#

\$T

Hombole Fl Ga St Hombole Fl Ga St

#

#

Endale(2008)

Fig. 1c. A location map of Case study 2, Nyumba Ya Mungu Reservoir, in the upstream part of the Pangani River catchment as adopted from (Ndomba, 2007)

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 269

2005). The surface runoff (Qsurf ) as input to the MUSLE equation is simulated by the runoff component of SWAT. The SWAT model uses water balance Equation 2 as a driving force

*t t day i surf i a i seep i gw i*

SWt is the final soil water content (mm), SWt-1 is the initial soil water content on day i (mm), t = 1, 2, 3,…,n where "n" is the total number of days during the simulation (days), Rday\_i is the amount of precipitation on day i (mm), Qsurf\_i is the amount of surface runoff on day i (mm), Ea\_i is the amount of evapotranspiration on day i (mm), Wseep\_i is the amount of water entering the vadose zone from the soil profile on day i (mm), and Qgw\_i is the amount of

SWAT uses Manning's equation to define the rate and velocity of flow. Water is routed through the channel network using the variable storage routing method or the Muskingum River routing method. Both the variable storage and Muskingum routing methods are

The sediment flow data are readily available (Table 2a-c). The quality and adequacy of data varies from one catchment to the other. In the two study cases, the Simiyu River and Koka Reservoir catchments, secondary data on streams flows, climate, sediment flow and spatial data were used to setup, calibrate and validate the model. These are typical data types used in most of the SWAT applications elsewhere in the region (Andualem and Yonas, 2008; Shimelis *et al*., 2010). Most of the sediment flow data are intermittent instantaneous sediment flow data. In one of the cases, the NYM Reservoir subcatchment, primary data on sediment flow was collected to complement the analysis. These are continuous subdaily sediment concentrations data plus multi-temporal reservoir survey information. As Table 2 stipulates, various sources of data were explored. The data preparation and analysis task involved analyzing statistics such as season mean, percent missing data, identifying outliers, length of the records, temporal and spatial variability of rainfall, and wet years' period. The wet years' period is defined as the period when the annual total rainfall is above the long term annual average. The analysis was meant to guide and provide data for the SWAT modelling. For instance, spatial variability justified the need for distributed modelling. The derived statistics were also used as inputs to weather generator module of SWAT. The module generates climatic data or fills in gaps in measured records. As presented in Tables 2a-1, 2b-1 and 2c-1, the input spatial data included base maps such as readily available topographic maps in the Ministries and global spatial thematic maps (*i.e.* Digital Elevation Models, DEM; Soil, and Landuse-cover) of various resolutions. One of the case studies reviewed in this paper (Mulungu and Munishi, 2007), used high-resolution data on land use from the 30 m LandSat TM Satellite, the 90 m Digital Elevation Model and the Soil and Terrain Database for Southern Africa (SOTERSAF). In some cases such as NYM, the soil types were extracted from Pauw (1984) digital map and complemented by the Soil Atlas of Tanzania (Hathout, 1983). Similarly, climatic data included rainfall data from the regular

*SW SW R Q E w Q*

1 \_ \_\_ \_ \_

(2)

behind everything that happens in the watershed (Neitsch *et al*., 2005).

1

*i*

variations of the kinematic wave model (Neitsch *et al*., 2005).

return flow on day i (mm).

**2.3 Data and data analysis** 

ground monitoring network.

*t*

Fig. 1d. A location map of Case study 3, Simiyu River catchment, Tanzania as adopted from Abdelhamid (2010)

#### **2.2 SWAT concept**

The SWAT model uses the Modified USLE (MUSLE) equation developed by Williams (1975) (Equation 1) to simulate the sediment yield from the upland catchments (Neitsch *et al*., 2005). The surface runoff (Qsurf ) as input to the MUSLE equation is simulated by the runoff component of SWAT. The SWAT model uses water balance Equation 2 as a driving force behind everything that happens in the watershed (Neitsch *et al*., 2005).

$$\text{SSV}\_{t} = \text{SVV}\_{t-1} + \sum\_{i=1}^{t} \left( R\_{day\\_i} - Q\_{surf\\_i} - E\_{a\\_i} - w\_{sep\\_i} - Q\_{gw\\_i} \right) \tag{2}$$

SWt is the final soil water content (mm), SWt-1 is the initial soil water content on day i (mm), t = 1, 2, 3,…,n where "n" is the total number of days during the simulation (days), Rday\_i is the amount of precipitation on day i (mm), Qsurf\_i is the amount of surface runoff on day i (mm), Ea\_i is the amount of evapotranspiration on day i (mm), Wseep\_i is the amount of water entering the vadose zone from the soil profile on day i (mm), and Qgw\_i is the amount of return flow on day i (mm).

SWAT uses Manning's equation to define the rate and velocity of flow. Water is routed through the channel network using the variable storage routing method or the Muskingum River routing method. Both the variable storage and Muskingum routing methods are variations of the kinematic wave model (Neitsch *et al*., 2005).

#### **2.3 Data and data analysis**

268 Advances in Data, Methods, Models and Their Applications in Geoscience

Fig. 1d. A location map of Case study 3, Simiyu River catchment, Tanzania as adopted from

The SWAT model uses the Modified USLE (MUSLE) equation developed by Williams (1975) (Equation 1) to simulate the sediment yield from the upland catchments (Neitsch *et al*.,

Abdelhamid (2010)

**2.2 SWAT concept** 

The sediment flow data are readily available (Table 2a-c). The quality and adequacy of data varies from one catchment to the other. In the two study cases, the Simiyu River and Koka Reservoir catchments, secondary data on streams flows, climate, sediment flow and spatial data were used to setup, calibrate and validate the model. These are typical data types used in most of the SWAT applications elsewhere in the region (Andualem and Yonas, 2008; Shimelis *et al*., 2010). Most of the sediment flow data are intermittent instantaneous sediment flow data. In one of the cases, the NYM Reservoir subcatchment, primary data on sediment flow was collected to complement the analysis. These are continuous subdaily sediment concentrations data plus multi-temporal reservoir survey information. As Table 2 stipulates, various sources of data were explored. The data preparation and analysis task involved analyzing statistics such as season mean, percent missing data, identifying outliers, length of the records, temporal and spatial variability of rainfall, and wet years' period. The wet years' period is defined as the period when the annual total rainfall is above the long term annual average. The analysis was meant to guide and provide data for the SWAT modelling. For instance, spatial variability justified the need for distributed modelling. The derived statistics were also used as inputs to weather generator module of SWAT. The module generates climatic data or fills in gaps in measured records. As presented in Tables 2a-1, 2b-1 and 2c-1, the input spatial data included base maps such as readily available topographic maps in the Ministries and global spatial thematic maps (*i.e.* Digital Elevation Models, DEM; Soil, and Landuse-cover) of various resolutions. One of the case studies reviewed in this paper (Mulungu and Munishi, 2007), used high-resolution data on land use from the 30 m LandSat TM Satellite, the 90 m Digital Elevation Model and the Soil and Terrain Database for Southern Africa (SOTERSAF). In some cases such as NYM, the soil types were extracted from Pauw (1984) digital map and complemented by the Soil Atlas of Tanzania (Hathout, 1983). Similarly, climatic data included rainfall data from the regular ground monitoring network.

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 271

No. Of data points 288 288 288 288 288 Average 54.9 0.724 0.098 4.025 19.1 Minimum 2.8 0.420 0.025 2.885 1 Maximum 830.0 2.270 0.190 12.227 359 Standard deviation 75.5 0.28 0.014 1.12 34.91

**T**able 2a-3. Daily suspended sediment flow data for the Nyumba Ya Mungu Reservoir catchment in the upstream part of the Pangani River catchment at the 1DC1-Ruvu site sampled using DH-48 sampler sampled at 9.00 hrs between April 19, 2005 and January 31,

> Data availability

6. Soil - 2010 - FAO/SOTER

Table 2b-1. Data types and sources for the Simiyu River catchment

Africa; FAO: Food and Agricultural Organization.

Note: (1) Fall signifies measured water gauge height difference between Base and Auxiliary gauging stations; and (2) Stream flow discharge data were derived from complex rating

1. Rainfall 13 1928-2003 8.8-69.8 MoWI/ TMA - Daily/

2. Climate 2 1970-1984 3.4-21 MoWI/ TMA - Daily 3. Flows 1 1969-2000 46 MoWI - Daily 4. DEM - 2010 - ASTER 30 m - 5. Landuse 2010 - GLCC 1 km -

Note: ASTER: Advanced Space borne Thermal Emission and Reflection Radiometer; GLCC: Global Land Cover Charactersization; SOTERSAF: Soil Terrain Database for Southern

%

missing Source

SAF

1 1999-2003 0 UDSM - Daily

Base Gauge Height [m]

4.45 0.02 0.001 0.07 2.06

Fall, [m]

Stream flow discharge [m3/s]

Sediment load, [t/day]

Resolution Spatial Temporal

10 km -

hourly

intermittent

Suspended sediment concentrations [mg/l]

Statistic

Standard Error of the

curve (Ndomba, 2007).

7. Sediment load

Data type No. of

stations

Mean (SEM)

2006

SN


Table 2a-1. Data types and sources for the Nyumba Ya Mungu Reservoir catchment located in the upstream part of the Pangani River catchment

Note: MoWI: Minstry of Water and Irrigation; TMA: Tanzania Meteorological Authority; PBWO: Pangani Basin Water Office; IRA: Institute of Resources Assessment based at University of Dar es Salaam (UDSM)


Table 2a-2. A summary of sediment flow data for the Nyumba Ya Mungu Reservoir catchment in the upstream part of the Pangani River catchment as sampled between March 18 and November 10, 2005 by an ISCO 6712 machine at 1DD1 site

missing Source

TMA/PBWO

TMA/PBWO

Hathout, (1983)


Gauge Height [m]

3 2005-2006 0 UDSM - Subdaily

Resolution Spatial Temporal

hourly

continuou

s

Streamflow discharge [m3/s]




%

2. Flows 3 1952-2005 1-54 MoWI/ /PBWO - Daily 3. DEM - 2006 - USGS/ HDRO 1K 1 km - 4. Landuse 1990's - IRA/Landsat 30 m -

Table 2a-1. Data types and sources for the Nyumba Ya Mungu Reservoir catchment located

Note: MoWI: Minstry of Water and Irrigation; TMA: Tanzania Meteorological Authority; PBWO: Pangani Basin Water Office; IRA: Institute of Resources Assessment based at

> Subdaily suspended sediment concentration [mg/l]

No. Of data points 291 291 291 Maximum 9110.0 4.44 256.53 Minimum 16.0 0.89 12.19 Mean 282.5 1.32 34.79

STD 801.7 0.49 30.02

Cv (%) 283.8 36.69 86.27

Mean, SEM 47.0 0.03 1.76

18 and November 10, 2005 by an ISCO 6712 machine at 1DD1 site

Table 2a-2. A summary of sediment flow data for the Nyumba Ya Mungu Reservoir catchment in the upstream part of the Pangani River catchment as sampled between March

SN

6. Sediment load

7. Reservoir bed contour

Data type No. of

stations

Data availability

1. Rainfall 31 1922-2005 1-60 MoWI/

Climate 9 1958-1999 0.1-62 MoWI/

5. Soil - 1983-1984 - Pauw(1984) &

1 1968 & 2005

in the upstream part of the Pangani River catchment

University of Dar es Salaam (UDSM)

Statistic

Standard Deviation,

Coefficient of Variation,

Standard Error of the


**T**able 2a-3. Daily suspended sediment flow data for the Nyumba Ya Mungu Reservoir catchment in the upstream part of the Pangani River catchment at the 1DC1-Ruvu site sampled using DH-48 sampler sampled at 9.00 hrs between April 19, 2005 and January 31, 2006

Note: (1) Fall signifies measured water gauge height difference between Base and Auxiliary gauging stations; and (2) Stream flow discharge data were derived from complex rating curve (Ndomba, 2007).


Table 2b-1. Data types and sources for the Simiyu River catchment Note: ASTER: Advanced Space borne Thermal Emission and Reflection Radiometer; GLCC: Global Land Cover Charactersization; SOTERSAF: Soil Terrain Database for Southern Africa; FAO: Food and Agricultural Organization.

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 273

Table 2c-2. A summary of continuous monthly sediment flow data for the Koka Reservoir catchment for the period from January 1990 to December 2004 at the Koka Reservoir as

It should be noted that SWAT, if not properly applied, may result in parameter uncertainty problems. Therefore, elaboration of the rationale of each application step is necessary. In these study cases the model was set up to represent the spatial variability of the main runoff-sediment yield controlling features such as soils, land use/cover, terrain (*i.e*, slope and slope length), river channels and reservoirs. The distributed nature of the sediment yield and erosion representation (lumped, semi and fully distributed) depended on the

The Latin Hypercube One-factor-At-a-Time (LH-OAT) design as proposed by Morris (1991) implemented in SWAT was used as a sensitivity analysis tool. Sensitivity analysis of hydrology and sediment transport components parameters were conducted without and/or with observed data before and after calibration. Various lengths of simulations (*i.e*. 2, 4, 6, 8 yrs and greater) were tested in order to capture model input (*i.e*., parameter and data)

Manual calibration, expert knowledge and automatic calibration techniques were tested for the calibration procedures. The autocalibration routine based on the Shuffled Complex Evolution-University of Arizona (SCE-UA) that is incorporated in the SWAT model has been used very often (Duan *et al*., 1992). In one of the study cases, SRC, the SUFI-2 program which combines calibration and uncertainty analysis (Abbaspour *et al*., 2004, 2007) was used. This tool is widely used in the region (Shimelis *et al*., 2010). The sensitive model parameters were adjusted within their feasible ranges during calibration to minimize model prediction errors for daily and monthly flow and sediment loads. In one of the study cases, *i.e*. the NYM Reservoir catchment, soil erodibility (KUSLE) (a MUSLE factor) was estimated according to the equation proposed by Mulengera and Payton (1999) for tropics. Biascorrected rating curves were developed and used to interpolate or extrapolate sediment loads (Ndomba *et al*., 2008b). It should be noted that to date there is no consensus on how to develop an excellent rating curve, especially from a short period of records. Ndomba *et al*. (2008b) developed the rating curve from continuous subdaily suspended sediment data (*i.e*. 2 to 12 samples a day) collected by an automatic pumping sampler (ISCO 6712). The ISCO 6712 sampler data were calibrated by daily-midway and intermittent-cross section sediment samples collected by a depth-integrating sampler (D-74). The sediment loads from rating

uncertainty. These analyses were used to identify the sensitive parameters.

No. 180 180.00 Max 421.930 179,841 Min 1.000 200 Mean 47.710 12,596 Standard Deviation, STD 78.010 18,178 Coefficient of Variation, Cv (%) 163.510 144 Standard Error of the Mean, SEM 5.810 1355

**2.4 SWAT model applications procedures and assumptions** 

availability of data and computation resources.

Mean monthly Stream flow discharges [m3/s]

Sediment load, [t/month]

Statistic

adopted from Endale (2008)


Table 2b-2. A summary of intermittent daily sediment flow data for the Simiyu River catchment as sampled between June 30, 1999 and May 29, 2004 at the Main Bridge site, the outlet


Table 2c-1. Data types and sources for the Koka Reservoir catchment Note: DH-MoWR: Department of Hydrology, Ministry of Water Resources, Ethiopia; NMSA: National Meteorological Service Agency, Ethiopia; and "-" Not applicable.

Daily Stream flow discharge [m3/s]

Gauge Height [m]

missing Source Resolution

HDRO 1K


RSAF

3 1990-2004 2.2-2.8 DH-MoWR - Daily

NMSA - Daily/ho

NMSA - Daily

DH-MoWR - Daily

1 km -

10 km -

Sediment load, [t/day]

Spatial Temporal

urly

intermitte nt

years, respective

ly

Suspended sediment concentrations [mg/l]

No. 102 70 13 70 Max 5067 213 3.250 20,707 Min 5.0 2.0 0.250 13.0 Mean 981.0 19.925 1.813 2,615 Standard Deviation, STD 1108.6 29.240 1.051 4,325 Coefficient of Variation, Cv (%) 113 146.748 57.949 165

SEM 109.8 3.495 0.291 517 Table 2b-2. A summary of intermittent daily sediment flow data for the Simiyu River catchment as sampled between June 30, 1999 and May 29, 2004 at the Main Bridge site, the

> Data availability

%

0.24

1.42

0.27

5. Landuse 2008 - GLCC 1 km -

Note: DH-MoWR: Department of Hydrology, Ministry of Water Resources, Ethiopia; NMSA: National Meteorological Service Agency, Ethiopia; and "-" Not applicable.

Statistic

Standard Error of the Mean,

SN Data type No. of

stations

1. Rainfall 3 1990-2004 0.12-

2. Climate 4 1990-2004 1.32-

3. Flows 3 1990-2004 0.15-

4. DEM 2008 - USGS/

1959, 1981,

6. Soil 2008 - FAO/SOTE

1988, 1999

Table 2c-1. Data types and sources for the Koka Reservoir catchment

outlet

7.

8.

Sediment load

Reservoir bed contour


Table 2c-2. A summary of continuous monthly sediment flow data for the Koka Reservoir catchment for the period from January 1990 to December 2004 at the Koka Reservoir as adopted from Endale (2008)

#### **2.4 SWAT model applications procedures and assumptions**

It should be noted that SWAT, if not properly applied, may result in parameter uncertainty problems. Therefore, elaboration of the rationale of each application step is necessary.

In these study cases the model was set up to represent the spatial variability of the main runoff-sediment yield controlling features such as soils, land use/cover, terrain (*i.e*, slope and slope length), river channels and reservoirs. The distributed nature of the sediment yield and erosion representation (lumped, semi and fully distributed) depended on the availability of data and computation resources.

The Latin Hypercube One-factor-At-a-Time (LH-OAT) design as proposed by Morris (1991) implemented in SWAT was used as a sensitivity analysis tool. Sensitivity analysis of hydrology and sediment transport components parameters were conducted without and/or with observed data before and after calibration. Various lengths of simulations (*i.e*. 2, 4, 6, 8 yrs and greater) were tested in order to capture model input (*i.e*., parameter and data) uncertainty. These analyses were used to identify the sensitive parameters.

Manual calibration, expert knowledge and automatic calibration techniques were tested for the calibration procedures. The autocalibration routine based on the Shuffled Complex Evolution-University of Arizona (SCE-UA) that is incorporated in the SWAT model has been used very often (Duan *et al*., 1992). In one of the study cases, SRC, the SUFI-2 program which combines calibration and uncertainty analysis (Abbaspour *et al*., 2004, 2007) was used. This tool is widely used in the region (Shimelis *et al*., 2010). The sensitive model parameters were adjusted within their feasible ranges during calibration to minimize model prediction errors for daily and monthly flow and sediment loads. In one of the study cases, *i.e*. the NYM Reservoir catchment, soil erodibility (KUSLE) (a MUSLE factor) was estimated according to the equation proposed by Mulengera and Payton (1999) for tropics. Biascorrected rating curves were developed and used to interpolate or extrapolate sediment loads (Ndomba *et al*., 2008b). It should be noted that to date there is no consensus on how to develop an excellent rating curve, especially from a short period of records. Ndomba *et al*. (2008b) developed the rating curve from continuous subdaily suspended sediment data (*i.e*. 2 to 12 samples a day) collected by an automatic pumping sampler (ISCO 6712). The ISCO 6712 sampler data were calibrated by daily-midway and intermittent-cross section sediment samples collected by a depth-integrating sampler (D-74). The sediment loads from rating

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 275

analysis of runoff plots-based data. Mulengera and Payton (1999) attempted to define KUSLE values for tropical regions. One may note from Table 3 that the same set of important parameters for all the cases is retained. There are few cases of swapping of the ranking of the parameter importance, *e.g*., for BIOMIX in SRC. The influence of these parameters in sediment yield is well documented in Neitsch *et al*. (2005) and Ndomba (2007). Besides, the independently performed simulation results for catchment sediment management scenarios in the study cases indicate that all sorts of farming practices as captured by the PUSLE and CUSLE parameters (Table 3) are the main determinants in reducing soil loss and sediment yield in the upland catchments and subsequent sedimentation problems in the downstream reservoirs (Endale, 2008). The results also suggest that micro river channels also act as important sources of sediment as represented by the high rank of the linear re-entrainment parameter for channel sediment routing factor, Csp. It should be noted that many other factors affect the sediment yield estimations. For instance, the hydrological parameters seem to have a high effect as well on the sediment computations (Ndomba, 2007). This may be explained by the fact that one of the parameters/factors in the sediment yield equation, the MUSLE, used in the SWAT model is the surface runoff, Qsurf. Experience also shows that the resolution of DEM and the monthly average rainfall intensities, which are provided in the weather generator database are fundamental and crucial. The results in Table 3 below compare well with that of Shimelis *et al*. (2010) who worked in Angeni Gauged watershed, Ethiopia. In their case the ranking for Csp and CCH are first and second, respectively, with

CUSLE ranked in the sixth position.

SN Parameter Description of parameter NYM

2. CCH Channel cover factor 2 5 2 3. PUSLE USLE support practice factor 3 3 3 4. KCH Channel erodibility factor [cm/h/Pa] 4 6 4

6. CUSLE Minimum USLE cover factor 6 7 6 7. BIOMIX Biological mixing efficiency. 7 1 7

9. RSDIN Initial residue cover [kg/ha] 10 10 10 Table 3. Sensitivity analysis results of sediment component of SWAT for three study cases,

The discussion in this section focuses on the study cases where there was relatively adequate data and where more modelling efforts were applied (Table 4). For instance, in KRC the results of the model performance according to CE for flow calibration and

1. Csp Linear re-entrainment parameter for channel sediment routing

5. SPEXP Exponential re-entrainment parameter

[t.ha.h./(ha.MJ.mm]

8. KUSLE USLE soil erodibility factor

i.e., NYM, SRC and KRC

**3.2 Model performance** 

for channel sediment routing

Rank

SRC Rank

1 2 1

5 4 5

10 10 10

KRC Rank

curve were bias corrected. Sediment load correction factors were derived from both statistical bias estimators (Ferguson, 1986) and actual sediment load approaches (Ndomba *et al*., 2008a). It was important to do this as it is known that uncorrected rating curves, developed by Ordinary Least Square (OLS) tend to underestimate sediment loads (Ferguson, 1986; Ndomba *et al.,* 2008b). The SWAT model simulation was validated with long term reservoir sediment accumulation and/or sediment loads.

Parameter uncertainty in this study was reduced by placing emphasis on the most sensitive parameters and reformulating the model. This was achieved in one case by estimating some important parameters outside the model using proposed equations/estimators for the Eastern Africa and tropics. This approach was also suggested by Melching (1995). In some cases, the degree of parameter estimation uncertainty of the catchment sediment yield model was reduced by calibrating the parameters during the wet years' period for which most of the hydro-climatic and sediment flow data required by the model are available, as suggested by Yapo, *et al*., (1996) who observed that the hydrographs of wet years produce more identifiable parameters.

The performance of the model using filled and raw rainfall was evaluated in order to assess input data uncertainty (Ndomba *et al*., 2008b). Model performances were mainly evaluated based on Nash-Sutcliffe Coefficient of Efficiency (CE), Relative Error (RE), and Total Mass Balance Controller (TMC). CE provides a normalized estimate of the relationship between the observed and predicted model values. The simulation results were considered good for CE values greater than 0.75, while for values of efficiency between 0.75 and 0.36 the simulation results were considered to be satisfactory. CE values between 0.36 and 0 were considered to be fair. A value of zero would indicate that the fit was as good as using the average value of all the measured data. RE was estimated as the ratio of the absolute error to the true value and expressed in percentage. RE of less than twenty percent (20%) is considered acceptable for most scientific applications.

SWAT applications in this study assume a number of things. Although the principal external dynamic agents of sedimentation are water, wind, gravity and ice (Vanoni, 1975) only the hydrospheric forces of rainfall, runoff and streamflow were considered. The computed sediment yield in SWAT is solely a result of sheet erosion processes in the catchment (Shimelis *et al*., 2010).

#### **3. Results and discussions**

#### **3.1 Sensitive parameters controlling sediment generation and routing**

Seven (7) out of nine (9) SWAT parameters that directly govern the sediment yield and transport in the study cases NYM, SRC and KRC were found to be sensitive (Table 3). It should be noted that rank 10 signifies that a parameter is not sensitive/influential at all. These parameters can be categorized into two groups: upland and channel factors. The former group includes parameters such as PUSLE, CUSLE, KUSLE, Biological mixing efficiency (BIOMIX), and Initial residual cover (RSDIN); whereas Linear re-entrainment parameter for channel sediment routing (Csp), Channel cover factor (CCH), Channel erodibility factor (KCH) and Exponential re-entrainment parameter for channel sediment routing (SPEXP) parameters belong to the latter group.

However, it should be noted that only channel routing parameters with serial numbers 1, 2, 4, and 5 in the Table 3 were calibrated in all cases. As described in Neitsch *et al*. (2005), SWAT upland factors according to the MUSLE equation are formed based on regression

curve were bias corrected. Sediment load correction factors were derived from both statistical bias estimators (Ferguson, 1986) and actual sediment load approaches (Ndomba *et al*., 2008a). It was important to do this as it is known that uncorrected rating curves, developed by Ordinary Least Square (OLS) tend to underestimate sediment loads (Ferguson, 1986; Ndomba *et al.,* 2008b). The SWAT model simulation was validated with

Parameter uncertainty in this study was reduced by placing emphasis on the most sensitive parameters and reformulating the model. This was achieved in one case by estimating some important parameters outside the model using proposed equations/estimators for the Eastern Africa and tropics. This approach was also suggested by Melching (1995). In some cases, the degree of parameter estimation uncertainty of the catchment sediment yield model was reduced by calibrating the parameters during the wet years' period for which most of the hydro-climatic and sediment flow data required by the model are available, as suggested by Yapo, *et al*., (1996) who observed that the hydrographs of wet years produce

The performance of the model using filled and raw rainfall was evaluated in order to assess input data uncertainty (Ndomba *et al*., 2008b). Model performances were mainly evaluated based on Nash-Sutcliffe Coefficient of Efficiency (CE), Relative Error (RE), and Total Mass Balance Controller (TMC). CE provides a normalized estimate of the relationship between the observed and predicted model values. The simulation results were considered good for CE values greater than 0.75, while for values of efficiency between 0.75 and 0.36 the simulation results were considered to be satisfactory. CE values between 0.36 and 0 were considered to be fair. A value of zero would indicate that the fit was as good as using the average value of all the measured data. RE was estimated as the ratio of the absolute error to the true value and expressed in percentage. RE of less than twenty percent (20%) is

SWAT applications in this study assume a number of things. Although the principal external dynamic agents of sedimentation are water, wind, gravity and ice (Vanoni, 1975) only the hydrospheric forces of rainfall, runoff and streamflow were considered. The computed sediment yield in SWAT is solely a result of sheet erosion processes in the

Seven (7) out of nine (9) SWAT parameters that directly govern the sediment yield and transport in the study cases NYM, SRC and KRC were found to be sensitive (Table 3). It should be noted that rank 10 signifies that a parameter is not sensitive/influential at all. These parameters can be categorized into two groups: upland and channel factors. The former group includes parameters such as PUSLE, CUSLE, KUSLE, Biological mixing efficiency (BIOMIX), and Initial residual cover (RSDIN); whereas Linear re-entrainment parameter for channel sediment routing (Csp), Channel cover factor (CCH), Channel erodibility factor (KCH) and Exponential re-entrainment parameter for channel sediment routing (SPEXP)

However, it should be noted that only channel routing parameters with serial numbers 1, 2, 4, and 5 in the Table 3 were calibrated in all cases. As described in Neitsch *et al*. (2005), SWAT upland factors according to the MUSLE equation are formed based on regression

**3.1 Sensitive parameters controlling sediment generation and routing** 

long term reservoir sediment accumulation and/or sediment loads.

considered acceptable for most scientific applications.

more identifiable parameters.

catchment (Shimelis *et al*., 2010).

**3. Results and discussions** 

parameters belong to the latter group.

analysis of runoff plots-based data. Mulengera and Payton (1999) attempted to define KUSLE values for tropical regions. One may note from Table 3 that the same set of important parameters for all the cases is retained. There are few cases of swapping of the ranking of the parameter importance, *e.g*., for BIOMIX in SRC. The influence of these parameters in sediment yield is well documented in Neitsch *et al*. (2005) and Ndomba (2007). Besides, the independently performed simulation results for catchment sediment management scenarios in the study cases indicate that all sorts of farming practices as captured by the PUSLE and CUSLE parameters (Table 3) are the main determinants in reducing soil loss and sediment yield in the upland catchments and subsequent sedimentation problems in the downstream reservoirs (Endale, 2008). The results also suggest that micro river channels also act as important sources of sediment as represented by the high rank of the linear re-entrainment parameter for channel sediment routing factor, Csp. It should be noted that many other factors affect the sediment yield estimations. For instance, the hydrological parameters seem to have a high effect as well on the sediment computations (Ndomba, 2007). This may be explained by the fact that one of the parameters/factors in the sediment yield equation, the MUSLE, used in the SWAT model is the surface runoff, Qsurf. Experience also shows that the resolution of DEM and the monthly average rainfall intensities, which are provided in the weather generator database are fundamental and crucial. The results in Table 3 below compare well with that of Shimelis *et al*. (2010) who worked in Angeni Gauged watershed, Ethiopia. In their case the ranking for Csp and CCH are first and second, respectively, with CUSLE ranked in the sixth position.


Table 3. Sensitivity analysis results of sediment component of SWAT for three study cases, i.e., NYM, SRC and KRC

#### **3.2 Model performance**

The discussion in this section focuses on the study cases where there was relatively adequate data and where more modelling efforts were applied (Table 4). For instance, in KRC the results of the model performance according to CE for flow calibration and

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 277

1/1/1976 7/19/1976 2/4/1977 8/23/1977 3/11/1978 9/27/1978

Fig. 3. Comparison between "observed" and simulated Simiyu daily sediment for the validation period from 1976 and 1978 at Main Bridge outlet (Abdelhamid, 2010)

Time(Days) Observed Sediment Validated sediment

0

1990

1991

1992

1993

Reservoir, between 1990 and 2004 (As adopted from Endale, 2008)

1994

1995

Fig. 4. SWAT simulations vs observed annual sediment loads at Case study 1, the Koka

1996

1997

1998

Observed sediment load Simulated Sediment load by SWAT

1999

2000

2001

2002

2003

2004

500,000

1,000,000

**Total Annual Sediment Load ( t)**

1,500,000

2,000,000

2,500,000

Sediment Load(T/d)

validation are 68 and 63%, respectively. For sediment calibration and validation, CE is 66 and 68%, respectively. The calibration and validation results have shown that measured and simulated values were closely related with RE of 7.5 %.

