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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]