GIS and Geocomputation for Water Resource Science and Engineering

Barnali Dixon is a Professor in the Department of Environmental Science, Policy and Geography at the University of South Florida St. Petersburg (USFSP) and the Director of the Geospatial Analytics Lab of USFSP. Venkatesh Uddameri is a Professor in the Department of Civil, Environmental and Construction Engineering at Texas Tech University and the Director of the TTU Water Resources Center. 

Preface xiii About the Companion Website xv

List of Acronyms xvii

Part I GIS, Geocomputation, and GIS Data 1

1 Introduction 3

1.1 What is geocomputation? 3

1.2 Geocomputation and water resources science and engineering 4

1.3 GIS-enabled geocomputation in water resources science and engineering 5

1.4 Why should water resources engineers and scientists study GIS 5

1.5 Motivation and organization of this book 6

1.6 Concluding remarks 7

References 9

2 A Brief History of GIS and Its Use in Water Resources Engineering 11

2.1 Introduction 11

2.2 Geographic Information Systems (GIS) – software and hardware 11

2.3 Remote sensing and global positioning systems and development of GIS 12

2.4 History of GIS in water resources applications 13

2.5 Recent trends in GIS 19

2.6 Benefits of using GIS in water resources engineering and science 20

2.7 Challenges and limitations of GIS-based approach to water resources engineering 20

2.7.1 Limitation 1: incompatibilities between real-world and GIS modeled systems 20

2.7.2 Limitation 2: inability of GIS to effectively handle time dimension 21

2.7.3 Limitation 3: subjectivity arising from the availability of multiple geoprocessing tools 21

2.7.4 Limitation 4: ground-truthing and caution against extrapolation 21

2.7.5 Limitation 5: crisp representation of fuzzy geographic boundaries 21

2.7.6 Limitation 6: dynamic rescaling of maps and intrinsic resampling operations by GIS software 22

2.7.7 Limitation 7: inadequate or improper understanding of scale and resolution of the datasets 22

2.7.8 Limitation 8: limited support for handling of advanced mathematical algorithms 22

2.8 Concluding remarks 23

References 25

3 Hydrologic Systems and Spatial Datasets 27

3.1 Introduction 27

3.2 Hydrological processes in a watershed 27

3.3 Fundamental spatial datasets for water resources planning: management and modeling studies 28

3.3.1 Digital elevation models (DEMs) 28

3.4 Sources of data for developing digital elevation models 30

3.4.1 Accuracy issues surrounding digital elevation models 30

3.5 Sensitivity of hydrologic models to DEM resolution 31

3.5.1 Land use and land cover (LULC) 32

3.5.2 Sources of data for developing digital land use land cover maps 32

3.6 Accuracy issues surrounding land use land cover maps 32

3.6.1 Anderson classification and the standardization of LULC mapping 33

3.7 Sensitivity of hydrologic models to LULC resolution 34

3.7.1 LULC, impervious surface, and water quality 34

3.7.2 Soil datasets 36

3.8 Sources of data for developing soil maps 36

3.9 Accuracy issues surrounding soil mapping 37

3.10 Sensitivity of hydrologic models to soils resolution 38

3.11 Concluding remarks 43

References 44

4 Water-Related Geospatial Datasets 47

4.1 Introduction 47

4.2 River basin, watershed, and subwatershed delineations 47

4.3 Streamflow and river stage data 48

4.4 Groundwater level data 48

4.5 Climate datasets 48

4.6 Vegetation indices 49

4.7 Soil moisture mapping 49

4.7.1 Importance of soil moisture in water resources applications 49

4.7.2 Methods for obtaining soil moisture data 50

4.7.3 Remote sensing methods for soil moisture assessments 50

4.7.4 Role of GIS in soil moisture modeling and mapping 51

4.8 Water quality datasets 51

4.9 Monitoring strategies and needs 51

4.10 Sampling techniques and recent advancements in sensing technologies 52

4.11 Concluding remarks 53

References 53

5 Data Sources and Models 55

5.1 Digital data warehouses and repositories 55

5.2 Software for GIS and geocomputations 55

5.3 Software and data models for water resources applications 59

5.4 Concluding remarks 60

References 60

Part II Foundations of GIS 61

6 Data Models for GIS 63

6.1 Introduction 63

