reach the

**4.2. Analysis of spatial distribution and temporal dynamic of soil sequestration** 

**4.3. Extraction methods for carbon storage and carbon sequestration capacity of** 

sponding to the net primary productivity of vegetation [9, 52, 54].

the average weight by the density.

biomass of aboveground part.

The carbon storage and carbon sequestration capacity of wetland vegetation are two different concepts, which are calculated, respectively, based on the vegetation biomass and the net primary productivity. The carbon storage of vegetation refers to carbon stored in its existing biomass, while the carbon sequestration capacity refers to the fixed carbon capacity corre-

**1.** Determination of vegetation biomass: in order to analyze the temporal characteristics of vegetation of carbon stocks, in all four seasons, setting up the sample plots in the survey area. A suitable number and area of parallel quadrats are set in each sample plot, after estimating the vegetation density in the quadrats, cutting the living part of plant by using the "W" or quincunx sampling method, bring back to the laboratory to calculated the aboveground biomass by multiplying the average weight by the density. After harvesting the ground parts of plant, dig out the corresponding roots, calculating the underground biomass by multiplying

**2.** Calculation of net primary productivity: the net primary productivity is the sum of biomass and ground litter of vegetation. The experiential proportion of allocation among aboveground and underground biomass was referenced in the relevant literature [9, 54], the ground litter is calculated from 5 to 10% of the existing biomass of aboveground part, and the net primary productivity of subsurface is calculated from 30 to 80% of the existing

**with respect to vegetation**

highest value [52].

**vegetation**

The index was classified into three groups for vulnerability evaluating, which including Exposure (*E*), Sensitivity (*S*) and Adaptation (*A*), and Vulnerability (*V*) and can be express in simple mathematical form: *V = E + S* − *A*. *E* refers to related climate change factors, mostly involved the index in '*Source*', such as rate of sea level rise, annual precipitation, etc. *S* represents the vegetation characteristic in response to climate change, mostly involved the index in '*Receptor*' and '*Consequence*', such as the change of community area and structure, landscape pattern indexes, etc. *A* represents the adapt ability of vegetation under the influence of climate change, mainly involved the index of analysis of '*Pathway*', such as sedimentation rate, annual sediment discharge, etc.

The evaluation indexes of *E, S, A* are digitized using the software platform of geographic information system (GIS), assign and store the indexes to the evaluation unit by combining the interpolation algorithm, and then building the geospatial quantization data of indexes in which spatial and attribute data are interrelated. Based on the above operation, the spatial overlay calculation of each vulnerability index layer was carried out, and then a composite layer with multiple index attributes was created, which is the ultimate vulnerability assessment index of vegetation.

## **4. Analysis of carbon sequestration characteristics of coastal wetland vegetation**

### **4.1. Analysis of spatial distribution and temporal dynamic of vegetation sequestration**

The spatial distribution of vegetation carbon storage is showing the trend of decreasing from high to low tidal flat, and the carbon storage increase gradually in the positive succession of vegetation [9, 50]. Growing in high-tidal flat and building a longer time of plant communities has become the main force of carbon sequestration. Invasive species have the absolute competitive advantage in the high salinity environment, because of its population density, carbon sequestration capacity is also higher. However, it is noteworthy that the strong reproductive and adaptive capacity of invasive species poses a great threat to indigenous plants, which makes their considerable carbon sequestration capacity lost its application value [51]. The temporal dynamic variation of vegetation carbon sequestration is similar to that of plant growth cycle, and the rapid accumulation of annual carbon sequestration occurs when the gradual enhancement of plant photosynthesis during April–July [51]. Aboveground biomass of vegetation is highest in summer and autumn, and then gradually falls down, which is allocated for breeding and root storage, so that the underground biomass is the highest in winter.

### **4.2. Analysis of spatial distribution and temporal dynamic of soil sequestration with respect to vegetation**

**(1) Analysis of '***Source***':** sea level rise will change the water level of the intertidal zone and depositional dynamic condition and affect the submerge time of vegetation. In addition, extreme climate will also directly change the succession process of vegetation. **(2) Analysis of '***Pathway***':** the silt by the river to the sea, sediment moved by the waves, decaying organic matter from the dead branches and fallen leaves, soil subsidence induced by the artificial coastal engineering are the key factors affecting the rate of sea level rise. **(3) Analysis of '***Receptor***':** in this step, the landscape pattern indices will be analyzed, such as community structure, community area and distribution position of vegetation. **(4) Analysis of '***Consequence***':** vegetation will produce various response under the influence of climate change, such as the variation of community structure, movement of the distribution position, changes in the vegetation succession. However, the dynamic analysis of vegetation based on the multi-temporal image deserves more attention. The index was classified into three groups for vulnerability evaluating, which including Exposure (*E*), Sensitivity (*S*) and Adaptation (*A*), and Vulnerability (*V*) and can be express in simple mathematical form: *V = E + S* − *A*. *E* refers to related climate change factors, mostly involved the index in '*Source*', such as rate of sea level rise, annual precipitation, etc. *S* represents the vegetation characteristic in response to climate change, mostly involved the index in '*Receptor*' and '*Consequence*', such as the change of community area and structure, landscape pattern indexes, etc. *A* represents the adapt ability of vegetation under the influence of climate change, mainly involved the index of analysis of '*Pathway*', such as sedimentation

The evaluation indexes of *E, S, A* are digitized using the software platform of geographic information system (GIS), assign and store the indexes to the evaluation unit by combining the interpolation algorithm, and then building the geospatial quantization data of indexes in which spatial and attribute data are interrelated. Based on the above operation, the spatial overlay calculation of each vulnerability index layer was carried out, and then a composite layer with multiple index attributes was created, which is the ultimate vulnerability assess-

**4. Analysis of carbon sequestration characteristics of coastal wetland** 

The spatial distribution of vegetation carbon storage is showing the trend of decreasing from high to low tidal flat, and the carbon storage increase gradually in the positive succession of vegetation [9, 50]. Growing in high-tidal flat and building a longer time of plant communities has become the main force of carbon sequestration. Invasive species have the absolute competitive advantage in the high salinity environment, because of its population density, carbon sequestration capacity is also higher. However, it is noteworthy that the strong reproductive and adaptive capacity of invasive species poses a great threat to indigenous plants, which makes their considerable carbon sequestration capacity lost its application value [51]. The temporal dynamic variation of vegetation carbon sequestration is similar to that of plant

**4.1. Analysis of spatial distribution and temporal dynamic of vegetation** 

rate, annual sediment discharge, etc.

74 Sea Level Rise and Coastal Infrastructure

ment index of vegetation.

**vegetation**

**sequestration**

The soil carbon stocks in the bare beach is lowest, which is because there is no higher plants distribution that organic carbon sources are limited [52]. In the middle-high-tidal flat, the carbon stocks of soil depend on the capture capacity of vegetation communities. Studies have shown that in the growth progress of plants, 10–40% of photosynthetic products migrate into the soil through root exudates, and most of the rest transform the organic carbon into soil through litter form, resulting in increased soil carbon stocks [53]. Therefore, soil carbon stocks reached the higher value in the dense area of plant root, which with the underground biomass of vegetation has a significant positive relationship. The soil carbon stocks in the wetland are the lowest in spring, and the plant residues in soil are decomposed rapidly with the increase of temperature, which forms the peak of carbon sequestration. In autumn and winter, the soil mineralization rate slows down with the temperature gradually decreases, the carbon accumulation rate is reduced, and the soil carbon stocks reach the highest value [52].

### **4.3. Extraction methods for carbon storage and carbon sequestration capacity of vegetation**

The carbon storage and carbon sequestration capacity of wetland vegetation are two different concepts, which are calculated, respectively, based on the vegetation biomass and the net primary productivity. The carbon storage of vegetation refers to carbon stored in its existing biomass, while the carbon sequestration capacity refers to the fixed carbon capacity corresponding to the net primary productivity of vegetation [9, 52, 54].


**3.** Calculation of carbon storage and carbon sequestration capacity: the biomass of perennial herb and wood plants with more developed organs will increase every year, therefore their biomass and net primary productivity are different, which means that carbon stock and carbon sequestration capacity are different. However, the biomass and net primary productivity of wet shrubs, artificial cash crop, underwater plants and other annual wetland plants are the same. The carbon storage and carbon sequestration capacity are calculated based on the organic matter production process of vegetation (i.e. Photosynthesis). Every plant forming 1 g dry organic matter needs to assimilate 1.62 g CO2 and fix 0.44 g carbon, and the carbon conversion coefficient can be determined to be 0.44. The calculating formula for the total carbon stock and total annual carbon fixation of wetland vegetation is:

$$\mathbf{C}\_{l} = p\mathbf{A}\_{l}\mathbf{Q}\_{l} \tag{1}$$

application value. The soil carbon storage has a significant linear relationship with the underground biomass of vegetation, and reached the highest value in winter. Carbon stock and carbon sequestration ability are calculated on the basis of biomass and net primary productivity, respectively. Unlike the annual plants, because of the biomass of perennial plants increased

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77

Combined with the above analysis results, the future research needs to be improved or expanded from the following aspects. (1) The reclamation of coastal wetland will accelerate the degradation of vegetation function. However, the relationship between the reclamation type and climate change, and the combined influence mechanism of various factors on the vegetation need to be further explored. (2) Remote sensing method shows the outstanding potential for vegetation stress analysis, while the field data collection is also an essential step. Therefore, the combination of the botany sampling method and remote sensing will help to improve the standardization of sampling data, so that the results of remote sensing survey from point to surface are more accurate. (3) Compared with other habitat environment, coastal wetlands are particularly special because of its periodically inundated with water. Therefore, it is very important to develop the remote sensing method considering the influence of seawater submergence, to extraction the information of vegetation community. In addition, the study on the landscape dynamics change of mangrove forest is relatively mature, but there are few researches on the other coastal wetland types, especially the typical river-sea interactive wetland. (4) Firstly, analyzing the distribution pattern of carbon source of vegetation by using "3S" detection method, and to realize the scale transformation from point to surface. Secondly, exploring vegetation carbon storage processes in response to climate change, especially seagrass beds. Finally, by combining the carbon storage process and remote sensing data, establishing a "coupling model of carbon process-remote sensing", to realize the scale conversion from process to region.

This project was jointly supported by the Key laboratory for Ecological Environment in Coastal Areas, State Oceanic Administration (201810) and PhD's Research Start-up Project of National Marine Environmental Monitoring Center (2017-A-06). The authors wish to thank the anonymous reviewers for their constructive comments that helped improve the scholarly

and Jianhua Zhao1

1 Key Laboratory for Ecological Environment in Coastal Areas (SOA), National Marine

2 Center for Housing Innovations, Chinese University of Hong Kong, Shatin, Hong Kong

every year, its biomass and net primary productivity is different.

**Acknowledgements**

quality of the paper.

**Author details**

\*, Kapo Wong2

\*Address all correspondence to: zhouc0316@126.com

Environmental Monitoring Center, Dalian, China

Chao Zhou<sup>1</sup>

where *Ai* refers to the area of class *i* vegetation, hm<sup>2</sup> . *Qi* refers to the vegetation biomass (kg/m<sup>2</sup> ) or net primary productivity of class *i* vegetation (kg/m<sup>2</sup> ·a). *p* refers to the carbon conversion coefficient (0.44). *Ci* refers to the total carbon stock (*t*, when *Qi* is the biomass of class *i* vegetation) or the total annual carbon fixation (*t/a*, when *Qi* is net primary productivity of class *i* vegetation) of class *i* vegetation.

#### **5. Summary and conclusions**

The relative elevation drop and spatial loss of the habitat are the main driving factors of the coastal wetland vegetation succession under influence of climate change. The relationship between sea level rise rate and sediment accumulation rate determines the change of relative elevation, and then affects the flooding degree of plants. Landward movement of coastal wetland can avoid the habitat loss to a certain extent, but depends more on the terrain in the moving path. Long-term salt stress leads to the withdrawal of the low-salt-tolerant plant from the community competition, and ocean acidification caused by an increase in dissolved inorganic carbon concentrations cannot be neglected, which changed the photosynthetic activity of submerged plants. The dispersion of vegetation landscape patches increased high salinity or artificial crops will gradually erode the natural vegetation communities. Finally, the reverse or secondary succession of vegetation will be resulted, which accelerated the alien species invasion, and even worse, it will lead to the vegetation transformed into the bare flat.

Remote sensing technology provides a more effective method to analyze the change of coastal wetland vegetation under the climate change. The relationship between the stress factor and vegetation spectrum is established by using the vegetation index which often used to express the leaf biochemical substances (pigment, water, etc.). A mature method system based on multispectral image has established to extraction the spatial information of vegetation community, however hyperspectral show a better potential, which is needed to further develop the specially algorithm. Various landscape indices are used to reflect the dynamic change of landscape pattern, which can reveal the change of landscape heterogeneity. A fragility evaluation model of coastal wetland vegetation was established base on the conceptual framework of SPRC model.

The rapid accumulation of vegetation carbon sequestration occurs in the period of stronger photosynthesis. However, the invasive species with considerable carbon stock has lost its application value. The soil carbon storage has a significant linear relationship with the underground biomass of vegetation, and reached the highest value in winter. Carbon stock and carbon sequestration ability are calculated on the basis of biomass and net primary productivity, respectively. Unlike the annual plants, because of the biomass of perennial plants increased every year, its biomass and net primary productivity is different.

Combined with the above analysis results, the future research needs to be improved or expanded from the following aspects. (1) The reclamation of coastal wetland will accelerate the degradation of vegetation function. However, the relationship between the reclamation type and climate change, and the combined influence mechanism of various factors on the vegetation need to be further explored. (2) Remote sensing method shows the outstanding potential for vegetation stress analysis, while the field data collection is also an essential step. Therefore, the combination of the botany sampling method and remote sensing will help to improve the standardization of sampling data, so that the results of remote sensing survey from point to surface are more accurate. (3) Compared with other habitat environment, coastal wetlands are particularly special because of its periodically inundated with water. Therefore, it is very important to develop the remote sensing method considering the influence of seawater submergence, to extraction the information of vegetation community. In addition, the study on the landscape dynamics change of mangrove forest is relatively mature, but there are few researches on the other coastal wetland types, especially the typical river-sea interactive wetland. (4) Firstly, analyzing the distribution pattern of carbon source of vegetation by using "3S" detection method, and to realize the scale transformation from point to surface. Secondly, exploring vegetation carbon storage processes in response to climate change, especially seagrass beds. Finally, by combining the carbon storage process and remote sensing data, establishing a "coupling model of carbon process-remote sensing", to realize the scale conversion from process to region.

### **Acknowledgements**

**3.** Calculation of carbon storage and carbon sequestration capacity: the biomass of perennial herb and wood plants with more developed organs will increase every year, therefore their biomass and net primary productivity are different, which means that carbon stock and carbon sequestration capacity are different. However, the biomass and net primary productivity of wet shrubs, artificial cash crop, underwater plants and other annual wetland plants are the same. The carbon storage and carbon sequestration capacity are calculated based on the organic matter production process of vegetation (i.e. Photosynthesis). Every

and the carbon conversion coefficient can be determined to be 0.44. The calculating formula for the total carbon stock and total annual carbon fixation of wetland vegetation is:

*Ci* = *pAi Qi* (1)

The relative elevation drop and spatial loss of the habitat are the main driving factors of the coastal wetland vegetation succession under influence of climate change. The relationship between sea level rise rate and sediment accumulation rate determines the change of relative elevation, and then affects the flooding degree of plants. Landward movement of coastal wetland can avoid the habitat loss to a certain extent, but depends more on the terrain in the moving path. Long-term salt stress leads to the withdrawal of the low-salt-tolerant plant from the community competition, and ocean acidification caused by an increase in dissolved inorganic carbon concentrations cannot be neglected, which changed the photosynthetic activity of submerged plants. The dispersion of vegetation landscape patches increased high salinity or artificial crops will gradually erode the natural vegetation communities. Finally, the reverse or secondary succession of vegetation will be resulted, which accelerated the alien species invasion, and even worse, it will lead to the vegetation transformed into the bare flat. Remote sensing technology provides a more effective method to analyze the change of coastal wetland vegetation under the climate change. The relationship between the stress factor and vegetation spectrum is established by using the vegetation index which often used to express the leaf biochemical substances (pigment, water, etc.). A mature method system based on multispectral image has established to extraction the spatial information of vegetation community, however hyperspectral show a better potential, which is needed to further develop the specially algorithm. Various landscape indices are used to reflect the dynamic change of landscape pattern, which can reveal the change of landscape heterogeneity. A fragility evaluation model of coastal wetland vegetation was established base on the conceptual framework of SPRC model. The rapid accumulation of vegetation carbon sequestration occurs in the period of stronger photosynthesis. However, the invasive species with considerable carbon stock has lost its

. *Qi*

and fix 0.44 g carbon,

)

refers to the vegetation biomass (kg/m<sup>2</sup>

·a). *p* refers to the carbon conversion

is net primary productivity of class *i*

is the biomass of class *i* vegeta-

plant forming 1 g dry organic matter needs to assimilate 1.62 g CO2

refers to the total carbon stock (*t*, when *Qi*

refers to the area of class *i* vegetation, hm<sup>2</sup>

or net primary productivity of class *i* vegetation (kg/m<sup>2</sup>

tion) or the total annual carbon fixation (*t/a*, when *Qi*

where *Ai*

coefficient (0.44). *Ci*

vegetation) of class *i* vegetation.

76 Sea Level Rise and Coastal Infrastructure

**5. Summary and conclusions**

This project was jointly supported by the Key laboratory for Ecological Environment in Coastal Areas, State Oceanic Administration (201810) and PhD's Research Start-up Project of National Marine Environmental Monitoring Center (2017-A-06). The authors wish to thank the anonymous reviewers for their constructive comments that helped improve the scholarly quality of the paper.

### **Author details**

Chao Zhou<sup>1</sup> \*, Kapo Wong2 and Jianhua Zhao1

\*Address all correspondence to: zhouc0316@126.com

1 Key Laboratory for Ecological Environment in Coastal Areas (SOA), National Marine Environmental Monitoring Center, Dalian, China

2 Center for Housing Innovations, Chinese University of Hong Kong, Shatin, Hong Kong

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**Chapter 6**

**Provisional chapter**

**Impact of** *Enteromorpha* **Blooms on Aquaculture**

**Research Off Qianliyan Island, Yellow Sea, China**

**Impact of** *Enteromorpha* **Blooms on Aquaculture** 

DOI: 10.5772/intechopen.71434

**Research Off Qianliyan Island, Yellow Sea, China**

Between 2008 and 2016, there were mass summer blooms of *Enteromorpha* in the Yellow Sea, China. They covered an area of thousands of square kilometers annually, lasting an average of 90 days. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the *Enteromorpha* blooms were used in this study of the Qianliyan Island area. Underwater robots survey *trepang*, *wrinkles abalone,* and submarine ecological status. We found that the time taken by *Enteromorpha* to cover the Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The *Enteromorpha* made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. *Enteromorpha* sedimentation during outbreaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of *trepang* and *wrinkles abalone* were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from

> © 2016 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,

© 2018 The Author(s). Licensee IntechOpen. 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.

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

Large summer blooms of *Enteromorpha* occurred in the Yellow Sea, China, from 2008 to 2016. These took place in the Yancheng shoals, Jiangsu Province, and covered an area of

**Keywords:** *Enteromorpha*, remote sensing data, SST, *trepang*, *wrinkles abalone*, Qianliyan

Guo Jie, Zhang Tianlong, Ji Diansheng, Mu Yankai,

Guo Jie, Zhang Tianlong, Ji Diansheng, Mu Yankai,

Yu Hongyang, Hou Chawei and Ji Ling

Yu Hongyang, Hou Chawei and Ji Ling

http://dx.doi.org/10.5772/intechopen.71434

**Abstract**

the blooms.

Island

**1. Introduction**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

**Provisional chapter**

### **Impact of** *Enteromorpha* **Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China Research Off Qianliyan Island, Yellow Sea, China**

**Impact of** *Enteromorpha* **Blooms on Aquaculture** 

DOI: 10.5772/intechopen.71434

Guo Jie, Zhang Tianlong, Ji Diansheng, Mu Yankai, Yu Hongyang, Hou Chawei and Ji Ling Yu Hongyang, Hou Chawei and Ji Ling Additional information is available at the end of the chapter

Guo Jie, Zhang Tianlong, Ji Diansheng, Mu Yankai,

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71434

#### **Abstract**

Between 2008 and 2016, there were mass summer blooms of *Enteromorpha* in the Yellow Sea, China. They covered an area of thousands of square kilometers annually, lasting an average of 90 days. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the *Enteromorpha* blooms were used in this study of the Qianliyan Island area. Underwater robots survey *trepang*, *wrinkles abalone,* and submarine ecological status. We found that the time taken by *Enteromorpha* to cover the Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The *Enteromorpha* made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. *Enteromorpha* sedimentation during outbreaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of *trepang* and *wrinkles abalone* were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from the blooms.

**Keywords:** *Enteromorpha*, remote sensing data, SST, *trepang*, *wrinkles abalone*, Qianliyan Island

#### **1. Introduction**

Large summer blooms of *Enteromorpha* occurred in the Yellow Sea, China, from 2008 to 2016. These took place in the Yancheng shoals, Jiangsu Province, and covered an area of

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. © 2018 The Author(s). Licensee IntechOpen. 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.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

thousands of square kilometers, lasting for about 90 days each year. The blooms seriously affected the ecological environment of the Yellow Sea and attracted the attention of scholars [1].

**2. Method and results**

**2.1. Study area and method**

The national aquatic germ plasm resources of Qianliyan Island sea area are located in the south Yellow Sea, China, at latitude 36°15′57″ N, longitude 121°23′09″ E. The reserve covers an area of 1766.27 hectares (**Figure 1**). The specific location of each monitoring station and its contents are shown in **Table 1**. Experimental *Enteromorpha* data were collected from the monitoring sites before, during, and after the blooms of 2010–2016. The main protected species in the reserve are the *trepang, wrinkles abalone, other varieties of protection are the blue dot mackerel, anchovy, small yellow croaker, hairtail*, *Penaeus chinensis, Portunus trituberculatus, and Charybdis japonica*. The reserve area is 24.8 sea miles distant from land, with no residents and away from the land-source pollution. The *Enteromorpha* blooms pass this protection zone every year. Remote sensing data (MODIS, Windsat, HY2, and Radarsat2), model prediction, and the marine environment ecological data before, during, and after the *Enteromorpha* blooms as measured by ships (2010–2016) were used in this research. An underwater robot was used to

Impact of *Enteromorpha* Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China

http://dx.doi.org/10.5772/intechopen.71434

85

**Figure 1.** Qianliyan Island sea area sketch map, Black grid is core area; slant lines area is experiment area; blace is

Qianliyan Island; rectangle ABCD is extract area of remote sensing data; black flag is monitoring station.

survey *trepang*, *wrinkles abalone,* and submarine ecological status.

Based on the differences in monitoring spectral caused by *Enteromorpha* covering the surface of the water, remote sensing data of MODIS data (TERRA/AQUA), image data of GF-1, HJ-1-A/1-B and Landsat-8 and so on, data source were used to study *Enteromorpha* drift, diffusion, and traceability [1–8]. Because the synthetic aperture radar (SAR) data are not affected by rain clouds, they are beginning to be used in *Enteromorpha* monitoring [9, 10]. Double polarization and cross-polarization retrieval factors were used to extract information about *Enteromorpha* on the sea surface. Although SAR has some limitations in the observation of large mass *Enteromorpha*, compact polarimetric synthetic aperture radar has solved this problem and will play an increasingly important role in such monitoring [11]. Researches show that the growth difference of *Enteromorpha* is not obvious when temperature is stable but nutrient conditions vary [11]. The growth difference in *Enteromorpha* is significant when nutrients are stable and temperature gradient is varying. The influence of temperature on *Enteromorpha* growth is considerable; the alga grows and quickly dies in water at 40°C; when the sea surface temperature (SST) is between 5 and 35°C, it can survive but it cannot maintain normal growth for a long time. In SST of 10–30°C, it grows normally. SSTs between 20and 25°C are most suitable for growth, and maximum growth takes place at the SSTs of 25°C*. Enteromorpha* can release spores at SSTs 15–35°C [11]. They are released in great quantities and grow normally when sea water salinity (SAL) is at a range of 12–40 psu; the most favorable range is when SAL is at 28–40 psu. At 32 psu, spore release is at its maximum [12]. The ability of *Enteromorpha* to adapt to high SAL is greater than its ability to adapt to low SAL. The most suitable conditions for *Enteromorpha* growth are when SAL is at 24–28 psu and the maximum are when SAL is at 24 psu [12]. Seawater pH values of 5 or 6 prevent *Enteromorpha* spore release; spore can be released at pH 7–10. A pH value of 9 allows maximum spore release [12]. *Enteromorpha* absorption of nitrogen and phosphorus is remarkable, the absorptive character for nitrogen and phosphorus have their own characteristics [13]. Wang [14, 15] simulated an indoor *Enteromorpha* tide using rotting leachate, and indicate that such tides may harm *wrinkles abalone* in the field. Results confirmed that *Enteromorpha* exudates, decaying and have an acute lethal effect for *wrinkles abalone*, *shrimps,* and *trepan*. The sulfide and ammonia in the leachate from *Enteromorpha* decay are the main causes of death of *wrinkles abalone*. Hypoxia stress for the green tide is an important cause of harm to farmed animals [12]. This chapter will study the impact of *Enteromorpha* on the ecological environment in the marine national aquatic germ plasm resources of Qianliyan Island, Yellow Sea, China. The greatest impact has been to *trepang* and abalone yield.

Section 2 provides an overview of the methods and results used to retrieve SST, chlorophyll concentration (CHL), sea state, and wind parameters from remote sensing; SAL and ecological environment data measured by ship. The remote sensing data and site monitoring data are discussed in Section 3, and Section 4 contains our conclusions.

### **2. Method and results**

thousands of square kilometers, lasting for about 90 days each year. The blooms seriously affected the ecological environment of the Yellow Sea and attracted the attention of

Based on the differences in monitoring spectral caused by *Enteromorpha* covering the surface of the water, remote sensing data of MODIS data (TERRA/AQUA), image data of GF-1, HJ-1-A/1-B and Landsat-8 and so on, data source were used to study *Enteromorpha* drift, diffusion, and traceability [1–8]. Because the synthetic aperture radar (SAR) data are not affected by rain clouds, they are beginning to be used in *Enteromorpha* monitoring [9, 10]. Double polarization and cross-polarization retrieval factors were used to extract information about *Enteromorpha* on the sea surface. Although SAR has some limitations in the observation of large mass *Enteromorpha*, compact polarimetric synthetic aperture radar has solved this problem and will play an increasingly important role in such monitoring [11]. Researches show that the growth difference of *Enteromorpha* is not obvious when temperature is stable but nutrient conditions vary [11]. The growth difference in *Enteromorpha* is significant when nutrients are stable and temperature gradient is varying. The influence of temperature on *Enteromorpha* growth is considerable; the alga grows and quickly dies in water at 40°C; when the sea surface temperature (SST) is between 5 and 35°C, it can survive but it cannot maintain normal growth for a long time. In SST of 10–30°C, it grows normally. SSTs between 20and 25°C are most suitable for growth, and maximum growth takes place at the SSTs of 25°C*. Enteromorpha* can release spores at SSTs 15–35°C [11]. They are released in great quantities and grow normally when sea water salinity (SAL) is at a range of 12–40 psu; the most favorable range is when SAL is at 28–40 psu. At 32 psu, spore release is at its maximum [12]. The ability of *Enteromorpha* to adapt to high SAL is greater than its ability to adapt to low SAL. The most suitable conditions for *Enteromorpha* growth are when SAL is at 24–28 psu and the maximum are when SAL is at 24 psu [12]. Seawater pH values of 5 or 6 prevent *Enteromorpha* spore release; spore can be released at pH 7–10. A pH value of 9 allows maximum spore release [12]. *Enteromorpha* absorption of nitrogen and phosphorus is remarkable, the absorptive character for nitrogen and phosphorus have their own characteristics [13]. Wang [14, 15] simulated an indoor *Enteromorpha* tide using rotting leachate, and indicate that such tides may harm *wrinkles abalone* in the field. Results confirmed that *Enteromorpha* exudates, decaying and have an acute lethal effect for *wrinkles abalone*, *shrimps,* and *trepan*. The sulfide and ammonia in the leachate from *Enteromorpha* decay are the main causes of death of *wrinkles abalone*. Hypoxia stress for the green tide is an important cause of harm to farmed animals [12]. This chapter will study the impact of *Enteromorpha* on the ecological environment in the marine national aquatic germ plasm resources of Qianliyan Island, Yellow Sea, China. The greatest impact has been to *trepang*

Section 2 provides an overview of the methods and results used to retrieve SST, chlorophyll concentration (CHL), sea state, and wind parameters from remote sensing; SAL and ecological environment data measured by ship. The remote sensing data and site monitoring data are

discussed in Section 3, and Section 4 contains our conclusions.

scholars [1].

