Barnali Dixon, Venkatesh Uddameri

GIS and Geocomputation for Water Resource Science and Engineering

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Wiley Works
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 576
• Erscheinungstermin: 26. Januar 2016
• Englisch
• Abmessung: 280mm x 216mm x 30mm
• Gewicht: 1410g
• ISBN-13: 9781118354131
• ISBN-10: 1118354133
• Artikelnr.: 41561812

Autorenporträt

Kristy Dixon graduated with a degree in English from the University of Utah. She started writing stories when she was seven and has been writing ever since. In the beginning she tried writing historical fiction, but soon found that to be boring. She started writing The Silver Eclipse at the urging of her children and found fantasy writing to be more fun. She has nine children and six chickens. If she isn't writing or playing board games with her kids, she is probably eating cookies or wishing that she were eating cookies. You can contact Kristy at [e-mail] and find her website at kristydixonbooks.com.

Inhaltsangabe

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 2.8 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 3.4 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 3.6 Accuracy issues
surrounding land use land cover maps 32 3.7 Sensitivity of hydrologic
models to LULC resolution 34 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 4.8 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 6.4 Database structure, storage, and organization 71 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 7.5 Aerial and satellite remote sensing and imagery 83 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 7.10 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 10.2 Topology and geometric computations (various
measurements) 137 10.3 Proximity and network analysis 143 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 11.3 Reclassification 155
11.4 Zonal operations 157 11.5 Calculation of area, perimeter, and shape
163 11.6 Statistical operations 164 11.7 Neighborhood operations 165 11.8
Determination of distance, proximity, and connectivity in raster 167 11.9
Physical distance and cost distance analysis 169 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
12.2 Topics in watershed characterization and analysis 191 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 13.5 Coupling mathematical models with GIS 209 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 15.6 Geostatistical Methods 227 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 16.3
Information theoretic approaches 238 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 17.4 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 19.5
Using ModelBuilder to automate geoprocessing tasks 279 19.6 Using Python
scripting for geoprocessing 287 19.7 Introduction to some useful Python
constructs 288 19.8 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 20.4 Concluding remarks 311 References 311
21 Loosely Coupled Hydrologic Model 313 21.1 Introduction 313 21.2 Study
area 313 21.3 Methods 314 21.4 Results and discussions 318 21.5 Conclusions
323 Acknowledgment 324 References 324 22 Watershed Characterization 325
22.1 Introduction 325 22.2 Background 325 22.3 Approach 326 22.4 Summary
and conclusions 332 References 345 23 Tightly Coupled Models with GIS for
Watershed Impact Assessment 347 23.1 Introduction 347 23.2 Methods 350 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 25.4 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 26.7 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 27.3 Methods 407 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 29.3 Results and discussion 433 29.4 Summary and
conclusions 437 References 437 30 Coupling of Statistical Methods with GIS
for Groundwater Vulnerability Assessment 439 30.1 Introduction 439 30.2
Methodology 440 30.3 Results and discussion 440 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 31.3 Results and discussion 453 31.4 Summary and
conclusions 457 References 457 32 Tight Coupling of Artificial Neural
Network (ANN) and GIS 461 32.1 Introduction 461 32.2 Methodology 463 32.3
Results and discussion 465 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 33.3 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 34.3 Results and discussion
486 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 36.7 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 37.6 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