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Abstract Seismic waves can shake mountainous landscapes, triggering thousands of landslides. Regional-scale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. Such an approach neglects the contribution of ground motion phase and amplitude and their variations over space and time. Here, we address this problem by developing an explainable deep-learning model able to treat the entire wavefield and benchmark it against a model equipped with scalar intensity parameters. The experiments run on the area affected by the 2015 Mw7.8 Gorkha, Nepal earthquake reveal a 16% improvement in predictive capacity when incorporating full waveforms. This improvement is achieved mainly on gentle (~25°) hillslopes exposed to low ground shaking (~0.2 m/s). Moreover, we can largely attribute this improvement to the ground motion before and much after the peak velocity arrival. This underscores the limits of single-intensity measures and the untapped potential of full waveform information.

Abstract Cryospheric hazards - in this case, thaw slumps (TS) and thermo-erosion gullies (TEG) - are phenomena typical of permafrost-dominated landscapes. Open datasets informing about their spatial, temporal and size distributions in the Arctic are still uncommon, as opposed to the systematic availability of this information for geomorphic processes in mid- to low- latitudes. To date, only the most populated region of Svalbard was covered with TS and TEG inventories. Here, we extend the respective information to most of the Archipelago, totalling 8491 polygons, out of which 3679 are TSs and 4812 are TEGs. These have been manually mapped from aerial photographs throughout Svalbard across the 14 largest ice-free regions. The SvalCryo inventory is highly relevant as the Arctic environment undergoes alarming changes in response to global warming. The idea behind the two inventories is to support the geoscientific community in the quest to evaluate the environmental response to climate change, by creating a baseline for change monitoring, and ultimately to serve as basis for susceptibility, hazard and risk assessment models.

Abstract The weakening of hillslopes during strong earthquakes increases landsliding rates in post-seismic periods. However, very few studies have addressed the amount of coseismic reduction in shear strength of hillslope materials. This makes estimation of post-seismic landslide susceptibility challenging. Here we propose a method to quantify the maximum shear-strength reduction expected on seismically disturbed hillslopes. We focus on a subset of the area affected by the 2008 Mw 7.9 Wenchuan, China earthquake. We combine physical and data-driven modeling approaches. First, we back-analyze shear-strength reduction at locations where post-seismic landslides occurred. Second, we regress the estimated shear-strength reduction against peak ground acceleration, local relief, and topographic position index to extrapolate the shear-strength reduction over the entire study area. Our results show a maximum of 60%–75% reduction in near-surface shear strength over a peak ground acceleration range of 0.5–0.9 g. Reduction percentages can be generalized using a data-driven model.