The sediment yield calibration results for SRC for the period from 1970 to 1975 in daily and monthly aggregated outputs are presented in Table 4 and Figure 2. The result from the SWAT model at a daily time step is fair with the model performance of CE= 24%. The sediment loads in the peak flood events such as those in 1970, 1972 and 1974 are overpredicted. However, the performance of the model when looking at the monthly sediment loads is good, with CE= 83%. The sediment modelling was validated for the period between 1976 to 1978 (Figure 3). Daily sediment load is fairly simulated with a model performance of CE = 16%. Sediment loads during the peak flood events are over-predicted such as in the months of April, 1976 and November, 1978. On the other hand, it under-predicted the loads in January and April of 1977. However, the performance of the model in simulating monthly sediment loads is good with CE = 80%. The improvement of the SWAT model performance when aggregating the outputs over a longer time period has also been observed by other researchers in the region and elsewhere (Schmidt and Volk, 2005; Shimelis *et al*., 2010).

Since the routing models for the SWAT model as well as the coupled SWAT-SOBEK models have shown to give acceptable and comparable results, the uncertainty of the sediment routing component has been evaluated to be minimal (Abdelhamid, 2010). The performance was measured against total sediment loads transported to the main outlet of the catchment. A similar result was obtained by Andualem and Yonas (2008) in Ethiopia where they applied SWAT and CCHE1D sediment transport models together in tandem. In Figure 4 shows that simulated and observed annual sediment loads are comparable for Case study 1, the Koka Reservoir catchment.

Fig. 2. Comparison between "observed" and simulated Simiyu daily sediment for calibration period from 1970and 1975 at Main Bridge outlet (Abdelhamid, 2010).

validation are 68 and 63%, respectively. For sediment calibration and validation, CE is 66 and 68%, respectively. The calibration and validation results have shown that measured and

The sediment yield calibration results for SRC for the period from 1970 to 1975 in daily and monthly aggregated outputs are presented in Table 4 and Figure 2. The result from the SWAT model at a daily time step is fair with the model performance of CE= 24%. The sediment loads in the peak flood events such as those in 1970, 1972 and 1974 are overpredicted. However, the performance of the model when looking at the monthly sediment loads is good, with CE= 83%. The sediment modelling was validated for the period between 1976 to 1978 (Figure 3). Daily sediment load is fairly simulated with a model performance of CE = 16%. Sediment loads during the peak flood events are over-predicted such as in the months of April, 1976 and November, 1978. On the other hand, it under-predicted the loads in January and April of 1977. However, the performance of the model in simulating monthly sediment loads is good with CE = 80%. The improvement of the SWAT model performance when aggregating the outputs over a longer time period has also been observed by other researchers in the region and elsewhere (Schmidt and Volk, 2005; Shimelis *et al*., 2010). Since the routing models for the SWAT model as well as the coupled SWAT-SOBEK models have shown to give acceptable and comparable results, the uncertainty of the sediment routing component has been evaluated to be minimal (Abdelhamid, 2010). The performance was measured against total sediment loads transported to the main outlet of the catchment. A similar result was obtained by Andualem and Yonas (2008) in Ethiopia where they applied SWAT and CCHE1D sediment transport models together in tandem. In Figure 4 shows that simulated and observed annual sediment loads are comparable for Case study 1,

1/1/1970 1/1/1971 1/1/1972 12/31/1972 12/31/1973 12/31/1974 12/31/1975

Time(Days)

Observed Sediment Calibrated sediment

Fig. 2. Comparison between "observed" and simulated Simiyu daily sediment for calibration period from 1970and 1975 at Main Bridge outlet (Abdelhamid, 2010).

simulated values were closely related with RE of 7.5 %.

the Koka Reservoir catchment.

Sediment Load(T/d)

Fig. 3. Comparison between "observed" and simulated Simiyu daily sediment for the validation period from 1976 and 1978 at Main Bridge outlet (Abdelhamid, 2010)

Fig. 4. SWAT simulations vs observed annual sediment loads at Case study 1, the Koka Reservoir, between 1990 and 2004 (As adopted from Endale, 2008)

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 279

approaches, , *i.e*., field observations and analysis of field-based sediment flow data. It was observed that the SWAT model could not capture the dynamics of sediment load delivery in some seasons to the catchment outlet (Ndomba *et al*., 2008b). The reach is known to transport most of the sediment loads delivered from the catchment (*i.e*. equilibrium river reach). The particular study linked the latter problem to model deficiency. The authors would like to note that it is difficult to compare the model performance objectively as the quality and quantity of data used are different. Notwithstanding, there is a general agreement on the performance based on the cumulative sediment yield amount as measured by a relative error below 20 percent for both (Table 4). It could also be observed that the catchment size and climate are relatively similar. However, there are differences in

catchment characteristics, *i.e*. geology, soils type, and topography.

Calibration, CE (%)

Validation, CE (%)

Calibration, CE (%\)

Validation, CE (%)

*Note: "-" not evaluated as a result of missing data*.

Relative Error (RE) in percent.

Runoff

Sediment yield rate

Variables Performance indicators Time step Study cases

IVF (%) - 100 104

Relative Error, (RE) (%) 7.5 2.6 0.76

Table 4. SWAT model performance for the 3 study cases, i.e., KRC, NYM and SRC

From the engineering perspective, sediment yield information is critical to estimating the design life of a reservoir as a result of sedimentation. A lumped spatial and temporal scales model could serve this purpose. However, if further insights into erosion processes and sediment sources are sought then finer temporal and spatial scales are important, *e.g.*, to evaluate best management practices, effects of land use and effects of land cover change. Besides, as the overall objective of this paper is to critically assess the suitability of the SWAT model for sediment yield modeling, various components and/or functionalities were evaluated. As presented earlier in this chapter, the SWAT model predicted satisfactorily the cumulative long-term sediment catchment yield, and the performance was measured using

The performances of the SWAT model in the study cases and others conducted in catchments of the Eastern Africa as reported in literatures based on CE and IVF and Relative

KRC NYM SRC

Daily 68 54.6 38

Monthly - 65 82

Daily 63 68 30

Monthly - 77.4 81

Daily 66 56 24

Monthly - 83

Daily 68 - 16

Monthly - - 80

Fig. 5. SWAT simulations vs "observed", rating curve based sediment loads at 1DD1 (annually), between January, 1969 – December, 2005 (Ndomba *et al*., 2008b)

For the case of the NYM Reservoir catchment the SWAT model captured 56 percent of the variance (CE) and underestimated the observed daily sediment loads by 0.9 percent according to TMC performance indices during a normal wet hydrological year, *i.e*., with a calibration period set between November 1, 1977 and October 31, 1978 (Table 4). The SWAT model predicted satisfactorily the long-term sediment catchment yield with a relative error of 2.6 percent (Table 4 & Figure 5). It should be noted that the "observed" sediment loads were estimated from the bias-corrected suspended sediment rating curve. Authors are aware that the SWAT model simulates bed-material load (*i.e.* bed and suspended load) while the rating curve computes only suspended sediment load. However, it should be noted that the sediment loads delivered to streams are characterized as fine (Ndomba, 2007). They would mostly be transported as suspended sediments loads. The accuracy achieved by using the SWAT model was expected because it was hypothesized that correct estimation of surface runoff would lead to a better prediction of the sediment yield. Other researchers such as Garde and Ranga Raju (2000) are of similar opinion. Also, the model has identified erosion sources spatially and has replicated some erosion processes as determined in other studies and field observations in the NYM (Ndomba *et al*., 2008b). This result suggests that for catchments where sheet erosion is dominant, the SWAT model may substitute other sediment yield estimation methods such as the sediment-rating curve. However, SWAT model simulations results for sediment storage in a reach such as the 1DD1 gauging station in the Myumba Ya Mungu Reservoir catchment differ from the findings based on other

**0**

**1969**

**1971**

**1973**

**Simulated mean sediment load by SWAT**

**1975**

**1977**

**1979**

**1981**

(annually), between January, 1969 – December, 2005 (Ndomba *et al*., 2008b)

**1983**

**Annual areal rainfall Mean annual areal rainfall**

Fig. 5. SWAT simulations vs "observed", rating curve based sediment loads at 1DD1

For the case of the NYM Reservoir catchment the SWAT model captured 56 percent of the variance (CE) and underestimated the observed daily sediment loads by 0.9 percent according to TMC performance indices during a normal wet hydrological year, *i.e*., with a calibration period set between November 1, 1977 and October 31, 1978 (Table 4). The SWAT model predicted satisfactorily the long-term sediment catchment yield with a relative error of 2.6 percent (Table 4 & Figure 5). It should be noted that the "observed" sediment loads were estimated from the bias-corrected suspended sediment rating curve. Authors are aware that the SWAT model simulates bed-material load (*i.e.* bed and suspended load) while the rating curve computes only suspended sediment load. However, it should be noted that the sediment loads delivered to streams are characterized as fine (Ndomba, 2007). They would mostly be transported as suspended sediments loads. The accuracy achieved by using the SWAT model was expected because it was hypothesized that correct estimation of surface runoff would lead to a better prediction of the sediment yield. Other researchers such as Garde and Ranga Raju (2000) are of similar opinion. Also, the model has identified erosion sources spatially and has replicated some erosion processes as determined in other studies and field observations in the NYM (Ndomba *et al*., 2008b). This result suggests that for catchments where sheet erosion is dominant, the SWAT model may substitute other sediment yield estimation methods such as the sediment-rating curve. However, SWAT model simulations results for sediment storage in a reach such as the 1DD1 gauging station in the Myumba Ya Mungu Reservoir catchment differ from the findings based on other

**1985**

**1987**

**Simulated sediment load by SWAT Suspended sediment load by Rating curve**

**1989**

**1991**

**1993**

**1995**

**1997**

**1999**

**2001**

**2003**

**2005**

**500**

**1000**

**1500**

**2000**

**2500**

**Total annual areal rainfall [mm]**

**3000**

**3500**

**4000**

**200,000**

**400,000**

**600,000**

**800,000**

**Total annual sediment loads [t]**

**1,000,000**

**1,200,000**

**1,400,000**

approaches, , *i.e*., field observations and analysis of field-based sediment flow data. It was observed that the SWAT model could not capture the dynamics of sediment load delivery in some seasons to the catchment outlet (Ndomba *et al*., 2008b). The reach is known to transport most of the sediment loads delivered from the catchment (*i.e*. equilibrium river reach). The particular study linked the latter problem to model deficiency. The authors would like to note that it is difficult to compare the model performance objectively as the quality and quantity of data used are different. Notwithstanding, there is a general agreement on the performance based on the cumulative sediment yield amount as measured by a relative error below 20 percent for both (Table 4). It could also be observed that the catchment size and climate are relatively similar. However, there are differences in catchment characteristics, *i.e*. geology, soils type, and topography.


Table 4. SWAT model performance for the 3 study cases, i.e., KRC, NYM and SRC *Note: "-" not evaluated as a result of missing data*.

From the engineering perspective, sediment yield information is critical to estimating the design life of a reservoir as a result of sedimentation. A lumped spatial and temporal scales model could serve this purpose. However, if further insights into erosion processes and sediment sources are sought then finer temporal and spatial scales are important, *e.g.*, to evaluate best management practices, effects of land use and effects of land cover change. Besides, as the overall objective of this paper is to critically assess the suitability of the SWAT model for sediment yield modeling, various components and/or functionalities were evaluated. As presented earlier in this chapter, the SWAT model predicted satisfactorily the cumulative long-term sediment catchment yield, and the performance was measured using Relative Error (RE) in percent.

The performances of the SWAT model in the study cases and others conducted in catchments of the Eastern Africa as reported in literatures based on CE and IVF and Relative

Suitability of SWAT Model for Sediment Yields Modelling in the Eastern Africa 281

i. Move from a rather "lumped calibration" on data at the outlet to a more distributed calibration by using internal gauging data for both flow, sediments and rainfalls. This can also be achieved through using higher resolution data, especially for DEM but also

ii. Improving the representation of important hydrological features, especially the water

iii. Improve the routing component of the model. In some cases the SWAT model simulations indicated sediment storage in a river reach to be unlike the findings based

It is important to continue efforts in applying SWAT in Eastern Africa. However, the authors appeal to those who want to apply SWAT in their studies is not to apply it blindly.

This work was co-funded by The Norwegian Programme for Development, Research and Education (NUFU) – Water Management in Pangani River Basin Tanzania Project at University of Dar es Salaam, Nile Basin Capacity Building Network-River Engineering Initiative, FRIEND/Nile based at UNESCO - Cairo Office in Cairo, Egypt and UNESCO-IHE Partnership Research Fund (UPaRF) through Adaptation to Climate Change Impacts on the Nile River Basin (ACCION) project. In addition, the authors wish to express their gratitude to Mr. F. Mashingia, a PhD fellow at University of Dar es Salaam, for preparing study area maps, as well as to anonymous reviewers, who helped to improve this chapter through their

Abbaspour, K.C., Johnson, C.A., van Genuchten, M.T., (2004). Estimating uncertain flow and

Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J.,

Arnold, J.G., Williams, J.R., & Maidment, D.R., (1995). Continuous-time water and sediment-

De Pauw, E. (1984). *Soils, Physiography and Agro ecological zones of Tanzania Publication*: Crop

Bagnold, R.A., (1977). Bedload transport in natural rivers. Water Resour. Res. 13:303-312. Bathurst, J.C., (2002). *Physically-based erosion and sediment yield modelling: the SHETRAN* 

alpine/alpine Thur watershed using SWAT. *J. Hydrol*. 333, 413–430. Abdelhamid, M.R., (2010). Sediment Transport Modelling in Simiyu Catchment of Lake

transport parameters using a sequential uncertainty fitting procedure. *Vadose Zone* 

Srinivasan, R., (2007). Modelling of hydrology and water quality in the pre-

Victoria Basin, Tanzania. MSc Thesis WSE-HI.10-06. UNESCO-IHE, Institute for

routing model for large basins. *Journal of Hydraulic Engineering,* Vol. 121(2): pp. 171-

*concept*. In: Wolfgang Summer and Desmond E.Walling (ed.), Modelling erosion, sediment transport and sediment yield. IHP-VI Technical Documents in

monitoring and early warning systems Project GCPS/URT/047/NET. Ministry of

They need to consult experience from previous studies in Eastern Africa.

the land use and soil maps;

on other approaches.

**5. Acknowledgements** 

thorough review

**6. References** 

183.

*J*. 3, 1340–1352.

Water Education, Netherlands.

Hydrology, No.60, pp47-68.

ponds, wetlands/marsh or swamps;

Errors (RE) criteria suggest that the model can fairly/satisfactorily estimate sediment yield for even poorly gauged catchments (Ndomba *et al*., 2005; Ndomba, 2007; Mulungu and Munishi, (2007); Ndomba and Birhanu, 2008; Ndomba *et al*., 2008b; Andualem and Yonas, 2008; Shimelis *et al*., 2010). The latter mainly proves that the models are able to capture the dynamics in time, but it provides little validation on the identification of the processes and/or the parameters in space which would require observations at several internal points in the catchment.

#### **4. Conclusions and recommendations**

#### **4.1 Conclusions**

This chapter presents three study cases in Eastern Africa where the SWAT model was applied extensively using the available data. In few cases primary data on sediment loads were explored. These cases represent various climatic conditions within the equatorial and/or tropical region. Based on the results of this study, the SWAT model seems to be robust and can be relied upon as a tool for catchment sediment management in the tropics. However, the model could not capture dynamics of sediment load delivery (i*.e*. equilibrium river regime) in some seasons in one catchment. The particular study linked the latter problem to model deficiency. Based on the simulation results the study has found that all sorts of farming practices captured by PUSLE and CUSLE parameters are the main determining management techniques in reducing soil loss/sediment yield and subsequently sedimentation problems in the reservoir. Besides, the performances of the SWAT model in these study cases as well as others conducted in Eastern Africa suggest that the model can satisfactorily estimate sediment yield for even poorly gauged catchments. The temporal variability is quite well captured. It should be noted that the calibration of the distributed parameters was typically done in a "lumped" way using sediment observations at the outlet instead of using observations at interior locations in the river basin. Therefore, the physical meaning of these parameters as well as the spatial representativeness could be questioned. The performance of the model suggests that the model can be used as a research tool in reservoir sedimentation and sediment yield modelling studies in the region.

The results of these study cases are not conclusive enough because some challenges have not been addressed. Although the input data varies in type and quality, from coarse-resolution to high-resolution measured spatial and climate data, there is a general lack of highresolution spatial input data. More high-resolution spatial input data may not necessarily improve the performance of the model, but it may contribute to a better representation of the spatial variability. For erosion modelling, a high DEM resolution is especially important because the DEM is used to compute the slopes. Slopes have dual role: they affect the runoff processes in the hydrology that directly influence the erosion computations, and they are also directly used in the MUSLE equation.

It should be noted that the authors are aware that the performance of the SWAT model applications in the study cases can not be compared objectively because the performance is affected by modelling efforts and techniques, input data quality and catchment representation of important hydrological features.

#### **4.2 Recommendations**

A general recommendation is that more attention needs to be given to the spatial representativeness of the processes, the process parameters and the input data. The latter involves:


It is important to continue efforts in applying SWAT in Eastern Africa. However, the authors appeal to those who want to apply SWAT in their studies is not to apply it blindly. They need to consult experience from previous studies in Eastern Africa.

### **5. Acknowledgements**

280 Advances in Data, Methods, Models and Their Applications in Geoscience

Errors (RE) criteria suggest that the model can fairly/satisfactorily estimate sediment yield for even poorly gauged catchments (Ndomba *et al*., 2005; Ndomba, 2007; Mulungu and Munishi, (2007); Ndomba and Birhanu, 2008; Ndomba *et al*., 2008b; Andualem and Yonas, 2008; Shimelis *et al*., 2010). The latter mainly proves that the models are able to capture the dynamics in time, but it provides little validation on the identification of the processes and/or the parameters in

This chapter presents three study cases in Eastern Africa where the SWAT model was applied extensively using the available data. In few cases primary data on sediment loads were explored. These cases represent various climatic conditions within the equatorial and/or tropical region. Based on the results of this study, the SWAT model seems to be robust and can be relied upon as a tool for catchment sediment management in the tropics. However, the model could not capture dynamics of sediment load delivery (i*.e*. equilibrium river regime) in some seasons in one catchment. The particular study linked the latter problem to model deficiency. Based on the simulation results the study has found that all sorts of farming practices captured by PUSLE and CUSLE parameters are the main determining management techniques in reducing soil loss/sediment yield and subsequently sedimentation problems in the reservoir. Besides, the performances of the SWAT model in these study cases as well as others conducted in Eastern Africa suggest that the model can satisfactorily estimate sediment yield for even poorly gauged catchments. The temporal variability is quite well captured. It should be noted that the calibration of the distributed parameters was typically done in a "lumped" way using sediment observations at the outlet instead of using observations at interior locations in the river basin. Therefore, the physical meaning of these parameters as well as the spatial representativeness could be questioned. The performance of the model suggests that the model can be used as a research tool in

space which would require observations at several internal points in the catchment.

reservoir sedimentation and sediment yield modelling studies in the region.

The results of these study cases are not conclusive enough because some challenges have not been addressed. Although the input data varies in type and quality, from coarse-resolution to high-resolution measured spatial and climate data, there is a general lack of highresolution spatial input data. More high-resolution spatial input data may not necessarily improve the performance of the model, but it may contribute to a better representation of the spatial variability. For erosion modelling, a high DEM resolution is especially important because the DEM is used to compute the slopes. Slopes have dual role: they affect the runoff processes in the hydrology that directly influence the erosion computations, and they are

It should be noted that the authors are aware that the performance of the SWAT model applications in the study cases can not be compared objectively because the performance is affected by modelling efforts and techniques, input data quality and catchment

A general recommendation is that more attention needs to be given to the spatial representativeness of the processes, the process parameters and the input data. The latter

**4. Conclusions and recommendations** 

also directly used in the MUSLE equation.

**4.2 Recommendations** 

involves:

representation of important hydrological features.

**4.1 Conclusions** 

This work was co-funded by The Norwegian Programme for Development, Research and Education (NUFU) – Water Management in Pangani River Basin Tanzania Project at University of Dar es Salaam, Nile Basin Capacity Building Network-River Engineering Initiative, FRIEND/Nile based at UNESCO - Cairo Office in Cairo, Egypt and UNESCO-IHE Partnership Research Fund (UPaRF) through Adaptation to Climate Change Impacts on the Nile River Basin (ACCION) project. In addition, the authors wish to express their gratitude to Mr. F. Mashingia, a PhD fellow at University of Dar es Salaam, for preparing study area maps, as well as to anonymous reviewers, who helped to improve this chapter through their thorough review

#### **6. References**


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**14** 

*Brazil* 

**Fuzzy Hydrologic Model in** 

Natural elements such as soil, geology and vegetation are usually represented as map classes, whose boundaries are sharply defined. However not all entries in geo-objects datasets are sharp and this is true both for their attribute values as well as for their spatial

This traditional representation between geo-objects is considered as an oversimplification of a more complex pattern. In some conditions, these boundaries are recognized more easily because are associated to significant and abrupt land changes, such as situations in which the boundaries are located in river banks, in geologic phenomenon (intrusions, flaws, fractures) or associated with sudden relief variations (Burrough, 1986). Apart from these situations, the boundaries are associated to uncertainties caused by limited observations (Hadzilacos, 1996). In all these cases, fuzzy methods are more suitable than boolean logic. Zadeh (1965) developed fuzzy set theory allowing the mathematical modeling in zones of imprecisions and uncertainties. Fuzzy set theory is a generalization of the boolean logic to situations where data are modelled by entities whose attributes have zones of gradual transition, rather than sharp boundaries. Studies of natural phenomena and natural objects demonstrated that the use of boolean logic is an inadequate method and brings much

The objective of this study was to develop a fuzzy rule-based modelling to predict runoff in a watershed using the Soil Conservation Service Curve Number (SCSCN) model (SCS,

Although the SCSCN model was developed primarily based on small watersheds, it can be applied in medium and large watersheds, with a diversified variety of soils and vegetation, if integrated to a geographical information system (GIS) (Johnson & Miller, 1997; Thompson,

The study area is the Quilombo River watershed, located in Ribeira Valley, South of the State of São Paulo, Brazil (Fig. 1). The land-cover of the area is composed of Atlantic forest (dominant) and pasture. The choice to study this watershed was driven by the availability of

soil map, rain record gage, and stream discharge record gage.

**1. Introduction** 

distribution (Burrough, 1996).

inferior results (Burrough, 1986).

1972).

1999).

**2. Study area** 

**Tropical Watershed** 

Aurélio Azevedo Barreto-Neto *Federal Institute of Espírito Santo,* 

*sources: Proceedings of the sediment-yield workshop*, USDA Sedimentation Lab., Oxford, MS, November 28-30, 1972. ARS-S-40.

Yapo, P.O., Gupta, H.V., & Sorooshian, S., (1996). Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. *Journal of Hydrology*, vol.181, pp23-48.

## **Fuzzy Hydrologic Model in Tropical Watershed**

Aurélio Azevedo Barreto-Neto *Federal Institute of Espírito Santo, Brazil* 

#### **1. Introduction**

284 Advances in Data, Methods, Models and Their Applications in Geoscience

Yapo, P.O., Gupta, H.V., & Sorooshian, S., (1996). Automatic calibration of conceptual

Oxford, MS, November 28-30, 1972. ARS-S-40.

pp23-48.

*sources: Proceedings of the sediment-yield workshop*, USDA Sedimentation Lab.,

rainfall-runoff models: sensitivity to calibration data. *Journal of Hydrology*, vol.181,

Natural elements such as soil, geology and vegetation are usually represented as map classes, whose boundaries are sharply defined. However not all entries in geo-objects datasets are sharp and this is true both for their attribute values as well as for their spatial distribution (Burrough, 1996).

This traditional representation between geo-objects is considered as an oversimplification of a more complex pattern. In some conditions, these boundaries are recognized more easily because are associated to significant and abrupt land changes, such as situations in which the boundaries are located in river banks, in geologic phenomenon (intrusions, flaws, fractures) or associated with sudden relief variations (Burrough, 1986). Apart from these situations, the boundaries are associated to uncertainties caused by limited observations (Hadzilacos, 1996). In all these cases, fuzzy methods are more suitable than boolean logic.

Zadeh (1965) developed fuzzy set theory allowing the mathematical modeling in zones of imprecisions and uncertainties. Fuzzy set theory is a generalization of the boolean logic to situations where data are modelled by entities whose attributes have zones of gradual transition, rather than sharp boundaries. Studies of natural phenomena and natural objects demonstrated that the use of boolean logic is an inadequate method and brings much inferior results (Burrough, 1986).

The objective of this study was to develop a fuzzy rule-based modelling to predict runoff in a watershed using the Soil Conservation Service Curve Number (SCSCN) model (SCS, 1972).

Although the SCSCN model was developed primarily based on small watersheds, it can be applied in medium and large watersheds, with a diversified variety of soils and vegetation, if integrated to a geographical information system (GIS) (Johnson & Miller, 1997; Thompson, 1999).

#### **2. Study area**

The study area is the Quilombo River watershed, located in Ribeira Valley, South of the State of São Paulo, Brazil (Fig. 1). The land-cover of the area is composed of Atlantic forest (dominant) and pasture. The choice to study this watershed was driven by the availability of soil map, rain record gage, and stream discharge record gage.

Fuzzy Hydrologic Model in Tropical Watershed 287

rain event. The CN ranges from 0 to 100, where larger CN represents greater proportion of

Basically, four steps are necessary to evaluate runoff from a rainfall by the SCSCN model: (i) to determine the hydrologic soil group (Table 1); (ii) to determine the five-day antecedent moisture condition of the soil from the precipitation record; (iii) to determine the runoff CN (on the basis of land cover, soil treatment, plus hydrologic condition and hydrologic soil group of the soil); and (iv) to calculate the runoff volume for one rain event. Concepts

In this model, the soils are classified to one of four HSG (A, B, C or D) defined by the SCS. This classification was accomplished by the analysis of the infiltration capacity of the soil. The description of each group, according to SCS (1972) and Rawls et al. (1992), is listed in

In the SCSCN model, the watershed surface setting is assessed as a function of land cover, type of soil treatment and soil hydrologic condition. Land cover varies with landuse and can include key categories such as forests, swamps, pasture, bare soil, impermeable areas, etc. The soil treatment is related to automated farming practices (plantation along topographic contour lines and terraces) and management practices (pasture control, crop rotation and reduction). The association between landuse and the type of soil treatment is named class. Some examples of classes are: cereal plantations on topographic contour lines; dense forests;

The association between specific HSG, land cover and type of soil treatment is referred to as soil-cover hydrologic complex, for which the CN attribute can be derived from the

Antecedent Moisture Conditions (AMC) are related to the soil moisture due to accumulated rain, but considering the five last days that precede a particular rain event. There are three types of AMC: AMC I = soil is dry; AMC II = soil moisture is medium; and AMC III = soil is

The CNs were firstly obtained by measures made in a great number of watersheds for AMC II. The CN derived for AMC II can be converted to AMC I or AMC III through a transfer

The runoff begins when the portion of lost rain by infiltration, evapotranspiration, interception and depression storage, denominated initial abstractions, is less than the total precipitation. The runoff equation defined by SCS and detailed on the National Engineering

specialized literature (SCS, 1972; Rawls et al., 1992; Pilgrim & Cordery, 1993).

surface runoff.

Table 1.

saturated in water.

table provided by SCS (SCS, 1972).

Handbook (SCS, 1972) is the following:

related to these four main steps are given below.

dense pasture, flat bare soil, paved highways, etc.

HSG Characteristics

A Soils with high infiltration rates

C Soils with low infiltration rates

Table 1. Hydrologic soil group (HSG) according to SCSCN model

B Soils with moderate infiltration rates

D Soils with very low infiltration rates

Fig. 1. Locality map

#### **3. Fuzzy theory**

A fuzzy set is defined mathematically as follows: if X = {x} is a finite set (for space) of points, then the fuzzy set A in X is the set of ordered pairs:

$$\mathbf{A} = \{ \mathbf{x}, \mu\_{\mathbf{A}}(\mathbf{x}) \} \quad \mathbf{x} \in \mathcal{X} \tag{1}$$

where A(x) is known as the grade of membership of x in A and x X mean that x is contained in X. For all A, A(x) represents the grade of membership of x in A and is a real number in the interval [0, 1], with 1 representing full membership of the set and 0 nonmembership (Zadeh, 1965). In practice, X = { x1, x2 …, xn) and the Eq. (1) can be written as:

$$\mathbf{A} = \{ \mathbf{x}\_{\mathsf{L}}, \boldsymbol{\mu}\_{\mathsf{A}}(\mathbf{x}\_{\mathsf{L}}); \mathbf{x}\_{\mathsf{2}\prime}, \boldsymbol{\mu}\_{\mathsf{A}}(\mathbf{x}\_{\mathsf{2}}); \dots; \mathbf{x}\_{\mathsf{n}\prime}, \boldsymbol{\mu}\_{\mathsf{A}}(\mathbf{x}\_{\mathsf{n}}) \} \tag{2}$$

#### **4. The Soil Conservation Service Curve Number (SCSCN) hydrologic model**

The SCSCN model is a well known archetype for estimating the storm runoff depth from storm rainfall depth for watershed and thus, stream flow, infiltration, soil moisture content and transport of sediments. Therefore, the model can assist hydraulic projects, soil conservation projects and flood control (SCS, 1972; Engel et al., 1993; Mack, 1995; Johnson & Miller, 1997; Thompson, 1999; Pullar & Springer, 2000;

In the SCSCN model, the physical characteristics of the watershed, such as hydrologic soil group (HGS), land cover and antecedent moisture conditions, are important because these characteristics determine the curve number (CN) parameter that estimate the runoff from a

A fuzzy set is defined mathematically as follows: if X = {x} is a finite set (for space) of points,

where A(x) is known as the grade of membership of x in A and x X mean that x is contained in X. For all A, A(x) represents the grade of membership of x in A and is a real number in the interval [0, 1], with 1 representing full membership of the set and 0 nonmembership (Zadeh, 1965). In practice, X = { x1, x2 …, xn) and the Eq. (1) can be written as:

**4. The Soil Conservation Service Curve Number (SCSCN) hydrologic model**  The SCSCN model is a well known archetype for estimating the storm runoff depth from storm rainfall depth for watershed and thus, stream flow, infiltration, soil moisture content and transport of sediments. Therefore, the model can assist hydraulic projects, soil conservation projects and flood control (SCS, 1972; Engel et al., 1993; Mack, 1995; Johnson &

In the SCSCN model, the physical characteristics of the watershed, such as hydrologic soil group (HGS), land cover and antecedent moisture conditions, are important because these characteristics determine the curve number (CN) parameter that estimate the runoff from a

A = {x, A(x)} x X (1)

A = {x1, A(x1); x2, A(x2); ... ; xn, A(xn)} (2)

Fig. 1. Locality map

**3. Fuzzy theory** 

then the fuzzy set A in X is the set of ordered pairs:

Miller, 1997; Thompson, 1999; Pullar & Springer, 2000;

rain event. The CN ranges from 0 to 100, where larger CN represents greater proportion of surface runoff.

