A novel hydrogeophysical framework for developing conceptual site models and simulating groundwater flow conditions in heterogeneous aquifer systems

Numerical simulation of groundwater flow is crucial for understanding and managing aquifer systems, especially in heterogeneous environments. One of the key challenges in groundwater flow simulation is the limited availability of detailed geological and hydrogeological data, which is essential for accurately characterizing aquifer properties and flow dynamics. This study presents a novel approach to simulate the flow conditions in data-scare regions in which the primary inputs are obtained from the interpretation of the geophysical well-logging data. The conceptual model is established through the novel application of the Most Frequent Value-assisted Cluster Analysis (MFV-CA) and Csókás method, complemented by available water level data. MFV-CA is a robust clustering technique that uses Stiener Distance (Weighted Euclidean Distance) for rock differentiation mitigating the drawbacks of the standard k-means cluster analysis (CA) being sensitive to outliers in the dataset. On the other hand, the Csókás method is introduced as a modified version of the Kozeny-Carman equation to provide a continuous estimation of hydraulic conductivity. Accordingly, a 3D geological model is constructed, discretized, and characterized. The conceptual model is translated into a numerical model using the MODFLOW-USG framework employing a control volume finite difference unstructured grid that allows the representation of the system heterogeneity. The results showed an acceptable agreement between the observed and the calibrated hydraulic head. The study demonstrated the effectiveness of using geophysical data as an input for groundwater flow models that enhance data coverage and resolution. The proposed approach provides a practical decision-support tool for improving groundwater management and planning in data-scarce and geologically complex settings.