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 xvList of Acronyms xviiPart I GIS, Geocomputation, and GIS Data 11 Introduction 31.1 What is geocomputation? 31.2 Geocomputation and water resources science and engineering 41.3 GIS-enabled geocomputation in water resources science and engineering 51.4 Why should water resources engineers and scientists study GIS 51.5 Motivation and organization of this book 61.6 Concluding remarks 7References 92 A Brief History of GIS and Its Use in Water Resources Engineering 112.1 Introduction 112.2 Geographic Information Systems (GIS) – software and hardware 112.3 Remote sensing and global positioning systems and development of GIS 122.4 History of GIS in water resources applications 132.5 Recent trends in GIS 192.6 Benefits of using GIS in water resources engineering and science 202.7 Challenges and limitations of GIS-based approach to water resources engineering 202.7.1 Limitation 1: incompatibilities between real-world and GIS modeled systems 202.7.2 Limitation 2: inability of GIS to effectively handle time dimension 212.7.3 Limitation 3: subjectivity arising from the availability of multiple geoprocessing tools 212.7.4 Limitation 4: ground-truthing and caution against extrapolation 212.7.5 Limitation 5: crisp representation of fuzzy geographic boundaries 212.7.6 Limitation 6: dynamic rescaling of maps and intrinsic resampling operations by GIS software 222.7.7 Limitation 7: inadequate or improper understanding of scale and resolution of the datasets 222.7.8 Limitation 8: limited support for handling of advanced mathematical algorithms 222.8 Concluding remarks 23References 253 Hydrologic Systems and Spatial Datasets 273.1 Introduction 273.2 Hydrological processes in a watershed 273.3 Fundamental spatial datasets for water resources planning: management and modeling studies 283.3.1 Digital elevation models (DEMs) 283.4 Sources of data for developing digital elevation models 303.4.1 Accuracy issues surrounding digital elevation models 303.5 Sensitivity of hydrologic models to DEM resolution 313.5.1 Land use and land cover (LULC) 323.5.2 Sources of data for developing digital land use land cover maps 323.6 Accuracy issues surrounding land use land cover maps 323.6.1 Anderson classification and the standardization of LULC mapping 333.7 Sensitivity of hydrologic models to LULC resolution 343.7.1 LULC, impervious surface, and water quality 343.7.2 Soil datasets 363.8 Sources of data for developing soil maps 363.9 Accuracy issues surrounding soil mapping 373.10 Sensitivity of hydrologic models to soils resolution 383.11 Concluding remarks 43References 444 Water-Related Geospatial Datasets 474.1 Introduction 474.2 River basin, watershed, and subwatershed delineations 474.3 Streamflow and river stage data 484.4 Groundwater level data 484.5 Climate datasets 484.6 Vegetation indices 494.7 Soil moisture mapping 494.7.1 Importance of soil moisture in water resources applications 494.7.2 Methods for obtaining soil moisture data 504.7.3 Remote sensing methods for soil moisture assessments 504.7.4 Role of GIS in soil moisture modeling and mapping 514.8 Water quality datasets 514.9 Monitoring strategies and needs 514.10 Sampling techniques and recent advancements in sensing technologies 524.11 Concluding remarks 53References 535 Data Sources and Models 555.1 Digital data warehouses and repositories 555.2 Software for GIS and geocomputations 555.3 Software and data models for water resources applications 595.4 Concluding remarks 60References 60Part II Foundations of GIS 616 Data Models for GIS 636.1 Introduction 636.2 Data types, data entry, and data models 636.2.1 Discrete and continuous data 636.3 Categorization of spatial datasets 656.3.1 Raster and vector data structures 656.3.2 Content-based data classification 656.3.3 Data classification based on measurement levels 666.3.4 Primary and derived datasets 696.3.5 Data entry for GIS 696.3.6 GIS data models 706.4 Database structure, storage, and organization 716.4.1 What is a relational data structure? 