GIS and Geocomputation for Water Resource Science and Engineering

GIS and Geocomputation for Water Resource and Science 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. INDICE: List of Case Studies xi .A Preamble to Case–Studies xiii .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 (DEM) 28 .3.4 Sources of data for developing digital elevation models 29 .3.4.1 Accuracy issues surrounding digital elevation models 29 .3.5 Sensitivity of hydrologic models to DEM resolution 31 .3.5.1 Land use and land cover 31 .3.5.2 Sources of data for developing digital land use land cover maps 31 .3.6 Accuracy issues surrounding land use land cover maps 32 .3.6.1 Anderson classification and the standardization of LULC mapping 32 .3.7 Sensitivity of hydrologic models to LULC resolution 33 .3.7.1 LULC, impervious surface, and water quality 33 .3.7.2 Soil datasets 36 .3.8 Sources of data for developing soil maps 36 .3.9 Accuracy issues surrounding soil mapping 36 .3.10 Sensitivity of hydrologic models to soils resolution 38 .3.11 Concluding remarks 42 .References 44 .4 Water–Related Geospatial Datasets 47 .4.1 Introduction 47 .4.2 River basin, watershed, and sub–watershed 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 (advanced material) 52 .4.11 Conclusions 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 79 .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 remote–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 Twelve steps for integration of remotely sensed data into GIS 93 .7.11 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 199 .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: phenomenon, 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 .13.5 Physics–based models 208 .13.5.1 Expert–driven or stakeholder–driven models 208 .13.6 Coupling mathematical models with GIS 209 .13.6.1 Loose coupling of GIS and mathematical models 209 .13.6.2 Tight coupling of GIS and mathematical models 209 .13.6.3 What type of coupling to pursue? 210 .13.7 Closing 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 226 .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 233 .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 (SVM) 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 the 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 Closing 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 276 .19.5 Using ModelBuilder to automate geoprocessing tasks 277 .19.6 Using Python scripting for geoprocessing 283 .19.7 Introduction to some useful Python constructs 284 .19.7.1 Basic arithmetic and programming logic syntax 284 .19.7.2 Defining functions in Python 288 .19.7.3 Python classes 288 .19.7.4 Python modules and site–packages 290 .19.8 ArcPy geoprocessing modules and site–package 290 .19.9 Learning Python and scripting with ArcGIS 290 .19.10 Closing remarks 291 .References 291 .Part IV Illustrative Case Studies 293 .20 Watershed Delineation 295 .20.1 Introduction 295 .20.2 Background 295 .20.3 Methods 296 .20.3.1 Generalized methods 296 .20.3.2 Application 296 .20.3.3 Application of ArcGIS Spatial Analyst tools 296 .20.3.4 Application of ArcHydro for drainage analysis using digital terrain data 301 .20.4 Concluding remarks 309 .References 309 .21 Loosely Coupled Hydrologic Model 311 .21.1 Introduction 311 .21.2 Study area 311 .21.3 Methods 312 .21.3.1 Image processing 313 .21.3.2 ET/EV data 315 .21.3.3 Accuracy assessment 315 .21.3.4 Water budget spreadsheet model 315 .21.4 Results and discussions 316 .21.4.1 Image classification results 316 .21.4.2 Water budget calculation 317 .21.5 Conclusions 320 .Acknowledgment 322 .References 322 .22 Watershed Characterization 323 .22.1 Introduction 323 .22.2 Background 323 .22.3 Application and major tasks 324 .22.3.1 Analysis of watershed characteristics and reclassification 325 .22.3.2 Application of map algebra with reclassified maps 329 .22.3.3 Analysis of water quality parameters for the watershed 329 .References 341 .23 Tightly Coupled Models with GIS for Watershed Impact Assessment 343 .23.1 Introduction 343 .23.1.1 Overview of tasks 343 .23.1.2 Specific tasks 345 .23.2 Methods 347 .23.2.1 Study area 347 .23.2.2 Data processing 348 .23.2.3 Data layers 349 .23.3 Results and discussions 351 .23.4 Summary and conclusions 360 .References 360 .24 Tightly Coupled Models with GIS for Land Use Impact Assessment 363 .24.1 Introduction 363 .24.2 Description of study area and dataset 364 .24.3 Methodologies 365 .24.4 Results and discussions 374 .24.5 Conclusions 390 .References 391 .25 TMDL Curve Number 393 .25.1 Introduction 393 .25.1.1 Formulation of competing models 393 .25.1.2 Risk associated with different formulations 396 .25.2 Summary and conclusions 398 .References 399 .26 Tight Coupling MCDM Models in GIS 401 .26.1 Introduction 401 .26.2 Using GIS for groundwater vulnerability assessment 402 .26.3 Application of DRASTIC methodology in South Texas 402 .26.4 Study area 402 .26.5 Compiling the database for the DRASTIC index 402 .26.6 Development of DRASTIC vulnerability index 403 .26.6.1 Depth to groundwater 404 .26.6.2 Recharge 405 .26.6.3 Aquifer media 405 .26.6.4 Soil media 405 .26.6.5 Topography 406 .26.6.6 Impact of vadose zone 406 .26.6.7 Hydraulic conductivity 407 .26.7 DRASTIC index 407 .Summary 408 .References 408 .27 Advanced GIS MCDM Model Coupling for Assessing Human Health Risks 409 .27.1 Introduction 409 .27.2 Background information 410 .27.2.1 Groundwater vulnerability parameters 410 .27.2.2 Pathogen transport parameters 410 .27.2.3 Pathogen survival parameters 411 .27.3 Methods 411 .27.3.1 Study area 411 .27.3.2 Conceptual framework 411 .27.3.3 Data layers 412 .27.4 Results and discussion 416 .27.5 Conclusions 423 .References 423 .28 Embedded Coupling with JAVA 425 .28.1 Introduction 425 .28.2 Previous work 426 .28.3 Mathematical background 426 .28.4 Data formats of input files 427 .28.5 AFC structure and usage 427 .28.6 Illustrative example 428 .References 430 .29 GIS–Enabled Physicstable–Based Contaminant Transport Models for MCDM 431 .29.1 Introduction 431 .29.2 Methodology 432 .29.2.1 Conceptual model 432 .29.2.2 Mass–balance expressions 433 .29.2.3 Solutions of the steady–state mass–balance 434 .29.2.4 Model parameterization 435 .29.3 Results and discussion 437 .29.3.1 Sensitivity analysis 439 .29.4 Summary and conclusions 441 .References 441 .30 Coupling of Statistical Methods with GIS for Groundwater Vulnerability Assessment 443 .30.1 Introduction 443 .30.1.1 Logistic regression 443 .30.1.2 Akaike s information criterion (AIC) 444 .30.2 Methodology 444 .30.2.1 Application of logistic regression (LR) to DRASTIC vulnerability model 444 .30.2.2 Implementation in GIS 444 .30.3 Results and discussions 444 .30.3.1 Implementation in GIS 445 .30.4 Summary and conclusion 448 .References 448 .31 Coupling of Fuzzy Logic–Based Method with GIS for Groundwater Vulnerability Assessment 451 .31.1 Introduction 451 .31.2 Methodology 452 .31.2.1 Fuzzy sets and fuzzy numbers 452 .31.2.2 Fuzzy arithmetic 453 .31.2.3 Elementary fuzzy arithmetic for triangular fuzzy sets 453 .31.2.4 Approximate operations on triangular fuzzy sets 453 .31.2.5 Fuzzy aquifer vulnerability characterization 454 .31.2.6 Specification of weights 454 .31.2.7 Specification of ratings 454 .31.2.8 Defuzzification procedures 456 .31.2.9 Implementation 457 .31.3 Results and discussion 457 .31.3.1 Incorporation of fuzziness in decision–makers weights and ratings 457 .31.3.2 Comparison of exact and approximate fuzzy arithmetic for aquifer vulnerability estimation when .ratings and weights are fuzzy 457 .31.4 Summary and conclusions 461 .References 461 .32 Tight Coupling of Artificial Neural Network (ANN) and GIS 465 .32.1 Introduction 465 .32.1.1 The concept artificial neural network (ANN) 465 .32.2 Methodology 467 .32.2.1 Data development 467 .32.2.2 Application of feedforward neural network (FFNN) to DRASTIC groundwater vulnerability assessment model 467 .32.2.3 Application of radial basis function (RBF) neural network to DRASTIC groundwater vulnerability assessment model 468 .32.2.4 Performance evaluation of feedforward neural network (FFNN) and radial basis function (RBF) neural network models 468 .32.2.5 Implementation of artificial neural network in GIS 469 .32.3 Results and discussion 469 .32.3.1 Model performance evaluation for FFNN and RBF network models 472 .32.3.2 Results of ANN–GIS integration 476 .32.4 Summary and conclusion 476 .References 477 .33 Loose Coupling of Artificial Neuro–Fuzzy Information System (ANFIS) and GIS 479 .33.1 Introduction 479 .33.2 Methods 479 .33.2.1 Study area 479 .33.2.2 Data development 480 .33.2.3 Selection of the model inputs 480 .33.2.4 Development of artificial neuro–fuzzy models 481 .33.3 Results and discussion 482 .33.4 Conclusions 484 .References 485 .34 GIS and Hybrid Model Coupling 487 .34.1 Introduction 487 .34.2 Methodology 487 .34.2.1 Multicriteria decision–making model for assessing recharge potential 488 .34.2.2 Data compilation and GIS operations 489 .34.3 Results and discussion 490 .34.3.1 Identification of potential recharge areas and model evaluation 490 .34.3.2 Hydrogeological and geochemical assessment of identified recharge locations 494 .34.3.3 Artificial recharge locations in the context of demands 495 .34.4 Summary and conclusions 497 .Vitae 497 .References 497 .35 Coupling dynamic water resources models with GIS 499 .35.1 Introduction 499 .35.2 Modeling infiltration: Green Ampt approach 499 .35.3 Coupling Green Ampt modeling with regional–scale soil datasets 501 .35.4 Result and discussion 501 .Summary 502 .References 503 .36 Tight Coupling of MCDM models in GIS with Vector Datasets 505 .36.1 Introduction 505 .36.2 Methods for delineating well head protection areas 505 .36.3 Fixed radius model development 506 .36.4 Implementing well head protection models within GIS 507 .36.5 Data compilation 507 .36.6 Results and discussion 508 .36.6.1 Arbitrary fixed radius buffer 508 .36.6.2 Calculated variable radius buffer 508 .Summary 509 .References 510 .37 Loosely Coupled Models in GIS for Optimization 511 .37.1 Introduction 511 .37.2 Study area 512 .37.3 Mathematical model 513 .37.4 Data compilation and model application 514 .37.5 Results 515 .37.5.1 Baseline run 515 .37.5.2 Evaluation of certificate of convenience and necessity delineations 516 .37.5.3 Impacts of wastewater treatment efficiencies 516 .37.5.4 Impacts of influent characteristics 517 .37.5.5 Evaluation of current and future effluent discharge policies 517 .37.6 Summary and Conclusions 517 .References 518 .38 Epilogue 519 .References 521 .Index 523

• ISBN: 978-1-118-35413-1
• Editorial: Wiley–Blackwell
• Encuadernacion: Rústica
• Páginas: 504
• Fecha Publicación: 12/06/2015
• Nº Volúmenes: 1
• Idioma: Inglés