6.2 Data types, data entry, and data models 63

6.2.1 Discrete and continuous data 63

6.3 Categorization of spatial datasets 65

6.3.1 Raster and vector data structures 65

6.3.2 Content-based data classification 65

6.3.3 Data classification based on measurement levels 66

6.3.4 Primary and derived datasets 69

6.3.5 Data entry for GIS 69

6.3.6 GIS data models 70

6.4 Database structure, storage, and organization 71

6.4.1 What is a relational data structure? 71

6.4.2 Attribute data and tables 72

6.4.3 Geodatabase 73

6.4.4 Object-oriented database 75

6.5 Data storage and encoding 75

6.6 Data conversion 76

6.7 Concluding remarks 78

References 80

7 Global Positioning Systems (GPS) and Remote Sensing 81

7.1 Introduction 81

7.2 The global positioning system (GPS) 81

7.3 Use of GPS in water resources engineering studies 82

7.4 Workflow for GPS data collection 83

7.4.1 12 Steps to effective GPS data collection and compilation 83

7.5 Aerial and satellite remote sensing and imagery 83

7.5.1 Low-resolution imagery 84

7.5.2 Medium-resolution imagery 84

7.5.3 High-resolution imagery 84

7.6 Data and cost of acquiring remotely sensed data 84

7.7 Principles of remote sensing 85

7.8 Remote sensing applications in water resources engineering and science 88

7.9 Bringing remote sensing data into GIS 91

7.9.1 Twelve steps for integration of remotely sensed data into GIS 93

7.10 Concluding remarks 94

References 95

8 Data Quality, Errors, and Uncertainty 97

8.1 Introduction 97

8.2 Map projection, datum, and coordinate systems 97

8.3 Projections in GIS software 101

8.4 Errors, data quality, standards, and documentation 102

8.5 Error and uncertainty 106

8.6 Role of resolution and scale on data quality 107

8.7 Role of metadata in GIS analysis 109

8.8 Concluding remarks 109

References 109

9 GIS Analysis: Fundamentals of Spatial Query 111

9.1 Introduction to spatial analysis 111

9.2 Querying operations in GIS 116

9.2.1 Spatial query 116

9.3 Structured query language (SQL) 119

9.4 Raster data query by cell value 122

9.5 Spatial join and relate 125

9.6 Concluding remarks 128

References 128

10 Topics in Vector Analysis 129

10.1 Basics of geoprocessing (buffer, dissolve, clipping, erase, and overlay) 129

10.1.1 Buffer 129

10.1.2 Dissolve, clip, and erase 132

10.1.3 Overlay 132

10.2 Topology and geometric computations (various measurements) 137

10.2.1 Length and distance measurements 139

10.2.2 Area and perimeter-to-area ratio (PAR) calculations 140

10.3 Proximity and network analysis 143

10.3.1 Proximity 144

10.3.2 Network analysis 144

10.4 Concluding remarks 145

References 147

11 Topics in Raster Analysis 149

11.1 Topics in raster analysis 149

11.2 Local operations 149

11.2.1 Local operation with a single raster 151

11.2.2 Local operation with multiple rasters 151

11.2.3 Map algebra for geocomputation in water resources 153

11.3 Reclassification 155

11.4 Zonal operations 157

11.4.1 Identification of regions and reclassification 160

11.4.2 Category-wide overlay 161

11.5 Calculation of area, perimeter, and shape 163

11.6 Statistical operations 164

11.7 Neighborhood operations 165

11.7.1 Spatial aggregation analysis 165

11.7.2 Filtering 166

11.7.3 Computation of slope and aspect 167

11.7.4 Resampling 167

11.8 Determination of distance, proximity, and connectivity in raster 167

11.9 Physical distance and cost distance analysis 169

11.9.1 Cost surface analysis 172

11.9.2 Allocation and direction analysis 172

11.9.3 Path analysis 173

11.10 Buffer analysis in raster 174

11.11 Viewshed analysis 175

11.12 Raster data management (mask, spatial clip, and mosaic) 178

11.13 Concluding remarks 179

References 181

12 Terrain Analysis and Watershed Delineation 183

12.1 Introduction 183

12.1.1 Contouring 184

12.1.2 Hill shading and insolation 185

12.1.3 Perspective view 186

12.1.4 Slope and aspect 186

12.1.5 Surface curvature 191

12.2 Topics in watershed characterization and analysis 191

12.2.1 Watershed delineation 192

12.2.2 Critical considerations during watershed delineation 198

12.3 Concluding remarks 200

References 200

Part III Foundations of Modeling 203

13 Introduction to Water Resources Modeling 205

