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Introduction: The rock avalanches are a frequent and disruptive phenomenon in the Himalayas and other mountain chains. To minimize future losses, it is essential to investigate the engineering geological causative factors and mechanism of these mass wasting events. Study area: The present work is aimed at assessing the failure mechanism of the disastrous 2014 Jure rock avalanche along Araniko Highway, Northern Nepal. The event had blocked the Sunkoshi River and blocked an economically significant route to China. Geotechnical properties and analysis: Initially, rockmass characterization and intact strength attribute were determined for the site to classify the failure zone. The parameters measured and obtained from the field and laboratory were integrated into the analytical models to obtain a conclusive interpretation of the failure mechanism. Structural, kinematic, and key block theory analyses have been carried out for decipher the evolution of the failure zone. Results and discussion: Rock mass was found to be of fair quality, however, the structural instabilities and the presence of water has led to a progressive failure. Movement of the key block and subsequent sliding of wedges and foot failure appears to be a possible failure mechanism. Conclusion: The present research explores the contributory engineering geological aspects of the Jure rock avalanche. The investigation results can be used to tackle similar large scale rock avalanches in similar geological terrains and thus minimizing the losses. [ABSTRACT FROM AUTHOR]

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016–2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area. [ABSTRACT FROM AUTHOR]

In the present study, landslide hazard, vulnerability and the risk assessment of the Himalayan township Mussoorie, located in the lower part of the Lesser Himalaya has been undertaken. The area famous for tourism constitutes > 7000 buildings with ~ 30,000 habitations. Four bivariate statistical approaches viz. Weight of evidence (WoE), Frequency ratio (FR), Yule Coefficient (YC), and Information Value (InV) were used for landslide hazard assessment, and all these approaches with 75%—80% success and predication rate are found to be acceptable for landslide hazard mapping. The analyses indicate that the Nagar Palika Parisad ward, Library ward, Happy valley ward and Bhadraj ward exhibit the maximum area falling under the high and very high landslide hazard zones, whereas Landaur ward, Jalkii ward and Indra Colony ward exhibit a greater part of the area in the low and very low hazard zones. On the basis of the six elements at risk present in the study area viz. settlement, crop land, water body, roads, barren and degraded land, and dense forest, the ~ 40% of the area is very high and high vulnerable, ~ 11% moderate, and ~ 49% is low and very low vulnerable. High and very high vulnerable zones are located in the west and central portion mainly because of higher habitation and more anthropogenic activities. Finally, risk map prepared intersecting hazard and vulnerability maps exhibits that Nagar Palika Parisad ward has the largest part (~ 41%) of the area with 37% buildings falling in the high and very high risk zones, whereas the Landour ward is the safest ward having ~ 83% area in the low and very low risk. Overall ~ 1604 buildings with habitation of ~ 8,000 persons in the township, are prone to high and very high landslide risk. The results of this study may be utilized for further development and planning in the area. [ABSTRACT FROM AUTHOR]

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment. [ABSTRACT FROM AUTHOR]