Uplift rates in the Canton of Graubünden are among the highest in Switzerland. The rise of the Alps is countered by various erosion processes, such as overland flow erosion, landsliding and other mass movements. A network of sources, buffers and sinks controls the transport of sediment from detachment zones downstream. Recent sediment yields can be estimated at different spatial scales, e.g. by locally quantifying debris flow volumes in sediment traps or monitoring the basin-integrated suspension load into Lake Constance, yet many unknowns are remaining regarding the fate of sediment particles along their pathways.
We are an interdisciplinary team of geologists, geomorphologists, and hydrologists researching sediment sources and sinks in the Swiss Alpine Rhine landscape, the pathways that connect them, and the mechanisms that activate them. In this work we establish a link between long-term sediment erosion rates inferred from paired cosmogenic radionuclides (CRN) concentrations (10Be and 26Al) measured in river sand and modern catchment-scale meteorological and hydrological controls on sediment production by landsliding. By complementing CRN data with geochemical fingerprinting, we identify the spatial variability of sediment sources and their relative contribution across the basin on a millennial time scales. Using sequential machine learning techniques trained on the StorMe inventory of mass movements and gridded weather data, we predict which hydroclimatic conditions cause mass-failures on a daily scale. We then compare both rates of surface erosion and highlight differences in the long- and short-term erosion processes in the Alpine Rhine basin and its subcatchments.
We find that long-term erosion rates in the basin range from 0.3 to 2 mm/yr, with paired-CRN data indicating negligible storage and highly efficient sediment evacuation from the system. The highest erosion rates are linked to erosion hotspots: areas with present glaciers and, most importantly, abundant mass-wasting events particularly associated with Bündnerschiefer. Our machine learning model demonstrates that a majority of past rapid mass movements on subalpine slopes are conditioned by either intense rainfall in the summer months or oversaturated soils following snow melting at the onset and end of the shortening winter period.
These findings have vital implications for predicting how the Alpine landscape will respond to global warming. Our model predicts that the probability of landsliding in the subalpine areas is likely to increase in response to a shift of the snowline to higher elevations. A reduced period of continuous snowpack in winter reduces the buffer effect of the snow cover and increases the amount of liquid water available for infiltration into the critical soil zone, particularly in autumn and spring. Because the vulnerable areas underlain by Bündnerschists sit precisely within this critical elevation range, we anticipate they will become an even more dominant and active sediment source under a warming climate. Ultimately, unravelling these climate-sensitive erosion hotspots provides essential predictive insights for regional land management and natural hazard mitigation in Graubünden.