13.1 Mathematical modeling in water resources engineering and science 205

13.2 Overview of mathematical modeling in water resources engineering and science 206

13.3 Conceptual modeling: phenomena, processes, and parameters of a system 206

13.4 Common approaches used to develop mathematical models in water resources engineering 206

13.4.1 Data-driven models 207

13.4.2 Physics-based models 208

13.4.3 Expert-driven or stakeholder-driven models 208

13.5 Coupling mathematical models with GIS 209

13.5.1 Loose coupling of GIS and mathematical models 209

13.5.2 Tight coupling of GIS and mathematical models 209

13.5.3 What type of coupling to pursue? 210

13.6 Concluding remarks 210

References 211

14 Water Budgets and Conceptual Models 213

14.1 Flow modeling in a homogeneous system (boxed or lumped model) 213

14.2 Flow modeling in heterogeneous systems (control volume approach) 215

14.3 Conceptual model: soil conservation survey curve number method 217

14.4 Fully coupled watershed-scale water balance model: soil water assessment tool (SWAT) 218

14.5 Concluding remarks 219

References 220

15 Statistical and Geostatistical Modeling 221

15.1 Introduction 221

15.2 Ordinary least squares (OLS) linear regression 221

15.3 Logistic regression 222

15.4 Data reduction and classification techniques 223

15.5 Topics in spatial interpolation and sampling 223

15.5.1 Local area methods 224

15.5.2 Spline interpolation method 224

15.5.3 Thiessen polygons 224

15.5.4 Density estimation 225

15.5.5 Inverse distance weighted (IDW) 226

15.5.6 Moving average 226

15.5.7 Global area or whole area interpolation schemes 227

15.5.8 Trend surface analysis 227

15.6 Geostatistical Methods 227

15.6.1 Spatial autocorrelation 227

15.6.2 Variogram and semivariogram modeling 228

15.7 Kriging 230

15.8 Critical issues in interpolation 231

15.9 Concluding remarks 232

References 234

16 Decision Analytic and Information Theoretic Models 235

16.1 Introduction 235

16.2 Decision analytic models 235

16.2.1 Multiattribute decision-making models 235

16.2.2 Multiobjective decision-making models 238

16.3 Information theoretic approaches 238

16.3.1 Artificial neural networks (ANNs) 239

16.3.2 Support vector machines (SVMs) 239

16.3.3 Rule-based expert systems 240

16.3.4 Fuzzy rule-based inference systems 241

16.3.5 Neuro-fuzzy systems 243

16.4 Spatial data mining (SDM) for knowledge discovery in a database 245

16.5 The trend of temporal data modeling in GIS 245

16.6 Concluding remarks 246

References 246

17 Considerations for GIS and Model Integration 249

17.1 Introduction 249

17.2 An overview of practical considerations in adopting and integrating GIS into water resources projects 250

17.3 Theoretical considerations related to GIS and water resources model integration 251

17.3.1 Space and time scales of the problems and target outcomes 251

17.3.2 Data interchangeability and operability 253

17.3.3 Selection of the appropriate platform, models, and datasets 253

17.3.4 Model calibration and evaluation issues 255

17.3.5 Error and uncertainty analysis 255

17.4 Concluding remarks 256

References 257

18 Useful Geoprocessing Tasks While Carrying Out Water Resources Modeling 259

18.1 Introduction 259

18.2 Getting all data into a common projection 259

18.3 Adding point (X, Y) data and calculating their projected coordinates 260

18.4 Image registration and rectification 264

18.5 Editing tools to transfer information to vectors 266

18.6 GIS for cartography and visualization 270

18.7 Concluding remarks 271

References 271

19 Automating Geoprocessing Tasks in GIS 273

19.1 Introduction 273

19.2 Object-oriented programming paradigm 273

19.3 Vectorized (array) geoprocessing 274

19.4 Making nongeographic attribute calculations 274

19.4.1 Field calculator for vector attribute manipulation 274

19.4.2 Raster calculator for continuous data 278

19.5 Using ModelBuilder to automate geoprocessing tasks 279

19.6 Using Python scripting for geoprocessing 287

19.7 Introduction to some useful Python constructs 288

19.7.1 Basic arithmetic and programming logic syntax 288

19.7.2 Defining functions in Python 288

19.7.3 Python classes 288

