A Bayesian Geostatistical Approach to Analyzing Groundwater Depth in Mining Areas

This study addresses the spatial variability of groundwater levels within a mining basin in Greece. The objective is to develop an accurate spatial model of groundwater levels in the area to support an integrated groundwater management plan. Hydraulic heads were measured in 72 observation wells, which are irregularly distributed, primarily in mining zones. Multiple geostatistical approaches are evaluated to identify an optimal model based on cross-validation metrics. We introduce a novel trend model that includes the surface elevation gradient, as well as the proximity of wells to the riverbed, utilizing a modified Box–Cox transformation to normalize residuals. The results indicate that Regression Kriging with a non-differentiable Matérn variogram outperforms Ordinary Kriging in cross-validation accuracy. The study provides maps of the piezometric head and kriging variance within a Bayesian framework, being among the first to quantify and incorporate river-distance effects within regression kriging for groundwater.