Rapid Assessment of Landslide Exposure to Element at Risk: A Decision Support tool in Regional Landslide Forecasting

Rapid Assessment of Landslide Exposure to Element at Risk: A Decision Support tool in Regional Landslide Forecasting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rapid Assessment of Landslide Exposure to Element at Risk: A Decision Support tool in Regional Landslide Forecasting Gargi Singh, Sumit Kumar, Rabisankar Karmakar, Akshaya Kumar Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6261551/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Effective development of an landslide forecast and its use on ground requires granular knowledge on landslide risk of the area. The lack of detailed landslide impact information, limitations in complete historical landslide inventories, and inherent difficulties in quantifying elements-at-risk have long been a challenge in the development of effective regional landslide risk maps for disaster managers. In this research, we propose a rapid risk assessment methodology that integrates landslide occurrence information, landslide initiation and runout susceptibility, and physical and social parameters from census data. Using this approach, we ranked various administrative blocks in Darjeeling District, West Bengal, India, according to the elements exposed to landslide hazards. In total, 197 villages (Mauzas) were evaluated and ranked based on their exposure to landslides. This quasi-quantitative rapid assessment of administrative units provides valuable input for local authorities in resource management and planning and helps the scientific community prioritize investigations in sectors with higher vulnerability. The resulting risk exposure ranking helps as a decision support tool in regional landslide forecasting and supports informed decision-making for stakeholders. Landslide susceptibility runout susceptibility combined susceptibility risk Darjeeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Landslide risk analysis is one of the most challenging aspects of landslide investigation [ 34 ]. It is particularly difficult to assess exposure to landslides in areas with incomplete landslide information. The analysis of landslide risk is constrained by the highly localized nature of landslide occurrences, the paucity of temporal data on past landslides, incomplete landslide inventories, and difficulties in quantifying relevant social parameters. Regional Landslide forecasts are used to provide information on the level of landslide occurrence in space and time [ 21 ] using numerical models [ 38 ] and integrating them with static inputs for effective generation as well as utilization of the forecast information by stakeholders. However, regional landslide forecasts have relied heavily on the inputs of forecasted trigger parameters, such as rainfall. Regional landslide forecasts are known to use satellite products [ 6 , 27 ], global and local products of met offices [ 20 ], and ground rainfall measuring instruments. However, the resolution of these products is a coarse covering state, taluk, and block level, ranging from 144 to 16 Sq. km. The need for coarser-scale forecast information for site-specific hazards, such as landslides, can only be made more effective when the spatial impact information is converted into a risk landslide pose in the same administrative units. Such an approach has been used in other hazard forecasts such as flood by using risk maps [ 13 ], and the use of risk maps of administrative units for landslide initiation and impact is still not available. Such maps help the forecaster encrypt such information in the forecast for smaller risk units and stakeholders to decript the landslide forecast information for effective decision making. With increasing development and encroachment on hill slopes, landslide impacts have increased over time [ 14 ]. This trend highlights the need for an easy-to-follow mechanism for landslide risk analysis that can be used as a decision support tool by both forecasters and stakeholders in landslide disaster risk reduction planning. Several methods exist for calculating quantitative and qualitative landslide risks [ 2 , 8 , 41 ]. In general, risk calculation requires accounting for both direct (e.g., specific damage to property and life) and indirect losses (e.g., disruption of transportation infrastructure) [ 23 ]. A critical component of landslide risk assessment is estimating the vulnerability of exposed elements at risk. However, this step is often omitted because of a lack of appropriate data. Vulnerability refers to the degree of loss experienced by a given element-at-risk (EatR) as a consequence of a hazard event of a certain magnitude. Ideally, vulnerability is expressed on a scale from 0 (no damage) to 1 (complete loss). Vulnerability assessment frequently considers physical, social, economic, and environmental factors [ 13 , 15 , 25 ], evaluated either independently or in an integrated manner [ 24 ]. The variety of elements that may be exposed (e.g., buildings, roads, people) and their differing characteristics necessitate a complex, multi-level analysis [ 26 , 40 ]. Thus, to quantify landslide risk, it is essential to assess the vulnerability of the elements-at-risk (whether human or property) that are exposed to landslide hazards—often termed exposure vulnerability . Thorough landslide risk analysis is a prerequisite for prioritizing resources and planning, and it provides focused, informative inputs for decision support systems to manage risk in a given area[ 42 ]. There is a clear need for a simple, fast, and reliable risk assessment method that can serve as a decision-support tool for authorities to identify priority areas for intervention. In this study, we present such a methodology to evaluate the exposure of vulnerable elements-at-risk to landslide hazards over a large area (~ 2,000 sq km) characterized by a sporadic spatial distribution of elements-at-risk, spatiotemporal variations in landslide occurrences, and diverse land-use practices. According to the United Nations International Strategy for Disaster Reduction [ 39 ], elements-at-risk are defined as the population, properties, economic activities (including public services), or other values that are exposed to hazards in a given area. It is important to begin any risk assessment with a complete inventory of all important elements-at-risk in the hazard zone, even if some elements may ultimately not be affected by certain events (i.e., have vulnerability V = 0; SafeLand Project, [ 1 ]. In the context of landslides, elements located both at the landslide source (e.g., railroads, roads, and buildings in the Giddapahar landslide source area) and along the runout path or deposition zone (e.g., houses affected by debris flows in Mirik) are subject to landslide risk. Therefore, assessing the vulnerability of exposed elements-at-risk requires an understanding of the spatial distribution of both landslide initiation areas and potential runout/inundation zones. In this research, we develop a rapid assessment of landslide risk focusing on population metrics. Specifically, we consider population-related parameters (total population, number of households, working population, population below six years of age, and literate population) as indicators of exposure. The assessment is conducted at the Mauza level, where a Mauza is an administrative unit roughly equivalent to a village or cluster of villages. We chose Mauza boundaries (as opposed to geological or geomorphological units) for the analysis because administrative units are more practical for decision-makers to use in planning and resource allocation. Our vulnerability assessment emphasizes the physical aspects of the elements-at-risk (i.e., physical vulnerability), which determine the potential structural damage caused by landslide events of varying intensities. 2. Study area: Demographic distribution and landslide scenario The study area is Darjeeling District, the northernmost district of West Bengal, India (Fig. 1 ). It lies within an active Himalayan Fold-Thrust Belt (FTB) and falls in Zone IV of the Seismic Zonation Map of India [ 3 ], indicating high seismic hazard. The district includes several prominent hill towns—Darjeeling, Mirik, and Kurseong—that contain pockets of high population density. According to the 2011 Census of India, Darjeeling District had a population of 1,846,823 (937,259 males and 909,564 females)​, reflecting a 14.77% increase since 2001. (The 2001 census recorded a 23.79% increase over its 1991 population​). Studies worldwide have examined links between population growth and landslide occurrence [ 35 , 5 ]. In Darjeeling, no clear relationship has been found between overall population growth and the occurrence of large landslides. However, slope-cut failures have increased due to construction of buildings and roads on steep slopes without proper slope protection or scientific assessment​. Landslides in Darjeeling are triggered by both intense rainfall and earthquakes. The area receives between 1,365 mm and 5,365 mm of rainfall annually on average, with recorded extremes near 7,600 mm in a single year​. The June–September monsoon season contributes approximately 78–83% of the annual rainfall, and almost all landslide incidents occur during these monsoonal months​. Earthquake-triggered landslides have also been documented; notable examples include landslides triggered by the 1984 Bihar–Nepal earthquake (Mw 6.8) and the 2011 Sikkim earthquake (Mw 6.9), which added significantly to the landslide inventory of the region [ 17 , 30 ]. Overall, rainfall-induced landslides are more frequent and widespread than those triggered by earthquakes in this area​. Past landslide events have severely affected Darjeeling District, causing loss of life and damage to infrastructure. Table 1 below highlights a number of major landslide disasters in and around the Darjeeling Himalayas, illustrating the extent of their impacts: Table 1 Some prominent and fatal landslide events in Darjeeling Himalayas Date/ Time Loss/ Damage 24th September 1899 72 people died with huge loss of property 10th-12thJune, 1950 127 people died 2nd-5th October 1968 The most dreaded landslide and flood disaster accounting 677 deaths as per official record; profuse damage to infrastructure 3rd-4thSept., 1980 215 people lost their life 15th-16th Sept. 1991 2 people died and huge land and property got damaged; the Darjeeling-Siliguri Toy train track was severely damaged for 5 months 11th-13thJuly, 1993 15 people died 5th& 8th July 1998 Several deaths and road blockades are mainly along NH-55; the most affected terrain is Kurseong and its surroundings. 