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GIS and Geocomputation for Water Resource Science and Engineering


GIS and Geocomputation for Water Resource Science and Engineering


Wiley Works 1. Aufl.

von: Barnali Dixon, Venkatesh Uddameri

63,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 25.11.2015
ISBN/EAN: 9781118826218
Sprache: englisch
Anzahl Seiten: 576

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Beschreibungen

<p><i>GIS and Geocomputation for Water Resource </i><i>Science </i><i>and Engineering</i> 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.</p>
<p>Preface xiii</p> <p>About the Companion Website xv</p> <p>List of Acronyms xvii</p> <p><b>Part I GIS, Geocomputation, and GIS Data 1</b></p> <p><b>1 Introduction 3</b></p> <p>1.1 What is geocomputation? 3</p> <p>1.2 Geocomputation and water resources science and engineering 4</p> <p>1.3 GIS-enabled geocomputation in water resources science and engineering 5</p> <p>1.4 Why should water resources engineers and scientists study GIS 5</p> <p>1.5 Motivation and organization of this book 6</p> <p>1.6 Concluding remarks 7</p> <p>References 9</p> <p><b>2 A Brief History of GIS and Its Use in Water Resources Engineering 11</b></p> <p>2.1 Introduction 11</p> <p>2.2 Geographic Information Systems (GIS) – software and hardware 11</p> <p>2.3 Remote sensing and global positioning systems and development of GIS 12</p> <p>2.4 History of GIS in water resources applications 13</p> <p>2.5 Recent trends in GIS 19</p> <p>2.6 Benefits of using GIS in water resources engineering and science 20</p> <p>2.7 Challenges and limitations of GIS-based approach to water resources engineering 20</p> <p>2.8 Concluding remarks 23</p> <p>References 25</p> <p><b>3 Hydrologic Systems and Spatial Datasets 27</b></p> <p>3.1 Introduction 27</p> <p>3.2 Hydrological processes in a watershed 27</p> <p>3.3 Fundamental spatial datasets for water resources planning: management and modeling studies 28</p> <p>3.4 Sources of data for developing digital elevation models 30</p> <p>3.5 Sensitivity of hydrologic models to DEM resolution 31</p> <p>3.6 Accuracy issues surrounding land use land cover maps 32</p> <p>3.7 Sensitivity of hydrologic models to LULC resolution 34</p> <p>3.8 Sources of data for developing soil maps 36</p> <p>3.9 Accuracy issues surrounding soil mapping 37</p> <p>3.10 Sensitivity of hydrologic models to soils resolution 38</p> <p>3.11 Concluding remarks 43</p> <p>References 44</p> <p><b>4 Water-Related Geospatial Datasets 47</b></p> <p>4.1 Introduction 47</p> <p>4.2 River basin, watershed, and subwatershed delineations 47</p> <p>4.3 Streamflow and river stage data 48</p> <p>4.4 Groundwater level data 48</p> <p>4.5 Climate datasets 48</p> <p>4.6 Vegetation indices 49</p> <p>4.7 Soil moisture mapping 49</p> <p>4.8 Water quality datasets 51</p> <p>4.9 Monitoring strategies and needs 51</p> <p>4.10 Sampling techniques and recent advancements in sensing technologies 52</p> <p>4.11 Concluding remarks 53</p> <p>References 53</p> <p><b>5 Data Sources and Models 55</b></p> <p>5.1 Digital data warehouses and repositories 55</p> <p>5.2 Software for GIS and geocomputations 55</p> <p>5.3 Software and data models for water resources applications 59</p> <p>5.4 Concluding remarks 60</p> <p>References 60</p> <p><b>Part II Foundations of GIS 61</b></p> <p><b>6 Data Models for GIS 63</b></p> <p>6.1 Introduction 63</p> <p>6.2 Data types, data entry, and data models 63</p> <p>6.3 Categorization of spatial datasets 65</p> <p>6.4 Database structure, storage, and organization 71</p> <p>6.5 Data storage and encoding 75</p> <p>6.6 Data conversion 76</p> <p>6.7 Concluding remarks 78</p> <p>References 80</p> <p><b>7 Global Positioning Systems (GPS) and Remote Sensing 81</b></p> <p>7.1 Introduction 81</p> <p>7.2 The global positioning system (GPS) 81</p> <p>7.3 Use of GPS in water resources engineering studies 82</p> <p>7.4 Workflow for GPS data collection 