Basically, four steps are necessary to evaluate runoff from a rainfall by the SCSCN model: (i) to determine the hydrologic soil group (Table 1); (ii) to determine the five-day antecedent moisture condition of the soil from the precipitation record; (iii) to determine the runoff CN (on the basis of land cover, soil treatment, plus hydrologic condition and hydrologic soil group of the soil); and (iv) to calculate the runoff volume for one rain event. Concepts related to these four main steps are given below.


Table 1. Hydrologic soil group (HSG) according to SCSCN model

In this model, the soils are classified to one of four HSG (A, B, C or D) defined by the SCS. This classification was accomplished by the analysis of the infiltration capacity of the soil. The description of each group, according to SCS (1972) and Rawls et al. (1992), is listed in Table 1.

In the SCSCN model, the watershed surface setting is assessed as a function of land cover, type of soil treatment and soil hydrologic condition. Land cover varies with landuse and can include key categories such as forests, swamps, pasture, bare soil, impermeable areas, etc. The soil treatment is related to automated farming practices (plantation along topographic contour lines and terraces) and management practices (pasture control, crop rotation and reduction). The association between landuse and the type of soil treatment is named class. Some examples of classes are: cereal plantations on topographic contour lines; dense forests; dense pasture, flat bare soil, paved highways, etc.

The association between specific HSG, land cover and type of soil treatment is referred to as soil-cover hydrologic complex, for which the CN attribute can be derived from the specialized literature (SCS, 1972; Rawls et al., 1992; Pilgrim & Cordery, 1993).

Antecedent Moisture Conditions (AMC) are related to the soil moisture due to accumulated rain, but considering the five last days that precede a particular rain event. There are three types of AMC: AMC I = soil is dry; AMC II = soil moisture is medium; and AMC III = soil is saturated in water.

The CNs were firstly obtained by measures made in a great number of watersheds for AMC II. The CN derived for AMC II can be converted to AMC I or AMC III through a transfer table provided by SCS (SCS, 1972).

The runoff begins when the portion of lost rain by infiltration, evapotranspiration, interception and depression storage, denominated initial abstractions, is less than the total precipitation. The runoff equation defined by SCS and detailed on the National Engineering Handbook (SCS, 1972) is the following:

$$Q = \frac{(P - 0.2S)^2}{P + 0.8S} \tag{3}$$

Fuzzy Hydrologic Model in Tropical Watershed 289

Fig. 2. Leaf Pigment Index (LPI) map

Fig. 3. Hydrologic Soil Group (HGS) map

where Q is the direct runoff or excess precipitation, P is the precipitation, S is potential maximum storage in the watershed after beginning of the runoff . The CN parameter relates to S (mm) as:

$$S = \frac{25,400}{\text{CN}} - 254\tag{4}$$

#### **5. Model implementation**

#### **5.1 Landuse map**

The land use map was obtained by image processing of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (Abrams, 2000). Firstly, the ASTER image was compensated for atmospheric effects and converted into surface reflectance, through the Atmospheric Correction Now (ACORN) software, which involves a MODTRAN4-based method for radiative transfer calculation (Imspec, 2001). The Leaf Pigment Index (LPI) (Almeida & Souza Filho, 2004) was then calculated using ASTER reflectance data to represent the continuous surface associated to the vegetation coverage of the study area (Fig. 2). The LPI was calculated by:

$$\text{LPI} = \text{(ASTER 1)} \mid \text{(ASTER 2)} \tag{5}$$

where ASTER 1 is the band 1 (0.52-0.60 m - visible green) and ASTER 2 is the band 2 (0.63- 0.69 m - visible red). The LPI indicates the amount of chlorophyll in plant foliage – higher index values highlight areas in the image where photosyntetically active vegetation is denser. Other vegetation indices such as the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974) and the Moisture Stress Index (MSI) (Rock et al., 1986) were also tested, but the LPI showed to best represent the vegetation cover of the study area when the results were confronted with field observations. The map generated with LPI was converted to ASCII format, compatible with PCRaster EML.

#### **5.2 Soil map**

Soil data of the Quilombo River watershed were extracted from the soil map of Ribeira do Iguape Region at 1/100,000 scale (Sakai et al., 1983). Basically, the watershed is composed of four soil types: latosol, podzolic, inceptisol and organic soils. The soil map, originally in paper format, was converted to digital vector data. These vector data were transformed to raster data at 15 m resolution. The raster map was further converted to ASCII format.

The runoff estimate was obtained through the SCSCN model based on the hydrologic soil groups defined by the USA Soil Conservation Service, where the soil is classified into one of four different categories, ranging from A to D.

An important characteristic of the tropical soils in the São Paulo State is the fact that the clay-rich soils provide high infiltration rates (Lombardi-Neto et al., 1991). Another particular aspect of the studied watershed is that the organic soils are found in the bottom of the valleys and have high moisture content (Barreto-Neto, 2004). Based on these soil characteristics, the soil map was reclassified in agreement with the hydrologic soil groups (Table 2 and Fig. 3).

where Q is the direct runoff or excess precipitation, P is the precipitation, S is potential

25,400 <sup>254</sup> *CN <sup>S</sup>* (4)

The land use map was obtained by image processing of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (Abrams, 2000). Firstly, the ASTER image was compensated for atmospheric effects and converted into surface reflectance, through the Atmospheric Correction Now (ACORN) software, which involves a MODTRAN4-based method for radiative transfer calculation (Imspec, 2001). The Leaf Pigment Index (LPI) (Almeida & Souza Filho, 2004) was then calculated using ASTER reflectance data to represent the continuous surface associated to the vegetation coverage of

where ASTER 1 is the band 1 (0.52-0.60 m - visible green) and ASTER 2 is the band 2 (0.63- 0.69 m - visible red). The LPI indicates the amount of chlorophyll in plant foliage – higher index values highlight areas in the image where photosyntetically active vegetation is denser. Other vegetation indices such as the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974) and the Moisture Stress Index (MSI) (Rock et al., 1986) were also tested, but the LPI showed to best represent the vegetation cover of the study area when the results were confronted with field observations. The map generated with LPI was converted

Soil data of the Quilombo River watershed were extracted from the soil map of Ribeira do Iguape Region at 1/100,000 scale (Sakai et al., 1983). Basically, the watershed is composed of four soil types: latosol, podzolic, inceptisol and organic soils. The soil map, originally in paper format, was converted to digital vector data. These vector data were transformed to

The runoff estimate was obtained through the SCSCN model based on the hydrologic soil groups defined by the USA Soil Conservation Service, where the soil is classified into one of

An important characteristic of the tropical soils in the São Paulo State is the fact that the clay-rich soils provide high infiltration rates (Lombardi-Neto et al., 1991). Another particular aspect of the studied watershed is that the organic soils are found in the bottom of the valleys and have high moisture content (Barreto-Neto, 2004). Based on these soil characteristics, the soil map was reclassified in agreement with the hydrologic soil groups

raster data at 15 m resolution. The raster map was further converted to ASCII format.

maximum storage in the watershed after beginning of the runoff .

The CN parameter relates to S (mm) as:

the study area (Fig. 2). The LPI was calculated by:

to ASCII format, compatible with PCRaster EML.

four different categories, ranging from A to D.

**5. Model implementation** 

**5.1 Landuse map** 

**5.2 Soil map** 

(Table 2 and Fig. 3).

<sup>2</sup> ( 0.2 ) 0.8 *P S <sup>Q</sup> P S* 

(3)

LPI = (ASTER 1) / (ASTER 2) (5)

Fig. 2. Leaf Pigment Index (LPI) map

Fig. 3. Hydrologic Soil Group (HGS) map

Fuzzy Hydrologic Model in Tropical Watershed 291

The width of the transition zones was chosen based in the scale of the map, according to indications provided by Lagacherie et al. (1996) and Burrough and McDonnell (1998). Burrough and McDonnell (1998) exemplified that a sharp boundary drawn as a 0.2 mmthick line on a 1:25,000 scale map covers 50 m (25 m to the right and 25 m to the left from drawn boundary) and a diffuse boundary at the same scale might extend over 500 m. In this study, sharp boundaries were drawn as a 1 mm-thick line on a 1:100,000 scale map, so that the width of the spatial transition zone centered over the drawn boundary location was 200 m. Only the uncertainty related to the drawn boundaries of the map was used, although the

 In order to model fuzzy transition zones, the computer model involved the following steps: (i) separation of each soil unit (polygon boundary) in different map layers; (ii) isotropic spread of the boundary of each polygon (inside and outside the polygon); (iii) application of a membership function. Each soil unit was considered as a fuzzy set A = {x, µA(x)}. In this case, x denotes a point in geographic space that belongs to A, and µA(x) is a number that ranges from 0 to 1 and reflects the grade of membership of x in A. The fuzziness of the boundary between soil units A and B were indicated by both distributions of grades of membership, µA(x) and µB(x) (Fig. 5). Points located far enough from the boundary have either µA(x) = 1 and µB(x) = 0 (if x is contained in A) or µB(x) = 1 and µA(x) = 0 (if x is contained in B). Points located close to the boundary µA(x) and µB(x) have values between 0 and 1; (iv) the procedure was repeated for each soil unit, yielding a soil boundary fuzziness map for each soil unit. The width of the transition zone can be defined by the user before the computer program is run. Fig. 6 and Fig. 7 show the boundary of organic soil using the

Fig. 5. Linear membership function and illustration of the methodology employed in the

transition zones verified in the field show larger extensions.

fuzzy and boolean model, respectively.

conversion of crispy soil data to fuzzy soil data


Table 2. Soil types, % area in the watershed and their respective Hydrologic Soil Group (HGS) according to Lombardi- Neto et al. (1991) and Embrapa (1999)

#### **5.3 Fuzzy SCSCN model**

For the developed model, each input variable was coded, fuzzified and, subsequently, input into the fuzzy inference system for decision making, using the PCRaster EML (Wesseling et al., 1996). The implementation of the computer model followed three steps: (i) the soil and cover maps were transformed in a fuzzy set using the membership functions (linear and bell-shaped); (ii) using the fuzzy inference system, the CN map was generated based in the fuzzy soil map and the fuzzy cover map (both developed in the previous steps); (iii) runoff calculation.

#### **5.3.1 Fuzzy soil map**

Using the methods of fuzzy logic on polygon boundaries makes it simple to incorporate information about the nature of the boundaries. In this paper, the map-unit approach described for Burrough and McDonnell (1998) was employed. This approach assumes that the width of the transition zone is the same in all map boundaries. Information about the type of boundary was converted to parameters for two fuzzy membership functions (linear and bell-shaped) (Fig. 4), which were applied to the distance from the drawn boundary.

Fig. 4. Membership functions: (A) linear; (B) bell-shaped

Table 2. Soil types, % area in the watershed and their respective Hydrologic Soil Group

For the developed model, each input variable was coded, fuzzified and, subsequently, input into the fuzzy inference system for decision making, using the PCRaster EML (Wesseling et al., 1996). The implementation of the computer model followed three steps: (i) the soil and cover maps were transformed in a fuzzy set using the membership functions (linear and bell-shaped); (ii) using the fuzzy inference system, the CN map was generated based in the fuzzy soil map and the fuzzy cover map (both developed in the previous steps); (iii) runoff

Using the methods of fuzzy logic on polygon boundaries makes it simple to incorporate information about the nature of the boundaries. In this paper, the map-unit approach described for Burrough and McDonnell (1998) was employed. This approach assumes that the width of the transition zone is the same in all map boundaries. Information about the type of boundary was converted to parameters for two fuzzy membership functions (linear and bell-shaped) (Fig. 4), which were applied to the distance from the drawn boundary.

(HGS) according to Lombardi- Neto et al. (1991) and Embrapa (1999)

Fig. 4. Membership functions: (A) linear; (B) bell-shaped

**5.3 Fuzzy SCSCN model** 

calculation.

**5.3.1 Fuzzy soil map** 

**Soil types % of area HSG**  Latosol 2.3 A Podzolic 17.7 B Inceptisol 56.3 C Organic 23.7 D

The width of the transition zones was chosen based in the scale of the map, according to indications provided by Lagacherie et al. (1996) and Burrough and McDonnell (1998). Burrough and McDonnell (1998) exemplified that a sharp boundary drawn as a 0.2 mmthick line on a 1:25,000 scale map covers 50 m (25 m to the right and 25 m to the left from drawn boundary) and a diffuse boundary at the same scale might extend over 500 m. In this study, sharp boundaries were drawn as a 1 mm-thick line on a 1:100,000 scale map, so that the width of the spatial transition zone centered over the drawn boundary location was 200 m. Only the uncertainty related to the drawn boundaries of the map was used, although the transition zones verified in the field show larger extensions.

 In order to model fuzzy transition zones, the computer model involved the following steps: (i) separation of each soil unit (polygon boundary) in different map layers; (ii) isotropic spread of the boundary of each polygon (inside and outside the polygon); (iii) application of a membership function. Each soil unit was considered as a fuzzy set A = {x, µA(x)}. In this case, x denotes a point in geographic space that belongs to A, and µA(x) is a number that ranges from 0 to 1 and reflects the grade of membership of x in A. The fuzziness of the boundary between soil units A and B were indicated by both distributions of grades of membership, µA(x) and µB(x) (Fig. 5). Points located far enough from the boundary have either µA(x) = 1 and µB(x) = 0 (if x is contained in A) or µB(x) = 1 and µA(x) = 0 (if x is contained in B). Points located close to the boundary µA(x) and µB(x) have values between 0 and 1; (iv) the procedure was repeated for each soil unit, yielding a soil boundary fuzziness map for each soil unit. The width of the transition zone can be defined by the user before the computer program is run. Fig. 6 and Fig. 7 show the boundary of organic soil using the fuzzy and boolean model, respectively.

Fig. 5. Linear membership function and illustration of the methodology employed in the conversion of crispy soil data to fuzzy soil data

Fuzzy Hydrologic Model in Tropical Watershed 293

The fuzzy feature of the LPI map (land cover map) was calculated by the membership functions illustrated in Fig. 4. Field observations allowed the identification of transition zones on the vegetation cover (forest and pasture). The transition zones are covered by brushwood, as well as by degraded forest with grass fields. The diffuse boundaries observed in field were identified on the LPI map, so allowing proper membership function parameters to be used. This procedure generated four fuzzy maps: forest and pasture fuzzy

In the fuzzy rule-based modeling, the relationships between variables are represented by

 **If** x is A **then** y is B (6) where x and y are linguistic variables, A and B are linguistic constants. The if-part of the rule "x is A" is named the antecedent, while the then-part of the rule "y is B" is named the

In this study, the Sugeno's method of fuzzy inference (Sugeno, 1985) was used to calculate the CN of all cells in the watershed map. In this method the antecedent is a fuzzy proposition and the consequent is a crisp function. Two typical fuzzy rules used in a Sugeno

 **If** x1 is A11 **and** x2 is A12 **then** y is B1 (7)

 **If** x1 is A21 **and** x2 is A22 **then** y is B2 (8) where xi (i = 1, 2) is an input variable (e.g. soil, vegetation), y is an output variable (e.g. CN parameter), Aij (i = 1, 2 and j = 1, 2) is a fuzzy set (e.g. high infiltration capacity, forest), and

W1 = min(A11(x10), A12(x20)) (9)

 W2 = min(A21(x10), A22(x20)) (10) where "min" denotes "minimum value of". The global output y0, that can be the CN

 y0 = (W1B1 + W2B2)/(W1 + W2) (11) The fuzzy inference system of the Fuzzy SCSCN model was accomplished through the following steps: (i) transformation of the input data in a fuzzy set; (ii) application of the fuzzy rules (Table 3); (iii) computation of the information associated to transition zones on different soil and vegetation map units, using the Sugeno's method; (iv) generation of CN raster maps with the CN values of all pixels of the studied watershed (Fig. 8); and (v) runoff

parameter, is calculated by equation (11) (Kruse et al, 1994; Burrough, 1998):

0) the grade of pertinence then the

**5.3.2 Fuzzy land cover map** 

**5.3.3 Fuzzy rule-based modeling** 

consequent.

calculation.

maps using linear and bell-shaped membership functions.

means of fuzzy **if-then** rules that assume the form:

fuzzy model will be demonstrated as an example:

Bi is a number that represents the consequent of the rule. If x10 and x20 are values assumed by x1 and x2 and Aij(xi

consequent value (crisp function) is W1 and W2 :

Fig. 6. Organic soil map with fuzzy boundaries

Fig. 7. Organic soil map with Boolean boundaries

#### **5.3.2 Fuzzy land cover map**

292 Advances in Data, Methods, Models and Their Applications in Geoscience

Fig. 6. Organic soil map with fuzzy boundaries

Fig. 7. Organic soil map with Boolean boundaries

The fuzzy feature of the LPI map (land cover map) was calculated by the membership functions illustrated in Fig. 4. Field observations allowed the identification of transition zones on the vegetation cover (forest and pasture). The transition zones are covered by brushwood, as well as by degraded forest with grass fields. The diffuse boundaries observed in field were identified on the LPI map, so allowing proper membership function parameters to be used. This procedure generated four fuzzy maps: forest and pasture fuzzy maps using linear and bell-shaped membership functions.

#### **5.3.3 Fuzzy rule-based modeling**

In the fuzzy rule-based modeling, the relationships between variables are represented by means of fuzzy **if-then** rules that assume the form:

$$\mathbf{If } \mathbf{x} \text{ is } \mathbf{A} \text{ then } \mathbf{y} \text{ is } \mathbf{B} \tag{6}$$

where x and y are linguistic variables, A and B are linguistic constants. The if-part of the rule "x is A" is named the antecedent, while the then-part of the rule "y is B" is named the consequent.

In this study, the Sugeno's method of fuzzy inference (Sugeno, 1985) was used to calculate the CN of all cells in the watershed map. In this method the antecedent is a fuzzy proposition and the consequent is a crisp function. Two typical fuzzy rules used in a Sugeno fuzzy model will be demonstrated as an example:

$$\textbf{If } \mathbf{x\_1} \text{ is } A\_{11} \text{ and } \mathbf{x\_2} \text{ is } A\_{12} \text{ then } \mathbf{y} \text{ is } \textbf{B\_1} \tag{7}$$

$$\textbf{If } \ge\_1 \textbf{is} \text{ A}\_{21} \textbf{ and } \ge\_2 \textbf{is} \text{ A}\_{22} \textbf{ then } \text{y is } \text{B}\_2 \tag{8}$$

where xi (i = 1, 2) is an input variable (e.g. soil, vegetation), y is an output variable (e.g. CN parameter), Aij (i = 1, 2 and j = 1, 2) is a fuzzy set (e.g. high infiltration capacity, forest), and Bi is a number that represents the consequent of the rule.

If x10 and x20 are values assumed by x1 and x2 and Aij(xi 0) the grade of pertinence then the consequent value (crisp function) is W1 and W2 :

$$\mathbf{W}\_1 = \min(\mathbf{A}\_{11}(\mathbf{x}\_1 \mathbf{0}), \mathbf{A}\_{12}(\mathbf{x}\_2 \mathbf{0})) \tag{9}$$

$$\mathbf{W}\_2 = \min(\mathbf{A}\_{21}(\mathbf{x}\mathbf{\iota}^0), \mathbf{A}\_{22}(\mathbf{x}\mathbf{\iota}^0)) \tag{10}$$

where "min" denotes "minimum value of". The global output y0, that can be the CN parameter, is calculated by equation (11) (Kruse et al, 1994; Burrough, 1998):

$$\mathbf{y}^{0} = \left(\mathbf{W}\_{1}\mathbf{B}\_{1} + \mathbf{W}\_{2}\mathbf{B}\_{2}\right) / \left(\mathbf{W}\_{1} + \mathbf{W}\_{2}\right) \tag{11}$$

The fuzzy inference system of the Fuzzy SCSCN model was accomplished through the following steps: (i) transformation of the input data in a fuzzy set; (ii) application of the fuzzy rules (Table 3); (iii) computation of the information associated to transition zones on different soil and vegetation map units, using the Sugeno's method; (iv) generation of CN raster maps with the CN values of all pixels of the studied watershed (Fig. 8); and (v) runoff calculation.

Fuzzy Hydrologic Model in Tropical Watershed 295

**Runoff for Boolean SCSCN model (mm)** 

rain 1 5.5 2.1 0 0 0 rain 2 14 2.4 0 0 0 rain 3 21.2 3.4 0 0 0 rain 4 27.4 4.5 0.2 0 0 rain 5 32.8 5 0 1 1 rain 6 35.5 5 1.0 1 1 rain 7 45 7 2 3 3 rain 8 46 7 2 3 3 rain 9 56.5 12.6 5 6 6 rain 10 71.6 14 10 12.3 12.3 rain 11 87.0 19 16.6 18.4 18.6 rain 12 122.6 40 31 37 37 rain 13 134 49 43.2 50 50 rain 14 140.2 56 53 57 57 rain 15 150 59 54 58 58 rain 16 162 62 59 61.5 61.7

Table 4. Simulated runoff with the Boolean SCSCN and with the Fuzzy SCSCN using sixteen

Runoff simulations with the Fuzzy SCSCN model were accomplished using recorded precipitation data in the watershed (Table 4). Runoff modeling was also carried out using soil and vegetation cover data in Boolean format. The notion here was to compare the results derived from conventional SCSCN model and the Fuzzy SCSCN model, as presented

Figure 8 portrays two maps with the spatial distribution of the CNs calculated for the Fuzzy SCSCN model and for the boolean SCSCN model. These CN maps represent the capacity of the land to produce surface runoff from a rain event. It is clear that there is a greater variety of values of the parameter CN when it is calculated by the Fuzzy SCS model. Using the boolean SCSCN model it was possible to achieve only 8 CNs, whereas a larger range of CNs

The simulated runoff values derived from the Fuzzy SCSCN model were closer to measured runoff values in the watershed than the simulated runoff values yielded from the Boolean SCSCN model (Table 4). The better performance reached by the fuzzy model signifies that it can conveniently express natural phenomena, including zones of imprecision and/or uncertainties like transition zones among soil types and vegetation cover. Table 4 shows that the runoff data calculated by the models (from rain 1 to rain 9) is not in agreement with the

**Runoff for Fuzzy SCSCN model (mm)** 

> **Bell-shaped membership function**

**Linear membership function** 

**Event rain** 

rain events

in Table 4.

was yielded with the fuzzy SCSCN.

**(mm)** 

**Recorded runoff (mm)** 

Fig. 8. CN maps obtained by the Fuzzy SCSCN model (A) and by the standard SCSCN model (B)


Table 3. Fuzzy rule-based model for providing the *CN* parameters for the study area.

#### **6. Result discussions**

The CNs used here were selected on the basis of calibrations between modeled and observed runoffs. Key characteristics of the watershed, chiefly the hydrologic soil group, land cover and antecedent moisture conditions, plus CN tables available in the literature (e.g., SCS 1972; Thompson 1999), guided the CN selection. Once the CNs were selected, the runoff modelling was tested through a comparison between the modeled runoff depth and the recorded runoff depth observed in field. The validation of the CNs for the Quilombo River watershed was carried out for 16 rain events (Table 4). The results indicate that the modeled and the observed runoffs are akin and, therefore, the employed CNs proved suitable.

Fig. 8. CN maps obtained by the Fuzzy SCSCN model (A) and by the standard SCSCN

Rule no. (R*i*) *If* HSG *and* LPI *Then* CN

R1 *If* D (*v. low inf.*) *and* pasture *then* 80 (*v. high)* 

R3 *If* C (*low inf.) and* pasture *then* 74 *(High)*  R4 *If* C (*low inf.*) *and* forest *then* 62 *(Medium)*  R5 *If* A (*high inf.*) *and* pasture *then* 39 *(medium-low)* 

R6 *If* A (*high inf.*) *and* forest *then* 26 *(Low)*  R7 *If* B (*moderate inf.*) *and* pasture *then* 61 *(Medium)*  R8 *If* B (*moderate inf.*) *and* forest *then* 52 *(medium-low)* 

Table 3. Fuzzy rule-based model for providing the *CN* parameters for the study area.

The CNs used here were selected on the basis of calibrations between modeled and observed runoffs. Key characteristics of the watershed, chiefly the hydrologic soil group, land cover and antecedent moisture conditions, plus CN tables available in the literature (e.g., SCS 1972; Thompson 1999), guided the CN selection. Once the CNs were selected, the runoff modelling was tested through a comparison between the modeled runoff depth and the recorded runoff depth observed in field. The validation of the CNs for the Quilombo River watershed was carried out for 16 rain events (Table 4). The results indicate that the modeled and the observed runoffs are akin and, therefore, the employed CNs proved

R2 *If* D (*v. low inf.*) *and* forest *then* 69 *(medium-high)* 

model (B)

**6. Result discussions** 

suitable.


Table 4. Simulated runoff with the Boolean SCSCN and with the Fuzzy SCSCN using sixteen rain events

Runoff simulations with the Fuzzy SCSCN model were accomplished using recorded precipitation data in the watershed (Table 4). Runoff modeling was also carried out using soil and vegetation cover data in Boolean format. The notion here was to compare the results derived from conventional SCSCN model and the Fuzzy SCSCN model, as presented in Table 4.

Figure 8 portrays two maps with the spatial distribution of the CNs calculated for the Fuzzy SCSCN model and for the boolean SCSCN model. These CN maps represent the capacity of the land to produce surface runoff from a rain event. It is clear that there is a greater variety of values of the parameter CN when it is calculated by the Fuzzy SCS model. Using the boolean SCSCN model it was possible to achieve only 8 CNs, whereas a larger range of CNs was yielded with the fuzzy SCSCN.

The simulated runoff values derived from the Fuzzy SCSCN model were closer to measured runoff values in the watershed than the simulated runoff values yielded from the Boolean SCSCN model (Table 4). The better performance reached by the fuzzy model signifies that it can conveniently express natural phenomena, including zones of imprecision and/or uncertainties like transition zones among soil types and vegetation cover. Table 4 shows that the runoff data calculated by the models (from rain 1 to rain 9) is not in agreement with the

Fuzzy Hydrologic Model in Tropical Watershed 297

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Lagacherie, P., Andrieux, P. & Bouzigues, R. (1996). Fuzziness and Uncertainty of Soil

Lombardi-Neto, F., Junior, R. B., Lepsh, I. G., Oliveira, J. B., Bertolini, D., Galeti, P. A. &

Mack, M. J. (1995). HER-Hidrologic evaluation of runoff: the soil conservation service curve

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Sugeno, M. (1985). An introductory survey of fuzzy control. *Information Sciences*, Vol.36,

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(Ed.), McGraw-Hill, ISBN 9780070397323, New York

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pp. 439-445 ISSN 0006-3568

436-8 Rotterdam, The Netherlands

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data structure. *Computers & Geosciences*, Vol.23, No.3, (April 1997), pp. 267-272 ISSN

Boundaries: From Reality to Coding in GIS, In: *Geographic Objects with Indeterminate Boundaries,* P. A. Burrough & A. U. Frank, (Eds), Taylor & Francis, ISBN

Drugowich, M. I. (1991). *Terraceamento Agrícola*. Boletim técnico 206, Secretaria de

curve number technique as an interactive computer model. *Computer & Geosciences*,

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Remote Detection of Forest Damage. *BioScience*, Vol.36, No.7, (July-August 1986),

Systems in the Great Plains with ERTS, *Proceeding of Third Earth Resources Technology Satellite-1 Symposium*, pp. 310-317, Washington, December 10-14, 1973 Sakai, E., Lepsch, I. F. & Amaral, A. Z. (1983). *Levantamento Pedológico de Reconhecimento* 

*semidetalhado da Região de Ribeira do Iguape no Estado de São Paulo*, SAA/IAC, São

Integrating dynamic environmental models in GIS: The development of a Dynamic

runoff recorded in the field. This can be explained by the fact that the SCSCN model is inappropriate for estimating the storm runoff depth from small storm rainfall depth (SCS, 1972).

The choice of membership function employed in the Fuzzy SCSCN model, either linear or bell-shaped, showed no significant variation in the simulated runoff. The observed equivalence in the model using these membership functions can be explicated by four factors: (i) both functions are very similar in shape (Fig. 4); (ii) the map scale (1/100,000) is small; (iii) the watershed is medium-sized (270 km2) and this imparted a low runoff variability; and (iv) the transition area is of limited width (200 m).

### **7. Conclusions**

A methodology for runoff modeling using fuzzy sets, fuzzy membership functions and fuzzy rules was presented in this paper. The computer model was created within a GIS environment and its use can be extended to other watersheds in Brazil by simple changes on the database.

Fuzzy logic has a great potential in hydrologic sciences. The incorporation of the fuzzy theory to the SCSCN model allowed a better representation of natural phenomena because fuzzy theory considers the transition zones among geo-objects, which differs from the boolean logic that considers such boundaries as crisp. The calculated runoff by fuzzy model was closer from the measured runoff than the calculated runoff by the boolean model, confirming the adequacy of the fuzzy theory in modeling natural phenomena.

The Fuzzy SCSCN model can be used as a tool for predicting runoff and, consequently, soil erosio**n a**nd quality of water in watersheds. The model is relatively inexpensive because the PCRaster program, where the script is run, is freeware. The program developed here can produce fuzzy boundaries with different widths and can be used with numerous membership functions by simple changes in program script.

#### **8. References**


runoff recorded in the field. This can be explained by the fact that the SCSCN model is inappropriate for estimating the storm runoff depth from small storm rainfall depth (SCS,

The choice of membership function employed in the Fuzzy SCSCN model, either linear or bell-shaped, showed no significant variation in the simulated runoff. The observed equivalence in the model using these membership functions can be explicated by four factors: (i) both functions are very similar in shape (Fig. 4); (ii) the map scale (1/100,000) is small; (iii) the watershed is medium-sized (270 km2) and this imparted a low runoff

A methodology for runoff modeling using fuzzy sets, fuzzy membership functions and fuzzy rules was presented in this paper. The computer model was created within a GIS environment and its use can be extended to other watersheds in Brazil by simple changes on

Fuzzy logic has a great potential in hydrologic sciences. The incorporation of the fuzzy theory to the SCSCN model allowed a better representation of natural phenomena because fuzzy theory considers the transition zones among geo-objects, which differs from the boolean logic that considers such boundaries as crisp. The calculated runoff by fuzzy model was closer from the measured runoff than the calculated runoff by the boolean model,

The Fuzzy SCSCN model can be used as a tool for predicting runoff and, consequently, soil erosio**n a**nd quality of water in watersheds. The model is relatively inexpensive because the PCRaster program, where the script is run, is freeware. The program developed here can produce fuzzy boundaries with different widths and can be used with numerous

Abrams, M. (2000). The Advanced Spaceborne Thermal Emission and Reflection Radiometer

Almeida, T.I.R. & de Souza Filho, C. R. (2004). Principal Component Analysis Applied to

Barreto-Neto, A. A. (2004). *Modelagem dinâmica de processos ambientais*. Ph.D. Thesis,

Burrough, P. A. (1986). *Principles of Geographical Information Systems for Land Resources Assessment* (1st), Oxford University Press: Oxford, ISBN 0-19-854592-4 Burrough, P. A. (1996). Natural Objects with Indeterminate Boundaries, In: *Geographic* 

Burrough, P. A. & McDonnell, R. A. (1998). *Principles of Geographical Information Systems* (1st),

Universidade Estadual de Campinas, Campinas, Brazil.

