GIS and Geocomputation for Water Resource Science and Engineering

GIS and Geocomputation for Water Resource Science and Engineering not only provides a comprehensive introduction to the fundamentals of geographic information systems but also demonstrates how GIS and mathematical models can be integrated to develop spatial decision support systems to support water resources planning, management and engineering. The book uses a hands-on active learning approach to introduce fundamental concepts and numerous case-studies are provided to reinforce learning and demonstrate practical aspects. The benefits and challenges of using GIS in environmental and water resources fields are clearly tackled in this book, demonstrating how these technologies can be used to harness increasingly available digital data to develop spatially-oriented sustainable solutions. In addition to providing a strong grounding on fundamentals, the book also demonstrates how GIS can be combined with traditional physics-based and statistical models as well as information-theoretic tools like neural networks and fuzzy set theory.

Table of Contents:
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 2.8 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 3.4 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 3.6 Accuracy issues surrounding land use land cover maps 32 3.7 Sensitivity of hydrologic models to LULC resolution 34 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 4.8 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 6.4 Database structure, storage, and organization 71 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 7.5 Aerial and satellite remote sensing and imagery 83 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 7.10 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 10.2 Topology and geometric computations (various measurements) 137 10.3 Proximity and network analysis 143 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 11.3 Reclassification 155 11.4 Zonal operations 157 11.5 Calculation of area, perimeter, and shape 163 11.6 Statistical operations 164 11.7 Neighborhood operations 165 11.8 Determination of distance, proximity, and connectivity in raster 167 11.9 Physical distance and cost distance analysis 169 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 12.2 Topics in watershed characterization and analysis 191 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 13.5 Coupling mathematical models with GIS 209 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 15.6 Geostatistical Methods 227 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 16.3 Information theoretic approaches 238 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 17.4 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 19.5 Using ModelBuilder to automate geoprocessing tasks 279 19.6 Using Python scripting for geoprocessing 287 19.7 Introduction to some useful Python constructs 288 19.8 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 20.4 Concluding remarks 311 References 311 21 Loosely Coupled Hydrologic Model 313 21.1 Introduction 313 21.2 Study area 313 21.3 Methods 314 21.4 Results and discussions 318 21.5 Conclusions 323 Acknowledgment 324 References 324 22 Watershed Characterization 325 22.1 Introduction 325 22.2 Background 325 22.3 Approach 326 22.4 Summary and conclusions 332 References 345 23 Tightly Coupled Models with GIS for Watershed Impact Assessment 347 23.1 Introduction 347 23.2 Methods 350 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 25.4 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 26.7 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 27.3 Methods 407 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 29.3 Results and discussion 433 29.4 Summary and conclusions 437 References 437 30 Coupling of Statistical Methods with GIS for Groundwater Vulnerability Assessment 439 30.1 Introduction 439 30.2 Methodology 440 30.3 Results and discussion 440 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 31.3 Results and discussion 453 31.4 Summary and conclusions 457 References 457 32 Tight Coupling of Artificial Neural Network (ANN) and GIS 461 32.1 Introduction 461 32.2 Methodology 463 32.3 Results and discussion 465 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 33.3 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 34.3 Results and discussion 486 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 36.7 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 37.6 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