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The Himalayan Mountain range is infested by natural disasters annually. Mass movement and hillslope instability are among the most common phenomenon witnessed in those regions. The propensity to such activities is perplexing since it is not attributed to a definitive factor. The reason being the complex geological condition of this mountain range, topography, aided by anthropogenic intervention. A number of landslides reported in these regions are often rainfall induced landslides. However, there is always an interplay of geomorphology and tectonics which contribute to such mass movement.On September, 2017, a colossal landslide occurred along the char Dham (Hindu pilgrimage) corridor, located near Wazri (locally called Ojari) village in Uttarakhand (India). Resulting in the blockade of road for four consecutive days and stranding ~1313 pilgrims of the Char Dham Yatra, and as many as 14 villages were cut-off from the main district headquarters. In order to evaluate the landslide, slope stability, runout, structural, and precipitation analysis were carried out for both the pre- and post- failure event. In the pre-failure analysis, results showed that the maximum shear strain of about 0.16-0.26 was developed parallel to the slope and a total displacement of about 1.8-8 m was likely to take place, parallel to the slope. In the post-failure analysis, results indicated that the maximum shear strain had decreased to 0.14, with a decrease in total displacement to 4.4m. Indicating that much strain was still accumulated in the existing slope. This still implied that the slope was unstable and had accumulated much load on the slope, with high chance of further failure. This model was testified by the incident in the consecutive year of July 11, 2018, where the landslide was triggered due to incessant rainfall. Resulting in the disruption of the daily traffic and stranding ~90 pilgrims of the Char Dham Yatra. The debris flow simulation of the loose unconsolidated material, in case of excessive rainfall effect, revealed a flow accommodating 7-24 m height, 3-4 m/sec velocity, and 100-800 kPa pressure along the Yamuna River. To analyze the role of deformation in the landslide, the study area was subdivided into three distinct zones; (i) fault core, (ii) fault damage zone, and (iii) the intact zone (undeformed host rock), following the evidence from fields. From the visual observations, it was evident that the landslide was confined to the Main Central Thrust (MCT) damage zone.

We have mapped more than 400 major landslides (debris slides, rockfalls, and rock avalanches) in 5 fluvial valleys in Himalaya (India) between 77.3° E - 80.5° E longitudes. Field/high- resolution satellite imagery based landslide area mapping and field based landslide thickness approximation were used to determine landslide area and volume. Area-volume scaling exponents of these landslides revealed a lateral variation in the study area implying that landslide slopes in the eastern part of the study area retain relatively less volume that increases towards western part of the study area. We have hypothesized that such lateral variation is possibly caused by lateral variation in the landslide occurrence that in turn is mostly caused by lateral variation in the seismic-climatic regimes. Following the hypothesis, we noted that rainfall, surface runoff, soil moisture, and air moisture (climatic variables) data of years 1982-2020 represent a general decrease laterally from east to west in the study area. Further, the role of topography on the climate variables is also noted as it increases from east to west. Earthquake (Mw=>4) distribution (1960-2020), Arc Parallel Gravity Anomaly (APGA), cumulative seismic moment, shear stress accumulation rate, and convergence (India-Eurasia) rate (Seismic variables) also represent a general decrease laterally from east to west in the study area. The climatic variability is attributed to the spatial variability of the Indian Summer Monsoon (ISM), whereas seismic variability is referred to the spatial variability in the subsurface pattern of the Main Himalayan Thrust (MHT). Thus, such variability in the seismic-climatic regimes is noted to support our hypothesis.