Oxford University Press, ISBN 0198233655, USA

Francis, ISBN 0748403876, London

(ASTER): data products for the high spatial resolution imager on NASA´s Terra platform. *International Journal of Remote Sensing*, Vol.21, pp. 847-859, ISSN 0143-1161

Feature-Oriented Band Ratios of Hyperspectral Data: A Tool for Vegetation Studies. *International Journal of Remote Sensing*, Vol.25, No.22, pp. 5005-5024, ISSN

*Objects with Indeterminate Boundaries*, P. A. Burrough & A. U. Frank, (Eds), Taylor &

confirming the adequacy of the fuzzy theory in modeling natural phenomena.

membership functions by simple changes in program script.

variability; and (iv) the transition area is of limited width (200 m).

1972).

**7. Conclusions** 

the database.

**8. References** 

0143-1161


**15** 

*1Hong Kong 2Canada* 

**Analysis of Rocky Desertification** 

*1The Yuen Yuen Research Center for Satellite Remote Sensing, Institute of Space and* 

Rocky desertification (RD) is the process of land degradation characterized by soil erosion and bedrock exposure. It is one of the most serious land degradation problems in Karst areas especially in western Guangxi of southwest China, which is usually regarded as an obstacle to the local sustainable development. Recent investigations suggest that the RD is mainly caused by direct human activities, but some researchers also take account the climate change into a key factor of RD (e.g., Yao et al., 2001; Jing et al., 2003; Liao et al., 2004; Hu et

Most areas in the western part of Guangxi Province belong to the Karst region. Similar to the other Karst areas in the southwest China, the geological environment is fragile and sensitive, with a high density of population but a low degree economic development. These aspects make the environment degraded quickly. RD is one of the most serious weaknesses of sustainable development in western Guangxi of southwest China. The investigation of the

In Guangxi, the area of Karst regions is about 89,500 km2, which takes up 37.8% of the total area of Guangxi Province. Among them, the exposed surface of the Karst area is about 78,800 km2, at 88% of the total Karst area in Guangxi. In the past few decades, due to deforestation, over cutting and grazing, the forest in the mountains was severely damaged, which led to serious soil erosion. According to the recent survey, the RD land is more than 233×104 hm2, about 10% of the total area of Guangxi; the potential RD land area is more than 186×104 hm2, about 8% of the total area. The RD land is mainly distributed in the middle of Guangxi: Red River Basin, Liujiang Basin; and western Guangxi: Left, Right River Basin; northeast of Guangxi: the two sides of middle and lower reaches of Li River. There are 32 counties (cities, districts), and Karst areas take up more than 60% of the administrative areas. The typical characteristics of RD areas are lack of soil, water, food and fuel with a lower economic level. 28 counties are designated to be poor ones, and 23 of them

RD and its change monitoring are very significant and also necessarily meaningful.

**1. Introduction** 

al., 2004; Wang et al., 2004; Xiong et al., 2008).

*Earth Information Science, The Chinese University of Hong Kong, Shatin, 2Department of Geography, Queen's University, Kingston, Ontario,* 

**Monitoring Using MODIS Data** 

Yuanzhi Zhang1,\*, Jinrong Hu1, Hongyan Xi1,

**in Western Guangxi, China** 

Yuli Zhu1 and DongMei Chen2

Modelling language. *Transactions in GIS*, Vol.1, No.1, (January 1996), pp. 40-48, ISSN 1467-9671

Zadeh, L. A. (1965). Fuzzy sets. *Information and Control*, Vol.8, No.3, (June 1965), pp. 338-353

## **Analysis of Rocky Desertification Monitoring Using MODIS Data in Western Guangxi, China**

Yuanzhi Zhang1,\*, Jinrong Hu1, Hongyan Xi1, Yuli Zhu1 and DongMei Chen2 *1The Yuen Yuen Research Center for Satellite Remote Sensing, Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, 2Department of Geography, Queen's University, Kingston, Ontario, 1Hong Kong 2Canada* 

#### **1. Introduction**

298 Advances in Data, Methods, Models and Their Applications in Geoscience

Zadeh, L. A. (1965). Fuzzy sets. *Information and Control*, Vol.8, No.3, (June 1965), pp. 338-353

ISSN 1467-9671

Modelling language. *Transactions in GIS*, Vol.1, No.1, (January 1996), pp. 40-48,

Rocky desertification (RD) is the process of land degradation characterized by soil erosion and bedrock exposure. It is one of the most serious land degradation problems in Karst areas especially in western Guangxi of southwest China, which is usually regarded as an obstacle to the local sustainable development. Recent investigations suggest that the RD is mainly caused by direct human activities, but some researchers also take account the climate change into a key factor of RD (e.g., Yao et al., 2001; Jing et al., 2003; Liao et al., 2004; Hu et al., 2004; Wang et al., 2004; Xiong et al., 2008).

Most areas in the western part of Guangxi Province belong to the Karst region. Similar to the other Karst areas in the southwest China, the geological environment is fragile and sensitive, with a high density of population but a low degree economic development. These aspects make the environment degraded quickly. RD is one of the most serious weaknesses of sustainable development in western Guangxi of southwest China. The investigation of the RD and its change monitoring are very significant and also necessarily meaningful.

In Guangxi, the area of Karst regions is about 89,500 km2, which takes up 37.8% of the total area of Guangxi Province. Among them, the exposed surface of the Karst area is about 78,800 km2, at 88% of the total Karst area in Guangxi. In the past few decades, due to deforestation, over cutting and grazing, the forest in the mountains was severely damaged, which led to serious soil erosion. According to the recent survey, the RD land is more than 233×104 hm2, about 10% of the total area of Guangxi; the potential RD land area is more than 186×104 hm2, about 8% of the total area. The RD land is mainly distributed in the middle of Guangxi: Red River Basin, Liujiang Basin; and western Guangxi: Left, Right River Basin; northeast of Guangxi: the two sides of middle and lower reaches of Li River. There are 32 counties (cities, districts), and Karst areas take up more than 60% of the administrative areas. The typical characteristics of RD areas are lack of soil, water, food and fuel with a lower economic level. 28 counties are designated to be poor ones, and 23 of them

Analysis of Rocky Desertification Monitoring

Fig. 1. Location map of the study area in western Guangxi, China.

from previous research and the yearbook of Guangxi.

In this study, MODIS L1B data were used because of its large covered area with a coarse resolution. Other data sources included vector data, administrative maps, some information

The MODIS instrument is operating on both the Terra and Aqua spacecraft. It has a viewing swath width of 2,330 km and views the entire surface of the Earth every one to two days, its

**3. Data and methods** 

**3.1.1 MODIS data** 

**3.1 Data** 

Using MODIS Data in Western Guangxi, China 301

located in the Karst Rock Hill areas take up more than 30% of the administrative areas. RD in Guangxi has become a main cause of disaster and poverty, which constrains the regional economic and social development.

In China, studies on RD have been paid a lot of attention by many researchers since 1980s. Remote sensing and GIS technique always plays an important role in this research field. Landsat TM image, topographic map, geological map and GPS in-situ data were applied to produce a RD classification distribution map in Du'an Yao Autonomous County of Guangxi (Jiang et al., 2004) and to monitor the RD area in Wenshan County of Yunnan Province (Wu, 2009). ASTER image was used to study the situation of RD and its change trend from 2000 to 2005 in the Karst area of Guizhou Province (Chen et al., 2007). In addition, NOAA/AVHRR and MODIS data were used to monitor land desertification (Liu et al., 2007), in which humidity index was used to define the desertification area and two suitable classification methods were established to monitor the desertification dynamics from 1995 to 2001.

MODIS data was first applied in the western Guangxi of southwest China to monitor the rocky desertification with the change of land cover types from 2000 to 2010. The study area covers 30 counties in the western Guangxi. The study tends to give some suggestions to the local governments on the reconstruction of the rocky desertification and defense on new desertification in order to sustain the balance of the whole eco-geo-environment in western Guangxi of southwest China in the near future.

#### **2. Study area**

The study area is located in the western Guangxi province of southwest China (see Fig. 1), which is adjacent to Vietnam. The study area contains 30 counties with the total area of about 7.4×104km2, 31% of the whole province's area of Guangxi (23.76×104 km2). Its geographic location is north latitude 21°36′N to 25°40′N, and east longitude 104°20′E to 108°31′E. The area is mountainous region and belongs to subtropical zone with enough rainfall and rich natural resources.

The conflict between human and land in Guangxi is very sharp, but in the western Guangxi, it is even more severe. The land resources in this area have the following characteristics:


The study area is mainly composed of carbonate rocks, granite, purple sandstone and shale with weak anti-erosion properties. Climate in the study area is complex and changeable, and sunlight and rain is abundant all over a year, which may accelerate soil erosion. Additionally, with the increasing population and development of economy, human activities impact the probability of soil erosion. All of the characteristics of the study area made the Karst rocky desertification more seriously.

located in the Karst Rock Hill areas take up more than 30% of the administrative areas. RD in Guangxi has become a main cause of disaster and poverty, which constrains the regional

In China, studies on RD have been paid a lot of attention by many researchers since 1980s. Remote sensing and GIS technique always plays an important role in this research field. Landsat TM image, topographic map, geological map and GPS in-situ data were applied to produce a RD classification distribution map in Du'an Yao Autonomous County of Guangxi (Jiang et al., 2004) and to monitor the RD area in Wenshan County of Yunnan Province (Wu, 2009). ASTER image was used to study the situation of RD and its change trend from 2000 to 2005 in the Karst area of Guizhou Province (Chen et al., 2007). In addition, NOAA/AVHRR and MODIS data were used to monitor land desertification (Liu et al., 2007), in which humidity index was used to define the desertification area and two suitable classification

methods were established to monitor the desertification dynamics from 1995 to 2001.

MODIS data was first applied in the western Guangxi of southwest China to monitor the rocky desertification with the change of land cover types from 2000 to 2010. The study area covers 30 counties in the western Guangxi. The study tends to give some suggestions to the local governments on the reconstruction of the rocky desertification and defense on new desertification in order to sustain the balance of the whole eco-geo-environment in western

The study area is located in the western Guangxi province of southwest China (see Fig. 1), which is adjacent to Vietnam. The study area contains 30 counties with the total area of about 7.4×104km2, 31% of the whole province's area of Guangxi (23.76×104 km2). Its geographic location is north latitude 21°36′N to 25°40′N, and east longitude 104°20′E to 108°31′E. The area is mountainous region and belongs to subtropical zone with enough

The conflict between human and land in Guangxi is very sharp, but in the western Guangxi, it is even more severe. The land resources in this area have the following

a. the area of land is large but the farmland is very limited due to mountainous and rocky

b. a high density of population in Guangxi results in the small farmland per capita, which

c. the land cannot be utilized adequately as the whole agricultural productivity with a very low land quality. Most of the farmland belongs the second or third class.

The study area is mainly composed of carbonate rocks, granite, purple sandstone and shale with weak anti-erosion properties. Climate in the study area is complex and changeable, and sunlight and rain is abundant all over a year, which may accelerate soil erosion. Additionally, with the increasing population and development of economy, human activities impact the probability of soil erosion. All of the characteristics of the study area

is less than 0.1 hm2. In the study area it is much smaller;

d. soil fertility was lost due to a serious soil erosion.

made the Karst rocky desertification more seriously.

Moreover, the land is difficult to be utilized in the Karst area;

economic and social development.

Guangxi of southwest China in the near future.

rainfall and rich natural resources.

**2. Study area** 

characteristics:

landform;

Fig. 1. Location map of the study area in western Guangxi, China.

#### **3. Data and methods**

#### **3.1 Data**

In this study, MODIS L1B data were used because of its large covered area with a coarse resolution. Other data sources included vector data, administrative maps, some information from previous research and the yearbook of Guangxi.

#### **3.1.1 MODIS data**

The MODIS instrument is operating on both the Terra and Aqua spacecraft. It has a viewing swath width of 2,330 km and views the entire surface of the Earth every one to two days, its

Analysis of Rocky Desertification Monitoring

Fig. 2. The flowchart of data processing

250 m spatial resolution, respectively.

follows:

Using MODIS Data in Western Guangxi, China 303

The vegetation index is linear correlation to vegetation distribution density, the bigger of NDVI, the better of vegetation cover. The formula for NDVI calculation can be expressed as

 NDVI = (Rnir - Rred) /(Rnir + Rred) (1) Where, Rnir in the formula is the reflectance of near infrared band and Rred the reflectance of the red band, corresponding to the second band and the first band of MODIS L1B data with

NDVI value <0.2 0.2-0.4 0.4-0.6 >0.6

The extent of RD Intensity Moderate Mild Good protected

Table 1. The relationship of NDVI and the extent of RD (adopted from Hu et al., 2004)

There is a relationship between NDVI and the extent of RD (Hu et al., 2004) as shown in Table 1. From the table, one can see that if NDVI value is below 0.2, it means there is little

detectors measure 36 spectral bands, 0.405μm~14.385μm, covering the range of the electromagnetic spectrum. Among these bands, the 1-19 and 26 bands are for the visible and near-infrared channels, and the remaining 16 bands are thermal infrared channels. In addition, MODIS data have three spatial resolutions: 250 m (2 bands), 500 m (5 bands) and 1 km (29 bands). Compared with NOAA/AVHRR and MODIS data are of high spatial, temporal and spectral resolution. Therefore, MODIS data have been widely used in a lot of studies on land use land cover (LULC) mapping and LULC change detection at both global and local scales (Perera and Tsuchiya, 2009; Friedl et al., 2002).

MODIS Level 1B data with 250 m resolution were used in this study. Although the number of bands is limited, the two bands are in the red and near-infrared wavelengths, which are among the most important spectral regions for remote sensing of vegetation. MODIS L1B 250 m radiance data have been utilized for detection of vegetative cover conversion caused by recent significant natural events (burning and flooding) and human activities (deforestation) (Zhan et al., 2002). MODIS L1B data with 250 m resolution in November of 2000, 2003, 2006, 2008 and 2010 were downloaded to detect changes, because the weather in this month does not change too much and it is easier to get clear and cloudless images.

#### **3.1.2 Other supporting data**

The boundary vector data of the study area is from the National Fundamental Geographic Information System with a scale of 1:4,000,000. It contains the information of borders (national, province, city, county), rivers (the first, second, third class), main roads, main railways, and residences (e.g., city and county). In this study, the border and residence data are mainly used. In addition, local administrative maps, information from previous research and the yearbooks of Guangxi are used as supporting data in the study.

#### **3.2 Methods**

The processing steps of the study are shown in Fig. 2. Firstly, the MODIS L1B data were preprocessed and the study area of western Guangxi was retrieved using the border vector data. Secondly, the images and other data were projected to the same coordinate system and spatial resolution after geo-reference calibration. MODIS data with 250 m spatial resolution in 2000, 2003, 2006, 2008, and 2010 were obtained with two bands of red and infrared bands, respectively. Thereafter, two methods were used to monitor the RD: a) NDVI calculation to identify the extent of RD; and b) analysis on land cover change after classification on the two images. Finally, the changed information was extracted and compared. Through a statistical analysis, the RD results were quantitatively analyzed.

#### **3.2.1 RD identified by NDVI calculation**

NDVI (Normalized Difference Vegetation Index) is a simple numerical indicator that can be used to analyze remote sensing measurements. NDVI provides a crude estimate of vegetation health and a means of monitoring changes in vegetation over time. Vegetation index is extracted from the multi-spectral remote sensing data, it can quantized reflect the plants situation and helps strengthen our interpretation of remote sensing images. As a means of remote sensing, it is widely used in monitoring land-use cover, vegetation cover, density assessment, crop identification and crop forecasting. It has enhanced the ability of the classification in the topic mapping (Du, 2008).

detectors measure 36 spectral bands, 0.405μm~14.385μm, covering the range of the electromagnetic spectrum. Among these bands, the 1-19 and 26 bands are for the visible and near-infrared channels, and the remaining 16 bands are thermal infrared channels. In addition, MODIS data have three spatial resolutions: 250 m (2 bands), 500 m (5 bands) and 1 km (29 bands). Compared with NOAA/AVHRR and MODIS data are of high spatial, temporal and spectral resolution. Therefore, MODIS data have been widely used in a lot of studies on land use land cover (LULC) mapping and LULC change detection at both global

MODIS Level 1B data with 250 m resolution were used in this study. Although the number of bands is limited, the two bands are in the red and near-infrared wavelengths, which are among the most important spectral regions for remote sensing of vegetation. MODIS L1B 250 m radiance data have been utilized for detection of vegetative cover conversion caused by recent significant natural events (burning and flooding) and human activities (deforestation) (Zhan et al., 2002). MODIS L1B data with 250 m resolution in November of 2000, 2003, 2006, 2008 and 2010 were downloaded to detect changes, because the weather in this month does not change too much and it is easier to get clear and cloudless images.

The boundary vector data of the study area is from the National Fundamental Geographic Information System with a scale of 1:4,000,000. It contains the information of borders (national, province, city, county), rivers (the first, second, third class), main roads, main railways, and residences (e.g., city and county). In this study, the border and residence data are mainly used. In addition, local administrative maps, information from previous research

The processing steps of the study are shown in Fig. 2. Firstly, the MODIS L1B data were preprocessed and the study area of western Guangxi was retrieved using the border vector data. Secondly, the images and other data were projected to the same coordinate system and spatial resolution after geo-reference calibration. MODIS data with 250 m spatial resolution in 2000, 2003, 2006, 2008, and 2010 were obtained with two bands of red and infrared bands, respectively. Thereafter, two methods were used to monitor the RD: a) NDVI calculation to identify the extent of RD; and b) analysis on land cover change after classification on the two images. Finally, the changed information was extracted and compared. Through a statistical

NDVI (Normalized Difference Vegetation Index) is a simple numerical indicator that can be used to analyze remote sensing measurements. NDVI provides a crude estimate of vegetation health and a means of monitoring changes in vegetation over time. Vegetation index is extracted from the multi-spectral remote sensing data, it can quantized reflect the plants situation and helps strengthen our interpretation of remote sensing images. As a means of remote sensing, it is widely used in monitoring land-use cover, vegetation cover, density assessment, crop identification and crop forecasting. It has enhanced the ability of

and the yearbooks of Guangxi are used as supporting data in the study.

analysis, the RD results were quantitatively analyzed.

the classification in the topic mapping (Du, 2008).

**3.2.1 RD identified by NDVI calculation** 

and local scales (Perera and Tsuchiya, 2009; Friedl et al., 2002).

**3.1.2 Other supporting data** 

**3.2 Methods** 

Fig. 2. The flowchart of data processing

The vegetation index is linear correlation to vegetation distribution density, the bigger of NDVI, the better of vegetation cover. The formula for NDVI calculation can be expressed as follows:

$$\text{NDVI} = (\mathbf{R}\_{\text{vir}} \text{ - } \mathbf{R}\_{\text{red}}) \;/\left(\mathbf{R}\_{\text{vir}} + \mathbf{R}\_{\text{red}}\right) \tag{1}$$

Where, Rnir in the formula is the reflectance of near infrared band and Rred the reflectance of the red band, corresponding to the second band and the first band of MODIS L1B data with 250 m spatial resolution, respectively.


Table 1. The relationship of NDVI and the extent of RD (adopted from Hu et al., 2004)

There is a relationship between NDVI and the extent of RD (Hu et al., 2004) as shown in Table 1. From the table, one can see that if NDVI value is below 0.2, it means there is little

Analysis of Rocky Desertification Monitoring

Fig. 4. (continued)

Using MODIS Data in Western Guangxi, China 305

Fig. 3. Level slicing for the extent of RD (This is a segmentation method called level slicing).

vegetation cover on this area, and much rocky land exposed to the air, so the RD here is intense; if the NDVI value is between 0.2 and 0.4, the extent of RD is moderate; if NDVI is between 0.4 and 0.6, the extent of RD is mild; when the NDVI is above 0.6, it means these areas are good protected.

#### **3.2.2 Land cover classification**

After the detection of NDVI change, it is still needed to know the specific changes of land cover types in the study area. Generally, there are two methods to distinguish and interpret the remote sensing image: supervised classification and unsupervised classification. Supervised - image analyst "supervises" the selection of spectral classes that represent patterns or land cover features that the analyst can recognize. Unsupervised - statistical "clustering" algorithms used to select spectral classes inherent to the data, more computerautomated.

Supervised classification was used in this study. It is much more accurate for mapping classes, but depends heavily on the cognition and skills of the image specialist. The strategy is simple: the specialist must recognize conventional classes (real and familiar) or meaningful (but somewhat artificial) classes in a scene from prior knowledge, such as personal experience with what is present in the scene, or more generally, the region it is located in, by experience with thematic maps, or by on-site visits. This familiarity allows the individual(s) making the classification to choose and set up discrete classes (thus supervising the selection) and then, assign them category names.

Training ground and training sample selection is very important in supervised classification, the classification result will have a big different in supervised classification if the training sample is different. So it should be careful to select the training ground and choose the represented training sample correctly. These are the key points to produce a good classification result. In this study, supporting data and local land cover maps were used to help distinguish the land cover types. Due to the coarse resolution of MODIS data, it is not credible to classify many detailed land cover types. Thus based on information from supporting data and local land cover maps, six types of land cover were to be classified: water, wood, grassland, residence, farmland and unused land. After the classification, postprocessing of image classification should be performed to get more reliable land cover maps, whilst accuracy assessment would be done.

#### **4. Results**

#### **4.1 NDVI distribution and change of RD from 2000 to 2010**

NDVI values were calculated by equation (1) and their distribution maps of 2000 and 2010 were obtained using ENVI software. Difference can be seen in the NDVI maps of the two years. In north part of the study area, NDVI values decreased in November 2008 compared with that in November 2000. However, NDVI maps can not show these changes distinctly. To distinguish the extent of the RD from 2000 to 2010, a decision tree upon Table 1 was produced as shown in Fig. 3. The Decision Tree classifier performs multistage classifications by using a series of binary decisions to place pixels into classes. Each decision divides the pixels in a set of images into two classes based on an expression. Based on these rules, the extent distribution of RD from 2000 to 2010 can be mapped clearly (see Fig. 4).

vegetation cover on this area, and much rocky land exposed to the air, so the RD here is intense; if the NDVI value is between 0.2 and 0.4, the extent of RD is moderate; if NDVI is between 0.4 and 0.6, the extent of RD is mild; when the NDVI is above 0.6, it means these

After the detection of NDVI change, it is still needed to know the specific changes of land cover types in the study area. Generally, there are two methods to distinguish and interpret the remote sensing image: supervised classification and unsupervised classification. Supervised - image analyst "supervises" the selection of spectral classes that represent patterns or land cover features that the analyst can recognize. Unsupervised - statistical "clustering" algorithms used to select spectral classes inherent to the data, more computer-

Supervised classification was used in this study. It is much more accurate for mapping classes, but depends heavily on the cognition and skills of the image specialist. The strategy is simple: the specialist must recognize conventional classes (real and familiar) or meaningful (but somewhat artificial) classes in a scene from prior knowledge, such as personal experience with what is present in the scene, or more generally, the region it is located in, by experience with thematic maps, or by on-site visits. This familiarity allows the individual(s) making the classification to choose and set up discrete classes (thus

Training ground and training sample selection is very important in supervised classification, the classification result will have a big different in supervised classification if the training sample is different. So it should be careful to select the training ground and choose the represented training sample correctly. These are the key points to produce a good classification result. In this study, supporting data and local land cover maps were used to help distinguish the land cover types. Due to the coarse resolution of MODIS data, it is not credible to classify many detailed land cover types. Thus based on information from supporting data and local land cover maps, six types of land cover were to be classified: water, wood, grassland, residence, farmland and unused land. After the classification, postprocessing of image classification should be performed to get more reliable land cover

NDVI values were calculated by equation (1) and their distribution maps of 2000 and 2010 were obtained using ENVI software. Difference can be seen in the NDVI maps of the two years. In north part of the study area, NDVI values decreased in November 2008 compared with that in November 2000. However, NDVI maps can not show these changes distinctly. To distinguish the extent of the RD from 2000 to 2010, a decision tree upon Table 1 was produced as shown in Fig. 3. The Decision Tree classifier performs multistage classifications by using a series of binary decisions to place pixels into classes. Each decision divides the pixels in a set of images into two classes based on an expression. Based on these rules, the

extent distribution of RD from 2000 to 2010 can be mapped clearly (see Fig. 4).

supervising the selection) and then, assign them category names.

maps, whilst accuracy assessment would be done.

**4.1 NDVI distribution and change of RD from 2000 to 2010** 

areas are good protected.

automated.

**4. Results** 

**3.2.2 Land cover classification** 

Fig. 3. Level slicing for the extent of RD (This is a segmentation method called level slicing).

Fig. 4. (continued)

Analysis of Rocky Desertification Monitoring

2010 were shown in Fig. 5.

Fig. 5. (continued)

**4.2 Supervised classification and change analysis** 

Using MODIS Data in Western Guangxi, China 307

MODIS L1B images in 2000 and 2010 were classified by supervised classification, in which the maximum likelihood classifying algorithm was employed as the most typical and wide method. After the ground training and selection of training samples, the image classification of the MODIS data was performed under ENVI environment. Afterwards, the postprocessing classification was also made to reduce or eliminate the effect of noise caused by mixed scattered point features. Therefore, a filter kernel 3×3 matrix was used to make cluster analysis, which can smooth the classification maps and combine the similar areas to the neighbor region. The final results of the classification maps in 2000, 2003, 2006, 2008 and

Fig. 4. The RD extent mapping in 2000 (a) 2003 (b), 2006 (c), 2008 (d) and 2010(e)

Compared the results from 2000, 2003, 2006, 2008, and 2010, it is clearly found that the good protected area has decreased from 2000 to 2006, and increased again from 2006 to 2010 in west part of the study area; the mild and moderate RD area is increasing dramatically in middle and north part of the study area. Nevertheless, intense RD area is seldom noted in all years, which may indicate that the environment of the study area does not deteriorate very badly. A change table of class statistics was made as shown in Table 2, which gives the percentages of each class in the whole area. It is clear that good protected area decreased from 2000 to 2006 and increased again from 2006 to 2010, while intense and moderate areas are relatively stable, and mild area decreased remarkably from 2006 to 2010. This indicates that many mild areas have been converted to good protected lands since 2006 due to governmental land protection policies.


Table 2. The RD extent in 2000, 2003, 2006, 2008, and 2010

Fig. 4. The RD extent mapping in 2000 (a) 2003 (b), 2006 (c), 2008 (d) and 2010(e)

intensity moderate mild good protected

percent 0.12% 1.15% 37.98% 60.76% area(km2) 85.71 847.40 28104.77 44962.13

area(km2) 151.98 4548.10 49422.80 19877.12

percent 0.20% 3.91% 72.45% 23.44% area(km2) 145.59 2892.45 53613.76 17348.20

percent 0.35% 3.73% 57.49% 38.44% area(km2) 257.39 2756.87 42542.88 28442.86

percent 0.33% 1.68% 46.47% 51.52% area(km2) 247.24 1242.65 34387.04 38123.07

2003 percent 0.21% 6.15% 66.79% 26.86%

governmental land protection policies.

Table 2. The RD extent in 2000, 2003, 2006, 2008, and 2010

2000

**e** 

2006

2008

2010

Compared the results from 2000, 2003, 2006, 2008, and 2010, it is clearly found that the good protected area has decreased from 2000 to 2006, and increased again from 2006 to 2010 in west part of the study area; the mild and moderate RD area is increasing dramatically in middle and north part of the study area. Nevertheless, intense RD area is seldom noted in all years, which may indicate that the environment of the study area does not deteriorate very badly. A change table of class statistics was made as shown in Table 2, which gives the percentages of each class in the whole area. It is clear that good protected area decreased from 2000 to 2006 and increased again from 2006 to 2010, while intense and moderate areas are relatively stable, and mild area decreased remarkably from 2006 to 2010. This indicates that many mild areas have been converted to good protected lands since 2006 due to

#### **4.2 Supervised classification and change analysis**

MODIS L1B images in 2000 and 2010 were classified by supervised classification, in which the maximum likelihood classifying algorithm was employed as the most typical and wide method. After the ground training and selection of training samples, the image classification of the MODIS data was performed under ENVI environment. Afterwards, the postprocessing classification was also made to reduce or eliminate the effect of noise caused by mixed scattered point features. Therefore, a filter kernel 3×3 matrix was used to make cluster analysis, which can smooth the classification maps and combine the similar areas to the neighbor region. The final results of the classification maps in 2000, 2003, 2006, 2008 and 2010 were shown in Fig. 5.

Fig. 5. (continued)

Analysis of Rocky Desertification Monitoring

**5. Discussion and conclusion** 

from 2000 to 2010.

include:

classification were performed to detect the RD extent.

the RD extent than image classification in the study area.

Using MODIS Data in Western Guangxi, China 309

Fig. 6. The areas of each land cover type and its change from 2000 to 2010

In this study, MODIS L1B data with 250 m resolution were used to monitor the RD change in western Guangxi from 2000 to 2010. Two methods of NDVI calculation and supervised

From the above results, the distribution of RD areas extended from 2000 to 2008 and reduced from 2008 to 2010. The first method is based on the relationships between NDVI and RD. In general, if NDVI values of a region are high, it means the vegetation cover is well protected with a rare extent of RD. Otherwise the lower of the NDVI, the more serious of RD. Based on this assumption, the RD extent was determined in the study area

However, the RD areas were not only identified by NDVI. Some other factors may also affect the RD extent which can be extracted from MODIS data. To compare the RD extent with the change of land cover types, supervised classification was performed to determine six types of land cover in the study area (see Fig. 5). With the reference of previous studies of local land cover types (Li et al., 2006; Nong, 2007) and the yearbooks of Guangxi, the training sites and samples of six classes were selected and determined. Although there are misclassification errors involved, the results of image classification are reasonable to agree well with the previous results of land cover types and the statistic data in the yearbooks of Guangxi. Comparatively, the NDVI calculation is better and easier to be utilized to detect

It is reported that 37.6% RD is resulted from natural factors, while 62.4% of the RD area is caused by direct human activities (Nong, 2007). In this study, the natural factors may

Fig. 5. Land cover classification from MODIS in 2000 (a), 2003 (b), 2006 (c), 2008 (d) and 2010(e)

A lot of change of land cover types can be found during the period from 2000 to 2010. In the classification map of 2000, woodland is the main class, which takes up to 47.68% of the study area; grassland is about 31.82% and farmland takes up to 8.32%, while other classes are relatively small. However, in 2008, the woodland only takes 38.69%, and grassland and farmland are up to 37.03% and 10.97%, respectively, which may suggest that these woodland areas were degenerating into grassland and farmland areas in a large region with the intensifying degree of RD issues. But this trend stops from 2008 to 2010. From 2008 to 2010 the woodland has increased to 44.95% and grassland and farmland decreased to 35.50% and 8.23%, respectively.