84 Sea Level Rise and Coastal Infrastructure

and abalone yield.

### **2.1. Study area and method**

The national aquatic germ plasm resources of Qianliyan Island sea area are located in the south Yellow Sea, China, at latitude 36°15′57″ N, longitude 121°23′09″ E. The reserve covers an area of 1766.27 hectares (**Figure 1**). The specific location of each monitoring station and its contents are shown in **Table 1**. Experimental *Enteromorpha* data were collected from the monitoring sites before, during, and after the blooms of 2010–2016. The main protected species in the reserve are the *trepang, wrinkles abalone, other varieties of protection are the blue dot mackerel, anchovy, small yellow croaker, hairtail*, *Penaeus chinensis, Portunus trituberculatus, and Charybdis japonica*. The reserve area is 24.8 sea miles distant from land, with no residents and away from the land-source pollution. The *Enteromorpha* blooms pass this protection zone every year. Remote sensing data (MODIS, Windsat, HY2, and Radarsat2), model prediction, and the marine environment ecological data before, during, and after the *Enteromorpha* blooms as measured by ships (2010–2016) were used in this research. An underwater robot was used to survey *trepang*, *wrinkles abalone,* and submarine ecological status.

**Figure 1.** Qianliyan Island sea area sketch map, Black grid is core area; slant lines area is experiment area; blace is Qianliyan Island; rectangle ABCD is extract area of remote sensing data; black flag is monitoring station.


**Table 1.** Position monitoring information.

#### **2.2. Remote sensing and analysis of monitoring results**

MODIS, Radarsat2 data, and the ocean numerical model of the North China Sea Marine Forecasting Centre of SOA, to predict the drift of the blooms, were used to analyze the period when *Enteromorpha* covered the sea off Qianliyan Island (see **Table 2**) from 2009 to 2016. The longest duration of an *Enteromorpha* blooms in the Yellow Sea was in 2012 (106 days); in contrast, the longest off Qianliyan Island was 54 days. Eight-day average MODIS data (resolution of 4 km) were used to retrieve the chlorophyll concentration (CHL) and the SST data; the altimeter data of HY2 were used to retrieve the significant wave height (SWH) and ASCAT data (resolution of 50 km) to retrieve wind field in research area (**Figure 1**). As *Enteromorpha* blooms off Qianliyan Island often occurred in June, July, and August, so average SST, CHL, wind field, and SWH change in these months were the main indexes used by remote sensing from 2009 to 2016.

**Figure 2a** shows that CHL rose significantly in July 2009–2016; this was because SST was between 20 and 25°C (**Figure 2c**) and so most suitable for the *Enteromorpha* growth [12]. As the SST in June, July, and August 2012 was between 20 and 25°C (**Figure 2c**), the duration of the *Enteromorpha* bloom covering the Yellow Sea and the Qianliyan Island area was the longest (**Table 2**). The SWH retrieved by HY2 data and at sea level in the research area were at levels 4–5 (**Figure 2b**). The SWH in July was the highest of these 3 months.


The MODIS *Enteromorpha* data (resolution of 1 km) were extracted on 06 May, 20 June, 06 July 2012 and on 02, 20 and 29 June 2013 (**Figure 3a** and **b**). The Radarsat 2 *Enteromorpha* data (resolution of 100 m, VV) was extracted on 12 June and 14 July 2016 (**Figure 3c**). The floating algae index (FAI) was used to the *Enteromorpha* data [16]. There are wind scale from 3 to 4 in the study area and the advantages by south winds in June and July (**Figure 4a** and **b**). In August, the southwesterly wind becomes northwesterly (**Figure 4c**). These wind fields assist the *Enteromorpha* blooms in coming ashore. The drift direction of the *Enteromorpha* blooms was basically consis-

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**Figure 2.** Average CHL (a), SWH (b), and SST (c) in June, July, and August, respectively from 2009 to 2016.

Site observation data were measured at different water depths (0.5 m, upper; 10–18 m, middle; and 31.5–34 m, bottom) from ships (**Figure 1**). Observation data analyzed the impact of

tent with the wind direction.

**2.3. Real-time site observation data**

**Table 2.** The *Enteromorpha* starting and ending time in Yellow Sea and Qianliyan Island area.

Impact of *Enteromorpha* Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China http://dx.doi.org/10.5772/intechopen.71434 87

**Figure 2.** Average CHL (a), SWH (b), and SST (c) in June, July, and August, respectively from 2009 to 2016.

The MODIS *Enteromorpha* data (resolution of 1 km) were extracted on 06 May, 20 June, 06 July 2012 and on 02, 20 and 29 June 2013 (**Figure 3a** and **b**). The Radarsat 2 *Enteromorpha* data (resolution of 100 m, VV) was extracted on 12 June and 14 July 2016 (**Figure 3c**). The floating algae index (FAI) was used to the *Enteromorpha* data [16]. There are wind scale from 3 to 4 in the study area and the advantages by south winds in June and July (**Figure 4a** and **b**). In August, the southwesterly wind becomes northwesterly (**Figure 4c**). These wind fields assist the *Enteromorpha* blooms in coming ashore. The drift direction of the *Enteromorpha* blooms was basically consistent with the wind direction.

#### **2.3. Real-time site observation data**

**2.2. Remote sensing and analysis of monitoring results**

**Table 1.** Position monitoring information.

86 Sea Level Rise and Coastal Infrastructure

4–5 (**Figure 2b**). The SWH in July was the highest of these 3 months.

**End of the impact on the Qianliyan**

2009 July 7 August 4 20th May Outside the sea of

**Table 2.** The *Enteromorpha* starting and ending time in Yellow Sea and Qianliyan Island area.

 June 30 August 12 2nd June Yancheng coast 76 July 14 July 29 27th May Yancheng coast 82 June 4 July 27 16th May Yancheng coast 106 June 24 July 26 10th May Yancheng coast 96 June 12 August 3 12th May Yancheng coast 95 2015 June 10 July 31 16th May Yancheng coast 93 June 13 July 24 11th May Yancheng coast 90

**The time and place of first discovered by satellite**

Yancheng

**Duration time of Yellow Sea (Day)**

94

**Year Began to affect** 

**the Qianliyan**

MODIS, Radarsat2 data, and the ocean numerical model of the North China Sea Marine Forecasting Centre of SOA, to predict the drift of the blooms, were used to analyze the period when *Enteromorpha* covered the sea off Qianliyan Island (see **Table 2**) from 2009 to 2016. The longest duration of an *Enteromorpha* blooms in the Yellow Sea was in 2012 (106 days); in contrast, the longest off Qianliyan Island was 54 days. Eight-day average MODIS data (resolution of 4 km) were used to retrieve the chlorophyll concentration (CHL) and the SST data; the altimeter data of HY2 were used to retrieve the significant wave height (SWH) and ASCAT data (resolution of 50 km) to retrieve wind field in research area (**Figure 1**). As *Enteromorpha* blooms off Qianliyan Island often occurred in June, July, and August, so average SST, CHL, wind field, and SWH change in these months were the main indexes used by remote sensing from 2009 to 2016. **Figure 2a** shows that CHL rose significantly in July 2009–2016; this was because SST was between 20 and 25°C (**Figure 2c**) and so most suitable for the *Enteromorpha* growth [12]. As the SST in June, July, and August 2012 was between 20 and 25°C (**Figure 2c**), the duration of the *Enteromorpha* bloom covering the Yellow Sea and the Qianliyan Island area was the longest (**Table 2**). The SWH retrieved by HY2 data and at sea level in the research area were at levels

**Position the serial number Longitude (E) Latitude (N) Monitoring program** 1 121°25′12″ 36°21′00″ Water quality

3 121°22′12″ 36°15′00″ Water quality 4 121°17′60″ 36°16′12″ Water quality

2 121°24′00″ 36°16′48″ Water quality, sediment, biology

Site observation data were measured at different water depths (0.5 m, upper; 10–18 m, middle; and 31.5–34 m, bottom) from ships (**Figure 1**). Observation data analyzed the impact of

**Figure 3.** *Enteromorpha* image extraction by MODIS data on 26 May, 20 June, and 06 July 2012(a); 02, 20, and 29 June 2013(b); and on 12 June and 14 July 2016(c) by Radarsat 2 data from an area near Qianliyan Island.

*Enteromorpha* on the Qianliyan Island area (average data from stations 1, 2, 3, and 4) from 2010 to 2016. Samples were taken in February, March, and May, before *Enteromorpha* blooms; in July, during the *Enteromorpha* blooms; and in August, October, and November after the *Enteromorpha* blooms. **Figure 5a** shows that the SAL was between 30 psu and 32 psu in February, March, May, July, August, October, and November from 2012 to 2016. This allowed *Enteromorpha* to grow normally. During the *Enteromorpha* blooms, the SAL was between 30.8 and 31.7 psu in July of 2013. Spores dispersal is most favored when SAL is 30.5–32 psu [11]. The dissolved oxygen (DO) content of sea water is influenced by biological, chemical, physical, and other factors. When the density of phytoplankton is very great, the high concentrations of chlorophyll-a and active photosynthesis increase the oxygen content of the water. In contrast, when large numbers of phytoplankton die, oxidation is greater than photosynthesis and the DO content of the water falls sharply [17]. DO was clearly lower in upper, middle, and bottom water layers after blooms than before them, from 2014 to 2016 (**Figure 5b**). We suggest that this was because of *Enteromorpha* decomposition and settlement. After the large algal blooms, seaweed decomposition release large amounts of ammonium salts into the water, leading to a hypoxic environment and the

formation of hydrogen sulfide [18]. The inorganic nitrogen concentration (INC) of research area from upper, middle, and bottom layers in August was more than in May, in 2010 and 2015, only upper INC in August 2016 was more than in May 2016 (**Figure 5c**). In October 2016, the DIN

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**Figure 4.** Average wind field variation in Qianliyan area in June (a), July (b), and August (c) 2009–2016.

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**Figure 4.** Average wind field variation in Qianliyan area in June (a), July (b), and August (c) 2009–2016.

*Enteromorpha* on the Qianliyan Island area (average data from stations 1, 2, 3, and 4) from 2010 to 2016. Samples were taken in February, March, and May, before *Enteromorpha* blooms; in July, during the *Enteromorpha* blooms; and in August, October, and November after the *Enteromorpha* blooms. **Figure 5a** shows that the SAL was between 30 psu and 32 psu in February, March, May, July, August, October, and November from 2012 to 2016. This allowed *Enteromorpha* to grow normally. During the *Enteromorpha* blooms, the SAL was between 30.8 and 31.7 psu in July of 2013. Spores dispersal is most favored when SAL is 30.5–32 psu [11]. The dissolved oxygen (DO) content of sea water is influenced by biological, chemical, physical, and other factors. When the density of phytoplankton is very great, the high concentrations of chlorophyll-a and active photosynthesis increase the oxygen content of the water. In contrast, when large numbers of phytoplankton die, oxidation is greater than photosynthesis and the DO content of the water falls sharply [17]. DO was clearly lower in upper, middle, and bottom water layers after blooms than before them, from 2014 to 2016 (**Figure 5b**). We suggest that this was because of *Enteromorpha* decomposition and settlement. After the large algal blooms, seaweed decomposition release large amounts of ammonium salts into the water, leading to a hypoxic environment and the

**Figure 3.** *Enteromorpha* image extraction by MODIS data on 26 May, 20 June, and 06 July 2012(a); 02, 20, and 29 June

2013(b); and on 12 June and 14 July 2016(c) by Radarsat 2 data from an area near Qianliyan Island.

88 Sea Level Rise and Coastal Infrastructure

formation of hydrogen sulfide [18]. The inorganic nitrogen concentration (INC) of research area from upper, middle, and bottom layers in August was more than in May, in 2010 and 2015, only upper INC in August 2016 was more than in May 2016 (**Figure 5c**). In October 2016, the DIN

process and rivers. The RP of August, October, and November was more than in May in 2010 and 2016; but the RP of August in 2015 was less than in May 2015 (**Figure 5d**). Chemical oxygen demand (COD) as a characterization of the effective index of organic matter content in water, indirectly reflects the degree of organic pollution in a water body. The COD of August was less than in May in 2015 and 2016; but the COD of August was more than that of May in 2010. We found that the COD of August 2010, 2014, 2015, and 2016 was less than that of July 2012 and 2013 (**Figure 5e**). Death of *Enteromorpha* lethal effect of micro algae has a strong nutrient that is almost not consumed, but DO and pH got reduced [19]. The pH from upper, middle, and bottom layers in August were less than in May in 2010, 2015, and 2016 (8.10 < pH < 8.30) and the pH of July from upper and bottom layers was between 8.15 and 8.27 in 2012 and 2013. All the pH levels shown in **Figure 5f** are above 8 and less than 8.5. *Enteromorpha* spores can be released at this pH level (**Figure 5f**). **Figure 5g** shows that the oil content of upper layers in August was higher than those of May in 2015 and 2016, but the oil content in August was less than in May in 2010. The oil content in July 2012 was very low, but in July 2013, it was above 10 μg/L. The Zn content in August was more than in May in 2010 (upper layer) and 2015 (upper, middle, and bottom layers). Concentrations of Cu, Zn, Pb, and As were more than 0.2 mg/L, and *Enteromorpha* experienced an inhibitory effect on growth [20]. Concentrations of Cd and Hg were less than 0.2 mg/L. *Enteromorpha* drift introduced oil, Zn, Cu, Pb, Cd, Hg, and As into the reserve (**Figure 5g** and **f**) and polluted the environment. The underwater robot (**Figure 6a**) was used to survey the marine ecological environment in March and May of 2017 from Island 3 and 4 off Qianliyan Island (**Figure 1**), About 10 *trepangs* (**Figure 6b** and **c**) were distributed at a distance of 1000 m and there were many *starfish* in this area, but no *wrinkles abalone* could be found. Monitoring and field investigation data indicated that the total yield of *trepang* was decreasing yearly in the Qianliyan

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**Figure 6.** (a) The underwater robot; observations of (b, c) *trepang,* and (d) *starfish* in March and May 2017.

**Figure 5.** Variations in SAL, DO, INC, RP,COD, PH, oil; and Zn, Cu, Pb, Cd, Hg at surface, middle, and bottom layers of research area. Observation data from ship, 2010–2016.

of upper, middle, and bottom levels of the research area was more than in May and August of 2016. H3 PO4 is often referred to as reactive phosphate (RP), for around 10% of PO4 3− ion, and the rest are almost all HPO4 2− ion RP in sea water, the main source is input from mineral weathering process and rivers. The RP of August, October, and November was more than in May in 2010 and 2016; but the RP of August in 2015 was less than in May 2015 (**Figure 5d**). Chemical oxygen demand (COD) as a characterization of the effective index of organic matter content in water, indirectly reflects the degree of organic pollution in a water body. The COD of August was less than in May in 2015 and 2016; but the COD of August was more than that of May in 2010. We found that the COD of August 2010, 2014, 2015, and 2016 was less than that of July 2012 and 2013 (**Figure 5e**). Death of *Enteromorpha* lethal effect of micro algae has a strong nutrient that is almost not consumed, but DO and pH got reduced [19]. The pH from upper, middle, and bottom layers in August were less than in May in 2010, 2015, and 2016 (8.10 < pH < 8.30) and the pH of July from upper and bottom layers was between 8.15 and 8.27 in 2012 and 2013. All the pH levels shown in **Figure 5f** are above 8 and less than 8.5. *Enteromorpha* spores can be released at this pH level (**Figure 5f**). **Figure 5g** shows that the oil content of upper layers in August was higher than those of May in 2015 and 2016, but the oil content in August was less than in May in 2010. The oil content in July 2012 was very low, but in July 2013, it was above 10 μg/L. The Zn content in August was more than in May in 2010 (upper layer) and 2015 (upper, middle, and bottom layers). Concentrations of Cu, Zn, Pb, and As were more than 0.2 mg/L, and *Enteromorpha* experienced an inhibitory effect on growth [20]. Concentrations of Cd and Hg were less than 0.2 mg/L. *Enteromorpha* drift introduced oil, Zn, Cu, Pb, Cd, Hg, and As into the reserve (**Figure 5g** and **f**) and polluted the environment. The underwater robot (**Figure 6a**) was used to survey the marine ecological environment in March and May of 2017 from Island 3 and 4 off Qianliyan Island (**Figure 1**), About 10 *trepangs* (**Figure 6b** and **c**) were distributed at a distance of 1000 m and there were many *starfish* in this area, but no *wrinkles abalone* could be found. Monitoring and field investigation data indicated that the total yield of *trepang* was decreasing yearly in the Qianliyan

**Figure 6.** (a) The underwater robot; observations of (b, c) *trepang,* and (d) *starfish* in March and May 2017.

of upper, middle, and bottom levels of the research area was more than in May and August of

**Figure 5.** Variations in SAL, DO, INC, RP,COD, PH, oil; and Zn, Cu, Pb, Cd, Hg at surface, middle, and bottom layers of

2− ion RP in sea water, the main source is input from mineral weathering

3− ion, and the

is often referred to as reactive phosphate (RP), for around 10% of PO4

2016. H3

PO4

rest are almost all HPO4

90 Sea Level Rise and Coastal Infrastructure

research area. Observation data from ship, 2010–2016.


20 and 25°C from 2009 to 2016. The longest *Enteromorpha* bloom in the Yellow Sea was the 106 days and the longest time off Qianliyan Island was 54 days in 2012. SSTs in June, July, and August were between 20 and 25°C, and suitable for the growth of *Enteromorpha*. **Figure 3** shows that *Enteromorpha* did not cover the research area, uniformly; nevertheless, it was still found that the July SST and its corresponding CHL were the highest observed for 3 months from 2009 to 2016 (**Figure 2a** and **c**). *Enteromorpha* blooms moved closer to the shore under the action of wind and waves in August (**Figure 2b** and **4**). Site observation data were from *Enteromorpha* covering Qianliyan Island before the blooms (February, March, or May), during the blooms (July) and after the blooms (August or October) in 2010–2016. The SAL of the research area was between 30 and 32 psu, levels most favorable for *Enteromorpha* spore dispersal. After *Enteromorpha* blooms, DO measurements were significantly lower than beforehand. The DIN in August 2015 was higher than that of May. In the INC measurements in August 2016, only the upper layer was higher than that of May. The pH of sea water from the research area from 2010 to 2016 (8 < pH < 8.5) was suitable for release of *Enteromorpha* spores; and after the *Enteromorpha* blooms, the pH and COD declined. *Enteromorpha* outbreaks have introduced heavy metals and petroleum pollution into the Qianliyan Island area and their concentrations have inhibited the growth of *Enteromorpha*. Benthos sampling in July 2013 found high levels of *Enteromorpha* on the seabed near the Qianliyan Island area. **Table 4** shows that the diversity index has generally increased, while the abundance value has decreased significantly. Uniformity has fluctuated from 2010 to 2016 (except in 2011), and this indicates that the environmental quality

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Sections 2 and 3, examining the remote sensing data and the site observation data analysis, show that the SST, SAL, pH of the Qianliyan Island area are suitable for *Enteromorpha* growth and spore release. There is a direct relationship between the SST and the strength or weakness of an *Enteromorpha* bloom. After a bloom, pH and COD decline. Results of benthos sampling in July 2013 confirm that *Enteromorpha* settled in the region during June–August. *Enteromorpha* decomposition is a threat to the survival and reproduction of shellfish and zooplankton, and it has an especially acute, lethal effect on abalone and *trepang*. In consecutive years, *Enteromorpha* has covered the Qianliyan Island area, bringing significant effects including DO depression, COD rise, RP, and heavy metals into the research area and polluting the environment. Comparison with the biogenesis characteristics index found that the environmental quality of the survey has declined. Number of *starfish* increased and shellfish, especially abalone, were damaged. This explains why *wrinkles abalone* is hardly to be found at present and *trepang* are decreasing yearly. The subsidence of the *Enteromorpha* and the rise in SSTs are two main reasons for the decline in the ecological environment off the Qianliyan Island area. To protect the national aquatic germ plasm resources of Qianliyan Island, we need to: (1) halt the growth in starfish numbers; (2) salvage the *Enteromorpha* blooms when they cover the Qianliyan Island area; (3) reduce human interference; and (4) improve fishery resources and revegetation of the

of the survey area has declined.

**4. Conclusion**

eco-environment.

**Table 3.** Annual survey table of the sea cucumber of Qianliyan Island area.


**Table 4.** Biological community characteristics index comparison table. (D2 of benthonic organism has no observational data).

Island area (**Table 3**) and *wrinkles abalone* was rarely discovered. A site survey examined the average value of phytoplankton, zooplankter, and bentonic organisms from stations 1, 2, 3, and 4 found that the index of diversity (*H′* ) and degree of dominance (D2 ) were increasing, abundance (*d*) was declining, and uniformity (*J*) was swinging between the two. There were slight fluctuations in phytoplankton and zooplankton; but the *H′ , d,* and *J* of benthonic organism have slight fluctuations and **Table 4** shows an upward trend.

### **3. Discussion**

The analyses in Section 2.2 and 2.3 found that June, July, and August were the main periods of *Enteromorpha* cover of the Qianliyan Island area, when the July SST was between 20 and 25°C from 2009 to 2016. The longest *Enteromorpha* bloom in the Yellow Sea was the 106 days and the longest time off Qianliyan Island was 54 days in 2012. SSTs in June, July, and August were between 20 and 25°C, and suitable for the growth of *Enteromorpha*. **Figure 3** shows that *Enteromorpha* did not cover the research area, uniformly; nevertheless, it was still found that the July SST and its corresponding CHL were the highest observed for 3 months from 2009 to 2016 (**Figure 2a** and **c**). *Enteromorpha* blooms moved closer to the shore under the action of wind and waves in August (**Figure 2b** and **4**). Site observation data were from *Enteromorpha* covering Qianliyan Island before the blooms (February, March, or May), during the blooms (July) and after the blooms (August or October) in 2010–2016. The SAL of the research area was between 30 and 32 psu, levels most favorable for *Enteromorpha* spore dispersal. After *Enteromorpha* blooms, DO measurements were significantly lower than beforehand. The DIN in August 2015 was higher than that of May. In the INC measurements in August 2016, only the upper layer was higher than that of May. The pH of sea water from the research area from 2010 to 2016 (8 < pH < 8.5) was suitable for release of *Enteromorpha* spores; and after the *Enteromorpha* blooms, the pH and COD declined. *Enteromorpha* outbreaks have introduced heavy metals and petroleum pollution into the Qianliyan Island area and their concentrations have inhibited the growth of *Enteromorpha*. Benthos sampling in July 2013 found high levels of *Enteromorpha* on the seabed near the Qianliyan Island area. **Table 4** shows that the diversity index has generally increased, while the abundance value has decreased significantly. Uniformity has fluctuated from 2010 to 2016 (except in 2011), and this indicates that the environmental quality of the survey area has declined.

### **4. Conclusion**

Island area (**Table 3**) and *wrinkles abalone* was rarely discovered. A site survey examined the average value of phytoplankton, zooplankter, and bentonic organisms from stations 1, 2, 3, and 4

**Item Aug 2010 July 2012 July 2013 Aug 2014** Phytoplankton *D2* – *D2* 0.63 *D2* 0.62 *D2* 0.73

**Time (year) The sea cucumber yield Explain** 2000 5000 kg Two seasons 2011 2500~3000 kg A year

**Table 3.** Annual survey table of the sea cucumber of Qianliyan Island area.

 6.33 g/m2 Local biomass 4.53 g/m2 Local biomass 4.22 g/m2 Local biomass 4.41 g/m2 Local biomass

Zooplankter *D2 D2* 0.90 *D2* 0.75 *D2* 0.66

Bentonic organism *H*′ 0.68 *H*′ 1.89 *H′* 10 *H′* 14

*H*′ 1.98 *H*′ 2.32 *H*′ 2.45 *H*′ 2.05 *d* 2.39 *d* 0.47 *d* 0.53 *d* 0.51 *J* 0.54 *J* 0.83 *J* 0.75 *J* 0.67

*H*′ 1.28 *H*′ 1.23 *H*′ 1.90 *H*′ 2.19 *d* 1.39 *d* 0.84 *d* 1.15 *d* 1.34 *J* 0.62 *J* 0.51 *J* 0.55 *J* 0.72

*d* 0.62 *d* 1.05 *d* – *d* – *J* 0.41 *J* 0.85 *J* – *J* –

(*d*) was declining, and uniformity (*J*) was swinging between the two. There were slight fluctua-

The analyses in Section 2.2 and 2.3 found that June, July, and August were the main periods of *Enteromorpha* cover of the Qianliyan Island area, when the July SST was between

) and degree of dominance (D2

) were increasing, abundance

of benthonic organism has no observational

*, d,* and *J* of benthonic organism have slight

found that the index of diversity (*H′*

**3. Discussion**

**Time**

92 Sea Level Rise and Coastal Infrastructure

data).

tions in phytoplankton and zooplankton; but the *H′*

**Table 4.** Biological community characteristics index comparison table. (D2

fluctuations and **Table 4** shows an upward trend.

Sections 2 and 3, examining the remote sensing data and the site observation data analysis, show that the SST, SAL, pH of the Qianliyan Island area are suitable for *Enteromorpha* growth and spore release. There is a direct relationship between the SST and the strength or weakness of an *Enteromorpha* bloom. After a bloom, pH and COD decline. Results of benthos sampling in July 2013 confirm that *Enteromorpha* settled in the region during June–August. *Enteromorpha* decomposition is a threat to the survival and reproduction of shellfish and zooplankton, and it has an especially acute, lethal effect on abalone and *trepang*. In consecutive years, *Enteromorpha* has covered the Qianliyan Island area, bringing significant effects including DO depression, COD rise, RP, and heavy metals into the research area and polluting the environment. Comparison with the biogenesis characteristics index found that the environmental quality of the survey has declined. Number of *starfish* increased and shellfish, especially abalone, were damaged. This explains why *wrinkles abalone* is hardly to be found at present and *trepang* are decreasing yearly. The subsidence of the *Enteromorpha* and the rise in SSTs are two main reasons for the decline in the ecological environment off the Qianliyan Island area. To protect the national aquatic germ plasm resources of Qianliyan Island, we need to: (1) halt the growth in starfish numbers; (2) salvage the *Enteromorpha* blooms when they cover the Qianliyan Island area; (3) reduce human interference; and (4) improve fishery resources and revegetation of the eco-environment.

### **Acknowledgements**

This work was supported by the National Natural Science Foundation of China (No. 41576032), partially supported financially by international cooperation, CAS, Chinese-foreign cooperation in key projects ("The detection of oil spill and its ecological impact study" No. 133337KYSB20160002); and the "Strategic Priority Research Program" of the CAS (No. XDA11020305). Site monitoring data came from the Yantai Marine Environmental Monitoring Central Station, SOA.

[5] Liu D, Keesing J K, Xing Q, Shi P. World's largest macro algal bloom caused by expansion of seaweed aquaculture in China. Marine Pollution Bulletin. 2009;**58**(6):888-95. DOI:

Impact of *Enteromorpha* Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China

http://dx.doi.org/10.5772/intechopen.71434

95

[6] Zhang Z, Chen YL, Luo F. Temporal and spatial distribution characteristics of *Enteromorpha prolifera* in the south Yellow Sea based on remote sensing data of 2014. Journal of Huaihai Institute of Technology (Natureal Science Edition). 2016;**25**(1):80-85

[7] Zhao WJ, Zhang J, Cui TW, Hao YL, Sun L. *Enteromorpha prolifera* underwater spectral research based on simulation of radiation transmission. Spectroscopy and Spectral Analysis.

