Optimization of pumping rates in an island freshwater lens considering parameter, observation, and climate uncertainty

Numerical models and optimization algorithms can be valuable tools for decision-making in coastal and island aquifers, where pumping wells are threatened by salinization. Yet, the implementation of pumping optimization under uncertainty remains limited in practice, because of long simulation times and challenges associated with uncertainty propagation through series of models. A method was developed to optimize pumping rates in an island freshwater lens considering parameter, observation, and climate uncertainty. It was implemented in an island aquifer in the Magdalen Islands (Québec, Canada). A seawater intrusion model with rapid simulation times was developed using MODFLOW-SWI2. The iterative ensemble smoother algorithm implemented by PESTPP-IES allowed for history matching and nonlinear uncertainty quantification. The model predictive uncertainties were coupled with climate uncertainties, including recharge uncertainty (derived from various global circulation models and emission scenarios) and sea-level rise uncertainty. Using PESTPP-OPT, the pumping rates in the freshwater lens were then maximized while avoiding the risk of well salinization and considering parameter, observation, and climate uncertainty. Results of the pumping optimization were compared with estimates of water demand uncertainty. This study used widely available, model-independent software and could be used to support groundwater management decision-making in other insular or coastal areas.