716.4.2 Attribute data and tables 726.4.3 Geodatabase 736.4.4 Object-oriented database 756.5 Data storage and encoding 756.6 Data conversion 766.7 Concluding remarks 78References 807 Global Positioning Systems (GPS) and Remote Sensing 817.1 Introduction 817.2 The global positioning system (GPS) 817.3 Use of GPS in water resources engineering studies 827.4 Workflow for GPS data collection 837.4.1 12 Steps to effective GPS data collection and compilation 837.5 Aerial and satellite remote sensing and imagery 837.5.1 Low-resolution imagery 847.5.2 Medium-resolution imagery 847.5.3 High-resolution imagery 847.6 Data and cost of acquiring remotely sensed data 847.7 Principles of remote sensing 857.8 Remote sensing applications in water resources engineering and science 887.9 Bringing remote sensing data into GIS 917.9.1 Twelve steps for integration of remotely sensed data into GIS 937.10 Concluding remarks 94References 958 Data Quality, Errors, and Uncertainty 978.1 Introduction 978.2 Map projection, datum, and coordinate systems 978.3 Projections in GIS software 1018.4 Errors, data quality, standards, and documentation 1028.5 Error and uncertainty 1068.6 Role of resolution and scale on data quality 1078.7 Role of metadata in GIS analysis 1098.8 Concluding remarks 109References 1099 GIS Analysis: Fundamentals of Spatial Query 1119.1 Introduction to spatial analysis 1119.2 Querying operations in GIS 1169.2.1 Spatial query 1169.3 Structured query language (SQL) 1199.4 Raster data query by cell value 1229.5 Spatial join and relate 1259.6 Concluding remarks 128References 12810 Topics in Vector Analysis 12910.1 Basics of geoprocessing (buffer, dissolve, clipping, erase, and overlay) 12910.1.1 Buffer 12910.1.2 Dissolve, clip, and erase 13210.1.3 Overlay 13210.2 Topology and geometric computations (various measurements) 13710.2.1 Length and distance measurements 13910.2.2 Area and perimeter-to-area ratio (PAR) calculations 14010.3 Proximity and network analysis 14310.3.1 Proximity 14410.3.2 Network analysis 14410.4 Concluding remarks 145References 14711 Topics in Raster Analysis 14911.1 Topics in raster analysis 14911.2 Local operations 14911.2.1 Local operation with a single raster 15111.2.2 Local operation with multiple rasters 15111.2.3 Map algebra for geocomputation in water resources 15311.3 Reclassification 15511.4 Zonal operations 15711.4.1 Identification of regions and reclassification 16011.4.2 Category-wide overlay 16111.5 Calculation of area, perimeter, and shape 16311.6 Statistical operations 16411.7 Neighborhood operations 16511.7.1 Spatial aggregation analysis 16511.7.2 Filtering 16611.7.3 Computation of slope and aspect 16711.7.4 Resampling 16711.8 Determination of distance, proximity, and connectivity in raster 16711.9 Physical distance and cost distance analysis 16911.9.1 Cost surface analysis 17211.9.2 Allocation and direction analysis 17211.9.3 Path analysis 17311.10 Buffer analysis in raster 17411.11 Viewshed analysis 17511.12 Raster data management (mask, spatial clip, and mosaic) 17811.13 Concluding remarks 179References 18112 Terrain Analysis and Watershed Delineation 18312.1 Introduction 18312.1.1 Contouring 18412.1.2 Hill shading and insolation 18512.1.3 Perspective view 18612.1.4 Slope and aspect 18612.1.5 Surface curvature 19112.2 Topics in watershed characterization and analysis 19112.2.1 Watershed delineation 19212.2.2 Critical considerations during watershed delineation 19812.3 Concluding remarks 200References 200Part III Foundations of Modeling 20313 Introduction to Water Resources Modeling 20513.1 Mathematical modeling in water resources engineering and science 20513.2 Overview of mathematical modeling in water resources engineering and science 20613.3 Conceptual modeling: phenomena, processes, and parameters of a system 20613.4 Common approaches used to develop mathematical models in water resources engineering 20613.4.1 Data-driven models 20713.4.2 Physics-based models 20813.4.3 Expert-driven or stakeholder-driven models 20813.5 Coupling mathematical models with GIS 20913.5.1 Loose coupling of GIS and mathematical models 20913.5.2 Tight coupling of GIS and mathematical models 20913.5.3 What type of coupling to pursue? 