Water Resource Systems Planning and Management : : An Introduction to Methods, Models, and Applications.

• Author: Loucks, Daniel P.
• TeilnehmendeR: van Beek, Eelco.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2017.; ©2017.
• Year of Publication: 2017
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (635 pages)

Table of Contents

Intro -- Foreword -- Preface -- Contents -- 1 Water Resources Planning and Management: An Overview -- 1.1 Introduction -- 1.2 Planning and Management Issues: Some Case Studies -- 1.2.1 Kurds Seek Land, Turks Want Water -- 1.2.2 Sharing the Water of the Jordan River Basin: Is There a Way? -- 1.2.3 Mending the "Mighty and Muddy" Missouri -- 1.2.4 The Endangered Salmon -- 1.2.5 Wetland Preservation: A Groundswell of Support and Criticism -- 1.2.6 Lake Source Cooling: Aid to Environment, or Threat to Lake? -- 1.2.7 Managing Water in the Florida Everglades -- 1.2.8 Restoration of Europe's Rivers and Seas -- 1.2.8.1 North and Baltic Seas -- 1.2.8.2 The Rhine -- 1.2.8.3 The Danube -- 1.2.9 Flood Management on the Senegal River -- 1.2.10 Nile Basin Countries Striving to Share Its Benefits -- 1.2.11 Shrinking Glaciers at Top of the World -- 1.2.12 China, a Thirsty Nation -- 1.2.13 Managing Sediment in China's Yellow River -- 1.2.14 Damming the Mekong (S.E. Asia), the Amazon, and the Congo -- 1.3 So, Why Plan, Why Manage? -- 1.3.1 Too Little Water -- 1.3.2 Too Much Water -- 1.3.3 Too Polluted -- 1.3.4 Too Expensive -- 1.3.5 Ecosystem Too Degraded -- 1.3.6 Other Planning and Management Issues -- 1.3.6.1 Navigation -- 1.3.6.2 River Bank Erosion -- 1.3.6.3 Reservoir Related Issues -- 1.4 System Planning Scales -- 1.4.1 Spatial Scales for Planning and Management -- 1.4.2 Temporal Scales for Planning and Management -- 1.5 Planning and Management Approaches -- 1.5.1 Top-Down Planning and Management -- 1.5.2 Bottom-Up Planning and Management -- 1.5.3 Integrated Water Resources Management -- 1.5.4 Water Security and the Sustainable Development Goals (SDGs) -- 1.5.5 Planning and Management Aspects -- 1.5.5.1 Technical -- 1.5.5.2 Financial and Economic -- 1.5.5.3 Institutional and Governance -- 1.5.5.4 Models for Impact Prediction and Evaluation.

1.5.5.5 Models for Shared Vision or Consensus Building -- 1.5.5.6 Models for Adaptive Management -- 1.6 Planning and Management Characteristics -- 1.6.1 Integrated Policies and Development Plans -- 1.6.2 Sustainability -- 1.7 Meeting the Planning and Management Challenges-A Summary -- References -- Additional References (Further Reading) -- Exercises -- 2 Water Resource Systems Modeling: Its Role in Planning and Management -- 2.1 Introduction -- 2.2 Modeling Water Resource Systems -- 2.2.1 An Example Modeling Approach -- 2.2.2 Characteristics of Problems to be Modeled -- 2.3 Challenges Involving Modeling -- 2.3.1 Challenges of Planners and Managers -- 2.3.2 Challenges of Modelers -- 2.3.3 Challenges of Applying Models in Practice -- 2.3.4 Evaluating Modeling Success -- 2.4 Developments in Modeling -- 2.4.1 Technology -- 2.4.2 Algorithms -- 2.4.3 Interactive Model-Building Environments -- 2.4.4 Open Modeling Systems -- 2.5 Conclusions -- References -- Additional References (Further Reading) -- Exercises -- 3 Models for Identifying and Evaluating Alternatives -- 3.1 Introduction -- 3.1.1 Model Components -- 3.2 Plan Formulation and Selection -- 3.2.1 Plan Formulation -- 3.2.2 Plan Selection -- 3.3 Conceptual Model Development -- 3.4 Simulation and Optimization -- 3.4.1 Simulating a Simple Water Resources System -- 3.4.2 Defining What to Simulate -- 3.4.3 Simulation Versus Optimization -- 3.5 Conclusions -- Additional References (Further Reading) -- Exercises -- 4 An Introduction to Optimization Models and Methods -- 4.1 Introduction -- 4.2 Comparing Time Streams of Economic Benefits and Costs -- 4.2.1 Interest Rates -- 4.2.2 Equivalent Present Value -- 4.2.3 Equivalent Annual Value -- 4.3 Nonlinear Optimization Models and Solution Procedures -- 4.3.1 Solution Using Calculus -- 4.3.2 Solution Using Hill Climbing.