Table 3 and Fig. 6 show the total percentage and area of each land cover type from 2000 to 2010. It is clear that residential areas, farmland and grassland increased remarkably, whereas woodland decreased dramatically. In comparison, unused land areas decreased quite smaller, while water areas retains relative stable.


Table 3. The percentages of each land cover type in 2000, 2003, 2006, 2008 and 2010

Fig. 5. Land cover classification from MODIS in 2000 (a), 2003 (b), 2006 (c), 2008 (d) and

A lot of change of land cover types can be found during the period from 2000 to 2010. In the classification map of 2000, woodland is the main class, which takes up to 47.68% of the study area; grassland is about 31.82% and farmland takes up to 8.32%, while other classes are relatively small. However, in 2008, the woodland only takes 38.69%, and grassland and farmland are up to 37.03% and 10.97%, respectively, which may suggest that these woodland areas were degenerating into grassland and farmland areas in a large region with the intensifying degree of RD issues. But this trend stops from 2008 to 2010. From 2008 to 2010 the woodland has increased to 44.95% and grassland and farmland decreased to

Table 3 and Fig. 6 show the total percentage and area of each land cover type from 2000 to 2010. It is clear that residential areas, farmland and grassland increased remarkably, whereas woodland decreased dramatically. In comparison, unused land areas decreased

water wood grassland farmland residence unused land

2000 3.24% 47.68% 31.82% 8.32% 6.16% 2.78%

2003 4.05% 45.03% 33.52% 7.55% 3.20% 6.66%

2006 5.72% 44.09% 33.04% 6.16% 4.74% 6.24%

2008 3.30% 38.69% 37.03% 10.97% 3.74% 6.26%

2010 3.40% 44.95% 35.50% 8.23% 3.80% 4.13%

Table 3. The percentages of each land cover type in 2000, 2003, 2006, 2008 and 2010

2010(e)

**e** 

35.50% and 8.23%, respectively.

quite smaller, while water areas retains relative stable.

Fig. 6. The areas of each land cover type and its change from 2000 to 2010

#### **5. Discussion and conclusion**

In this study, MODIS L1B data with 250 m resolution were used to monitor the RD change in western Guangxi from 2000 to 2010. Two methods of NDVI calculation and supervised classification were performed to detect the RD extent.

From the above results, the distribution of RD areas extended from 2000 to 2008 and reduced from 2008 to 2010. The first method is based on the relationships between NDVI and RD. In general, if NDVI values of a region are high, it means the vegetation cover is well protected with a rare extent of RD. Otherwise the lower of the NDVI, the more serious of RD. Based on this assumption, the RD extent was determined in the study area from 2000 to 2010.

However, the RD areas were not only identified by NDVI. Some other factors may also affect the RD extent which can be extracted from MODIS data. To compare the RD extent with the change of land cover types, supervised classification was performed to determine six types of land cover in the study area (see Fig. 5). With the reference of previous studies of local land cover types (Li et al., 2006; Nong, 2007) and the yearbooks of Guangxi, the training sites and samples of six classes were selected and determined. Although there are misclassification errors involved, the results of image classification are reasonable to agree well with the previous results of land cover types and the statistic data in the yearbooks of Guangxi. Comparatively, the NDVI calculation is better and easier to be utilized to detect the RD extent than image classification in the study area.

It is reported that 37.6% RD is resulted from natural factors, while 62.4% of the RD area is caused by direct human activities (Nong, 2007). In this study, the natural factors may include:

Analysis of Rocky Desertification Monitoring

Meteorology, 2(1), 26-28.

22(3), 193-196.

19(3), 154-156.

Engineering, 2007, 23(10), 145-150.

Monit. Assess, 153, 339–349.

Space Research, 43, 1349–1355.

Scholarship at ISEIS of CUHK and CUHK Direct Grants.

**6. Acknowledgements** 

**7. References** 

Using MODIS Data in Western Guangxi, China 311

The MODIS Level 1B data downloaded from the website of NASA MODIS products and the vector data from National Fundamental Geographic Information System of China are highly appreciated. The authors would like to thank Mr. Xianzhi Hu for his help of image preprocessing. The research was partially supported by the Yuen Yuen Remote Sensing

Chen, Q., Xiong, K., and Lan, A., 2007. Analysis the Karst Rocky Desertification situation and change trend in Guizhou base on "3S". China Karst, 26(1), 37-42. Du, L., 2008. Preprocessing and NDVI calculation of MODIS 1B data. Desert and Oasis

Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D., Strahler,

Hu, B., Liao, C., Yan, Z., Jiang, S., Huang, Q., and Li, S., 2004. Diving Mechanism Diagnosis

Li, S., Shu, N, Wang, G., and Liao, S., 2006. The Origination Analysis and Progress of the

Liu, A., Wang, C., Wang, J., and Shao, 2007. Method for remote sensing monitoring of

Liu, L., Jing, X., Wang, J., and Zhao, C., 2009. Analysis of the changes of vegetation coverage

Luo, G., 2007. Rocky desertification and climatic factors in Guangxi. Journal of

Nong, S., 2007. Rock desertification status analysis in karst areas of Guangxi and control

Perera, K., and Tsuchiya, K., 2009. Experiment for mapping land cover and it's change in

Tang, X., He, X., Peng, H, and Chen, C., 2003. The causes and control measures of the

Wang, G., 2008. Features and preparation of MODIS data. China's scientific and technical papers online, http://www.paper.edu.cn, published on 27th August, 2008. Wei, M., 2002. Conditions and countermeasures of rocky desertification in Guangxi. Journal of Guangxi University (Philosophy and Social Science), 24(2), 42–47.

early results. Remote Sensing of Environment, 83(1), 287–302.

on RS and GIS. Journal of Mountain Science, 22(5). 583-590.

Meteorological Research and Application. 28(S1), 74-75.

measures. Guangxi Forestry Science, 36(3), 170–172.

A.H., Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C., 2002. Global land cover mapping from MODIS: algorithms and

of Karst Rocky Desertification in Du'an Yao Autonomous County of Guangxi based

Rocky Desert of Land in Guangxi. Journal of Guangxi Academy of Sciences,

desertification based on MODIS and NOAA/AVHRR data. Journal of Agricultural

of western Beijing mountainous areas using remote sensing and GIS. Environ.

southeastern Sri Lanka utilizing 250m resolution MODIS imageries. Advances in

Rocky Desertification in Guangxi Karst areas. Resource Development & Market,


On the other hand, direct human activities possibly cause RD or potential RD areas in the following ways:


The prevention and control measures of RD from the natural factors can be carried out by the following ways (Luo, 2007):


There are also various means on prevention and control measures of RD from the human activity factors, such as development of the biogas construction, population growth controlling, industrial pollution prevention (Xu, 2006). In addition, it is effective to propagate scientific knowledge on environment protection in the Karst area (Tang et al., 2003). The obvious increase of good protected area and woodland from 2008 to 2010 indicates these propagation and prevention policies have produces positive results of reducing RD in this area. However, there is still a long rough way to go for the public and the government to bring the RD under control in western Guangxi of southwest China in the future.

#### **6. Acknowledgements**

The MODIS Level 1B data downloaded from the website of NASA MODIS products and the vector data from National Fundamental Geographic Information System of China are highly appreciated. The authors would like to thank Mr. Xianzhi Hu for his help of image preprocessing. The research was partially supported by the Yuen Yuen Remote Sensing Scholarship at ISEIS of CUHK and CUHK Direct Grants.

#### **7. References**

310 Advances in Data, Methods, Models and Their Applications in Geoscience

 Climate effects: Guangxi is located in the subtropical climate with a long sunshine, much heat and rainfall. The average rainfall is usually 1400 ~ 1800mm per year, sometimes even more than 3000 mm. All these factors lead to serious soil loss, especially in the heavy rain season, in which the erodible soil is strongly rinsed off and

 The impact of geological conditions: the southeast of Guangxi is granite collapse Kong area, and the northwest region is limestone area. Both of these two types of geological

Vegetation influence: Rare vegetation cover is an important factor to result in soil

 Topography effects: Soil erosion may also be accelerated by hilly and flat ground, steep slope, broken terrain and cutting deep ravines in western Guangxi (Wei, 2002). On the other hand, direct human activities possibly cause RD or potential RD areas in the

 Human activities of production and life have an inextricably relationship with RD. The impact of anthropogenic factors is always through various forms. Along with the rapid growth of population, land and energy demand is increasing. This makes the original forest resource dissipated very quickly. Moreover, the inappropriate farming methods, such as the cultivation in high slope land, overgrazing, even excessive exploitation, as well as the quick exploration of local small mines, road building projects, and other industrial projects, make the ecological Karst areas brittle and weak, and showing a

The prevention and control measures of RD from the natural factors can be carried out by

 Water storage construction. Water storage project is an effective way to control RD extending. This can reduce the seepage of rainwater, then reduce the soil erosion, and can also satisfy the industrial and agricultural water demand as much as possible; Forest planting. Making full use of solar thermal and water resources in the small gap of the Karst land and planting more at these areas are both effective ways to reduce the rock surface temperature and water consumption. They improve the micro-climate

 Development of three-dimensional ecological agriculture. In such extreme degradation ecosystems of RD areas, the natural recovery of vegetation is very difficult. So it needs biological, engineering and management measures by adjusting the irrigation system and transformation of soil quality to improve soil fertility and improve the ecological

There are also various means on prevention and control measures of RD from the human activity factors, such as development of the biogas construction, population growth controlling, industrial pollution prevention (Xu, 2006). In addition, it is effective to propagate scientific knowledge on environment protection in the Karst area (Tang et al., 2003). The obvious increase of good protected area and woodland from 2008 to 2010 indicates these propagation and prevention policies have produces positive results of reducing RD in this area. However, there is still a long rough way to go for the public and the government to bring the RD under control in western Guangxi of southwest China in

rocks are more prone to form RD or potential RD areas;

Excessive deforestation makes the RD area enlarge seriously;

conditions and slow down the rock desert process;

environment, in order to make the RD area back to normal.

erosion and form RD (see Fig. 9 and Fig. 10);

rapid trend of RD (Li et al., 2006).

the following ways (Luo, 2007):

only bare bedrocks remain;

following ways:

the future.


**16** 

*Israel* 

**Lower Eocene Crustacean Burrows (Israel)** 

**Mode of Breeding Across the K-T Boundary** 

Crustacean burrows filled with chalk were found at the lowermost part of the Lower Eocene sequence in southwestern Israel. They are exposed on both sides of a road-cut about 40 m long (Fig. 1) on the way leading from the city of Be'er Sheva to the Israeli-Egyptian border (Fig. 2A; site A; N 300 57' 36", E 340 39' 20"). The road-cut exposes grey-greenish clay of the upper part of the Paleocene Taqiya Formation, overlain by white chalk with chert nodules of the Lower Eocene Mor Formation (Fig. 2B). The burrow system consists of horizontal galleries leading to heart-shaped flattened casts of chambers embedded in 12-cm-thick argillaceous chalk (Fig. 2B). This single type of chamber cast preserves on its surface ovoid blister-like elevations with transversal fine scratches. These peripheral structures are identical to those on the phosphatic casts of Campanian crustacean burrow-system chambers described from another exposure along the same road, some 18 km to the east where two types of chamber fillings were found (Lewy & Goldring, 2006). One is circular (D=45 mm) and replicates the arched ceiling with finely scratched elevated ovoid structures, whereas the cast of the floor comprises rings of about eight tubercles replicating pits 4 mm in diameter and 3.0-3.5 mm in depth. The pits were interpreted to host and protect large eggs in a brood chamber. The second kind of chamber changes shape and dimensions from circular (D=45 mm) to arrowhead-shaped up to 100 mm in length with one end rounded and tapering towards the opposite end. This gradually enlarged chamber was suggested to host the young (nursery chamber) and perhaps store food or provide gardening sites. The Lower Eocene burrow system lacks the brood chamber, whereas the heart-like chamber looks as a shortened modification of the Campanian arrowhead chamber, preserving the wall structure of the Campanian chambers. The diameter of the galleries of the Campanian burrows is about 14-17 mm compared to 10-15 mm of the Lower Eocene ones, and the greatest width of the Campanian chambers (about 7 cm) is similar to that of the Lower Eocene ones. Accordingly, the Campanian and Lower Eocene crustacean burrows into pelagic chalk have a similar structure of horizontal galleries connecting between chambers

**1. Introduction** 

**Reflect a Change from K- to r-Type** 

**End-Cretaceous Biological Crisis** 

**Clarifying the Process of the** 

Lydia Perelis-Grossowicz and Shimon Ilani

Zeev Lewy, Michael Dvorachek,

*Geological Survey of Israel,* 


## **Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis**

Zeev Lewy, Michael Dvorachek, Lydia Perelis-Grossowicz and Shimon Ilani *Geological Survey of Israel, Israel* 

#### **1. Introduction**

312 Advances in Data, Methods, Models and Their Applications in Geoscience

Wu, N., 2009. TM Image Based Monitoring on Rocky Desertification in Karst Area of

Xiong, Y., Qiu, G., Mo, D., Lin, H., Sun, H., Wang, Q., Zhao, S., and Yin, J., 2009. Rocky

Zhan, X., Sohiberg., R.A., Townshend, J.R.G., DiMiceli, C., Carroll, M.L., Eastman, J.C.,

MODIS 250 m data. Remote Sensing of Environment, 83(1), 336-350.

Hunan Province, China. Environ. Geol., 57, 1481-1488.

29(1), 62-66.

Wenshan Prefecture, Yunnan Province. Journal of Southwest Forestry University,

desertification and its causes in karst areas: a case study in Yongshun County,

Hansen, M.C., and Defries, R.S., 2002. Detection of land cover changes using

Crustacean burrows filled with chalk were found at the lowermost part of the Lower Eocene sequence in southwestern Israel. They are exposed on both sides of a road-cut about 40 m long (Fig. 1) on the way leading from the city of Be'er Sheva to the Israeli-Egyptian border (Fig. 2A; site A; N 300 57' 36", E 340 39' 20"). The road-cut exposes grey-greenish clay of the upper part of the Paleocene Taqiya Formation, overlain by white chalk with chert nodules of the Lower Eocene Mor Formation (Fig. 2B). The burrow system consists of horizontal galleries leading to heart-shaped flattened casts of chambers embedded in 12-cm-thick argillaceous chalk (Fig. 2B). This single type of chamber cast preserves on its surface ovoid blister-like elevations with transversal fine scratches. These peripheral structures are identical to those on the phosphatic casts of Campanian crustacean burrow-system chambers described from another exposure along the same road, some 18 km to the east where two types of chamber fillings were found (Lewy & Goldring, 2006). One is circular (D=45 mm) and replicates the arched ceiling with finely scratched elevated ovoid structures, whereas the cast of the floor comprises rings of about eight tubercles replicating pits 4 mm in diameter and 3.0-3.5 mm in depth. The pits were interpreted to host and protect large eggs in a brood chamber. The second kind of chamber changes shape and dimensions from circular (D=45 mm) to arrowhead-shaped up to 100 mm in length with one end rounded and tapering towards the opposite end. This gradually enlarged chamber was suggested to host the young (nursery chamber) and perhaps store food or provide gardening sites. The Lower Eocene burrow system lacks the brood chamber, whereas the heart-like chamber looks as a shortened modification of the Campanian arrowhead chamber, preserving the wall structure of the Campanian chambers. The diameter of the galleries of the Campanian burrows is about 14-17 mm compared to 10-15 mm of the Lower Eocene ones, and the greatest width of the Campanian chambers (about 7 cm) is similar to that of the Lower Eocene ones. Accordingly, the Campanian and Lower Eocene crustacean burrows into pelagic chalk have a similar structure of horizontal galleries connecting between chambers

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

**2. Crustacean burrows in pelagic chalk** 

**2.1 Lower eocene burrows** 

12-cm-thick layer.

chalky sediment (80% of natural size).

a stabilized level at which sediment removal stopped.

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 315

The road cut exposes the upper part of the greenish clay of the Taqiya Formation (Fig. 2) containing the latest Paleocene (Thanetian) planktonic foraminifer *Morozovella velascoensis*  (Cushman). It ranges into the overlying 20 cm of argillaceous chalk which forms a lithological transition to chalk of the Lower Eocene (Ypresian) Mor Formation. The formation begins with 75 cm of lithified chalk with chert nodules containing the Lower Eocene *Morozovella formosa formosa* Bolli and *Morozovella aragonensis* (Nuttall). The layer above is comprised of 12 cm of argillaceous chalk with secondary gypsum at the base and the crustacean burrow system in the upper part. The overlying sequence consists of 0.5-1.0 m thick units of hard chalk with chert nodules alternating with 10-15 cm thick beds of argillaceous chalk. The chalk-filled crustacean burrows consist of horizontal galleries connecting between chambers. Vertical shafts are not preserved. The present elliptical shape of the horizontal galleries attests to sediment compaction to 60-65% of the original vertical dimensions of the galleries as well as of the chambers. The periphery of these chalky casts is friable and part of the external features of the chamber cast is erased. None of the gallery fillings shows any scratches and their walls seem to have been smooth, forming tubes of 1.0-1.5 cm in diameter (Fig. 5G). About 27 heartshaped casts were collected, probably representing the only kind of chamber fill. Their orientation in the layer is with the heart-shape on the horizontal plane. A gallery enters into the middle of the floor and another one is connected close to the constricted end of the chamber ceiling, pointing a little upward and continues horizontally (Figs. 3, 4B). The chamber casts vary a little in dimensions and proportions whereby the longitudinal length (along the connecting galleries) may be shorter than the transversal width in some specimens. Length ranges between 55-75 mm and width between 55-73 mm. Despite sediment compaction, the thickness (chamber height) of all these casts decreases toward the end with the gallery opening (Figs. 4C, F, H, 5C, E) strengthening the pear-shape of the chamber in side view. Wellpreserved casts show ovoid blister-like elevations 5-6 mm broad and 5-9 mm long covering the whole cast, being compressed on the flanks by later compaction. These ovoid structures on the chamber ceiling (upper surface without a gallery entrance) of some specimens tend to orient into transversal lines forming slightly arched ribs about 7 mm in width (Fig. 5B). The horizontal galleries and the flattened chamber casts are concentrated in the upper half of the

Fig. 3. Lower Eocene burrowing crustacean chamber and connecting gallery within the

The absence of any relict of vertical shaft indicates the truncation of the unconsolidated sediment reaching close to the horizontal burrows. This sediment removal from the deep sea bottom by deep marine currents could have occurred before the fill of the burrows by the following deposition of foraminiferal ooze, or after sediment filled the burrows and formed

of similar dimensions and wall sculpture. These common features attest to a similar body structure. The main difference between these two burrow systems is the lack of brood chambers during the Lower Eocene, which reflects a change in breeding strategy sometime between the Campanian and the Early Eocene. Both burrow systems are in pelagic chalk and thus external ecological factors turned the specially constructed brood chamber useless. These local ecological changes were probably associated with the global biological turnover at the K-T boundary. Their evaluation in comparison with other biological changes clarifies the natural processes which resulted in the end-Cretaceous biological crisis.

Fig. 1. Road-cut exposing Taqiya Fm. greenish clay overlain by Mor Fm. chalk (M. Kitin pointing at the fossiliferous bed).

Fig. 2. A. Location map. B. Columnar section of the Upper Paleocene-Lower Eocene fossiliferous interval.

### **2. Crustacean burrows in pelagic chalk**

#### **2.1 Lower eocene burrows**

314 Advances in Data, Methods, Models and Their Applications in Geoscience

of similar dimensions and wall sculpture. These common features attest to a similar body structure. The main difference between these two burrow systems is the lack of brood chambers during the Lower Eocene, which reflects a change in breeding strategy sometime between the Campanian and the Early Eocene. Both burrow systems are in pelagic chalk and thus external ecological factors turned the specially constructed brood chamber useless. These local ecological changes were probably associated with the global biological turnover at the K-T boundary. Their evaluation in comparison with other biological changes clarifies

the natural processes which resulted in the end-Cretaceous biological crisis.

Fig. 1. Road-cut exposing Taqiya Fm. greenish clay overlain by Mor Fm. chalk (M. Kitin

Fig. 2. A. Location map. B. Columnar section of the Upper Paleocene-Lower Eocene

pointing at the fossiliferous bed).

fossiliferous interval.

The road cut exposes the upper part of the greenish clay of the Taqiya Formation (Fig. 2) containing the latest Paleocene (Thanetian) planktonic foraminifer *Morozovella velascoensis*  (Cushman). It ranges into the overlying 20 cm of argillaceous chalk which forms a lithological transition to chalk of the Lower Eocene (Ypresian) Mor Formation. The formation begins with 75 cm of lithified chalk with chert nodules containing the Lower Eocene *Morozovella formosa formosa* Bolli and *Morozovella aragonensis* (Nuttall). The layer above is comprised of 12 cm of argillaceous chalk with secondary gypsum at the base and the crustacean burrow system in the upper part. The overlying sequence consists of 0.5-1.0 m thick units of hard chalk with chert nodules alternating with 10-15 cm thick beds of argillaceous chalk. The chalk-filled crustacean burrows consist of horizontal galleries connecting between chambers. Vertical shafts are not preserved. The present elliptical shape of the horizontal galleries attests to sediment compaction to 60-65% of the original vertical dimensions of the galleries as well as of the chambers. The periphery of these chalky casts is friable and part of the external features of the chamber cast is erased. None of the gallery fillings shows any scratches and their walls seem to have been smooth, forming tubes of 1.0-1.5 cm in diameter (Fig. 5G). About 27 heartshaped casts were collected, probably representing the only kind of chamber fill. Their orientation in the layer is with the heart-shape on the horizontal plane. A gallery enters into the middle of the floor and another one is connected close to the constricted end of the chamber ceiling, pointing a little upward and continues horizontally (Figs. 3, 4B). The chamber casts vary a little in dimensions and proportions whereby the longitudinal length (along the connecting galleries) may be shorter than the transversal width in some specimens. Length ranges between 55-75 mm and width between 55-73 mm. Despite sediment compaction, the thickness (chamber height) of all these casts decreases toward the end with the gallery opening (Figs. 4C, F, H, 5C, E) strengthening the pear-shape of the chamber in side view. Wellpreserved casts show ovoid blister-like elevations 5-6 mm broad and 5-9 mm long covering the whole cast, being compressed on the flanks by later compaction. These ovoid structures on the chamber ceiling (upper surface without a gallery entrance) of some specimens tend to orient into transversal lines forming slightly arched ribs about 7 mm in width (Fig. 5B). The horizontal galleries and the flattened chamber casts are concentrated in the upper half of the 12-cm-thick layer.

Fig. 3. Lower Eocene burrowing crustacean chamber and connecting gallery within the chalky sediment (80% of natural size).

The absence of any relict of vertical shaft indicates the truncation of the unconsolidated sediment reaching close to the horizontal burrows. This sediment removal from the deep sea bottom by deep marine currents could have occurred before the fill of the burrows by the following deposition of foraminiferal ooze, or after sediment filled the burrows and formed a stabilized level at which sediment removal stopped.

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 317

Fig. 5. Lower Eocene, burrowing crustacean, chamber casts (85% of natural size). A: Lower side; B: Upper side showing ovoid blister-like elevations arranged 80% of transversal

direction; C: Side view. D: Lower side of heart-shaped specimen; E: Upper side; F: side view; G: Gallery fragment without any distinct sculpture (A-C sample GSI 8990:3; D-F sample GSI

8990:4; G Sample 8990:6).

Fig. 4. Lower Eocene, burrowing crustacean, chamber casts (85% of natural size). A: Upper side with relic of broken gallery at the lower (narrow) end. B: Lower side with the gallery exiting from the middle; C: Side view with relics of both galleries. D: Upper side; E: Lower side; F: Side view; G-H: Smallest chamber cast; G: Upper side; H: side view; (A-C sample GSI 8990:1; C-F sample GSI 8990:2; G-H: sample GSI 8990:5).

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 317

316 Advances in Data, Methods, Models and Their Applications in Geoscience

Fig. 4. Lower Eocene, burrowing crustacean, chamber casts (85% of natural size). A: Upper side with relic of broken gallery at the lower (narrow) end. B: Lower side with the gallery exiting from the middle; C: Side view with relics of both galleries. D: Upper side; E: Lower side; F: Side view; G-H: Smallest chamber cast; G: Upper side; H: side view; (A-C sample

GSI 8990:1; C-F sample GSI 8990:2; G-H: sample GSI 8990:5).

Fig. 5. Lower Eocene, burrowing crustacean, chamber casts (85% of natural size). A: Lower side; B: Upper side showing ovoid blister-like elevations arranged 80% of transversal direction; C: Side view. D: Lower side of heart-shaped specimen; E: Upper side; F: side view; G: Gallery fragment without any distinct sculpture (A-C sample GSI 8990:3; D-F sample GSI 8990:4; G Sample 8990:6).

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 319

Fig. 6. A-D: Phosphatic molds of the two types of chambers in Campanian crustacean burrow system (samples GSI 5597). A & B: Nursery and storage chambers. A: Side view of vertically enlarged chamber; B: The pattern of the chamber wall consisting of low, circular to ovoid, blister-like elevations in irregular orientation of their length as indicated by fine transversal scratches in different directions. C & D: Casts of chamber floor comprising rings of pits arranged in a honeycomb pattern, leaving alternating non-pitted parts of the floor, which enables crustacean to cross the chamber without stepping on the pit's content. The pits were interpreted as protected sites for individual large eggs in a brood chamber. E: Sketch of the single type of chamber of Lower Eocene burrowing crustaceans suggesting the position of the egg-mass and the hatched young in relation to adult crustaceans crossing the chamber within the burrow network. H: Sketch of Pleistocene *Spongeliomorpha sicula* (Sicily,

Italy; D'Alessandro & Bromley, 1995). All figures in 80% natural size.

#### **2.2 Campanian burrows**

The Campanian crustacean burrows into chalk filled with granular phosphorite were described by Lewy & Goldring (2006). They comprise straight and slightly bent casts of galleries covered by longitudinal scratches. Most of them were compressed by sediment compaction but some preserve the circular cross-section of 14-17 mm in diameter. One of the two kinds of chamber casts occurs in a constant size and a rounded shape 45 mm in diameter. The biscuit-like chamber cast has a gently arched ceiling, with circular to ovoid blister-like elevations covered by transversal fine ribs replicating scratches. The lower surface of the cast comprises rings of about 8 tubercles 4 mm in diameter and 3.0-3.5 mm high in a honeycomb pattern extending over the whole surface (Fig. 6 C, D). The tubercles are smooth, but in an unfinished state of chamber the few tubercles bear fine scratches. On opposite sides of the cast are relics of the pair of connecting galleries. This structure construction replicates a pitted chamber-floor in which the honeycomb configuration of the pits enables crustaceans to cross the chamber by stepping on the floor in the center of the pit rings. This carefully constructed chamber floor comprises 60-70 pits which were interpreted as individual sites for a large egg in a brood chamber within the network of the burrow system. The other chamber type is represented by different shapes and dimensions suggesting its continuous enlargement. The smallest chamber cast is flattened and biscuitlike of a diameter of 4-5 cm, with both sides covered by the finely scratched blister-like elevations. Further enlargement changes the round periphery into an arrowhead shape with one end broad and rounded, tapering toward the opposite end (Fig. 6B). Above a length of 9-10 cm the enlargement of the chamber is vertical (Fig. 6A). The scratched elevations cover the whole cast up to the longitudinally scratched gallery casts on both ends of the elongated cast (Fig. 6B). Only movable objects could be stored in these gradually enlarged chambers. Therefore they were interpreted as nursing chamber for the young, for storing food, and perhaps also as gardening sites.

#### **2.3 Comparison between the campanian and the lower eocene burrow systems**

Both crustacean burrow systems were dug into pelagic chalk suggesting rather deep marine bottom conditions of several hundred meters in depth. Both occur in the same region, which at the time of deposition were on the seaward flank of the anticlinal structures of the Syrian Arc fold system (Krenkel, 1924), which has been operating and intensifying the folded structures from the Late Coniacian to the Middle Eocene. The chambers in both systems posses the same wall structure carved by the crustacean appendages, and the similar diameter of the galleries in both systems suggest morphological similarity of the producers. The crustaceans living in the Campnian burrow system had to cross the chambers. Therefore the brood chamber was carefully constructed to avoid damage to the eggs. The size of the pits (D=4 mm) attests to the rather large size of the eggs hosted in the brood chamber, probably being cared for until hatching. It seems that only the large eggs laid by the females were kept to assure total recovery (K-type breeding) whereby the number of the young of each hatching phase was the same, keeping more or less a constant size of the community. The construction of the brood chamber, exclusively for egg development, required to transfer the hatchling (larvae) to a nursery chamber where the young developed.

The Lower Eocene burrow system lacks the brood chamber and the single type of chamber has a nearly constant flattened heart-like shape as if it was a concise shortened modification of the Campanian nursery chamber. The entrance and exit at the opposite ends of the Campanian nursery chamber, which required the crustaceans to cross the whole length of

The Campanian crustacean burrows into chalk filled with granular phosphorite were described by Lewy & Goldring (2006). They comprise straight and slightly bent casts of galleries covered by longitudinal scratches. Most of them were compressed by sediment compaction but some preserve the circular cross-section of 14-17 mm in diameter. One of the two kinds of chamber casts occurs in a constant size and a rounded shape 45 mm in diameter. The biscuit-like chamber cast has a gently arched ceiling, with circular to ovoid blister-like elevations covered by transversal fine ribs replicating scratches. The lower surface of the cast comprises rings of about 8 tubercles 4 mm in diameter and 3.0-3.5 mm high in a honeycomb pattern extending over the whole surface (Fig. 6 C, D). The tubercles are smooth, but in an unfinished state of chamber the few tubercles bear fine scratches. On opposite sides of the cast are relics of the pair of connecting galleries. This structure construction replicates a pitted chamber-floor in which the honeycomb configuration of the pits enables crustaceans to cross the chamber by stepping on the floor in the center of the pit rings. This carefully constructed chamber floor comprises 60-70 pits which were interpreted as individual sites for a large egg in a brood chamber within the network of the burrow system. The other chamber type is represented by different shapes and dimensions suggesting its continuous enlargement. The smallest chamber cast is flattened and biscuitlike of a diameter of 4-5 cm, with both sides covered by the finely scratched blister-like elevations. Further enlargement changes the round periphery into an arrowhead shape with one end broad and rounded, tapering toward the opposite end (Fig. 6B). Above a length of 9-10 cm the enlargement of the chamber is vertical (Fig. 6A). The scratched elevations cover the whole cast up to the longitudinally scratched gallery casts on both ends of the elongated cast (Fig. 6B). Only movable objects could be stored in these gradually enlarged chambers. Therefore they were interpreted as nursing chamber for the young, for storing food, and

**2.3 Comparison between the campanian and the lower eocene burrow systems** 

transfer the hatchling (larvae) to a nursery chamber where the young developed.

