Modelling catchment susceptibility to alpine mass movements

Gravitational mass movements in alpine regions, such as landslides, debris flows and rockfall, are driven by complex physical processes. While the translation and runout of these events can be reasonably well modelled once they occur, the predisposing and triggering mechanisms leading to failure are very challenging to assess. This is particularly demanding for practitioners who need to take decisions on the ground to ensure the safety of the population. There is potential to improve the situation by using a variety of new space-time climate and land surface datasets to describe the hydrogeomorphic system state and relate it to possible failure by confronting it with past observed events. In this work we focus on the local susceptibility to the initiation of mass wasting events (shallow landslides, debris flows and rockfall) in low- and subalpine regions by exploring the predictive power of various hydro-meteorological drivers related to rainfall, snowmelt, high soil moisture, freezing, etc.

To provide spatially and temporally consistent information, we model all hydro-meteorological drivers governing the hydrogeomorphic catchment state of the Alpine Rhine (GR, Switzerland) over the period 1998-2022 based on globally available soil information (SoilGrids) as well as national climate (Federal Office of Meteorology and Climatology MeteoSwiss), snow (WSL Institute for Snow and Avalanche Research SLF) and terrain data (Federal Office of Topography Swisstopo). The temporal and spatial resolution of the analysis is daily over a 1x1km grid. We determine the seasonally varying contribution of each driver to the triggering of each individual mass movement type utilizing the concept of receiver operating characteristics (ROC) and its area under the curve (AUC) as performance metrics. The underlying events recorded in the Swiss natural hazard database comprise 459 shallow landslides, 295 debris flows and 761 rockfalls (StorMe, Swiss Federal Office for the Environment FOEN) in the study period. The best-performing hydro-meteorological drivers then serve as input to predict the occurrence of mass wasting events with data driven models. We test both a traditional statistical approach and machine learning algorithms to compare their capability of modelling the susceptibility to alpine mass movements.

Compared to a purely rainfall-based prediction of landslide or debris flow activity, which is commonly done in the literature, this approach benefits from the availability of further spatially distributed climate variables and terrain characteristics. Our findings contribute to a better understanding of the role of catchment state on predisposing and triggering conditions of alpine mass movements, and illustrate also the limits of predictability for such events due to the inherent randomness in the triggering processes.