21013.6 Concluding remarks 210References 21114 Water Budgets and Conceptual Models 21314.1 Flow modeling in a homogeneous system (boxed or lumped model) 21314.2 Flow modeling in heterogeneous systems (control volume approach) 21514.3 Conceptual model: soil conservation survey curve number method 21714.4 Fully coupled watershed-scale water balance model: soil water assessment tool (SWAT) 21814.5 Concluding remarks 219References 22015 Statistical and Geostatistical Modeling 22115.1 Introduction 22115.2 Ordinary least squares (OLS) linear regression 22115.3 Logistic regression 22215.4 Data reduction and classification techniques 22315.5 Topics in spatial interpolation and sampling 22315.5.1 Local area methods 22415.5.2 Spline interpolation method 22415.5.3 Thiessen polygons 22415.5.4 Density estimation 22515.5.5 Inverse distance weighted (IDW) 22615.5.6 Moving average 22615.5.7 Global area or whole area interpolation schemes 22715.5.8 Trend surface analysis 22715.6 Geostatistical Methods 22715.6.1 Spatial autocorrelation 22715.6.2 Variogram and semivariogram modeling 22815.7 Kriging 23015.8 Critical issues in interpolation 23115.9 Concluding remarks 232References 23416 Decision Analytic and Information Theoretic Models 23516.1 Introduction 23516.2 Decision analytic models 23516.2.1 Multiattribute decision-making models 23516.2.2 Multiobjective decision-making models 23816.3 Information theoretic approaches 23816.3.1 Artificial neural networks (ANNs) 23916.3.2 Support vector machines (SVMs) 23916.3.3 Rule-based expert systems 24016.3.4 Fuzzy rule-based inference systems 24116.3.5 Neuro-fuzzy systems 24316.4 Spatial data mining (SDM) for knowledge discovery in a database 24516.5 The trend of temporal data modeling in GIS 24516.6 Concluding remarks 246References 24617 Considerations for GIS and Model Integration 24917.1 Introduction 24917.2 An overview of practical considerations in adopting and integrating GIS into water resources projects 25017.3 Theoretical considerations related to GIS and water resources model integration 25117.3.1 Space and time scales of the problems and target outcomes 25117.3.2 Data interchangeability and operability 25317.3.3 Selection of the appropriate platform, models, and datasets 25317.3.4 Model calibration and evaluation issues 25517.3.5 Error and uncertainty analysis 25517.4 Concluding remarks 256References 25718 Useful Geoprocessing Tasks While Carrying Out Water Resources Modeling 25918.1 Introduction 25918.2 Getting all data into a common projection 25918.3 Adding point (X, Y) data and calculating their projected coordinates 26018.4 Image registration and rectification 26418.5 Editing tools to transfer information to vectors 26618.6 GIS for cartography and visualization 27018.7 Concluding remarks 271References 27119 Automating Geoprocessing Tasks in GIS 27319.1 Introduction 27319.2 Object-oriented programming paradigm 27319.3 Vectorized (array) geoprocessing 27419.4 Making nongeographic attribute calculations 27419.4.1 Field calculator for vector attribute manipulation 27419.4.2 Raster calculator for continuous data 27819.5 Using ModelBuilder to automate geoprocessing tasks 27919.6 Using Python scripting for geoprocessing 28719.7 Introduction to some useful Python constructs 28819.7.1 Basic arithmetic and programming logic syntax 28819.7.2 Defining functions in Python 28819.7.3 Python classes 28819.7.4 Python modules and site-packages 28919.8 ArcPy geoprocessing modules and site-package 28919.9 Learning Python and scripting