Optimal wellfield operation management using Pymoo-based dynamic simulation for the Abu Jarjur desalination plant in the Kingdom of Bahrain

Study region Bahrain. Study Focus In Bahrain, a limited, non-renewable brackish groundwater resource supplies a reverse osmosis (RO) desalination plant, requiring a balance between maximizing water production and preserving aquifer viability. This study presents a dynamic simulation–optimization framework based on a fully integrated MODFLOW–FloPy–pymoo platform to determine optimal wellfield pumping schedules. The framework couples groundwater flow simulation with NSGA-II multi-objective optimization to maximize groundwater abstraction while minimizing aquifer drawdown under operational constraints. Six multi-criteria decision-making (MCDA) methods were employed to rank Pareto-optimal wellfield management strategies and support robust decision-making. New hydrological insights for the region The optimized solution increased groundwater abstraction by approximately 54% while reducing mean drawdown by 8% in the principal production zone, while well-specific groundwater levels improved by 3.8% relative to the baseline scenario. The optimal abstraction of approximately 35 million m³ /year is sufficient to sustain the RO plant production capacity of 26.53 million m³ /year at 75% recovery. The proposed integrated MODFLOW–NSGA-II–MCDA framework provides a practical decision-support tool for identifying sustainable pumping schedules and adaptive wellfield operating strategies, offering a transferable methodology for groundwater management in Bahrain and other water-scarce regions dependent on non-renewable coastal aquifers.