Impact of Climate Change on Natural Resource Management (eBook)

The following authors contributed with their articles: Prof. Dr. Asis Mazumdar (BME, MME, PhD (Jadavpur University (JU)) who is the Director of School of Water Resources Engineering and Co-ordinator of Regional Center, National Afforestation and Eco-development Board; Dr. Debasri Roy (PhD (Calcutta University)) is a Reader of the School of Water Resources Engineering; Dr. Pankaj Kr. Roy (BCE (National Institute of Technology, Silchar), ME, PhD(JU)), Dr. Rajib Das (BE (Utkal University), ME (JU), PhD (Pisa)) are the Lecturers and Mr. Sanjib Das (BE (JU) is the Technical Assistant of the School of Water Resources Engineering; Mr. Arnab Barua (BEE (West Bengal University of Technology (WBUT)), ME(JU), Mr. Biswajit Majumder (BE (National Institute of Technology, Agartala (NITA), ME(JU)), Miss Debapriya Basu (BEE (WBUT), ME (JU) and Mr. Sabyasachi Pramanik (BChE (WBUT), ME (JU)) are the former Master of Engineering students of School of Water Resources Engineering; Mr. Rabindra Nath Barman (BME, ME, PhD (submitted) (JU)) is the Assistant Professor of NITA; Mr.Sashi Sonkar and Miss Suchita Dutta are the Junior Research fellow and Research Assistant of Regional Center, National Afforestation and Eco-development Board whereas Mr. Chinmoy Boral is presently doing his PhD at the School of Water Resources Engineering, Jadavpur University.The book was compiled by Mr. Bipal K. Jana (MSc (CU), PhD (submitted) (JU)) and Dr. Mrinmoy Majumder (BEE (Biju Patnaik University of Technology), ME, PhD (JU)).Bipal Jana is Senior Research Fellow in the School of Water Resources Engineering. He has more than 17 years of experience in the fields of environmental engineering and management. He has completed his bachelor and master degrees in Science at Calcutta University and MBA (P G D Environmental Management) at the Indian Institute of Social Welfare and Business Management(IISWBM). He has authored over 10 papers in national and international publications.Mrinmoy Majumder was Senior Research Fellow in the School of Water Resources Engineering, Jadavpur University, Kolkata. He has obtained his bachelor in Electrical Engineering from Utkal University and his master degree in Water Resources from Jadavpur University. He is the author of 2 books and more than 10 publications in national and international journals.

Editors 5
Preface 6
Acknowledgments 10
Contents 12
Chapter 1: Estimation of Carbon Dioxide Emission Contributing GHG Level in Ambient Air of a Metro City: A Case Study for Kolkata 17
1.1 Introduction 18
1.2 The Study Area 19
1.3 CO2 Emission from Vehicular Exhaust 20
1.4 CO2 Emission from Small-Scale Industries in KMA 24
1.5 CO2 Emission from Human Respiration 25
1.5.1 World Population Changes over Time 25
1.5.2 Regional Population Growth 26
1.5.3 CO2 Emission from Human Respiration 28
1.6 CO2 Emission from Soil Respiration 29
1.6.1 Soil Quality 29
1.6.2 CO2 Emission for Soil Respiration 30
1.7 Conclusion 31
References 32
Chapter 2: Impact of Climate Change on the Availability of Virtual Water Estimated with the Help of Distributed Neurogenetic Models 33
2.1 Introduction 34
2.1.1 Impact of Global Warming 34
2.1.2 Climate Models 35
2.1.3 Coupled Climatic and Hydrologic Models 36
2.1.4 Application of Computer Models in Hydrology 36
2.1.4.1 Application of Artificial Neural Network in Hydrology 36
2.1.4.2 Virtual Water and Its Impacts 37
2.1.4.3 Limitations of Virtual Water Concept 37
2.1.5 Objective and Scope 38
2.1.6 Study Area 38
2.2 Methodology 38
2.2.1 Neurogenetic Spatially Distributed Rainfall–Runoff Model (NSRRM) 39
