Multiobjective Spatial Pumping Optimization for Groundwater Management in a Multiaquifer System

Abstract

Challenges exist in managing groundwater resources because of spatiotemporally variable pumping activities as well as complex subsurface hydrogeology. In addition, excessive water exploitation induces an imbalance among multistakeholder benefits. In this study, a nonlinear high-order multiobjective optimization model was constructed to derive optimal freshwater pumping strategies and explore the optimality through regulation of pumping locations. Three objectives concerning water supply, energy cost, and environmental problems were formulated into a groundwater management framework that maximizes the total groundwater withdrawal from potential wells and minimizes the total energy cost for well pumping, and groundwater level variations at monitoring locations. Binary variables were incorporated into the groundwater management model to control the operative status of the pumping wells. An improved Nondominated Sorting Genetic Algorithm II (NSGA-II) was developed to increase solution convergence and linked with a high-fidelity groundwater model (MODFLOW-2005) to solve the optimization problem. The improved NSGA-II was expedited on a parallel computing platform to alleviate the computational burden. The effectiveness of the proposed methodology was demonstrated by an application to the Baton Rouge multiaquifer system in southeastern Louisiana. Nondominated trade-off solutions were successfully achieved through the proposed approach and were an optimum with regard to the goals and corresponding consequences. Operative status of the pumping wells, pumping rates, and distances from observation wells to the pumping wells produced distinctive optimization responses. In conclusion, the proposed approach is an appealing method for determining the optimal extent to which the three objectives concerning water supply, energy cost, and environmental problems can be achieved.