Modelling local susceptibility to rapid mass movements in a changing climate

Abstract ID: 3.12163 | Accepted as Talk | Talk/Oral | TBA | TBA

Sophia Demmel (0)
Molnar, Peter (1)
Sophia Demmel (1)
Molnar, Peter (1)

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(1) ETH Zürich, Laura-Hezner-Weg 7, 8093, Zürich, CH

(1) ETH Zürich, Laura-Hezner-Weg 7, 8093, Zürich, CH

Categories: Cryo- & Hydrosphere, Hazards
Keywords: mass movements, sediment erosion, hydrometeorological drivers, machine learning, climate change

Categories: Cryo- & Hydrosphere, Hazards
Keywords: mass movements, sediment erosion, hydrometeorological drivers, machine learning, climate change

Rapid mass movements, particularly debris flows, contribute substantially to the sediment discharge of alpine catchments. Large volumes of material are eroded by these events, a major fraction of which is then available for downstream transport in the fluvial system. It is subject to ongoing research to what extent the sediment flux from mountain rivers will alter with warming temperatures, less snowfall and more intense precipitation events until the end of the 21st century. In this work we examine future changes in the provision of material eroded by alpine mass movements and its seasonality.
We model the local susceptibility to rapid gravitational mass movements such as shallow landslides and debris flows in the Alpine Rhine catchment in the Canton of Grisons, Switzerland, based on current climate input and future climate projections. We leverage the predictive power of various hydrometeorological drivers and local characteristics of terrain and lithology, all of which contribute to the hydrogeomorphic catchment state. In a first step, the hydrometeorological drivers such as rainfall, snowmelt, soil saturation and frost processes are modelled 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). Secondly, a data-driven algorithm simulates daily susceptibility to the occurrence of rapid mass movements on a 1x1km grid. This machine learning framework is informed by the hydrogeomorphic catchment variables and over 1000 shallow landslide and debris flow observations (StorMe, Swiss Federal Office for the Environment FOEN) over the period 1998-2022. We then compare the modelled susceptibility under today’s climate to the projected changes in susceptibility at mid-century and end-century.
Estimating the susceptibility of alpine regions to the initiation of gravitational mass movements based on hydrometeorological variables allows us to shed light on the role of the hydrogeomorphic catchment state on the underlying triggering mechanisms. By assessing the effects of a warming climate on the occurrence of these events, we contribute to a better understanding of future sediment input into the fluvial system from erosion.

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