[8] Tang JW, Wang XM, Song QJ, Li TJ, Chen JZ, Huang HJ, Ren JP. The statistic inversion algrithms of water constituents for the Huanghai Sea and the East China Sea. Acta

[9] Li Y, Liang G, Yu SM, Chen P. Selection of microwave remte sensing data of monitoring of *Entermorpha prolifera* disaster. Marine Environmental Science. 2011;**30**(5):739-742

[10] Guo J, Guo S. Status and trend of remote sensing study to monitor sea surface oil spill and *enteromorpha*. Journal of Guangxi Academy of Sciences. 2016;**32**(2):73-82

[11] Shen H, William P, Liu QR, He YJ. Detection of macroalgae blooms by complex SAR

[12] Wang JW, Yan BL, Lin AP, Hu JP, Shen SD. Ecological factor research on the growth and induction of spores release in *Enteromorpha prolifera* (Chlorophyta). Marine Science

[13] Zhang XH, Wang ZL, Li RX, Li Y, Wang X. Microscopic observation on population growth and reproduction of *Entromorphra prolifera* under different temperature and

[14] Wang C. Primary Studies on the Harmful Effects and Mechanisms of Ulvaprolifera Green Tide [Thesis]. Qingdao: Institute of Oceanology Chinese Academy of Sciences; 2010

[15] Wang C, Yu RC, Zhou MJ.Effects of the decomposing green macroalga *Ulva* (*Enteromorpha*) *prolifera* on the growth of four red-tide species. Chinese Journal of Oceanology and

[16] Hu CM. A novel ocean color index to detect floating algae in the global oceans. Remote

[17] Sun XX, Du M, Pu YL. A study on interval taking out to extend the life of the electroless nickel plating solution. Periodical of Ocean University of China. 2006;**36**(2):287-290

[18] Norkko A, Bonsdorff E. Altered benthic prey-availability due to episodic oxygen defi-

ciency caused by drifting algal mats. Marine Ecology. 1996;**17**:355-372

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2009;**23**(4):617-626

Bulietin. 2007;**26**(2):60-65

Limnology. 2011;**29**(3):541-546

### **Author details**

Guo Jie1 \*, Zhang Tianlong1,2, Ji Diansheng3 , Mu Yankai1,2, Yu Hongyang4 , Hou Chawei3 and Ji Ling3

\*Address all correspondence to: jguo@yic.ac.cn

1 Key Laboratory of Coastal Zone Environmental Processes of Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences (CAS); Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, Yantai, China

2 University of Chinese Academy of Sciences, Beijing, China

3 Yantai Marine Environmental Monitoring Central Station, State Oceanic Administration (SOA), Yantai, China

4 School of Environmental and Material Engineering, Yantai University, Yantai, Shandong, China

### **References**


[5] Liu D, Keesing J K, Xing Q, Shi P. World's largest macro algal bloom caused by expansion of seaweed aquaculture in China. Marine Pollution Bulletin. 2009;**58**(6):888-95. DOI: http:// dx.doi.org/10.1016/j.marpolbul.2009.01.013

**Acknowledgements**

94 Sea Level Rise and Coastal Infrastructure

**Author details**

(SOA), Yantai, China

\*, Zhang Tianlong1,2, Ji Diansheng3

of Coastal Zone Environmental Processes, Yantai, China

2 University of Chinese Academy of Sciences, Beijing, China

Transactions American Geophysical Union. 2008;**89**:302-303

**115**:C05017. DOI: http://dx.doi.org/10.1029/2009JC005561

Natural Hazards. 2015;**78**:7-21

ing images. Spectroscopy and Spectral Analysis. 2011;**31**:1644-1647

\*Address all correspondence to: jguo@yic.ac.cn

Guo Jie1

Ji Ling3

China

**References**

This work was supported by the National Natural Science Foundation of China (No. 41576032), partially supported financially by international cooperation, CAS, Chinese-foreign cooperation in key projects ("The detection of oil spill and its ecological impact study" No. 133337KYSB20160002); and the "Strategic Priority Research Program" of the CAS (No. XDA11020305). Site monitoring

, Mu Yankai1,2, Yu Hongyang4

, Hou Chawei3

and

data came from the Yantai Marine Environmental Monitoring Central Station, SOA.

1 Key Laboratory of Coastal Zone Environmental Processes of Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences (CAS); Shandong Provincial Key Laboratory

3 Yantai Marine Environmental Monitoring Central Station, State Oceanic Administration

4 School of Environmental and Material Engineering, Yantai University, Yantai, Shandong,

[1] Hu C, He MX. Origin and offshore extent of floating algae in Olympic sailing area. Eos

[2] Hu C, Li D, Chen C, Ge J, Muller-Karger FE, Liu J, He MX. On the recurrent *Ulva prolifera* blooms in the Yellow Sea and East China Sea. Journal of Geophysical Research. 2010;

[3] Xing Q, Zheng X, Shi P, Hao J, Yu D, Liang S, Zhang Y. Monitoring "GreenTide" in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sens-

[4] Xing Q, Tosi L, Braga F, Gao X, Gao M. Interpreting the progressive eutro-phication behind the world's largest macroalgal blooms with water quality and ocean color data.


[19] Liu Q. The Interactions Study between Bloom-Forming *Ulva prolifera* and Phytoplankton in the Yellow Sea [Thesis]. Qingdao: Institute of Oceanology Chinese Academy of Sciences; 2015

**Section 5**

**Coastal Geohazards**

[20] Wu LW, Han XR, Wu T. Effects of heavy metals on the uptake of nitrate by *Ulva prolifera*. China Environmental Science. 2016;**36**(4):1173-1180

**Section 5**

**Coastal Geohazards**

[19] Liu Q. The Interactions Study between Bloom-Forming *Ulva prolifera* and Phytoplankton in the Yellow Sea [Thesis]. Qingdao: Institute of Oceanology Chinese Academy of Sciences;

[20] Wu LW, Han XR, Wu T. Effects of heavy metals on the uptake of nitrate by *Ulva prolifera*.

China Environmental Science. 2016;**36**(4):1173-1180

2015

96 Sea Level Rise and Coastal Infrastructure

**Chapter 7**

Provisional chapter

**Geohazards in the Fjords of Northern Patagonia, Chile**

DOI: 10.5772/intechopen.71435

Geohazards in the Fjords of Northern Patagonia, Chile

A geomorphological analysis of the Comau Fjord was carried out to identify geohazards that are a product of current landform dynamics and processes. The geological setting of the area includes fractured metamorphic and volcanic rocks forming steep hillslopes in an active tectonic context due to the Liquiñe-Ofqui Fault Zone (LOFZ). Geomorphological and hazard mapping was performed using aerial photographs, GIS geoprocessing and fieldwork in January and May 2016 and February 2017. The susceptibility of landsliding was statistically assessed and validated with the inventory of landslides completed during fieldwork. The triggering of geohazards such as landslides and fluvial floods in the study area is associated with high annual precipitation (>5000 mm annually) with a concentration of rainfall that has increased in the last 50 years. Geohazard mapping demonstrated the potential for rock and earth falls, debris flows and river floods, as well as the potential impact of these geohazards on the area's intensive aquaculture industry and a main national highway projected for the eastern flank of the fjord. In a geographical scenario of environmental and territorial change, the present and future human occupation of Comau Fjord's coast constitutes potential hazard and

risk conditions for aquaculture infrastructure and highway users.

considered to be an unresolved social problem [7].

Keywords: geohazard, North Patagonia, Comau Fjord, landslides, GIS

Natural hazards are due to the interaction between environmental and social changes, and the patterns of land use associated with economic development and the processes of urbanization which increase people's exposure and vulnerability [1–6]. In such a context, natural risk is also

> © 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 eproduction in any medium, provided the original work is properly cited.

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

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

María-Victoria Soto, Pablo Sarricolea,

María-Victoria Soto, Pablo Sarricolea, Sergio A. Sepúlveda, Misael Cabello, Ignacio Ibarra, Constanza Molina and

Constanza Molina and Michael Maerker

http://dx.doi.org/10.5772/intechopen.71435

Michael Maerker

Abstract

1. Introduction

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Sergio A. Sepúlveda, Misael Cabello, Ignacio Ibarra,

### **Geohazards in the Fjords of Northern Patagonia, Chile** Geohazards in the Fjords of Northern Patagonia, Chile

DOI: 10.5772/intechopen.71435

María-Victoria Soto, Pablo Sarricolea, Sergio A. Sepúlveda, Misael Cabello, Ignacio Ibarra, Constanza Molina and Michael Maerker María-Victoria Soto, Pablo Sarricolea, Sergio A. Sepúlveda, Misael Cabello, Ignacio Ibarra, Constanza Molina and

Additional information is available at the end of the chapter Michael Maerker Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71435

#### Abstract

A geomorphological analysis of the Comau Fjord was carried out to identify geohazards that are a product of current landform dynamics and processes. The geological setting of the area includes fractured metamorphic and volcanic rocks forming steep hillslopes in an active tectonic context due to the Liquiñe-Ofqui Fault Zone (LOFZ). Geomorphological and hazard mapping was performed using aerial photographs, GIS geoprocessing and fieldwork in January and May 2016 and February 2017. The susceptibility of landsliding was statistically assessed and validated with the inventory of landslides completed during fieldwork. The triggering of geohazards such as landslides and fluvial floods in the study area is associated with high annual precipitation (>5000 mm annually) with a concentration of rainfall that has increased in the last 50 years. Geohazard mapping demonstrated the potential for rock and earth falls, debris flows and river floods, as well as the potential impact of these geohazards on the area's intensive aquaculture industry and a main national highway projected for the eastern flank of the fjord. In a geographical scenario of environmental and territorial change, the present and future human occupation of Comau Fjord's coast constitutes potential hazard and risk conditions for aquaculture infrastructure and highway users.

Keywords: geohazard, North Patagonia, Comau Fjord, landslides, GIS

### 1. Introduction

Natural hazards are due to the interaction between environmental and social changes, and the patterns of land use associated with economic development and the processes of urbanization which increase people's exposure and vulnerability [1–6]. In such a context, natural risk is also considered to be an unresolved social problem [7].

© 2018 The Author(s). Licensee IntechOpen. 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 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 eproduction in any medium, provided the original work is properly cited.

Climate change scenarios are a new challenge for the study of natural hazards, risks and adaptation to hazards [8–11]. For example, during the period between 1900 and 2013, floods were the most frequent natural disaster, affecting more people than any other event of natural origin, [12], phenomena that intensively affected urban and rural areas alike [13].

Kappes et al. [14] suggest that the different types of hazards, scales of analysis, magnitudes of measures and risks constitute a scenario of multiple risks, which are also associated with environmental changes and human impact. Furthermore, human impact also functions as an agent of change in the processes and behavior of morphological systems [15].

Glacial and periglacial areas are geomorphological systems of high sensibility to climatic agents [16, 17] that are accentuated by the marine influence of the South Pacific Ocean. The climate changes modeled for the sector [18–21] that add future productive interventions particular to anthropic agents suggest an even more complex future for natural and territorial systems [22–24].

The most frequent natural hazards in mountainous environments are shallow landslides, associated with a landscape modeled by glacial and tectonic processes, such as those identified in the fjords of Norway [25–27]. Furthermore, large tsunamis from landslides have also been registered in Norway's fjords.

The purpose of this study is to establish the geomorphic conditions in the Comau Fjord, the associated natural hazards, and the impacts on vulnerable areas caused by these hazards, including the direct impact on a highway currently under construction and the potential population of the area.

The Comau Fjord is located directly on the Liquiñe-Ofqui Fault Zone (LOFZ, Figures 1 and 2), a regional fault system parallel to the Nazca and South American tectonic plate boundary, with activity during the late Cenozoic era [28, 29]. The LOFZ is a seismically active fault system [28, 29] in Chile's Northern Patagonia, which in 2007 generated a seismic swarm in the Aysén Fjord

with the LOFZ. The volcano experienced a significant eruption in 2008 [33], and its ash was

Though detailed geological studies of the area are not available, the geological map of [34] shows that the predominant lithological units are Jurassic intrusions (diorites, gabbros, monzodiorites), metamorphic rocks of schist with amphibolitic characteristics of the Paleozoic

Climatically, the region is subjected to winds from the west and the alternating cold front systems associated with a subpolar low-pressure zone, creating temperate oceanic and subpolar climates [35]. Extreme climate scenarios, from the most optimistic to the most severe (B2 and A1F1) of the IPCC [36] suggest that at the end of the twenty-first century (1971–2100) there will be a notable advance of Mediterranean climates with mild summers as far south as 46�S [37], while maintaining a zone of a more oceanic climate type (Cfb). Sarricolea and Figueroa [38] show precipitation diminishing by more than 200 mm and temperatures increasing up to 4�C of average annual temperature until the end of the twenty-first century. Using all models

of the AR5 for precipitation and temperature, the following information was obtained:

nami wave reached 10 m [30–32]. The Chaitén Volcano (42�50<sup>0</sup>

W), with a principal earthquake (Mw 6.2), landslides and tsunamis. The tsu-

S/72�32<sup>0</sup>

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435 101

W) is also associated

(45�25<sup>0</sup>

S/72�581<sup>0</sup>

Figure 1. Study area.

identified in the zone of Comau (Figure 1).

era and Quaternary volcanic rock.

### 2. Study area

The Gulf of Ancud and the Comau Fjord are part of the Andean landscape, with marked tectonic, volcanic, and glacial activity and climatic-environmental changes from the Pleistocene era to the present. The Comau Fjord is located between the mouth of the rivers/fjords Quintupeu and Vodudahue, covering a distance approximately 24 km long, with a maximum and minimum width of 11 and 2 km, respectively. The flanks of the fjord have a steep gradient (>30) with heights that reach above 1000 m.a.s.l. (Figure 1).

Furthermore, the Comau Fjord area is scarcely populated (300 inhabitants during the year) with an incipient aquaculture industry oriented to large international markets. The area's connectivity is poor, with access only by sea (private and with State subsidy). Nevertheless the construction of a highway on the fjord's eastern flank is projected and currently under design that could stimulate development of the local and regional economy. Because of the incipient aquaculture industry in the fjord, an increase in population and economic activities in the area can be projected, but these activities will be located in an area with threats of natural hydro-meteorological, seismic-tectonic and oceanographic events of an Andean Patagonian fjord with a meso-tidal regime.

Figure 1. Study area.

Climate change scenarios are a new challenge for the study of natural hazards, risks and adaptation to hazards [8–11]. For example, during the period between 1900 and 2013, floods were the most frequent natural disaster, affecting more people than any other event of natural

Kappes et al. [14] suggest that the different types of hazards, scales of analysis, magnitudes of measures and risks constitute a scenario of multiple risks, which are also associated with environmental changes and human impact. Furthermore, human impact also functions as an

Glacial and periglacial areas are geomorphological systems of high sensibility to climatic agents [16, 17] that are accentuated by the marine influence of the South Pacific Ocean. The climate changes modeled for the sector [18–21] that add future productive interventions particular to anthropic agents suggest an even more complex future for natural and territorial

The most frequent natural hazards in mountainous environments are shallow landslides, associated with a landscape modeled by glacial and tectonic processes, such as those identified in the fjords of Norway [25–27]. Furthermore, large tsunamis from landslides have also been

The purpose of this study is to establish the geomorphic conditions in the Comau Fjord, the associated natural hazards, and the impacts on vulnerable areas caused by these hazards, including the direct impact on a highway currently under construction and the potential

The Gulf of Ancud and the Comau Fjord are part of the Andean landscape, with marked tectonic, volcanic, and glacial activity and climatic-environmental changes from the Pleistocene era to the present. The Comau Fjord is located between the mouth of the rivers/fjords Quintupeu and Vodudahue, covering a distance approximately 24 km long, with a maximum and minimum width of 11 and 2 km, respectively. The flanks of the fjord have a steep gradient

Furthermore, the Comau Fjord area is scarcely populated (300 inhabitants during the year) with an incipient aquaculture industry oriented to large international markets. The area's connectivity is poor, with access only by sea (private and with State subsidy). Nevertheless the construction of a highway on the fjord's eastern flank is projected and currently under design that could stimulate development of the local and regional economy. Because of the incipient aquaculture industry in the fjord, an increase in population and economic activities in the area can be projected, but these activities will be located in an area with threats of natural hydro-meteorological, seismic-tectonic and oceanographic events of an Andean Patagonian

(>30) with heights that reach above 1000 m.a.s.l. (Figure 1).

origin, [12], phenomena that intensively affected urban and rural areas alike [13].

agent of change in the processes and behavior of morphological systems [15].

systems [22–24].

registered in Norway's fjords.

100 Sea Level Rise and Coastal Infrastructure

fjord with a meso-tidal regime.

population of the area.

2. Study area

The Comau Fjord is located directly on the Liquiñe-Ofqui Fault Zone (LOFZ, Figures 1 and 2), a regional fault system parallel to the Nazca and South American tectonic plate boundary, with activity during the late Cenozoic era [28, 29]. The LOFZ is a seismically active fault system [28, 29] in Chile's Northern Patagonia, which in 2007 generated a seismic swarm in the Aysén Fjord (45�25<sup>0</sup> S/72�581<sup>0</sup> W), with a principal earthquake (Mw 6.2), landslides and tsunamis. The tsunami wave reached 10 m [30–32]. The Chaitén Volcano (42�50<sup>0</sup> S/72�32<sup>0</sup> W) is also associated with the LOFZ. The volcano experienced a significant eruption in 2008 [33], and its ash was identified in the zone of Comau (Figure 1).

Though detailed geological studies of the area are not available, the geological map of [34] shows that the predominant lithological units are Jurassic intrusions (diorites, gabbros, monzodiorites), metamorphic rocks of schist with amphibolitic characteristics of the Paleozoic era and Quaternary volcanic rock.

Climatically, the region is subjected to winds from the west and the alternating cold front systems associated with a subpolar low-pressure zone, creating temperate oceanic and subpolar climates [35]. Extreme climate scenarios, from the most optimistic to the most severe (B2 and A1F1) of the IPCC [36] suggest that at the end of the twenty-first century (1971–2100) there will be a notable advance of Mediterranean climates with mild summers as far south as 46�S [37], while maintaining a zone of a more oceanic climate type (Cfb). Sarricolea and Figueroa [38] show precipitation diminishing by more than 200 mm and temperatures increasing up to 4�C of average annual temperature until the end of the twenty-first century. Using all models of the AR5 for precipitation and temperature, the following information was obtained:

3. Methodology

was completed.

The methodological focus was based on the geomorphology and GIS processes carried out to

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435 103

Geomorphological mapping: This was undertaken by means of aerial photograph interpretation and complemented with the geological map at scale 1:250,000 [34]. Due to difficult access to the study area, the high slope angle and elevation of both sides of the fjord, boat and helicopter access were used to examine landforms and validate the geomorphological mapping. The fieldwork was done in January and May 2016 and February 2017. A geomorphological classification for Andean cordillera environments was used [39], combined with lithology and morpho-structure [40]. Furthermore, an inventory of the fjord's hanging lakes and glaciers

GIS and proxies for modeling topographic indexes: The methodology of Märker et al. [41–43] was used to elaborate a high resolution Digital Elevation Model (DEM) (Alos/Prism 10 m o SRTM 25x) hydrologically corrected according to the Planchón and Darboux algorithm [44], reprocessed for analysis of terrain using SAGA GIS. Processes of hydrological erosion were modeled applying the following indexes: the Stream Power Index (SPI), the Transport Capacity Index (TCI) and the Topographic Wetness Index (TWI). The SPI was used to identify susceptibility to erosion and transport of sediments frequently accumulated in turbulent flows. This model describes the effects of the processes of fluvial lineal erosion and stream incisions, such as gullies, ravines and lateral erosion of beds. The Transport Capacity Index (TCI) was used to indicate areas prone to laminar erosion, sediment transport and processes of deposition. The Topographic Wetness Index (TWI) provides information on the accumulation of water and soil saturation around flat terrain units or topographical depressions, suggesting processes of surface runoff with substrate saturation and areas susceptible to flooding. This index was used to estimate inundation zones along fluvial riverbeds and ravines. Furthermore, modeling of topographical data using GIS tools was carried out to complement the geomorphological analysis,

especially for those parts of the mountain on which fieldwork could not be done.

Landslide inventory: A photointerpretation of aerial photographs (1982, 1997, 1:20,000 and 1:70,000) and Google Earth was completed and validated by fieldwork. Landslides were

establish the natural hazard conditions using the following scheme:

Figure 2. Geomorphological map with an inventory of landslides, glaciers and hanging lakes.

The temperature will increase in the RCP 2.6 and RCP 8.5 scenarios in the same way until 2020. Subsequently in the RCP 2.6 scenario (and until the end of the twenty-first century), temperatures will establish 1.5C above the average temperatures for the twentieth century. While the RCP 8.5 continues increasing until the end of the twenty-first century with temperatures 3.7C above those registered in the twentieth century.

Precipitation does not show significant differences between the RCP 2.5 and RCP 8.5 scenarios until 2050, but toward the end of the twenty-first century, the RCP 2.6 maintains the amounts of precipitation, while the RCP 8.5 decreases by 200 mm.

### 3. Methodology

The temperature will increase in the RCP 2.6 and RCP 8.5 scenarios in the same way until 2020. Subsequently in the RCP 2.6 scenario (and until the end of the twenty-first century), temperatures will establish 1.5C above the average temperatures for the twentieth century. While the RCP 8.5 continues increasing until the end of the twenty-first century with temperatures 3.7C

Figure 2. Geomorphological map with an inventory of landslides, glaciers and hanging lakes.

Precipitation does not show significant differences between the RCP 2.5 and RCP 8.5 scenarios until 2050, but toward the end of the twenty-first century, the RCP 2.6 maintains the amounts

above those registered in the twentieth century.

102 Sea Level Rise and Coastal Infrastructure

of precipitation, while the RCP 8.5 decreases by 200 mm.

The methodological focus was based on the geomorphology and GIS processes carried out to establish the natural hazard conditions using the following scheme:

Geomorphological mapping: This was undertaken by means of aerial photograph interpretation and complemented with the geological map at scale 1:250,000 [34]. Due to difficult access to the study area, the high slope angle and elevation of both sides of the fjord, boat and helicopter access were used to examine landforms and validate the geomorphological mapping. The fieldwork was done in January and May 2016 and February 2017. A geomorphological classification for Andean cordillera environments was used [39], combined with lithology and morpho-structure [40]. Furthermore, an inventory of the fjord's hanging lakes and glaciers was completed.

GIS and proxies for modeling topographic indexes: The methodology of Märker et al. [41–43] was used to elaborate a high resolution Digital Elevation Model (DEM) (Alos/Prism 10 m o SRTM 25x) hydrologically corrected according to the Planchón and Darboux algorithm [44], reprocessed for analysis of terrain using SAGA GIS. Processes of hydrological erosion were modeled applying the following indexes: the Stream Power Index (SPI), the Transport Capacity Index (TCI) and the Topographic Wetness Index (TWI). The SPI was used to identify susceptibility to erosion and transport of sediments frequently accumulated in turbulent flows. This model describes the effects of the processes of fluvial lineal erosion and stream incisions, such as gullies, ravines and lateral erosion of beds. The Transport Capacity Index (TCI) was used to indicate areas prone to laminar erosion, sediment transport and processes of deposition. The Topographic Wetness Index (TWI) provides information on the accumulation of water and soil saturation around flat terrain units or topographical depressions, suggesting processes of surface runoff with substrate saturation and areas susceptible to flooding. This index was used to estimate inundation zones along fluvial riverbeds and ravines. Furthermore, modeling of topographical data using GIS tools was carried out to complement the geomorphological analysis, especially for those parts of the mountain on which fieldwork could not be done.

Landslide inventory: A photointerpretation of aerial photographs (1982, 1997, 1:20,000 and 1:70,000) and Google Earth was completed and validated by fieldwork. Landslides were identified by morphological evidence, according to the criteria of Náquira [45], Sepúlveda and Serey [30] and Sepúlveda et al. [32–47] for fjord environments. Vegetated landslides were also identified that occurred in the past but at unknown dates.

hillslope instability are debris cones and localized slopes at the base of the cirques as well as on

Relevant factors of geomorphology are the numerous fractures and structural lineaments related with the LOZF that are identified in the granite, metamorphic and volcanic rock

With regards to alluvial processes, the geomorphological map (Figure 2) shows that alluvial fans on the riverbeds of the fjord are almost inexistent given that the gradient and morphology of the fjord are unfavorable for their development, except for three hydrographic basins, with

fans ranges from ~300 to ~750 m (i.e. linear distance from the apex to the distal zone). On the other hand, in the eastern zone of the fjord, macro tidal fan deltas have formed associated with the three principal Andean hydrographic basins of the Cahuelmó, Huinay and Vodudahue rivers. These are large alluvial fans in a macro tidal environment, formed by coalesced lobes of gravel [53], except for Cahuelmó, which is itself a delta, according to the predominant sandy

Eight hanging lakes were identified on the eastern flank of the fjord on steep slopes with dense native forests (Figures 2 and 3). These are systems of three and four interconnected lakes, filled

Slope Area km2 Area % Horizontal (0–2) 3.96 1.63 Gentle (2.1–5) 3.96 1.62 Moderate (5.1–10) 10.81 4.43 Strong (10.1–20) 39.06 16.02 Very strong to moderately steep (20.1–30) 60.92 24.99 Steep (30.1–45) 88.74 36.39 Very steep (>45) 36.4 14.93

Figure 3. Hanging lakes. (a) Image showing the border of the cirque (red line) and two connected hanging lakes developed as the glacier retreats. (b) Lateral and oblique view of the hanging lakes on massive and fractured igneous

, that discharge on the western shore of the fjord. The size of these alluvial

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435 105

Total area km<sup>2</sup>

: 243.84

(Figure 2). These factors were incorporated in landslide modeling.

the hillslopes of the fjord.

surfaces over ~15km<sup>2</sup>

sedimentology identified in fieldwork.

Table 1. Classification of slopes on the Comau fjord.

rock, and glacier at the back.

Landslide susceptibility: The analysis of landslide susceptibility was carried out using the bivariate statistical analysis of Van Westen [48], applied by Molina [49], using as variables the gradient, orientation and height of the hillslope, curvature and profile, distance to the drainage networks and to the faults, density of drainage and faults/lineations, and the lithological units as evidence of geomorphological, hydrologic and geological characteristics of the fjord. The method requires a landslide inventory. The landslide susceptibility map was developed using weighing factors according to the method of Dahal et al. [50], dividing the factors in representative classes of the study area and intersecting with the inventory results. Weights were assigned as a function of landslide density found in each pixel factor. The ranges were reclassified for the study zone by Náquira [45] as high susceptibility, with 35% of the highest weighted values; moderate susceptibility, represented between 35 and 62%; and low susceptibility, between 62 and 100% of the weight [49].

Gutenberg-Richter Law and seismic activity: This was used to identify seismic interplate continental or cortical events occurring in the area (41�27<sup>0</sup> - 43�30<sup>0</sup> S), according to the data from the USGS in the period 1919–2016. The model connects the frequency and the magnitude of earthquakes [51]. This information is used only as an indicator of recurrent superficial seismic activity associated with the LOFZ and as a potential trigger for landslides and tsunamis.

The results of the Geohazard map are subject to uncertainty given the variability of precipitation and temperature in a climate change scenario, which was not modeled in this study.

### 4. Results and discussion

#### 4.1. Geomorphological mapping

The geomorphological map shows a predominance of hillslopes of metamorphic and plutonic rock [52] that have been classified as active slopes because of the strong lineal incision, the presence of free-faces with several discontinuities such as joints, faults and fractures creating planes of weakness around rocky slopes in the zone of seasonal nivation, and the presence of talus, covered by dense vegetation of Pilgerodendron uviferum, with slopes above 30� (the altitude limit of vegetation has been identified as 1000 m.a.s.l.). A slope classification according to thresholds of morphogenetic processes (Table 1) was applied demonstrating that 61% have a 20�–30� slope and more than 45� of incline. Such measures favor processes of slope dynamics by gravitational effect, which are further favored by the high rainfall distributed throughout the year (5000 mm/year).

Numerous individual cirques and systems of coalesced cirques on both flanks of the fjord are the inherited forms of glacial excavation. The flanks' altitudinal slopes oscillate between ~600 and ~1000 m.a.s.l. (Figure 2) with respect to the base of the fjord. Landforms indicating active hillslope instability are debris cones and localized slopes at the base of the cirques as well as on the hillslopes of the fjord.