In this paper, we present a novel, one-step synthesis of SnO

The trans-Himalayan highway, between Gangtok and Yumthang, winds along steep valley sides, including a long section above the Teesta River. Many villages are precariously perched above the V-shaped valley bottoms. The highway is subject to frequent rainfall-triggered landslide events during monsoon season, disrupting transport and destroying infrastructure. The area has also experienced at least three large rock slope failures (RSF) within the past 40 years and many smaller RSF after the 2011 Sikkim earthquake (Martha et al, 2015). Earlier RSF, many prehistoric, have left at least 30 large boulder deposits along the valley. Several of those such as the Lanta Khola landslide get reactivated each monsoon season (Sengupta et al., 2011). A number of villages are located on these deposits, as they are frequently found in shallower sections of the valley slopes.In the present study, Persistent Scatterer InSAR (PSI) has been employed, using Sentinel-1A and -1B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2021 for selected historical landslides and landslide-prone areas along the Dzongu and Yumthang Valleys. Among them are the massive translational Dzongu landslide that occurred in 2016 near Mantam village forming a landslide dam (Morken et al., 2020), a large rock avalanche that occurred in 2015 in Yumthang valley (Penna et al., 2021), and several slope instabilities in the cities of Mangan and Mangshila.Despite the challenges of dense vegetation and winter snow, we detected sufficient targets within the landslides, mainly over the scar areas, rock outcrops, building roofs, and landslide deposits. In this study, we compare the movement/settlement of these historic deposits with ongoing movement in prehistoric deposits. We look at linear vs seasonal components of ongoing deformation within the settlements built upon RSF deposits and discuss the implications with respect to possible catastrophic reactivation. Martha, T. R., Govindharaj, K. B., & Kumar, K. V. (2015). Damage and geological assessment of the 18 September 2011 Mw 6.9 earthquake in Sikkim, India using very high-resolution satellite data. Geoscience Frontiers, 6(6), 793-805.Morken, O. A., Hermanns, R. L., Penna, I., Dehls, J. F., & Bhasin, R. (2020, June). The Dzongu landslide dam: high sedimentation rate contributing to dam stability. In ISRM International Symposium-EUROCK 2020. OnePetro.Penna, I. M., Hermanns, R. L., Nicolet, P., Morken, O. A., Dehls, J., Gupta, V., & Jaboyedoff, M. (2021). Airblasts caused by large slope collapses. Bulletin, 133(5-6), 939-948.Sengupta, A., Gupta, S., and Anbarasu, K., 2010, Rainfall thresholds for the initiation of landslide at Lanta Khola in north Sikkim, India: Natural Hazards, v. 52, no. 1, p. 31-42.

Landslide is one of the most destructive natural hazard in Himalaya. It is mainly caused by numerous geological, geomorphological and hydrological characteristics of the terrain, and generally triggered either by rainfall or earthquake. It poses a serious threat to human lives, environment and the built infrastructures of the region. It has been reported that every year around 300 - 400 fatalities occur in the Himalayan region and monetary loss incurred is ~ 100 million USD. Therefore it is necessary to demarcate different landslide susceptible zones in the region. This will help in the sustainable development of region and minimize the destruction caused by landslides. For the present study, large scale landslide susceptibility mapping for the state of Sikkim encompassing northern and eastern districts using Artificial Neural Network has been carried out. Landslide susceptibility, the relative probability of occurrence of landslides in an area, is one of the prerequisites for the development of the area in this mountain terrain. To assess the landslide susceptibility in a region, it is essential to understand the spatial distribution of the active landslides and landslide deposits, and their controlling factors. The relative weightage to each landslide controlling factor is determined using appropriate models and finally the landslide susceptibility map is prepared.Geologically, the area encompasses the rocks of the Lesser Himalaya and Higher Himalaya, demarcated from one another by Main Central Thrust (MCT) and mainly constitutes phyllite, schist, quartzite, schist and gneiss. An inventory of 247 active landslides and landslide deposits ranging in area from ~ 200 m2 to ~ 450700 m2 and thematic layers of fifteen possible causative factors of landslides viz. lithology, slope angle & aspect, elevation, curvature-plan, curvature-profile, topographic wetness index, stream power index, distance to drainage, road & thrusts, land use and land cover, normalized difference vegetation index (NDVI), and peak ground acceleration (PGA) have been prepared. Of the 247 landslides, 70% were randomly selected for the assessment of landslide susceptibility, and the remaining 30% were used for validating the model. The dependency rate of landslides on each causative factor were estimated using information gain value analysis and subsequently landslide susceptibility map was computed using artificial neural network (ANN) algorithm.It has been noted that high and very high susceptible zones are mainly concentrated along the strike of the MCT, on south facing slopes as these are slopes experience concentrated rainfall due to the orographic barrier. The success rate of our model is 92% and prediction rate is 89%.