4.3.3 Solution Using Lagrange Multipliers -- 4.3.3.1 Approach -- 4.3.3.2 Meaning of Lagrange Multiplier λ -- 4.4 Dynamic Programming -- 4.4.1 Dynamic Programming Networks and Recursive Equations -- 4.4.2 Backward-Moving Solution Procedure -- 4.4.3 Forward-Moving Solution Procedure -- 4.4.4 Numerical Solutions -- 4.4.5 Dimensionality -- 4.4.6 Principle of Optimality -- 4.4.7 Additional Applications -- 4.4.7.1 Capacity Expansion -- 4.4.7.2 Reservoir Operation -- 4.4.8 General Comments on Dynamic Programming -- 4.5 Linear Programming -- 4.5.1 Reservoir Storage Capacity-Yield Models -- 4.5.2 A Water Quality Management Problem -- 4.5.2.1 Model Calibration -- 4.5.2.2 Management Model -- 4.5.3 A Groundwater Supply Example -- 4.5.3.1 A Simplified Model -- 4.5.3.2 A More Detailed Model -- 4.5.3.3 An Extended Model -- 4.5.3.4 Piecewise Linear Model -- 4.5.4 A Review of Linearization Methods -- 4.6 A Brief Review -- Additional References (Further Reading) -- Exercises -- 5 Data-Fitting, Evolutionary, and Qualitative Modeling -- 5.1 Introduction -- 5.2 Artificial Neural Networks -- 5.2.1 The Approach -- 5.2.2 An Example -- 5.3 Evolutionary Algorithms -- 5.3.1 Genetic Algorithms -- 5.3.2 Example Iterations -- 5.3.3 Differential Evolution -- 5.3.4 Covariance Matrix Adaptation Evolution Strategy -- 5.4 Genetic Programming -- 5.5 Qualitative Functions and Modeling -- 5.5.1 Linguistic Functions -- 5.5.2 Membership Functions -- 5.5.3 Illustrations of Qualitative Modeling -- 5.5.3.1 Water Allocation -- 5.5.3.2 Qualitative Reservoir Storage and Release Targets -- 5.5.3.3 Qualitative Water Quality Management Objectives and Constraints -- 5.6 Conclusions -- References -- Additional References (Further Reading) -- Exercises -- 6 An Introduction to Probability, Statistics, and Uncertainty -- 6.1 Introduction -- 6.2 Probability Concepts and Methods.

