Groundwater–Surface Water Management With Stochastic Surface Water Supplies: A Simulation Optimization Approach

Abstract

A new simulation optimization model for groundwater-surface water management identifies efficient strategies for meeting water demand and controlling a regional water quality problem, while accounting for varying availability of surface water supplies. Optimal values of model variables are computed for ranges of surface water availability. Expected groundwater levels are computed using a response function approach, which incorporates a probability for each hydraulic stress. Three objective functions are considered: (1) minimize need for supplemental water, (2) minimize imposed water use reductions, and (3) minimize changes from current pumping patterns. The model imposes constraints on expected groundwater levels, expected hydraulic gradients (surrogates for controlling contaminant transport), capacities of pipeline and recharge facilities, and water demand requirements. Capacity and demand constraints must be met every year for all possible surface water conditions. Application to the Santa Clara-Calleguas Basin in southern California indicates that a large quantity of supplemental water or significant pumpage reductions, particularly in the lower aquifer, may be required to control seawater intrusion with current facilities. Supplemental water would be delivered directly to users through pipelines and artificially recharged. Results also indicate that the current artificial-recharge program has been valuable and that construction of new artificial-recharge facilities might be beneficial. It should be noted that local agencies are currently developing several potential sources of supplemental water. Monte Carlo simulations yield estimates of the reliability of gradient constraints in controlling advective transport and the likelihood that water level constraints will be violated. 2845 2846 REICHARD:GROUNDWATER-SURFACE WATER MANAGEMENT In any given year there is a probability prob(j) that the actual river diversion will equal riv(j). Annual decisions on pumping, pipeline delivery, and artificial recharge will depend on the actual river diversion in that year. The approach taken to analyze this water management problem is to link a distributed-parameter groundwater simulation model with mathematical optimization techniques. Bredehoeft and Young [1970] were the first to combine groundwater simulation and optimization. Their approach involved iteration between simulation and optimization. Direct linkage of groundwater simulation and optimization was subsequently developed and advanced by Maddock [1972],Aguado and Remson [1974], and others (see review papers by Gorelick [1983] and Yeh [1992]).Several previous studies have applied deterministic simulation optimization methods to analyze regional groundwater management issues [Heidari Gharbi and Peralta, 1994]. Most of these studies focused strictly on quantity issues. Willis and Finney [1988], Lefkoff and Gorelick [1990a, b], and Gharbi and Peralta [1994] explicitly considered water quality issues. Willis and Finney linked nonlinear optimization with a sharp-interfa...

Optimal conjunctive‐use operations and plans

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Application of Modified Tank Model to Simulate Groundwater Level Fluctuations in Kabul Basin, Afghanistan

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MODOPTIM: A general optimization program for ground-water flow model calibration and ground-water management with MODFLOW

“…The theory and application of optimization methods to the simulation of fluid flow in porous media has been documented by many investigators in both the hydrologic sciences and petroleum engineering. Optimization terminology includes many names, such as parameter estimation (Townley and Wilson, 1985;Carrera and Neuman, 1986), nonlinear regression (Cooley, 1982;Vecchia and Cooley, 1987;Hill, 1992), inverse problem (Kuiper, 1986), automatic calibration (Holmes and others, 1989;Poeter and Hill, 1997), automatic history-matching (Jacquard and Jain, 1965;Jahns, 1966;Coats and others, 1970;Thomas and others, 1971;Chen and others, 1974;Watson and Lee, 1986;Yang and others, 1987), optimal control (Chavent and others, 1975;Wasserman and others, 1975), optimal ground-water management (Gorelick, 1983;Dougherty and Marryott, 1991;Chau, 1992), systems analysis (Yeh, 1992), and optimization (Gorelick and Wagner, 1986;Reichard, 1995).…”