The Lower Eocene burrow system lacks the brood chamber and the single type of chamber has a nearly constant flattened heart-like shape as if it was a concise shortened modification of the Campanian nursery chamber. The entrance and exit at the opposite ends of the Campanian nursery chamber, which required the crustaceans to cross the whole length of

Both crustacean burrow systems were dug into pelagic chalk suggesting rather deep marine bottom conditions of several hundred meters in depth. Both occur in the same region, which at the time of deposition were on the seaward flank of the anticlinal structures of the Syrian Arc fold system (Krenkel, 1924), which has been operating and intensifying the folded structures from the Late Coniacian to the Middle Eocene. The chambers in both systems posses the same wall structure carved by the crustacean appendages, and the similar diameter of the galleries in both systems suggest morphological similarity of the producers. The crustaceans living in the Campnian burrow system had to cross the chambers. Therefore the brood chamber was carefully constructed to avoid damage to the eggs. The size of the pits (D=4 mm) attests to the rather large size of the eggs hosted in the brood chamber, probably being cared for until hatching. It seems that only the large eggs laid by the females were kept to assure total recovery (K-type breeding) whereby the number of the young of each hatching phase was the same, keeping more or less a constant size of the community. The construction of the brood chamber, exclusively for egg development, required to

**2.2 Campanian burrows** 

perhaps also as gardening sites.

Fig. 6. A-D: Phosphatic molds of the two types of chambers in Campanian crustacean burrow system (samples GSI 5597). A & B: Nursery and storage chambers. A: Side view of vertically enlarged chamber; B: The pattern of the chamber wall consisting of low, circular to ovoid, blister-like elevations in irregular orientation of their length as indicated by fine transversal scratches in different directions. C & D: Casts of chamber floor comprising rings of pits arranged in a honeycomb pattern, leaving alternating non-pitted parts of the floor, which enables crustacean to cross the chamber without stepping on the pit's content. The pits were interpreted as protected sites for individual large eggs in a brood chamber. E: Sketch of the single type of chamber of Lower Eocene burrowing crustaceans suggesting the position of the egg-mass and the hatched young in relation to adult crustaceans crossing the chamber within the burrow network. H: Sketch of Pleistocene *Spongeliomorpha sicula* (Sicily, Italy; D'Alessandro & Bromley, 1995). All figures in 80% natural size.

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

faunal groups (discussed herein).

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 321

eggs that could be obtained in the Eocene brood seasons is in sharp contrast to the Campanian brood in which the number of eggs was limited by the number of pits in the brood chambers, and hence the number of hatchlings even if all the eggs were fertile. The interpreted reduction in egg size did not affect the size of the mature crustaceans as attested to by the similar range of diameters of the Campanian (D=14-17 mm) and Lower Eocene (D=10-15 mm) galleries. This reflects a change in breeding strategy that is neither the result of changes in body or population size, nor of food shortage. It reflects a transition to a more economic mode of life to which the Lower Eocene (or earlier) crustacean population had to adapt under ecological pressure within the same pelagic habitat of their Late Cretaceous ancestors. The reduction in egg size increased the number of eggs that each female laid and hence the overall quantity of eggs in each breeding period. It probably increased the number of hatchlings despite some egg loss due to reduced brood-care. This interpreted need for more hatchlings must have compensated for the loss of individuals being killed while swimming outside the underground tunnel network to look for food. This fatal threat of predation has profoundly increased during the Late Cretaceous as evidenced by other

The evolutionary trend expressed by changes in crustacean burrowing systems can be extended to Early Pleistocene times as exemplified by a burrow system of *Spongeliomorpha sicula* D'Alessansro & Bromley (1995) from Sicily, Italy. Cylindrical vertical shafts and horizontal galleries about 10 mm in diameter bear longitudinal fine ridges replicating scratches. Plum-shaped chamber casts 30 mm high and 25 mm in diameter with similar longitudinal striations occur at gallery junctions every few centimeters (Fig. 6F). They are associated with much shorter cylindrical inflations. The plum-shaped chambers were interpreted as microbial gardening sites (D'Alessandro & Bromley, 1995). Following the study of the Campanian and Lower Eocene burrow systems, we are inclined to refer these chambers to brood and nursery chambers as in the Lower Eocene example. However, in the Pleistocene example each chamber has its own entrance from the lower side, whereby the chamber content is not jeopardized by chamber-crossing crustaceans as in the Eocene example. The associated sediments indicate shallow marine environments which were rich in food sources and should not require production of special nourishment in specially constructed gardening chambers. On the other hand, eggs and larvae shed into the shallow marine water were subjected to rapid consumption by many predators. Thus keeping the hatchlings until they were capable to defend and feed themselves would have been needed to protect the species. This interpretation coincides with the evolutionary trend in this group of burrowing crustaceans from the Late Cretaceous to almost present times. The interpreted care for the brood and the young attests to communal organization at least until the Early Pleistocene and probably might be detected in extant *Spongeliomorpha* species. It seems that these burrowing Crustaceans have maintained communal organizations from times when they inhabited deep water bottoms exposed to predators, and continued to experience the

benefits of this co-operation in shallower marine environments.

**3. The ecological affinities of the upper part of the Cretaceous Period** 

MacLeod (2005) summarized the characteristic affinities of the Cretaceous Period which ranged between 145.5-65.5 Myr and is generally divided at the Albian-Cenomanian boundary (99.6 Myr) into the Lower and Upper Cretaceous. The warm equable climate (warmer than today) extended into the high latitudes, and the poles were probably without

the room, shifted in the Lower Eocene single chamber type to a new position. One gallery tube enters into the middle of the floor and the other tube exits from the upper narrow end of the chamber (Figs. 3, 4C). In that way, a large part of the space is not disturbed while crustaceans cross the chamber. In most chamber casts, the horizontally narrow part is also vertically smaller than the opposite side, which forms a greater space undisturbed by crustaceans crossing the chamber mainly over the floor. This constant chamber configuration attests to its special functions which required digging all chambers in the same design. When compared to the functions of the two Campanian chambers, the single Lower Eocene type of chamber probably fulfilled the necessary functions of brood chamber and nursery room. The lack of special sites protecting the eggs does not mean that the brood was released into the water and the carefully constructed chambers served for storing food and gardening. It is more reasonable to assume that in this case the whole brood mass laid by several females was concentrated in one place in the chamber, protected underground from potential predators and undergoing minimal brood-care. Accordingly total recovery was not expected and the brood consisted of numerous, hence small eggs which in previous Late Cretaceous times would not have been preserved. This breeding strategy reflects the early stages of r-type mode of breeding which in its fully developed stage the brood of numerous small eggs laid by each female was shed into open water. Thus, a small number of hatchling survived to maturity enough to maintain the species' population size relative to the associated organisms. The Lower Eocene hatched young probably developed in a corner where the eggs were concentrated. These free-swimming young swam within the chamber above the egg mass on the bottom of the broader part of the chamber (Fig. 6E).

The communal organization concluded for the Campanian burrowing crustaceans (Lewy & Goldring, 2006) can be extended to the Lower Eocene ones, which were constructed of one type of chamber where the brood was of several females was assembled and the hatching young were cared for in another corner.

#### **2.4 Possible cause for the change in breeding strategy**

The Campanian brood chambers were constructed to host each of the selected eggs in individual pits, where they were protected from incidental damage by the crustacean crossing the chamber while swimming through the burrow network. The size of the pits (D=4 mm) suggests that the largest (yolk-rich) eggs were selected from the brood of the females whereas small eggs were not included in the processed brood of every breeding season. Each brood chamber comprised a limited number of eggs, yielding 60-70 hatchlings under full recovery. This limited number of young probably maintained the community, reflecting optimal living conditions under 'luxurious' stable ecological settings. The disappearance of the brood chamber indicates that their function lost its previous significance, though underground protection of the brood continued in the Lower Eocene single type of chamber. What probably rendered these brood chambers useless seems to have been a change toward more eggs in every breeding season, which would require less attention and will supply offspring in the quantity required to maintain the size of a living community. It is suggested that the small eggs which were previously discarded were now gathered into an egg-mass placed at a corner of the underground chambers. These continued to provide a connecting passage within the burrow system and probably served as nursery and storage rooms as well (Fig. 6E). This multifunction may have involved some damage to the free-lying eggs, but their large quantity assured the maintenance of the population size and even an increase in the number of hatchlings. The unlimited number of

the room, shifted in the Lower Eocene single chamber type to a new position. One gallery tube enters into the middle of the floor and the other tube exits from the upper narrow end of the chamber (Figs. 3, 4C). In that way, a large part of the space is not disturbed while crustaceans cross the chamber. In most chamber casts, the horizontally narrow part is also vertically smaller than the opposite side, which forms a greater space undisturbed by crustaceans crossing the chamber mainly over the floor. This constant chamber configuration attests to its special functions which required digging all chambers in the same design. When compared to the functions of the two Campanian chambers, the single Lower Eocene type of chamber probably fulfilled the necessary functions of brood chamber and nursery room. The lack of special sites protecting the eggs does not mean that the brood was released into the water and the carefully constructed chambers served for storing food and gardening. It is more reasonable to assume that in this case the whole brood mass laid by several females was concentrated in one place in the chamber, protected underground from potential predators and undergoing minimal brood-care. Accordingly total recovery was not expected and the brood consisted of numerous, hence small eggs which in previous Late Cretaceous times would not have been preserved. This breeding strategy reflects the early stages of r-type mode of breeding which in its fully developed stage the brood of numerous small eggs laid by each female was shed into open water. Thus, a small number of hatchling survived to maturity enough to maintain the species' population size relative to the associated organisms. The Lower Eocene hatched young probably developed in a corner where the eggs were concentrated. These free-swimming young swam within the chamber

above the egg mass on the bottom of the broader part of the chamber (Fig. 6E).

young were cared for in another corner.

**2.4 Possible cause for the change in breeding strategy** 

The communal organization concluded for the Campanian burrowing crustaceans (Lewy & Goldring, 2006) can be extended to the Lower Eocene ones, which were constructed of one type of chamber where the brood was of several females was assembled and the hatching

The Campanian brood chambers were constructed to host each of the selected eggs in individual pits, where they were protected from incidental damage by the crustacean crossing the chamber while swimming through the burrow network. The size of the pits (D=4 mm) suggests that the largest (yolk-rich) eggs were selected from the brood of the females whereas small eggs were not included in the processed brood of every breeding season. Each brood chamber comprised a limited number of eggs, yielding 60-70 hatchlings under full recovery. This limited number of young probably maintained the community, reflecting optimal living conditions under 'luxurious' stable ecological settings. The disappearance of the brood chamber indicates that their function lost its previous significance, though underground protection of the brood continued in the Lower Eocene single type of chamber. What probably rendered these brood chambers useless seems to have been a change toward more eggs in every breeding season, which would require less attention and will supply offspring in the quantity required to maintain the size of a living community. It is suggested that the small eggs which were previously discarded were now gathered into an egg-mass placed at a corner of the underground chambers. These continued to provide a connecting passage within the burrow system and probably served as nursery and storage rooms as well (Fig. 6E). This multifunction may have involved some damage to the free-lying eggs, but their large quantity assured the maintenance of the population size and even an increase in the number of hatchlings. The unlimited number of eggs that could be obtained in the Eocene brood seasons is in sharp contrast to the Campanian brood in which the number of eggs was limited by the number of pits in the brood chambers, and hence the number of hatchlings even if all the eggs were fertile. The interpreted reduction in egg size did not affect the size of the mature crustaceans as attested to by the similar range of diameters of the Campanian (D=14-17 mm) and Lower Eocene (D=10-15 mm) galleries. This reflects a change in breeding strategy that is neither the result of changes in body or population size, nor of food shortage. It reflects a transition to a more economic mode of life to which the Lower Eocene (or earlier) crustacean population had to adapt under ecological pressure within the same pelagic habitat of their Late Cretaceous ancestors. The reduction in egg size increased the number of eggs that each female laid and hence the overall quantity of eggs in each breeding period. It probably increased the number of hatchlings despite some egg loss due to reduced brood-care. This interpreted need for more hatchlings must have compensated for the loss of individuals being killed while swimming outside the underground tunnel network to look for food. This fatal threat of predation has profoundly increased during the Late Cretaceous as evidenced by other faunal groups (discussed herein).

The evolutionary trend expressed by changes in crustacean burrowing systems can be extended to Early Pleistocene times as exemplified by a burrow system of *Spongeliomorpha sicula* D'Alessansro & Bromley (1995) from Sicily, Italy. Cylindrical vertical shafts and horizontal galleries about 10 mm in diameter bear longitudinal fine ridges replicating scratches. Plum-shaped chamber casts 30 mm high and 25 mm in diameter with similar longitudinal striations occur at gallery junctions every few centimeters (Fig. 6F). They are associated with much shorter cylindrical inflations. The plum-shaped chambers were interpreted as microbial gardening sites (D'Alessandro & Bromley, 1995). Following the study of the Campanian and Lower Eocene burrow systems, we are inclined to refer these chambers to brood and nursery chambers as in the Lower Eocene example. However, in the Pleistocene example each chamber has its own entrance from the lower side, whereby the chamber content is not jeopardized by chamber-crossing crustaceans as in the Eocene example. The associated sediments indicate shallow marine environments which were rich in food sources and should not require production of special nourishment in specially constructed gardening chambers. On the other hand, eggs and larvae shed into the shallow marine water were subjected to rapid consumption by many predators. Thus keeping the hatchlings until they were capable to defend and feed themselves would have been needed to protect the species. This interpretation coincides with the evolutionary trend in this group of burrowing crustaceans from the Late Cretaceous to almost present times. The interpreted care for the brood and the young attests to communal organization at least until the Early Pleistocene and probably might be detected in extant *Spongeliomorpha* species. It seems that these burrowing Crustaceans have maintained communal organizations from times when they inhabited deep water bottoms exposed to predators, and continued to experience the benefits of this co-operation in shallower marine environments.

#### **3. The ecological affinities of the upper part of the Cretaceous Period**

MacLeod (2005) summarized the characteristic affinities of the Cretaceous Period which ranged between 145.5-65.5 Myr and is generally divided at the Albian-Cenomanian boundary (99.6 Myr) into the Lower and Upper Cretaceous. The warm equable climate (warmer than today) extended into the high latitudes, and the poles were probably without

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

**4. Victims of disturbed prey-predator relationships under climatic and** 

Keller, 2002; Abramovich et al., 2010).

**ecological instability** 

and on land.

**4.1 Vertebrates: Reptiles and fishes** 

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 323

the top predators and thus changed prey-predator relationships. Micropaleontological analyses of Late Maastrichtian pelagic sediments detect short-term paleotemperature fluctuations (Li & Keller, 1998). A progressive cooling trend between ~66.8-65.45 Myr was followed by rapid extreme warming 400-200 Kyr before the end of the Maastrichtian, which was succeeded by a cooler climate during the last 100 Kyr of this stage (Abramovich &

Despite the overall flourishing of the Upper Cretaceous marine fauna and flora, the local balance of prey-predator relationship was very fragile, threatening this global paradise. Optimal living conditions on land increased animal diversity, populations size and the dimensions of individuals (gigantism), as in the marine environments. However, any temporary ecological disturbance might have reduced reproduction, resulting in much less young, which played a significant role in the food-chain. The further collapse of the food chain, as the result of increased predatory stress, is examined herein, by comparing the affinities of the organisms which became extinct at the end of the Cretaceous Period to those which survived the biological crisis. The present study builds on the detailed analysis of the geological record of most faunal and floral groups around the K-T boundary, carried out by a large team of experts (MacLeod et al., 1997). They pointed out the terrestrial and extraterrestrial factors which affected life on Earth during a long period as well as shortcatastrophic processes close to the K-T boundary. However, the control of the extinctionsurvivorship pattern was not defined. Re-evaluation of the characteristics of representative marine and terrestrial faunal groups will demonstrate that all those which became extinct at the end of the Cretaceous, were at some stage of their life unable to avoid their predation, thus being victims of temporary extreme predatory stress. This selective over-predation of the vulnerable organisms was caused by the collapse of the food chain as the result of climatic and ecological instability. These are partly reflected by paleotemperature fluctuations of the oceans surface water and deeper levels (Li & Keller, 1998; Abramovich & Keller, 2002), as well as reduction in oxygen content in seawater and dwarfing in marine calcareous planktonic microorganisms. All these phenomena can be related to fluctuations in the intensity of the volcaniclastic dust screening the sunlight, hence affecting photosynthetic activity of the flora and disturbing the biological clock of animals in the sea

The disappearance of the dinosaurs close to the end of the Cretaceous Period is presented in scientific and popular publications and films as evidence to the most impressive catastrophic event in Earth history. These reptiles ruled over the land while their flying relatives (pterosaurs) and the marine ones (e.g., plesiosaurs and mosasaurs) were the top predators in the sky and the sea. They diversified during the Late Cretaceous and many of their species grew to giant dimensions (9-15 m long mosasaurs; 11-12 m wing-span of the pterosaur *Quetzalcoatlus*). Their apparent simultaneous disappearance from over the whole world was puzzling. Whatever caused it did not kill the related crocodiles and did not harm the sensitive frogs and salamanders. This selective elimination of the most skillful predators resulted from their early ontogenetic stage. Dinosaurs and pterosaurs were oviparous,

ice cover most of the year (Hay, 2008). High sea-levels with their peak during the Turonian characterized the general transgressive trend of the oceans throughout the Upper Cretaceous and the Lower Tertiary. The continental plates continued to move toward their present-day position whereby the Atlantic Ocean further opened perpendicular to the Tethys Ocean improving the north-south water circulation. This global plate movement triggered local tectonic movements which differentiated plate margins and intensified the reduction of the previous Albian-Cenomanian-Turonian broad (50-300 km wide) shelves, where rudistid bivalves thrived together with sessile ostreids and chondrodentids. These could survive temporary exposure or cover by sediment, in contrast to hermatypic corals which occupied protected regions in this shallow marine ecosystem. A broad intertidal flat extended landward of this rudistid reefal belt, where calcareous detritus (bioclasts) accumulated and was partly dolomitized under the high salinity of the lagoon and sabkha settings. The abundance of calcitic mollusk conchs highly increased during the middle part of the Cretaceous Period, represented by the shallow marine rudists, oysters and chondrodonts, with Inoceramidae inhabiting the deeper water. The expansion of pelagic environments during the Upper Cretaceous was associated with an increase in the diversity and abundance of planktonic foraminifera and calcareous nannoplankton (Coccolithophorida) all having calcitic endoskeletons. This abundance of biologically precipitated calcite suggested a very low Mg/Ca ratio in Cretaceous seawater (MacLeod, 2005). Keeled planktonic foraminifera diversified from the uppermost part of the Lower Cretaceous (Albian) onward throughout the Late Cretaceous, and were associated with globular forms. Their calcitic tests accumulated as foraminiferal ooze in the outer shelf and deeper marine bottoms forming chalk characterizing the Upper Cretaceous, such as the Lower and Upper Chalk in northwest Europe. The gradual increase in the plankton bloom in the broad oceans triggered a gradual rise in marine productivity evidenced in the later Upper Cretaceous (Campanian-Maastrichtian) by extensive accumulations of organic-rich ('bituminous') chalk with chert (from dissolved diatoms and radiolarians) and phosphorite beds mainly in the Tethys ocean (Lucas & Prévot-Lucas, 1996). These optimal living conditions are corroborated by the increasing diversity of the marine fauna and the development of gigantic organisms. The largest ammonite *Parapuzosia seppenradensis*  (Landois) with a diameter of about 2.50 m (Summesberger, 1979) and *P. bradyi* Miller & Yongquist (D=1.37 m) from Wyoming, USA (Larson et al., 1997) are from the Lower Campanian. Large inoceramid bivalves with an axial length of 1 m, and occasionally over 2- 3 m in size of the genus *Platyceramus,* occur in the Santonian-Lower Campanian of Colorado (USA) (Kauffman et al., 2007). Marine reptiles related to plesiosaurs and mosasaurs grew to a length of 9-15 m (MacLeod et al., 1997). Their flying relatives (Pterosauria) reached in latest Cretaceous time wide wing spans up to 11-12 m in *Quetzalcoatlus* (Langston, 1981). The marine high productivity extended into early Cenozoic times (Lower Eocene) despite the profound change in zoo- and phytoplankton composition, suggesting that the Late Cretaceous marine physical and chemical properties were neither affected by the Deccan volcanism (India) nor by the asteroid impact. These marine conditions were controlled by the continuing movement of the plates and the general transgressive trend of the widening oceans during the Late Cretaceous and Early Tertiary. Thereby the size of the shelves and the neritic habitats were considerably reduced. Organisms living within the 'reefal' habitat of the rudistid and ostreid buildups decreased in abundance and disappeared from many shrinking shallow marine environments, surviving only in restricted regions. The expansion of the pelagic habitats increased the abundance of the nektonic organisms which comprised the top predators and thus changed prey-predator relationships. Micropaleontological analyses of Late Maastrichtian pelagic sediments detect short-term paleotemperature fluctuations (Li & Keller, 1998). A progressive cooling trend between ~66.8-65.45 Myr was followed by rapid extreme warming 400-200 Kyr before the end of the Maastrichtian, which was succeeded by a cooler climate during the last 100 Kyr of this stage (Abramovich & Keller, 2002; Abramovich et al., 2010).

#### **4. Victims of disturbed prey-predator relationships under climatic and ecological instability**

Despite the overall flourishing of the Upper Cretaceous marine fauna and flora, the local balance of prey-predator relationship was very fragile, threatening this global paradise. Optimal living conditions on land increased animal diversity, populations size and the dimensions of individuals (gigantism), as in the marine environments. However, any temporary ecological disturbance might have reduced reproduction, resulting in much less young, which played a significant role in the food-chain. The further collapse of the food chain, as the result of increased predatory stress, is examined herein, by comparing the affinities of the organisms which became extinct at the end of the Cretaceous Period to those which survived the biological crisis. The present study builds on the detailed analysis of the geological record of most faunal and floral groups around the K-T boundary, carried out by a large team of experts (MacLeod et al., 1997). They pointed out the terrestrial and extraterrestrial factors which affected life on Earth during a long period as well as shortcatastrophic processes close to the K-T boundary. However, the control of the extinctionsurvivorship pattern was not defined. Re-evaluation of the characteristics of representative marine and terrestrial faunal groups will demonstrate that all those which became extinct at the end of the Cretaceous, were at some stage of their life unable to avoid their predation, thus being victims of temporary extreme predatory stress. This selective over-predation of the vulnerable organisms was caused by the collapse of the food chain as the result of climatic and ecological instability. These are partly reflected by paleotemperature fluctuations of the oceans surface water and deeper levels (Li & Keller, 1998; Abramovich & Keller, 2002), as well as reduction in oxygen content in seawater and dwarfing in marine calcareous planktonic microorganisms. All these phenomena can be related to fluctuations in the intensity of the volcaniclastic dust screening the sunlight, hence affecting photosynthetic activity of the flora and disturbing the biological clock of animals in the sea and on land.

#### **4.1 Vertebrates: Reptiles and fishes**

322 Advances in Data, Methods, Models and Their Applications in Geoscience

ice cover most of the year (Hay, 2008). High sea-levels with their peak during the Turonian characterized the general transgressive trend of the oceans throughout the Upper Cretaceous and the Lower Tertiary. The continental plates continued to move toward their present-day position whereby the Atlantic Ocean further opened perpendicular to the Tethys Ocean improving the north-south water circulation. This global plate movement triggered local tectonic movements which differentiated plate margins and intensified the reduction of the previous Albian-Cenomanian-Turonian broad (50-300 km wide) shelves, where rudistid bivalves thrived together with sessile ostreids and chondrodentids. These could survive temporary exposure or cover by sediment, in contrast to hermatypic corals which occupied protected regions in this shallow marine ecosystem. A broad intertidal flat extended landward of this rudistid reefal belt, where calcareous detritus (bioclasts) accumulated and was partly dolomitized under the high salinity of the lagoon and sabkha settings. The abundance of calcitic mollusk conchs highly increased during the middle part of the Cretaceous Period, represented by the shallow marine rudists, oysters and chondrodonts, with Inoceramidae inhabiting the deeper water. The expansion of pelagic environments during the Upper Cretaceous was associated with an increase in the diversity and abundance of planktonic foraminifera and calcareous nannoplankton (Coccolithophorida) all having calcitic endoskeletons. This abundance of biologically precipitated calcite suggested a very low Mg/Ca ratio in Cretaceous seawater (MacLeod, 2005). Keeled planktonic foraminifera diversified from the uppermost part of the Lower Cretaceous (Albian) onward throughout the Late Cretaceous, and were associated with globular forms. Their calcitic tests accumulated as foraminiferal ooze in the outer shelf and deeper marine bottoms forming chalk characterizing the Upper Cretaceous, such as the Lower and Upper Chalk in northwest Europe. The gradual increase in the plankton bloom in the broad oceans triggered a gradual rise in marine productivity evidenced in the later Upper Cretaceous (Campanian-Maastrichtian) by extensive accumulations of organic-rich ('bituminous') chalk with chert (from dissolved diatoms and radiolarians) and phosphorite beds mainly in the Tethys ocean (Lucas & Prévot-Lucas, 1996). These optimal living conditions are corroborated by the increasing diversity of the marine fauna and the development of gigantic organisms. The largest ammonite *Parapuzosia seppenradensis*  (Landois) with a diameter of about 2.50 m (Summesberger, 1979) and *P. bradyi* Miller & Yongquist (D=1.37 m) from Wyoming, USA (Larson et al., 1997) are from the Lower Campanian. Large inoceramid bivalves with an axial length of 1 m, and occasionally over 2- 3 m in size of the genus *Platyceramus,* occur in the Santonian-Lower Campanian of Colorado (USA) (Kauffman et al., 2007). Marine reptiles related to plesiosaurs and mosasaurs grew to a length of 9-15 m (MacLeod et al., 1997). Their flying relatives (Pterosauria) reached in latest Cretaceous time wide wing spans up to 11-12 m in *Quetzalcoatlus* (Langston, 1981). The marine high productivity extended into early Cenozoic times (Lower Eocene) despite the profound change in zoo- and phytoplankton composition, suggesting that the Late Cretaceous marine physical and chemical properties were neither affected by the Deccan volcanism (India) nor by the asteroid impact. These marine conditions were controlled by the continuing movement of the plates and the general transgressive trend of the widening oceans during the Late Cretaceous and Early Tertiary. Thereby the size of the shelves and the neritic habitats were considerably reduced. Organisms living within the 'reefal' habitat of the rudistid and ostreid buildups decreased in abundance and disappeared from many shrinking shallow marine environments, surviving only in restricted regions. The expansion of the pelagic habitats increased the abundance of the nektonic organisms which comprised

The disappearance of the dinosaurs close to the end of the Cretaceous Period is presented in scientific and popular publications and films as evidence to the most impressive catastrophic event in Earth history. These reptiles ruled over the land while their flying relatives (pterosaurs) and the marine ones (e.g., plesiosaurs and mosasaurs) were the top predators in the sky and the sea. They diversified during the Late Cretaceous and many of their species grew to giant dimensions (9-15 m long mosasaurs; 11-12 m wing-span of the pterosaur *Quetzalcoatlus*). Their apparent simultaneous disappearance from over the whole world was puzzling. Whatever caused it did not kill the related crocodiles and did not harm the sensitive frogs and salamanders. This selective elimination of the most skillful predators resulted from their early ontogenetic stage. Dinosaurs and pterosaurs were oviparous,

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

end-Cretaceous biological crisis (Lewy, 1996, 2002).

The straight (orthocone) baculitids are heteromorph ammonites which did not modify the last growth stage and probably had another breeding strategy. All baculitid species seem to have formed local evolutionary lineages and were hence indigenous species like several planispiral ammonites, which characterize biogeographic provinces in contrast to the 'cosmopolitan' distribution of most heteromorphs. The 'pelagic' mode of breeding resulted in the wide distribution of these species, whereas in the 'stationary' mode of breeding the hatchlings remained in the area where they hatched and formed indigenous species. This

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 325

constricting the terminal aperture. Others added apertural appendages. All of these modifications in the last growth stage limited the mobility of the ammonoid by the constricted or upward oriented aperture, complicated nourishment, and in some cases must have resulted in death of starvation. These rather fatal modifications could not have been intended to live further in a different way (Westermann, 1990) and were interpreted to have served the last and most important biological duty of breeding by providing protected brood chambers (Lewy, 1996). The female was situated beside numerous tiny eggs (detected in fossil ammonites) being drifted by currents across the ocean while the eggs developed. This drifting process is corroborated by the wide distribution of many of these nonstreamlined heteromorphy ammonite species along the Tethys Sea, which could not have been explained by swimming. Such mode of breeding in cephalopods is known in extant octopods which carry out two breeding strategies. The common one is laying large, yolkrich eggs in bundles attached to submarine substrates ('stationary' mode of breeding), in which both parents care for the brood during several months without eating. They die of starvation close to when the young hatch, each with some yolk for their nourishment during the first hours of free swimming. In the single group of argonautids the female *Argonauta*  (larger than the male) secrets from the expanded edge of two of its tentacles a thin calcitic, widely-coiled shell. It situates itself in this boat-shaped shell and lays numerous tiny spherical eggs about 1 mm in diameter, similar in shape and size to spheres detected in fossil ammonites (Lewy, 1996). Together they drift over the sea while the eggs develop ('pelagic' mode of breeding) and the young are shed into the water to cope with life. The shapes of all of the argonautid brood chambers (several fossil and extant species) are identical to latest Cretaceous ammonites. This fragile conch cannot protect its content and merely carries the female and the brood. For this purpose, a smooth boat-shaped shell would be sufficient and the ammonite-like complex sculpture cannot be explained by convergent evolution. Argonautid egg-cases occur since the Late Oligocene (Saul & Stadum, 2005). It is unlikely that only then the 'pelagic' mode of breeding was introduced into octopod breeding strategy. It is more reasonable to explain the first occurrence in Late Oligocene times of fossils of ammonite-like argonautids by the preservation of their calcitic shell. This brood-case might have been made in earlier times of an organic substance (conchiolin) which disintegrated close to after burial in the marine sediment. Octopods have neither an external conch nor an internal hard feature, which renders their geological record sparse. The earliest fossil octopod is an imprint from the Middle Jurassic, but the development of these cephalopods must have been earlier. The few octopod fossils preserved in restricted environments of unusual burial conditions resulted in the dispute over their systematic position among cephalopods. The comparison between the anatomy and physiology of extant octopods and the functional morphology of ammonites suggests close genetic relationships between these two cephalopod groups, one of which survived the

laying 1-7 eggs (or more) in nests on a rather flat land, such as sea-shores (Sanz et al., 1995), tidal flats (López-Martínez et al., 2000) and beside estuaries, lagoons, marshes and fluvial plains (Vianey-Liaud & Lopez-Martinez, 1997). Despite brood-care by the adults, the eggs and the hatchlings were frequently exposed to potential predators and could be snatched from the nest or consumed on site after the distraction of the parent. Dinosaurs and pterosaurs living in this region were probably involved in the killing of their kind. This common process among birds was suspected to have occurred in dinosaurs as indicated by the name *Oviraptor*='egg stealer', given to a dinosaur situated in an egg nest. Other oviparous reptiles such as crocodiles, land lizards and sea-turtles hid the brood and only experienced predators knew were to search for them, whereby most of the eggs hatched and the young quickly looked for shelter. The marine reptiles gave birth to young which were likewise vulnerable to predation at this stage as hinted by bones of small, probably young mosasaurs in Upper Campanian sediments in southern Israel. The dinosaurian branch of birds survived this predatory threat thanks to their rather small body, and hence their egg size. These could be laid in nests high above the ground hidden in trees, in bushes or at inaccessible sites such as on cliffs. The threat to the brood was thus minimized and restricted to the few predators which could discover and reach these breeding sites. The extant large ostrich exemplifies the mode of breeding of the dinosaurs on open ground whereby some of the 10-12 eggs might be consumed by predators.

