Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions

Abstract

Abstract. Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific focus on the value of antecedent soil wetness. First, we show that landslide susceptibility predicted by the factor of safety in the infinite slope model is strongly dependent on soil data inputs, limiting the hydrologically active range where landslides can occur to only ∼20 % of the country with typical soil parameters and soil depth models, not accounting for uncertainty. Second, we find the soil saturation estimate provided by a conceptual hydrological model (PREVAH) to be more informative for landslide prediction than that estimated by the physically based coarse-resolution model (TerrSysMP), which we attribute to the lack of temporal variability and coarse spatial resolution in the latter. Nevertheless, combining the soil water state estimates in TerrSysMP with the infinite slope approach improves the separation between landslide triggering and non-triggering rainfall events. Third, we demonstrate the added value of antecedent soil saturation in combination with rainfall thresholds. We propose a sequential threshold approach, where events are first split into dry and wet antecedent conditions by an N d (day) antecedent soil saturation threshold, and then two different total rainfall–duration threshold curves are estimated. This, among all different approaches explored, is found to be the most successful for landslide prediction.