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

Bibliographic Details
Main Author: Loucks, Daniel P.
Other Authors: van Beek, Eelco.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2017.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
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.

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