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Rainfall-induced landslide masses often change into disastrous debris flows and damage large areas in Nepal's mountainous region. The area covered by debris flow inundation or debris flow runout is a most essential component for landslide hazard assessments leading to development of land use plans. However, there is presently no tool that can allow to assess and predict debris flow runout from the initial landslide on the watershed scale in Nepal. In this paper, a method and simulation tool are developed for the assessment and prediction of debris flow runout in Kulekhani Watershed, Nepal. The Flow-R model with user-defined landslide-susceptible areas was chosen for debris flow runout analysis. The Flow-R model with various algorithms has the capability to analyze debris flow inundation with limited input information. Two recent debris flow events are taken as case studies to identify the appropriate algorithms for runout analysis of the study area. After comparison of the observed and simulated results for debris flow runout, the algorithms proposed by Holmgren (Hydrol Process 8:327–334, 1994) (modified) are found suitable for the study watershed. These algorithms are employed for debris flow inundation analysis in the study area with pre-defined landslide sources plus debris flow inundation map in GIS environment. The results obtained from this modeling for the debris flow area induced by 540 mm of rainfall in 24 h period was 2.68% of the watershed, which is comparable to previously observed debris flow area in the study watershed. The findings of this research and GIS simulation tool developed will enable the optimization of planning and investment for land development in the studied region. [ABSTRACT FROM AUTHOR]

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps are modelled either by knowledge-driven methods (qualitative) or data-driven methods (quantitative) depending on apriori expert knowledge or complete landslide inventory data, respectively, which hinders land regulatory authorities from taking quick decisions for land use planning at a regional scale. To overcome these limitations, a new modelling methodology is proposed which can perform equally with the existing established models and needs minimum knowledge of geo-environmental variables without any model testing data. In this method, common variances of the geo-environmental variables are extracted from initially equally weighted variables and are termed as equal weight method (EWM). The extracted common variances are used to weight the variables for the preparation of a landslide susceptibility map on a regional scale. The output of the model can be interpreted to understand the inter-relationship of different geo-environmental variables along with the identification and prioritization of variables on model performance by delineating the non-performing variables. The proposed modelling methodology is tested at the eastern Himalayan road corridor, the result of which is compared with the results of the analytical hierarchy process (qualitative) and frequency ratio (quantitative) modelling methods by area under curve (AUC) of receiver operating characteristic (ROC) curves and cost analysis. AUC of ROC curves for the analytical hierarchy process, frequency ratio method and equal weight method models are 0.755, 0.809 and 0.774, respectively, indicating little performance differences. Cost analysis of all models shows a maximum bounding cost of 0.4 indicating that all models are cost-effective. The study concludes that the equal weight method is quick, robust and cost-effective at par with other well tested knowledge-driven and data-driven methods. [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]

The majority of landslides in the Indian Himalayas are triggered by rainfall and earthquake. The geology and geomorphology also play an important role and constitute the major predisposing factor controlling landslides. Additionally, active tectonics modify the landscape, impacting the occurrence and distribution of landslides. The objective of this study is to investigate the role of some of the major predisposing factors to assess landslide patterns in the Yamuna River Basin as it is prone to recurrent landslide hazards. The approach mainly deals with the application of geographical information system-based statistical analysis and quantifying frequency-area distribution of rainfall-triggered landslides for the years 2015 to 2017. The analysis shows that the frequency of landslides is highest in the slope range 40°–70° and the mean landslide size is higher in the slope range 0°–20° and 30°–40°, remaining constant on further increase in slope angle. The study shows that some critical slope range controls both the frequency and size of landslides. Landslides were mostly restricted to south facing slopes. About 91% of the landslide occur in the drainage density range of 1.2 to 2.1 km/km2 while landslides show very low occurrence in either lower (<1.2) or higher (>2.1) drainage density. Frequency-size distribution analysis of landslides depicts a distinct cutoff point but an indistinct rollover point. Under-sampling of small landslides can be a reasonable cause for this. Like other studies, the present data fits very well for medium and large landslides following an inverse power law with a scaling exponent of −2.16. Around 80% of landslides are smaller than the cutoff size and 20% larger than this value. The geology and tectonics of Lesser Himalayas and Sivaliks offer favorable conditions for frequent landslides. Areas such as Dehradun, Uttarkashi, Sirmaur, Shimla, and Solan are very prone to landslide activity owing to the presence of poorly consolidated sandstone, conglomerate, quartzites, phyllites, etc. Moreover, the majority of larger landslides are in the Dehradun district where precipitation, drainage density, and lithology are highly favorable to trigger large events. The present study has identified the critical range of predisposing factors responsible for landslides and presents the frequency-size distribution of landslides in the Yamuna River Basin. These are the key inputs in landslide susceptibility mapping and risk assessment. Therefore, the findings from the present analysis can be very helpful in landslide risk reduction and landslide hazard zonation and probably plan critical locations for installation of early warning signals. [ABSTRACT FROM AUTHOR]