Relevant factors of geomorphology are the numerous fractures and structural lineaments related with the LOZF that are identified in the granite, metamorphic and volcanic rock (Figure 2). These factors were incorporated in landslide modeling.

With regards to alluvial processes, the geomorphological map (Figure 2) shows that alluvial fans on the riverbeds of the fjord are almost inexistent given that the gradient and morphology of the fjord are unfavorable for their development, except for three hydrographic basins, with surfaces over ~15km<sup>2</sup> , that discharge on the western shore of the fjord. The size of these alluvial fans ranges from ~300 to ~750 m (i.e. linear distance from the apex to the distal zone). On the other hand, in the eastern zone of the fjord, macro tidal fan deltas have formed associated with the three principal Andean hydrographic basins of the Cahuelmó, Huinay and Vodudahue rivers. These are large alluvial fans in a macro tidal environment, formed by coalesced lobes of gravel [53], except for Cahuelmó, which is itself a delta, according to the predominant sandy sedimentology identified in fieldwork.

Eight hanging lakes were identified on the eastern flank of the fjord on steep slopes with dense native forests (Figures 2 and 3). These are systems of three and four interconnected lakes, filled


Table 1. Classification of slopes on the Comau fjord.

identified by morphological evidence, according to the criteria of Náquira [45], Sepúlveda and Serey [30] and Sepúlveda et al. [32–47] for fjord environments. Vegetated landslides were also

Landslide susceptibility: The analysis of landslide susceptibility was carried out using the bivariate statistical analysis of Van Westen [48], applied by Molina [49], using as variables the gradient, orientation and height of the hillslope, curvature and profile, distance to the drainage networks and to the faults, density of drainage and faults/lineations, and the lithological units as evidence of geomorphological, hydrologic and geological characteristics of the fjord. The method requires a landslide inventory. The landslide susceptibility map was developed using weighing factors according to the method of Dahal et al. [50], dividing the factors in representative classes of the study area and intersecting with the inventory results. Weights were assigned as a function of landslide density found in each pixel factor. The ranges were reclassified for the study zone by Náquira [45] as high susceptibility, with 35% of the highest weighted values; moderate susceptibility, represented between 35 and 62%; and low suscepti-

Gutenberg-Richter Law and seismic activity: This was used to identify seismic interplate

data from the USGS in the period 1919–2016. The model connects the frequency and the magnitude of earthquakes [51]. This information is used only as an indicator of recurrent superficial seismic activity associated with the LOFZ and as a potential trigger for landslides

The results of the Geohazard map are subject to uncertainty given the variability of precipitation and temperature in a climate change scenario, which was not modeled in this study.

The geomorphological map shows a predominance of hillslopes of metamorphic and plutonic rock [52] that have been classified as active slopes because of the strong lineal incision, the presence of free-faces with several discontinuities such as joints, faults and fractures creating planes of weakness around rocky slopes in the zone of seasonal nivation, and the presence of talus, covered by dense vegetation of Pilgerodendron uviferum, with slopes above 30� (the altitude limit of vegetation has been identified as 1000 m.a.s.l.). A slope classification according to thresholds of morphogenetic processes (Table 1) was applied demonstrating that 61% have a 20�–30� slope and more than 45� of incline. Such measures favor processes of slope dynamics by gravitational effect, which are further favored by the high rainfall distributed throughout

Numerous individual cirques and systems of coalesced cirques on both flanks of the fjord are the inherited forms of glacial excavation. The flanks' altitudinal slopes oscillate between ~600 and ~1000 m.a.s.l. (Figure 2) with respect to the base of the fjord. Landforms indicating active


S), according to the

identified that occurred in the past but at unknown dates.

104 Sea Level Rise and Coastal Infrastructure

bility, between 62 and 100% of the weight [49].

and tsunamis.

4. Results and discussion

4.1. Geomorphological mapping

the year (5000 mm/year).

continental or cortical events occurring in the area (41�27<sup>0</sup>

Figure 3. Hanging lakes. (a) Image showing the border of the cirque (red line) and two connected hanging lakes developed as the glacier retreats. (b) Lateral and oblique view of the hanging lakes on massive and fractured igneous rock, and glacier at the back.

by a fusion of glacial waters from the receding glacier identified in the last 30 years. These lakes are associated with glacier cirque, located between ~1200 and 1800 m.a.s.l., with a S-SE orientation. In the glacial lakes there are only rocky thresholds, without moraines; these glacial lakes supply water to the hanging falls. There is no glacial lake outburst floods (GLOF) observed.

The Topographic Wetness Index (TWI), as can be seen in Figure 4C, shows areas with the highest potential of water accumulation are the valley bottoms of the principal catchment basins (Cahuelmo, Huinay, Vododahué), where the low fluvial terraces (Figure 2) present flood risk. The TWI results in the study area clearly indicate the presence of hanging lakes and cirque glaciers. The western slope of the fjord presents scarce areas of water accumulation because of the steepness of its slope, but a moderate potential for saturation that can be associated with areas prone to landslides. The results of the index's application were validated

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435 107

The model of landslide susceptibility shown in Figure 5 demonstrates high susceptibility on the eastern side of the fjord, which can be associated with steep slopes of metamorphic and igneous rock, a factor that Oppikofer et al. [27], Blikra et al. [25] indicate as highly favorable for this type of phenomena. The model included the density of structural lineaments (faults) and

Figure 5 also shows the location of debris flows, which show spatial agreement with the SPI and the TCI (Figure 4), associated with hydrologic action in micro catchment areas and ravines. On the eastern hillslope, the presence of receding glaciers and their associated lakes constitute potential areas for debris flows. There are also a large number of coalesced cirque glaciers. Consequently, the results of landslide susceptibility, geomorphology and GIS Index

The rock fall features cover large sections of the slopes as evidence of geological processes that have been masked by the vegetation of the austral forest and demonstrate probable synergic action of the LOZF's tectonic action (Figure 6A and B). The predominant forms are earth and rock slides, observed as much of plutonic as of metamorphic rock (Figure 6C and D). Many landslides were found to be covered by vegetation, principally ferns, pioneer vegetation that date from an indeterminate time in the past (Figure 6E); P. uviferum forests take decades to grow and up to 200 years to reach adult size (These forms were classified in the inventory as vegetated earth and rock slide and supply the principal evidence of dynamic processes on the slopes of the fjord. On the slopes associated with volcanic rock, above all on the western side,

The debris flows identified do not present a pattern associated with a specific type of rock, rather they are associated with ravines and streams in micro catchments (Figure 7) and glacial

Areas subjected to fluvial inundation are identified in the mid and lower sections of the principal valley and sub-catchment area tributaries (Figure 5). The morphology of fluvial terraces eroded by historic fluvial rises is evidence of this activity, above all in the most distal parts where the Holocene fluvial terraces still present evidence of seasonal fluvial action

lakes, which contribute to the propensity to landsliding in the eastern zone.

in fieldwork by helicopter flight.

coincide.

retreat catchments.

5. Geohazards: landslide and floods

rocks and earth slides predominate (Figure 6F).

#### 4.2. GIS and proxies for modeling topographic indexes

The results showed that because of the steep gradient of Comau Fjord's western hillslope (slope > 45), high levels of lineal erosion were generated, identified in Figure 4A. The areas with the most significant processes of lineal erosion (red) are principally associated with stream incisions developed on the granite and metamorphic rock slopes with structural lineaments to which are associated the ravines that connect the cirque glaciers and the hanging lakes. In addition, these ravines dissect the fjord walls, permitting the transfer of runoff and detritus from the cirques up to the fjord's base. The fluvial valleys of the Vodudahue, Huinay and Cahuelmó present elevated ranges of lineal and lateral erosion, associated with the development of a drainage network of glacial and periglacial Andean catchment areas. By contrast, areas with low SPI show good agreement with ridges of divides and fluvial plains, where the slope tends to be lower than 5.

The results of the Transport Capacity Index (TCI), in Figure 4B shows the marked influence of the gradient on the slopes and the adjacent valleys; these are areas that present the surfaces most affected by laminar erosion (red colors). These areas show evidence of landslides and talus in the slopes, as a response to the high susceptibility to the soil erosion of the hillslopes. The red lines on the bottom of the valley and ravines show the action of the water. There are marked incisions in the ravines and streams, many of which come from glacier-lake or fluvial valley systems, with distal deposits, alluvial fans or fan deltas. Just as with SPI, areas with low TCI are associated with fluvial plains and fjord divides.

Figure 4. A. Stream Power Index (SPI), B. Transport Capacity Index (TCI), C. Topographic Wetness Index (TWI).

The Topographic Wetness Index (TWI), as can be seen in Figure 4C, shows areas with the highest potential of water accumulation are the valley bottoms of the principal catchment basins (Cahuelmo, Huinay, Vododahué), where the low fluvial terraces (Figure 2) present flood risk. The TWI results in the study area clearly indicate the presence of hanging lakes and cirque glaciers. The western slope of the fjord presents scarce areas of water accumulation because of the steepness of its slope, but a moderate potential for saturation that can be associated with areas prone to landslides. The results of the index's application were validated in fieldwork by helicopter flight.

### 5. Geohazards: landslide and floods

by a fusion of glacial waters from the receding glacier identified in the last 30 years. These lakes are associated with glacier cirque, located between ~1200 and 1800 m.a.s.l., with a S-SE orientation. In the glacial lakes there are only rocky thresholds, without moraines; these glacial lakes supply water to the hanging falls. There is no glacial lake outburst floods (GLOF)

The results showed that because of the steep gradient of Comau Fjord's western hillslope (slope > 45), high levels of lineal erosion were generated, identified in Figure 4A. The areas with the most significant processes of lineal erosion (red) are principally associated with stream incisions developed on the granite and metamorphic rock slopes with structural lineaments to which are associated the ravines that connect the cirque glaciers and the hanging lakes. In addition, these ravines dissect the fjord walls, permitting the transfer of runoff and detritus from the cirques up to the fjord's base. The fluvial valleys of the Vodudahue, Huinay and Cahuelmó present elevated ranges of lineal and lateral erosion, associated with the development of a drainage network of glacial and periglacial Andean catchment areas. By contrast, areas with low SPI show good agreement with ridges of divides and fluvial plains, where the slope tends to be lower than 5.

The results of the Transport Capacity Index (TCI), in Figure 4B shows the marked influence of the gradient on the slopes and the adjacent valleys; these are areas that present the surfaces most affected by laminar erosion (red colors). These areas show evidence of landslides and talus in the slopes, as a response to the high susceptibility to the soil erosion of the hillslopes. The red lines on the bottom of the valley and ravines show the action of the water. There are marked incisions in the ravines and streams, many of which come from glacier-lake or fluvial valley systems, with distal deposits, alluvial fans or fan deltas. Just as with SPI, areas with low

Figure 4. A. Stream Power Index (SPI), B. Transport Capacity Index (TCI), C. Topographic Wetness Index (TWI).

4.2. GIS and proxies for modeling topographic indexes

TCI are associated with fluvial plains and fjord divides.

observed.

106 Sea Level Rise and Coastal Infrastructure

The model of landslide susceptibility shown in Figure 5 demonstrates high susceptibility on the eastern side of the fjord, which can be associated with steep slopes of metamorphic and igneous rock, a factor that Oppikofer et al. [27], Blikra et al. [25] indicate as highly favorable for this type of phenomena. The model included the density of structural lineaments (faults) and lakes, which contribute to the propensity to landsliding in the eastern zone.

Figure 5 also shows the location of debris flows, which show spatial agreement with the SPI and the TCI (Figure 4), associated with hydrologic action in micro catchment areas and ravines. On the eastern hillslope, the presence of receding glaciers and their associated lakes constitute potential areas for debris flows. There are also a large number of coalesced cirque glaciers. Consequently, the results of landslide susceptibility, geomorphology and GIS Index coincide.

The rock fall features cover large sections of the slopes as evidence of geological processes that have been masked by the vegetation of the austral forest and demonstrate probable synergic action of the LOZF's tectonic action (Figure 6A and B). The predominant forms are earth and rock slides, observed as much of plutonic as of metamorphic rock (Figure 6C and D). Many landslides were found to be covered by vegetation, principally ferns, pioneer vegetation that date from an indeterminate time in the past (Figure 6E); P. uviferum forests take decades to grow and up to 200 years to reach adult size (These forms were classified in the inventory as vegetated earth and rock slide and supply the principal evidence of dynamic processes on the slopes of the fjord. On the slopes associated with volcanic rock, above all on the western side, rocks and earth slides predominate (Figure 6F).

The debris flows identified do not present a pattern associated with a specific type of rock, rather they are associated with ravines and streams in micro catchments (Figure 7) and glacial retreat catchments.

Areas subjected to fluvial inundation are identified in the mid and lower sections of the principal valley and sub-catchment area tributaries (Figure 5). The morphology of fluvial terraces eroded by historic fluvial rises is evidence of this activity, above all in the most distal parts where the Holocene fluvial terraces still present evidence of seasonal fluvial action

(Figures 5 and 7). The fan delta morphology (Figure 8) that drains the principal valleys also shows evidence of flooding due to tidal changes. The results of the TWI (Figure 4) permitted analysis of areas difficult to access, and these results were also validated by helicopter flight in

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435 109

Figure 6. Inventory of landslides. A–B: vegetated rock fall area and present day rock fall feature. C: earth and rockslides on the eastern side of fjord. D: the lower deposits associated to the great event occurred in 1957. E: vegetated earth and

Figure 7. Oblique view of the Vodudahue river valley showing low alluvial terraces prone to flooding, which corresponds to areas with high Topographic Wetness Index (TWI). On the right side of the figure a fan delta is shown with a meso-tidal regime in contact with the fjord. The figure also shows granitic and vegetated hillslopes that border both sides

rockslide. F: rocks and earth (soil) slides on volcanic slopes (See location of the photos in Figure 5).

of the valley. Source: Photography taken by the authors during helicopter flight.

January 2016.

Figure 5. Geohazard map: landslide susceptibility and inventory, rivers and tsunami flood areas.

(Figures 5 and 7). The fan delta morphology (Figure 8) that drains the principal valleys also shows evidence of flooding due to tidal changes. The results of the TWI (Figure 4) permitted analysis of areas difficult to access, and these results were also validated by helicopter flight in January 2016.

Figure 6. Inventory of landslides. A–B: vegetated rock fall area and present day rock fall feature. C: earth and rockslides on the eastern side of fjord. D: the lower deposits associated to the great event occurred in 1957. E: vegetated earth and rockslide. F: rocks and earth (soil) slides on volcanic slopes (See location of the photos in Figure 5).

Figure 7. Oblique view of the Vodudahue river valley showing low alluvial terraces prone to flooding, which corresponds to areas with high Topographic Wetness Index (TWI). On the right side of the figure a fan delta is shown with a meso-tidal regime in contact with the fjord. The figure also shows granitic and vegetated hillslopes that border both sides of the valley. Source: Photography taken by the authors during helicopter flight.

Figure 5. Geohazard map: landslide susceptibility and inventory, rivers and tsunami flood areas.

108 Sea Level Rise and Coastal Infrastructure

Figure 8. Fan delta and changes of tide in Huinay river outlet (A) High tide, January 28 (15:30 hrs.), (B) Low tide, January 22 (20:32 hrs.). Source: González [54].

the regression analysis and the data is low (0.83), and does not allow for quantifying the return period, a recurrent condition of seismic activity in the LOFZ can be established. Furthermore, this activity is a factor in the geohazard of landslips and tsunamis, as evidenced by Sepúlveda

Table 2. Parameters of the Gutenberg-Richter Law (Log N =a+bM, where N is number of earthquakes and M the

Type of seismic event N de seismic events Magnitude Depth Coefficient of

Complete register 142 2.9 7.2 0 165.4 5.012 0.670 Intraplate continental seismic events 16 3 7.2 7.8 36.2 2.859 0.440 Interplate seismic events 85 3.4 7 7.8 47.5 4.674 0.631 Intraplate oceanic seismic events 13 3.9 6 52.2 165.4 3.505 0.585 Volcanic seismic events 28 2.9 5.2 0 10 8.539 1.655

lineal regression

111

Min. Max. Min. Max. a b

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435

In the context of potential activation of seismic activity that could generate a tsunami, a map was created with areas affected by flooding, adding three wave heights, according to the standards of the State of Chile and the known event that occurred in the Aysén Fjord in 2007. Figure 9 shows the areas that are potentially susceptible and vulnerable to flooding by tsunami. The State of Chile has defined 30 m above sea level as the safety zone [55]; the wave height of 50 m is the maximum level modeled for the Aysén Fjord [56]. To this level, 7

The risk of landslides in fjords has been studied in the northern hemisphere because of its impact on human life and economic loss [26, 27, 57], but in Chile the region of the fjords constitutes an isolated territory uncoupled from the urban-regional-national system, unpopulated and lacking

This situation is in the process of changing with the construction of a land route to foment inter-regional integration. The route is projected to benefit the growth of the aquaculture industry, above all for the export of salmon and the development of economic activity in the valleys of the Huinay and Vodudahue, where small settlements already exist (Figure 9). The case of the Vodudahue is more complex because the valley is quite wide, apt for forestry, livestock and farming, and has internal roads to connect to the route under construction (CH 7), that will connect to the port, continental Chile and South Patagonia. Fishing lodges and an ecotourism industry are being constructed for an elite socio-

additional meters must be added from high tide as a worst-case scenario.

et al. [32] in the Aysén Fjord.

magnitude) for seismic activity 1919–2016.

Gutenberg-Richter Law

infrastructure [6].

economic international market.

Figure 9. Areas of potential flooding by tsunamis. The low lands with current and projected uses would be the most vulnerable.

### 6. Seismic activity and landslide-tsunami trigger

Results of the calculation of seismic susceptibility using the Gutenberg-Richter Law, identified 16 intraplate continental seismic events in the 97 years analyzed (Table 2). Cortical earthquakes correspond to 14% of the total events. Though the coefficient of correlation between


#### Gutenberg-Richter Law

6. Seismic activity and landslide-tsunami trigger

Results of the calculation of seismic susceptibility using the Gutenberg-Richter Law, identified 16 intraplate continental seismic events in the 97 years analyzed (Table 2). Cortical earthquakes correspond to 14% of the total events. Though the coefficient of correlation between

Figure 9. Areas of potential flooding by tsunamis. The low lands with current and projected uses would be the most

Figure 8. Fan delta and changes of tide in Huinay river outlet (A) High tide, January 28 (15:30 hrs.), (B) Low tide, January

22 (20:32 hrs.). Source: González [54].

110 Sea Level Rise and Coastal Infrastructure

vulnerable.

Table 2. Parameters of the Gutenberg-Richter Law (Log N =a+bM, where N is number of earthquakes and M the magnitude) for seismic activity 1919–2016.

the regression analysis and the data is low (0.83), and does not allow for quantifying the return period, a recurrent condition of seismic activity in the LOFZ can be established. Furthermore, this activity is a factor in the geohazard of landslips and tsunamis, as evidenced by Sepúlveda et al. [32] in the Aysén Fjord.

In the context of potential activation of seismic activity that could generate a tsunami, a map was created with areas affected by flooding, adding three wave heights, according to the standards of the State of Chile and the known event that occurred in the Aysén Fjord in 2007.

Figure 9 shows the areas that are potentially susceptible and vulnerable to flooding by tsunami. The State of Chile has defined 30 m above sea level as the safety zone [55]; the wave height of 50 m is the maximum level modeled for the Aysén Fjord [56]. To this level, 7 additional meters must be added from high tide as a worst-case scenario.

The risk of landslides in fjords has been studied in the northern hemisphere because of its impact on human life and economic loss [26, 27, 57], but in Chile the region of the fjords constitutes an isolated territory uncoupled from the urban-regional-national system, unpopulated and lacking infrastructure [6].

This situation is in the process of changing with the construction of a land route to foment inter-regional integration. The route is projected to benefit the growth of the aquaculture industry, above all for the export of salmon and the development of economic activity in the valleys of the Huinay and Vodudahue, where small settlements already exist (Figure 9). The case of the Vodudahue is more complex because the valley is quite wide, apt for forestry, livestock and farming, and has internal roads to connect to the route under construction (CH 7), that will connect to the port, continental Chile and South Patagonia. Fishing lodges and an ecotourism industry are being constructed for an elite socioeconomic international market.

### 7. Conclusions

The predominant feature of Comau Fjord's geomorphological setting is its steeply graded hillslopes, 36% of its territory in thresholds above 30 (61% have a 20–30 slope and more than 45 of incline), in addition to fractured intrusive and metamorphic rocks, most of them classified as free-faces and talus landforms, above all on its higher eastern flank. The talus are vegetated providing evidence of a timberline 1000 m.a.s.l., above which are bare rocks exposed to glacial rainfall activity, and processes of nivation. The presence of vegetated talus on both sides of the fjord is a constant, as are the numerous ravines because of intense precipitation and glacial melting activity in the fjord.

Acknowledgements

Author details

Ignacio Ibarra<sup>1</sup>

References

for their support with field work and information.

\*Address all correspondence to: mvsoto@uchilefau.cl

We would like to thank Fondecyt for financing the project "Recognizing the hotspot in the periglacial environment of the fjords and interior sea through the integrated evaluation of geohazard drivers, risks and impact on territory resources in the Gulf of Ancud: a methodological contribution" (1151087). We would like to acknowledge Huinay Scientific Field Station

María-Victoria Soto1,2\*, Pablo Sarricolea1,2, Sergio A. Sepúlveda2,3,4, Misael Cabello5

, Constanza Molina3 and Michael Maerker<sup>6</sup>

2 CITRID, Risk Reduction And Disaster Program, University of Chile, Chile

4 Institute of Engineering Sciences, University of O'Higgins, Rancagua, Chile

6 Department of Earth and Environmental Sciences, University of Pavia, Italy

5 Physical Geography Lab. Department of Geography, University of Chile, Chile

urbano: Aplicación a Santiago (Chile) y Zaragoza (España). 2011;XX:86-95

ment for regional application. Natural Hazards. 2012;64(3):1977-1999

[1] Soto MV, Moreno R. Implicancias del crecimiento urbano en el piedmont andino de Santiago: un tema de sustentabilidad urbana. Chile. En: Sobre la medición de la forma del espacio

[2] Holsten A, Kropp JP. An integrated and transferable climate change vulnerability assess-

[3] Sahin O, Mohamed S. Coastal vulnerability to sea-level rise: a spatial–temporal assessment framework. Natural Hazards. 2014;70(1):395-414. DOI: 10.1007/s11069-013-0818-4

[4] Castro CP, Ibarra I, Lukas M, Sarmiento JP. Disaster risk construction in the progressive consolidation of informal settlements: Iquique and Puerto Montt (Chile) case studied. International Journal of Disaster Risk Reduction. 2015;13:109-127. DOI: 10.1016/j.ijdrr.2015.05.001

[5] Ibarra I, Castro CP, Soto MV, Rauld R. Applied Geomorphology to assessment of natural hazards at the southern area of Pichilemu district, O'Higgins Region, Chile. Revista

Investigaciones Geográficas. 2016;51:61-80. DOI: 10.5354/0719-5370.2016.42521

1 Department of Geography, University of Chile, Santiago, Chile

3 Department of Geology, University of Chile, Santiago, Chile

,

113

Geohazards in the Fjords of Northern Patagonia, Chile http://dx.doi.org/10.5772/intechopen.71435

The landslide inventory performed in fieldwork is geomorphic evidence of the dynamic processes acting on the hillslopes. The landslide susceptibility modeled for the study area, based methodologically on the inventory and statistical analysis, strongly suggests that the study area shows conditions for landsliding. Furthermore, the seismic activity identified for this zone as part of the influence of the LOZF was described based on the magnitudefrequency relationships using the Gutenberg-Richter Law. Although results show a low coefficient of linear regressions (0.4) for cortical seismic activity near the study area, this can be considered as another latent trigger for landslides and tsunamis. Historical reports, in fact, have documented the triggering of landsliding inducing tsunamis in Aysen 2007.

GIS modeling of topographic indexes is an important methodological tool for geomorphological terrain analysis in areas with difficult access to the Andean fjords and catchment areas of the Chilean Patagonia. This tool has been useful to show areas prone to be flooded and zones with landslide susceptibility.

The zone's climatic conditions of very high rainfall (5000 mm/year), together with the presence of low alluvial terraces, favor the dynamic processes that generate fluvial flooding. Identification of areas of fluvial flooding is associated with high annual rainfall and a winter peak level, but also with the increase in spring and summer temperatures that contribute to the receding nature of the Andean glaciers, and the increasing river flow. The trend of climatic change in the zone is set to continue throughout this century. Identified areas of fluvial flooding correspond to the Holocene terraces, currently not in use. The projected highway in the fluvial valleys will be affected by this threat.

The construction of Route CH 7 will be a strong incentive for economic activity in the fjord area, principally in aquaculture and tourism. As shown in the geomorphological and geohazards maps (Figures 2, 5, and 9), most of the planned highway that will be built at the eastern base of the fjord can be disrupted by rock falls, rock and earth slides and debris flows. Indeed, at least 24 critical zones of exposure can be identified on the hazard maps. Moreover, areas that currently have human settlements are located on geomorphological units such us low alluvial terraces and deltas, prone to flooding. These economic activities will, thus, be exposed and vulnerable to future risks given the identified geohazards. For this reason, direct or indirect mitigation controls such as land use planning should be considered to reduce the risk of disaster at the study site.

### Acknowledgements

7. Conclusions

112 Sea Level Rise and Coastal Infrastructure

glacial melting activity in the fjord.

with landslide susceptibility.

be affected by this threat.

risk of disaster at the study site.

The predominant feature of Comau Fjord's geomorphological setting is its steeply graded hillslopes, 36% of its territory in thresholds above 30 (61% have a 20–30 slope and more than 45 of incline), in addition to fractured intrusive and metamorphic rocks, most of them classified as free-faces and talus landforms, above all on its higher eastern flank. The talus are vegetated providing evidence of a timberline 1000 m.a.s.l., above which are bare rocks exposed to glacial rainfall activity, and processes of nivation. The presence of vegetated talus on both sides of the fjord is a constant, as are the numerous ravines because of intense precipitation and

The landslide inventory performed in fieldwork is geomorphic evidence of the dynamic processes acting on the hillslopes. The landslide susceptibility modeled for the study area, based methodologically on the inventory and statistical analysis, strongly suggests that the study area shows conditions for landsliding. Furthermore, the seismic activity identified for this zone as part of the influence of the LOZF was described based on the magnitudefrequency relationships using the Gutenberg-Richter Law. Although results show a low coefficient of linear regressions (0.4) for cortical seismic activity near the study area, this can be considered as another latent trigger for landslides and tsunamis. Historical reports, in fact,

GIS modeling of topographic indexes is an important methodological tool for geomorphological terrain analysis in areas with difficult access to the Andean fjords and catchment areas of the Chilean Patagonia. This tool has been useful to show areas prone to be flooded and zones

The zone's climatic conditions of very high rainfall (5000 mm/year), together with the presence of low alluvial terraces, favor the dynamic processes that generate fluvial flooding. Identification of areas of fluvial flooding is associated with high annual rainfall and a winter peak level, but also with the increase in spring and summer temperatures that contribute to the receding nature of the Andean glaciers, and the increasing river flow. The trend of climatic change in the zone is set to continue throughout this century. Identified areas of fluvial flooding correspond to the Holocene terraces, currently not in use. The projected highway in the fluvial valleys will

The construction of Route CH 7 will be a strong incentive for economic activity in the fjord area, principally in aquaculture and tourism. As shown in the geomorphological and geohazards maps (Figures 2, 5, and 9), most of the planned highway that will be built at the eastern base of the fjord can be disrupted by rock falls, rock and earth slides and debris flows. Indeed, at least 24 critical zones of exposure can be identified on the hazard maps. Moreover, areas that currently have human settlements are located on geomorphological units such us low alluvial terraces and deltas, prone to flooding. These economic activities will, thus, be exposed and vulnerable to future risks given the identified geohazards. For this reason, direct or indirect mitigation controls such as land use planning should be considered to reduce the

have documented the triggering of landsliding inducing tsunamis in Aysen 2007.

