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The paper describes the recent devastating landslide of Kotrupi, Himachal Pradesh in India which had claimed many human lives and properties along its course on 13 August 2017. This event was the third reactivation of the slide as per data available. The study for this landslide involves a preliminary investigation of the site and comparison of pre- and post-event slope stability analysis. Other than rainfall, the combined effect of differential weathering rate of the debris and soil layers, low material strength, and the presence of a fault zone was found to be the primary cause of instability. The analysis was carried out with the geotechnical investigation and numerical modeling with the help of finite element model-based Phase 2 simulator. The significant findings of this paper include numerical simulation of pre- and post-event slide as stated under unsaturated and saturated condition. The study finds a low factor of safety of the present slope. The unstable condition of the slope may trigger another event of the same intensity shortly. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides on steep slopes are potential hazards to life and property. The state of Kerala in India is witnessing unprecedented death and damage since last 3 years due to landslides triggered by heavy rainfall. In this paper, we present a cause-impact analysis of landslides that occurred due to the monsoon rainfall in the years 2018, 2019 and 2020. We have created an event-based landslide inventory post-rainfall season for the entire state and then selected six major landslides for detailed analysis. Event-based landslide inventory maps are prepared from Earth observation satellite data such as Resourcesat-2, 2A, Cartosat-2S, WorldView-2, Pleiades-1A and 1B, SPOT-6 and 7 and Sentinel-2 acquired before and after the rainfall events. Then, rainfall data were analysed to find out the role of daily and antecedent rainfalls on landslide trigger. Finally, we have carried out numerical debris flow modeling to characterise landslides and assess the impact. Our results show that landslides are triggered by antecedent rainfall of variable durations and are mostly debris flow type that has followed the channel path. The runout length and lateral spreads are controlled by variable frictional parameters of the slope material. Furthermore, reactivation at the crown region is also noticed in few cases that need to be monitored. [ABSTRACT FROM AUTHOR]

India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India's landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India. [ABSTRACT FROM AUTHOR]

Several landslides and floods were triggered by unprecedented, incessant heavy monsoon rainfall from 4 August 2019 to 8 August 2019, along the northern slopes, at Kavalappara, in the Malappuram district and Puthumala, in the Wayanad district of Kerala, India. The geological and geomorphological field settings were analyzed within the sub-basin wide catchment areas, for a preliminary evaluation of the causative factors of the landslides. This paper provides insights into the causative factors of landslides, together with suggestions on appropriate land use planning and deployment of real-time flood and landslide monitoring systems, besides building an adaptable community resilience model in high landslide-prone areas. The structurally disturbed weakened bedrocks and generally incompetent residual alteration products, exacerbated by nonchalant human interventions, reduction of cohesive strength, on super-saturation of underlying earth material, were inferred to be the primary causes of major landslides, triggered during 5 days of excessively heavy rainfall (400% over the normal average). Additionally, massive toe erosion was observed by streams gushing with course clastic sediments and debris. The destructive nature of such multi-hazards was evoked by unscientific modifications of slopes, mismanagement of water drainage, unplanned residential developments, across the channel courses and within active floodplain of rivers, and lack of early warning schemes for landslide. [ABSTRACT FROM AUTHOR]

In this paper, we present a state-of-the-art wireless sensor network (WSN) of deep-earth probes (DEPs) that has been deployed to monitor an active landslide in the Western Ghats mountain range of South India. While India has one of the highest incidences of landslides and landslide-induced fatalities-primarily in the Himalayas of North India and in the Western Ghats of Central and South India-our study is perhaps the first comprehensive attempt to instrumentally detect landslides in the Western Ghats. Wireless networks have enabled us, since June 2009, to continuously monitor the deployment site in real time and from anywhere around the globe. There have been a few earlier landslide monitoring WSNs using accelerometers in Emilia Romagna Apennines, Italy; global navigation satellite system (GNSS) sensors to monitor the Hornbergl landslide, Austria; and vibrating wire stress sensors to monitor a slope in China. We improved upon these WSN systems by incorporating a variety of sensors-piezometers, dielectric moisture sensors, strain gauges, tiltmeters, a geophone, and a weather station-and installing some of these sensors as deep as 20 m below the ground surface. We present the salient aspects of the field deployment of DEPs: the selection of sensors and their incorporation in DEPs, the methodology we used in embedding these DEPs into the soil, and a few of the key aspects of the wireless sensor network. We also present a description of the deployment site and some of the results of geotechnical investigations carried out on borehole corings. Finally, we present the more interesting field data collected from the monitoring system during a rainy season in July and August 2009. [ABSTRACT FROM AUTHOR]

Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed. [ABSTRACT FROM AUTHOR]