10th-11thJuly 2003 24 people died at the Gayabari landslide near Mirik 15th-17thJuly 2007 Damage of properties in Darjeeling and adjoining areas 7th-9th September 2007 12 lives were lost along with severe damage to properties in Darjeeling Himalayas 26th-27thMay, 2009 25 lives were lost along with profuse damage to properties in Darjeeling Himalayas 18th September 2011 Occurrence of a large number of landslides induced by a major earthquake 14th September 2012 Extensive damage in six tea gardens (Takdah, Lopchu tea gardens) of Darjeeling district owing to landslides. 1st July 2015 19 people died in the Limbudhura village of Mirik sub-division; land and property of Limbudhura village also got washed away. To better understand landslide distribution, we prepared a comprehensive landslide inventory for Darjeeling District using multiple data sources and field surveys. This inventory includes a total of 1,356 landslides, comprising 1,035 debris slides/flows and 261 rock slides/falls​. The corridor from Balasun to Chota Ringtong emerged as an epicenter of major landslides during multiple extreme events. Landslides in this corridor have repeatedly disrupted the National Highway and the Darjeeling Himalayan Railway (a UNESCO World Heritage “Toy Train” line), leaving these vital transport links inoperative for days at a time​. The study area also encompasses a known “sinking zone” (an area of gradual ground subsidence and instability) identified by previous studies [ 16 , 29 ]. 3. Material and method The goal of this study was to devise a rapid assessment methodology using available information, such that the output can serve as a decision-support tool for landslide risk management​. We selected the Mauza (the smallest administrative unit in the region) as the mapping unit for analysis. Using administrative units ensures that the results align with governance and decision-making frameworks. The chosen mapping scale corresponds to the scale of the available input data. 3.1. Data Sources We utilized various data sets to assess the exposure and vulnerability of elements-at-risk in Darjeeling. The main inputs to our analysis included: Historical records and archives: Legacy reports (e.g., Geological Survey of India publications) and old documents (including newspaper archives) providing information on past landslides​. Remote sensing imagery: High-resolution Earth observation data such as Cartosat-1 (2.5 m) and IRS-P6 LISS-IV (5.8 m) satellite images, along with Google Earth imagery and base maps from ESRI’s ArcGIS (DigitalGlobe, GeoEye, etc.)​. Topographic maps: Survey of India topographic sheets (1:50,000 scale; map sheet nos. 78A/04, 78A/08, 78B/01, 78B/05, surveyed in 1961–64) that mark landslide scars and the year of survey​. Landslide inventory: A compiled inventory of landslides (both historical and recent) in the region, including the Darjeeling landslide inventory described in the Study Area section. Census data: Demographic and socio-economic data from the 2011 Census of India at the Mauza level (described below). Administrative boundaries: Digital shapefiles of Mauza boundaries obtained from the Darjeeling district authorities​. Active landslide data: Locations of known active landslides (from recent observations) for inclusion in the susceptibility analysis. Landslide susceptibility models: Results of landslide initiation and runout susceptibility modeling for the study area. 3.1. Landslide Inventory Historical information on landslide occurrences is a fundamental component of landslide susceptibility studies, as it provides insight into the frequency, size, damage, and types of landslides [ 12 , 41 , 36 , 43 ]. Past and present landslide occurrences form the basis for predicting future landslides, establishing rainfall or trigger thresholds, and conducting hazard and vulnerability analyses. For this study, we prepared a detailed landslide inventory by compiling information from multiple sources. These included high-resolution satellite imagery (Cartosat-1, LISS-IV), Google Earth and other online imagery, and base maps in ArcGIS​. We also incorporated landslide records from topographic maps, the Darjeeling Himalayan Railway (DHR) slip failure register, legacy reports of the Geological Survey of India, local newspaper reports, and information from internet blogs​. We paid special attention to consistent and reliable data sources. For example, IRS P6 LISS-IV imagery from various dates (17 October 2006; 5 November 2006; 4 December 2006; 9 February 2008; 18 November 2008; 5 January 2010; and 18 March 2010) and Cartosat-1 panchromatic imagery were used extensively to map landslides (Fig. 4 )​. The DHR slip register, which documents every slope failure affecting the railway line between Sukna and Darjeeling from 1991 to 2016, was used to validate and supplement our inventory​. Older landslides visible on the 1960s Survey of India toposheets were also added to the inventory; those maps provide the locations of historical landslide scars along with the survey year​. Additionally, we included landslide locations from GSI’s past investigations and any new landslides identified through recent satellite imagery and field surveys​ Using all these sources, we collated a landslide inventory database of approximately 2,986 landslides across the West Bengal Himalayas (Fig. 2 )​. This comprehensive inventory underpins the susceptibility analysis described later. 3.2. Administrative boundaries A Mauza is a land revenue unit or small administrative area in Darjeeling District, typically encompassing one or more settlements (villages or hamlets) under a common jurisdiction. We obtained the Mauza boundary map for Darjeeling from the state government web portal and the district administration (as a GIS shapefile)​.. There are 210 Mauzas in Darjeeling District, with areas ranging from as small as 0.01 sq. km. (Dhwaipani Mauza) to as large as 155 sq. km. (Singalila Forest Mauza)​. These Mauza boundaries serve as the spatial units for our exposure and risk calculations. 3.3 Census data We used the latest available population and socio-economic data from the 2011 Census of India for each Mauza. The census provides detailed information including total population, number of households, male and female population, population under 6 years of age, literacy and illiteracy rates, main and marginal workforce, and non-working population​. From this comprehensive dataset, we selected five key parameters for our analysis that are most relevant to landslide impact and community vulnerability: Total population Number of households Working population (i.e., number of people engaged in income-generating work, including main and marginal workers) Population below 6 years (young children) Literate population (as an indicator of literacy rate) These parameters capture important aspects of the social profile and potential coping capacity of each Mauza in the face of landslides. For instance, a higher total population or more households means more people and homes are exposed to risk; a larger working population might indicate significant economic activity at risk; and a higher literate population could correlate with better access to information and resources for disaster response. It should be noted that official census data were unavailable for 28 of the 210 Mauzas in Darjeeling District​. Our analysis for those locations had to rely on partial or estimated information. 3.4 Elements at Risk Using the census data, we generated spatial distribution maps for the selected social parameters (total population, households, working population, population under 6, and literate population) across all Mauzas (Fig. 3 ). These maps represent the key elements-at-risk in terms of population and settlements. For context, we identified the Mauzas that have the minimum and maximum values for each parameter in the distric: Total population : minimum of 49 (Sum Forest Mauza) and maximum of 126,935 (Darjeeling town Mauza). Households : minimum of 8 (Sum Forest) and maximum of 27,470 (Darjeeling). Population below 6 years : minimum of 14 (Phuguri Forest) and maximum of 11,696 (Darjeeling). Literate population : minimum of 38 (Sum Forest) and maximum of 93,091 (Darjeeling). Working population : minimum of 10 (Sum Forest) and maximum of 52,356 (Darjeeling). These figures highlight the wide range of population exposure across different Mauzas. (Note: “Sum Forest” refers to a sparsely populated forest Mauza, whereas Darjeeling Mauza represents the main urban area.) 3.5 Landslide Susceptibility Landslide susceptibility represents the likelihood of landslide occurrence in an area based on local terrain conditions [ 4 , 19 ]. In our context, susceptibility encompasses both the landslide initiation zones and the areas that could potentially be affected by the moving debris (runout areas) [ 31 ]. Numerous studies have modeled landslide initiation and runout e.g., [ 33 , 11 , 7 , 8 , 22 ]. For a regional-scale vulnerability assessment, it is crucial to consider both initiation and runout susceptibility together to identify the most hazard-prone zones. In this study, we generated separate initiation and runout susceptibility maps and then combined them to obtain an overall susceptibility map for the region. 