We would like to thank Fondecyt for financing the project "Recognizing the hotspot in the periglacial environment of the fjords and interior sea through the integrated evaluation of geohazard drivers, risks and impact on territory resources in the Gulf of Ancud: a methodological contribution" (1151087). We would like to acknowledge Huinay Scientific Field Station for their support with field work and information.

### Author details

María-Victoria Soto1,2\*, Pablo Sarricolea1,2, Sergio A. Sepúlveda2,3,4, Misael Cabello5 , Ignacio Ibarra<sup>1</sup> , Constanza Molina3 and Michael Maerker<sup>6</sup>

\*Address all correspondence to: mvsoto@uchilefau.cl


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**Chapter 8**

Provisional chapter

**Coastal Disasters and Remote Sensing Monitoring**

DOI: 10.5772/intechopen.72460

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sealevel rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning

Coastal Disasters and Remote Sensing Monitoring

Yan Yu, Shengbo Chen, Tianqi Lu and Siyu Tian

Yan Yu, Shengbo Chen, Tianqi Lu and Siyu Tian

Additional information is available at the end of the chapter

advices for coastal infrastructure construction.

infrastructure, multi-source

1. Introduction

Keywords: chlorophyll-a, coastline, oil spilling, monitoring, forecast, coastal

The coastal zone is the intersection zone of the lithosphere, hydrosphere, biosphere and atmosphere interaction where the continent connects with the ocean. The disaster in the coastal

> © 2016 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 eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. 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.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72460

**Methods**

Abstract

Methods


#### **Coastal Disasters and Remote Sensing Monitoring Methods** Coastal Disasters and Remote Sensing Monitoring Methods

DOI: 10.5772/intechopen.72460

Yan Yu, Shengbo Chen, Tianqi Lu and Siyu Tian Yan Yu, Shengbo Chen, Tianqi Lu and Siyu Tian

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72460

#### Abstract

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shoa.cl/servicios/citsu/pdf/citsu\_aysen\_low.pdf

ijdrr.2012.11.002

118 Sea Level Rise and Coastal Infrastructure

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sealevel rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning advices for coastal infrastructure construction.

Keywords: chlorophyll-a, coastline, oil spilling, monitoring, forecast, coastal infrastructure, multi-source

### 1. Introduction

The coastal zone is the intersection zone of the lithosphere, hydrosphere, biosphere and atmosphere interaction where the continent connects with the ocean. The disaster in the coastal

© 2016 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 eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. 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.

zone is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to morphology, marine disasters could be classified as disastrous waves, sea ice, red tide, Tsunami and storm tide. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Most common marine disasters happened in coastal areas of China are coastal disaster, storm tide, seawater intrusion, disastrous wave, Tsunami and so on. These disasters caused great loss to the residents of coastal areas, economic and ecological environment. Besides, the disaster caused by human activities has become more and more serious, such as red tide, rising sea-level and ocean oil spill. With the development of satellite technology, remote sensing technology has gradually become an important means for monitoring coastal disasters and environmental changes that and providing data source for coastal infrastructure planning. Climate and ecological change caused by human activities is a hot issue of social concern at present. Taken Bohai Bay located in the South China Sea as an example, this paper will discuss remote sensing method to monitor and prevent three coastal disasters including red tide and, sea-level rise and oil spill, which could be applied in coastal infrastructure planning.

algorithm is too simple to consider the absorption and scattering properties of water and bio optical model. The semi empirical considers the absorption and scattering coefficient of different group of water (chlorophyll, seawater, suspended sediment, CDOM) of [7–9] in Case 2 algorithm using CZCS. The inversion of coastal chlorophyll is limited by the spatial resolution, for example MODIS with spatial resolution of data could not meet the inversion requirements though the spectral resolution is high, so this paper adopts experimental algorithm that uses band 1 and band 2 of MODIS with resolution of 500 m, proposed by Fan et al., using, this algorithm is more consistent with the requirements of offshore chloro-

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Inter Government Panel on Climate Change (IPCC) has been assessing the impact of global climate change on natural ecology and human socio-economic systems five times since 1990 [10]. IPCC's fourth report on climate change (AR4) pointed out that since 1961, the global average sea-level rise as the rate of 1.8 mm/a, and by 1993, this data has increased by 3.1 mm/a. Over the past 30 years, China's average annual sea-level rise as the rate of 2.9 mm/a. Melting glaciers is the main reason of rising sea-level since 1960s. Asian glaciers that has the core of Qinghai Tibet plateau continue to retreat [11, 12]; in the past 20 years, the Antarctic ice sheet and the Greenland ice sheet keep melting, and the ablation rate continues to accelerate, leading to rising sea-level [13–15]. Sea-level rise caused by global climate change have significant impact on the coast. The coastal area exerts most important influence on social and economic development, is also the most serious and direct area affected by sea-level changes. More than about half of the world population, production and consumption are distributed in the coastal areas within the shoreline, and most of the world's wealth are concentrated in the regional economy. Monitoring the coastline changes for many years by remote sensing images can help to analyze the impact of sea-level rise on different types of coastline. The coastline extraction can be divided into automatic extraction and visual interpretation. Bouchahma et al. [16] applied Canny operator to binary NDWI from Landsat TM. Xu [17] proposed the modified normalized difference water index MNDWI (Modified NDWI), the NDWI index in the near infrared band is replaced by infrared band to get better accurate extraction for water information. But most methods are not perfect in the extraction of biological shoreline and mud shoreline. In the coastal areas with smaller scales, high resolution remote sensing data can be used for visual interpretation for different types of coastline, and better results can be obtained. With the rapid development of Marine transportation and offshore oil development, the high frequency of the oil spill accidents has caused a lot of damage to the coastal ecological environment. There are several commonly used operational oil spill models, such as MOTHY (Modèle Océanique de Transport d'Hydrocarbures, a French operational oil spill drift forecast system), OSCAR (Oil Spill Contingency and Response), OILMAP (Oil Spill Model 30 and Response System), ADIOS2 (Automated Data Inquiry for Oil Spills), GNOME (General NOAA Operational Modeling Environment) [18, 19]. The models above have several parameters, such as wind, current and waves are affected by different environment. Thus, it is necessary to calibrate Lagrangian transport model to get the optimal model coefficients for certain oil spill accident with the special datasets. Tian et al. [20] calibrated two parameters, Mean Center Position distance difference (MCPD) and particles' Standard Deviational Ellipses (SDEs), to evaluate the performance of Lagrangian transport model with different coefficient combinations, and forecast the diffusion of the oil slick.

phyll inversion, which is more suitable for chlorophyll inversion.

Satellite remote sensing technology can monitor and prevent inshore disasters in large area scale and in real time, which has been successfully applied in monitoring red tide, sea-level rise and marine oil spill. Taking effective measures to prevent the occurrence or control the area of red tide before its outburst is a long-term goal of red tide management. In 1950s, the red tide often strikes the coastal areas of industrially developed countries such as the USA and Western Europe, and the research on red tide has been conducted earlier, which mainly focuses on statistics in the red tide causes, ecotoxicology, monitoring management and so on. Doucette et al. [1] carried out toxicological studies on the nutrient conditions under the red tide. Moisn [2] investigates the effect of temperature on the growth rate of red tide organisms. Temperature and nutrient salts are the main factors in the formation of red tide. Based on the water temperature of red tide, Huang and Lou [3] is established the artificial neural network method, but the rise of flow region with low temperature could also result in the red tide, so extracting red tide through water temperature has rather great limitation. Based on the water temperature of red tide, Huang and Lou [3] established the artificial neural network method, but the rise of flow region with low temperature could also result in the red tide, so extracting red tide through water temperature has rather great limitation. Autotrophic algae photosynthesis is the main energy source of most red tide, and the cell have abundant chlorophyll, thus the red tide could be identified through abnormal increase of chlorophyll content. Steidinger & Haddad [4] used CZCS (Coastal Zone Color Scanner) algorithm to obtain chlorophyll content to identify the short bloom dinoflagellate blooms in the western waters of the Florida shelf in 1981. The CZCS algorithm has been successfully applied in Case1 water. There exist 10 algorithms calculated from the ratio of blue and green using water color sensors like MODIS, SeaWiFS, MERIS, OCTS [5]. However, the effect of traditional algorithms of Case 2 is not so good because of suspended sediment. Based on the relative reflectance spectra of red tide algae measured in the East China Sea, Mao and Huang [6] proposed a combination method of three bands for eliminating the disturbance of suspended sediment on the retrieval of chlorophyll concentration. Chlorophyll empirical

algorithm is too simple to consider the absorption and scattering properties of water and bio optical model. The semi empirical considers the absorption and scattering coefficient of different group of water (chlorophyll, seawater, suspended sediment, CDOM) of [7–9] in Case 2 algorithm using CZCS. The inversion of coastal chlorophyll is limited by the spatial resolution, for example MODIS with spatial resolution of data could not meet the inversion requirements though the spectral resolution is high, so this paper adopts experimental algorithm that uses band 1 and band 2 of MODIS with resolution of 500 m, proposed by Fan et al., using, this algorithm is more consistent with the requirements of offshore chlorophyll inversion, which is more suitable for chlorophyll inversion.

zone is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to morphology, marine disasters could be classified as disastrous waves, sea ice, red tide, Tsunami and storm tide. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Most common marine disasters happened in coastal areas of China are coastal disaster, storm tide, seawater intrusion, disastrous wave, Tsunami and so on. These disasters caused great loss to the residents of coastal areas, economic and ecological environment. Besides, the disaster caused by human activities has become more and more serious, such as red tide, rising sea-level and ocean oil spill. With the development of satellite technology, remote sensing technology has gradually become an important means for monitoring coastal disasters and environmental changes that and providing data source for coastal infrastructure planning. Climate and ecological change caused by human activities is a hot issue of social concern at present. Taken Bohai Bay located in the South China Sea as an example, this paper will discuss remote sensing method to monitor and prevent three coastal disasters including red tide and, sea-level rise and oil spill, which could

Satellite remote sensing technology can monitor and prevent inshore disasters in large area scale and in real time, which has been successfully applied in monitoring red tide, sea-level rise and marine oil spill. Taking effective measures to prevent the occurrence or control the area of red tide before its outburst is a long-term goal of red tide management. In 1950s, the red tide often strikes the coastal areas of industrially developed countries such as the USA and Western Europe, and the research on red tide has been conducted earlier, which mainly focuses on statistics in the red tide causes, ecotoxicology, monitoring management and so on. Doucette et al. [1] carried out toxicological studies on the nutrient conditions under the red tide. Moisn [2] investigates the effect of temperature on the growth rate of red tide organisms. Temperature and nutrient salts are the main factors in the formation of red tide. Based on the water temperature of red tide, Huang and Lou [3] is established the artificial neural network method, but the rise of flow region with low temperature could also result in the red tide, so extracting red tide through water temperature has rather great limitation. Based on the water temperature of red tide, Huang and Lou [3] established the artificial neural network method, but the rise of flow region with low temperature could also result in the red tide, so extracting red tide through water temperature has rather great limitation. Autotrophic algae photosynthesis is the main energy source of most red tide, and the cell have abundant chlorophyll, thus the red tide could be identified through abnormal increase of chlorophyll content. Steidinger & Haddad [4] used CZCS (Coastal Zone Color Scanner) algorithm to obtain chlorophyll content to identify the short bloom dinoflagellate blooms in the western waters of the Florida shelf in 1981. The CZCS algorithm has been successfully applied in Case1 water. There exist 10 algorithms calculated from the ratio of blue and green using water color sensors like MODIS, SeaWiFS, MERIS, OCTS [5]. However, the effect of traditional algorithms of Case 2 is not so good because of suspended sediment. Based on the relative reflectance spectra of red tide algae measured in the East China Sea, Mao and Huang [6] proposed a combination method of three bands for eliminating the disturbance of suspended sediment on the retrieval of chlorophyll concentration. Chlorophyll empirical

be applied in coastal infrastructure planning.

120 Sea Level Rise and Coastal Infrastructure

Inter Government Panel on Climate Change (IPCC) has been assessing the impact of global climate change on natural ecology and human socio-economic systems five times since 1990 [10]. IPCC's fourth report on climate change (AR4) pointed out that since 1961, the global average sea-level rise as the rate of 1.8 mm/a, and by 1993, this data has increased by 3.1 mm/a. Over the past 30 years, China's average annual sea-level rise as the rate of 2.9 mm/a. Melting glaciers is the main reason of rising sea-level since 1960s. Asian glaciers that has the core of Qinghai Tibet plateau continue to retreat [11, 12]; in the past 20 years, the Antarctic ice sheet and the Greenland ice sheet keep melting, and the ablation rate continues to accelerate, leading to rising sea-level [13–15]. Sea-level rise caused by global climate change have significant impact on the coast. The coastal area exerts most important influence on social and economic development, is also the most serious and direct area affected by sea-level changes. More than about half of the world population, production and consumption are distributed in the coastal areas within the shoreline, and most of the world's wealth are concentrated in the regional economy. Monitoring the coastline changes for many years by remote sensing images can help to analyze the impact of sea-level rise on different types of coastline. The coastline extraction can be divided into automatic extraction and visual interpretation. Bouchahma et al. [16] applied Canny operator to binary NDWI from Landsat TM. Xu [17] proposed the modified normalized difference water index MNDWI (Modified NDWI), the NDWI index in the near infrared band is replaced by infrared band to get better accurate extraction for water information. But most methods are not perfect in the extraction of biological shoreline and mud shoreline. In the coastal areas with smaller scales, high resolution remote sensing data can be used for visual interpretation for different types of coastline, and better results can be obtained. With the rapid development of Marine transportation and offshore oil development, the high frequency of the oil spill accidents has caused a lot of damage to the coastal ecological environment. There are several commonly used operational oil spill models, such as MOTHY (Modèle Océanique de Transport d'Hydrocarbures, a French operational oil spill drift forecast system), OSCAR (Oil Spill Contingency and Response), OILMAP (Oil Spill Model 30 and Response System), ADIOS2 (Automated Data Inquiry for Oil Spills), GNOME (General NOAA Operational Modeling Environment) [18, 19]. The models above have several parameters, such as wind, current and waves are affected by different environment. Thus, it is necessary to calibrate Lagrangian transport model to get the optimal model coefficients for certain oil spill accident with the special datasets. Tian et al. [20] calibrated two parameters, Mean Center Position distance difference (MCPD) and particles' Standard Deviational Ellipses (SDEs), to evaluate the performance of Lagrangian transport model with different coefficient combinations, and forecast the diffusion of the oil slick.

### 2. Study areas and remote sensing monitoring methods

Remote sensing data in coastal disasters monitoring and prevention has the characteristics of large area, real-time, using the method of remote sensing monitoring of red tides and sea-level rise, oil spilling has many effective results. This paper, using remote sensing data inversion and interpretation results as an example, introduces several methods of monitoring coastal disasters. poisoning. The formation of red tide can be divided into four stages: start, development, maintenance and extinction. Water temperature and sewage content of inorganic matter are the main factors causing the red tide, but the topography is also an important factor. Sea-level rise is caused by global warming, polar glaciers melting, upper oceanic thermal expansion and other causes of global sea-level rise phenomenon. Sea-level rise leads to coastal degradation, mangrove disappearance, and coastal ecological damage. The Hainan Island study area

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123

The geographical conditions of Bohai Sea and South China Sea are very different. The three sides of the Bohai sea are surrounded by land, and the whole shape of the Bohai coastline is similar to gourd. The Bohai Sea of gourd mouth is only 59 nautical miles, and the average depth of Bohai Sea is 25 m. Bohai Sea is more closed terrain, reducing the stormy storms, and the exchange rate with the ocean is slow, so it is a natural farm and natural harbor. However, a large number of ships parked and oil spill accident cause the Bohai oil pollution more and more serious. Human activities lead to about 10 million tons of oil into the ocean per year according to incomplete statistics, and the data accounts for about 0.5% of the world's annual oil production. There are 1 million to 1.5 million tons of oil is due to shipping operations and ship accidents into the ocean among them. Oil pollution is mainly caused by the oil industry, shipbreaking wastewater discharge, oil transport vessel cleaning, accidents and oil production

remote sensing image is shown in Figure 1.

Figure 1. Hainan Island study area map.

#### 2.1. Study area

Hainan Province is located in the southernmost part of China, Hainan Island is the second largest island in China, and is separated from mainland by Qiongzhou Strait. It belongs to the tropical monsoon climate. Most of the coastal waters of Hainan Island are open sea areas with large waves, and the average wave height of 0.5–1.5 m. Hainan Island blows southeast wind and southwest wind in summer. There are more typhoons with the speed is extremely unstable. The average wind speed is 25 m/s. It can reach 30–60 m/s with the super typhoon. Hainan Province is the largest province in China suffering from marine disasters. The areas of disaster are mainly concentrated along the coastline of Hainan Island. The main disasters are storm surge, catastrophic waves, tsunami, red tide and coastal degradation caused by rising sealevel. Storm surge is due to strong atmospheric disturbances, such as the phenomenon of abnormal sea surface caused by strong winds or air pressure changes. If it superimposes with natural high tide, it will result in tidal surge, seawater immersion inland, even lead to catastrophe. The storm surges in Hainan Province usually occurs in summer and autumn, mainly caused by tropical cyclones, and more than the warning line of storm surge is 0.6 times. Catastrophic waves usually refer to the catastrophic waves that mean sea waves up to 6 meters above the waves. It can often overthrow the ship, destroy marine engineering and coastal engineering, and bring disasters to navigation, maritime construction, maritime military activities and fishing. Catastrophic waves are mainly caused by tropical cyclones and cold air in South China Sea, cold weather in winter is more likely to produce catastrophic waves. The Tsunami is usually caused by a seabed earthquake that is less than 100 kilometers beneath the seabed and more than 6.5 on the Richter scale. Underwater or coastal landslides or volcanic eruptions may also cause a Tsunami. There was a Tsunami caused by an earthquake in Hainan Island in January 1992. Tsunami wave speed as high as 700–800 km/h, and cross the ocean in a few hours. The wavelength can be up to hundreds of kilometers, and can travel thousands of miles with low loss energy. The ocean waves are less than a meter high, but when they reach the shallow coastal waters, the wavelengths decrease and the wave height increases dramatically. Tsunami forms a "water wall" with great energy up to tens of meters. Red tide is an anomalous phenomenon in marine ecosystems. Under certain environmental conditions, some phytoplankton, protozoa or bacteria in the seawater are proliferated or highly aggregated to cause the water to discolor a harmful ecological phenomenon. When the large amount of nutrients containing domestic sewage, industrial waste water and agricultural wastewater into the ocean, resulting in seawater eutrophication, red tide organisms will be rapidly multiply, they formed a red tide. Excessive amounts of phytoplankton consume the oxygen in seawater, and the dead phytoplankton release harmful substances that kill other creatures by hypoxia or poisoning. The formation of red tide can be divided into four stages: start, development, maintenance and extinction. Water temperature and sewage content of inorganic matter are the main factors causing the red tide, but the topography is also an important factor. Sea-level rise is caused by global warming, polar glaciers melting, upper oceanic thermal expansion and other causes of global sea-level rise phenomenon. Sea-level rise leads to coastal degradation, mangrove disappearance, and coastal ecological damage. The Hainan Island study area remote sensing image is shown in Figure 1.

The geographical conditions of Bohai Sea and South China Sea are very different. The three sides of the Bohai sea are surrounded by land, and the whole shape of the Bohai coastline is similar to gourd. The Bohai Sea of gourd mouth is only 59 nautical miles, and the average depth of Bohai Sea is 25 m. Bohai Sea is more closed terrain, reducing the stormy storms, and the exchange rate with the ocean is slow, so it is a natural farm and natural harbor. However, a large number of ships parked and oil spill accident cause the Bohai oil pollution more and more serious. Human activities lead to about 10 million tons of oil into the ocean per year according to incomplete statistics, and the data accounts for about 0.5% of the world's annual oil production. There are 1 million to 1.5 million tons of oil is due to shipping operations and ship accidents into the ocean among them. Oil pollution is mainly caused by the oil industry, shipbreaking wastewater discharge, oil transport vessel cleaning, accidents and oil production

Figure 1. Hainan Island study area map.

2. Study areas and remote sensing monitoring methods

2.1. Study area

122 Sea Level Rise and Coastal Infrastructure

Remote sensing data in coastal disasters monitoring and prevention has the characteristics of large area, real-time, using the method of remote sensing monitoring of red tides and sea-level rise, oil spilling has many effective results. This paper, using remote sensing data inversion and interpretation results as an example, introduces several methods of monitoring coastal disasters.

Hainan Province is located in the southernmost part of China, Hainan Island is the second largest island in China, and is separated from mainland by Qiongzhou Strait. It belongs to the tropical monsoon climate. Most of the coastal waters of Hainan Island are open sea areas with large waves, and the average wave height of 0.5–1.5 m. Hainan Island blows southeast wind and southwest wind in summer. There are more typhoons with the speed is extremely unstable. The average wind speed is 25 m/s. It can reach 30–60 m/s with the super typhoon. Hainan Province is the largest province in China suffering from marine disasters. The areas of disaster are mainly concentrated along the coastline of Hainan Island. The main disasters are storm surge, catastrophic waves, tsunami, red tide and coastal degradation caused by rising sealevel. Storm surge is due to strong atmospheric disturbances, such as the phenomenon of abnormal sea surface caused by strong winds or air pressure changes. If it superimposes with natural high tide, it will result in tidal surge, seawater immersion inland, even lead to catastrophe. The storm surges in Hainan Province usually occurs in summer and autumn, mainly caused by tropical cyclones, and more than the warning line of storm surge is 0.6 times. Catastrophic waves usually refer to the catastrophic waves that mean sea waves up to 6 meters above the waves. It can often overthrow the ship, destroy marine engineering and coastal engineering, and bring disasters to navigation, maritime construction, maritime military activities and fishing. Catastrophic waves are mainly caused by tropical cyclones and cold air in South China Sea, cold weather in winter is more likely to produce catastrophic waves. The Tsunami is usually caused by a seabed earthquake that is less than 100 kilometers beneath the seabed and more than 6.5 on the Richter scale. Underwater or coastal landslides or volcanic eruptions may also cause a Tsunami. There was a Tsunami caused by an earthquake in Hainan Island in January 1992. Tsunami wave speed as high as 700–800 km/h, and cross the ocean in a few hours. The wavelength can be up to hundreds of kilometers, and can travel thousands of miles with low loss energy. The ocean waves are less than a meter high, but when they reach the shallow coastal waters, the wavelengths decrease and the wave height increases dramatically. Tsunami forms a "water wall" with great energy up to tens of meters. Red tide is an anomalous phenomenon in marine ecosystems. Under certain environmental conditions, some phytoplankton, protozoa or bacteria in the seawater are proliferated or highly aggregated to cause the water to discolor a harmful ecological phenomenon. When the large amount of nutrients containing domestic sewage, industrial waste water and agricultural wastewater into the ocean, resulting in seawater eutrophication, red tide organisms will be rapidly multiply, they formed a red tide. Excessive amounts of phytoplankton consume the oxygen in seawater, and the dead phytoplankton release harmful substances that kill other creatures by hypoxia or at sea. It not only destroyed the coastal scenery, but also seriously endanger marine life. Bohai bathymetry map and the locations of PL19-3 B and C platforms are shown in Figure 2.

### 2.2. Data

In this paper, five kinds of optical data and one kind of microwave data are used. The optical data are MODIS, Spot6, Landsat 7 ETM, Landsat6 ETM, Landsat1 MSS and the microwave data is ASAR. The date, spatial resolution, wavelength range of remote sensing data used in this paper are in Table 1.

#### 2.3. Red tide monitoring

Nutrient concentration and temperature are two major factors of the outburst of red tides, but upwelling also can bring nutrient with lower temperature, so the chlorophyll-a concentration inversion using remote sensing data is more suitable for coastal zone to forecast red tide. Coastal water chlorophyll-a concentration inversion is based on the Case 2 chlorophyll-a algorithm, which include empirical model and semi-analytical, bio-optical model of the water-leaving radiance Rrs(λ) [8, 21–23]. The empirical model utilizes the linear regression method to build model describing the relationship between chlorophyll-a concentration and Rrs(λ). Relatively, the semi-analytical, bio-optical model have two free variables, the absorption coefficient due to phytoplankton at 675 nm, aψ (675),and the absorption coefficient due to colored dissolved organic matter (CDOM) at 400 nm, ag(400). Although the semi-analytical,

bio-optical model with the inherent optical properties, it does not work well in the coastal zone effected heavily by suspended sediment. We choose an improved OC3M model using more

Data Date Spatial resolution (m) Wavelength range (μm)

MODIS 2/3/2015 250 0.62–0.92 SPOT6 22/1/2013 1.5 0.45–0.75 Landsat6 ETM 24/2/2001 30 0.57–1.75 Landsat1 MSS 15/1/1973 80 0.5–11 ASAR 5/11/2011 <sup>150</sup> 5.6 � 104

A blue green ratio experience algorithm of CZCS developed by NASA such as Eq. (1), was the earliest model to calibrate pigment (including chlorophyll-a and brown pigment) concentra-

> <sup>C</sup> <sup>¼</sup> <sup>A</sup> <sup>R</sup>ð Þ <sup>433</sup> <sup>R</sup>ð Þ <sup>550</sup> <sup>B</sup>

The MODIS chlorophyll-a production provides multiband algorithm, OC3M model, which is

<sup>∗</sup>X^<sup>2</sup> <sup>þ</sup> <sup>a</sup><sup>3</sup>

<sup>∗</sup>X^<sup>3</sup> <sup>þ</sup> <sup>a</sup><sup>4</sup>

<sup>R</sup><sup>12</sup> (3)

Coastal Disasters and Remote Sensing Monitoring Methods

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<sup>∗</sup><sup>X</sup> <sup>þ</sup> <sup>a</sup><sup>2</sup>

<sup>X</sup> <sup>¼</sup> log maxð Þ <sup>R</sup>9;R<sup>10</sup>

where a0, a1, a2, a<sup>3</sup> are undetermined coefficients, R9, R10, R<sup>12</sup> are the reflections of MODIS band

In order to obtain more accurate resolution inversion results, and as a result of lack of MODIS nearshore chlorophyll product, we decided to adopt OC3M model improved by 1/2 band, which is more suitable for nearshore areas. Then, we use two kinds of data to establish model, the one is MODIS water-color product, the other one is South China Sea measured value, and

(1)

125

<sup>∗</sup>X^<sup>4</sup> (2)

tion. A and B in Eq. (1) are empirical parameters, and C is pigment concentration.

the most widely used empirical algorithm for MODIS data, such as Eqs. (2) and (3).

log ½ �¼ Chla a<sup>0</sup> þ a<sup>1</sup>

finally, we contrast and analysis the model accuracy.

high spatial resolution MODIS data (band 1, 2).

13/5/2001 14/5/2001 19/5/2001

2.3.1. OC3M model

Table 1. Used remote sensing data.

9, band 10, band 12.

2.3.2. Provided OC3M model

Figure 2. Bohai Bay study area map.


Table 1. Used remote sensing data.

bio-optical model with the inherent optical properties, it does not work well in the coastal zone effected heavily by suspended sediment. We choose an improved OC3M model using more high spatial resolution MODIS data (band 1, 2).

#### 2.3.1. OC3M model

at sea. It not only destroyed the coastal scenery, but also seriously endanger marine life. Bohai

In this paper, five kinds of optical data and one kind of microwave data are used. The optical data are MODIS, Spot6, Landsat 7 ETM, Landsat6 ETM, Landsat1 MSS and the microwave data is ASAR. The date, spatial resolution, wavelength range of remote sensing data used in

Nutrient concentration and temperature are two major factors of the outburst of red tides, but upwelling also can bring nutrient with lower temperature, so the chlorophyll-a concentration inversion using remote sensing data is more suitable for coastal zone to forecast red tide. Coastal water chlorophyll-a concentration inversion is based on the Case 2 chlorophyll-a algorithm, which include empirical model and semi-analytical, bio-optical model of the water-leaving radiance Rrs(λ) [8, 21–23]. The empirical model utilizes the linear regression method to build model describing the relationship between chlorophyll-a concentration and Rrs(λ). Relatively, the semi-analytical, bio-optical model have two free variables, the absorption coefficient due to phytoplankton at 675 nm, aψ (675),and the absorption coefficient due to colored dissolved organic matter (CDOM) at 400 nm, ag(400). Although the semi-analytical,

bathymetry map and the locations of PL19-3 B and C platforms are shown in Figure 2.

