ABSTRACT:

This is the data and code release for the manuscript "From Resistivity to Hydraulic Properties: Calibrating a Groundwater Flow Model by Integrating Airborne Electromagnetic and Borehole Data into a Probabilistic Multi-Texture Framework" by Leland Scantlebury & Thomas Harter.

Manuscript 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 heterogeneous groundwater-surface water model. ER and texture observations are first combined through consensus clustering into five meta‑texture classes; log‑normal ER-texture distributions derived with the Knight et al. (2018) method then transform the 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 NSE = 0.84; GW heads r2 = 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.