6.2.1 Random Variables and Distributions -- 6.2.2 Expected Values -- 6.2.3 Quantiles, Moments, and Their Estimators -- 6.2.4 L-Moments and Their Estimators -- 6.3 Distributions of Random Events -- 6.3.1 Parameter Estimation -- 6.3.2 Model Adequacy -- 6.3.3 Normal and Lognormal Distributions -- 6.3.4 Gamma Distributions -- 6.3.5 Log-Pearson Type 3 Distribution -- 6.3.6 Gumbel and GEV Distributions -- 6.3.7 L-Moment Diagrams -- 6.4 Analysis of Censored Data -- 6.5 Regionalization and Index-Flood Method -- 6.6 Partial Duration Series -- 6.7 Stochastic Processes and Time Series -- 6.7.1 Describing Stochastic Processes -- 6.7.2 Markov Processes and Markov Chains -- 6.7.3 Properties of Time Series Statistics -- 6.8 Synthetic Streamflow Generation -- 6.8.1 Introduction -- 6.8.2 Streamflow Generation Models -- 6.8.3 A Simple Autoregressive Model -- 6.8.4 Reproducing the Marginal Distribution -- 6.8.5 Multivariate Models -- 6.8.6 Multiseason, Multisite Models -- 6.8.6.1 Disaggregation Model -- 6.8.6.2 Aggregation Models -- 6.9 Stochastic Simulation -- 6.9.1 Generating Random Variables -- 6.9.2 River Basin Simulation -- 6.9.3 The Simulation Model -- 6.9.4 Simulation of the Basin -- 6.9.5 Interpreting Simulation Output -- 6.10 Conclusions -- References -- Additional References (Further Reading) -- Exercises -- 7 Modeling Uncertainty -- 7.1 Introduction -- 7.2 Generating Values from Known Probability Distributions -- 7.3 Monte Carlo Simulation -- 7.4 Chance Constrained Models -- 7.5 Markov Processes and Transition Probabilities -- 7.6 Stochastic Optimization -- 7.6.1 Probabilities of Decisions -- 7.6.2 A Numerical Example -- 7.7 Summary -- Additional References (Further Reading) -- Exercises -- 8 System Sensitivity and Uncertainty Analysis -- 8.1 Introduction -- 8.2 Issues, Concerns, and Terminology -- 8.3 Variability and Uncertainty in Model Output.

8.3.1 Natural Variability -- 8.3.2 Knowledge Uncertainty -- 8.3.2.1 Parameter Value Uncertainty -- 8.3.2.2 Model Structural and Computational Errors -- 8.3.3 Decision Uncertainty -- 8.3.3.1 Surprises -- 8.4 Sensitivity and Uncertainty Analyses -- 8.4.1 Uncertainty Analyses -- 8.4.1.1 Model and Model Parameter Uncertainties -- 8.4.1.2 What Uncertainty Analysis Can Provide -- 8.4.2 Sensitivity Analyses -- 8.4.2.1 Sensitivity Coefficients -- 8.4.2.2 A Simple Deterministic Sensitivity Analysis Procedure -- 8.4.2.3 Multiple Errors and Interactions -- 8.4.2.4 First-Order Sensitivity Analysis -- An Example of First-Order Sensitivity Analysis -- Warning on Accuracy -- 8.4.2.5 Fractional Factorial Design Method -- 8.4.2.6 Monte Carlo Sampling Methods -- Simple Monte Carlo Sampling -- Sampling Uncertainty -- Making Sense of the Results -- Standardized Monte Carlo Analysis -- Generalized Likelihood Estimation -- 8.4.2.7 Latin Hypercube Sampling -- 8.5 Performance Indicator Uncertainties -- 8.5.1 Performance Measure Target Uncertainty -- 8.5.2 Distinguishing Differences Between Performance Indicator Distributions -- 8.6 Communicating Model Output Uncertainty -- 8.7 Conclusions -- References -- Additional References (Further Reading) -- Exercises -- 9 Performance Criteria -- 9.1 Introduction -- 9.2 Informed Decision-Making -- 9.3 Performance Criteria and General Alternatives -- 9.3.1 Constraints on Decisions -- 9.3.2 Tradeoffs Among Performance Criteria -- 9.4 Quantifying Performance Criteria -- 9.4.1 Economic Criteria -- 9.4.1.1 Benefit and Cost Estimation -- Market Prices Equal Social Values -- Market Prices not Equal to Social Values -- No Market Processes -- 9.4.1.2 A Note Concerning Costs -- 9.4.1.3 Long- and Short-Run Benefit Functions -- 9.4.2 Environmental Criteria -- 9.4.3 Ecological Criteria -- 9.4.4 Social Criteria -- 9.5 Multicriteria Analyses.

9.5.1 Dominance.