2.2.1.1 Model Variables of NSRRM 39
Determination of Peak Average Monthly Rainfall and Runoff 39
Determination of Basin Loss Coefficient 40
Determination of Channel Loss Coefficient 40
Determination of Loss Coefficient 40
Calculation of Time of Concentration 40
2.2.1.2 Model Development 41
2.2.2 Categorized Neurogenetic Spatially Distributed Rainfall–Runoff Model (CNSRRM) 41
2.2.2.1 Model Variable 41
2.2.3 Model Validation and Uncertainty Analysis 44
2.3 Result and Discussion 45
2.4 Conclusion 55
References 55
Chapter 3: Use of Forest Index or PLANOBAY in Estimation of Water Availability Due to Climate Change 58
3.1 Introduction 59
3.1.1 Forest Hydrology 59
3.1.1.1 Forests and the Hydrologic Cycle 59
3.1.1.2 Water–Forest Interactions in Riparian Catchments 60
3.1.1.3 Aquatic Biodiversity 60
3.1.1.4 Forest Management and Watershed Quality 60
3.1.1.5 Impact of Forest on Basin Runoff 61
3.1.2 Hydrologic Models 61
3.1.3 Neurogenetic Models 61
3.1.4 Objective and Scope 64
3.1.5 Study Area 64
3.2 Methodology 64
3.2.1 Model Variables 65
3.2.1.1 Determination of VAIn 65
3.2.2 Data Collection 66
3.2.3 Development of Neurogenetic Models 67
3.2.4 Model Validation and Uncertainty Analysis 68
3.2.5 Estimation of Climate Change and Water Availability 70
3.2.5.1 Determination of Basin Loss Coefficient 71
3.2.5.2 Determination of Channel Loss Coefficient 71
3.2.5.3 Determination of Loss Coefficient 71
3.3 Result and Discussion 72
3.3.1 Validation of PLANOBAY Model 72
3.4 Conclusion 80
References 81
Chapter 4: Application of Parity Classified Neurogenetic Models to Analyze the Impact of Climatic Uncertainty on Water Footprint 83
4.1 Introduction 84
4.1.1 Water Footprint 84
4.1.2 Climatic Uncertainty 85
4.1.3 Climate Change and Climate Models 86
4.1.4 Hydrologic Models 86
4.1.5 Neurogenetic Models 86
4.1.6 Objective and Scope 86
4.1.7 Study Area 87
4.2 Methodology 87
4.2.1 Estimation of Stream Flow with the Help of PARITYCGD Model 87
4.2.1.1 Model Variables 87
Determination of PCI 87
Determination of Basin Runoff Index 88
Model Development 88
4.2.2 Estimation of Stream Flow with the Help of Neurogenetic Model for Estimation of Basin Hydrograph (NGHYD) 89
4.2.2.1 Model Variables 90
Data Collection 91
Determination of Incremental Rainfall Abstraction 91
Determination of Incremental Basin Runoff 92
4.2.2.2 Model Development 92
4.2.3 Calculation of Water Footprint of a Region 92
4.2.4 Model Validation 95
4.3 Result and Discussion 95
4.3.1 Model Validation 95
4.3.2 Impact of Climate Change on Basin Runoff 98
4.3.3 Estimation of Future Water Footprint 100
4.3.4 Impact of Climatic Uncertainty 100
4.4 Conclusion 103
References 104
Chapter 5: Impact of Climatic Uncertainty on Water Sequestration of a Subtropical River Basin 105
5.1 Introduction 106
5.1.1 Hydrological Cycle 106
5.1.2 Water Sequestration 106
5.1.3 Influencing Factors 107
5.1.3.1 Infiltration Capacity 107
5.1.3.2 Soil Texture 107
5.1.3.3 Soil Erosion 107
5.1.3.4 Plant Population 108
5.1.4 Literature Review 108
5.1.5 Artificial Neural Network and Genetic Algorithm 109
5.1.6 GIS and Hydrology 109
5.1.7 Climate Change and Climate Models 109
5.1.8 Objective and Scope 109
5.1.9 Study Area 110
5.2 Methodology 110
5.2.1 Conceptual Coupled Neurogenetic Distributed Hydrologic Model (CONCONGDHM) 110
5.2.1.1 Model Variables 112
Determination of Estimated Basin Runoff from the Conceptual Models 112
5.2.1.2 Development of Neurogenetic Models 112
5.2.1.3 Estimation of Basin Runoff 113
5.2.2 PARITYCGD Model 113
5.2.3 Model Validation 113
5.2.4 Calculation of Water Sequestration 115