2.2. Data

this paper are in Table 1.

124 Sea Level Rise and Coastal Infrastructure

2.3. Red tide monitoring

Figure 2. Bohai Bay study area map.

A blue green ratio experience algorithm of CZCS developed by NASA such as Eq. (1), was the earliest model to calibrate pigment (including chlorophyll-a and brown pigment) concentration. A and B in Eq. (1) are empirical parameters, and C is pigment concentration.

$$C = A \left[ \frac{R(433)}{R(550)} \right]^B \tag{1}$$

The MODIS chlorophyll-a production provides multiband algorithm, OC3M model, which is the most widely used empirical algorithm for MODIS data, such as Eqs. (2) and (3).

$$\log\left[\text{Cbla}\right] = a\_0 + a\_1^\* X + a\_2^\* X^{\prime 2} + a\_3^\* X^{\prime 3} + a\_4^\* X^{\prime 4} \tag{2}$$

$$X = \log\left[\frac{\max(R\_9, R\_{10})}{R\_{12}}\right] \tag{3}$$

where a0, a1, a2, a<sup>3</sup> are undetermined coefficients, R9, R10, R<sup>12</sup> are the reflections of MODIS band 9, band 10, band 12.

#### 2.3.2. Provided OC3M model

In order to obtain more accurate resolution inversion results, and as a result of lack of MODIS nearshore chlorophyll product, we decided to adopt OC3M model improved by 1/2 band, which is more suitable for nearshore areas. Then, we use two kinds of data to establish model, the one is MODIS water-color product, the other one is South China Sea measured value, and finally, we contrast and analysis the model accuracy.

#### 2.3.2.1. Inverting model MODIS water color production

First of all, the two-band reflectivity model is established by the reflectance of the red-band and near-infrared bands of MODIS data (n = 1, 2), X<sup>1</sup> is ratio of two-band (Eq. (4)), X<sup>2</sup> is vegetation index (Eq. (5)).

$$X\_1 = R\_2 / R\_1 \tag{4}$$

sensing methods to detect sea-level changes mostly use active microwave radar including altimeter, scatterometer and synthetic aperture radar to measure sea surface height, significant wave height, sea surface topography, simultaneously measure the ocean current, sea wave, tide, sea surface wind. Nonetheless, the spatial resolution of microwave data is much lower than the visible data, which is more suitable for the coastal zone research. Coastline changes without anthropogenic impact can be used to estimate sea-level changes. The interpretation of coastline based on the different interpretation marks of coastline types, and the main types of southwest of Hainan island include sandy coastline, rocky coastline, estuary coastline and artificial coastline. This part will introduce the interpretation marks of different coastline.

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The mark of the sandy shoreline is the highest traced line consisting of small gravel, coarse sand, shell debris, driftwood, water grass at the foot of the lateral coastal sand bank. Figure 3 is

The rocky coastlines are divided into the one with beach and without beach. Although both of the water edge of rocky coastlines have seabed, the trace line of coastline with beach is the same as the one of sandy coastline (Figure 4). The trace line of coastline without beach is below

The estuary coastline has two parts: the boundary between sea and land; the boundary between sea and river. The interpretation mark of former is vegetation on the land edge, and

The main artificial coastlines of the research area are mariculture and artificial pier. The

the latter is the suddenly broadening line between sea and river, such as Figure 6.

2.4.1. Sandy coastline

2.4.2. Rocky coastline

the latest sea cliff (Figure 5).

2.4.3. Estuary coastline

2.4.4. Artificial coastline

the sandy coastline in SPOT6 and outside picture.

interpretation mark is artificial building, such as Figure 7.

Figure 3. Sandy coastline in SPOT6 and outside picture.

$$X\_2 = (R\_2 - R\_1)/(R\_2 + R\_1) \tag{5}$$

Chlorophyll concentration (Eq. (6)) is calculate by linear regression analysis using (n = 1, 2) and MODIS chlorophyll product provided by NASA Ocean Color Processing Center (http:// oceancolor.gsfc.nasa.gov/cms).

$$\text{Clla} = b\_1 \times X\_n^{\prime 3} + b\_2 \times X\_n^{\prime 2} + b\_3 \times X\_n + b\_4 \tag{6}$$

Stepwise regression analysis is used to input each band's information into the model and to carry out the significant tests of the band taken into the model. The model will test the significance of the front band, simultaneously, delete the bands whose significance is decreasing due to later band adding. The eventual band is the optimal one. Due to the stepwise regression analysis can evaluate the significance of bands, the model is selected in this paper. The inverting model, such as Eq. (7), acquired by the stepwise regression analysis of the spectral data (red spectral band, near-infrared band) and MODIS chlorophyll-a production.

$$\text{Chla} = c\_1 \times R\_1 + c\_2 \times R\_2 + c\_3 \tag{7}$$

In Eq. (7), Chla is chlorophyll-a concentration, b1, b2, b3, b4, c1, c2, c<sup>3</sup> are undetermined parameters, R1, R<sup>2</sup> are the reflection data of MODIS red band and near-infrared band.

#### 2.3.2.2. Inverting model of measured southern China Sea data

The inverting model of measured Southern China Sea also need calculate the Xn (n = 1, 2)in Eqs. (4) and (5). The different part is the chlorophyll-a concentration data modeling that comes from measured Southern China Sea data.

$$\text{Clla} = d\_1 \, ^\ast X\_n ^\ast + d\_2 \, ^\ast X\_n ^\ast + d\_3 \, ^\ast X\_n + d\_4 \tag{8}$$

$$\text{Chla} = e\_1 \, ^\ast R\_1 + e\_2 \, ^\ast R\_2 + e\_3 \, \tag{9}$$

In Eqs. (8) and (9), Chla is chlorophyll-a concentration (μg/L), d1, d2, d3, d4, e1, e2, e<sup>3</sup> are undetermined parameters during the regression analysis.

#### 2.4. Sea-level rise

Sea-level rise caused by global climate change has significant impacts on coastal zone. Coastal ecosystems are particularly sensitive to sea-level rise, which is gradually causing the coastline to degenerate, and the area of mangroves to reduce or even disappear. Traditional remote sensing methods to detect sea-level changes mostly use active microwave radar including altimeter, scatterometer and synthetic aperture radar to measure sea surface height, significant wave height, sea surface topography, simultaneously measure the ocean current, sea wave, tide, sea surface wind. Nonetheless, the spatial resolution of microwave data is much lower than the visible data, which is more suitable for the coastal zone research. Coastline changes without anthropogenic impact can be used to estimate sea-level changes. The interpretation of coastline based on the different interpretation marks of coastline types, and the main types of southwest of Hainan island include sandy coastline, rocky coastline, estuary coastline and artificial coastline. This part will introduce the interpretation marks of different coastline.

### 2.4.1. Sandy coastline

2.3.2.1. Inverting model MODIS water color production

vegetation index (Eq. (5)).

126 Sea Level Rise and Coastal Infrastructure

oceancolor.gsfc.nasa.gov/cms).

First of all, the two-band reflectivity model is established by the reflectance of the red-band and near-infrared bands of MODIS data (n = 1, 2), X<sup>1</sup> is ratio of two-band (Eq. (4)), X<sup>2</sup> is

Chlorophyll concentration (Eq. (6)) is calculate by linear regression analysis using (n = 1, 2) and MODIS chlorophyll product provided by NASA Ocean Color Processing Center (http://

<sup>n</sup> <sup>þ</sup> <sup>b</sup><sup>2</sup> � <sup>X</sup>^<sup>2</sup>

Stepwise regression analysis is used to input each band's information into the model and to carry out the significant tests of the band taken into the model. The model will test the significance of the front band, simultaneously, delete the bands whose significance is decreasing due to later band adding. The eventual band is the optimal one. Due to the stepwise regression analysis can evaluate the significance of bands, the model is selected in this paper. The inverting model, such as Eq. (7), acquired by the stepwise regression analysis of the spectral data (red spectral band, near-infrared band) and MODIS chlorophyll-a production.

In Eq. (7), Chla is chlorophyll-a concentration, b1, b2, b3, b4, c1, c2, c<sup>3</sup> are undetermined param-

The inverting model of measured Southern China Sea also need calculate the Xn (n = 1, 2)in Eqs. (4) and (5). The different part is the chlorophyll-a concentration data modeling that comes

<sup>∗</sup>R<sup>1</sup> <sup>þ</sup> <sup>e</sup><sup>2</sup>

In Eqs. (8) and (9), Chla is chlorophyll-a concentration (μg/L), d1, d2, d3, d4, e1, e2, e<sup>3</sup> are

Sea-level rise caused by global climate change has significant impacts on coastal zone. Coastal ecosystems are particularly sensitive to sea-level rise, which is gradually causing the coastline to degenerate, and the area of mangroves to reduce or even disappear. Traditional remote

eters, R1, R<sup>2</sup> are the reflection data of MODIS red band and near-infrared band.

∗ X^<sup>3</sup> <sup>n</sup> þ d<sup>2</sup> ∗ X^<sup>2</sup> <sup>n</sup> þ d<sup>3</sup> ∗

Chla ¼ e<sup>1</sup>

2.3.2.2. Inverting model of measured southern China Sea data

undetermined parameters during the regression analysis.

Chla ¼ d<sup>1</sup>

from measured Southern China Sea data.

2.4. Sea-level rise

Chla <sup>¼</sup> <sup>b</sup><sup>1</sup> � <sup>X</sup>^<sup>3</sup>

X<sup>1</sup> ¼ R2=R<sup>1</sup> (4)

<sup>n</sup> þ b<sup>3</sup> � Xn þ b<sup>4</sup> (6)

Xn þ d<sup>4</sup> (8)

<sup>∗</sup>R<sup>2</sup> <sup>þ</sup> <sup>e</sup><sup>3</sup> (9)

X<sup>2</sup> ¼ ð Þ R<sup>2</sup> � R<sup>1</sup> =ð Þ R<sup>2</sup> þ R<sup>1</sup> (5)

Chla ¼ c<sup>1</sup> � R<sup>1</sup> þ c<sup>2</sup> � R<sup>2</sup> þ c<sup>3</sup> (7)

The mark of the sandy shoreline is the highest traced line consisting of small gravel, coarse sand, shell debris, driftwood, water grass at the foot of the lateral coastal sand bank. Figure 3 is the sandy coastline in SPOT6 and outside picture.

### 2.4.2. Rocky coastline

The rocky coastlines are divided into the one with beach and without beach. Although both of the water edge of rocky coastlines have seabed, the trace line of coastline with beach is the same as the one of sandy coastline (Figure 4). The trace line of coastline without beach is below the latest sea cliff (Figure 5).

#### 2.4.3. Estuary coastline

The estuary coastline has two parts: the boundary between sea and land; the boundary between sea and river. The interpretation mark of former is vegetation on the land edge, and the latter is the suddenly broadening line between sea and river, such as Figure 6.

#### 2.4.4. Artificial coastline

The main artificial coastlines of the research area are mariculture and artificial pier. The interpretation mark is artificial building, such as Figure 7.

Figure 3. Sandy coastline in SPOT6 and outside picture.

Figure 4. Rocky coastline with beach in SPOT6 and outside picture.

2.5. Oil spilling trajectory simulation model

Figure 7. Artificial coastline in SPOT6.

<sup>i</sup> and xtþΔ<sup>t</sup>

Eqs. (10)–(11)).

where parameters xt

be obtained timely.

The oil spilling trajectory simulation can help predict the area where the oil spill might reach and where it might be contaminated. However, the Lagrange model is available (such as

time interval setting Δt equal 1 h in the text. When the model forecasts the oil trajectory, the symbol of Δt is plus; when the model traces oil trajectory, the symbol of Δt is minus. Parameter vi is the drifting speed of oil particle i at the t moment. The location of each oil particle calculated as the linear combination of wind velocity, current velocity, wave-induced Stokes drift and turbulent diffusive velocity. Where Cc is current drift coefficient, uc is the surface current velocity, CD is wind drag coefficient, DW is the transformation matrix taking the wind deflection angle into account, uw is wind velocity above sea surface 10 m, CH is wave-induced Stokes drift coefficient, uH is wave-induced Stokes drift velocity. Cc, CD, CH are empirical parameters reducing the model accuracy. However there are some methods using the buoy data and monitoring data on correcting the oil trajectory correction at present, the data cannot

Aiming at this problem, we calibrate Lagrangian numerical model with remote sensing image to improve Lagrangian model, which is independent of weather, climate and low cost. We correct the Lagrangian model using to simulate the oil spilling based on the ENVISAT ASAR remote sensing data which time phase is during the PL19-3 oil spilling accident on Bohai. The oil simulation trajectory is made of points by remote sensing data inverting results. In Figure 7, the points D, E, I are the oil slick locations of remoting sensing image on June 11, June 13, June

<sup>i</sup> þ viΔt (10)

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v ¼ Ccuc þ CDDW uW þ CHuH þ ud (11)

<sup>i</sup> are the locations of oil particles at t and t þ Δt moment, Δt is

x ð Þ tþΔt <sup>i</sup> <sup>¼</sup> xt

Figure 5. Rocky coastline without beach in SPOT6.

Figure 6. Estuary coastline in SPOT6.

Figure 7. Artificial coastline in SPOT6.

Figure 4. Rocky coastline with beach in SPOT6 and outside picture.

128 Sea Level Rise and Coastal Infrastructure

Figure 5. Rocky coastline without beach in SPOT6.

Figure 6. Estuary coastline in SPOT6.

#### 2.5. Oil spilling trajectory simulation model

The oil spilling trajectory simulation can help predict the area where the oil spill might reach and where it might be contaminated. However, the Lagrange model is available (such as Eqs. (10)–(11)).

$$\mathbf{x}\_{i}^{(t+\Delta t)} = \mathbf{x}\_{i}^{t} + \upsilon\_{i}\Delta t \tag{10}$$

$$
\sigma = \mathsf{C}\_{c}\mathsf{u}\_{c} + \mathsf{C}\_{D}D\_{W}\mathsf{u}\_{W} + \mathsf{C}\_{H}\mathsf{u}\_{H} + \mathsf{u}\_{d} \tag{11}
$$

where parameters xt <sup>i</sup> and xtþΔ<sup>t</sup> <sup>i</sup> are the locations of oil particles at t and t þ Δt moment, Δt is time interval setting Δt equal 1 h in the text. When the model forecasts the oil trajectory, the symbol of Δt is plus; when the model traces oil trajectory, the symbol of Δt is minus. Parameter vi is the drifting speed of oil particle i at the t moment. The location of each oil particle calculated as the linear combination of wind velocity, current velocity, wave-induced Stokes drift and turbulent diffusive velocity. Where Cc is current drift coefficient, uc is the surface current velocity, CD is wind drag coefficient, DW is the transformation matrix taking the wind deflection angle into account, uw is wind velocity above sea surface 10 m, CH is wave-induced Stokes drift coefficient, uH is wave-induced Stokes drift velocity. Cc, CD, CH are empirical parameters reducing the model accuracy. However there are some methods using the buoy data and monitoring data on correcting the oil trajectory correction at present, the data cannot be obtained timely.

Aiming at this problem, we calibrate Lagrangian numerical model with remote sensing image to improve Lagrangian model, which is independent of weather, climate and low cost. We correct the Lagrangian model using to simulate the oil spilling based on the ENVISAT ASAR remote sensing data which time phase is during the PL19-3 oil spilling accident on Bohai. The oil simulation trajectory is made of points by remote sensing data inverting results. In Figure 7, the points D, E, I are the oil slick locations of remoting sensing image on June 11, June 13, June 14 respectively. The simulation points constitute the oil trajectory. The oil slick D acts as the testing trajectory to correct the simulation result.

Eventually, we use the ellipse center and the ellipse long axis of oil slick area to evaluate the simulation result. The center of ellipse calculation method is in Eqs. (12) and (13), the long axis of ellipse calculation method is in Eq. (14).

$$\overline{X} = \frac{1}{n} \sum\_{i=1}^{n} x\_i \tag{12}$$

estuary. But the magnitude is not completely related to the distance of the estuary, so there are other reasons for that. We add the contours of the water depth and the chlorophyll-an inversion result to discuss the relationship between the shallow water terrain and chlorophyll-a

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We add the counter of water depth and chlorophyll-a concentration. In order to see the relationship between the two parts, we enlarge three parts of Figure 8 in Figure 9. The adding results suggest that, in normal condition, the chlorophyll-a concentrate is higher than 2.25 μg/L in the depth lower than 15 m, and lower than 0.75 μg/L in the depth higher than 20. In the c of Figure 9, the concentration is low (<0.75 μg/L) near the estuary. Maybe the slop of shallow

The coastline interpretation results in Figure 10 show that the coastline without anthropogenic influence whose main part is sandy coastline is deteriorating year by year. The most heavily

concentration.

3.2. Sea-level rise

water terrain is larger than other coastal zone.

Figure 9. Water depth and chlorophyll a concentration adding.

$$\overline{Y} = \frac{1}{n} \sum\_{i=1}^{n} y\_i \tag{13}$$

$$\tan \theta = \frac{\left(\sum\_{i=1}^{n} \mathbf{x}\_i^2 - \sum\_{i=1}^{n} y\_i^2\right) + \sqrt{\left(\sum\_{i=1}^{n} \mathbf{x}\_i^2 + \sum\_{i=1}^{n} y\_i^2\right) + 4\left(\sum\_{i=1}^{n} \mathbf{x}\_i y\_i\right)^2}}{2\sum\_{i=1}^{n} \mathbf{x}\_i y\_i} \tag{14}$$

#### 3. Result and discussion

#### 3.1. Chlorophyll-a inverting result

Through the chlorophyll-a inverting result (Figure 8) in the southwest Hainan Island, we can see the distribution regularities. The high concentration of chlorophyll-a is found near the

Figure 8. Chlorophyll a inverting result.

estuary. But the magnitude is not completely related to the distance of the estuary, so there are other reasons for that. We add the contours of the water depth and the chlorophyll-an inversion result to discuss the relationship between the shallow water terrain and chlorophyll-a concentration.

We add the counter of water depth and chlorophyll-a concentration. In order to see the relationship between the two parts, we enlarge three parts of Figure 8 in Figure 9. The adding results suggest that, in normal condition, the chlorophyll-a concentrate is higher than 2.25 μg/L in the depth lower than 15 m, and lower than 0.75 μg/L in the depth higher than 20. In the c of Figure 9, the concentration is low (<0.75 μg/L) near the estuary. Maybe the slop of shallow water terrain is larger than other coastal zone.

#### 3.2. Sea-level rise

14 respectively. The simulation points constitute the oil trajectory. The oil slick D acts as the

Eventually, we use the ellipse center and the ellipse long axis of oil slick area to evaluate the simulation result. The center of ellipse calculation method is in Eqs. (12) and (13), the long axis

xi (12)

yi (13)

<sup>i</sup>¼<sup>1</sup> xiyi

(14)

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

<sup>i</sup>¼<sup>1</sup> <sup>y</sup><sup>2</sup> i � � <sup>þ</sup> <sup>4</sup> <sup>P</sup><sup>n</sup>

� �<sup>2</sup> <sup>q</sup>

<sup>i</sup> <sup>þ</sup> <sup>P</sup><sup>n</sup>

<sup>X</sup> <sup>¼</sup> <sup>1</sup> n Xn i¼1

<sup>Y</sup> <sup>¼</sup> <sup>1</sup> n Xn i¼1

> P<sup>n</sup> <sup>i</sup>¼<sup>1</sup> <sup>x</sup><sup>2</sup>

2 P<sup>n</sup> <sup>i</sup>¼<sup>1</sup> xiyi

Through the chlorophyll-a inverting result (Figure 8) in the southwest Hainan Island, we can see the distribution regularities. The high concentration of chlorophyll-a is found near the

testing trajectory to correct the simulation result.

of ellipse calculation method is in Eq. (14).

130 Sea Level Rise and Coastal Infrastructure

P<sup>n</sup> <sup>i</sup>¼<sup>1</sup> <sup>x</sup><sup>2</sup>

<sup>i</sup> � <sup>P</sup><sup>n</sup>

� � <sup>þ</sup>

<sup>i</sup>¼<sup>1</sup> <sup>y</sup><sup>2</sup> i

tan θ ¼

3. Result and discussion

3.1. Chlorophyll-a inverting result

Figure 8. Chlorophyll a inverting result.

The coastline interpretation results in Figure 10 show that the coastline without anthropogenic influence whose main part is sandy coastline is deteriorating year by year. The most heavily

Figure 9. Water depth and chlorophyll a concentration adding.

Figure 10. Sandy coastline changes between 1973 and 2013.

eroded part of up to more than 30 m from 1973 to 2013. Some part of the sandy coastline eroded from 1973 to 2013, but it was increasing from 1991 to 2013, such as the c of Figure 10 because of the reclaiming land from the sea.

3.4. Coastal infrastructure

Along with the rapid development of coastal cities, the number of human activities is increasing, so the planning and construction of the coastal infrastructures become increasingly important in the terms of coastal ecological protection, aquaculture, marine transport and so on. The constructions of different coastal infrastructures are based on the different types of coasts, which include bedrock coast, plain coast and biological coast. Bedrock coast include cape and bay, and many bedrock coasts are natural deep harbor. There are three kinds of plain coasts: muddy coast, sandy coast and deltaic coast. Most of the plain coasts are straight, and the terrains are flat, and thus are suitable for building saltern, lidos and the fisheries. Biological coast is divided into two kinds of mangroves and coral reefs. As the biological coasts have the function of protecting biological diversity and wetlands, this kind coasts should not be set up large coastal infrastructures. Although nearshore ecological environment is of great

Figure 11. The distribution of simulation particles with calibrated model with start particle on June 13 (Note: Initial points\_1314 means that the initial points are distributed at UTC 14:00 on June 13 and simulated\_oil\_particles\_1402 means

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t that these simulated oil particles are obtained as the moment of UTC 02:00 on June 14).

#### 3.3. Oil spilling trajectory simulation

Figure 11 is comparison of the oil slick simulation trajectory (points) and oil actual location (irregular polygon). The result shows that oil slick simulation and real oil slick are similar in distributional pattern and location. In the third figure of Figure 11, the location of oil slick is accordance with real oil slick moving with current, but the model can simulate the oil slick staying in the area where the accident occurred.

Figure 11. The distribution of simulation particles with calibrated model with start particle on June 13 (Note: Initial points\_1314 means that the initial points are distributed at UTC 14:00 on June 13 and simulated\_oil\_particles\_1402 means t that these simulated oil particles are obtained as the moment of UTC 02:00 on June 14).

#### 3.4. Coastal infrastructure

eroded part of up to more than 30 m from 1973 to 2013. Some part of the sandy coastline eroded from 1973 to 2013, but it was increasing from 1991 to 2013, such as the c of Figure 10

Figure 11 is comparison of the oil slick simulation trajectory (points) and oil actual location (irregular polygon). The result shows that oil slick simulation and real oil slick are similar in distributional pattern and location. In the third figure of Figure 11, the location of oil slick is accordance with real oil slick moving with current, but the model can simulate the oil slick

because of the reclaiming land from the sea.

Figure 10. Sandy coastline changes between 1973 and 2013.

132 Sea Level Rise and Coastal Infrastructure

staying in the area where the accident occurred.

3.3. Oil spilling trajectory simulation

Along with the rapid development of coastal cities, the number of human activities is increasing, so the planning and construction of the coastal infrastructures become increasingly important in the terms of coastal ecological protection, aquaculture, marine transport and so on. The constructions of different coastal infrastructures are based on the different types of coasts, which include bedrock coast, plain coast and biological coast. Bedrock coast include cape and bay, and many bedrock coasts are natural deep harbor. There are three kinds of plain coasts: muddy coast, sandy coast and deltaic coast. Most of the plain coasts are straight, and the terrains are flat, and thus are suitable for building saltern, lidos and the fisheries. Biological coast is divided into two kinds of mangroves and coral reefs. As the biological coasts have the function of protecting biological diversity and wetlands, this kind coasts should not be set up large coastal infrastructures. Although nearshore ecological environment is of great

Figure 12. The biggest saltern in China Yingge saltern image.

significance to human beings, the pollution of coastal water by inland waters and the changing

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The coastal infrastructure planning also should consider the coastal disaster factors. The high chlorophyll-a concentration coastal zone usually with a gentle bottom topography need to reduce the discharge of sewage. These coastal regions can be used to build saltern, such as Figure 12. The shallow water regions with steep bottom topography have lower chlorophyll-a concentration and clear water. These regions with sandy coastline are suitable for lido, such as Figure 13; the ones with rocky coastline are suitable for deep harbor, such as Figure 14. The remote sensing methods forecast the area where disaster may occur, and make coastal infra-

Overall, we have inversed chlorophyll-a centration and coastline interpretation in southwest Hainan Island, and used the improved Lagrangian model to simulate the oil slick movement trend and distribution. Through the research in this paper, we acquire a couple of conclusions. 1. The adding of chlorophyll-a centration and counter of shallow water depth suggest that not only the nutrient salt brought by the land runoff and temperature are the main reasons for the increase of chlorophyll concentration, but also the shallow Marine terrain in the

of the coastline through reclamation works destroy the coastal biodiversity.

structure construction more reasonable.

Figure 14. Suitable location for deep harbor image.

nearshore area is an important factor.

4. Conclusion

Figure 13. Sanya Bay lido image.

Figure 14. Suitable location for deep harbor image.

significance to human beings, the pollution of coastal water by inland waters and the changing of the coastline through reclamation works destroy the coastal biodiversity.

The coastal infrastructure planning also should consider the coastal disaster factors. The high chlorophyll-a concentration coastal zone usually with a gentle bottom topography need to reduce the discharge of sewage. These coastal regions can be used to build saltern, such as Figure 12. The shallow water regions with steep bottom topography have lower chlorophyll-a concentration and clear water. These regions with sandy coastline are suitable for lido, such as Figure 13; the ones with rocky coastline are suitable for deep harbor, such as Figure 14. The remote sensing methods forecast the area where disaster may occur, and make coastal infrastructure construction more reasonable.

### 4. Conclusion

Figure 12. The biggest saltern in China Yingge saltern image.

134 Sea Level Rise and Coastal Infrastructure

Figure 13. Sanya Bay lido image.

Overall, we have inversed chlorophyll-a centration and coastline interpretation in southwest Hainan Island, and used the improved Lagrangian model to simulate the oil slick movement trend and distribution. Through the research in this paper, we acquire a couple of conclusions.

1. The adding of chlorophyll-a centration and counter of shallow water depth suggest that not only the nutrient salt brought by the land runoff and temperature are the main reasons for the increase of chlorophyll concentration, but also the shallow Marine terrain in the nearshore area is an important factor.

2. The coastline is degenerating in 40s from 1973 to 2013, and the most heavily eroded part of up to more than 30 m. The sea-level is rising year by year.

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### Author details

Yan Yu<sup>1</sup> \*, Shengbo Chen<sup>1</sup> , Tianqi Lu1 and Siyu Tian<sup>2</sup>

\*Address all correspondence to: yuyan14@mails.jlu.edu.cn


### References


[7] Morel A, Ahn YH. Optical efficiency factors of free-living marine bacteria: Influence of bacterioplankton upon the optical properties and particulate organic carbon in oceanic waters. Journal of Marine Research. 1990;48(1):145-175. DOI: 10.1357/002224090784984632

2. The coastline is degenerating in 40s from 1973 to 2013, and the most heavily eroded part of

3. The oil slick trajectory result suggest that a part of oil slick moves with the current, and the other part stays in the area where the accident occurred. The improved Lagrangian model can simulate the trajectory of oil slick efficiently, but it is not suitable for prediction of the

4. The remote sensing methods can forecast the area where coastal disasters caused by human activity may occur, and interpret the types of coastline. The remote sensing data application

can help to make coastal infrastructure construction planning more reasonable.

1 College of Geo-exploration Science and Technology, Jilin University, Changchun, China

2 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

[1] Doucette GJ, Turner JT, Powell CL, et al. Trophic accumulation of PSP toxins in zooplankton during Alexandrium fundyense, blooms in Casco Bay, Gulf of Maine, April–June 1998. I. Toxin levels in A. fundyense, and zooplankton size fractions. Deep Sea Research Part II: Topical Studies in Oceanography. 2005;52(19-21):2764-2783. DOI: 10.1016/j.dsr2.2005.06.031

[2] Moisan JR, Moisan TA, Abbott MR. Modelling the effect of temperature on the maximum growth rates of phytoplankton populations. Ecological Modelling. 2002;153(3):197-215.

