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 Bud.