with ArcGIS 28919.10 Concluding remarks 290References 291Part IV Illustrative Case Studies 293A Preamble to Case Studies 29520 Watershed Delineation 29720.1 Introduction 29720.2 Background 29720.3 Methods 29820.3.1 Generalized methods 29820.3.2 Application 29820.3.3 Application of ArcGIS Spatial Analyst tools 29820.3.4 Application of ArcHydro for drainage analysis using digital terrain data 30320.4 Concluding remarks 311References 31121 Loosely Coupled Hydrologic Model 31321.1 Introduction 31321.2 Study area 31321.3 Methods 31421.3.1 Image processing 31521.3.2 ET/EV data 31721.3.3 Accuracy assessment 31721.3.4 Water budget spreadsheet model 31721.4 Results and discussions 31821.4.1 Image classification results 31821.4.2 Water budget calculation 31921.5 Conclusions 323Acknowledgment 324References 32422 Watershed Characterization 32522.1 Introduction 32522.2 Background 32522.3 Approach 32622.3.1 Analysis of watershed characteristics and reclassification 32722.3.2 Integrated evaluation of watershed runoff potential 33022.4 Summary and conclusions 332References 34523 Tightly Coupled Models with GIS for Watershed Impact Assessment 34723.1 Introduction 34723.1.1 Land use and soil influences on runoff and the curve number (CN) 34723.2 Methods 35023.2.1 Study area 35023.2.2 Data processing 35023.2.3 Data layers 35123.3 Results and discussion 35323.4 Summary and conclusions 357References 35724 GIS for Land Use Impact Assessment 35924.1 Introduction 35924.2 Description of study area and datasets 36024.3 Results and discussion 37024.4 Conclusions 386References 38725 TMDL Curve Number 38925.1 Introduction 38925.2 Formulation of competing models 38925.3 Use of Geographic Information System to obtain parameters for use in the NRCS method 39025.3.1 Nonpoint source loading determination 39125.4 Risk associated with different formulations 39225.5 Summary and conclusions 394References 39526 Tight Coupling MCDM Models in GIS 39726.1 Introduction 39726.2 Using GIS for groundwater vulnerability assessment 39826.3 Application of DRASTIC methodology in South Texas 39826.4 Study area 39826.5 Compiling the database for the DRASTIC index 39826.6 Development of DRASTIC vulnerability index 39926.6.1 Depth to groundwater 40026.6.2 Recharge 40126.6.3 Aquifer media 40126.6.4 Soil media 40126.6.5 Topography 40226.6.6 Impact of vadose zone 40226.6.7 Hydraulic conductivity 40326.7 DRASTIC index 40326.8 Summary 404References 40427 Advanced GIS MCDM Model Coupling for Assessing Human Health Risks 40527.1 Introduction 40527.2 Background information 40627.2.1 Groundwater vulnerability parameters 40627.2.2 Pathogen transport parameters 40627.2.3 Pathogen survival parameters 40727.3 Methods 40727.3.1 Study area 40727.3.2 Conceptual framework 40727.3.3 Data layers 40827.4 Results and discussion 41227.5 Conclusions 419References 41928 Embedded Coupling with JAVA 42128.1 Introduction 42128.2 Previous work 42228.3 Mathematical background 42228.4 Data formats of input files 42328.5 AFC structure and usage 42328.6 Illustrative example 424References 42629 GIS-Enabled Physics-Based Contaminant Transport Models for MCDM 42729.1 Introduction 42729.2 Methodology 42829.2.1 Conceptual model 42829.2.2 Mass-balance expressions 42929.2.3 Solutions of the steady-state mass-balance equation 43029.2.4 Model parameterization 43129.3 Results and discussion 43329.3.1 Sensitivity analysis 43529.4 Summary and conclusions 437References 43730 Coupling of Statistical Methods with GIS for Groundwater Vulnerability Assessment 43930.1 Introduction 43930.1.1 Logistic regression 43930.1.2 Akaike’s information criterion (AIC) 44030.2 Methodology 44030.2.1 Application of logistic regression (LR) to DRASTIC vulnerability model 44030.2.2 Implementation in GIS 44030.3 Results and discussion 44030.3.1 Implementation in GIS 44130.4 Summary and conclusions 444References 44431 Coupling of Fuzzy Logic-Based Method with GIS for Groundwater