[3] Huang WG, Lou XL. AVHRR detection of red tides with neural networks. International Journal of Remote Sensing. 2003;24(10):1991-1996. DOI:10.1080/0143116031000068213 [4] Steidinger KA, Haddad K. Biologic and hydrographic aspects of red tides. Bioscience.

[5] Pinkerton MH, Richardson KM, Boyd PW, et al. Intercomparison of ocean colour bandratio algorithms for chlorophyll concentration in the subtropical front east of New Zealand.

[6] Mao XM, Huang WG. Algorithms of multiband remote sensing for coastal red tie waters.

Remote Sensing of Environment. 2005;97(3):382-402. DOI: 10.1016/j.rse.2005.05.004

, Tianqi Lu1 and Siyu Tian<sup>2</sup>

up to more than 30 m. The sea-level is rising year by year.

part of oil slick which does not move with current.

\*Address all correspondence to: yuyan14@mails.jlu.edu.cn

Author details

136 Sea Level Rise and Coastal Infrastructure

\*, Shengbo Chen<sup>1</sup>

DOI: 10.1002/ecy.1581

1981;31(11):814-819. DOI: 10.2307/1308678

Chinese Journal of Applied Ecology. 2003;14(7):1200-1202

Yan Yu<sup>1</sup>

References


[21] Lee Z, Carder KL, Hawes SK, et al. Model for the interpretation of hyperspectral remotesensing reflectance. Applied Optics. 1994;33(24):5721. DOI: 10.1364/AO.33.005721

**Chapter 9**

**Provisional chapter**

**Revealing Landscape Planning Strategies for Disaster-**

**Disaster-Prone Coastal Urban Environments: The Case** 

Regarding the challenges of the twenty-first century, this study aims to explore the role of landscape architecture within the multidisciplinary setting of the studies on coastal disasters. Thus, it focuses on Istanbul, which deserves being one of the most well-known coastal megacities of the world, not only due to its long history dating back to 6700 BC but also due its unique coastal configuration. This ever-expanding but disaster-prone megacity stands on two peninsulas belonging to different continents, holds the only strait connecting the Black Sea to the other seas, and accommodates 12 lakes with more than 100 streams. These coastal features promote the vulnerability of the megacity to a wide range of natural and man-made disasters, such as earthquake, tsunami, flood, sea level rise, and salinization. The evaluation process of this study benefits from the GIS and comprises five major phases: examining the urban-landscape change, defining the major coastal disasters, identifying the disaster-prone environments, and defining multilayered landscape planning strategies. This study develops landscape planning strategies for disaster-prone coastal urban environments by deriving from the complex dynamics of the Istanbul megacity. This study is an attempt to further disaster-sensitive landscape studies in the belief that not only Istanbul but also the other coastal megaci-

**Keywords:** landscape planning, coastal megacities, disaster-prone urban environments,

© 2016 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.

© 2018 The Author(s). Licensee IntechOpen. 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.

DOI: 10.5772/intechopen.73567

**Prone Coastal Urban Environments: The Case of**

**Revealing Landscape Planning Strategies for** 

**Istanbul Megacity**

**of Istanbul Megacity**

Fatma Aycim Turer Baskaya

Fatma Aycim Turer Baskaya

**Abstract**

http://dx.doi.org/10.5772/intechopen.73567

ties will benefit from them.

GIS, Istanbul

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter


#### **Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments: The Case of Istanbul Megacity Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments: The Case of Istanbul Megacity**

DOI: 10.5772/intechopen.73567

Fatma Aycim Turer Baskaya Fatma Aycim Turer Baskaya

[21] Lee Z, Carder KL, Hawes SK, et al. Model for the interpretation of hyperspectral remotesensing reflectance. Applied Optics. 1994;33(24):5721. DOI: 10.1364/AO.33.005721

[22] Morel A, Gentili B. Diffuse reflectance of oceanic waters. II. Bidirectional aspects. Applied

[23] Lee Z, Carder KL, Hawes SK, et al. Model for the interpretation of hyperspectral remotesensing reflectance. Applied Optics. 1994;33(24):5721-5732. DOI: 10.1364/AO.33.005721

Optics. 1993;32(33):6864-6879. DOI: 10.1364/AO.32.006864

138 Sea Level Rise and Coastal Infrastructure

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73567

#### **Abstract**

Regarding the challenges of the twenty-first century, this study aims to explore the role of landscape architecture within the multidisciplinary setting of the studies on coastal disasters. Thus, it focuses on Istanbul, which deserves being one of the most well-known coastal megacities of the world, not only due to its long history dating back to 6700 BC but also due its unique coastal configuration. This ever-expanding but disaster-prone megacity stands on two peninsulas belonging to different continents, holds the only strait connecting the Black Sea to the other seas, and accommodates 12 lakes with more than 100 streams. These coastal features promote the vulnerability of the megacity to a wide range of natural and man-made disasters, such as earthquake, tsunami, flood, sea level rise, and salinization. The evaluation process of this study benefits from the GIS and comprises five major phases: examining the urban-landscape change, defining the major coastal disasters, identifying the disaster-prone environments, and defining multilayered landscape planning strategies. This study develops landscape planning strategies for disaster-prone coastal urban environments by deriving from the complex dynamics of the Istanbul megacity. This study is an attempt to further disaster-sensitive landscape studies in the belief that not only Istanbul but also the other coastal megacities will benefit from them.

**Keywords:** landscape planning, coastal megacities, disaster-prone urban environments, GIS, Istanbul

© 2016 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. © 2018 The Author(s). Licensee IntechOpen. 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.

### **1. Introduction**

The twenty-first century brings about chaotic environmental issues that are likely to be challenging and need innovative strategies. With a population of 15 million, Istanbul is one of the most well-known coastal megacities of the world, due to its history dating back to 6700 BC and its unique coastal configuration. This ever-expanding but disaster-prone megacity stands on two peninsulas belonging to different continents, holds the only strait connecting the Black Sea to the world. Due to its coastal identity, this megacity is open to a wide range of natural and man-made disasters, such as earthquake, tsunami, flood, sea level rise, and salinization.

ADRC [1] defines disaster as a severe disruption of the function of a community leading extensive human, material, economic, and environmental failures which exceeds the ability of the pertinent community to get over through its own resources.

In the case of at-risk megacity of Istanbul, limited open space, increasing number of people, gigantic urban infrastructure, old urban fabric, official plans' incapability to catch the rapid change of the city, urban expansion to the drinking water basins, buried urban streams, and instantly decided megaprojects are the significant internal features that are increasing the vulnerability of the city.

In 1999, a major earthquake struck the Istanbul surrounding area and acted as a turning point for the country as it revealed that not only the megacity but the whole country is unprepared for the disasters. Several credible academic and governmental studies have been conducted since 1999 but more or less with a focus on the earthquake.

However, in the case of Istanbul where even the environmental plans cannot easily keep up with the increasing population and rapid spatial development, which are rendering them unable to protect their validity, it is a hard challenge to implement a sustainable disaster management system.

Cities rely on the functionality of their infrastructures. In case of a disaster, this functional network itself can already turn into a dominant component of urban vulnerability [2]. The existence of an expanding urban population and density already brings about an exponentially increased complexity within the urban infrastructure [3]. Megacities are characteristic of the complexity of their infrastructures. Thus, they are open to drastically severe multihazards, which are defined by the UNISDR Terminology [4] as the context capturing interrelated simultaneous, cascading, or cumulative hazard events.

the images pertinent to the two of the cultural coastal areas of Istanbul such as Golden Horn

As a discipline, dealing with multiscale studies pertinent with open spaces, landscape architecture executes landscape analysis, planning, designing, and management for the benefit of built and natural environments. Its ability to conduct multiscale studies makes the profession

Disaster management involving two major components, which are risk management and crisis management, stands on a multidisciplinary structure. Thus, this study examines the role of landscape architecture within this setting. Landscape planning can undertake several roles in the phases of disaster management as mitigation to preparedness before the disaster and

of landscape architecture competent to take part in multidisciplinary studies.

**Figure 2.** Images from the Golden Horn and Bosphorus, respectively [6].

**Figure 1.** Location of the megacity and its coastline types due to natural and cultural characteristics.

Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments…

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141

and Bosphorus Strait.

The reason for the concerns in Istanbul is not only the vulnerability of the urban infrastructure but the existence of unique cultural heritages representing the synthesis of western and eastern cultures. Cultural accumulation of the city has a densely knitted spatial pattern with its natural coastal formations especially along the strait of Bosphorus, which is binding the Black Sea to the Sea of Marmara, the Golden Horn standing in the oldest part of the megacity, and the opening to the Sea of Marmara [5].

**Figure 1** represents the location and the 10 coastline types of the megacity that are defined within this study due to their natural and cultural characteristics, while **Figure 2** illustrates Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments… http://dx.doi.org/10.5772/intechopen.73567 141

**Figure 1.** Location of the megacity and its coastline types due to natural and cultural characteristics.

**Figure 2.** Images from the Golden Horn and Bosphorus, respectively [6].

**1. Introduction**

140 Sea Level Rise and Coastal Infrastructure

vulnerability of the city.

management system.

The twenty-first century brings about chaotic environmental issues that are likely to be challenging and need innovative strategies. With a population of 15 million, Istanbul is one of the most well-known coastal megacities of the world, due to its history dating back to 6700 BC and its unique coastal configuration. This ever-expanding but disaster-prone megacity stands on two peninsulas belonging to different continents, holds the only strait connecting the Black Sea to the world. Due to its coastal identity, this megacity is open to a wide range of natural and man-made disasters, such as earthquake, tsunami, flood, sea level rise, and salinization.

ADRC [1] defines disaster as a severe disruption of the function of a community leading extensive human, material, economic, and environmental failures which exceeds the ability

In the case of at-risk megacity of Istanbul, limited open space, increasing number of people, gigantic urban infrastructure, old urban fabric, official plans' incapability to catch the rapid change of the city, urban expansion to the drinking water basins, buried urban streams, and instantly decided megaprojects are the significant internal features that are increasing the

In 1999, a major earthquake struck the Istanbul surrounding area and acted as a turning point for the country as it revealed that not only the megacity but the whole country is unprepared for the disasters. Several credible academic and governmental studies have been conducted

However, in the case of Istanbul where even the environmental plans cannot easily keep up with the increasing population and rapid spatial development, which are rendering them unable to protect their validity, it is a hard challenge to implement a sustainable disaster

Cities rely on the functionality of their infrastructures. In case of a disaster, this functional network itself can already turn into a dominant component of urban vulnerability [2]. The existence of an expanding urban population and density already brings about an exponentially increased complexity within the urban infrastructure [3]. Megacities are characteristic of the complexity of their infrastructures. Thus, they are open to drastically severe multihazards, which are defined by the UNISDR Terminology [4] as the context capturing interrelated

The reason for the concerns in Istanbul is not only the vulnerability of the urban infrastructure but the existence of unique cultural heritages representing the synthesis of western and eastern cultures. Cultural accumulation of the city has a densely knitted spatial pattern with its natural coastal formations especially along the strait of Bosphorus, which is binding the Black Sea to the Sea of Marmara, the Golden Horn standing in the oldest part of the megacity, and

**Figure 1** represents the location and the 10 coastline types of the megacity that are defined within this study due to their natural and cultural characteristics, while **Figure 2** illustrates

of the pertinent community to get over through its own resources.

since 1999 but more or less with a focus on the earthquake.

simultaneous, cascading, or cumulative hazard events.

the opening to the Sea of Marmara [5].

the images pertinent to the two of the cultural coastal areas of Istanbul such as Golden Horn and Bosphorus Strait.

As a discipline, dealing with multiscale studies pertinent with open spaces, landscape architecture executes landscape analysis, planning, designing, and management for the benefit of built and natural environments. Its ability to conduct multiscale studies makes the profession of landscape architecture competent to take part in multidisciplinary studies.

Disaster management involving two major components, which are risk management and crisis management, stands on a multidisciplinary structure. Thus, this study examines the role of landscape architecture within this setting. Landscape planning can undertake several roles in the phases of disaster management as mitigation to preparedness before the disaster and response to recovery after the disaster. However, an insufficient number of studies have analyzed the management of disaster types as a whole from natural to man-made ones from the scope of landscape planning.

The megacity of Istanbul might capture a priority through the site-specific hazard-based further studies regarding its strategic financial and cultural importance but also its specific location in between two inner seas as the Black Sea and Sea of Marmara. Thus, this study attempts to identify the significant coastal disaster types and the disaster-prone environments at the megacity to develop the multilayered landscape planning strategies that will work both

Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments…

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143

In order to reveal the power of landscape planning in disaster mitigation, this study utilized GIS technology by means of the Arc GIS 10.0 software to examine the interplay between the identified disaster types within this research and the coastal landscapes for revealing the planning strategies for the disaster-prone landscapes of Istanbul. The 1:5000 scale digital base map files in dwg format were obtained from the Istanbul Metropolitan Municipality's Directorate

Controlling and updating of the dwg files for such a rapidly changing city are done by benefiting from the current aerial photographs available from the online city map service of the

Within this study, GIS-based data were registered to Universal Transverse Mercator 3 Degree coordinate system with European Datum 1950. JICA and IMM [17] forwarded the basic disaster mitigation plan of the megacity refer to this registration, as the central meridian of 30° east is close to Istanbul. Further information on coastal disasters and disaster management was gathered from the literature during the development of the method of this study. **Figure 3** illustrates the evaluation process used in this study for developing landscape planning strategies for the disaster-prone coastal urban environments of the

This study involves a four-phased evaluation process. Istanbul is a dynamic and ever-expanding coastal megacity welcoming a significant number of megaprojects altering the coastlines [18]. Thus, the evaluation process starts with the classification of the coastlines of Istanbul and

In the case of Istanbul, urban development throughout the centuries indicates a strong dependency on the coastal areas. In the last decades, this development has turned into a megaurbanization holding huge and rapid spatial alterations. Thus, the coastal risks are increased. Within the second phase of the evaluation process, the most significant coastal disasters are

Identification of the disaster-prone environments holds the third phase of the study and interrogates the interplays between the urban pattern-dynamics and the disaster types. The disasters are handled as earthquake, flood, tsunami, sea level rise, megaprojects, salinization, and terrorist attacks. In the case of Istanbul, vulnerable water basins, lakes, and lagoons appear to be important as they are under the impacts of rapid urban development, megaprojects,

identified and then classified as natural or man-made and current or projected.

figuring out their spatial interplay with the urban macroform.

before and after the emergence of the disasters.

**2. Materials and methods**

Istanbul Metropolitan Municipality [16].

of Cartography.

Istanbul megacity.

According to UNISDR Terminology [4], resilience is the capacity of a system or a community exposed to hazards to withstand, absorb, accommodate, adapt to, transform, and overcome from the effects of a hazard or a multihazard in a timely and effective manner, including through the preservation and restoration of its critical infrastructures and functions through risk management.

Besides the many other landscape strategies, this study interrogates the interplay between resiliency and green infrastructure with a focus on disaster-prone coastal urban environments. According to Hagerman [7], green infrastructure refers to the existence of an interconnected system composed of soil, water, air, fauna, and flora. Thus, it forms the basis of a healthy ecosystem, which forwards the services to mankind. Schiappacasse and Müller [8] and EEA-Green Infrastructure [9] defined the integration of green infrastructure planning into spatial planning system as a source of urban and regional resilience. Thus, it highlights that multilayered system of the green infrastructure is the best fit with the disaster-prone areas requiring resilience.

This study reveals the current and projected disasters/hazards pertinent with the megacity like earthquake, sea level rise, and coastal megaprojects. Earthquake emerges to be the most notable and widely examined current hazard of Istanbul. Erdik and Durukal [10] remarked that Istanbul will face a major earthquake while this area has an annual probability of approximately 2%, one of the highest in the world. As Altan and Kemper [11] identified, the North Anatolian Fault, which is standing 50 km away from the city center and passing through the Sea of Marmara, is one of the largest and most active tectonic fault lines in the world. The most recent major earthquake in the region generated by this fault brought about a massive destruction with its measure of 7.4 on Richter scale.

Sea level rise appears to be a serious hazard within the projected ones. By discussing the potential vulnerability of the countries to climate change, GCP [12] figured out that among 116 countries, Turkey gets intermediate rating within the Likert scale of 5. Turkey holds a coastline with a length of 8333 km [13]. Regarding this coastal character, 40% of the population of Turkey lives in coastal areas below 5 m altitude (as a general measure to compare all countries), which may be at risk due to the sea level rise in the course of global warming [12].

Turkey has a diverse coastal pattern due to a variety of geomorphologic and socioeconomic attributes. This diversity brings about a need for site-specific studies on different coastal regions of Turkey in order to capture a further understanding of the climate-induced impacts on the coastal environments [14, 15].

Different than the other coastal cities of Turkey, Istanbul welcomes drastic coastal megaprojects such as turning western peninsula of Istanbul into an island through the opening of a canal binding Marmara Sea to the Black Sea. Megaprojects have mega-impacts on the environment. Impacts of such projects may be regarded as adverse or good due to the level of working with nature rather than in opposition to it.

The megacity of Istanbul might capture a priority through the site-specific hazard-based further studies regarding its strategic financial and cultural importance but also its specific location in between two inner seas as the Black Sea and Sea of Marmara. Thus, this study attempts to identify the significant coastal disaster types and the disaster-prone environments at the megacity to develop the multilayered landscape planning strategies that will work both before and after the emergence of the disasters.

### **2. Materials and methods**

response to recovery after the disaster. However, an insufficient number of studies have analyzed the management of disaster types as a whole from natural to man-made ones from the

According to UNISDR Terminology [4], resilience is the capacity of a system or a community exposed to hazards to withstand, absorb, accommodate, adapt to, transform, and overcome from the effects of a hazard or a multihazard in a timely and effective manner, including through the preservation and restoration of its critical infrastructures and functions through

Besides the many other landscape strategies, this study interrogates the interplay between resiliency and green infrastructure with a focus on disaster-prone coastal urban environments. According to Hagerman [7], green infrastructure refers to the existence of an interconnected system composed of soil, water, air, fauna, and flora. Thus, it forms the basis of a healthy ecosystem, which forwards the services to mankind. Schiappacasse and Müller [8] and EEA-Green Infrastructure [9] defined the integration of green infrastructure planning into spatial planning system as a source of urban and regional resilience. Thus, it highlights that multilayered system of the green infrastructure is the best fit with the disaster-prone

This study reveals the current and projected disasters/hazards pertinent with the megacity like earthquake, sea level rise, and coastal megaprojects. Earthquake emerges to be the most notable and widely examined current hazard of Istanbul. Erdik and Durukal [10] remarked that Istanbul will face a major earthquake while this area has an annual probability of approximately 2%, one of the highest in the world. As Altan and Kemper [11] identified, the North Anatolian Fault, which is standing 50 km away from the city center and passing through the Sea of Marmara, is one of the largest and most active tectonic fault lines in the world. The most recent major earthquake in the region generated by this fault brought about a massive

Sea level rise appears to be a serious hazard within the projected ones. By discussing the potential vulnerability of the countries to climate change, GCP [12] figured out that among 116 countries, Turkey gets intermediate rating within the Likert scale of 5. Turkey holds a coastline with a length of 8333 km [13]. Regarding this coastal character, 40% of the population of Turkey lives in coastal areas below 5 m altitude (as a general measure to compare all countries), which may be at risk due to the sea level rise in the course of global warming [12]. Turkey has a diverse coastal pattern due to a variety of geomorphologic and socioeconomic attributes. This diversity brings about a need for site-specific studies on different coastal regions of Turkey in order to capture a further understanding of the climate-induced impacts

Different than the other coastal cities of Turkey, Istanbul welcomes drastic coastal megaprojects such as turning western peninsula of Istanbul into an island through the opening of a canal binding Marmara Sea to the Black Sea. Megaprojects have mega-impacts on the environment. Impacts of such projects may be regarded as adverse or good due to the level of

scope of landscape planning.

142 Sea Level Rise and Coastal Infrastructure

risk management.

areas requiring resilience.

destruction with its measure of 7.4 on Richter scale.

working with nature rather than in opposition to it.

on the coastal environments [14, 15].

In order to reveal the power of landscape planning in disaster mitigation, this study utilized GIS technology by means of the Arc GIS 10.0 software to examine the interplay between the identified disaster types within this research and the coastal landscapes for revealing the planning strategies for the disaster-prone landscapes of Istanbul. The 1:5000 scale digital base map files in dwg format were obtained from the Istanbul Metropolitan Municipality's Directorate of Cartography.

Controlling and updating of the dwg files for such a rapidly changing city are done by benefiting from the current aerial photographs available from the online city map service of the Istanbul Metropolitan Municipality [16].

Within this study, GIS-based data were registered to Universal Transverse Mercator 3 Degree coordinate system with European Datum 1950. JICA and IMM [17] forwarded the basic disaster mitigation plan of the megacity refer to this registration, as the central meridian of 30° east is close to Istanbul. Further information on coastal disasters and disaster management was gathered from the literature during the development of the method of this study. **Figure 3** illustrates the evaluation process used in this study for developing landscape planning strategies for the disaster-prone coastal urban environments of the Istanbul megacity.

This study involves a four-phased evaluation process. Istanbul is a dynamic and ever-expanding coastal megacity welcoming a significant number of megaprojects altering the coastlines [18]. Thus, the evaluation process starts with the classification of the coastlines of Istanbul and figuring out their spatial interplay with the urban macroform.

In the case of Istanbul, urban development throughout the centuries indicates a strong dependency on the coastal areas. In the last decades, this development has turned into a megaurbanization holding huge and rapid spatial alterations. Thus, the coastal risks are increased. Within the second phase of the evaluation process, the most significant coastal disasters are identified and then classified as natural or man-made and current or projected.

Identification of the disaster-prone environments holds the third phase of the study and interrogates the interplays between the urban pattern-dynamics and the disaster types. The disasters are handled as earthquake, flood, tsunami, sea level rise, megaprojects, salinization, and terrorist attacks. In the case of Istanbul, vulnerable water basins, lakes, and lagoons appear to be important as they are under the impacts of rapid urban development, megaprojects,

**3. Results and discussion**

Each landscape generates a unique signature on the Earth [20]. The urban development of Istanbul represents a template how a city can interact with a grift coastal land involving peninsulas, islands, gulfs, straits, and bays throughout the centuries. Spatial development of the megacity from Byzantium period to today holds the initial stage of the evaluation process of this study to understand the interaction of the city with the ongoing environmental dynamics. This study benefits from three main sources such as 1/100.000 Environment Plan of Istanbul [21], online land cover data of European Environment Agency [22], and the aerial photographs available from the Istanbul Metropolitan Municipality [16] to reveal the spatiotempo-

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**Figure 4** illustrates that old settlement areas of Istanbul take place along the waterfronts and within the surrounding area of the coastal historical hub of the city. This hub represents the intersection area of three water bodies, which are Golden Horn, Bosphorus, and the Marmara Sea.

There are several critiques about the spatial development of the city happened under the impact of the nonpermanent processes declared by the administrative units and the international diffusion on the planning ideas especially after the second half of the 1950s [23]. The 1950s were also a significant turning point for the megacity as it has begun to gain economic dominance within the country, which brings about a rural migration increasing with an accelerated ratio [24]. Linear development approach of the city along the coastal areas altered

ral changes within the urban macroform from Byzantium period to today.

**Figure 4.** Altering urban macroform and its interplay with the water basins.

**Figure 3.** Evaluation process of this study and its phases.

salinization, and sea level rise. Istanbul examines huge amount of coastal megaprojects [18, 19]. This study focuses on one of them, which is an integrated project of Canal Istanbul and third Airport. This project is a significant one as it aims to turn western peninsula of Istanbul into an island. The third phase ends by the dispersion of the disaster types with varying levels along the coastlines.

The final phase of the evaluation process promotes the citywide spatial findings by developing the landscape planning strategies for the disaster-prone coastal urban environments.

## **3. Results and discussion**

Each landscape generates a unique signature on the Earth [20]. The urban development of Istanbul represents a template how a city can interact with a grift coastal land involving peninsulas, islands, gulfs, straits, and bays throughout the centuries. Spatial development of the megacity from Byzantium period to today holds the initial stage of the evaluation process of this study to understand the interaction of the city with the ongoing environmental dynamics.

This study benefits from three main sources such as 1/100.000 Environment Plan of Istanbul [21], online land cover data of European Environment Agency [22], and the aerial photographs available from the Istanbul Metropolitan Municipality [16] to reveal the spatiotemporal changes within the urban macroform from Byzantium period to today.

**Figure 4** illustrates that old settlement areas of Istanbul take place along the waterfronts and within the surrounding area of the coastal historical hub of the city. This hub represents the intersection area of three water bodies, which are Golden Horn, Bosphorus, and the Marmara Sea.

There are several critiques about the spatial development of the city happened under the impact of the nonpermanent processes declared by the administrative units and the international diffusion on the planning ideas especially after the second half of the 1950s [23]. The 1950s were also a significant turning point for the megacity as it has begun to gain economic dominance within the country, which brings about a rural migration increasing with an accelerated ratio [24]. Linear development approach of the city along the coastal areas altered

**Figure 4.** Altering urban macroform and its interplay with the water basins.

salinization, and sea level rise. Istanbul examines huge amount of coastal megaprojects [18, 19]. This study focuses on one of them, which is an integrated project of Canal Istanbul and third Airport. This project is a significant one as it aims to turn western peninsula of Istanbul into an island. The third phase ends by the dispersion of the disaster types with varying levels

The final phase of the evaluation process promotes the citywide spatial findings by developing the landscape planning strategies for the disaster-prone coastal urban environments.

along the coastlines.

144 Sea Level Rise and Coastal Infrastructure

**Figure 3.** Evaluation process of this study and its phases.

drastically within the 1970s (**Figure 4**). Throughout the decades, the built-up spaces expanded to the water basins due to the combination of legal, illegal, and informal residential areas occurred at the urban–rural fringe.

Old development areas of megacity represent the dense built-up spaces involving narrow roads within a mazy road network. Such a pattern with an attached building collapse potential brings about a chaotic evacuation road network and an insufficient amount of open spaces for the evacuation areas. Thus, earthquake and accompanying secondary disasters can easily

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OYO International [26] examines the interplay between the earthquake and secondary hazards. Hence, the GIS-based maps generated within this study benefitted from it for the spatial

Alpar et al. [27] forwarded the fact of near-field tsunami for Istanbul. Thus, they highlighted that it is rather problematic to estimate a near-field tsunami impact on the islands and southern coastal districts of Istanbul due to the nonexhaustive historical documents, the longtime interval between the devastating earthquakes, and the limited distance between the fault and coastline. In the case of Istanbul, fragmented open space network appears to be important for the disaster management. Turer Baskaya [5] highlighted the issue that open reserve areas should not be used at least for the major evacuation facilities as their future concerns a big question mark

Due to the high land prices and limited empty area for development within the inner city, there are some instant transformation projects and pertinent implementations ongoing from varying scales. Today, amendment plans are the reality of the megacity, which is a tool for the planning system authorities to catch the rapid spatial change. Thus, new constructions may

An evacuation system should not rely on the reserve open areas/green fields at least for the major facilities but prefer already designed open spaces, semipublic open spaces of administrative, educational, healthcare, and religious buildings or protected lands for the evacuation hubs [5]. A continuous green connection between the coastline and interior lands is vital for the disaster-prone coastal cities. Green represents here not only planted public areas but also semipublic areas, pedestrianized streets or multifunctional land uses involving plenty of pervious surfaces and let semipublic-public accesses. In this study, human and ecosystem friendly cor-

Coastal megacities are considered to be disadvantaged regarding sea-borne risks. However, due to the existence of their public gates to the sea in case of a disaster like earthquake, they can rely on sea transportation and stay in access to urban, national, or even international traffic.