83</p> <p>7.4.1 12 Steps to effective GPS data collection and compilation 83</p> <p>7.5 Aerial and satellite remote sensing and imagery 83</p> <p>7.6 Data and cost of acquiring remotely sensed data 84</p> <p>7.7 Principles of remote sensing 85</p> <p>7.8 Remote sensing applications in water resources engineering and science 88</p> <p>7.9 Bringing remote sensing data into GIS 91</p> <p>7.10 Concluding remarks 94</p> <p>References 95</p> <p><b>8 Data Quality, Errors, and Uncertainty 97</b></p> <p>8.1 Introduction 97</p> <p>8.2 Map projection, datum, and coordinate systems 97</p> <p>8.3 Projections in GIS software 101</p> <p>8.4 Errors, data quality, standards, and documentation 102</p> <p>8.5 Error and uncertainty 106</p> <p>8.6 Role of resolution and scale on data quality 107</p> <p>8.7 Role of metadata in GIS analysis 109</p> <p>8.8 Concluding remarks 109</p> <p>References 109</p> <p><b>9 GIS Analysis: Fundamentals of Spatial Query 111</b></p> <p>9.1 Introduction to spatial analysis 111</p> <p>9.2 Querying operations in GIS 116</p> <p>9.3 Structured query language (SQL) 119</p> <p>9.4 Raster data query by cell value 122</p> <p>9.5 Spatial join and relate 125</p> <p>9.6 Concluding remarks 128</p> <p>References 128</p> <p><b>10 Topics in Vector Analysis 129</b></p> <p>10.1 Basics of geoprocessing (buffer, dissolve, clipping, erase, and overlay) 129</p> <p>10.2 Topology and geometric computations (various measurements) 137</p> <p>10.3 Proximity and network analysis 143</p> <p>10.4 Concluding remarks 145</p> <p>References 147</p> <p><b>11 Topics in Raster Analysis 149</b></p> <p>11.1 Topics in raster analysis 149</p> <p>11.2 Local operations 149</p> <p>11.3 Reclassification 155</p> <p>11.4 Zonal operations 157</p> <p>11.5 Calculation of area, perimeter, and shape 163</p> <p>11.6 Statistical operations 164</p> <p>11.7 Neighborhood operations 165</p> <p>11.8 Determination of distance, proximity, and connectivity in raster 167</p> <p>11.9 Physical distance and cost distance analysis 169</p> <p>11.10 Buffer analysis in raster 174</p> <p>11.11 Viewshed analysis 175</p> <p>11.12 Raster data management (mask, spatial clip, and mosaic) 178</p> <p>11.13 Concluding remarks 179</p> <p>References 181</p> <p><b>12 Terrain Analysis and Watershed Delineation 183</b></p> <p>12.1 Introduction 183</p> <p>12.2 Topics in watershed characterization and analysis 191</p> <p>12.3 Concluding remarks 200</p> <p>References 200</p> <p><b>Part III Foundations of Modeling 203</b></p> <p><b>13 Introduction to Water Resources Modeling 205</b></p> <p>13.1 Mathematical modeling in water resources engineering and science 205</p> <p>13.2 Overview of mathematical modeling in water resources engineering and science 206</p> <p>13.3 Conceptual modeling: phenomena, processes, and parameters of a system 206</p> <p>13.4 Common approaches used to develop mathematical models in water resources engineering 206</p> <p>13.5 Coupling mathematical models with GIS 209</p> <p>13.6 Concluding remarks 210</p> <p>References 211</p> <p><b>14 Water Budgets and Conceptual Models 213</b></p> <p>14.1 Flow modeling in a homogeneous system (boxed or lumped model) 213</p> <p>14.2 Flow modeling in heterogeneous systems (control volume approach) 215</p> <p>14.3 Conceptual model: soil conservation survey curve number method 217</p> <p>14.4 Fully coupled watershed-scale water balance model: soil water assessment tool (SWAT) 218</p> <p>14.5 Concluding remarks 219</p> <p>References 220</p> <p><b>15 Statistical and Geostatistical Modeling 221</b></p> <p>15.1 Introduction 221</p> <p>15.2 Ordinary least squares (OLS) linear regression 221</p> <p>15.3 Logistic regression 222</p> <p>15.4 Data reduction and classification techniques 223</p> <p>15.5 Topics in spatial interpolation and sampling 223</p> <p>15.6 Geostatistical Methods 227</p> <p>15.7 Kriging 230</p> <p>15.8 Critical issues in interpolation 231</p> <p>15.9 Concluding remarks 232</p> <p>References 