19.7.4 Python modules and site-packages 289

19.8 ArcPy geoprocessing modules and site-package 289

19.9 Learning Python and scripting with ArcGIS 289

19.10 Concluding remarks 290

References 291

Part IV Illustrative Case Studies 293

A Preamble to Case Studies 295

20 Watershed Delineation 297

20.1 Introduction 297

20.2 Background 297

20.3 Methods 298

20.3.1 Generalized methods 298

20.3.2 Application 298

20.3.3 Application of ArcGIS Spatial Analyst tools 298

20.3.4 Application of ArcHydro for drainage analysis using digital terrain data 303

20.4 Concluding remarks 311

References 311

21 Loosely Coupled Hydrologic Model 313

21.1 Introduction 313

21.2 Study area 313

21.3 Methods 314

21.3.1 Image processing 315

21.3.2 ET/EV data 317

21.3.3 Accuracy assessment 317

21.3.4 Water budget spreadsheet model 317

21.4 Results and discussions 318

21.4.1 Image classification results 318

21.4.2 Water budget calculation 319

21.5 Conclusions 323

Acknowledgment 324

References 324

22 Watershed Characterization 325

22.1 Introduction 325

22.2 Background 325

22.3 Approach 326

22.3.1 Analysis of watershed characteristics and reclassification 327

22.3.2 Integrated evaluation of watershed runoff potential 330

22.4 Summary and conclusions 332

References 345

23 Tightly Coupled Models with GIS for Watershed Impact Assessment 347

23.1 Introduction 347

23.1.1 Land use and soil influences on runoff and the curve number (CN) 347

23.2 Methods 350

23.2.1 Study area 350

23.2.2 Data processing 350

23.2.3 Data layers 351

23.3 Results and discussion 353

23.4 Summary and conclusions 357

References 357

24 GIS for Land Use Impact Assessment 359

24.1 Introduction 359

24.2 Description of study area and datasets 360

24.3 Results and discussion 370

24.4 Conclusions 386

References 387

25 TMDL Curve Number 389

25.1 Introduction 389

25.2 Formulation of competing models 389

25.3 Use of Geographic Information System to obtain parameters for use in the NRCS method 390

25.3.1 Nonpoint source loading determination 391

25.4 Risk associated with different formulations 392

25.5 Summary and conclusions 394

References 395

26 Tight Coupling MCDM Models in GIS 397

26.1 Introduction 397

26.2 Using GIS for groundwater vulnerability assessment 398

26.3 Application of DRASTIC methodology in South Texas 398

26.4 Study area 398

26.5 Compiling the database for the DRASTIC index 398

26.6 Development of DRASTIC vulnerability index 399

26.6.1 Depth to groundwater 400

26.6.2 Recharge 401

26.6.3 Aquifer media 401

26.6.4 Soil media 401

26.6.5 Topography 402

26.6.6 Impact of vadose zone 402

26.6.7 Hydraulic conductivity 403

26.7 DRASTIC index 403

26.8 Summary 404

References 404

27 Advanced GIS MCDM Model Coupling for Assessing Human Health Risks 405

27.1 Introduction 405

27.2 Background information 406

27.2.1 Groundwater vulnerability parameters 406

27.2.2 Pathogen transport parameters 406

27.2.3 Pathogen survival parameters 407

27.3 Methods 407

27.3.1 Study area 407

27.3.2 Conceptual framework 407

27.3.3 Data layers 408

27.4 Results and discussion 412

27.5 Conclusions 419

References 419

28 Embedded Coupling with JAVA 421

28.1 Introduction 421

28.2 Previous work 422

28.3 Mathematical background 422

28.4 Data formats of input files 423

28.5 AFC structure and usage 423

28.6 Illustrative example 424

References 426

29 GIS-Enabled Physics-Based Contaminant Transport Models for MCDM 427

29.1 Introduction 427

29.2 Methodology 428

29.2.1 Conceptual model 428

29.2.2 Mass-balance expressions 429

29.2.3 Solutions of the steady-state mass-balance equation 430

29.2.4 Model parameterization 431

29.3 Results and discussion 433

29.3.1 Sensitivity analysis 435

29.4 Summary and conclusions 437

References 437

30 Coupling of Statistical Methods with GIS for Groundwater Vulnerability Assessment 439

30.1 Introduction 439

30.1.1 Logistic regression 439

30.1.2 Akaike’s information criterion (AIC) 440

30.2 Methodology 440

30.2.1 Application of logistic regression (LR) to DRASTIC vulnerability model 440

30.2.2 Implementation in GIS 440

30.3 Results and discussion 440

30.3.1 Implementation in GIS 441

