Water Resources Systems Planning and Management

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Elsevier, 2003 M09 12 - 882 pages
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This book is divided into four parts. The first part, Preliminaries, begins by introducing the basic theme of the book. It provides an overview of the current status of water resources utilization, the likely scenario of future demands, and advantages and disadvantages of systems techniques. An understanding of how the hydrological data are measured and processed is important before undertaking any analysis. The discussion is extended to emerging techniques, such as Remote Sensing, GIS, Artificial Neural Networks, and Expert Systems. The statistical tools for data analysis including commonly used probability distributions, parameter estimation, regression and correlation, frequency analysis, and time-series analysis are discussed in a separate chapter.

Part 2 Decision Making, is a bouquet of techniques organized in 4 chapters. After discussing optimization and simulation, the techniques of economic analysis are covered. Recently, environmental and social aspects, and rehabilitation and resettlement of project-affected people have come to occupy a central stage in water resources management and any good book is incomplete unless these topics are adequately covered. The concept of rational decision making along with risk, reliability, and uncertainty aspects form subject matter of a chapter. With these analytical tools, the practitioner is well equipped to take a rational decision for water resources utilization.

Part 3 deals with Water Resources Planning and Development. This part discusses the concepts of planning, the planning process, integrated planning, public involvement, and reservoir sizing.

The last part focuses on Systems Operation and Management. After a resource is developed, it is essential to manage it in the best possible way. Many dams around the world are losing some storage capacity every year due to sedimentation and therefore, the assessment and management of reservoir sedimentation is described in details. No analysis of water resources systems is complete without consideration of water quality. A river basin is the natural unit in which water occurs. The final chapter discusses various issues related to holistic management of a river basin.

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Decision Making
Water Resources Planning and Development
Systems Operation and Management
Appendix A Conversion Factors
Appendix B Useful Internet Sites

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Page 314 - An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state resulting from the first decision.
Page 28 - Council and for the water planning and development programs of the US Bureau of Reclamation and the US Army Corps of Engineers in Idaho.
Page 180 - A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: 1 . Knowledge is acquired by the network through a learning process; 2. Interneuron connection strengths known as synaptic weights are used to store the knowledge.
Page 152 - Nonforested wetland 71 Dry salt flats 72 Beaches 73 Sandy areas other than beaches 74 Bare exposed rock 75 Strip mines, quarries, and gravel pits 76 Transitional areas 77 Mixed barren land...
Page 83 - ... the greatest depth of precipitation for a given duration that is physically possible over a given size storm area at a particular geographical location at a certain tIme of the year.
Page 196 - INTELLIGENCE (AI) is the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior — understanding language, learning, reasoning, solving problems, and so on.
Page 152 - Croplands and pasture 22 Orchards, groves, vineyards, nurseries, and ornamental horticultural areas 23 Confined feeding operations 24 Other agricultural land...
Page 34 - Dooge (1) defines a system as 'any structure, device, scheme or procedure (real or abstract) that interrelates in a given time reference, an input, cause or stimulus, of matter, energy or information, and an output, effect or response, of information, energy or matter1.
Page 180 - MLPs are supervised learning classifiers that consist of an input layer, an output layer, and one or more hidden layers that extract useful information during learning and assign modifiable weighting coefficients to components of the input layers.

About the author (2003)

Sharad Jain is the Director General of the National Water Development Agency in New Delhi. He has both R & D and teaching experience of more than 35 years in the field of hydrology and water resources. His research interests include Hydrology; Water Resources Planning and Management; Advanced Tools such as Artificial Neural Networks; and Remote Sensing and Decision Support Systems. He developed a software package for Reservoir Analysis and worked with International and Indian Standards. Jain has organized about 35 short-term courses on International Water, Environment, Energy, and Society (WEES-2009) and developed a web-based course on “Hydrologic measurement and analysis of data for NPTEL (E-learning program of IITs) and is a Consultant to Atomic Energy Agency (Vienna) under their IWAVE program. administrative duties., NWDA contribute towards the use of water for the benefit of society.

V.P. Singh is a Professor of Water Engineering at Texas A and M University. Over his career he has won numerous awards including the Engineering Medal of Achievement (University of Guelph, Guelph, Canada, 2017) and the Ven Te Chow lecture and Award (International Water Resources Association, 2017). He has published numerous books and journal articles and his research focuses on surface-water Hydrology, Groundwater Hydrology, Hydraulics, Irrigation Engineering, Environmental Quality and Water Resources. Principal research topics have encompassed: 1. Watershed modeling, 2. Erosion and Sediment Transport in Upland Watersheds, 3. Streamflow Forecasting, 4. Dam Break Analysis, 5. Entropy-Based Modeling, 6. Network Design, 7. Groundwater Modeling, and 8. Hydrologic Impacts of Climate Change.

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