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Coseismic avalanches and rockfalls, as well as their simultaneous air blast and muddy flow, which were induced by the 2015 Gorkha earthquake in Nepal, destroyed the village of Langtang. In order to reveal volume and structure of the deposit covering the village, as well as sequence of the multiple events, we conducted an intensive in situ observation in October 2015. Multitemporal digital elevation models created from photographs taken by helicopter and unmanned aerial vehicles reveal that the deposit volumes of the primary and succeeding events were 6.81 ± 1.54 × 106 and 0.84 ± 0.92 × 106 m3, respectively. Visual investigations of the deposit and witness statements of villagers suggest that the primary event was an avalanche composed mostly of snow, while the collapsed glacier ice could not be dominant source for the total mass. Succeeding events were multiple rockfalls which may have been triggered by aftershocks. From the initial deposit volume and the area of the upper catchment, we estimate an average snow depth of 1.82 ± 0.46 m in the source area. This is consistent with anomalously large snow depths (1.28–1.52 m) observed at a neighboring glacier (4800–5100 m a.s.l.), which accumulated over the course of four major snowfall events between October 2014 and the earthquake on 25 April 2015. Considering long-term observational data, probability density functions, and elevation gradients of precipitation, we conclude that this anomalous winter snow was an extreme event with a return interval of at least 100 years. The anomalous winter snowfall may have amplified the disastrous effects induced by the 2015 Gorkha earthquake in Nepal.

Glacial lakes are potentially dangerous sources of glacial lake outburst floods (GLOFs), and represent a serious natural hazard in Himalayan countries. Despite the development of various indices aimed at determining the outburst probability, an objective evaluation of the thousands of Himalayan glacial lakes has yet to be completed. In this study we propose a single index, based on the depression angle from the lakeshore, which allows the lakes to be assessed using remotely sensed digital elevation models (DEMs). We test our approach on five lakes in Nepal, Bhutan, and Tibet using images taken by the declassified Hexagon KH-9 satellite before these lakes experienced an outburst flood. All five lakes had a steep lakefront area (SLA), on which a depression angle was steeper than our proposed threshold of 10° before the GLOF event, but the SLA was no longer evident after the events. We further calculated the potential flood volume (PFV); i.e., the maximum volume of floodwater that could be released if the lake surface was lowered sufficiently to eradicate the SLA. This approach guarantees repeatability to assess the possibility of GLOF hazards because it requires no particular expertise to carry out, though the PFV does not quantify the GLOF risk. We calculated PFVs for more than 2000 Himalayan glacial lakes using visible band images and DEMs of ASTER data. The PFV distribution follows a power-law function. We found that 794 lakes did not have an SLA, and consequently had a PFV of zero, while we also identified 49 lakes with PFVs of over 10 million m3, which is a comparable volume to that of recorded major GLOFs. This PFV approach allows us to preliminarily identify and prioritize those Himalayan glacial lakes that require further detailed investigation on GLOF hazards and risk.