30.4 Summary and conclusions 444

References 444

31 Coupling of Fuzzy Logic-Based Method with GIS for Groundwater Vulnerability Assessment 447

31.1 Introduction 447

31.2 Methodology 448

31.2.1 Fuzzy sets and fuzzy numbers 448

31.2.2 Fuzzy arithmetic 449

31.2.3 Elementary fuzzy arithmetic for triangular fuzzy sets 449

31.2.4 Approximate operations on triangular fuzzy sets 449

31.2.5 Fuzzy aquifer vulnerability characterization 450

31.2.6 Specification of weights 450

31.2.7 Specification of ratings 450

31.2.8 Defuzzification procedures 452

31.2.9 Implementation 453

31.3 Results and discussion 453

31.3.1 Incorporation of fuzziness in decision-makers’ weights and ratings 453

31.3.2 Comparison of exact and approximate fuzzy arithmetic for aquifer vulnerability estimation when ratings and weights are fuzzy 453

31.4 Summary and conclusions 457

References 457

32 Tight Coupling of Artificial Neural Network (ANN) and GIS 461

32.1 Introduction 461

32.1.1 The concept of artificial neural network (ANN) 461

32.2 Methodology 463

32.2.1 Data development 463

32.2.2 Application of feedforward neural network (FFNN) to DRASTIC groundwater vulnerability assessment model 463

32.2.3 Application of radial basis function (RBF) neural network to DRASTIC groundwater vulnerability assessment model 464

32.2.4 Performance evaluation of feedforward neural network (FFNN) and radial basis function (RBF) neural network models 464

32.2.5 Implementation of artificial neural network in GIS 465

32.3 Results and discussion 465

32.3.1 Model performance evaluation for FFNN and RBF network models 468

32.3.2 Results of ANN-GIS integration 472

32.4 Summary and conclusion 472

References 473

33 Loose Coupling of Artificial Neuro-Fuzzy Information System (ANFIS) and GIS 475

33.1 Introduction 475

33.2 Methods 475

33.2.1 Study area 475

33.2.2 Data development 476

33.2.3 Selection of the model inputs 476

33.2.4 Development of artificial neuro-fuzzy models 477

33.3 Results and discussion 478

33.4 Conclusions 479

References 480

34 GIS and Hybrid Model Coupling 483

34.1 Introduction 483

34.2 Methodology 483

34.2.1 Multicriteria decision-making model for assessing recharge potential 484

34.2.2 Data compilation and GIS operations 485

34.3 Results and discussion 486

34.3.1 Identification of potential recharge areas and model evaluation 486

34.3.2 Hydrogeological and geochemical assessment of identified recharge locations 490

34.3.3 Artificial recharge locations in the context of demands 491

34.4 Summary and conclusions 493

References 493

35 Coupling Dynamic Water Resources Models with GIS 495

35.1 Introduction 495

35.2 Modeling infiltration: Green–Ampt approach 495

35.3 Coupling Green–Ampt modeling with regional-scale soil datasets 497

35.4 Result and discussion 497

35.5 Summary 498

References 499

36 Tight Coupling of Well Head Protection Models in GIS with Vector Datasets 501

36.1 Introduction 501

36.2 Methods for delineating well head protection areas 501

36.3 Fixed radius model development 502

36.4 Implementing well head protection models within GIS 503

36.5 Data compilation 503

36.6 Results and discussion 504

36.6.1 Arbitrary fixed radius buffer 504

36.6.2 Calculated variable radius buffer 504

36.7 Summary 505

References 506

37 Loosely Coupled Models in GIS for Optimization 507

37.1 Introduction 507

37.2 Study area 508

37.3 Mathematical model 509

37.4 Data compilation and model application 510

37.5 Results 511

37.5.1 Baseline run 511

37.5.2 Evaluation of certificate of convenience and necessity delineations 512

37.5.3 Impacts of wastewater treatment efficiencies 512

37.5.4 Impacts of influent characteristics 513

37.5.5 Evaluation of current and future effluent discharge policies 513

37.6 Summary and conclusions 513

References 514

38 Epilogue 515

References 517

Example of a Syllabus: For Graduate 6000 Level Engineering Students 519

Example of a Syllabus: For Graduate 6000 Level Environmental Science and Geography Students 523

Example of a Syllabus: For Undergraduate 4000 Level Engineering Students 527

Example of a Syllabus: For Undergraduate 4000 Level Environmental Science and Geography Students 531

Index 535