In recent years, the aquifer simulation model has been combined with techniques of optimization to address important groundwater management problems. The combined simulation and optimization model accounts for the complex behavior of the groundwater system and identifies the best management strategy under consideration of the management objectives and constraints. Simulation-optimization groundwater management models have been developed for a variety of applications, such as restoration of contaminated groundwater, control of aquifer hydraulics, allocation of ground and surface water resources, and evaluation of groundwater policies [see reviews by Gorelick, 1983, 1990; Yeh, 1992; Ahlfeld and Heidari, 1994; Bredehoeft et al, 1994].

Gorelick [1983] divides groundwater management models into three categories: groundwater hydraulic management, groundwater quality management, and groundwater policy evaluation and allocation. This review focuses in detail on the recent advances made in groundwater hydraulic and groundwater quality management modeling, which encompasses most of the aquifer management work done in the past four years. For a review of recent research in groundwater policy evaluation and allocation, see Bredehoeft et al. [1994].

Recent advances in simulation-optimization groundwater management modeling

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.

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

Mathematical (analytical, numerical and optimization) models are employed in many disciplines including the water resources planning, engineering and management These models can vary from a simple black-box model to a sophisticated distributed physics-based model Recently, development and employment of modern optimization methods (MOMs) have become popular in the area of mathematical modeling This paper overviews the MOMs based on the evolutionary search which were developed over mostly the last 30 years These methods have wide application in practice from finance to engineering and this paper focuses mostly on the applications in the area of water resources planning, engineering and management Although there are numerous optimization algorithms, the paper outlines the ones that have been widely employed especially in the last three decades; such as the Genetic Algorithm (GA), Ant Colony (AC), Differential Evolution (DE), Particle Swarm (PS), Harmony Search (HS), Genetic Programming (GP), and Gene Expression Programming (GEP) The paper briefly introduces theoretical background of each algorithm and its applications and discusses the merits and, if any, shortcomings The wide spectrum of applications include, but not limited to, flood control and mitigation, reservoir operation, irrigation, flood routing, river training, flow velocity, rainfall-runoff processes, sediment transport, groundwater management, water quality, hydropower, dispersion, and aquifers

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Modern optimization methods in water resources planning, engineering and management

Reservoir operation has been described as a multistage stochastic control problem, yet solutions to an explicitly stochastic formulated multi-reservoir operation problem remain problematic. Despite development of new techniques, implicitly stochastic optimization (ISO) models solved using linear programming (LP) remain one of the most readily applicable to the analysis of complex systems. However the attribute of perfect foresight limits the immediate usefulness of such models and hinders the derivation of operating rules for subsequent testing in simulation models. This dissertation presents a modification to the traditional ISO model that overcomes the problem of perfect foresight and incorporates consideration of risk in the prescribed reservoir operation. The proposed method is implemented using sequential runs of an ISO model, where each run has an optimized terminal or carryover storage value function. The time horizon for each run is reduced to a fraction (1/n) of the period-of-analysis. The series of n linked consecutive runs form the optimal operating policy over the entire period-of-analysis. An iterative non-linear search algorithm is used to define the optimal carryover storage value function. Balancing rules are used to reduce the dimensionality of the multi-reservoir operation problem. The method is demonstrated using three case studies: single reservoir operation; single reservoir operation with conjunctive use of groundwater; and multireservoir operation. In all three cases the reservoir(s) have the dual purpose of flood control and water conservation. The objective function is to minimize the economic cost of shortage associated with downstream agricultural deliveries. A generalized network flow algorithm with gains is used to find the optimal solution. ............................................. Chairperson, Dissertation Committee

Implicit Stochastic Optimization with Limited Foresight for Reservoir Systems

An optimization model is developed for selecting between candidate surface-water reservoirs and ground-water development. The model may be used to perform a preliminary screening of alternatives for water-supply development, and to identify storage capacities at reservoir sites, as well as aquifer pumping at candidate locations. A hybrid simulation-optimization strategy is used to consider monthly operation of the reservoir and aquifer system using historical or synthetic hydrologic data. A modified sequent peak algorithm is used for reservoir sizing, and a unit response matrix approach is used to model the ground-water subsystem. A target aggregate firm yield from the surface water–ground water system may be specified. Decisions on the failure of firm yield at each reservoir site during a critical period for streamflow, with a corresponding increase in ground-water pumping such that the system firm yield is maintained are considered. Example applications with data from the Jordan River basin in Utah demo...

Ywh Chu Lin

Papers

A groundwater management model for Salt Lake County, Utah with some water rights and water quality considerations