Coupled Hydrogeophysical Modeling to Constrain Unsaturated Soil Parameters for a Slow‐Moving Landslide

Boyd, J. P. and Chambers, J. E. and Wilkinson, P. B. and Meldrum, P. I. and Bruce, E. and Binley, A. (2024) Coupled Hydrogeophysical Modeling to Constrain Unsaturated Soil Parameters for a Slow‐Moving Landslide. Water Resources Research, 60 (10): e2023WR036. ISSN 0043-1397

Abstract

Geophysical methods have proven to be useful for investigating unstable slopes as they are both non‐invasive and sensitive to the spatial distribution of physical properties in the subsurface. Of particular interest are the links between electrical resistivity and near‐surface moisture content; recent work has demonstrated that it is possible to calibrate hydrological models using geophysical measurements. In this study we explore the use of in‐field electrical resistivity data for calibrating unsaturated soil retention parameters and saturated hydraulic conductivity used for modeling unsaturated fluid flow. We study a synthetic case study, and a well‐characterized site in the northeast of England and develop an approach to calibrate retention parameters for a mudstone and a sandstone formation, the former being an actively failing unit. Petrophysical relationships between electrical resistivity and moisture content (or saturation) are established for both formations. 2D hydrological models are driven by effective rainfall estimations; subsequently these models are coupled with a geophysical forward model via a Markov chain Monte Carlo approach. For the synthetic case, we show that our modeling approach is sensitive to the moisture retention parameters, while less so to saturated hydraulic conductivity. We observe the same characteristics and sensitivities for the field case, albeit with a greater data misfit. Further hydrological simulations suggest that the slope retained high moisture contents in the months preceding a rotational failure. Therefore, we propose that coupled hydrological and geophysical modeling approaches could aid in enhancing landslide monitoring, modeling, and early warning efforts.