In this paper, a quantitative landslide hazard model is presented for transportation lines, with an example for a road and railroad alignment, in parts of Nilgiri hills in southern India. The data required for the hazard assessment were obtained from historical records available for a 21-year period from 1987 to 2007. A total of 901 landslides from cut slopes along the railroad and road alignment were included in the inventory. The landslides were grouped into three magnitude classes based on the landslide type, volume, scar depth, and run-out distance. To calculate landslide hazard, we estimated the total number of individual landslides per kilometer of the (rail) road for different return periods, based on the relationship between past landslides (recorded in our database) and triggering events. These were multiplied by the probability that the landslides belong to a given magnitude class. This gives the hazard for a given return period expressed as the number of landslides of a given magnitude class per kilometer of (rail) road. The relationship between the total number of landslides and the return period was established using a Gumbel distribution model, and the probability of landslide magnitude was obtained from frequency-volume statistics. The results of the analysis indicate that the total number of landslides, from 1- to 50-year return period, varies from 56 to 197 along the railroad and from 14 to 82 along the road. In total, 18 hazard scenarios were generated using the three magnitude classes and six return periods (1, 3, 5, 15, 25, and 50 years). The hazard scenarios derived from the model form the basis for future direct and indirect landslide risk analysis along the transportation lines. The model was validated with landslides that occurred in the year 2009. [ABSTRACT FROM AUTHOR]

In the present study, the Sataun landslide was investigated along the National Highway-72 (NH-72) between Sirmauri Tal and Sataun towns of the Sirmaur district of Himachal Pradesh, Northwestern Himalaya, India. Geologically, the Sataun landslide is composed of Middle Siwalik sandstones, which are highly fractured and faulted due to the interaction of the active Sirmauri Tal Fault zone and the Malgi Fault at the site of the Sataun landslide. Through the geological field investigations, three unstable zones were identified, which are prone to landslides. A major failure along one of the unstable zones (Sataun landslide, Zone 1) occurred on the 9th of October 2019. The investigation of the Sataun landslide was carried out using geological, geomorphological, geotechnical, and kinematic analysis. A pre-landslide topography was used to analyse the slope stability using FEM (finite element modelling) technique. Geomorphological studies and field investigations suggest the presence of an active fault zone (Sirmauri Tal Fault zone) passing through the Sataun landslide zone. Grain size analysis demonstrates that the soil is non-cohesive poorly graded sandy soil and geotechnical parameters indicate low cohesion (0.17 kPa) and low angle of internal friction (33.09°). Pre-landslide slope modelling suggests a low factor of safety (0.76). Toe cutting by the Giri River, the presence of radial, transverse cracks on the landslide body, and tension cracks on the crown propose the active and progressive nature of the landslide. Although the role of active fault is the most crucial factor for the Sataun landslide failure, rainfall played a critical triggering role in the Sataun landslide. This study shows that the unstable condition of the Sataun landslide may trigger another event of similar or greater intensity in the near future; therefore, proper monitoring is required to mitigate this landslide. [ABSTRACT FROM AUTHOR]

Probabilistic modelling is gaining increased attention in the field of assessing the landslide hazard due to the ability to account for the spatial and temporal uncertainties related to the variability of geological, hydrological, geotechnical, seismological and geomorphological parameters. In this study, a seismic landslide hazard assessment was carried out for Uttarakhand state, located in the Indian Himalayan region. A methodology was developed to model the parametric uncertainties incorporated in the modified Newmark slope stability analysis model, which considers the rock joint shear strength properties in permanent displacement computation. The uncertainties related to input parameters were taken into account by utilizing statistical distributions to represent these parameters. On a pixel-by-pixel basis, several probability density functions were simulated using the Monte Carlo method, and the simulation results were retained throughout the computation process. As a result, there were no constraints on the mathematical complexity or symmetry of the underlying distributions when casting the derived quantities into probabilistic hazard maps. The hazard map showed the probability of exceedance of seismic slope displacement beyond a threshold value of 5 cm. High probability values were observed in the Middle and Greater Himalayas, emphasizing the likelihood of a large number of earthquake-induced landslides in this region. Finally, the results were validated using the landslide inventory of the 1999 Chamoli earthquake. The prepared seismic landslide hazard map will give infrastructural planners and local authorities a tool for evaluating the risk associated with a seismic landslide for land use planning and taking appropriate mitigation measures to reduce the losses. [ABSTRACT FROM AUTHOR]

Analysis of high-resolution multibeam bathymetry, sub-bottom profiles, and multichannel seismic reflection data provides a novel understanding of a large-scale submarine landslide in the Cochin offshore region, southwestern continental margin of India. This massive submarine landslide is referred to name as the "Cochin slide". The Cochin slide exhibits a U-shaped slide scar with an average gradient of ~ 2°, which opens in the NNE-SSW trend in the lower slope and Laccadive Basin. The slide scar has a width of ~ 46 km and a total perimeter length of ~ 60 km, estimated with a volume of ~ 240 km3 excavated from this region. A non-sinuous channel with a length of ~ 36 km and having incisions varying from ~ 140 to 40 m from head to toe has developed within the slide scar. The submarine channel might have formed after the slide event facilitating channel incisions in the upper slope. The mass transport deposits associated with the slide have distributed over a wide area (~ 5700 sq. km.) in the lower slope and Laccadive Basin, with a maximum run-out length of ~ 138 km. The preconditioning factors or trigger mechanisms for the Cochin slide could not be precisely established yet. However, sedimentation during different geological times and the occurrence of weak layers might have contributed to the slope instability. The morphology of the Cochin slide, its correlation with the extensive faulting in the continental shelf-slope province, the presence of structural elements, and records of previous earthquakes suggest that the tectonic/geological processes strongly influence the triggering of the slide. [ABSTRACT FROM AUTHOR]