234</p> <p><b>16 Decision Analytic and Information Theoretic Models 235</b></p> <p>16.1 Introduction 235</p> <p>16.2 Decision analytic models 235</p> <p>16.3 Information theoretic approaches 238</p> <p>16.4 Spatial data mining (SDM) for knowledge discovery in a database 245</p> <p>16.5 The trend of temporal data modeling in GIS 245</p> <p>16.6 Concluding remarks 246</p> <p>References 246</p> <p><b>17 Considerations for GIS and Model Integration 249</b></p> <p>17.1 Introduction 249</p> <p>17.2 An overview of practical considerations in adopting and integrating GIS into water resources projects 250</p> <p>17.3 Theoretical considerations related to GIS and water resources model integration 251</p> <p>17.4 Concluding remarks 256</p> <p>References 257</p> <p><b>18 Useful Geoprocessing Tasks While Carrying Out Water Resources Modeling 259</b></p> <p>18.1 Introduction 259</p> <p>18.2 Getting all data into a common projection 259</p> <p>18.3 Adding point (<i>X</i>, <i>Y</i>) data and calculating their projected coordinates 260</p> <p>18.4 Image registration and rectification 264</p> <p>18.5 Editing tools to transfer information to vectors 266</p> <p>18.6 GIS for cartography and visualization 270</p> <p>18.7 Concluding remarks 271</p> <p>References 271</p> <p><b>19 Automating Geoprocessing Tasks in GIS 273</b></p> <p>19.1 Introduction 273</p> <p>19.2 Object-oriented programming paradigm 273</p> <p>19.3 Vectorized (array) geoprocessing 274</p> <p>19.4 Making nongeographic attribute calculations 274</p> <p>19.5 Using ModelBuilder to automate geoprocessing tasks 279</p> <p>19.6 Using Python scripting for geoprocessing 287</p> <p>19.7 Introduction to some useful Python constructs 288</p> <p>19.8 ArcPy geoprocessing modules and site-package 289</p> <p>19.9 Learning Python and scripting with ArcGIS 289</p> <p>19.10 Concluding remarks 290</p> <p>References 291</p> <p><b>Part IV Illustrative Case Studies 293</b></p> <p><b>A Preamble to Case Studies 295</b></p> <p><b>20 Watershed Delineation 297</b></p> <p>20.1 Introduction 297</p> <p>20.2 Background 297</p> <p>20.3 Methods 298</p> <p>20.4 Concluding remarks 311</p> <p>References 311</p> <p><b>21 Loosely Coupled Hydrologic Model 313</b></p> <p>21.1 Introduction 313</p> <p>21.2 Study area 313</p> <p>21.3 Methods 314</p> <p>21.4 Results and discussions 318</p> <p>21.5 Conclusions 323</p> <p>Acknowledgment 324</p> <p>References 324</p> <p><b>22 Watershed Characterization 325</b></p> <p>22.1 Introduction 325</p> <p>22.2 Background 325</p> <p>22.3 Approach 326</p> <p>22.4 Summary and conclusions 332</p> <p>References 345</p> <p><b>23 Tightly Coupled Models with GIS for Watershed Impact Assessment 347</b></p> <p>23.1 Introduction 347</p> <p>23.2 Methods 350</p> <p>23.3 Results and discussion 353</p> <p>23.4 Summary and conclusions 357</p> <p>References 357</p> <p><b>24 GIS for Land Use Impact Assessment 359</b></p> <p>24.1 Introduction 359</p> <p>24.2 Description of study area and datasets 360</p> <p>24.3 Results and discussion 370</p> <p>24.4 Conclusions 386</p> <p>References 387</p> <p><b>25 TMDL Curve Number 389</b></p> <p>25.1 Introduction 389</p> <p>25.2 Formulation of competing models 389</p> <p>25.3 Use of Geographic Information System to obtain parameters for use in the NRCS method 390</p> <p>25.4 Risk associated with different formulations 392</p> <p>25.5 Summary and conclusions 394</p> <p>References 395</p> <p><b>26 Tight Coupling MCDM Models in GIS 397</b></p> <p>26.1 Introduction 397</p> <p>26.2 Using GIS for groundwater vulnerability assessment 398</p> <p>26.3 Application of DRASTIC methodology in South Texas 398</p> <p>26.4 Study area 398</p> <p>26.5 Compiling the database for the DRASTIC index 398</p> <p>26.6 Development of DRASTIC vulnerability index 399</p> <p>26.7 DRASTIC index 403</p> <p>26.8 Summary 404</p> <p>References 404</p> <p><b>27 Advanced GIS MCDM Model Coupling for Assessing Human Health Risks 405</b></p> <p>27.1 Introduction 