5.3 Result and Discussion 115
5.3.1 Model Validation 115
5.3.2 Estimation of Future WSC of the Rivers 117
5.4 Conclusion 121
References 122
Chapter 6: Estimating Spatial Variation of River Discharge in Face of Desertification Induced Uncertainty 123
6.1 Introduction 124
6.1.1 Hydrologic Models 124
6.1.1.1 Rational OC and MODRAT Hydrologic Model 124
6.1.2 Combined Modeling System 125
6.1.3 Desertification 126
6.1.3.1 Causes of Desertification 127
6.1.4 Objective and Scope 128
6.1.5 Study Area 129
6.2 Methodology 129
6.2.1 Data Collection 130
6.2.2 Model Development 130
6.2.3 Model Validation 132
6.2.4 Development of Desertification-Induced Model Uncertainty Scenario 132
6.3 Result and Discussion 133
6.3.1 Model validation 133
6.3.2 Estimation of Desertification Induced Basin Runoff 134
6.4 Conclusion 141
References 142
Chapter 7: Determination of Urbanization Impact on Rain Water Quality with the Help of Water Quality Index and Urbanization Index 143
7.1 Introduction 144
7.1.1 Rain Water Harvesting: Justification and Utility 144
7.1.2 Water Quality and Pollution 145
7.1.3 Urbanization 146
7.1.4 Urbanization of Kolkata 146
7.1.5 Literature Review 147
7.1.5.1 Objective and Scope 148
7.1.5.2 Study Area 148
7.2 Methods and Materials 149
7.2.1 Sampling Stations and Sample Collection 149
7.2.2 Laboratory Analysis of Samples 150
7.2.3 Estimation of Water Quality Index of the Sampling Location 150
7.2.4 Estimation of Urbanization Index 150
7.2.5 Results and Discussion 152
7.3 Conclusion 153
References 154
Chapter 8: Identification of Water-Stressed Regions of Two Tropical and Subtropical River Basins with the Help of Representative Elementary Area Concept and Neurogenetic Models 155
8.1 Introduction 156
8.1.1 Hydrologic Models 157
8.1.2 Study Area 158
8.2 Methodology 158
8.2.1 Representative Elementary Area Hydrologic Model 158
8.2.1.1 Model Variables 158
Determination of Representative Elementary Area and Representative Elementary Runoff 159
8.2.1.2 Model Development 159
8.2.2 Development of Neurogenetic Model for Estimation of Basin Hydrograph (NGHYD) 160
8.2.2.1 Model Variables 161
8.2.3 Development of the Continuously Distributed Hydrologic Model 162
8.2.4 Development of the Discretely Distributed Hydrologic Model 163
8.3 Result and Discussion 164
8.3.1 Model Validation 164
8.3.2 Estimation of Future Water Stress 167
8.4 Conclusion 172
References 172
Chapter 9: Estimation of the Spatial Variation of Stream Flow by Neural Models and Surface Algorithms 173
9.1 Introduction 174
9.1.1 Lumped and Distributed Hydrologic Models 174
9.1.1.1 Deterministic Hydrologic Models 174
9.1.1.2 Models Based on Data or Stochastic Hydrologic Models 175
9.1.2 Application of Artificial Neural Network in Hydrology 175
9.1.3 Climate Models 176
9.1.4 Surface Algorithms 177
9.1.5 Objective and Scope 177
9.1.6 Study Area 177
9.2 Methodology 178
9.2.1 Conceptual Coupled Neurogenetic Distributed Hydrologic Model (CONCONGDHM) 178
9.2.2 Neurogenetic Spatially Distributed Rainfall–Runoff Model 178
9.2.3 Categorized Neurogenetic Spatially Distributed Rainfall-Runoff Model 179
9.2.4 Neurogenetic Model for Estimation of Basin Hydrograph (NGHYD) 179
9.2.5 Plantation-Prioritized Basin Yield Estimation Hydrologic Model 180
9.2.6 Representative Elementary Area Hydrologic Model 180
9.2.7 PARITY Neurogenetic Distributed Hydrologic Model 181
9.2.8 Model Validation 181
9.2.9 Estimation of Spatial Variability 182
9.3 Result and Discussion 182
9.3.1 Model Validation 182
9.3.2 Estimation of Future Stream flow 183
9.4 Conclusion 191
References 192