The reliable landslide hazard assessment entails a robust understanding of frequency-magnitude analysis of the landslide inventory. Previous studies proposed that the landslide frequency-size distribution follows a power-law distribution even though 75–90% of data deviated from the power-law fit. This deviation from the power-law fit was ubiquitous in various landslide studies irrespective of their triggering factor, spatial and temporal resolution of the landslide inventory and geological and morphological settings of the area. This study conducted a detailed frequency-size distribution of the landslides at four different locations in India's Himalayan regions to check the validity of power law to represent the landslide data. The landslide inventory of Kathua, Shimla, Pithoragarh and Darjeeling regions are complied, consisting of 1942, 905, 2151 and 393 landslide events, respectively. The landslide frequency distribution is tested for the power-law, exponential and lognormal distribution. After a detailed statistical analysis, our finding suggests that medium-large landslide size events follow a power-law distribution, accounting for 8–25% of the total data; the rest deviates significantly from the power-law fit and follows a lognormal distribution. The cut-off between the lognormal and power-law distributions, or the minimum size for medium-large landslides, is 102.9–104.3 m2. The Kolmogorov–Smirnov (KS) and Lilliefors tests are used to validate the findings. This study provides insight into the landslide size probability distribution in different locations situated in India's Himalayan regions. [ABSTRACT FROM AUTHOR]

Assessment of the spatial probability of future landslide occurrences for disaster risk reduction is done through landslide susceptibility modelling. In this study, we investigated the effect of time and space partitioning strategies of samples on the performance of regional landslide susceptibility models on macro-scale mapping in the state of Mizoram, India, covering 21,087 km2 area. We used landslide inventory data of 2014 and 2017 periods consisting of 1205 and 2265 landslides, respectively, to train and test the models with four sampling strategies such as spatial, temporal, temporal (size constrained) and temporal (geographic constrained). We used five commonly inherited models such as multiclass weighted overlay (MCWO), information value (IV), weights of evidence (WoE), logistic regression (LR) and artificial neural network (ANN) to evaluate the effect of sampling strategies on the model performance for regional landslide susceptibility mapping. Validation of model performance was done using receiver operating characteristic (ROC) curve. Traditional spatial sampling strategy applied to landslides in 2014 with a random split in 70:30 proportion provided a high performance of all the five models but failed to predict landslides in 2017. The landslide incidences in 2017, when used for model validation either entirely or in different split conditions (both size and geographic constrained), provided consistent performance, even though the testing sample size is large or have a different spatial disposition, if the training was carried out with non-linear susceptibility models such as LR and ANN using landslide incidences in 2014. Results show the importance of sample selection during validation of landslide susceptibility models on a regional scale. [ABSTRACT FROM AUTHOR]

Heavy rainfall in June 2013 triggered flash flooding and landslides throughout the Indian Himalayan state of Uttarakhand, killing more than 6000 people. The vast majority of fatalities and destruction resulted directly from a lake outburst and debris flow disaster originating from above the village of Kedarnath on June 16 and 17. Here, we provide a systematic analysis of the contributing factors leading to the Kedarnath disaster, both in terms of hydrometeorological triggering and topographic predisposition. Topographic characteristics of the lake watershed above Kedarnath are compared with other glacial lakes across the north-western Himalayan states of Uttarakhand and Himachal Pradesh, and implications for glacier lake outburst hazard assessment in a changing climate are discussed. Our analysis suggests that the early onset of heavy monsoon rainfall (390 mm, June 10-17) immediately following a 4-week period of unusually rapid snow cover depletion and elevated streamflow was the crucial hydrometeorological factor, resulting in slope saturation and significant run-off into the small seasonal glacial lake. Between mid-May and mid-June 2013, snow-covered area above Kedarnath decreased by around 50 %. The unusual situation of the lake being dammed in a steep, unstable paraglacial environment but fed entirely from snowmelt and rainfall within a fluvial dominated watershed is important in the context of this disaster. A simple scheme enabling large-scale recognition of such an unfavourable topographic setting is introduced. In view of projected 21st century changes in monsoon timing and heavy precipitation in South Asia, more emphasis should be given to potential hydrometeorological triggering of lake outburst and debris flow disasters in the Himalaya. [ABSTRACT FROM AUTHOR]