This study highlights the current and projected hazards as in the case of instantly decided megaprojects. Megaprojects [19] remarked that in between the years of 1998 and 2017, a total of 120 megaprojects (completed or continuing) have taken place in Istanbul. This amount raises a question about the feasibility and sustainability of the projects. This study handles one megaproject as a sample to interrogate their interplay with the environmental dynamics, which is the combined projects of Canal Istanbul, surrounding development areas, and the third airport. Besides, this combined project is not taking place in the official environment

easily occur on the green fields, which had been available for the evacuation areas.

ridors with varying ratios in between capture the meaning of green connectivity.

dispersion of the tsunami run-up heights reaching 10 m as the highest (**Figure 5**).

create cascading infrastructure failures within the old urban setting.

in such a rapidly changing megacity.

plan of Istanbul dated 2009.

Today, urban sprawl is concerned to be a problem as the ecologically important and protected areas of Istanbul even in the transfrontier scale exist in the northern part of the megacity. As an indicator for both the ecologically important and sensitive areas, this study handles the water basins. To explore the interplay between the urban macroform and water basins of Istanbul, boundaries of water basins are obtained as jpg files from the online city map service of the Istanbul Metropolitan Municipality and then rectified and adapted to the GIS studies.

Western peninsula of the megacity is more abundant than the eastern one about the water bodies involving lakes and lagoons. Expansion of the built-up areas of the eastern peninsula to the water basin is larger than the development within the western one. These spatial expansions are, to some extent, the results of the approval-, amendment-, and implementationbased problems within the official urban plans.

Urban macroform development represents the age, and the network of the urban building pattern and its potential interact with the disasters as in the case of earthquake, flood, and terrorist attacks.

North Anatolian Fault passing through the Sea of Marmara is a major hazard for the megacity. According to the official map of the dispersion of earthquake zones in Istanbul [25], within the total five level of earthquake zones of Turkey, the megacity captures the highest four levels. Dispersion of the zones reveals a concern to the southern districts that involve the old coastal settlements (**Figure 5**).

**Figure 5.** Dispersion of the earthquake zones and tsunami run-up heights in Istanbul (adapted from [25, 26]).

Old development areas of megacity represent the dense built-up spaces involving narrow roads within a mazy road network. Such a pattern with an attached building collapse potential brings about a chaotic evacuation road network and an insufficient amount of open spaces for the evacuation areas. Thus, earthquake and accompanying secondary disasters can easily create cascading infrastructure failures within the old urban setting.

drastically within the 1970s (**Figure 4**). Throughout the decades, the built-up spaces expanded to the water basins due to the combination of legal, illegal, and informal residential areas

Today, urban sprawl is concerned to be a problem as the ecologically important and protected areas of Istanbul even in the transfrontier scale exist in the northern part of the megacity. As an indicator for both the ecologically important and sensitive areas, this study handles the water basins. To explore the interplay between the urban macroform and water basins of Istanbul, boundaries of water basins are obtained as jpg files from the online city map service of the Istanbul Metropolitan Municipality and then rectified and adapted to the GIS studies. Western peninsula of the megacity is more abundant than the eastern one about the water bodies involving lakes and lagoons. Expansion of the built-up areas of the eastern peninsula to the water basin is larger than the development within the western one. These spatial expansions are, to some extent, the results of the approval-, amendment-, and implementation-

Urban macroform development represents the age, and the network of the urban building pattern and its potential interact with the disasters as in the case of earthquake, flood, and

North Anatolian Fault passing through the Sea of Marmara is a major hazard for the megacity. According to the official map of the dispersion of earthquake zones in Istanbul [25], within the total five level of earthquake zones of Turkey, the megacity captures the highest four levels. Dispersion of the zones reveals a concern to the southern districts that involve the old

**Figure 5.** Dispersion of the earthquake zones and tsunami run-up heights in Istanbul (adapted from [25, 26]).

occurred at the urban–rural fringe.

146 Sea Level Rise and Coastal Infrastructure

based problems within the official urban plans.

terrorist attacks.

coastal settlements (**Figure 5**).

OYO International [26] examines the interplay between the earthquake and secondary hazards. Hence, the GIS-based maps generated within this study benefitted from it for the spatial dispersion of the tsunami run-up heights reaching 10 m as the highest (**Figure 5**).

Alpar et al. [27] forwarded the fact of near-field tsunami for Istanbul. Thus, they highlighted that it is rather problematic to estimate a near-field tsunami impact on the islands and southern coastal districts of Istanbul due to the nonexhaustive historical documents, the longtime interval between the devastating earthquakes, and the limited distance between the fault and coastline.

In the case of Istanbul, fragmented open space network appears to be important for the disaster management. Turer Baskaya [5] highlighted the issue that open reserve areas should not be used at least for the major evacuation facilities as their future concerns a big question mark in such a rapidly changing megacity.

Due to the high land prices and limited empty area for development within the inner city, there are some instant transformation projects and pertinent implementations ongoing from varying scales. Today, amendment plans are the reality of the megacity, which is a tool for the planning system authorities to catch the rapid spatial change. Thus, new constructions may easily occur on the green fields, which had been available for the evacuation areas.

An evacuation system should not rely on the reserve open areas/green fields at least for the major facilities but prefer already designed open spaces, semipublic open spaces of administrative, educational, healthcare, and religious buildings or protected lands for the evacuation hubs [5].

A continuous green connection between the coastline and interior lands is vital for the disaster-prone coastal cities. Green represents here not only planted public areas but also semipublic areas, pedestrianized streets or multifunctional land uses involving plenty of pervious surfaces and let semipublic-public accesses. In this study, human and ecosystem friendly corridors with varying ratios in between capture the meaning of green connectivity.

Coastal megacities are considered to be disadvantaged regarding sea-borne risks. However, due to the existence of their public gates to the sea in case of a disaster like earthquake, they can rely on sea transportation and stay in access to urban, national, or even international traffic.

This study highlights the current and projected hazards as in the case of instantly decided megaprojects. Megaprojects [19] remarked that in between the years of 1998 and 2017, a total of 120 megaprojects (completed or continuing) have taken place in Istanbul. This amount raises a question about the feasibility and sustainability of the projects. This study handles one megaproject as a sample to interrogate their interplay with the environmental dynamics, which is the combined projects of Canal Istanbul, surrounding development areas, and the third airport. Besides, this combined project is not taking place in the official environment plan of Istanbul dated 2009.

The draft plans of the Canal Istanbul forward the pertinent sizes as 25 m depth, 200 m width, and 42 km length [28]. Kundak and Baypinar [29] compared the main artificial canals in the world, Bosphorus Strait and Canal Istanbul. They indicated that these artificial ones acting as the megaprojects of their period were built to gain substantial benefits like shortening the sea navigation distance, diminishing the risks pertinent to the duration of the journey together with the severe environmental conditions. However, in the case of the Bosphorus Strait and Canal Istanbul, there is neither a shorter nor longer one to attach the Black Sea to the Sea of Marmara; therefore, the duration of the cruise does not change.

Touching the equilibrium between different seas should regard cautiousness. Besides many others, this study highlights a credible 2015 dated report of World Wildlife Fund (WWF), which is prepared by 21 scholars with a title of "either canal or Istanbul" in Turkish. Physical (e.g., temperature), chemical (e.g., salinity), and biological (e.g., chlorophyll concentration) differences between the Mediterranean Sea and the Black Sea are in the balance by means of the Istanbul and Canakkale straits together with the Marmara Sea. The Black Sea and Mediterranean waters with different intensities (temperature, salinity) are divided into two layers with an obvious interface. The top and bottom layer waters flowing in opposite directions to each other join with each other by the effect of shear stress and turbulence along the distance, providing water, heat, and matter exchange between them. Mixing mechanisms and two water bodies arising from different seas are undergoing rapid change along their way, especially in the shallow Istanbul and Dardanelles Straits. Thus, any intervention to this system should require well analyzing supported by the reliable data [30].

According to Saydam [31], this canal will have an impact on the lower waters of the Marmara Sea approaching the Bosphorus to reach the Black Sea by such a short period that it can be expressed in months. Therefore, this project may lead to even the end of the life in the Marmara Sea.

**Figures 6** and **7** illustrate the route of the canal, location of the airport at the north, and the new development areas surrounding the watercourse. These construction areas with an area of 38,500 ha announced through the 13.08.2012 dated [32] decree of the council of ministers.

The Turkish Foundation for Combating Soil Erosion, for Reforestation and the Protection of Natural Habitats (TEMA) highlights that the proposed construction areas are located in the ecologically important internationally protected areas. It forwards the adverse impacts of the third Bridge, third Airport, and Canal Istanbul combined projects on the forest ecosystem and endemic species, flora and fauna richness, fertile lands, currents and marine ecosystem, local climate and climate change, bird migration routes, freshwater resources, urban development, and transportation system [33].

These projects will change the urban development pattern and generate a massive amount of settlement areas in the northern part of the western peninsula. These projects will turn the already existing built-up space of the western peninsula into an island and welcome more population to the city (**Figure 6**).

the current built-up spaces. Turer Baskaya and Ayatac [35] figured out the historical urban streams of the megacity regarding their interplay with the surrounding built-up spaces. With a focus on eight of the major historical urban streams, it highlights that the role of stream ecosystems in urban planning has been disregarded especially after the mid 1970s, which has

**Figure 7.** Cascading hazards of flood, sea level rise, salinization, and the megaprojects of the Canal Istanbul and third airport.

**Figure 6.** Interplay between the urban development, water basins, and the megaprojects of Canal Istanbul and third airport.

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149

Flooding is natural but turns into a disaster in case the built-up spaces are developed in the flood-prone areas without enough regard to natural dynamics [34]. Istanbul is rich about its urban streams, which are generating a water network both hidden "within" and "under" Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments… http://dx.doi.org/10.5772/intechopen.73567 149

The draft plans of the Canal Istanbul forward the pertinent sizes as 25 m depth, 200 m width, and 42 km length [28]. Kundak and Baypinar [29] compared the main artificial canals in the world, Bosphorus Strait and Canal Istanbul. They indicated that these artificial ones acting as the megaprojects of their period were built to gain substantial benefits like shortening the sea navigation distance, diminishing the risks pertinent to the duration of the journey together with the severe environmental conditions. However, in the case of the Bosphorus Strait and Canal Istanbul, there is neither a shorter nor longer one to attach the Black Sea to the Sea of

Touching the equilibrium between different seas should regard cautiousness. Besides many others, this study highlights a credible 2015 dated report of World Wildlife Fund (WWF), which is prepared by 21 scholars with a title of "either canal or Istanbul" in Turkish. Physical (e.g., temperature), chemical (e.g., salinity), and biological (e.g., chlorophyll concentration) differences between the Mediterranean Sea and the Black Sea are in the balance by means of the Istanbul and Canakkale straits together with the Marmara Sea. The Black Sea and Mediterranean waters with different intensities (temperature, salinity) are divided into two layers with an obvious interface. The top and bottom layer waters flowing in opposite directions to each other join with each other by the effect of shear stress and turbulence along the distance, providing water, heat, and matter exchange between them. Mixing mechanisms and two water bodies arising from different seas are undergoing rapid change along their way, especially in the shallow Istanbul and Dardanelles Straits. Thus, any intervention to this sys-

According to Saydam [31], this canal will have an impact on the lower waters of the Marmara Sea approaching the Bosphorus to reach the Black Sea by such a short period that it can be expressed in months. Therefore, this project may lead to even the end of the life in the

**Figures 6** and **7** illustrate the route of the canal, location of the airport at the north, and the new development areas surrounding the watercourse. These construction areas with an area of 38,500 ha announced through the 13.08.2012 dated [32] decree of the council of ministers. The Turkish Foundation for Combating Soil Erosion, for Reforestation and the Protection of Natural Habitats (TEMA) highlights that the proposed construction areas are located in the ecologically important internationally protected areas. It forwards the adverse impacts of the third Bridge, third Airport, and Canal Istanbul combined projects on the forest ecosystem and endemic species, flora and fauna richness, fertile lands, currents and marine ecosystem, local climate and climate change, bird migration routes, freshwater resources, urban development,

These projects will change the urban development pattern and generate a massive amount of settlement areas in the northern part of the western peninsula. These projects will turn the already existing built-up space of the western peninsula into an island and welcome more

Flooding is natural but turns into a disaster in case the built-up spaces are developed in the flood-prone areas without enough regard to natural dynamics [34]. Istanbul is rich about its urban streams, which are generating a water network both hidden "within" and "under"

Marmara; therefore, the duration of the cruise does not change.

tem should require well analyzing supported by the reliable data [30].

Marmara Sea.

148 Sea Level Rise and Coastal Infrastructure

and transportation system [33].

population to the city (**Figure 6**).

**Figure 6.** Interplay between the urban development, water basins, and the megaprojects of Canal Istanbul and third airport.

**Figure 7.** Cascading hazards of flood, sea level rise, salinization, and the megaprojects of the Canal Istanbul and third airport.

the current built-up spaces. Turer Baskaya and Ayatac [35] figured out the historical urban streams of the megacity regarding their interplay with the surrounding built-up spaces. With a focus on eight of the major historical urban streams, it highlights that the role of stream ecosystems in urban planning has been disregarded especially after the mid 1970s, which has brought about cascading environmental problems. The flood also appears to be a gradually increasing problem within the densely populated areas standing on old urban infrastructures.

Ozacar [36] interrogated the impacts of urbanization on flood and soil erosion hazards in Istanbul and forwarded a scored watershed map representing the distribution of the floods from 1997 to 2010 (**Figure 7**). Flood risk areas holding the levels of the highest and high are standing in the urban development areas. Scarcity of the pervious surfaces and the buried urban streams are the features of daily landscapes of Istanbul, which promote the flood risk. Development areas of the western peninsula examine a higher flood risk level, which will grow even more by the construction of the megaprojects of Canal Istanbul and the third airport.

Resilient landscape planning and designing can forward a systems-based approach to give an adapted new way of life to the people living in flood-prone areas [37]. Even in the world cities, there are implementations of such resilient studies which prove that regarding the unique natural features it owns, Istanbul can benefit from this systems-based approach. However, Istanbul should first figure out how the megaprojects can be sensitive to environmental-natural dynamics.

Intervention to degraded landscapes is an initial integral to the systems-based approach regarding the megacity of Istanbul. Industrial facilities and brownfields within the expanding development areas and buried urban streams existing even in the most densely settled areas might be the subject of landscape intervention studies. Regarding the mosaic of the diverse landscape features, combined intervention techniques of reclamation, rehabilitation, naturalization, and enhancement should be developed through a multiscale perspective.

Today, salinization is not a robust current hazard, but Canal Istanbul Project will increase it and promote it probably to the priority level. **Figure 6** represents the megaproject and the sea level rise impacts on the water bodies. Western peninsula will lose a great amount of water sources.

nodes and urban vantage point calculations to the assessment of mobility modes of the pedestrians within the pedestrianized areas. Thus, multiscale and multidisciplinary studies are necessary for the new multilayered understanding of the public spaces of the twenty-first century.

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151

Dynamics altering the urbanized areas differ throughout the decades by the proportional changes and technical promotions of the already existing dynamics and the emerging new ones together with the redefined interplays between them. This brings about a necessity of

Based on the GIS-based findings of this study, **Table 1** indicates the hazards distribution along the coastlines regarding their levels. Gray-colored ones in the table represent the levels that will increase following the establishment of the megaproject. Planning strategies should be developed according to not only the hazards' but also the coastlines' characteristics.

Insufficient amount of open spaces brings about the strategy of generating hybrid and transformable landscapes to provide efficient usage of the available open spaces in the disasterprone cities. Turer Baskaya [5] defined the concept of disaster-sensitive hybrid spaces. In case of a disaster, open spaces of daily life instantly may transform into hybrid spaces for the emergency evacuation. Hybrid open spaces hold varying public to semipublic open spaces and strategically important buildings/facilities to bind them to each other to enlarge the capacity of services. Thus, both the internal and external spaces of these facilities turn into

ever up-to-date planning and design approaches and techniques.

**Figure 8.** Dispersion of the terrorist attacks within the last 15 years.

One of the significant potential impacts of the accelerated sea level rise on Istanbul is saltwater intrusion as two of the big lagoons, one lake as a drinking water supply and the historical estuary of Golden Horn are in great vulnerability [14, 38]. Another concern about the city is the impact of the sea level rise on the spatial matrix of the cultural heritages, which extends along the strait of Bosphorus and the northern centrum of Marmara Sea (**Figure 7**).

Rural migration and the seasonal demographic change due to the high tourism capacity compose the chaotic demographic pattern of the megacity. This demographic profile together with a mazy pattern of the built-up spaces generates prone areas to man-made disasters. Within the last 15 years, 13 terrorist attacks occurred there, while all of them were in the western peninsula (**Figure 8**).

Historical squares and cultural heritages surrounding areas appear to be attack-prone due to the incredibly high amount of mobile people, narrow roads within a mazy road network, and blocked views within the built-up space configurations.

For the enhancement of attack-prone existing spatial configurations, diverse issues should be taken into consideration ranging from enlightenment techniques to the design of transportation Revealing Landscape Planning Strategies for Disaster-Prone Coastal Urban Environments… http://dx.doi.org/10.5772/intechopen.73567 151

**Figure 8.** Dispersion of the terrorist attacks within the last 15 years.

brought about cascading environmental problems. The flood also appears to be a gradually increasing problem within the densely populated areas standing on old urban infrastructures. Ozacar [36] interrogated the impacts of urbanization on flood and soil erosion hazards in Istanbul and forwarded a scored watershed map representing the distribution of the floods from 1997 to 2010 (**Figure 7**). Flood risk areas holding the levels of the highest and high are standing in the urban development areas. Scarcity of the pervious surfaces and the buried urban streams are the features of daily landscapes of Istanbul, which promote the flood risk. Development areas of the western peninsula examine a higher flood risk level, which will grow even more by the construction of the megaprojects of Canal Istanbul and the third airport.

Resilient landscape planning and designing can forward a systems-based approach to give an adapted new way of life to the people living in flood-prone areas [37]. Even in the world cities, there are implementations of such resilient studies which prove that regarding the unique natural features it owns, Istanbul can benefit from this systems-based approach. However, Istanbul should first figure out how the megaprojects can be sensitive

Intervention to degraded landscapes is an initial integral to the systems-based approach regarding the megacity of Istanbul. Industrial facilities and brownfields within the expanding development areas and buried urban streams existing even in the most densely settled areas might be the subject of landscape intervention studies. Regarding the mosaic of the diverse landscape features, combined intervention techniques of reclamation, rehabilitation, natural-

Today, salinization is not a robust current hazard, but Canal Istanbul Project will increase it and promote it probably to the priority level. **Figure 6** represents the megaproject and the sea level rise impacts on the water bodies. Western peninsula will lose a great amount of water

One of the significant potential impacts of the accelerated sea level rise on Istanbul is saltwater intrusion as two of the big lagoons, one lake as a drinking water supply and the historical estuary of Golden Horn are in great vulnerability [14, 38]. Another concern about the city is the impact of the sea level rise on the spatial matrix of the cultural heritages, which extends

Rural migration and the seasonal demographic change due to the high tourism capacity compose the chaotic demographic pattern of the megacity. This demographic profile together with a mazy pattern of the built-up spaces generates prone areas to man-made disasters. Within the last 15 years, 13 terrorist attacks occurred there, while all of them were in the west-

Historical squares and cultural heritages surrounding areas appear to be attack-prone due to the incredibly high amount of mobile people, narrow roads within a mazy road network, and

For the enhancement of attack-prone existing spatial configurations, diverse issues should be taken into consideration ranging from enlightenment techniques to the design of transportation

ization, and enhancement should be developed through a multiscale perspective.

along the strait of Bosphorus and the northern centrum of Marmara Sea (**Figure 7**).

to environmental-natural dynamics.

150 Sea Level Rise and Coastal Infrastructure

sources.

ern peninsula (**Figure 8**).

blocked views within the built-up space configurations.

nodes and urban vantage point calculations to the assessment of mobility modes of the pedestrians within the pedestrianized areas. Thus, multiscale and multidisciplinary studies are necessary for the new multilayered understanding of the public spaces of the twenty-first century.

Dynamics altering the urbanized areas differ throughout the decades by the proportional changes and technical promotions of the already existing dynamics and the emerging new ones together with the redefined interplays between them. This brings about a necessity of ever up-to-date planning and design approaches and techniques.

Based on the GIS-based findings of this study, **Table 1** indicates the hazards distribution along the coastlines regarding their levels. Gray-colored ones in the table represent the levels that will increase following the establishment of the megaproject. Planning strategies should be developed according to not only the hazards' but also the coastlines' characteristics.

Insufficient amount of open spaces brings about the strategy of generating hybrid and transformable landscapes to provide efficient usage of the available open spaces in the disasterprone cities. Turer Baskaya [5] defined the concept of disaster-sensitive hybrid spaces. In case of a disaster, open spaces of daily life instantly may transform into hybrid spaces for the emergency evacuation. Hybrid open spaces hold varying public to semipublic open spaces and strategically important buildings/facilities to bind them to each other to enlarge the capacity of services. Thus, both the internal and external spaces of these facilities turn into


Hazard levels that will increase after the implementation of the megaproject.

**Table 1.** Dispersion of the hazard types along the coastline types of the megacity of Istanbul.

new components of the emergency response [5]. Besides increasing the capacity, letting citizens perceive and get aware of the defined elements of the disaster management is essential.

When we handle the disaster-prone urban environments through human scale, benefiting from landscape mental map arrives. Sulsters [39] states that every person deals with his unique city experience and a mental map as the byproduct of this experience. These maps are involving not only the direct experiences but also the perceptional ones attached to their fund of life.

In this study, application of mental mapping in urban legibility studies appears to be important to estimate the behaviors of victims of the disasters in case of an emergency. Revealing the way they are going to act, the landmarks they are going to use for the orientation, and places they will select for their evacuations are essential for disaster-sensitive spatial designs and even for placing awareness raising features within these places. Awareness raising through the spatial design of daily landscapes stands as a successful mitigation tool.

**Figure 9** illustrates the urban dynamics and characteristics of the disaster-prone megacity of Istanbul and proposes landscape strategies and their interplay with the hazard types as a summary of the so far discussed findings of this study. As a rapidly altering megacity open to massive coastal changes, Istanbul is a demanding case study but capable of forwarding varying strategies.

**Figure 9.** Interplay between the urban pattern-dynamics, disaster types, and landscape strategies.

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153

**Figure 9.** Interplay between the urban pattern-dynamics, disaster types, and landscape strategies.

new components of the emergency response [5]. Besides increasing the capacity, letting citizens perceive and get aware of the defined elements of the disaster management is essential. When we handle the disaster-prone urban environments through human scale, benefiting from landscape mental map arrives. Sulsters [39] states that every person deals with his unique city experience and a mental map as the byproduct of this experience. These maps are involving not only the direct experiences but also the perceptional ones attached to their fund of life. In this study, application of mental mapping in urban legibility studies appears to be important to estimate the behaviors of victims of the disasters in case of an emergency. Revealing the way they are going to act, the landmarks they are going to use for the orientation, and places they will select for their evacuations are essential for disaster-sensitive spatial designs and even for placing awareness raising features within these places. Awareness raising through

**Figure 9** illustrates the urban dynamics and characteristics of the disaster-prone megacity of Istanbul and proposes landscape strategies and their interplay with the hazard types as a summary of the so far discussed findings of this study. As a rapidly altering megacity open to massive coastal changes, Istanbul is a demanding case study but capable of forwarding

the spatial design of daily landscapes stands as a successful mitigation tool.

**Flood Earthquake Tsunami Sea level rise** 

available

available

Hazard levels (++++: very high, +++: high, ++: medium, +: low, −: in between non to very low). Hazard levels that will increase after the implementation of the megaproject.

**Table 1.** Dispersion of the hazard types along the coastline types of the megacity of Istanbul.

Eastern Bosphorus ++ ++ + +++ — — +++ Eastern Marmara ++ ++++ +++ +++ — — ++

Golden Horn +++ ++ ++ ++++ — ++ ++++ Princes' Islands ++ ++++ ++++ +++ — — ++

**(projected)**

++ ++ + + — + +++

++++ ++++ ++ ++ — + +++

+ ++ + — ++++ — —

+ +++ — — + — —

**Salinization (projected)**

++++ — — —

++++ — — —

**Terrorist attacks**

**Old built-up space**

varying strategies.

Western Bosphorus

(Urbanized areas of) Western Marmara

152 Sea Level Rise and Coastal Infrastructure

Western Lakes & Lagoons

Eastern Lakes & Lagoons

Western Black Sea + + Not

Eastern Black Sea + ++ Not

### **4. Conclusion**

Istanbul is worldwide known coastal megacity having mega-impacts on the natural and cultural environment. This megacity captures a unique coastal location, which has promoted it as a cultural bridge and a world scale financial node inviting more or less inevitably mega coastal projects to itself. However, this coastal identity also renders it as one of the most hazard-prone settlements of the world.

[3] Jha AK, Miner TW, Stanton-Geddes Z. Building Urban Resilience Principles, Tools, and Practice. Washington, DC: World Bank; 2013. DOI: 10.1596/978-0-8213-8865-5

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[4] UNISDR Terminology on Disaster Risk Reduction [Internet]. United Nations Office for Risk Reduction; 2017. [Cited 2017 August 10]. Available from: http://www.unisdr.org/

[5] Turer Baskaya FA. Disaster sensitive landscape planning for the coastal megacity of Istanbul. Journal of Coastal Conservation – Planning and Management. 2015;**19**:729-742.

[6] Eurosoft. Licence Free Istanbul Photographs [Cd]. Istanbul: Eurosoft Software Distribu-

[7] Hagerman C. Green infrastructure, In: Cohen N, Robbins P, editors. Green Cities- An A-to- Z Guide. The SAGE reference series on green society. United States of America:

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[10] Erdik M, Durukal E. Earthquake risk and its mitigation in Istanbul. Natural Hazards.

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Studies. 2015;**2**(1):36-43

IFRC [40] highlighted the mighty transaction between hazard, vulnerability, and risk. When the vulnerability of the community and the adverse impacts of the risk cannot be decreased, risk management fails, and hazards turn into disasters.

This study handles the coastal megacity of Istanbul and interrogates its chaotic characteristics and dynamics to reveal the power of multilayered and multiscale landscape strategies for preventing the hazards turning into disasters. These landscape strategies are adaptable to other hazard-prone coastal cities and take the initial steps for further studies to handle a disaster-free future.

### **Acknowledgements**

The author would like to thank the Istanbul Technical University Board of Scientific Research Projects as the initial idea of studying on hazard-prone landscapes emerged following the findings of a research project "Developing landscape planning strategies for the historical urban steams- case of Istanbul" with project number 38165.

### **Author details**

Fatma Aycim Turer Baskaya

Address all correspondence to: turerfat@itu.edu.tr

Department of Landscape Architecture, Istanbul Technical University, Istanbul, Turkey

### **References**


[3] Jha AK, Miner TW, Stanton-Geddes Z. Building Urban Resilience Principles, Tools, and Practice. Washington, DC: World Bank; 2013. DOI: 10.1596/978-0-8213-8865-5

**4. Conclusion**

154 Sea Level Rise and Coastal Infrastructure

disaster-free future.

**Author details**

**References**

2017-07-10]

DOI: 10.1007/s11069-006-9073-2

Fatma Aycim Turer Baskaya

**Acknowledgements**

ard-prone settlements of the world.

risk management fails, and hazards turn into disasters.

urban steams- case of Istanbul" with project number 38165.

Address all correspondence to: turerfat@itu.edu.tr

Istanbul is worldwide known coastal megacity having mega-impacts on the natural and cultural environment. This megacity captures a unique coastal location, which has promoted it as a cultural bridge and a world scale financial node inviting more or less inevitably mega coastal projects to itself. However, this coastal identity also renders it as one of the most haz-

IFRC [40] highlighted the mighty transaction between hazard, vulnerability, and risk. When the vulnerability of the community and the adverse impacts of the risk cannot be decreased,

This study handles the coastal megacity of Istanbul and interrogates its chaotic characteristics and dynamics to reveal the power of multilayered and multiscale landscape strategies for preventing the hazards turning into disasters. These landscape strategies are adaptable to other hazard-prone coastal cities and take the initial steps for further studies to handle a

The author would like to thank the Istanbul Technical University Board of Scientific Research Projects as the initial idea of studying on hazard-prone landscapes emerged following the findings of a research project "Developing landscape planning strategies for the historical

Department of Landscape Architecture, Istanbul Technical University, Istanbul, Turkey

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**Section 6**

**Coastal Geomorphology**

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