Chapter 10: Estimation of the Spatial Variation of Water Quality by Neural Models and Surface Algorithms 195
10.1 Introduction 196
10.1.1 Important Water Quality Parameters 196
10.1.1.1 Biochemical Oxygen Demand 196
10.1.1.2 Dissolved Oxygen 197
10.1.1.3 pH 197
10.1.1.4 Turbidity 197
10.1.1.5 Total Dissolved Solids 198
10.1.1.6 Total and Fecal Coliform 198
10.1.1.7 Hardness 199
10.1.1.8 Dissolved Organic Compounds 199
10.1.1.9 Temperature 199
10.1.2 Different Types of Water 200
10.1.2.1 Utility Water 200
10.1.2.2 Softened Water 200
10.1.2.3 Drinking Water 200
10.1.2.4 Steam Condensate 200
10.1.2.5 Waste Water 200
10.1.3 Water Quality Standards 201
10.1.4 Water Quality/Pollutant Estimation Models 201
10.1.4.1 Biofilm Model 201
10.1.4.2 Large-Eddy Simulation with a One-Equation Subgrid-Scale 202
10.1.4.3 QUAL2K Model 202
10.1.4.4 WaterGEMS 202
10.1.4.5 Hydrological Simulation Program–Fortran 203
10.1.4.6 AQUASEA Finite Element Model 203
10.1.4.7 XPSWMM or XP-SWMM Flood Hydrology and Hydraulic Modeling Software 203
10.2 Methodology 203
10.2.1 Calculation of Weighted Average of Water Quality 204
10.2.2 Development of the Neurogenetic Models 204
10.2.3 Estimation of Spatial Variability 204
10.3 Result and Discussion 205
10.3.1 Model Validation 205
10.3.2 Estimation of Future Quality of River Runoff 205
10.4 Conclusion 212
References 213
Chapter 11: Estimation of the Spatial Variation of Pollution Load by Neural Models and Surface Algorithms 214
11.1 Introduction 215
11.1.2 Types of Water Pollution 217
11.1.2.1 Point Source Pollution 217
11.1.2.2 Nonpoint Source Pollution 217
11.1.2.3 Groundwater Pollution 217
11.1.3 Control of Water Pollution 218
11.1.3.1 Industrial Waste Water 218
11.1.4 Water Pollution in India 219
11.2 Models for Water Pollutions 220
11.2.1 Methodology 220
11.3 Result and Discussion 221
11.3.1 Model Validation 221
11.3.2 Estimation of Future Industrial Pollution of River Runoff 221
11.3.3 Estimation of Future Organic Pollution of River Runoff 227
11.4 Conclusion 236
References 236
Chapter 12: Impact of Stressed Climatic Condition on a Small Tropical Tributary 238
12.1 Introduction 239
12.1.1 Location 239
12.1.1.1 River System 240
12.2 Data Description 240
12.3 Methodology 240
12.3.1 Artificial Neural Network 240
12.3.1.1 Network Building Procedure 243
Selection of Network Topology 243
Training Phase 244
Testing Phase 244
12.3.2 Evaluation of the Network 245
12.4 Result and Discussion 246
12.4.1 Model Development 246
12.4.2 Overstressed Conditions 246
12.4.3 1Stressed Condition 248
12.4.4 Response for Overstressed Condition 248
12.4.5 Response for Stressed Condition 248
12.5 Conclusion 257
References 257
Chapter 13: Determination of Evapotranspiration from Stream Flow with the Help of Classified Neurogenetic Model 258
13.1 Introduction 259
13.2 Study Area 260
13.2.1 Description of the River Basin 260
13.2.2 Climate of the River Basin 261
13.2.3 Geomorphology 261
13.2.4 Control Structures 261
13.3 Data Description 262
13.4 Methodology 264
13.4.1 Artificial Neural Network 264
13.4.1.1 Network Building Procedure 265
Selection of Network Topology 265
Training Phase 266
Testing Phase 266
13.4.2 HEC-HMS 267
13.4.3 Evaluation of the Networks 267
13.5 Result and Discussion 268
13.5.1 Model Development 268
13.5.1.1 Data Classification 268
13.5.1.2 Model Selection 270
13.6 Concluding Remarks 274
References 275
Chapter 14: Determination of Urban and Rural Monsoonal Evapotranspiration by Neurogenetic Models 278
14.1 Introduction 279
14.1.1 Artificial Neural Network 279
14.1.2 Least Square Method 280
141.3 Time Series Method 281
14.1.4 Mayer’s Formula 281
14.1.5 Model Validation 281
