Groundwater Optimization Handbook Flow, Contaminant Transport, and Conjunctive Management

Existing and impending water shortages argue for improving water quantity and quality management. Groundwater Optimization Handbook: Flow, Contaminant Transport, and Conjunctive Management helps you formulate and solve groundwater optimization problems to ensure sustainable supplies of adequate quality and quantity. It shows you how to more effectively use simulation-optimization (S-O) modeling, an economically valuable groundwater management tool that couples simulation models with mathematical optimization techniques. Written for readers of varying familiarity with groundwater hydrology and mathematical optimization, the handbook approaches complex problems realistically. Its techniques have been applied in many legal settings, with produced strategies providing up to 57% improvement over those developed without S-O modeling. These techniques supply constructible designs, planning and management strategies, and metrics for performance-based contracts. Learn how to: Recognize opportunities for applying S-O models Lead client, agency, and consultant personnel through the strategy design and adaptation process Formulate common situations as clear deterministic/stochastic and single/multiobjective mathematical optimization problems Distinguish between problem nonlinearities resulting from physical system characteristics versus management goals Create an S-O model appropriate for your specific needs or select an existing transferrable model Develop acceptable feasible solutions and compute optimal solutions Quantify tradeoffs between multiple objectives Evaluate and adapt a selected optimal strategy, or use it as a metric for comparison Drawing on the author’s numerous real-world designs and more than 30 years of research, consulting, and teaching experience, this practical handbook supplies design procedures, detailed flowcharts, solved problems, lessons learned, and diverse applications. It guides you through the maze of multiple objectives, constraints, and uncertainty to calculate the best strategies for managing flow, contamination, and conjunctive use of groundwater and surface water. Ancillary materials are available from the Downloads tab on the book page at www.crcpress.com.

PART I Introduction to S-O Concepts Essence of Optimizing Groundwater ManagementBook GoalsThe Need for and Benefits of OptimizationConsiderations When Using OptimizationGroundwater Systems Analysis Perspective and ToolsSpecific Reader Goals Introduction to Mathematical Optimization for Groundwater Strategy DesignSimulation (S) and S-O Modeling and Basic Optimization TerminologySimple Optimization ProblemManual Simplex Solution PART II Optimization Theory Optimization Problem Types and CategoriesIntroductionCommon Optimization Problem Types (LP, QP, IP, MIP, NLP, MINLP)Linearity and Nonlinearity in S-O ModelingSingle-Objective and Multiobjective OptimizationDeterministic and Stochastic OptimizationOptimization of Multiple Physical ProcessesVariable, Constraint, and Objective Function Flexibility Deterministic OptimizationIntroductionSolution Space GeometryOverview of Optimizer Type OptionsClassical Optimization TypesNon-Classical Optimization TypesSimplifying Optimization Techniques Optimization with UncertaintyIntroductionAddressing UncertaintyStochastic Modeling ToolsRobustness Optimization Multiobjective Optimization ApproachesIntroductionMultiobjective OptimizationIllustrative Multiobjective LP and QP Problems PART III Exact and Approximation Simulator Theory Embedded Numerical and Analytical EquationsIntroduction and TerminologyEmbedded Numerical EquationEmbedded Analytical EquationEmbedded Discretized Numerical Model Response Matrix SimulatorsIntroductionDiscretized Convolution Integrals (Response Matrix or Approximator)Example: Predicting Head Changes Resulting from Assumed Transient Pumping StrategyInfluence Coefficient Development ProcessInfluence Coefficient Computation Approximation and Other SimulatorsIntroductionStatistical Regression Equations and Power FunctionsArtificial Neural NetworksBasic Economic and Fiscal Simulators PART IV S-O Processes and Guidance Formulating Optimization Problems and Selecting S-O ToolsIntroductionIdentify the S-O Model PurposeState the Optimization Problem Conceptually and Refine ItPrepare Preliminary Optimization Problem Formulation(s), without Selecting S-O ApproachClarify Linearity-Nonlinearity of Physical System and Management ProblemSelect an S-O ApproachSelect S-O Modeling Tool and Obtain or Develop S-O Model and Postprocessor Preparing Data Input and Implementing S-O ToolGeneral ConceptsFlow Optimization IllustrationTransport Optimization IllustrationsSelect Candidate Stimuli LocationsPrepare Initial Feasible Solution (Strategy) and Optimization Parameters as Input DataRun S-O ModelAnalyze Results and SensitivityReport ResultsImplement Strategy and Monitor System Groundwater and Conjunctive Management S-O Application GuidanceIntroductionWater Supply and Flow Hydraulic Management for Nonlinear River-Aquifer System (with Multiobjective)Flow Optimization: Limiting Surface Water Depletion in Dynamic Stream-Aquifer SystemFlow Optimization: Conjunctive Management of Dynamic Stream-Aquifer SystemContainment Optimization: Plume Management via Hydraulic OptimizationOptimal Site Dewatering System Design Groundwater Contamination and Transport Management S-O Application GuidanceOverviewBackground Situation and Optimization NeedsS-O Approach SelectionInitial Screening RunsOptimization Scenarios OverviewSolving MINLP Minimizing Residual Mass Optimization Problem Using GA-TSIllustrating the Effect of Minimizing Total Pumping on Maximum Concentration and Residual MassThe Effect of Minimizing Cost on the Optimal ResultContrasting Minimizing Mass Remaining, Pumping, and CostSolving MINLP Minimizing Residual Mass Optimization Problem Using ANN-GAClosure PART V Applications Hydraulic S-O Modeling ApplicationsIntroductionArkansas Grand Prairie and Northeastern Arkansas—Sustainable Conjunctive UseCache Valley, Utah—Safe Yield Practice While Protecting Surface Water ResourcesNorton Air Force Base, Southwest Boundary TCE Plume—Hydraulic Plume Containment (California) Contaminant Transport S-O Modeling ApplicationsIntroductionMassachusetts Military Reservation, Chemical Spill 10 Plume (Massachusetts)Blaine Naval Ammunition Depot Multiple Plume Management (Nebraska)Optimal Robust Pumping Strategy Design for Umatilla Chemical Depot (Oregon)Multiple Realization Pump and Treat System Optimization (California) Closure Glossary Index Each chapter includes a bibliography.

Richard Peralta, PhD, PE, has used S-O modeling to design strategies for more than 20 sites or real-world projects. As a Utah Cooperative Extension Service water quality coordinator, he optimized nonpoint and point source contamination management, and collaborated with state and federal agencies in technology transfer and public education. Through the University of Arkansas, and subsequently Utah State University, private work, and the U.S. Air Force Reserve, he worked in 25 U.S. states and in numerous countries. For the military, he participated in and led many environmental contamination remediation evaluation teams and helped provide optimal solutions that were successfully implemented in the field. After several years of advising on environmental matters in the Pentagon, Colonel Peralta retired from the U.S. Air Force Reserve as a chief bioenvironmental engineer. He is a professor in the Civil and Environmental Engineering Department at Utah State University, consults privately, and is the distributor of SOMOS software. For more information, see Dr. Peralta’s page at the College of Engineering at Utah State University. Contributing author Ineke M. Kalwij, PhD, PEng, collaborates with Dr. Peralta, working on groundwater optimization software development and publications. She also provides consulting services to clients, primarily in the area of groundwater system management. For more information, see Kalwij Water Dynamics Inc.