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. Multi-temporal satellite data provide evidence of a precursor event in 2016 and expansion of a linear fracture along joint planes, indicating 2021 rock-ice avalanche is a retrogressive wedge failure. The deformation history shows that the fracture propagated at a velocity of ~0.07 m day-1 until September 2020, and with an accelerated velocity (~0.14 m day-1 on average) lately. Analysis of recent similar cases in HMA supported our inference on global warming-induced glacier retreat and thermomechanical effects in enhancing the weakening of fractured rock masses in tectonically active mountain belts. Recent advances in Earth observation and seismic monitoring system can offer clues to the location and timing of impending catastrophic failures in high mountain regions.

Basin-wide flood depth and exposure mapping from SAR images and machine learning models.

Recent years recorded an increasing number of short duration - high-intensity rainfall events in the Indian subcontinent consequent with urban and riverine flash floods. Rapid assessments of flooded areas are key for effective mitigation strategies and disaster risk plans, as well as to prepare operative policies for future events. Herein, we present an integrated methodology for rapidly mapping the flood extent, and depths based on Synthetic Aperture Radar (SAR) images and a digital elevation model (DEM). Incessant rain during August 2019 brought heavy riverine flooding in southern India, killed at least 280 people, and displaced about one million inhabitants from low-lying areas. We used SAR images by Sentinel-1 before, and during the flooding, and the MERIT DEM which enabled us to map the flood extent and flood depth of the inundation zones. Because the coverage of Sentinel-1 scene was limited to the Kabini river section during the flood period, flood extent and depth maps for the adjacent basin was generated by mapping the susceptibility for flooding using the training set obtained from the flood time Sentinel-1 images, and a set of predictive variables derived from DEM using random forest model. Qualitative analysis and cross-comparison with a numerical flood model proved the proposed approach is highly reliable with an accuracy value of 90% and 86% respectively for training and validation data, thus allowing a precise, simple, and fast flood mapping. The methodology presented here could be applied to other flooded areas having incomplete inventory in the context of flood risk assessment.