3.5.1 Initiation susceptibility We evaluated landslide initiation susceptibility using statistical modeling. An ensemble landslide susceptibility map was produced by combining three modeling techniques: Logistic Regression (LRM), Quadratic Discriminant Analysis (QDA), and Linear Discriminant Analysis (LDA). We utilized the open-source software LAND-SE [ 37 ] to build these models. The ensemble model (Fig. 4 ) classified about 28% of the study area as having high landslide probability (P > 0.55)​. Among the individual models and their ensemble, QDA and the combined LRM-LDA-QDA ensemble performed best, achieving the highest prediction accuracy and specificity, and the lowest false-positive rate (FPR)​. These models were the most effective in correctly identifying areas likely to experience future landslides in the study area. 3.5.2 Runout susceptibility To assess landslide runout (debris flow) susceptibility, we applied a conceptual modeling tool called r.randomwalk [ 32 ] within a GIS. This tool simulates the paths of debris flows by releasing hypothetical mass movements from identified source points and allowing them to “run” downslope until a stopping criterion is met. For the simulation, we used two primary inputs: (1) a high-resolution Digital Elevation Model (ALOS PALSAR DEM, 12.5 m resolution) to define potential release (initiation) pixels, and (2) the mapped inventory of 66 channelized debris flows in the area to calibrate and validate the model​. The output of this analysis was a debris flow susceptibility map (Fig. 5 ), which we classified into zones of high, moderate, and low susceptibility based on the modeled impact probability. The results showed that approximately 57% of the Darjeeling study area is susceptible to debris flows; within this susceptible zone, about 21% of the area was identified as having very high debris flow probability​. Historical evidence underscores the importance of debris flow hazards: for example, destructive debris flow events in 2015 and in September 2007 caused extensive loss of life and property in Darjeeling District. These events (as documented by news reports and GSI studies) highlight that debris flows are a major threat in the region. Therefore, mapping the potential impact areas of debris flows is crucial for identifying vulnerable locations. 3.5.3 Combined Susceptibility In order to get a comprehensive picture of landslide hazard, we combined the initiation and runout susceptibility maps. We focused on the highest hazard classes to delineate an integrated high-susceptibility zone. Specifically, we overlaid the high-susceptibility zones from the initiation model with the high-impact probability zones from the runout model to identify areas that are highly susceptible to either form of landsliding (or both). These overlapping areas were designated as combined high susceptibility zones. The resulting combined susceptibility map (Fig. 6 ) highlights the areas most likely to experience landslides (either slope failures at the source or debris flow impacts downslope). While our demonstration emphasizes the high-susceptibility class, the same approach can be applied to moderate and low susceptibility classes if needed. 3.8 Exposure Vulnerability Assessment Using the hazard information and exposure data described above, we developed a methodology to rank each Mauza by its exposure vulnerability to landslides. Figure 7 presents a flowchart of this methodology. The inputs to the assessment include the combined landslide susceptibility map (Fig. 6 ), the elements-at-risk maps (population and infrastructure), the census data parameters, and the Mauza boundaries​. We made two key assumptions in this assessment: Maximum Vulnerability : All elements-at-risk in the path of a landslide are assumed to have a vulnerability value of 1 (i.e., they would suffer complete damage in the event of a landslide). This conservative assumption is reasonable in the steep terrain of the Himalayas, where landslides are typically severe and anything in their path is likely to be fully affected. High Susceptibility as Trigger Zones : During extreme rainfall events (exceeding established thresholds), landslides are most likely to occur in the high-susceptibility zones. Therefore, any element-at-risk located within those zones is considered to be exposed and likely to be affected by a landslide under such conditions​. With these assumptions, we calculated an exposure vulnerability index for each Mauza using the following steps​: Identify High Susceptibility Areas : Use the GIS Union tool to combine the high-susceptibility zones from the combined susceptibility map with the locations of known active landslides. This produces a consolidated map of areas that are considered highly susceptible to landslides. Calculate Susceptibility Area Density : For each Mauza, compute the density of highly susceptible area by dividing the area of the Mauza that falls within high-susceptibility zones (from Step 1) by the total area of that Mauza. Calculate Exposure Parameter Density : For each Mauza, calculate the density of each exposure parameter (the five census indicators and the transportation network). For the population parameters, this is done by dividing the parameter value (e.g., total population count) by the Mauza area, yielding values such as population per square kilometer. For the transportation network, we calculated the length of roads (or other transport infrastructure) in the Mauza per square kilometer. Compute Exposure Vulnerability : Multiply the susceptibility area density (Step 2) by the density of each exposure parameter (Step 3) to obtain the exposure vulnerability value for each parameter in each Mauza. This yields, for example, a value representing "population exposure vulnerability" for a given Mauza (indicating how much of its population is exposed in high-susceptibility zones), and similarly for households, working population, etc. Rank the Mauzas : Rank the Mauzas based on their exposure vulnerability values for each parameter. A higher rank (1 being the highest) indicates a greater level of exposure for that parameter. We produced separate rankings for each of the five social parameters, as well as a composite consideration of all factors. This step-by-step approach provides a quantitative basis to compare administrative units in terms of their relative exposure to landslide hazards. The output is a set of ranked lists or maps that highlight which Mauzas are most at risk when considering different facets of exposure (population, infrastructure, etc.). 4. Results and Discussion Using the above methodology, we analyzed landslide exposure vulnerability for the selected social parameters across Darjeeling District (Fig. 8 ). The results indicate clear spatial differences in which areas are most at risk: Total Population, Households, and Literate Population : The highest exposure vulnerability for total population, number of households, and literate population was found in Sukhiapokhri (CT) Mauza , followed (in descending order) by Kurseong , Simana Basti , Darjeeling , and other Mauzas. In these areas, a large portion of the population resides in zones that are highly susceptible to landslides. Population Below 6 Years and Working Population : The highest exposure for young children and for working population occurred in Kurseong Mauza , followed by Sukhiapokhri (CT) , Simana Basti , Darjeeling , and others. These findings suggest that certain Mauzas consistently rank high in vulnerability across multiple parameters, indicating hotspots of social exposure to landslides. A more qualitative examination of overall risk also shows that some smaller settlements, such as Singtam Mauza , Shivakhola Forest , and Tung Mauza , are highly vulnerable to landslide impacts. These areas might not have the largest populations, but their location and context make them particularly prone to disaster. Identifying and ranking the most vulnerable Mauzas is crucial for disaster preparedness and risk reduction in Darjeeling. This information allows authorities to prioritize specific areas for detailed landslide forecasting, early warning, and mitigation efforts. Focusing communication and emergency response on the high-risk areas can improve the effectiveness of disaster management. For instance, if two Mauzas are forecast to have a similar likelihood of landslide occurrence, responders can give higher priority to the Mauza that has a greater exposure vulnerability (i.e., more people or assets in harm’s way). In this way, the limited resources available for landslide monitoring and response can be directed to where they are needed most. 5. Conclusion A significant portion of India’s land area—approximately 0.43 million sq. km. or about 12.6% (excluding snow-covered regions)—is prone to landslide hazards​. However, comprehensive data for quantitative risk or vulnerability analysis are lacking in most regions. In the Indian context, two data sets are commonly and readily available across the country: (1) basic landslide inventory data compiled by agencies such as the Geological Survey of India (the national nodal agency for landslides), the National Remote Sensing Centre, and state/district disaster management departments; and (2) demographic data from the national census (e.g., via the Census of India website). The methodology presented in this study leverages these widely available data sets to perform a rapid landslide risk assessment that is well-suited for data-scarce environments. This research is helpful in providing a broad overview of the areas that are most at risk from landslides in Darjeeling District. By integrating landslide susceptibility with exposure data, we can highlight which communities and assets are in harm’s way, and produce a ranked list of vulnerable locations. Further such information not only helps in decsison support for encrypting and decrypting the forecast information but also can be valuable for decision-makers, as it helps in allocating resources and planning interventions (such as slope stabilization, community preparedness programs, or early warning systems) in the most at-risk areas. The method can also guide the prioritization of areas for implementing or enhancing landslide forecasting and monitoring systems. In summary, the rapid assessment methodology developed in this research offers an effective decision-support tool for regional landslide risk management, especially in regions where detailed data are limited but prompt risk evaluation is needed. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Clinical trial number Not Applicable. Consent to Publish Declaration Not Applicable Ethics and Consent to Participate declarations Not Applicable Competing Interest No, I declare that thye authors have no competing interests that might be perceived to influence the results and/or discussion in the paper. Data availability Data supporting this study's findings are available from the corresponding author upon reasonable request. Author Contribution G.S: Conceptualization, Methodology, Writing- Original draft. S.K: Data curation, Visualization, Investigation. R.K: Investigation, Data curation. A.K.M: Supervision. References Bazin S, 2012. SafeLand deliverable 4.8. Guidelines for Landslide Monitoring and Early Warning Systems in Europe — Design and Required Technology. European Project SafeLand, Grant Agreement No. 226479. Deliverable 4.8, 153 pp., available at: http://www.safeland-fp7 eu, 2012. Bell. R and Glade.T. 2004. Quantitative risk analysis for landslides – Examples from B´ıldudalur, NW-Iceland. 