MacLeod et al. (1997) summarize the record of the Upper Cretaceous cartilaginous **fishes** many of which survived into the Tertiary like many of the bony fishes. Among those which became extinct are *Enchodus* and *Stratodus. Enchodus* species reached 1 m of length whereas *Stratodus* species were over 3 m long (Lewy et al., 1992). These rather large bony fishes, as well as most of the cartilaginous ones seem to have swum as individuals in contrast to present-day small fish, forming vortex-like swarms, hence confusing predators. Thus the surviving potential of these small fish is higher than of individual large ones, despite their skills as vicious predators which could be overcome by larger sharks and by big marine reptiles.

#### **4.2 Cephalopods**

**Ammonites** are another example of a large group seemingly to suddenly disappear at the end of the Cretaceous Period like the dinosaurs. These conch bearing cephalopods diversified during the Upper Cretaceous, providing excellent biostratigraphic markers. Ammonite species gradually disappeared during the Upper Maastrictian, with 12 reaching close to the K-T boundary in the section exposed in northern Spain, in which other species disappear in groups or individual species during the Upper Maastrichtian (Marshall & ward, 1996). This is probably the most complete latest Cretaceous sedimentary sequence with ammonites. The simultaneous disappearance of twelve species close to the K-T boundary gives the impression of a catastrophic event that killed all ammonites in this area and seems to corroborate the total elimination of the order Ammonoidea throughout the world.

Lewy (1996, 2002a, b) analyzed the functional morphology of ammonites (ammonoid conchs), especially of heteromorphs, but also of the planispirally coiled ones. Most of the heteromorph ammonites (except for the Baculitidae) developed a U-shaped terminal whorl with an upward facing aperture, which in some species was partly occluded by the previous whorls. Some planispiral ammonites changed the shape of the last whorl, inflating it and

laying 1-7 eggs (or more) in nests on a rather flat land, such as sea-shores (Sanz et al., 1995), tidal flats (López-Martínez et al., 2000) and beside estuaries, lagoons, marshes and fluvial plains (Vianey-Liaud & Lopez-Martinez, 1997). Despite brood-care by the adults, the eggs and the hatchlings were frequently exposed to potential predators and could be snatched from the nest or consumed on site after the distraction of the parent. Dinosaurs and pterosaurs living in this region were probably involved in the killing of their kind. This common process among birds was suspected to have occurred in dinosaurs as indicated by the name *Oviraptor*='egg stealer', given to a dinosaur situated in an egg nest. Other oviparous reptiles such as crocodiles, land lizards and sea-turtles hid the brood and only experienced predators knew were to search for them, whereby most of the eggs hatched and the young quickly looked for shelter. The marine reptiles gave birth to young which were likewise vulnerable to predation at this stage as hinted by bones of small, probably young mosasaurs in Upper Campanian sediments in southern Israel. The dinosaurian branch of birds survived this predatory threat thanks to their rather small body, and hence their egg size. These could be laid in nests high above the ground hidden in trees, in bushes or at inaccessible sites such as on cliffs. The threat to the brood was thus minimized and restricted to the few predators which could discover and reach these breeding sites. The extant large ostrich exemplifies the mode of breeding of the dinosaurs on open ground whereby some of

MacLeod et al. (1997) summarize the record of the Upper Cretaceous cartilaginous **fishes** many of which survived into the Tertiary like many of the bony fishes. Among those which became extinct are *Enchodus* and *Stratodus. Enchodus* species reached 1 m of length whereas *Stratodus* species were over 3 m long (Lewy et al., 1992). These rather large bony fishes, as well as most of the cartilaginous ones seem to have swum as individuals in contrast to present-day small fish, forming vortex-like swarms, hence confusing predators. Thus the surviving potential of these small fish is higher than of individual large ones, despite their skills as vicious predators which could be overcome by larger sharks and by big marine

**Ammonites** are another example of a large group seemingly to suddenly disappear at the end of the Cretaceous Period like the dinosaurs. These conch bearing cephalopods diversified during the Upper Cretaceous, providing excellent biostratigraphic markers. Ammonite species gradually disappeared during the Upper Maastrictian, with 12 reaching close to the K-T boundary in the section exposed in northern Spain, in which other species disappear in groups or individual species during the Upper Maastrichtian (Marshall & ward, 1996). This is probably the most complete latest Cretaceous sedimentary sequence with ammonites. The simultaneous disappearance of twelve species close to the K-T boundary gives the impression of a catastrophic event that killed all ammonites in this area and seems to corroborate the total elimination of the order Ammonoidea throughout the

Lewy (1996, 2002a, b) analyzed the functional morphology of ammonites (ammonoid conchs), especially of heteromorphs, but also of the planispirally coiled ones. Most of the heteromorph ammonites (except for the Baculitidae) developed a U-shaped terminal whorl with an upward facing aperture, which in some species was partly occluded by the previous whorls. Some planispiral ammonites changed the shape of the last whorl, inflating it and

the 10-12 eggs might be consumed by predators.

reptiles.

world.

**4.2 Cephalopods** 

constricting the terminal aperture. Others added apertural appendages. All of these modifications in the last growth stage limited the mobility of the ammonoid by the constricted or upward oriented aperture, complicated nourishment, and in some cases must have resulted in death of starvation. These rather fatal modifications could not have been intended to live further in a different way (Westermann, 1990) and were interpreted to have served the last and most important biological duty of breeding by providing protected brood chambers (Lewy, 1996). The female was situated beside numerous tiny eggs (detected in fossil ammonites) being drifted by currents across the ocean while the eggs developed. This drifting process is corroborated by the wide distribution of many of these nonstreamlined heteromorphy ammonite species along the Tethys Sea, which could not have been explained by swimming. Such mode of breeding in cephalopods is known in extant octopods which carry out two breeding strategies. The common one is laying large, yolkrich eggs in bundles attached to submarine substrates ('stationary' mode of breeding), in which both parents care for the brood during several months without eating. They die of starvation close to when the young hatch, each with some yolk for their nourishment during the first hours of free swimming. In the single group of argonautids the female *Argonauta*  (larger than the male) secrets from the expanded edge of two of its tentacles a thin calcitic, widely-coiled shell. It situates itself in this boat-shaped shell and lays numerous tiny spherical eggs about 1 mm in diameter, similar in shape and size to spheres detected in fossil ammonites (Lewy, 1996). Together they drift over the sea while the eggs develop ('pelagic' mode of breeding) and the young are shed into the water to cope with life. The shapes of all of the argonautid brood chambers (several fossil and extant species) are identical to latest Cretaceous ammonites. This fragile conch cannot protect its content and merely carries the female and the brood. For this purpose, a smooth boat-shaped shell would be sufficient and the ammonite-like complex sculpture cannot be explained by convergent evolution. Argonautid egg-cases occur since the Late Oligocene (Saul & Stadum, 2005). It is unlikely that only then the 'pelagic' mode of breeding was introduced into octopod breeding strategy. It is more reasonable to explain the first occurrence in Late Oligocene times of fossils of ammonite-like argonautids by the preservation of their calcitic shell. This brood-case might have been made in earlier times of an organic substance (conchiolin) which disintegrated close to after burial in the marine sediment. Octopods have neither an external conch nor an internal hard feature, which renders their geological record sparse. The earliest fossil octopod is an imprint from the Middle Jurassic, but the development of these cephalopods must have been earlier. The few octopod fossils preserved in restricted environments of unusual burial conditions resulted in the dispute over their systematic position among cephalopods. The comparison between the anatomy and physiology of extant octopods and the functional morphology of ammonites suggests close genetic relationships between these two cephalopod groups, one of which survived the end-Cretaceous biological crisis (Lewy, 1996, 2002).

The straight (orthocone) baculitids are heteromorph ammonites which did not modify the last growth stage and probably had another breeding strategy. All baculitid species seem to have formed local evolutionary lineages and were hence indigenous species like several planispiral ammonites, which characterize biogeographic provinces in contrast to the 'cosmopolitan' distribution of most heteromorphs. The 'pelagic' mode of breeding resulted in the wide distribution of these species, whereas in the 'stationary' mode of breeding the hatchlings remained in the area where they hatched and formed indigenous species. This

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

2009).

**4.3 Bivalvia** 

Maastrichtian coped with the usual predation rate.

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 327

common morphological affinities with Early Tertiary sepiids (Coleoidea) suggesting an evolutionary transition across the K-T boundary rather than belemnoid extinction (Lewy,

Five Late Cretaceous **nautiloid** genera crossed the K-T boundary (Kummel, 1964). Many had a spherical shape which was not easy to catch and crush, despite the fact that the ammonoids and the nautiloids were predated by mosasaurs (Kauffman, 2004). All cephalopod groups descended from ancestral nautiloids and the extant ones have similar anatomical features, except for nautilids. The latter have a primitive eye structure, two pairs of gills and numerous small tentacles in contrast to vertebrates-like eyes, a single pair of gills and ten or eight tentacles as seen, for example in cuttlefish, squids and octopods. These anatomical and physiological differences suggest that the extant nautilid adapted to darkness and oxygen deficiency, such as exists in the deep ocean where they are found today, restricted to the southwestern Pacific Ocean (Kummel, 1964). Mesozoic fossil nautiloids occur in shallow and deep marine sediments. It is reasonable to assume that nautiloids swimming in open marine waters were attacked by sharks, large fish, large octopods and squids. The slow swimming nautiloids escaped into deeper marine environments already millions of years ago during which their anatomy and physiology considerably changed and therefore cannot be applied to Mesozoic and older nautiloids (Lewy, 2000). This trend explains how nautiloid genera survived the Late Cretaceous biological crisis which affected their associated cochleate ammonites. This crisis was not caused by acid rain (Prinn & Fegley, 1987) which would have killed most nektonic organisms, but reflects an increase in predation pressure (as reflected by other faunal groups) from which nautiloids escaped into deeper water and less menacing habitats.

Most bivalves are burrowers into the sediment and are thus hidden from predators, in contrast to epifaunal species. Among the few groups which did not survive into the Cenozoic are the sessile, epifaunal, gregarious **incoceramid** bivalves, which thrived in large communities on rather deep marine bottoms of calcareous shale and chalk. These sediments preserved organic matter in some places, suggesting temporary reduced oxygen content and hence living conditions unfavorable to other organisms (Kauffman et al., 2007). The decimeter to over a meter long bivalves are found up to the base of the Upper Maastrichtian, with questionable relics at higher levels. Their small relative *Tenuipteria* survived to the end of the Maastrichtian (Dhondt, 1983; Marshall & Ward, 1996). This selective extinction can be explained by increased predation by sharks and mosasaurian reptiles (Kauffman, 1972), which dived into the deep bottom for the easy prey due to the fragile nature of the prismatic shell structure of these sessile, rather large bivalves, which until the beginning of the Upper

**Rudists** were individual marine bivalves attached to substrates or reclining on the soft sediment. Whether or not they hosted photosynthesizing zooxanthellae, they concentrated in shallow water where food supply and aeration were optimal. They probably had a short larval stage and could not have drifted far from their ancestral rudists before settling down and undergoing metamorphism. Therefore the young rudists are found attached beside, or on top of the previous generation accumulating into wide thickets forming the carbonateplatform framework, or building elongated or lenticular biogenic buildups (bioherms) with or without hermatypic corals, stromatoporoids, calcareous algae and other attached faunal groups. Some rudists reclined on the bottom in the low-energy neritic zone. The general

means that the two modes of octopod breeding occurred in ammonoids and controlled their distribution. The similarity of the argonautid brood cases to Upper Cretaceous ammonites strengthens these ammonoid-octopodid relationships, suggesting phylogenetic connections. Lewy (1996) suggested that octopods descended from ammonoids in which the conch degenerated until total loss like in opistobranch gastropods. This evolutionary trend can be explained by the diversification of fish, sharks, marine reptiles and belemnites (Lewy, 2009) predating, among others on slow swimming ammonites which their conch did not protect anymore from these skillful hunters. On the other hand, the conch limited the expansion of the mantle cavity and hence the expelled water jet which controlled swimming speed. The lack of an external conch overcame these restrictions and improved maneuverability, while additional strategies improved octopods' escape from predators. The earliest octopods descended from several ammonoid groups, whereby some carried out the 'stationary' mode of breeding and the others- the 'pelagic' mode. These conchless creatures were physiologically required to carry out the two modes of breeding in which the 'pelagic' one a floating egg-case was needed. Empty ammonites floated for some time over the Jurassic and Cretaceous seas before sinking into the depths. These were occupied by the relevant octopods and amended into suitable egg-cases. In Late Cretaceous times, the common ammonites *Hoplitoplacenticeras*, *Jeletkytes* and *Phylloceras* were amended into floating eggcases by the breaking off of the terminal part of the conch and its extention in an uncoiling shape, enabling the octopod female to enter and care for the brood. This added part was probably made of conchiolin secreted from glands developed at the end of two tentacles as reflected by extant argonautid octopods. The disappearance of floating conchs in earliest Cenozoic times forced the surviving octopods to produce the whole brood-case, which they did in the shape of the Late Cretaceous ammonites, which their ancestors had learned to construct. The short longevity of extant octopods (1-3 years), when applied to ammonoids provided an explanation to the function of the fluted margins of ammonite septa (Lewy, 2002a), ammonoid high evolution rate and other phenomena in ammonoids (Lewy, 2002b), corroborating the deduced ammonoid-octopodid genetic relationships as hinted by other common characteristics (Lewy, 1996). Accordingly, the order Ammonoidea did not completely disappear at the K-T boundary, but only the conch-bearing ones. In this respect the Ammonoidea are comparable to those dinosaurs from which the birds descended.

The endoskeleton of the cephalopod order of **Belemnitida** has been suggested to balance the horizontal orientation in the water while the belemnoid preys and swallows skeletal fragments (Lewy, 2009). These were mainly made of calcareous composition of a specific gravity twice that of flesh, being temporarily stored in the frontal crop. The change in weight in the anterior side through the accumulation and regurgitation of these fragments was balanced by water-gas exchange in the phragmocone. The rapid evolution of the belemnites since the Early Jurassic was associated with the appearance of calcitic opercula (aptychi) in ammonites. This calcification of the pair of 'wings' of the lower jaw in the shape of the aperture was intended to protect the ammonoid by preventing crustacean claws, belemnite tentacles and other means of predation from penetrating into the conch. Most aptychi are found associated with belemnites suggesting prey-predator relationships. The same ammonite genera (e.g., *Baculites*) in regions without belemnites, lack any associated aptychi plates (Lewy, 2009). Some Late Cretaceous belemnites reduced the size of the guard (rostrum) up to complete disappearance (e.g., *Naefia*, *Groenlandibelus*) suggesting a change in their diet comprising less skeletal parts- a fact which was attributed to the profound reduction in the abundance of ammonites as prey. These latest Cretaceous belemnites share common morphological affinities with Early Tertiary sepiids (Coleoidea) suggesting an evolutionary transition across the K-T boundary rather than belemnoid extinction (Lewy, 2009).

Five Late Cretaceous **nautiloid** genera crossed the K-T boundary (Kummel, 1964). Many had a spherical shape which was not easy to catch and crush, despite the fact that the ammonoids and the nautiloids were predated by mosasaurs (Kauffman, 2004). All cephalopod groups descended from ancestral nautiloids and the extant ones have similar anatomical features, except for nautilids. The latter have a primitive eye structure, two pairs of gills and numerous small tentacles in contrast to vertebrates-like eyes, a single pair of gills and ten or eight tentacles as seen, for example in cuttlefish, squids and octopods. These anatomical and physiological differences suggest that the extant nautilid adapted to darkness and oxygen deficiency, such as exists in the deep ocean where they are found today, restricted to the southwestern Pacific Ocean (Kummel, 1964). Mesozoic fossil nautiloids occur in shallow and deep marine sediments. It is reasonable to assume that nautiloids swimming in open marine waters were attacked by sharks, large fish, large octopods and squids. The slow swimming nautiloids escaped into deeper marine environments already millions of years ago during which their anatomy and physiology considerably changed and therefore cannot be applied to Mesozoic and older nautiloids (Lewy, 2000). This trend explains how nautiloid genera survived the Late Cretaceous biological crisis which affected their associated cochleate ammonites. This crisis was not caused by acid rain (Prinn & Fegley, 1987) which would have killed most nektonic organisms, but reflects an increase in predation pressure (as reflected by other faunal groups) from which nautiloids escaped into deeper water and less menacing habitats.

#### **4.3 Bivalvia**

326 Advances in Data, Methods, Models and Their Applications in Geoscience

means that the two modes of octopod breeding occurred in ammonoids and controlled their distribution. The similarity of the argonautid brood cases to Upper Cretaceous ammonites strengthens these ammonoid-octopodid relationships, suggesting phylogenetic connections. Lewy (1996) suggested that octopods descended from ammonoids in which the conch degenerated until total loss like in opistobranch gastropods. This evolutionary trend can be explained by the diversification of fish, sharks, marine reptiles and belemnites (Lewy, 2009) predating, among others on slow swimming ammonites which their conch did not protect anymore from these skillful hunters. On the other hand, the conch limited the expansion of the mantle cavity and hence the expelled water jet which controlled swimming speed. The lack of an external conch overcame these restrictions and improved maneuverability, while additional strategies improved octopods' escape from predators. The earliest octopods descended from several ammonoid groups, whereby some carried out the 'stationary' mode of breeding and the others- the 'pelagic' mode. These conchless creatures were physiologically required to carry out the two modes of breeding in which the 'pelagic' one a floating egg-case was needed. Empty ammonites floated for some time over the Jurassic and Cretaceous seas before sinking into the depths. These were occupied by the relevant octopods and amended into suitable egg-cases. In Late Cretaceous times, the common ammonites *Hoplitoplacenticeras*, *Jeletkytes* and *Phylloceras* were amended into floating eggcases by the breaking off of the terminal part of the conch and its extention in an uncoiling shape, enabling the octopod female to enter and care for the brood. This added part was probably made of conchiolin secreted from glands developed at the end of two tentacles as reflected by extant argonautid octopods. The disappearance of floating conchs in earliest Cenozoic times forced the surviving octopods to produce the whole brood-case, which they did in the shape of the Late Cretaceous ammonites, which their ancestors had learned to construct. The short longevity of extant octopods (1-3 years), when applied to ammonoids provided an explanation to the function of the fluted margins of ammonite septa (Lewy, 2002a), ammonoid high evolution rate and other phenomena in ammonoids (Lewy, 2002b), corroborating the deduced ammonoid-octopodid genetic relationships as hinted by other common characteristics (Lewy, 1996). Accordingly, the order Ammonoidea did not completely disappear at the K-T boundary, but only the conch-bearing ones. In this respect the Ammonoidea are comparable to those dinosaurs from which the birds descended. The endoskeleton of the cephalopod order of **Belemnitida** has been suggested to balance the horizontal orientation in the water while the belemnoid preys and swallows skeletal fragments (Lewy, 2009). These were mainly made of calcareous composition of a specific gravity twice that of flesh, being temporarily stored in the frontal crop. The change in weight in the anterior side through the accumulation and regurgitation of these fragments was balanced by water-gas exchange in the phragmocone. The rapid evolution of the belemnites since the Early Jurassic was associated with the appearance of calcitic opercula (aptychi) in ammonites. This calcification of the pair of 'wings' of the lower jaw in the shape of the aperture was intended to protect the ammonoid by preventing crustacean claws, belemnite tentacles and other means of predation from penetrating into the conch. Most aptychi are found associated with belemnites suggesting prey-predator relationships. The same ammonite genera (e.g., *Baculites*) in regions without belemnites, lack any associated aptychi plates (Lewy, 2009). Some Late Cretaceous belemnites reduced the size of the guard (rostrum) up to complete disappearance (e.g., *Naefia*, *Groenlandibelus*) suggesting a change in their diet comprising less skeletal parts- a fact which was attributed to the profound reduction in the abundance of ammonites as prey. These latest Cretaceous belemnites share

Most bivalves are burrowers into the sediment and are thus hidden from predators, in contrast to epifaunal species. Among the few groups which did not survive into the Cenozoic are the sessile, epifaunal, gregarious **incoceramid** bivalves, which thrived in large communities on rather deep marine bottoms of calcareous shale and chalk. These sediments preserved organic matter in some places, suggesting temporary reduced oxygen content and hence living conditions unfavorable to other organisms (Kauffman et al., 2007). The decimeter to over a meter long bivalves are found up to the base of the Upper Maastrichtian, with questionable relics at higher levels. Their small relative *Tenuipteria* survived to the end of the Maastrichtian (Dhondt, 1983; Marshall & Ward, 1996). This selective extinction can be explained by increased predation by sharks and mosasaurian reptiles (Kauffman, 1972), which dived into the deep bottom for the easy prey due to the fragile nature of the prismatic shell structure of these sessile, rather large bivalves, which until the beginning of the Upper Maastrichtian coped with the usual predation rate.

**Rudists** were individual marine bivalves attached to substrates or reclining on the soft sediment. Whether or not they hosted photosynthesizing zooxanthellae, they concentrated in shallow water where food supply and aeration were optimal. They probably had a short larval stage and could not have drifted far from their ancestral rudists before settling down and undergoing metamorphism. Therefore the young rudists are found attached beside, or on top of the previous generation accumulating into wide thickets forming the carbonateplatform framework, or building elongated or lenticular biogenic buildups (bioherms) with or without hermatypic corals, stromatoporoids, calcareous algae and other attached faunal groups. Some rudists reclined on the bottom in the low-energy neritic zone. The general

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

Cretaceous Period.

predation of previously untouched organisms.

systems (Tantawy et al., 2009).

**5. Marine microorganisms with symbiotic zooxanthellae** 

**4.4 Gastropoda** 

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 329

biological crisis. This is in contrast to the Exogyrini tribe which lived attached to firm substrates and therefore inhabited mainly shallow marine environments such as gregarious oysters which were subjected to local exposure by long low-tides, being covered by sediment from terrestrial runoff, as well as predation. There they were associated with the oyster-like *Chondrodonta* which could withstand wave impact and thus thrived in highenergy environments and disappeared before the Exogyrini during the Upper Campanian (Stenzel, 1971).Perhaps *Chondrodonta* attained larger dimension and had a thin fragile shell in contrast to *Exogyra* species. The pectinid **Neithea** Group thrived in Upper Cretaceous neritic and continental-slope sediments and probably disappeared at the end of the Mesozoic (MacLeod et al., 1997). After an early byssate stage they reclined on the sea bottom and occasionally leaped for a short distance, thus being exposed most of the time to diving predators, subject to over predation and extinction at the end of the

Most gastropod families crossed the Cretaceous-Tertiary boundary nearly unaffected except for the **Nerineidae** and **Actaeonellidae**. These two families comprised rather large gastropods which thrived on the Tethyan warm-water carbonate platforms and their marginal mainly low-salinity zones (Sohl & Kollmann, 1985). The elongated nerineids had a thick external shell reinforced by folds of the inner shell layer. Actaeonellids likewise had a thick shell and most of them formed ovate conchs which probably slipped through the teeth of predators, increasing the resistance of both gastropod groups to predation. Though these gastropods were mobile, they formed layered concentrations and in places lenticular structures. These accumulations suggest that these gastropods lived close below (shallow burrowers) or on the bottom and were thus subjected to exhumation and concentration by turbulent water in the neritic zone. Generally their representative species survived up to the end of the Cretaceous although some disappeared earlier from many provinces (Sohl & Kollmann, 1985, fig. 14) probably as the result of the reduction of the neritic zones and

Marine floral and faunal microorganisms flourished and diversified throughout the Late Cretaceous. Therefore the disappearance of most of them at the end of the period seemed catastrophic (MacLeod et al., 1997). Close to the K-T boundary the calcareous tests of **nannoplankton** and the **planktonic foraminifera** reduced their size (dwarfing) and the assemblage became dominated by low-oxygen-tolerant small heterohelicid foraminifera and the disaster opportunist nannofossil *Micula decussata* (Abramovich & Keller, 2002; Keller & Abramovich, 2009). These latest Maastrichtian affected microfossils occur in Indian Ocean drilling samples with volcanic sediments attributed to the Deccan volcanism, hinting to a connection between the intensive volcanism and the deterioration of the marine ecological

**6. The end-Cretaceous biological crisis caused by the Deccan volcanism** 

The main volcanic phase, comprising ~80% of the total Deccan Trap volume, occurred around the K-T boundary and is interpreted as being active during a short time interval in

narrowing of these neritic habitats during the Late Cretaceous reduced rudistid abundance (e.g., central-eastern Mediterranean and Middle East region; Steuber and Löser, 2000) and the associated 'reefal' communities. Late Cretaceous rudist buildups prevailed in restricted regions of the Tethys Sea, such as in the Caribbean province (e.g., Jamaica; Mitchell et al., 2004) where a few genera reached the K-T boundary and became suddenly extinct probably as the result of the asteroid impact at the Yukatan Peninsula in the same region (Steuber et al., 2002). The following initiated tsunami waves might have broken and killed the rudists or covered them by sediment (e.g., Scasso et al., 2005; Bralower et al., 2010; with references). However, the dating of these turbulence-induced deposits relative to the age of the asteroid impact and the K-T boundary are still controversial (Keller et al., 2007).

Most **oysters** are attached to substrates in shallow marine environments tending to concentrate and form oyster banks. A few genera recline on soft bottoms in low-energy environments (e.g., *Gryphaea, Pycnodonte*). The 'tribe' Exogyrini (Stenzel, 1971) was highly abundant in the Cretaceous neritic zone and their calcitic shells are well preserved in carbonate platform sediments beside the long-ranging *Ostrea*. Their attached (left) valve first grew in a spiral pattern which opened and straightened into an elongated or rounded cupshape in which the posterior margin stretched over the substrate and the anterior margin was raised, whereby the flattened upper valve was inclined to the substrate. This mode of growth subjected these oysters to penetration of sedimentary particles in between the valves as well as total cover by sediment. The disadvantageous growth orientation in shallow marine environments added to possible exposure at low tide or predation, all of which resulted in the extinction of this group at the end of the Cretaceous. Thereby they differ from the subfamilies Gryphaeinae and Pycnodonteinae in which the lower valve grew in a nearly planispiral curvature into a cup-shape, whereby the valve commissure (margins) was elevated above the substrate and the flat upper valve was in horizontal orientation (Stenzel, 1971). The larvae of these oysters had to attach before undergoing metamorphism. The shallow marine habitats of the Upper Triassic-Jurassic Gryphaeinae consisted mainly of friable sediment such as sand and marl. Because these sediments lacked large firm substrates, any grain or small fragment served as attachment site as evidenced by the small attachment scar at the oyster beak. With further growth the small substrate lost its anchorage function and the oyster was tilted, raising the substrate above the ground whereby the ventral margins of the oyster nearly sunk into the friable sediment. To avoid the penetration of sedimentary particles, the oyster increased the upward growth of the lower valve whereby the oyster balance changed and required further tilt and upward growth. The resulting planispiral curvature increased the living space in between the two valves beyond the size of the mollusk which was compensated by secondary deposition of shell material on the inner surface of the lower shell (Lewy, 1976). However, the precipitated calcitic foliated shell structure increased the oyster total weight and enhanced its sinking into the sediment. Thereby the curvature and thickness of the Gryphaeinae lower valve reflect the plasticity of the sediment on which it reclined. The crucial effect of this secondary deposit in the lower valve was partly solved in Cretaceous times by changing the compact structure into a vesicular one which characterized the similarly looking Pycnodonteinae. These oysters thickened their valves by layers of light vesicular structures in between layers of foliated structures minimizing the weight of the secondary fill. Thereby the Pycnodonteinae could inhabit very soft bottoms and thrive on marl and planktonic foraminiferal ooze forming chalk in the Upper Cretaceous. Thanks to their adaptation to rather deep marine environments, the Pycnodonteinae survived the end-Cretaceous biological crisis. This is in contrast to the Exogyrini tribe which lived attached to firm substrates and therefore inhabited mainly shallow marine environments such as gregarious oysters which were subjected to local exposure by long low-tides, being covered by sediment from terrestrial runoff, as well as predation. There they were associated with the oyster-like *Chondrodonta* which could withstand wave impact and thus thrived in highenergy environments and disappeared before the Exogyrini during the Upper Campanian (Stenzel, 1971).Perhaps *Chondrodonta* attained larger dimension and had a thin fragile shell in contrast to *Exogyra* species. The pectinid **Neithea** Group thrived in Upper Cretaceous neritic and continental-slope sediments and probably disappeared at the end of the Mesozoic (MacLeod et al., 1997). After an early byssate stage they reclined on the sea bottom and occasionally leaped for a short distance, thus being exposed most of the time to diving predators, subject to over predation and extinction at the end of the Cretaceous Period.

