Tobias Halter

It has been widely recognised that the degree of soil wetness before precipitation events can be decisive for whether or not shallow rainfall-induced landslides occur. While there are methods to measure and/or model soil wetness in complex topography, they often exhibit limitations in spatial or temporal resolution, hindering their application in regional landside initiation modelling. In this study, we address the need for high-resolution predictions of initial saturation before rainfall events by employing data-driven linear regression models. The models were trained using in-situ soil moisture data collected from six measurement stations located in a landslide-prone region in Switzerland. Various topographic attributes, along with multiple antecedent rainfall and evapotranspiration variables were tested as input for the models. The final model consisted of five measurable variables, including cumulative antecedent rainfall, cumulative evapotranspiration, and the topographic wetness index (TWI). The model effectively reproduced the observed spatial and temporal variability of the in-situ measurements with a coefficient of determination R² = 0.62 and a root mean square error RMSE = 0.07. Subsequently, we applied the regression model to predict the spatial soil saturation at the onset of actual landslide triggering rainfall events and integrated these patterns into the hydromechanical model STEP-TRAMM. The results demonstrate improvements in predicting observed landslide occurrences compared to simulations assuming spatially uniform initial saturation conditions, highlighting the importance of in-situ measurements and a realistic extrapolation of such data in space and time for accurate modelling of shallow landslide initiation.

In view of a national Landslide Early Warning System (LEWS) for Switzerland, a pilot study is currently running in the Napf-region (central Switzerland), a hilly semi-forested area (186 km²) with a substantial number of shallow landslide observations in the past decades. The study includes a comprehensive soil wetness monitoring, a test of a numerical model to simulate distributed landslide triggering, as well as the application of Distributed Acoustic Sensing (DAS) at the scale of a steep slope. We show that the applied tools provide a promising basis for a systematic observation, assessment and warning of critical landslide situations. At the same time, this work also suggests that the soil physical conditions can and need to be addressed in an adequate way to correctly rate the spatial and temporal criticality for landslide triggering.