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In: Landslide Processes: from Geomorphologic Mapping to Dynamic Modeling: Proceedings of the Landslide Processes, edited by Malet JP, Remaitre A and Bogaard T, European Centre on Geomorphological Hazards (CERG), 2009, Strasbourg, France 37-44. Ghosh, S., Chakraborty, I., Bhattacharya, D., Bora, A. and Kumar, A. (2012b)Generating field-based inventory of earthquake-induced landslides in the Himalayas-an aftermath of the 18 September 2011 Sikkim Earthquake. Indian Journal of Geosciences, 66 (1): 27–38. lade T, Crozier MJ, 1996. Towards a national landslide information base for New Zealand. N Z Geogr 52(1), pp 29–40. Ghosh, S., Ghoshal, T.B., Mukherjee, J., Bhowmik, S., 2016. Landslide Compendium on Darjeeling-Sikkim Himalayas, GSI Spl Publication No 103. Godt JW, Baum RL, Savage WZ , Salciarini D, Schulz WH , Harp EL , 2008. Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework. 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Micro-sized enterprises: vulnerability to flash floods. Nat Hazards 84 , 1091–1107. https://doi.org/10.1007/s11069-016-2476-9. Kienberger, S., Lang, S., Zeil, P., 2009. Spatial vulnerability units – expert-based spatial modeling of socio-economic vulnerability in the Salzach catchment, Austria. Natural Hazards and Earth System Sciences 9 (3), 767–778. Kienholz H, 1977. Kombinierte geomorphologische Gefahrenkarte 1 : 10 000 von Grindelwald Catena, 3 (1977), pp. 265-294. Kirschbaum, D., & Stanley, T. (2018). Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness. Earth’s Future , 6 (3), 505–523. https://doi.org/10.1002/2017EF000715. Lang T, and Barros AP, 2002. An investigation of the onsets of the 1999 and 2000 monsoons in Central Nepal, Mon. Wea. Rev., 130, pp 1299–1316. Mandal, S. (2015). A comprehensive review on Paglajhora sinking zone landslide in the Shivkhola watershed of Darjeeling Himalaya. Int J Geol Earth Environ Sci , 5 (2), 156-170. Martha, Tapas R, K. Babu Govindharaj, K. Vinod Kumar. (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, Volume 6, Issue 6, 2015, Pages 793-805, ISSN 1674-9871, https://doi.org/10.1016/j.gsf.2013.12.011. Melo R, Zêzere JL, 2017: Modeling debris flow initiation and run-out in recently burned areas using data-driven methods, Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer; International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1373-1407. Mergili M, Krenn J, Chu HJ, 2015. r.randomwalk v1, a multi-functional conceptual tool for mass movement routing. Geosci Model Dev 8:4027 4043. Montgomery DR, Dietrich WE, 1994. A physically based model for the topographic control on shallow landsliding: Water Resources Research, Vol. 30, No. 4, 1153-1171. Pellicani, R., Van Westen, C.J. & Spilotro, G. (2014). Assessing landslide exposure in areas with limited landslide information. Landslides 11 , 463–480. https://doi.org/10.1007/s10346-013-0386-4 Petley, D. (2010). Landslide Hazards. In A. Goudie, & I. Alcantara-Ayala (Eds.), Geomorphological Hazards and Disaster Prevention (pp. 63-74). Cambridge University Press. Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences , 7 , 725-742. Rossi, M. and Reichenbach, P. 2016. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0, Geosci. Model Dev., 9, 3533–3543, https://doi.org/10.5194/gmd-9-3533-2016. Segoni, S., Battistini, A., Rossi, G., Rosi, A., Lagomarsino, D., Catani, F., Moretti, S., & Casagli, N. (2015). Technical Note: An operational landslide early warning system at regional scale based on space–time-variable rainfall thresholds. Natural Hazards and Earth System Sciences , 15 (4), 853–861. https://doi.org/10.5194/nhess-15-853-2015 UNISDR (2004) Living with Risk: A Global Review of Disaster Reduction Initiatives. United Nations Inter-Agency Secretariat of the ISDR (UNISDR), Geneva. UNISDR (2009) 2009 UNISDR terminology on disaster risk reduction. UNISDR, Geneva. Available at: http://www.unisdr.org/we/inform/publicaitons/7817. Accessed on December 15, 2012. van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? Bull Eng Geol Environ 65(5):167–184. Wang, H. B., Wu, S. R., Shi, J. S., & Li, B. (2011). Qualitative hazard and risk assessment of landslides: a practical framework for a case study in China. Natural Hazards, 69(3), 1281–1294. doi:10.1007/s11069-011-0008-1 Youssef, A.M., Al-Kathery, M. & Pradhan, B. (2015). Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19 , 113–134. https://doi.org/10.1007/s12303-014-0032-8 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 May, 2025 Reviews received at journal 08 May, 2025 Reviews received at journal 03 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Editor assigned by journal 18 Apr, 2025 Submission checks completed at journal 18 Apr, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6261551","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446787701,"identity":"0ac8d995-b251-4559-aa5b-a0ae21ac9f22","order_by":0,"name":"Gargi Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYLCCBAYGHiBifMDAIAEXIUoLswHxWiCAh02CKHXy7mcfv3jAUCdjzr/2WOWXPxZAkR4Dhoc7cGsxPJNuZpHAcJjHcsa7tNuybRJAkTMGDIln8GhpSGMzSGA4wGNw44zZbckGoJYZaQkMiW14tPQ/A2mpA2splvhDhBZ5iTTmBwkMzDwG53vMGD8AQ0BeIvkAXi0GEs/YGBIMDgNt4TGWZmyT4DHgOXzgAF5b+tOYP/6oqLM3OH/G8OOPP3Vy8u2NjQ9/4rPlAAPQMcA4ZJAAOQ8YO0ARhgO4NQBtaWBg/gBm8R9gYPwBERkFo2AUjIJRgAIAGPZOMq7M0BIAAAAASUVORK5CYII=","orcid":"","institution":"Geological Survey of India, Central Headquarter","correspondingAuthor":true,"prefix":"","firstName":"Gargi","middleName":"","lastName":"Singh","suffix":""},{"id":446787702,"identity":"a979f8dd-b6bf-4b7e-ab5c-ca24395df431","order_by":1,"name":"Sumit Kumar","email":"","orcid":"","institution":"Geological Survey of India, Central Headquarter","correspondingAuthor":false,"prefix":"","firstName":"Sumit","middleName":"","lastName":"Kumar","suffix":""},{"id":446787703,"identity":"cc57a864-777b-4b2c-a5bc-75032652e711","order_by":2,"name":"Rabisankar Karmakar","email":"","orcid":"","institution":"Geological Survey of India, Central Headquarter","correspondingAuthor":false,"prefix":"","firstName":"Rabisankar","middleName":"","lastName":"Karmakar","suffix":""},{"id":446787704,"identity":"ff526a5d-aa17-4788-a0bc-1bd3d8ffd811","order_by":3,"name":"Akshaya Kumar Mishra","email":"","orcid":"","institution":"Geological Survey of India, Central Headquarter","correspondingAuthor":false,"prefix":"","firstName":"Akshaya","middleName":"Kumar","lastName":"Mishra","suffix":""}],"badges":[],"createdAt":"2025-03-19 12:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6261551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6261551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81365864,"identity":"5d72facc-548c-401c-bac0-c83c82ab0595","added_by":"auto","created_at":"2025-04-25 09:26:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":544393,"visible":true,"origin":"","legend":"\u003cp\u003eshows the location map of the study area in Darjeeling District.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/67f2de67b3745ef172e59fd0.jpeg"},{"id":81365865,"identity":"86860cc3-8122-47d7-9425-ea0665cbf977","added_by":"auto","created_at":"2025-04-25 09:26:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":947843,"visible":true,"origin":"","legend":"\u003cp\u003eOpen hillslope and Channelised debris flow incidence map in Darjeeling District, West Bengal\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/aee0dd593754ed1d625288f6.jpeg"},{"id":81365872,"identity":"e0a33db9-e384-49a0-93e2-b35db607057a","added_by":"auto","created_at":"2025-04-25 09:26:45","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":739867,"visible":true,"origin":"","legend":"\u003cp\u003eshows spatial distribution of different social parameters of Darjeeling District, West Bengal, A. Population, B. Household, C. Literacy rate, D. Population below 6 years age and E. Working population\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/f923f7943c9e507aa63e1382.jpeg"},{"id":81366152,"identity":"f9ea1487-66a9-417f-9e0b-b5e924ffef13","added_by":"auto","created_at":"2025-04-25 09:34:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":994327,"visible":true,"origin":"","legend":"\u003cp\u003eLandslide susceptibility map of the Darjeeling District obtained using Ensemble methodology\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/3765068c83a7567e85d851af.jpeg"},{"id":81365869,"identity":"3ba32c32-846f-457a-a0dd-673bffe760b2","added_by":"auto","created_at":"2025-04-25 09:26:45","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1450078,"visible":true,"origin":"","legend":"\u003cp\u003eDebris flow susceptibility map of the study area\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/9a6201ad4b6723fec6cd8e0a.jpeg"},{"id":81366151,"identity":"0e4806ad-dedf-41c5-a541-08748c1cfbdb","added_by":"auto","created_at":"2025-04-25 09:34:45","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":757710,"visible":true,"origin":"","legend":"\u003cp\u003eCombine the susceptibility map of the study area (initiation and runout susceptibility).