From Resistivity to Hydraulic Properties: Calibrating a Groundwater Flow Model by Integrating Airborne Electromagnetic and Borehole Data Into a Probabilistic Multi‐Texture Framework

Abstract

Airborne electromagnetic (AEM) surveys offer rapid, cost‐effective subsurface imaging, yet converting their electrical resistivity (ER) models into physically meaningful hydraulic property fields for groundwater models remains a challenge. We develop and demonstrate a data‐driven workflow for an unconsolidated sedimentary aquifer system in Scott Valley, northern California, USA, that incorporates AEM ER data with borehole logs to build and calibrate a geologically heterogeneous groundwater‐surface water model. ER and texture observations are first combined through consensus clustering into five meta‐texture classes; a probabilistic ER‐texture transform then converts ER data to cell‐scale texture probabilities. These probabilities and borehole data are combined using Texture2Par to create a three‐dimensional texture model, which is translated to grid‐scale hydraulic conductivity and storage via power‐law averaging. During calibration, we parameterized the ER‐texture distributions, essentially allowing parameter estimation to adjust the estimated textures along the AEM flight lines. The texture‐based groundwater‐surface water model attains the same high goodness‐of‐fit as the previous zonal calibration (Fort Jones streamflow Nash‐Sutcliffe efficiency = 0.84; groundwater heads r ²  = 0.98), with more geologically plausible heterogeneity and improved simulation of groundwater‐driven seasonally low streamflow. The proposed ER‐to‐texture workflow provides an adaptable workflow for embedding AEM information into basin‐scale groundwater models.