Landslide and related mass movement activities are common and one of the most destructive natural hazards in the mountainous terrain including the Himalayas. Of the 11 administrative states in the Indian Himalayan region, the state of Uttarakhand has witnessed enhanced activities of these phenomena. It is therefore essential to understand the regional scale landslide susceptibility assessment of the state and in the present study, landslide susceptibility mapping for the entire state has been carried out using bivariate weight of evidence and information value methods which depict that around 51% of the area is located in the high and very high landslide susceptible zones, 22–23% in the moderate and ~26–27% in the low and very low landslide susceptible zones, and slopes ranging between 40° and 60°, located at an elevation of 2000–4000 m, facing towards southern sides and covered with limestone, gneiss, quartzite and phyllite, have higher propensity towards development of landslides in the region. [ABSTRACT FROM AUTHOR]

This article deals with the analysis of landslide causes and associated damages in the Kashmir Himalayas of Pakistan. The present study is based on Muzaffarabad, which lies in the lesser Himalayas. Geologically, the Kashmir Himalaya is the young and most dynamic system in the world. In Muzaffarabad, mostly, people live on the fragile mountain slopes, and therefore, they are highly vulnerable to the risk of landslides. To achieve the objectives of the study, data were collected both from primary and secondary sources. Primary data were obtained through intensive field work and human perception survey, while secondary data were obtained from the related line agencies. The analysis reveals that in the study area, immature geology, active seismic zone, wide range of temperature and seasonal rain are the major physical factors, whereas human interventions on the fragile slopes are intensifying factors which in effect contribute to the landslide incidence. As a result, the adverse impacts on housing, sources of livelihood earnings and human casualties are escalating day-by-day. There are several implementing agencies which are responsible for reducing the risk of landsliding. So far, these agencies have not reduced the landslide damages rather their intensity and frequency have been increased especially after 2005 Kashmir earthquake. [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]

The torrential rains in June 2013 combined with melting of snow caused voluminous floods in the rivers of Uttarakhand and subsequently triggered widespread mud, landslides and debris deposition. The event caused instability of the channel by shifting the banks. Erosion rendered many locations along the banks vulnerable to economic and human loss. The shifts in reaches are calculated by digitizing the bank line using satellite imageries of year 2005, 2010 and 2015. The extent and magnitude of risks have been assessed based on information of past events, rapid field assessments, current mitigation measures and interactions with the locals. The findings from these interactions, and secondary data based on geospatial analysis of bank line changes have been used in the identification of vulnerable reaches along the major rivers. Criteria to identify the vulnerable reaches are based on risk, exposure and hazards in that area. The magnitude of risks due to flood hazards on various exposures along the riverbank is calculated based on qualitatively derived scores. River basins focusing on rainfall, topography, drainage pattern, soil, landslide and exiting infrastructure in relation to vulnerability of the region using GIS data are discussed in details. A fuller understanding will enable decision makers towards more efficient resources management for prevention and protection of river banks due to flood events. In addition to this, an official online decision support system (www.urmis.dhi-india.com) with collaborating partners and organizations for relevant data, information and document has been created. [ABSTRACT FROM AUTHOR]