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/eed7f782b71a8aee62fff916.jpeg"},{"id":81366149,"identity":"245003d3-9e50-4626-8b06-13fdce6f8a0e","added_by":"auto","created_at":"2025-04-25 09:34:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":139760,"visible":true,"origin":"","legend":"\u003cp\u003eFramework of methodology for exposure vulnerability assessment\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/28d4c2d5f508823ed3746ac2.png"},{"id":81365873,"identity":"cd2704bf-1e6c-45b8-aef1-369bf54f296a","added_by":"auto","created_at":"2025-04-25 09:26:45","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":925148,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of the mauzas based on different social parameters of Darjeeling District, West Bengal, A. Population, B. Household, C. Population below 6 years age, D. Literacy rate and E. Working population\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/1caf299f2c2b4b2771cdf7f8.jpeg"},{"id":81367570,"identity":"ddcfc0bb-b5ce-46c5-afef-de2f347e5751","added_by":"auto","created_at":"2025-04-25 09:50:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7384120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6261551/v1/6dd16db7-3047-4abb-bfec-11430423e7d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid Assessment of Landslide Exposure to Element at Risk: A Decision Support tool in Regional Landslide Forecasting","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLandslide risk analysis is one of the most challenging aspects of landslide investigation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It is particularly difficult to assess exposure to landslides in areas with incomplete landslide information. The analysis of landslide risk is constrained by the highly localized nature of landslide occurrences, the paucity of temporal data on past landslides, incomplete landslide inventories, and difficulties in quantifying relevant social parameters. Regional Landslide forecasts are used to provide information on the level of landslide occurrence in space and time [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] using numerical models [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and integrating them with static inputs for effective generation as well as utilization of the forecast information by stakeholders. However, regional landslide forecasts have relied heavily on the inputs of forecasted trigger parameters, such as rainfall. Regional landslide forecasts are known to use satellite products [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], global and local products of met offices [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and ground rainfall measuring instruments. However, the resolution of these products is a coarse covering state, taluk, and block level, ranging from 144 to 16 Sq. km. The need for coarser-scale forecast information for site-specific hazards, such as landslides, can only be made more effective when the spatial impact information is converted into a risk landslide pose in the same administrative units. Such an approach has been used in other hazard forecasts such as flood by using risk maps [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the use of risk maps of administrative units for landslide initiation and impact is still not available. Such maps help the forecaster encrypt such information in the forecast for smaller risk units and stakeholders to decript the landslide forecast information for effective decision making. With increasing development and encroachment on hill slopes, landslide impacts have increased over time [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This trend highlights the need for an easy-to-follow mechanism for landslide risk analysis that can be used as a decision support tool by both forecasters and stakeholders in landslide disaster risk reduction planning.\u003c/p\u003e \u003cp\u003eSeveral methods exist for calculating quantitative and qualitative landslide risks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In general, risk calculation requires accounting for both direct (e.g., specific damage to property and life) and indirect losses (e.g., disruption of transportation infrastructure) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA critical component of landslide risk assessment is estimating the vulnerability of exposed elements at risk. However, this step is often omitted because of a lack of appropriate data. \u003cem\u003eVulnerability\u003c/em\u003e refers to the degree of loss experienced by a given element-at-risk (EatR) as a consequence of a hazard event of a certain magnitude. Ideally, vulnerability is expressed on a scale from 0 (no damage) to 1 (complete loss). Vulnerability assessment frequently considers physical, social, economic, and environmental factors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], evaluated either independently or in an integrated manner [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The variety of elements that may be exposed (e.g., buildings, roads, people) and their differing characteristics necessitate a complex, multi-level analysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Thus, to quantify landslide risk, it is essential to assess the vulnerability of the elements-at-risk (whether human or property) that are exposed to landslide hazards\u0026mdash;often termed \u003cem\u003eexposure vulnerability\u003c/em\u003e. Thorough landslide risk analysis is a prerequisite for prioritizing resources and planning, and it provides focused, informative inputs for decision support systems to manage risk in a given area[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a clear need for a simple, fast, and reliable risk assessment method that can serve as a decision-support tool for authorities to identify priority areas for intervention. In this study, we present such a methodology to evaluate the exposure of vulnerable elements-at-risk to landslide hazards over a large area (~\u0026thinsp;2,000 sq km) characterized by a sporadic spatial distribution of elements-at-risk, spatiotemporal variations in landslide occurrences, and diverse land-use practices.\u003c/p\u003e \u003cp\u003eAccording to the United Nations International Strategy for Disaster Reduction [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], \u003cem\u003eelements-at-risk\u003c/em\u003e are defined as the population, properties, economic activities (including public services), or other values that are exposed to hazards in a given area. It is important to begin any risk assessment with a complete inventory of all important elements-at-risk in the hazard zone, even if some elements may ultimately not be affected by certain events (i.e., have vulnerability V\u0026thinsp;=\u0026thinsp;0; SafeLand Project, [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the context of landslides, elements located both at the landslide source (e.g., railroads, roads, and buildings in the Giddapahar landslide source area) and along the runout path or deposition zone (e.g., houses affected by debris flows in Mirik) are subject to landslide risk. Therefore, assessing the vulnerability of exposed elements-at-risk requires an understanding of the spatial distribution of both landslide initiation areas and potential runout/inundation zones.\u003c/p\u003e \u003cp\u003eIn this research, we develop a rapid assessment of landslide risk focusing on population metrics. Specifically, we consider population-related parameters (total population, number of households, working population, population below six years of age, and literate population) as indicators of exposure. The assessment is conducted at the \u003cb\u003eMauza\u003c/b\u003e level, where a Mauza is an administrative unit roughly equivalent to a village or cluster of villages. We chose Mauza boundaries (as opposed to geological or geomorphological units) for the analysis because administrative units are more practical for decision-makers to use in planning and resource allocation. Our vulnerability assessment emphasizes the physical aspects of the elements-at-risk (i.e., physical vulnerability), which determine the potential structural damage caused by landslide events of varying intensities.\u003c/p\u003e"},{"header":"2. Study area: Demographic distribution and landslide scenario","content":"\u003cp\u003eThe study area is Darjeeling District, the northernmost district of West Bengal, India (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It lies within an active Himalayan Fold-Thrust Belt (FTB) and falls in Zone IV of the Seismic Zonation Map of India [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], indicating high seismic hazard. The district includes several prominent hill towns\u0026mdash;Darjeeling, Mirik, and Kurseong\u0026mdash;that contain pockets of high population density. According to the 2011 Census of India, Darjeeling District had a population of 1,846,823 (937,259 males and 909,564 females)​, reflecting a 14.77% increase since 2001. (The 2001 census recorded a 23.79% increase over its 1991 population​). Studies worldwide have examined links between population growth and landslide occurrence [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Darjeeling, no clear relationship has been found between overall population growth and the occurrence of large landslides. However, slope-cut failures have increased due to construction of buildings and roads on steep slopes without proper slope protection or scientific assessment​.\u003c/p\u003e \u003cp\u003eLandslides in Darjeeling are triggered by both intense rainfall and earthquakes. The area receives between 1,365 mm and 5,365 mm of rainfall annually on average, with recorded extremes near 7,600 mm in a single year​. The June\u0026ndash;September monsoon season contributes approximately 78\u0026ndash;83% of the annual rainfall, and almost all landslide incidents occur during these monsoonal months​. Earthquake-triggered landslides have also been documented; notable examples include landslides triggered by the 1984 Bihar\u0026ndash;Nepal earthquake (Mw 6.8) and the 2011 Sikkim earthquake (Mw 6.9), which added significantly to the landslide inventory of the region [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Overall, rainfall-induced landslides are more frequent and widespread than those triggered by earthquakes in this area​.\u003c/p\u003e \u003cp\u003ePast landslide events have severely affected Darjeeling District, causing loss of life and damage to infrastructure. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below highlights a number of major landslide disasters in and around the Darjeeling Himalayas, illustrating the extent of their impacts:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSome prominent and fatal landslide events in Darjeeling Himalayas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDate/ Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss/ Damage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24th September 1899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 people died with huge loss of property\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th-12thJune, 1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 people died\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd-5th October 1968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe most dreaded landslide and flood disaster accounting 677 deaths as per official record; profuse damage to infrastructure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd-4thSept., 1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 people lost their life\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15th-16th Sept. 1991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 people died and huge land and property got damaged; the Darjeeling-Siliguri Toy train track was severely damaged for 5 months\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11th-13thJuly, 1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 people died\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th\u0026amp; 8th July 1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeveral deaths and road blockades are mainly along NH-55; the most affected terrain is Kurseong and its surroundings.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th-11thJuly 2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 people died at the Gayabari landslide near\u003c/p\u003e \u003cp\u003eMirik\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15th-17thJuly 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDamage of properties in Darjeeling and adjoining areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7th-9th September 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 lives were lost along with severe damage to properties in Darjeeling Himalayas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26th-27thMay, 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 lives were lost along with profuse damage to properties in Darjeeling Himalayas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18th September 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccurrence of a large number of landslides induced by a major earthquake\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14th September 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtensive damage in six tea gardens (Takdah, Lopchu tea gardens) of Darjeeling district owing to landslides.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st July 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 people died in the Limbudhura village of Mirik sub-division; land and property of Limbudhura village also got washed away.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo better understand landslide distribution, we prepared a comprehensive landslide inventory for Darjeeling District using multiple data sources and field surveys. This inventory includes a total of 1,356 landslides, comprising 1,035 debris slides/flows and 261 rock slides/falls​. The corridor from Balasun to Chota Ringtong emerged as an epicenter of major landslides during multiple extreme events. Landslides in this corridor have repeatedly disrupted the National Highway and the Darjeeling Himalayan Railway (a UNESCO World Heritage \u0026ldquo;Toy Train\u0026rdquo; line), leaving these vital transport links inoperative for days at a time​. The study area also encompasses a known \u0026ldquo;sinking zone\u0026rdquo; (an area of gradual ground subsidence and instability) identified by previous studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Material and method","content":"\u003cp\u003eThe goal of this study was to devise a rapid assessment methodology using available information, such that the output can serve as a decision-support tool for landslide risk management​. We selected the Mauza (the smallest administrative unit in the region) as the mapping unit for analysis. Using administrative units ensures that the results align with governance and decision-making frameworks. The chosen mapping scale corresponds to the scale of the available input data.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.1. Data Sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWe utilized various data sets to assess the exposure and vulnerability of elements-at-risk in Darjeeling. The main inputs to our analysis included:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHistorical records and archives: Legacy reports (e.g., Geological Survey of India publications) and old documents (including newspaper archives) providing information on past landslides​.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRemote sensing imagery: High-resolution Earth observation data such as Cartosat-1 (2.5 m) and IRS-P6 LISS-IV (5.8 m) satellite images, along with Google Earth imagery and base maps from ESRI\u0026rsquo;s ArcGIS (DigitalGlobe, GeoEye, etc.)​.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTopographic maps: Survey of India topographic sheets (1:50,000 scale; map sheet nos. 78A/04, 78A/08, 78B/01, 78B/05, surveyed in 1961\u0026ndash;64) that mark landslide scars and the year of survey​.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLandslide inventory: A compiled inventory of landslides (both historical and recent) in the region, including the Darjeeling landslide inventory described in the Study Area section.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCensus data: Demographic and socio-economic data from the 2011 Census of India at the Mauza level (described below).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdministrative boundaries: Digital shapefiles of Mauza boundaries obtained from the Darjeeling district authorities​.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eActive landslide data: Locations of known active landslides (from recent observations) for inclusion in the susceptibility analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLandslide susceptibility models: Results of landslide initiation and runout susceptibility modeling for the study area.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Landslide Inventory\u003c/h2\u003e \u003cp\u003eHistorical information on landslide occurrences is a fundamental component of landslide susceptibility studies, as it provides insight into the frequency, size, damage, and types of landslides [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Past and present landslide occurrences form the basis for predicting future landslides, establishing rainfall or trigger thresholds, and conducting hazard and vulnerability analyses.\u003c/p\u003e \u003cp\u003eFor this study, we prepared a detailed landslide inventory by compiling information from multiple sources. These included high-resolution satellite imagery (Cartosat-1, LISS-IV), Google Earth and other online imagery, and base maps in ArcGIS​. We also incorporated landslide records from topographic maps, the Darjeeling Himalayan Railway (DHR) slip failure register, legacy reports of the Geological Survey of India, local newspaper reports, and information from internet blogs​. We paid special attention to consistent and reliable data sources. For example, IRS P6 LISS-IV imagery from various dates (17 October 2006; 5 November 2006; 4 December 2006; 9 February 2008; 18 November 2008; 5 January 2010; and 18 March 2010) and Cartosat-1 panchromatic imagery were used extensively to map landslides (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)​. The DHR slip register, which documents every slope failure affecting the railway line between Sukna and Darjeeling from 1991 to 2016, was used to validate and supplement our inventory​. Older landslides visible on the 1960s Survey of India toposheets were also added to the inventory; those maps provide the locations of historical landslide scars along with the survey year​. Additionally, we included landslide locations from GSI\u0026rsquo;s past investigations and any new landslides identified through recent satellite imagery and field surveys​\u003c/p\u003e \u003cp\u003eUsing all these sources, we collated a landslide inventory database of approximately 2,986 landslides across the West Bengal Himalayas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)​. This comprehensive inventory underpins the susceptibility analysis described later.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Administrative boundaries\u003c/h2\u003e \u003cp\u003eA Mauza is a land revenue unit or small administrative area in Darjeeling District, typically encompassing one or more settlements (villages or hamlets) under a common jurisdiction. We obtained the Mauza boundary map for Darjeeling from the state government web portal and the district administration (as a GIS shapefile)​.. There are 210 Mauzas in Darjeeling District, with areas ranging from as small as 0.01 sq. km. (Dhwaipani Mauza) to as large as 155 sq. km. (Singalila Forest Mauza)​. These Mauza boundaries serve as the spatial units for our exposure and risk calculations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Census data\u003c/h2\u003e \u003cp\u003eWe used the latest available population and socio-economic data from the 2011 Census of India for each Mauza. The census provides detailed information including total population, number of households, male and female population, population under 6 years of age, literacy and illiteracy rates, main and marginal workforce, and non-working population​. From this comprehensive dataset, we selected five key parameters for our analysis that are most relevant to landslide impact and community vulnerability:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNumber of households\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWorking population (i.e., number of people engaged in income-generating work, including main and marginal workers)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePopulation below 6 years (young children)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLiterate population (as an indicator of literacy rate)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese parameters capture important aspects of the social profile and potential coping capacity of each Mauza in the face of landslides. For instance, a higher total population or more households means more people and homes are exposed to risk; a larger working population might indicate significant economic activity at risk; and a higher literate population could correlate with better access to information and resources for disaster response. It should be noted that official census data were unavailable for 28 of the 210 Mauzas in Darjeeling District​. Our analysis for those locations had to rely on partial or estimated information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Elements at Risk\u003c/h2\u003e \u003cp\u003eUsing the census data, we generated spatial distribution maps for the selected social parameters (total population, households, working population, population under 6, and literate population) across all Mauzas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These maps represent the key \u003cb\u003eelements-at-risk\u003c/b\u003e in terms of population and settlements. For context, we identified the Mauzas that have the minimum and maximum values for each parameter in the distric:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTotal population\u003c/b\u003e: minimum of 49 (Sum Forest Mauza) and maximum of 126,935 (Darjeeling town Mauza).