An Optimization-Simulation Approach for Groundwater Abstraction under Recharge Uncertainty

Droughts and climate variability cause uncertainties on water supply especially in arid regions and coastal aquifers' over-exploitation causes seawater intrusion. Since the rate and extent of aquifer recharge is often very uncertain, determining the optimal groundwater abstraction is a challenging task. In this paper a framework is proposed for estimating the optimal abstraction of groundwater for urban supply under uncertainty and under complex conditions of water table fluctuations and seawater intrusion. It is based on a combination of several models: (i) a Monte-Carlo Simulation (MCS) to incorporate the uncertainties in groundwater recharge, (ii) a numerical groundwater flow model, MODFLOW to simulate the effects of abstractions on the water table fluctuations and seawater intrusion and (iii) a multi-objective optimization model to generate the set of Pareto optimal solutions for each recharging scenario. Maximizing the benefit to the water utility, minimizing the average groundwater table level fluctuations and minimizing the seawater intrusion are the objectives of the model. A fast multi-objective evolutionary algorithm is used to obtain the Pareto efficient solutions for each recharging scenario. Compromise programming (CP) is then used to select the closest solutions to the ideal. Finally, the amount of optimal reliable groundwater abstraction is estimated using a cumulative distribution function. The proposed methodology is applied to a coastal aquifer in the western part of Muscat metropolitan area, Oman. The results have shown that annual groundwater abstraction volume may range from 12.7 to 18.8 Mm(3) compared to 6.8 Mm(3) currently pumped. This would result in an economic benefit of $10.5 million to $15.4 million/year. On the other hand the aquifer's maximum annual mean drawdown would range from 0.7 to 0.9 m.