Integrated approach for characterizing aquifer heterogeneity in alluvial plains

Abstract

Alluvial aquifers serve as vital groundwater resources worldwide. Due to their complex heterogeneity, accurate characterization requires the integration of multiple data types. This study presents a systematic framework to address aquifer heterogeneity through hydrofacies analysis, combining borehole data, electrical resistivity tomography (ERT) and stochastic modeling. The approach was tested in the Varaždin aquifer, where geostatistical and stochastic tools were used to simulate the spatial distribution of four hydrofacies: gravel (G), gravel, sandy to clayey (Gsc), sand with gravel, clayey to silty (Sgcs), and clay to silt, sandy (CSs). As the thin and electrically conductive lenses of Sgcs-CSs material below 20 m depth limited the ERT resolution, synthetic models were used to infer their possible geometry and resistivity magnitudes, estimating a model of the hydrofacies distribution up to 35 m depth, consistent with field-data based model. The resulting dimensions of the lens-shaped structures revealed the horizontal extent of the hydrofacies, and were incorporated into horizontal Markov chain models. The 3D Markov chain models were used to generate 10 stochastic realizations of the hydrofacies distribution. Validation identified the representative hydrofacies model for the Varaždin aquifer with a prediction accuracy of 63 %. Results from simulations focused on the Vinokovščak wellfield area show that incorporating ERT-derived lens lengths into the model development slightly improved hydrofacies prediction accuracy by 0.3 % to 5.0 %, depending on hydrofacies model grid resolution. The analysis of different grid resolutions demonstrates that increasing model detail beyond the characteristic lens dimensions provided no accuracy improvement, suggesting that the optimal cell size is closely related to the estimated lens lengths. In contrast, coarser grids provide a simplified hydrofacies model, potentially increasing prediction accuracy but losing spatial resolution. This methodology forms a basis for integrating spatial heterogeneity into groundwater models, providing a useful tool for sustainable management in alluvial and similar sedimentary environments.