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHouseholds\u003c/b\u003e: minimum of 8 (Sum Forest) and maximum of 27,470 (Darjeeling).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePopulation below 6 years\u003c/b\u003e: minimum of 14 (Phuguri Forest) and maximum of 11,696 (Darjeeling).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLiterate population\u003c/b\u003e: minimum of 38 (Sum Forest) and maximum of 93,091 (Darjeeling).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWorking population\u003c/b\u003e: minimum of 10 (Sum Forest) and maximum of 52,356 (Darjeeling).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese figures highlight the wide range of population exposure across different Mauzas. (Note: \u0026ldquo;Sum Forest\u0026rdquo; refers to a sparsely populated forest Mauza, whereas Darjeeling Mauza represents the main urban area.)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Landslide Susceptibility\u003c/h2\u003e \u003cp\u003eLandslide susceptibility represents the likelihood of landslide occurrence in an area based on local terrain conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In our context, susceptibility encompasses both the landslide initiation zones and the areas that could potentially be affected by the moving debris (runout areas) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Numerous studies have modeled landslide initiation and runout e.g., [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For a regional-scale vulnerability assessment, it is crucial to consider both initiation and runout susceptibility together to identify the most hazard-prone zones. In this study, we generated separate initiation and runout susceptibility maps and then combined them to obtain an overall susceptibility map for the region.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Initiation susceptibility\u003c/h2\u003e \u003cp\u003eWe evaluated landslide initiation susceptibility using statistical modeling. An ensemble landslide susceptibility map was produced by combining three modeling techniques: Logistic Regression (LRM), Quadratic Discriminant Analysis (QDA), and Linear Discriminant Analysis (LDA). We utilized the open-source software LAND-SE [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] to build these models. The ensemble model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) classified about 28% of the study area as having high landslide probability (P\u0026thinsp;\u0026gt;\u0026thinsp;0.55)​. Among the individual models and their ensemble, QDA and the combined LRM-LDA-QDA ensemble performed best, achieving the highest prediction accuracy and specificity, and the lowest false-positive rate (FPR)​. These models were the most effective in correctly identifying areas likely to experience future landslides in the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Runout susceptibility\u003c/h2\u003e \u003cp\u003eTo assess landslide runout (debris flow) susceptibility, we applied a conceptual modeling tool called \u003cem\u003er.randomwalk\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] within a GIS. This tool simulates the paths of debris flows by releasing hypothetical mass movements from identified source points and allowing them to \u0026ldquo;run\u0026rdquo; downslope until a stopping criterion is met. For the simulation, we used two primary inputs: (1) a high-resolution Digital Elevation Model (ALOS PALSAR DEM, 12.5 m resolution) to define potential release (initiation) pixels, and (2) the mapped inventory of 66 channelized debris flows in the area to calibrate and validate the model​. The output of this analysis was a debris flow susceptibility map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which we classified into zones of high, moderate, and low susceptibility based on the modeled impact probability.\u003c/p\u003e \u003cp\u003eThe results showed that approximately 57% of the Darjeeling study area is susceptible to debris flows; within this susceptible zone, about 21% of the area was identified as having very high debris flow probability​. Historical evidence underscores the importance of debris flow hazards: for example, destructive debris flow events in 2015 and in September 2007 caused extensive loss of life and property in Darjeeling District. These events (as documented by news reports and GSI studies) highlight that debris flows are a major threat in the region. Therefore, mapping the potential impact areas of debris flows is crucial for identifying vulnerable locations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Combined Susceptibility\u003c/h2\u003e \u003cp\u003eIn order to get a comprehensive picture of landslide hazard, we combined the initiation and runout susceptibility maps. We focused on the highest hazard classes to delineate an integrated high-susceptibility zone. Specifically, we overlaid the high-susceptibility zones from the initiation model with the high-impact probability zones from the runout model to identify areas that are highly susceptible to either form of landsliding (or both). These overlapping areas were designated as combined high susceptibility zones. The resulting combined susceptibility map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) highlights the areas most likely to experience landslides (either slope failures at the source or debris flow impacts downslope). While our demonstration emphasizes the high-susceptibility class, the same approach can be applied to moderate and low susceptibility classes if needed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Exposure Vulnerability Assessment\u003c/h2\u003e \u003cp\u003eUsing the hazard information and exposure data described above, we developed a methodology to rank each Mauza by its \u003cem\u003eexposure vulnerability\u003c/em\u003e to landslides. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents a flowchart of this methodology. The inputs to the assessment include the combined landslide susceptibility map (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the elements-at-risk maps (population and infrastructure), the census data parameters, and the Mauza boundaries​.\u003c/p\u003e \u003cp\u003eWe made two key assumptions in this assessment:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMaximum Vulnerability\u003c/b\u003e: All elements-at-risk in the path of a landslide are assumed to have a vulnerability value of 1 (i.e., they would suffer complete damage in the event of a landslide). This conservative assumption is reasonable in the steep terrain of the Himalayas, where landslides are typically severe and anything in their path is likely to be fully affected.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigh Susceptibility as Trigger Zones\u003c/b\u003e: During extreme rainfall events (exceeding established thresholds), landslides are most likely to occur in the high-susceptibility zones. Therefore, any element-at-risk located within those zones is considered to be exposed and likely to be affected by a landslide under such conditions​.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eWith these assumptions, we calculated an exposure vulnerability index for each Mauza using the following steps​:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIdentify High Susceptibility Areas\u003c/b\u003e: Use the GIS \u003cem\u003eUnion\u003c/em\u003e tool to combine the high-susceptibility zones from the combined susceptibility map with the locations of known active landslides. This produces a consolidated map of areas that are considered \u003cem\u003ehighly susceptible\u003c/em\u003e to landslides.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCalculate Susceptibility Area Density\u003c/b\u003e: For each Mauza, compute the density of highly susceptible area by dividing the area of the Mauza that falls within high-susceptibility zones (from Step 1) by the total area of that Mauza.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCalculate Exposure Parameter Density\u003c/b\u003e: For each Mauza, calculate the density of each exposure parameter (the five census indicators and the transportation network). For the population parameters, this is done by dividing the parameter value (e.g., total population count) by the Mauza area, yielding values such as population per square kilometer. For the transportation network, we calculated the length of roads (or other transport infrastructure) in the Mauza per square kilometer.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCompute Exposure Vulnerability\u003c/b\u003e: Multiply the susceptibility area density (Step 2) by the density of each exposure parameter (Step 3) to obtain the exposure vulnerability value for each parameter in each Mauza. This yields, for example, a value representing \"population exposure vulnerability\" for a given Mauza (indicating how much of its population is exposed in high-susceptibility zones), and similarly for households, working population, etc.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRank the Mauzas\u003c/b\u003e: Rank the Mauzas based on their exposure vulnerability values for each parameter. A higher rank (1 being the highest) indicates a greater level of exposure for that parameter. We produced separate rankings for each of the five social parameters, as well as a composite consideration of all factors.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis step-by-step approach provides a quantitative basis to compare administrative units in terms of their relative exposure to landslide hazards. The output is a set of ranked lists or maps that highlight which Mauzas are most at risk when considering different facets of exposure (population, infrastructure, etc.