#### **4.4 Gastropoda**

328 Advances in Data, Methods, Models and Their Applications in Geoscience

narrowing of these neritic habitats during the Late Cretaceous reduced rudistid abundance (e.g., central-eastern Mediterranean and Middle East region; Steuber and Löser, 2000) and the associated 'reefal' communities. Late Cretaceous rudist buildups prevailed in restricted regions of the Tethys Sea, such as in the Caribbean province (e.g., Jamaica; Mitchell et al., 2004) where a few genera reached the K-T boundary and became suddenly extinct probably as the result of the asteroid impact at the Yukatan Peninsula in the same region (Steuber et al., 2002). The following initiated tsunami waves might have broken and killed the rudists or covered them by sediment (e.g., Scasso et al., 2005; Bralower et al., 2010; with references). However, the dating of these turbulence-induced deposits relative to the age of the asteroid

Most **oysters** are attached to substrates in shallow marine environments tending to concentrate and form oyster banks. A few genera recline on soft bottoms in low-energy environments (e.g., *Gryphaea, Pycnodonte*). The 'tribe' Exogyrini (Stenzel, 1971) was highly abundant in the Cretaceous neritic zone and their calcitic shells are well preserved in carbonate platform sediments beside the long-ranging *Ostrea*. Their attached (left) valve first grew in a spiral pattern which opened and straightened into an elongated or rounded cupshape in which the posterior margin stretched over the substrate and the anterior margin was raised, whereby the flattened upper valve was inclined to the substrate. This mode of growth subjected these oysters to penetration of sedimentary particles in between the valves as well as total cover by sediment. The disadvantageous growth orientation in shallow marine environments added to possible exposure at low tide or predation, all of which resulted in the extinction of this group at the end of the Cretaceous. Thereby they differ from the subfamilies Gryphaeinae and Pycnodonteinae in which the lower valve grew in a nearly planispiral curvature into a cup-shape, whereby the valve commissure (margins) was elevated above the substrate and the flat upper valve was in horizontal orientation (Stenzel, 1971). The larvae of these oysters had to attach before undergoing metamorphism. The shallow marine habitats of the Upper Triassic-Jurassic Gryphaeinae consisted mainly of friable sediment such as sand and marl. Because these sediments lacked large firm substrates, any grain or small fragment served as attachment site as evidenced by the small attachment scar at the oyster beak. With further growth the small substrate lost its anchorage function and the oyster was tilted, raising the substrate above the ground whereby the ventral margins of the oyster nearly sunk into the friable sediment. To avoid the penetration of sedimentary particles, the oyster increased the upward growth of the lower valve whereby the oyster balance changed and required further tilt and upward growth. The resulting planispiral curvature increased the living space in between the two valves beyond the size of the mollusk which was compensated by secondary deposition of shell material on the inner surface of the lower shell (Lewy, 1976). However, the precipitated calcitic foliated shell structure increased the oyster total weight and enhanced its sinking into the sediment. Thereby the curvature and thickness of the Gryphaeinae lower valve reflect the plasticity of the sediment on which it reclined. The crucial effect of this secondary deposit in the lower valve was partly solved in Cretaceous times by changing the compact structure into a vesicular one which characterized the similarly looking Pycnodonteinae. These oysters thickened their valves by layers of light vesicular structures in between layers of foliated structures minimizing the weight of the secondary fill. Thereby the Pycnodonteinae could inhabit very soft bottoms and thrive on marl and planktonic foraminiferal ooze forming chalk in the Upper Cretaceous. Thanks to their adaptation to rather deep marine environments, the Pycnodonteinae survived the end-Cretaceous

impact and the K-T boundary are still controversial (Keller et al., 2007).

Most gastropod families crossed the Cretaceous-Tertiary boundary nearly unaffected except for the **Nerineidae** and **Actaeonellidae**. These two families comprised rather large gastropods which thrived on the Tethyan warm-water carbonate platforms and their marginal mainly low-salinity zones (Sohl & Kollmann, 1985). The elongated nerineids had a thick external shell reinforced by folds of the inner shell layer. Actaeonellids likewise had a thick shell and most of them formed ovate conchs which probably slipped through the teeth of predators, increasing the resistance of both gastropod groups to predation. Though these gastropods were mobile, they formed layered concentrations and in places lenticular structures. These accumulations suggest that these gastropods lived close below (shallow burrowers) or on the bottom and were thus subjected to exhumation and concentration by turbulent water in the neritic zone. Generally their representative species survived up to the end of the Cretaceous although some disappeared earlier from many provinces (Sohl & Kollmann, 1985, fig. 14) probably as the result of the reduction of the neritic zones and predation of previously untouched organisms.

#### **5. Marine microorganisms with symbiotic zooxanthellae**

Marine floral and faunal microorganisms flourished and diversified throughout the Late Cretaceous. Therefore the disappearance of most of them at the end of the period seemed catastrophic (MacLeod et al., 1997). Close to the K-T boundary the calcareous tests of **nannoplankton** and the **planktonic foraminifera** reduced their size (dwarfing) and the assemblage became dominated by low-oxygen-tolerant small heterohelicid foraminifera and the disaster opportunist nannofossil *Micula decussata* (Abramovich & Keller, 2002; Keller & Abramovich, 2009). These latest Maastrichtian affected microfossils occur in Indian Ocean drilling samples with volcanic sediments attributed to the Deccan volcanism, hinting to a connection between the intensive volcanism and the deterioration of the marine ecological systems (Tantawy et al., 2009).

#### **6. The end-Cretaceous biological crisis caused by the Deccan volcanism**

The main volcanic phase, comprising ~80% of the total Deccan Trap volume, occurred around the K-T boundary and is interpreted as being active during a short time interval in

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 331

opportunist *Guembelitria* (Keller & Abramovich, 2009). The shading of sunlight reduced the depth of the marine euphotic zone and affected water temperature. A thick cover by ash dust probably lowered sea-water and Earth surface temperature. A less dense screen may have resulted in a greenhouse effect, keeping the warmth of partially penetrating sun-light from escaping into the atmosphere. Paleotemperature fluctuations in the latest Maastrictian marine environments (Li & Keller, 1998) are accordingly related to fluctuations in the

The polyps of reef-building hermatypic **corals** house symbiotic zooxanthellae which are involved in the precipitation of the calcareous skeleton and in other physiological processes, but they can also become part of the coelenterate diet. Unlike in planktonic foraminifera these symbiotic relationships observed on extant corals in the Great Barrier Reef of Australia can be stopped for a while (coral bleaching) during which coral growth slows down while the polyps feed on other algae and microorganisms, organic debris and bacteria (Vernon, 1993). This may explain the survival of some hermatypic coral groups, though a great deal

The fatal influence of the Deccan volcanism on the latest Cretaceous marine planktonic microorganisms applies to marine and large terrestrial creatures as well. The darkening of the atmosphere by dispersed volcaniclasts blurred the distinction between the annual seasons controlling plant growth and blooming, as well as the biological clock of animal reproduction, which provides a significant food source to carnivores after months of near starvation. Seasonality controls the timing of sperm and egg spawning into the water, most of which is consumed by predators awaiting this process. Mating and reproduction among larger animals is coordinated with availability of food supply (plant and meat) which will assure the survival and development of the young. Long-ranging darkness confused the instincts and physiology of animals, reducing birth rate and hence food supply crucial for predating mammals to feed the young as well as for reptiles and birds. The reduction in birth rate (including the laying of eggs) immediately reduced the food supply. The aggressive and large predators (mainly among the reptiles) were forced to consume part of the prey that smaller predators used to eat, whereby the 'normal' food chain collapsed, resulting in the intensive predation of the temporarily unprotected ones. These were dinosaurs and pterosaurs eggs in nests on flat-land and their hatched young, as well as mature ones sitting on the eggs, and other creatures which for a moment were careless. The over-predation of this easy prey reduced the size of the victim's population which gradually diminished until the remaining ones could not preserve the species, leading to extinction. The organisms which were not affected by the collapse of the food chain were small creatures which could escape and hide themselves or their brood such as crocodiles and turtles which covered their brood, birds which laid the small eggs in between plants, small mammals which could hide underground or among bushes, and amphibians and fishes capable of hiding in aquatic environments. The darkening effect of the Deccan volcaniclasts must have slowed down the metabolism of cold-blooded reptiles, among which were probably some large dinosaurs. During the severe darkening they were completely unable to defend themselves even from small predators. The selective elimination of the temporary vulnerable ones is an extreme example of **natural selection** as the result of catastrophic changes in the regular pattern of the long-operating ecological system in which organisms and plants lived in harmony. The additional destructive effect of a single or multiple

intensity of the Deccan volcanic eruptions and world-wide dispersal of volcaniclasts.

disappeared during the latest Cretaceous (MacLeod et al., 1997).

the middle of the paleomagnetic chron 29r (Keller et al., 2009, fig. 5). Oxygen isotope analyses (Li & Keller, 1998) and the response of microorganisms to water temperature (e.g., Abramovich & Keller, 2002; Abramovich et al., 2010) reflect fluctuations in surface and intermediate depth ocean water temperature during the Late Maastrichtian, especially in the latest 0.5 Myr. Fluctuating cool temperatures (average degrees of 9.90 C intermediate and 15.40 C surface water) during 66.85 and 65.52 Myr were followed by a short-term warming between 65.45 and 65.11 Myr which increased intermediate water temperatures by 2-30 C, and decreased the vertical thermal gradient to an average of 2.70 C (Li & Keller, 1998). A previous study by Stüben et al. (2003) on hemipelagic sediments of Tunisia differentiated three cool periods (65.50-65.55, 65.26-65.33, 65.04-65.12 Myr) and three warm periods (65.33-65.38, 65.12-65.26, 65.00-65.04 Myr). Tantawy et al. (2009, p. 85) point out that "**the biotic effects of volcanism have long been the unknown factors in creating biotic stress. The contribution of the Deccan volcanism to the K-T mass extinction remained largely unknown, although recent investigations revealed that the main phase of Deccan volcanism coincided with the K-T mass extinction**". Keller et al. (2009, p. 723-4) refer to "**the dust clouds obscuring sunlight and causing short-term global cooling"** as the result of the volcanic eruptions, but **"how Deccan volcanism affected the environment and how it may have led to the mass extinction of dinosaurs and other organisms in India and globally is still speculative**". The direct cause for seawater temperature fluctuations during the Maastrichtian last half million years and the dwarfed microfossils in this time interval (Keller, 2008) are herein related to sunlight screening by volcaniclastic dust from the Deccan volcanism, suggesting that its main activity extended over the same period.

Most Late Maastrichtian planktonic microorganisms reduced their size (dwarfing) at about 65.4 Myr (Keller, 2008) reaching sexual maturity at smaller dimensions and probably more rapid than their normally-sized ancestors. This assemblage became dominated by lowoxygen-tolerant small heterohelicids (Keller & Abramovich, 2009). All these globally detected abnormal morphological and ecological aspects of the latest Cretaceous marine microfossils attest to the deterioration of the ecological conditions, as the result of sunlight screening and darkening of the Earth to various extents and periods. Global darkening of the atmosphere by fine volcaniclasts decreased photosynthetic activity of the symbiotic zooxanthellae in extreme cases these useless symbionts were digested by their host. The dwarfing of the latest Maastrichtian microfossils was artificially demonstrated by the elimination of these symbiotic dinoflagellates from within the planktonic foraminifer *Globigerinoides sacculifer* (Bé et al., 1982). The loss of symbionts resulted in early gametogenesis (at small size), short life span of the foraminifer and its smaller shell size at sexual maturity (dwarfing), exactly as described from the latest Maastrichtian planktonic foraminifera and calcareous nannoplankton. When the tested live foraminifers were reinfected by zooxanthellae they resumed normal shell growth and size as before the removal of the symbiotic zooxanthellae (Bé et al., 1982). The lack of planktonic microfossils of normal size in the latest Maastrichtian 0.5 Myr indicates that solar radiation was, during this period, too low to resume symbiotic relationships between these photosynthesizing dinoflagellates and the microorganisms. The drastically reduced photosynthetic activity lowered the oxygen content in the upper water column as attested to by the increased abundance of low-oxygen-tolerant small heterohelicids and the blooming of the disaster

the middle of the paleomagnetic chron 29r (Keller et al., 2009, fig. 5). Oxygen isotope analyses (Li & Keller, 1998) and the response of microorganisms to water temperature (e.g., Abramovich & Keller, 2002; Abramovich et al., 2010) reflect fluctuations in surface and intermediate depth ocean water temperature during the Late Maastrichtian, especially in the latest 0.5 Myr. Fluctuating cool temperatures (average degrees of 9.90 C intermediate and 15.40 C surface water) during 66.85 and 65.52 Myr were followed by a short-term warming between 65.45 and 65.11 Myr which increased intermediate water temperatures by 2-30 C, and decreased the vertical thermal gradient to an average of 2.70 C (Li & Keller, 1998). A previous study by Stüben et al. (2003) on hemipelagic sediments of Tunisia differentiated three cool periods (65.50-65.55, 65.26-65.33, 65.04-65.12 Myr) and three warm periods (65.33-65.38, 65.12-65.26, 65.00-65.04 Myr). Tantawy et al. (2009, p. 85) point out that "**the biotic effects of volcanism have long been the unknown factors in creating biotic stress. The contribution of the Deccan volcanism to the K-T mass extinction remained largely unknown, although recent investigations revealed that the main phase of Deccan volcanism coincided with the K-T mass extinction**". Keller et al. (2009, p. 723-4) refer to "**the dust clouds obscuring sunlight and causing short-term global cooling"** as the result of the volcanic eruptions, but **"how Deccan volcanism affected the environment and how it may have led to the mass extinction of dinosaurs and other organisms in India and globally is still speculative**". The direct cause for seawater temperature fluctuations during the Maastrichtian last half million years and the dwarfed microfossils in this time interval (Keller, 2008) are herein related to sunlight screening by volcaniclastic dust from the Deccan volcanism, suggesting that its main activity extended

Most Late Maastrichtian planktonic microorganisms reduced their size (dwarfing) at about 65.4 Myr (Keller, 2008) reaching sexual maturity at smaller dimensions and probably more rapid than their normally-sized ancestors. This assemblage became dominated by lowoxygen-tolerant small heterohelicids (Keller & Abramovich, 2009). All these globally detected abnormal morphological and ecological aspects of the latest Cretaceous marine microfossils attest to the deterioration of the ecological conditions, as the result of sunlight screening and darkening of the Earth to various extents and periods. Global darkening of the atmosphere by fine volcaniclasts decreased photosynthetic activity of the symbiotic zooxanthellae in extreme cases these useless symbionts were digested by their host. The dwarfing of the latest Maastrichtian microfossils was artificially demonstrated by the elimination of these symbiotic dinoflagellates from within the planktonic foraminifer *Globigerinoides sacculifer* (Bé et al., 1982). The loss of symbionts resulted in early gametogenesis (at small size), short life span of the foraminifer and its smaller shell size at sexual maturity (dwarfing), exactly as described from the latest Maastrichtian planktonic foraminifera and calcareous nannoplankton. When the tested live foraminifers were reinfected by zooxanthellae they resumed normal shell growth and size as before the removal of the symbiotic zooxanthellae (Bé et al., 1982). The lack of planktonic microfossils of normal size in the latest Maastrichtian 0.5 Myr indicates that solar radiation was, during this period, too low to resume symbiotic relationships between these photosynthesizing dinoflagellates and the microorganisms. The drastically reduced photosynthetic activity lowered the oxygen content in the upper water column as attested to by the increased abundance of low-oxygen-tolerant small heterohelicids and the blooming of the disaster

over the same period.

opportunist *Guembelitria* (Keller & Abramovich, 2009). The shading of sunlight reduced the depth of the marine euphotic zone and affected water temperature. A thick cover by ash dust probably lowered sea-water and Earth surface temperature. A less dense screen may have resulted in a greenhouse effect, keeping the warmth of partially penetrating sun-light from escaping into the atmosphere. Paleotemperature fluctuations in the latest Maastrictian marine environments (Li & Keller, 1998) are accordingly related to fluctuations in the intensity of the Deccan volcanic eruptions and world-wide dispersal of volcaniclasts.

The polyps of reef-building hermatypic **corals** house symbiotic zooxanthellae which are involved in the precipitation of the calcareous skeleton and in other physiological processes, but they can also become part of the coelenterate diet. Unlike in planktonic foraminifera these symbiotic relationships observed on extant corals in the Great Barrier Reef of Australia can be stopped for a while (coral bleaching) during which coral growth slows down while the polyps feed on other algae and microorganisms, organic debris and bacteria (Vernon, 1993). This may explain the survival of some hermatypic coral groups, though a great deal disappeared during the latest Cretaceous (MacLeod et al., 1997).

The fatal influence of the Deccan volcanism on the latest Cretaceous marine planktonic microorganisms applies to marine and large terrestrial creatures as well. The darkening of the atmosphere by dispersed volcaniclasts blurred the distinction between the annual seasons controlling plant growth and blooming, as well as the biological clock of animal reproduction, which provides a significant food source to carnivores after months of near starvation. Seasonality controls the timing of sperm and egg spawning into the water, most of which is consumed by predators awaiting this process. Mating and reproduction among larger animals is coordinated with availability of food supply (plant and meat) which will assure the survival and development of the young. Long-ranging darkness confused the instincts and physiology of animals, reducing birth rate and hence food supply crucial for predating mammals to feed the young as well as for reptiles and birds. The reduction in birth rate (including the laying of eggs) immediately reduced the food supply. The aggressive and large predators (mainly among the reptiles) were forced to consume part of the prey that smaller predators used to eat, whereby the 'normal' food chain collapsed, resulting in the intensive predation of the temporarily unprotected ones. These were dinosaurs and pterosaurs eggs in nests on flat-land and their hatched young, as well as mature ones sitting on the eggs, and other creatures which for a moment were careless. The over-predation of this easy prey reduced the size of the victim's population which gradually diminished until the remaining ones could not preserve the species, leading to extinction. The organisms which were not affected by the collapse of the food chain were small creatures which could escape and hide themselves or their brood such as crocodiles and turtles which covered their brood, birds which laid the small eggs in between plants, small mammals which could hide underground or among bushes, and amphibians and fishes capable of hiding in aquatic environments. The darkening effect of the Deccan volcaniclasts must have slowed down the metabolism of cold-blooded reptiles, among which were probably some large dinosaurs. During the severe darkening they were completely unable to defend themselves even from small predators. The selective elimination of the temporary vulnerable ones is an extreme example of **natural selection** as the result of catastrophic changes in the regular pattern of the long-operating ecological system in which organisms and plants lived in harmony. The additional destructive effect of a single or multiple

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

the Deccan volcanic activity.

**9. Acknowledgment** 

Breeding Across the K-T Boundary Clarifying the Process of the End-Cretaceous Biological Crisis 333

starvation. Food shortage resulted in intensive predation of those which could not escape or hide. The comparison between Campanian and Lower Eocene crustacean burrow systems into pelagic chalk suggest a change in the mode of breeding from a few large eggs specially treated (K-type) to numerous tiny eggs partly cared for (transition to r-type) to compensate their over-predation. Dinosaur and pterosaur eggs were laid in nests on open-flat land such as estuaries, tidal-flats and shores. They and the few successfully hatched young provided easy prey, most probably to carnivorous reptiles living in the same region. The hatched young of marine reptiles were vulnerable to predation by other reptiles, sharks and large fishes. Reptiles which hid their brood (e.g., crocodiles, sea-turtles), birds laying their small eggs among plants or at sites inaccessible to non-flying organisms were not much affected by the predatory stress. The small mammals of that time could hide in the underground and in hidden places where they survived the predatory threat. The detected fluctuations in seawater temperature during the Cretaceous last half million years (Li & Keller, 1998) resulted from variations in the amount of volcaniclastic dust released into the atmosphere by the Deccan volcanic eruptions of different intensities and duration. A thick, long-lasting dust screen blocked the solar radiation resulting in the cooling of Earth's surface land and ocean-water. A thin volcaniclastic screen created a 'greenhouse' effect raising the temperature on the Earth. The associated darkening reduced the metabolism and the activity of cold-blooded reptiles, whereby the large ones living on land could not withstand even small predators. Darkening reduced and stopped the photosynthetic activity of the symbiotic zooxanthellae in planktonic foraminifera and calcareous nannoplankton lowering the oxygen content in the reduced euphotic zone as reflected by an increase in abundance of microorganisms tolerating low-oxygen conditions (e.g., Tantawy et al., 2009). The lack of these symbionts lowered the rate of calcium-carbonate precipitation, as attested to by smaller test sizes (dwarfing) of the planktonic microfossils during the last 0.5 Myr before the K-T boundary (Keller, 2008), being demonstrated in laboratory experiments on extant planktonic foraminifera (Bé et al., 1982). The recovery of most of the Late Mesozoic life forms during the Early Cenozoic suggests that all those organisms and plants survived predation thanks to their capabilities as well as by retreating to restricted and protected habitats. There they adapted to the local and changing ecological settings during a few million years until most of them succeeded in returning to the open-large marine and terrestrial habitats. Thereby they re-appeared in the fossil record, some in a new shape as the result of adaptation to changing settings during a few million years of the recovery of Earth's ecological systems. All of these seemingly new taxa took part in continuous evolutionary lineages ranging across the K-T boundary and during the aftermath of the biological crisis. They passed most of this period in hitherto undiscovered sites and therefore these intermediate evolutionary stages do not appear in the fossil record. The resulting different nomenclature of taxa between the Late Cretaceous and the Early Tertiary was erroneously referred to the mass extinction of the Cretaceous species. The end-Cretaceous biological crisis was actually an extreme example of natural selection caused by

We thank Michail Kitin (GSI) for the technical assistance in the field and in the laboratory; to

Chana Netzer-Cohen and Nili Almog (GSI) for their graphic work.

asteroid impacts (Keller, 2008) would have had little contribution to the gradual collapse of Earth's biological systems.

#### **7. Early Tertiary biological recovery**

The Deccan volcanic activity extended into the lowermost Tertiary (~64.8 Myr; Keller et al., 2009) and thereafter the climatic and ecologic systems began their recovery. Small globigerinid planktonic foraminifera survived the end-Cretaceous biological crisis and appear in Early Paleocene sediments (e.g., Orue-Etxebarria & Apellaniz, 2000). Their trochospiral coiling with 4-7 nearly globular chambers in the last whorl resembles other associated species such as *Parvularugoglobigerina eugobina* (Olsson et al., 1999). This general shape and size has neen observed in one of the earliest planktonic foraminifer from the Lower Jurassic (probably Hettangian; ca 190 Myr) of Hungary (Görög, 1994). Keeled planktonic foraminifera appeared in the Upper Albian (*Rotalipora*) about 90 Myr later (Leckie, 1987). The earliest Tertiary keeled (pseudo-keel) planktonic foraminifer [e.g., *Morozovella angulata* (White)] appeared at the base of the Upper Paleocene (Thanatian, 58.7 Myr) about 6 million years after the recovery of the ocean ecological setting. This rapid introduction of the keel structure among pelagic foraminifera suggests that the survivors in a 'primitive' appearance preserved in their genome the ability to secrete keels and other morphologies under suitable conditions. The relative quick recovery of planktonic microorganisms after their near elimination at the end of the Cretaceous Period explains the similar recovery of life in the marine and terrestrial bioprovinces. Many Late Cretaceous species retreated to small-restricted niches protected from the side effects of the Deccan volcanism. They continued living in these numerous small habitats, adapting to the restricted ecological settings and thereby gradually changing their physiology, anatomy and the skeleton. With the recovery and stabilization of the ecological systems all of these 'hidden' communities tried to enter and adapt to the physical and chemical conditions of the open-large habitats and share the environment with other communities. Only those which succeeded to accommodate themselves in these extensive bioprovinces in large populations were discovered. The fossil record of all earlier small communities which lived in restricted areas is still missing, giving a misleading impression of a big hiatus in taxa ranges and sudden first appearance of new ones. These are actually members of evolutionary lineages, of which the earliest Paleocene ancestors have not been yet discovered. This all took part in an evolutionary biological continuum from the latest Mesozoic into the Cenozoic. The technical comparison of taxa names between these eras intensified the apparent catastrophic aspect of the end-Cretaceous biological crisis, being erroneously referred to a **mass extinction**.

#### **8. Conclusion**

Sunlight screening by volcaniclast dust from the Deccan volcanic eruptions blurred the distinction between annual seasons and disordered the biological clock of organisms and plants on land and in the sea. Flowering plants produced less fruits for vegetarians, reducing their birth-rate. The disturbed sexual cycle of carnivores likewise lowered their birth rate and drastically reduced the amount of food (eggs and young born), on which adults depended for feeding themselves and their young ones after a long period of near starvation. Food shortage resulted in intensive predation of those which could not escape or hide. The comparison between Campanian and Lower Eocene crustacean burrow systems into pelagic chalk suggest a change in the mode of breeding from a few large eggs specially treated (K-type) to numerous tiny eggs partly cared for (transition to r-type) to compensate their over-predation. Dinosaur and pterosaur eggs were laid in nests on open-flat land such as estuaries, tidal-flats and shores. They and the few successfully hatched young provided easy prey, most probably to carnivorous reptiles living in the same region. The hatched young of marine reptiles were vulnerable to predation by other reptiles, sharks and large fishes. Reptiles which hid their brood (e.g., crocodiles, sea-turtles), birds laying their small eggs among plants or at sites inaccessible to non-flying organisms were not much affected by the predatory stress. The small mammals of that time could hide in the underground and in hidden places where they survived the predatory threat. The detected fluctuations in seawater temperature during the Cretaceous last half million years (Li & Keller, 1998) resulted from variations in the amount of volcaniclastic dust released into the atmosphere by the Deccan volcanic eruptions of different intensities and duration. A thick, long-lasting dust screen blocked the solar radiation resulting in the cooling of Earth's surface land and ocean-water. A thin volcaniclastic screen created a 'greenhouse' effect raising the temperature on the Earth. The associated darkening reduced the metabolism and the activity of cold-blooded reptiles, whereby the large ones living on land could not withstand even small predators. Darkening reduced and stopped the photosynthetic activity of the symbiotic zooxanthellae in planktonic foraminifera and calcareous nannoplankton lowering the oxygen content in the reduced euphotic zone as reflected by an increase in abundance of microorganisms tolerating low-oxygen conditions (e.g., Tantawy et al., 2009). The lack of these symbionts lowered the rate of calcium-carbonate precipitation, as attested to by smaller test sizes (dwarfing) of the planktonic microfossils during the last 0.5 Myr before the K-T boundary (Keller, 2008), being demonstrated in laboratory experiments on extant planktonic foraminifera (Bé et al., 1982). The recovery of most of the Late Mesozoic life forms during the Early Cenozoic suggests that all those organisms and plants survived predation thanks to their capabilities as well as by retreating to restricted and protected habitats. There they adapted to the local and changing ecological settings during a few million years until most of them succeeded in returning to the open-large marine and terrestrial habitats. Thereby they re-appeared in the fossil record, some in a new shape as the result of adaptation to changing settings during a few million years of the recovery of Earth's ecological systems. All of these seemingly new taxa took part in continuous evolutionary lineages ranging across the K-T boundary and during the aftermath of the biological crisis. They passed most of this period in hitherto undiscovered sites and therefore these intermediate evolutionary stages do not appear in the fossil record. The resulting different nomenclature of taxa between the Late Cretaceous and the Early Tertiary was erroneously referred to the mass extinction of the Cretaceous species. The end-Cretaceous biological crisis was actually an extreme example of natural selection caused by the Deccan volcanic activity.

#### **9. Acknowledgment**

332 Advances in Data, Methods, Models and Their Applications in Geoscience

asteroid impacts (Keller, 2008) would have had little contribution to the gradual collapse of

The Deccan volcanic activity extended into the lowermost Tertiary (~64.8 Myr; Keller et al., 2009) and thereafter the climatic and ecologic systems began their recovery. Small globigerinid planktonic foraminifera survived the end-Cretaceous biological crisis and appear in Early Paleocene sediments (e.g., Orue-Etxebarria & Apellaniz, 2000). Their trochospiral coiling with 4-7 nearly globular chambers in the last whorl resembles other associated species such as *Parvularugoglobigerina eugobina* (Olsson et al., 1999). This general shape and size has neen observed in one of the earliest planktonic foraminifer from the Lower Jurassic (probably Hettangian; ca 190 Myr) of Hungary (Görög, 1994). Keeled planktonic foraminifera appeared in the Upper Albian (*Rotalipora*) about 90 Myr later (Leckie, 1987). The earliest Tertiary keeled (pseudo-keel) planktonic foraminifer [e.g., *Morozovella angulata* (White)] appeared at the base of the Upper Paleocene (Thanatian, 58.7 Myr) about 6 million years after the recovery of the ocean ecological setting. This rapid introduction of the keel structure among pelagic foraminifera suggests that the survivors in a 'primitive' appearance preserved in their genome the ability to secrete keels and other morphologies under suitable conditions. The relative quick recovery of planktonic microorganisms after their near elimination at the end of the Cretaceous Period explains the similar recovery of life in the marine and terrestrial bioprovinces. Many Late Cretaceous species retreated to small-restricted niches protected from the side effects of the Deccan volcanism. They continued living in these numerous small habitats, adapting to the restricted ecological settings and thereby gradually changing their physiology, anatomy and the skeleton. With the recovery and stabilization of the ecological systems all of these 'hidden' communities tried to enter and adapt to the physical and chemical conditions of the open-large habitats and share the environment with other communities. Only those which succeeded to accommodate themselves in these extensive bioprovinces in large populations were discovered. The fossil record of all earlier small communities which lived in restricted areas is still missing, giving a misleading impression of a big hiatus in taxa ranges and sudden first appearance of new ones. These are actually members of evolutionary lineages, of which the earliest Paleocene ancestors have not been yet discovered. This all took part in an evolutionary biological continuum from the latest Mesozoic into the Cenozoic. The technical comparison of taxa names between these eras intensified the apparent catastrophic aspect of the end-Cretaceous biological crisis, being erroneously referred to a **mass** 

Sunlight screening by volcaniclast dust from the Deccan volcanic eruptions blurred the distinction between annual seasons and disordered the biological clock of organisms and plants on land and in the sea. Flowering plants produced less fruits for vegetarians, reducing their birth-rate. The disturbed sexual cycle of carnivores likewise lowered their birth rate and drastically reduced the amount of food (eggs and young born), on which adults depended for feeding themselves and their young ones after a long period of near

Earth's biological systems.

**extinction**.

**8. Conclusion** 

**7. Early Tertiary biological recovery** 

We thank Michail Kitin (GSI) for the technical assistance in the field and in the laboratory; to Chana Netzer-Cohen and Nili Almog (GSI) for their graphic work.

Lower Eocene Crustacean Burrows (Israel) Reflect a Change from K- to r-Type Mode of

25, pp. 45-50, ISSN 0021-2164

No. 1, pp. 63-69, ISSN 0022-3360

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26, pp. 995-998, ISSN 0091-7613

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## *Edited by Dongmei Chen*

With growing attention on global environmental and climate change, geoscience has experienced rapid change and development in the last three decades. Many new data, methods and modeling techniques have been developed and applied in various aspects of geoscience. The chapters collected in this book present an excellent profile of the current state of various data, analysis methods and modeling techniques, and demonstrate their applications from hydrology, geology and paleogeomorphology, to geophysics, environmental and climate change. The wide range methods and techniques covered in the book include information systems and technology, global position system (GPS), digital sediment core image analysis, fuzzy set theory for hydrology, spatial interpolation, spectral analysis of geophysical data, GIS-based hydrological models, high resolution geological models, 3D sedimentology, change detection from remote sensing, etc. Besides two comprehensive review articles, most chapters focus on in-depth studies of a particular method or technique.

Advances in Data,

Methods, Models and Their

Applications in Geoscience

*Edited by Dongmei Chen*

ISBN 978-953-307-737-6

ISBN 978-953-51-4385-7

Advances in Data, Methods, Models and Their Applications in Geoscience

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