405</p> <p>27.2 Background information 406</p> <p>27.3 Methods 407</p> <p>27.4 Results and discussion 412</p> <p>27.5 Conclusions 419</p> <p>References 419</p> <p><b>28 Embedded Coupling with JAVA 421</b></p> <p>28.1 Introduction 421</p> <p>28.2 Previous work 422</p> <p>28.3 Mathematical background 422</p> <p>28.4 Data formats of input files 423</p> <p>28.5 AFC structure and usage 423</p> <p>28.6 Illustrative example 424</p> <p>References 426</p> <p><b>29 GIS-Enabled Physics-Based Contaminant Transport Models for MCDM 427</b></p> <p>29.1 Introduction 427</p> <p>29.2 Methodology 428</p> <p>29.3 Results and discussion 433</p> <p>29.4 Summary and conclusions 437</p> <p>References 437</p> <p><b>30 Coupling of Statistical Methods with GIS for Groundwater Vulnerability Assessment 439</b></p> <p>30.1 Introduction 439</p> <p>30.2 Methodology 440</p> <p>30.3 Results and discussion 440</p> <p>30.4 Summary and conclusions 444</p> <p>References 444</p> <p><b>31 Coupling of Fuzzy Logic-Based Method with GIS for Groundwater Vulnerability Assessment 447</b></p> <p>31.1 Introduction 447</p> <p>31.2 Methodology 448</p> <p>31.3 Results and discussion 453</p> <p>31.4 Summary and conclusions 457</p> <p>References 457</p> <p><b>32 Tight Coupling of Artificial Neural Network (ANN) and GIS 461</b></p> <p>32.1 Introduction 461</p> <p>32.2 Methodology 463</p> <p>32.3 Results and discussion 465</p> <p>32.4 Summary and conclusion 472</p> <p>References 473</p> <p><b>33 Loose Coupling of Artificial Neuro-Fuzzy Information System (ANFIS) and GIS 475</b></p> <p>33.1 Introduction 475</p> <p>33.2 Methods 475</p> <p>33.3 Results and discussion 478</p> <p>33.4 Conclusions 479</p> <p>References 480</p> <p><b>34 GIS and Hybrid Model Coupling 483</b></p> <p>34.1 Introduction 483</p> <p>34.2 Methodology 483</p> <p>34.3 Results and discussion 486</p> <p>34.4 Summary and conclusions 493</p> <p>References 493</p> <p><b>35 Coupling Dynamic Water Resources Models with GIS 495</b></p> <p>35.1 Introduction 495</p> <p>35.2 Modeling infiltration: Green–Ampt approach 495</p> <p>35.3 Coupling Green–Ampt modeling with regional-scale soil datasets 497</p> <p>35.4 Result and discussion 497</p> <p>35.5 Summary 498</p> <p>References 499</p> <p><b>36 Tight Coupling of Well Head Protection Models in GIS with Vector Datasets 501</b></p> <p>36.1 Introduction 501</p> <p>36.2 Methods for delineating well head protection areas 501</p> <p>36.3 Fixed radius model development 502</p> <p>36.4 Implementing well head protection models within GIS 503</p> <p>36.5 Data compilation 503</p> <p>36.6 Results and discussion 504</p> <p>36.7 Summary 505</p> <p>References 506</p> <p><b>37 Loosely Coupled Models in GIS for Optimization 507</b></p> <p>37.1 Introduction 507</p> <p>37.2 Study area 508</p> <p>37.3 Mathematical model 509</p> <p>37.4 Data compilation and model application 510</p> <p>37.5 Results 511</p> <p>37.6 Summary and conclusions 513</p> <p>References 514</p> <p><b>38 Epilogue 515</b></p> <p>References 517</p> <p>Example of a Syllabus: For Graduate 6000 Level Engineering Students 519</p> <p>Example of a Syllabus: For Graduate 6000 Level Environmental Science and Geography Students 523</p> <p>Example of a Syllabus: For Undergraduate 4000 Level Engineering Students 527</p> <p>Example of a Syllabus: For Undergraduate 4000 Level Environmental Science and Geography Students 531</p> <p>Index 535</p>
<p><b>BARNALI DIXON</b> is a Professor in the Department of Environmental Science, Policy and Geography, University of South Florida St. Petersburg (USFSP) and the Director of the Geospatial Analytics Lab of USFSP. <p><b>VENKATESH UDDAMERI</b> 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.
<p><b>GIS and Geocomputation for Water Resource Science and Engineering</b> <p><i>GIS and Geocomputation for Water Resource and Science Engineering</i> 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.

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