This article examines the relative impacts of anthropogenic interventions and global climate change on the dynamics of saltwater intrusion in highly urbanized coastal aquifers. For this purpose, simulations of the impacts of sea-level rise and abstraction scenarios for the near future were undertaken for a pilot aquifer using a multi-objective 3D variable-density flow and solute transport model. We find that sea-level rise associated with climate change has less influence on the encroachment of salinity than anthropogenic abstraction, which has a more appreciable impact on saltwater intrusion through greater sensitivity to water consumption and seasonality.

Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data‐worth analysis in order to conduct a three‐dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start‐up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.