Vulnerability Assessment 44731.1 Introduction 44731.2 Methodology 44831.2.1 Fuzzy sets and fuzzy numbers 44831.2.2 Fuzzy arithmetic 44931.2.3 Elementary fuzzy arithmetic for triangular fuzzy sets 44931.2.4 Approximate operations on triangular fuzzy sets 44931.2.5 Fuzzy aquifer vulnerability characterization 45031.2.6 Specification of weights 45031.2.7 Specification of ratings 45031.2.8 Defuzzification procedures 45231.2.9 Implementation 45331.3 Results and discussion 45331.3.1 Incorporation of fuzziness in decision-makers’ weights and ratings 45331.3.2 Comparison of exact and approximate fuzzy arithmetic for aquifer vulnerability estimation when ratings and weights are fuzzy 45331.4 Summary and conclusions 457References 45732 Tight Coupling of Artificial Neural Network (ANN) and GIS 46132.1 Introduction 46132.1.1 The concept of artificial neural network (ANN) 46132.2 Methodology 46332.2.1 Data development 46332.2.2 Application of feedforward neural network (FFNN) to DRASTIC groundwater vulnerability assessment model 46332.2.3 Application of radial basis function (RBF) neural network to DRASTIC groundwater vulnerability assessment model 46432.2.4 Performance evaluation of feedforward neural network (FFNN) and radial basis function (RBF) neural network models 46432.2.5 Implementation of artificial neural network in GIS 46532.3 Results and discussion 46532.3.1 Model performance evaluation for FFNN and RBF network models 46832.3.2 Results of ANN-GIS integration 47232.4 Summary and conclusion 472References 47333 Loose Coupling of Artificial Neuro-Fuzzy Information System (ANFIS) and GIS 47533.1 Introduction 47533.2 Methods 47533.2.1 Study area 47533.2.2 Data development 47633.2.3 Selection of the model inputs 47633.2.4 Development of artificial neuro-fuzzy models 47733.3 Results and discussion 47833.4 Conclusions 479References 48034 GIS and Hybrid Model Coupling 48334.1 Introduction 48334.2 Methodology 48334.2.1 Multicriteria decision-making model for assessing recharge potential 48434.2.2 Data compilation and GIS operations 48534.3 Results and discussion 48634.3.1 Identification of potential recharge areas and model evaluation 48634.3.2 Hydrogeological and geochemical assessment of identified recharge locations 49034.3.3 Artificial recharge locations in the context of demands 49134.4 Summary and conclusions 493References 49335 Coupling Dynamic Water Resources Models with GIS 49535.1 Introduction 49535.2 Modeling infiltration: Green–Ampt approach 49535.3 Coupling Green–Ampt modeling with regional-scale soil datasets 49735.4 Result and discussion 49735.5 Summary 498References 49936 Tight Coupling of Well Head Protection Models in GIS with Vector Datasets 50136.1 Introduction 50136.2 Methods for delineating well head protection areas 50136.3 Fixed radius model development 50236.4 Implementing well head protection models within GIS 50336.5 Data compilation 50336.6 Results and discussion 50436.6.1 Arbitrary fixed radius buffer 50436.6.2 Calculated variable radius buffer 50436.7 Summary 505References 50637 Loosely Coupled Models in GIS for Optimization 50737.1 Introduction 50737.2 Study area 50837.3 Mathematical model 50937.4 Data compilation and model application 51037.5 Results 51137.5.1 Baseline run 51137.5.2 Evaluation of certificate of convenience and necessity delineations 51237.5.3 Impacts of wastewater treatment efficiencies 51237.5.4 Impacts of influent characteristics 51337.5.5 Evaluation of current and future effluent discharge policies 51337.6 Summary and conclusions 513References 51438 Epilogue 515References 517Example of a Syllabus: For Graduate 6000 Level Engineering Students 519Example of a Syllabus: For Graduate 6000 Level Environmental Science and Geography Students 523Example of a Syllabus: For Undergraduate 4000 Level Engineering Students 527Example of a Syllabus: For Undergraduate 4000 Level Environmental Science and Geography Students 531Index 535