14.1.6 Study Area 282
14.2 Methodology 283
14.2.1 Development of CANN Model 283
14.2.2 Model Validation 283
14.2.3 Estimation of ET for Extreme Values 283
14.3 Result and Discussion 284
14.4 Conclusions 286
References 287
Chapter 15: Accumulation of Carbon Stock Through Plantation in Urban Area 291
15.1 Introduction 292
15.2 The Site and Study Area 293
15.3 Materials and Methods 295
15.3.1 Carbon Dioxide Measurement 295
15.3.2 Measurement of Aboveground Biomass of the Tree 296
15.3.3 Carbon Content in Accumulated Biomass 296
15.4 Results and Discussion 297
15.4.1 Ambient CO2 Level 297
15.4.2 Carbon Sequestration Rate (CSR) by A. lebbek and A. integrifolia 297
15.4.3 Biomass Carbon Content 299
15.4 Conclusion 302
References 302
Chapter 16: Conservation of Natural Resource with the Application of Carbon Sequestration and Carbon Economy 304
16.1 Climate Change 305
16.2 Contribution of Forests 306
16.2.1 Introduction 306
16.2.2 Forest Benefits and Their Valuation 307
16.2.2.1 Direct Benefits Associated with Consumptive Uses 309
16.2.2.2 Nonconsumptive Uses 309
16.2.2.3 Indirect Benefits Associated with 309
16.2.2.4 Option and Existence Values 309
16.2.2.5 Direct Economic Benefits from Forests 309
16.2.2.6 Indirect Economic Benefits from Forests 310
16.3 Joint Forest Management 310
16.4 Carbon Reduction and CDM 312
16.5 Carbon Sequestration and Carbon Economy 313
16.6 Conclusion 314
References 315
Chapter 17: Measurement of Diurnal Carbon Sequestration Rate and Aboveground Biomass Carbon Potential of Two Young Species and Soil Respiration in Two Natural Forests in India 318
17.1 Introduction 319
17.2 The Site and Study Area 321
17.3 Materials and Methods 322
17.3.1 Carbon Dioxide Measurement 322
17.3.2 Measurement of Aboveground Biomass of the Tree 323
17.3.3 Carbon Content in Aboveground Biomass 324
17.3.4 Soil Characteristics and Soil Respiration 325
17.4 Results and Discussion 325
17.4.1 Ambient CO2 Level 325
17.4.2 Soil Carbon and Soil Respiration 325
17.4.3 Carbon Sequestration Rate by S. robusta and T. grandis 329
17.4.4 Biomass Carbon Content 331
17.5 Conclusion 334
References 335
Chapter 18: Estimation of Soil Carbon Stock and Soil Respiration Rate of Recreational and Natural Forests in India 338
18.1 Introduction 339
18.2 Study Area 341
18.3 Materials and Methods 341
18.4 Results and Discussions 343
18.5 Conclusion 350
References 351
Chapter 19: Estimation of Reservoir Discharge with the Help of Clustered Neurogenetic Algorithm 353
19.1 Introduction 354
19.1.1 Artificial Neural Networks in Hydrology 354
19.1.2 Artificial Neural Networks in Stream-flow Forecasting 354
19.1.3 Description of the River Basin 355
19.1.3.1 Climate of the River Basin 356
19.1.3.2 Geomorphology 356
19.1.3.3 Control Structures 356
19.2 Data Description 357
19.3 Methodology 357
19.3.1 Artificial Neural Network 357
19.3.1.1 Selection of Network Topology 358
19.3.1.2 Training Phase 359
19.3.1.4 Testing Phase 359
19.3.2 Evaluation of the Network 360
19.4 Result and Discussion 360
19.4.1 Model Development 360
19.5 Conclusion 363
References 364
Chapter 20: Water Availability Analysis and Estimation of Optimal Power Generation for a Fixed Head Multi-Reservoir Hydropower Plant 367
20.1 Introduction 368
20.2 Literature Review 368
20.3 Study Area 369
20.4 Methodology 369
20.4.1 Estimation of Stream Flow 371
20.4.2 Determination of Water Availability 372
20.4.3 Generation of Load Duration Curve 372
20.4.4 Identification of Optimal Power Generation 372
20.5 Results and Discussion 373
20.5.1 Data Analysis 373
20.5.2 Estimated Discharge of the Study Area 375