The ca. 80 km long trans-Himalayan highway between Gangtok and Yumthang has experienced at least three large rock slope failures (RSF) within the past 40 years and tens of smaller RSF related to the 2011 Sikkim earthquake. More than 30 conspicuous boulder deposits suggest that similar failures happened in the past. Since the largest of these deposits are located within the shallowest sections of otherwise 60 – 75° steep slopes, they are often the location of settlements. We have used Terrestrial Cosmogenic Nuclide (TCN) dating to understand better where and how often these events are likely to occur.The trans-Himalayan highway connects the Lesser Himalaya, with a tropical to subtropical climate, with the cold-temperate climate in the Higher Himalaya north of the Main Central Thrust (MCT). This highway also crosses the orographic barrier, with rainfalls exceeding 3000 mm/yr in the south and less than 500 mm/yr in the north. On September 10th, 1983, a large RSF was triggered by “exceptional” rainfall and impacted the settlement of Manul, with an estimated life loss of 200 persons. Today, the deposit is covered by a dense tropical forest 30-m high that restricts detailed analysis. However, boulder size and boulder density on the surface suggest that it was a rock avalanche.The second reported RSF is a rock avalanche with a volume of 12 million m3 that occurred close to the village of Yumthang on March 11th, 2015. This deposit overlies two generations of prehistoric rock-avalanche deposits. No trigger was reported.The last reported RSF involved a volume of 8.7 million m3, occurred on August 13th, 2016 at Dzongu, NW of Mangan. While no trigger for the collapse was reported, satellite footage indicates at least ten years of pre-failure rock-slope deformation. The deposit has the typical carapace of a rock avalanche, but videos posted on social media instead suggest that it was a collapse that took place over several hours.RSF deposits are found in similar numbers in both the Higher and Lesser Himalaya, with the highest concentration in the vicinity of the MCT and a second cluster close to the village of Yumthang. We sampled ten of the deposits for TCN dating, including two of the historic events. Both historic events returned zero ages. The two older deposits overlain by the 2015 Yumthang rock avalanche returned equally young ages, suggesting multiple recent events at that site within a short time. The zero ages of both historical events suggest that inheritance of nuclides prior to failure in the samples can be ruled out. The ages of the remaining deposits range from 0.2 to ~12 kyr. Several deposits have bimodal age distributions. Others have three different ages in different sectors of the deposit. These results show that multiple RSF similar to the Yumthang site often can affect the same slope sector, leaving deposits on the same slope sections. Thus, the 30 identified deposits by far are the lower limit of RSF failures in the study area and that the threat of RSF is high.

Objective: In today’s day and age with the advent of smartphones along with the handy apps available for download, there is increasing opportunities for surgeons to integrate such technology into clinical practice with great ease. This study aims to provide a systematic classification of apps in order to provide dependable data for choosing the right app by both surgeons and trainees. Material and Methods: A series of methodical searches were carried out on “Google Play Store” and “Apple’s App Store” with pre-decided keywords. The results were then sorted and segregated into relevant categories like core surgery, apps related to surgical practice, patient utility apps and other surgical branches. Thereafter, the apps that met with our cut-offs, were assessed for their credibility and utility, based on predefined parameters. Results: There were a lot of variations in between the categories we segregated the apps into. Using predefined cutoff criteria, (rating >3 and reviews >30), 48 of the apps were assessed finally for their utility and credibility. Out of these 48 apps, 42 were on android platform while the remaining 6 were on iOS. Ten apps were found to be having high credibility and 15 apps have high utility. Conclusion: The role of smartphone apps in surgery and surgical training appears highly promising and using apps with high credibility and utility will provide dependable and updated information for the surgeons and trainees.