Hänge in Bewegung: Rutschung, Steinschlag und Felssturz ¶

On 17 October 2015, a large landslide entered the marine waters of Taan Fiord, Alaska, and generated a displacement wave with a 193 m runup. The wave scoured the surrounding hillslopes of soil and vegetation and deposited significant volumes of material into the fjord, onto hillslopes on the opposite side of the fjord, and on top of Tyndall Glacier. For this study, we generated six, 2-m posting Digital Surface Models (DSMs) using DigitalGlobe/Maxar satellite imagery acquired near-annually between 2012 and 2019, and the Surface Extraction with TIN-based Search-space Minimization (SETSM) high-performance computing algorithm. We aligned the DSMs to exposed bedrock in the 01 March 2014 DSM acquisition, and then used them to characterize topographic and volumetric changes from before and after the 2015 Taan Fiord rock avalanche. We find that the landslide mobilized roughly 77. 0 ± 0.9 Mm³ of material, of which approximately 56.3 Mm³ were deposited in the fjord waters. Furthermore, we quantified an additional 27.2 ± 3.8 Mm³ of material scoured from fjord-adjacent hillslopes and deposited in the fjord waters, providing new constraints on the subaqueous deposition. This is the first time that DSMs have been used to estimate the volume of scour caused by a tsunami and the subsequent changes in extent and volume with time. Our results for the landslide and runout are consistent with field measurements published previously. This study offers improved estimates of both subaerial and subaqueous deposition for the 2015 Taan Fiord landslide and describes additional regional environmental conditions. We identify precursory motion prior to the 2015 landslide, characterize several smaller-scale landslides in the larger Taan Fiord region, delineate terminus positions and associated ice dynamics of the Tyndall Glacier, and detail seasonal changes in vegetation growth and snow melt/accumulation. This work provides important new insights into the geomorphic features and dynamics of this landslide and subsequent tsunami. The interdisciplinary applications associated with DSMs and the accuracy of the measurements presented here demonstrate that these methods are an effective tool to improve our understanding of the pre- and post-landslide processes, for monitoring areas at risk for landslides and other natural hazards, and for rapid response to catastrophic events.

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 g1/420g % 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 Ng 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.

An increase in debris-flow frequency is expected in steep Alpine catchments after the occurrence of a large landslide, such as a rock avalanche. Herein we describe changes in debris-flow activity following increases in sediment availability due to landslides, or accelerated rock-glacier movement, for five catchments in the Swiss Alps, the Spreitgraben, Schipfenbach, Bondasca, Riascio, and Dorfbach catchments. Documentation on debris-flow activity is available from both before and after the landslide that generated the new sediment deposits. Data from nearby meteorological stations were used to explore possible changes in rainfall activity, and how the intensity and duration of rainfall events may have changed. In all cases there was a considerable increase in debris-flows frequency for one to eight years following the landslide. The annual number of days with debris-flow activity following the landslide was similar to that observed for the Illgraben catchment, where many such landslides occur annually. No clear change in precipitation totals preceding debris flows was apparent for the Riascio catchment, suggesting that the increase in frequency of debris flows is related to the increase in the amount of sediment that can be readily mobilized. In the two cases where rainfall data were available on an hourly basis, no systematic changes in the intensity or duration of rainfall related to debris-flow triggering were apparent, as shown by the close-clustering of storms on the intensity-duration plots. Following the sediment-generating event, an initial and sudden increase of the sediment yield was observed, followed by a decrease over time towards pre-disturbance values. The response of the catchments appears to be related to the amount of debris-flow activity prior to the landslide: sediment yield from catchments with frequent debris flows prior to the landslide activity did not increase as dramatically as in catchments where debris-flow activity was less common prior to the landslide.

Debris flows and landslides induced by heavy rainfall represent an ubiquitous and destructive natural hazard in steep mountainous regions. For debris flows initiated by shallow landslides, the prediction of the resulting pathways and associated hazard is often hindered by uncertainty in determining initiation locations, volumes and mechanical state of the mobilized debris (and by model parameterization). We propose a framework for linking a simplified physically-based debris flow runout model with a novel Landslide Hydro-mechanical Triggering (LHT) model to obtain a coupled landslide-debris flow susceptibility and hazard assessment. We first compared the simplified debris flow model of Perla (1980) with a state-of-the art continuum-based model (RAMMS) and with an empirical model of Rickenmann (1999) at the catchment scale. The results indicate that predicted runout distances by the Perla model are in reasonable agreement with inventory measurements and with the other models. Predictions of localized shallow landslides by LHT model provides information on water content of released mass. To incorporate effects of water content and flow viscosity as provided by LHT on debris flow runout, we adapted the Perla model. The proposed integral link between landslide triggering susceptibility quantified by LHT and subsequent debris flow runout hazard calculation using the adapted Perla model provides a spatially and temporally resolved framework for real-time hazard assessment at the catchment scale or along critical infrastructure (roads, railroad lines).

A high-resolution gridded daily precipitation data set was combined with a landslide inventory containing over 2000 events in the period 1972–2012 to analyze rainfall thresholds which lead to landsliding in Switzerland. We colocated triggering rainfall to landslides, developed distributions of triggering and nontriggering rainfall event properties, and determined rainfall thresholds and intensity-duration ID curves and validated their performance. The best predictive performance was obtained by the intensity-duration ID threshold curve, followed by peak daily intensity I_max and mean event intensity I_mean. Event duration by itself had very low predictive power. A single country-wide threshold of I_max = 28 mm/d was extended into space by regionalization based on surface erodibility and local climate (mean daily precipitation). It was found that wetter local climate and lower erodibility led to significantly higher rainfall thresholds required to trigger landslides. However, we showed that the improvement in model performance due to regionalization was marginal and much lower than what can be achieved by having a high-quality landslide database. Reference cases in which the landslide locations and timing were randomized and the landslide sample size was reduced showed the sensitivity of the I_max rainfall threshold model. Jack-knife and cross-validation experiments demonstrated that the model was robust. The results reported here highlight the potential of using rainfall ID threshold curves and rainfall threshold values for predicting the occurrence of landslides on a country or regional scale with possible applications in landslide warning systems, even with daily data.