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eUsing the above methodology, we analyzed landslide exposure vulnerability for the selected social parameters across Darjeeling District (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The results indicate clear spatial differences in which areas are most at risk:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTotal Population, Households, and Literate Population\u003c/b\u003e: The highest exposure vulnerability for total population, number of households, and literate population was found in \u003cb\u003eSukhiapokhri (CT) Mauza\u003c/b\u003e, followed (in descending order) by \u003cb\u003eKurseong\u003c/b\u003e, \u003cb\u003eSimana Basti\u003c/b\u003e, \u003cb\u003eDarjeeling\u003c/b\u003e, and other Mauzas. In these areas, a large portion of the population resides in zones that are highly susceptible to landslides.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePopulation Below 6 Years and Working Population\u003c/b\u003e: The highest exposure for young children and for working population occurred in \u003cb\u003eKurseong Mauza\u003c/b\u003e, followed by \u003cb\u003eSukhiapokhri (CT)\u003c/b\u003e, \u003cb\u003eSimana Basti\u003c/b\u003e, \u003cb\u003eDarjeeling\u003c/b\u003e, and others.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese findings suggest that certain Mauzas consistently rank high in vulnerability across multiple parameters, indicating hotspots of social exposure to landslides. A more qualitative examination of overall risk also shows that some smaller settlements, such as \u003cb\u003eSingtam Mauza\u003c/b\u003e, \u003cb\u003eShivakhola Forest\u003c/b\u003e, and \u003cb\u003eTung Mauza\u003c/b\u003e, are highly vulnerable to landslide impacts. These areas might not have the largest populations, but their location and context make them particularly prone to disaster.\u003c/p\u003e \u003cp\u003eIdentifying and ranking the most vulnerable Mauzas is crucial for disaster preparedness and risk reduction in Darjeeling. This information allows authorities to prioritize specific areas for detailed landslide forecasting, early warning, and mitigation efforts. Focusing communication and emergency response on the high-risk areas can improve the effectiveness of disaster management. For instance, if two Mauzas are forecast to have a similar likelihood of landslide occurrence, responders can give higher priority to the Mauza that has a greater exposure vulnerability (i.e., more people or assets in harm\u0026rsquo;s way). In this way, the limited resources available for landslide monitoring and response can be directed to where they are needed most.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eA significant portion of India\u0026rsquo;s land area\u0026mdash;approximately 0.43\u0026nbsp;million sq. km. or about 12.6% (excluding snow-covered regions)\u0026mdash;is prone to landslide hazards​. However, comprehensive data for quantitative risk or vulnerability analysis are lacking in most regions. In the Indian context, two data sets are commonly and readily available across the country: (1) basic landslide inventory data compiled by agencies such as the Geological Survey of India (the national nodal agency for landslides), the National Remote Sensing Centre, and state/district disaster management departments; and (2) demographic data from the national census (e.g., via the Census of India website). The methodology presented in this study leverages these widely available data sets to perform a rapid landslide risk assessment that is well-suited for data-scarce environments.\u003c/p\u003e \u003cp\u003eThis research is helpful in providing a broad overview of the areas that are most at risk from landslides in Darjeeling District. By integrating landslide susceptibility with exposure data, we can highlight which communities and assets are in harm\u0026rsquo;s way, and produce a ranked list of vulnerable locations.\u003c/p\u003e \u003cp\u003eFurther such information not only helps in decsison support for encrypting and decrypting the forecast information but also can be valuable for decision-makers, as it helps in allocating resources and planning interventions (such as slope stabilization, community preparedness programs, or early warning systems) in the most at-risk areas. The method can also guide the prioritization of areas for implementing or enhancing landslide forecasting and monitoring systems. In summary, the rapid assessment methodology developed in this research offers an effective decision-support tool for regional landslide risk management, especially in regions where detailed data are limited but prompt risk evaluation is needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo, I declare that thye authors have no competing interests that might be perceived to influence the results and/or discussion in the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting this study's findings are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.S: Conceptualization, Methodology, Writing- Original draft. S.K: Data curation, Visualization, Investigation. R.K: Investigation, Data curation. A.K.M: Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBazin S, 2012. SafeLand deliverable 4.8. Guidelines for Landslide Monitoring and Early Warning Systems in Europe \u0026mdash; Design and Required Technology. European Project SafeLand, Grant Agreement No. 226479. Deliverable 4.8, 153 pp., available at: http://www.safeland-fp7 eu, 2012.\u003c/li\u003e\n\u003cli\u003eBell. R and Glade.T. 2004. Quantitative risk analysis for landslides \u0026ndash; Examples from B\u0026acute;ıldudalur, NW-Iceland. Natural Hazards and Earth System Sciences 4: pp117\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eBIS (2002) IS 1893 (Part 1)\u0026mdash;2002: Indian Standard Criteria for Earthquake Resistant Design of Structures. \u003c/li\u003e\n\u003cli\u003eBrabb E, 1984. 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Technical Note: An operational landslide early warning system at regional scale based on space\u0026ndash;time-variable rainfall thresholds. \u003cem\u003eNatural Hazards and Earth System Sciences\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 853\u0026ndash;861. https://doi.org/10.5194/nhess-15-853-2015\u003c/li\u003e\n\u003cli\u003eUNISDR (2004) Living with Risk: A Global Review of Disaster Reduction Initiatives. United Nations Inter-Agency Secretariat of the ISDR (UNISDR), Geneva.\u003c/li\u003e\n\u003cli\u003eUNISDR (2009) 2009 UNISDR terminology on disaster risk reduction. UNISDR, Geneva. Available at: http://www.unisdr.org/we/inform/publicaitons/7817. Accessed on December 15, 2012.\u003c/li\u003e\n\u003cli\u003evan Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation\u0026mdash;why is it still so difficult? Bull Eng Geol Environ 65(5):167\u0026ndash;184.\u003c/li\u003e\n\u003cli\u003eWang, H. B., Wu, S. R., Shi, J. S., \u0026amp; Li, B. (2011). Qualitative hazard and risk assessment of landslides: a practical framework for a case study in China. Natural Hazards, 69(3), 1281\u0026ndash;1294. doi:10.1007/s11069-011-0008-1 \u003c/li\u003e\n\u003cli\u003eYoussef, A.M., Al-Kathery, M. \u0026amp; Pradhan, B. (2015). Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. \u003cem\u003eGeosci J\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 113\u0026ndash;134. https://doi.org/10.1007/s12303-014-0032-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Landslide susceptibility, runout susceptibility, combined susceptibility, risk, Darjeeling","lastPublishedDoi":"10.21203/rs.3.rs-6261551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6261551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEffective development of an landslide forecast and its use on ground requires granular knowledge on landslide risk of the area. The lack of detailed landslide impact information, limitations in complete historical landslide inventories, and inherent difficulties in quantifying elements-at-risk have long been a challenge in the development of effective regional landslide risk maps for disaster managers. In this research, we propose a rapid risk assessment methodology that integrates landslide occurrence information, landslide initiation and runout susceptibility, and physical and social parameters from census data. Using this approach, we ranked various administrative blocks in Darjeeling District, West Bengal, India, according to the elements exposed to landslide hazards. In total, 197 villages (Mauzas) were evaluated and ranked based on their exposure to landslides. This quasi-quantitative rapid assessment of administrative units provides valuable input for local authorities in resource management and planning and helps the scientific community prioritize investigations in sectors with higher vulnerability. The resulting risk exposure ranking helps as a decision support tool in regional landslide forecasting and supports informed decision-making for stakeholders.\u003c/p\u003e","manuscriptTitle":"Rapid Assessment of Landslide Exposure to Element at Risk: A Decision Support tool in Regional Landslide Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-25 09:26:40","doi":"10.21203/rs.3.rs-6261551/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-22T19:50:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-08T05:32:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T06:56:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144912434641159283141416997643666365819","date":"2025-04-30T05:02:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333197679634280849464163435388696957626","date":"2025-04-23T08:34:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T07:31:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-18T12:50:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-18T12:47:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-03-19T12:21:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52f68644-18b4-414f-a0df-ecd21eed3eaf","owner":[],"postedDate":"April 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T00:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-25 09:26:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6261551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6261551","identity":"rs-6261551","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}