20.5.3 Uncertainty Analysis and Identification of Optimal Power Generation Point 375
20.6 Conclusion 376
20.6.1 Limitations 377
20.6.2 Future Scope 377
References 379
Chapter 21: An Overview of Hydrologic Modeling 381
21.1 Hydrologic Models 382
21.2 Data Statistics and Modeling Watershed Change 382
21.3 Identification of Watershed Change 383
21.4 Time Series Modeling 384
21.5 Modeling the Spatial Variability 385
21.5.1 Spatially Sensitive Hydrological Models 386
21.5.2 Distributed Hydrologic Models 387
21.6 Spatiotemporal Hydrologic Models 389
21.6.1 Reduction of Spatial Nonlinearity 389
21.6.2 Reduction of Temporal Variability 390
21.6.3 Simplification of Process Volatility 390
21.7 Some Popular Hydrologic Modeling Systems 392
21.7.1 Hydrologic Engineering Centre: Hydrologic Modeling System (HEC-HMS) 392
21.7.1.1 Governing Equation 392
21.7.2 Trend Research Manual 55 (TR55) 393
21.7.2.1 Governing Equations for TR55 393
21.7.2.2 Required Input Parameters and Data 394
21.7.2.3 Advantages and Limitations 394
21.7.3 MODified RATional Hydrologic Model 394
21.7.3.1 Governing Equation 395
21.7.3.2 Required Input Parameters 395
21.7.3.3 Limitations 395
References 395
Chapter 22: A Generalized Overview of Artificial Neural Network and Genetic Algorithm 398
22.1 Artificial Neural Network (ANN) 399
22.2 Mathematical Representation of Artificial Neural Network 399
22.3 Network Building Procedure 401
22.3.1 Selection of Network Topology 401
22.3.2 Training Phase 402
22.3.3 Testing Phase 402
22.4 Structure of Neural Network 402
22.4.1 Feed-Forward Connections 402
22.4.2 Feedback Connections 403
22.4.3 Lateral Connections 403
22.4.4 Time-Delayed Connections 403
22.5 Classification of Neural Networks 403
22.5.1 Feedforward Neural Network 403
22.5.2 Radial Basis Function (RBF) Network 403
22.5.3 Kohonen Self-organizing Network 404
22.5.4 Recurrent Network (RN) 405
22.5.4.1 Simple Recurrent Network 405
22.5.4.2 Hopfield Network 405
22.5.4.3 Echo State Network 405
22.5.4.4 Long Short-Term Memory Network 405
22.5.5 Stochastic Neural Networks 406
22.5.5.1 Boltzmann Machine 406
22.5.6 Modular Neural Networks 406
22.5.6.1 Committee of Machines 406
22.5.6.2 Associative Neural Network (ASNN) 406
22.6 Activation Functions 407
22.6.1 Threshold Function (Step or Ramp) 407
22.6.2 Piecewise-Linear Function 407
22.6.3 Sigmoid Function 407
22.6.4 Linear Basis Function 408
22.6.5 Radial Basis Function 408
22.6.6 Gaussian Function 408
22.7 Learning Rules 408
22.7.1 Supervised Learning 409
22.7.2 Unsupervised Learning 409
22.7.3 Reinforcement Learning 410
22.8 Training Algorithms 410
22.8.1 Single or Multilayer Perceptron Learning Algorithm 410
22.8.2 Back Propagation Learning Algorithm 411
22.8.3 Gradient Descent Learning Algorithm 414
22.9 Genetic Algorithm 415
22.9.1 Concept of GA 415
22.9.2 Procedures to Solve Problems with GA 416
22.9.3 Outline of the Basic Genetic Algorithm 416
22.9.4 Limitations of GA 417
22.10 Application of ANN and GA in Hydrology 417
22.10.1 Application of ANN in Hydrologic Problems 417
22.10.2 Application of GA in Hydrologic Problems 417
References 418
Chapter 23: Introduction to Climate Change and Climate Models 421
23.1 Climate Change and Climate Models 421
23.2 Impact of Global Warming 422
23.3 Special Report on Emissions Scenarios 422
23.3.1 Purpose 422
23.3.2 Scenario Families 423
23.3.2.1 A1 Scenario 423
23.3.2.2 A2 Scenario 423
23.3.2.3 B1 Scenario 423
21.3.2.4 B2 Scenario 424
23.3.3 SRES Scenarios and Climate Change Initiatives 424
23.4 Climate Models 425
23.4.1 Global Climate Models 425
23.4.1.1 Structure of Global Climatic Models 426
