GIS and Geocomputation for Water Resource Science and Engineering

Content: 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