The Impact of Groundwater Model Parametrization on Calibration Fit and Prediction Accuracy—Assessment in the Form of a Post-Audit at the SLOVNAFT Oil Refinery Site, in Slovakia

Abstract

The present work is focused on the effect of increasing model complexity on calibration fit and prediction accuracy. Groundwater flow was numerically simulated at a field site with a hydraulic groundwater protection system in operation with many pumping and observation wells at the site of the Slovnaft refinery in southwestern Slovakia. The adjusted parameters during the calibration included hydraulic conductivity, as well as recharge, evapotranspiration, and riverbed conductance. Four model scenarios were built (V1–V4) within the model calibration for the conditions in the year 2008, with increasing complexity mainly within artificial K-field zonation, which was created and step-wise upgraded based on groundwater head residuals’ distribution. Selected descriptive statistics were evaluated together with chosen information criteria after the models were calibrated. Subsequently, the real predictive accuracy of individual calibrated scenarios was evaluated for conditions in the year 2019 in the form of a post-audit. Within the overall evaluation, the calibration fit increased with increased parameterization complexity. However, the Akaike information criterion, corrected Akaike information criterion, and Bayesian information criterion detected opposite trends for model predictability. A post-audit of prediction accuracy revealed a significant improvement of the V2, V3, and V4 scenarios against the simplest V1 scenario. However, among the V2–V4 scenarios, the degree of prediction accuracy improvement was almost insignificant. The level of effort spent on V3 and V4 parameterization seems disproportionate to the benefit of a negligible improvement in prediction accuracy. Groundwater flow path analysis showed that similarly successful scenarios (measured by prediction accuracy) can generate very different groundwater pathlines.