Earthquake-induced soil landslides: volume estimates and uncertainties with the existing scaling exponents.

Quantifying landslide volumes in earthquake affected areas is critical to understand the orogenic processes and their surface effects at different spatio-temporal scales. Here, we build an accurate scaling relationship to estimate the volume of shallow soil landslides based on 1 m pre- and post-event LiDAR elevation models. On compiling an inventory of 1719 landslides for 2018 M 6.

Combining geophysics, remote sensing and numerical simulation to assess GLOFs: Case study of the Namulacuo Lake in the Southeastern Tibetan Plateau.

The current highest glacial lake outburst floods (GLOFs) risk level is centered in the eastern Himalaya. GLOFs represent a serious threat to downstream inhabitants and ecological environment. In the context of climate warming on the Tibetan Plateau, such GLOFs will continue or even intensify in the future.

Imminent threat of rock-ice avalanches in High Mountain Asia.

Upsurge of glacier-related hazards in High Mountain Asia (HMA) has been evident in recent years due to global warming. While many glacial-related hazards are instantaneous, some large landslides were preceded by slow gravitational deformation, which can be predicted to evade catastrophes. Here, we present robust evidence of historical deformation in 2021 Chamoli rock-ice avalanche of Himalaya using space imaging techniques.

Surface temperature controls the pattern of post-earthquake landslide activity.

The patterns and controls of the transient enhanced landsliding that follows strong earthquakes remain elusive. Geostatistical models can provide clues on the underlying processes by identifying relationships with a number of physical variables. These models do not typically consider thermal information, even though temperature is known to affect the hydro-mechanical behavior of geomaterials, which, in turn, controls slope stability.

Unraveling the drivers of intensified landslide regimes in Western Ghats, India.

The Western Ghats (WG) mountain range in the Indian sub-continent is a biodiversity hotspot, now faces a severe threat to the valley population and ecosystem because of changing rainfall patterns and land-use changes. Here, we use the 2018-2019 landslide inventory data together with various geo-environmental factors and show that the landslide activity in the WG region is amplified by anthropogenic disturbances. We applied a generalized feature selection algorithm and a random forest susceptibility model to demonstrate the major topographic controls of landslides and the risk associated with them in the WG region.