Monitoring critically saturated conditions for shallow landslide occurrence using electrical resistivity tomography

Soil wetness is an important property in determining the variable disposition of hillslopes to shallow landslides. Recent studies have demonstrated the potential of in situ soil wetness information for landslide early warning. However, the spatial representativeness of in situ sensors may be affected by local heterogeneities of soil properties and hydrological processes, and their installation may be destructive. Electrical resistivity tomography (ERT) has been used in the past to estimate plot‐scale soil moisture variation and may overcome these limitations. In this study, we installed and operated an automated ERT monitoring system on a landslide‐prone hillslope in the Napf region (Switzerland). The system was operational during a period of 9 mo, and measurements were conducted at high temporal resolution and under different soil hydrological conditions. Electrical resistivity was measured along two perpendicular profile lines in Wenner–Schlumberger configuration at 0.25‐m electrode spacing. Soil saturation was calculated by the Archie's law and the parameters were fitted with colocated soil moisture sensors. Comparison of ERT‐derived soil moisture with soil wetness from in situ sensors showed a good correlation, and infiltration properties critical for landslide early warning could be reliably reproduced. Further, analysis of spatial saturation variation revealed that ERT was capable to detect heterogeneities of soil hydrological process. Under highly saturated conditions, the reliability of the saturation estimation was affected by an increased number of faulty measurements and the spatial heterogeneity of the infiltration process. Core Ideas In situ soil wetness monitoring is used for landslide early warning. Electrical resistivity tomography (ERT) is a noninvasive alternative to sensors for soil moisture estimation. Spatial variation of soil hydrological processes can be assessed using ERT. Temporal soil moisture variation can be well reproduced at high temporal resolution. Data quality depends on the water saturation and the petrophysical function parametrization.