23.4.1.2 Model Grids 427
23.4.1.3 Different Types of GCMs Based on Model Structure 428
One-Dimensional Models 428
One-Dimensional Radiative-Convective Atmospheric Models 428
One-Dimensional Upwelling-Diffusion Ocean Models 428
One-Dimensional Energy Balance Models 429
Two-Dimensional Atmosphere and Ocean Models 429
Three-Dimensional Atmosphere and Ocean General Circulation Models 429
23.4.1.4 Different Types of GCMs Based on Model Parameters 430
Models of Carbon Parameter 430
Models of Atmospheric Chemistry and Aerosols 432
Models of Ice Sheets 433
23.4.2 Regional Climate Models 433
23.4.3 Examples of Climate Models 437
23.4.3.1 Reading Intermediate General Circulation Model (IGCM) 437
23.4.3.2 Hadley Centre Coupled Model, Version 3 (HadCM3) 437
23.4.3.3 Hadley Centre Atmospheric Model, Version 3 (HadAM3) 438
23.4.3.4 Hadley Centre Global Environmental Model, Version 1 (HadGEM1) 438
23.4.3.5 Geophysical Fluid Dynamics Laboratory Coupled Model, Version 2.X (GFDL CM2.X) 438
23.4.3.6 EdGCM 438
23.4.3.7 Providing REgional Climates for Impacts Studies (PRECIS) 439
23.4.3.8 Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) 439
23.5 Use of Connected Climatic and Hydrologic Models 440
References 440
Chapter 24: A Brief Introduction to Remote Sensing and GIS 444
24.1 The Concept of Remote Sensing 444
24.1.1 Electromagnetic Spectrum: Transmittance, Absorptance, and Reflectance 445
24.2 Concept of Geographical Information System 445
24.2.1 Components of Geographical Information System 446
24.2.2 Computer Hardware 446
24.2.3 Software Modules 446
24.2.3.1 Data Input, Editing and Verification 446
24.2.3.2 Data Storage and Database Management 446
24.2.3.3 Data Analysis, Modeling, and Cartographic Manipulation 447
24.2.3.4 Data Output and Presentation 447
24.2.4 Spatial Data Models and Structures 447
24.3 Sensors and Satellites 447
24.3.1 Sensors 447
24.3.1.1 Example of Sensors 448
Photographic Camera 448
Vidicon Television Camera 448
Optical Scanner 448
Optical Mechanical Scanners 449
Thematic Mapper 449
Push Broom Scanners 449
24.3.1.2 Satellites 450
SPOT-HRV 450
IRS-LISS 450
INSAT-VHRR 451
NOAA-AVHRR 451
Microwave Radiometers 451
Microwave Radar 451
Shuttle Imaging Radar 452
LANDSAT Satellite 452
24.3.1.3 Indian Remote Sensing (IRS) Satellites 452
LISS 454
IRS-1C-D 454
IRS-P2 455
24.4 Interpretation of Remotely Sense Data 455
24.4.1 Soil 456
24.4.2 Water 456
24.4.3 Vegetation 456
24.5 Global Positioning Satellites 457
24.6 Aerial Photography 458
24.6.1 Tone (Closely Related to Hue or Color) 458
24.6.2 Size 458
24.6.3 Shape 458
24.6.4 Texture 459
24.6.5 Pattern (Spatial Arrangement) 459
24.6.6 Shadow 459
24.6.7 Site 459
24.6.8 Association 459
24.7 Application of Remote Sensing and GIS in Hydrology 460
24.7.1 Catchment Delineation 460
24.7.2 Distributive Modeling 461
24.7.3 Identification of Hydrological Representative Unit (HRU) 462
References 463
Chapter 25: An Introduction and Current Trends of Damodar and Rupnarayan River Network 464
25.1 An Introduction to Damodar River Network 468
25.1.1 Damodar Valley Corporation 468
25.1.2 Catchment Description 471
25.1.3 Geomorphological Features 472
25.1.4 Hydrologic and Meterologic Characteristic 473
25.1.5 Industrial and Agricultural Status 478
25.1.6 Water Pollution 479
25.2 An Introduction to Rupnarayan River Network 479
25.2.1 Catchment Description 479
25.2.2 Geomorphological Features 479
25.2.2.1 The Puruliya High Plain 480
25.2.2.2 The Rarh Upland 480
25.2.2.3 The Riverine Delta 481
25.2.3 Hydrologic and Meterologic Characteristic 482
25.2.4 Industrial and Agricultural Status 482
References 483
Index 484