A National Level Landslide Risk Index for Land Use Planning in Bhutan: Towards Assessing Landslide Hazard, Exposure, and Vulnerability Indexes

A National Level Landslide Risk Index for Land Use Planning in Bhutan: Towards Assessing Landslide Hazard, Exposure, and Vulnerability Indexes | 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 A National Level Landslide Risk Index for Land Use Planning in Bhutan: Towards Assessing Landslide Hazard, Exposure, and Vulnerability Indexes Indra Bahadur Chhetri, Mim Prasad Phuyel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6472965/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Bhutan, landslides are a common natural hazard posing a greater impact on human settlements, infrastructure, and the environment. However, studies on the landslide risk to understand these impacts at a Gewog (smallest territorial units) level are limited. This study proposes an indicator-based approach to assessing the three risk dimensions; hazard, exposure, and physical vulnerability of buildings. The hazard component integrates a national landslide susceptibility index and an extreme precipitation susceptibility index. Exposure is assessed through population and building density, while vulnerability is determined by construction features such as construction technique and materials; number of rooms, and type of roofing, all weighted empirically. The final landslide risk index is derived by multiplying these risk dimensions. Cluster analysis further identifies key risk drivers across Gewogs. Results indicate that 47.5% of Gewogs (96) are at high to very high landslide risk, while only 19% (41) are at low to very low risk. High-risk areas are often rural Gewogs with dense populations and structurally vulnerable buildings. Additionally, 56% of houses nationwide fall into high or very high vulnerability categories. This integrative, localized risk assessment supports more targeted and context-sensitive landslide risk management strategies and offers a model adaptable to other regions for improved disaster risk reduction and land use planning. Environmental Engineering Climatology Civil Engineering Geographic Information Systems Landslide landslide hazard index landslide exposure index landslide vulnerability index landslide risk index Bhutan Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 1. Introduction The term “landslides” refers to the downward and outward movement of slope-forming materials such as a mass of rock, earth, debris, or a combination of these (Highland & Bobrowsky, 2008 ). They are categorized based on the type of slope movement (fall, topple, spread, flow, slide, etc.), the material involved (rock, soil, debris, etc.), and the speed of movement. On the other hand, landslide risk is a measure of the probability and severity of an adverse effect on the property, or the environment as a result of landslides (Corominas et al., 2014 ; Pasang & Kubíček, 2020 ). Generally, there are three dimensions of landslide risk: hazard, exposure, and vulnerability which must be specifically considered that can help understand the spatial distribution and trends of landslide disasters (Corominas et al., 2014 ). Schneiderbauer & Ehrlich ( 2004 ) define a hazard as “a potentially damaging physical event, phenomenon and/or human activity, which may cause loss of life or injury, property damage, social and economic disruption or environmental degradation”. The landslide hazard is a function of susceptibility (spatial propensity to landslide activity) and temporal frequency of landslide triggers, i.e. the probability of occurrence. This means that for landslides, geophysical methods of assessment at a very local level become less practical considering the spatial scales (in terms of area and volume) and the temporal frequency (Emberson et al., 2021 ). However, to ascertain risk to society, infrastructure, and the environment, risk analysts often delineate where the geographic footprints of such elements intersect with a defined hazard footprint. This will help represent the proportion of the value (lives, buildings, and environment) of the element expected to be lost as a result of the hazard to obtain the estimated risk. Consequently, landslide exposure is the relationship between past evidence of landslides and the spatial arrangement of exposed elements that best characterize such a relationship (Emberson et al., 2021 ; Pereira et al., 2020 ). On the other hand, landslide vulnerability is the quantitative evaluation of vulnerability components (physical exposure and social characteristics) to identify the elements at risk. To understand the landslide risk i.e probability of occurrence or expected degree of loss, a multi-level analysis is required due to the range of potentially exposed elements (e.g., buildings, roads, people, and environment), or elements located in hazardous zones that are consequently prone to possible losses, and their different characteristics (Garcia et al., 2016 ). Globally, 67,052 deaths were reported due to landslides and Asia alone has reported a total of 25,604 deaths which is about 38.2% between 1910 to 2020 (Thongley & Vansarochana, 2021b ). In Bhutan, a total of 1665 landslide events have been identified constituting 0.10% of the total geographical area of Bhutan (3,730.22 km 2 out of 38,394 km 2 ). Over 380 individuals died and about 87,000 people were affected by natural disasters in Bhutan between 1994 and 2016 and the number is only expected to rise further (Ministry of Health, 2016 ). The landslides in Bhutan are mainly rainfall-induced and further aggravated by the toe-cutting of slopes along the road corridor and deforestation emphasizing infrastructure development (Dikshit, Sarkar, Pradhan, Acharya, et al., 2020; Pasang & Kubíček, 2020 ). Worldwide, several methodologies and models have been studied for prediction and susceptibility mapping. Some of the past research and the frequently used models are frequency ratio (FR) (Lee & Sambath, 2006 ; Li et al., 2022 ; Shahabi et al., 2012 ; Thambidurai et al., 2023 ), the weight of evidence (WoE), information value (InV) (Chen et al., 2020 ; Tang et al., 2021 ; Wubalem & Meten, 2020 ; Zhao et al., 2021 ), fuzzy neural network (FNN) (Huang et al., 2022 ; Lucchese et al., 2021 ; Ozdemir, 2020 ), index of entropy (IoE) (Barman et al., 2023 ; Wubalem et al., 2022 ; XU et al., 2020 ), analytical hierarchy process (AHP) (Orejuela & Toulkeridis, 2020 ; Panchal & Shrivastava, 2020 , 2022 ; Teshnizi et al., 2022 ), logistic regression (LR) (Cemiloglu et al., 2023 ; Jin et al., 2022 ; Riegel et al., 2020 ; Sujatha & Sridhar, 2021 ), and fuzzy logic (FL) (Baharvand et al., 2020 ; Oleng et al., 2024 ; Wang & Nanehkaran, 2024 ). Nevertheless, in Bhutan, all researchers have conducted studies regarding susceptibility mapping considering the small pockets of an area rather than across the entire nation (Dikshit, Sarkar, Pradhan, Acharya, et al., 2020; Dikshit, Sarkar, Pradhan, Jena, et al., 2020 ; Pasang & Kubíček, 2020 ; Saha et al., 2021 , 2022 ; Sarkar et al., 2022 ; Tempa et al., 2021 ; Thongley & Vansarochana, 2021a , 2021b ). In their study, no researchers have conducted studies to explore the possible impact of landslides on humans, property, infrastructure, and the environment at a Gewog (smallest territorial units) level in the country making their assessment either generalized or incomplete. The present study would allow accessing social, infrastructure, and environmental exposure to landslides at a Gewog level across the entire nation. Further, the study will also propose a landslide risk index (LRi) for Bhutan at a Gewog level. This Gewog level landslide risk index correlates to the scale of strategic risk management and is a crucial step in defining the proper public policy measures for land use planning and civil protection management to mitigate disaster risk and reduce future losses. Furthermore, adaption methods for disaster risk reduction should concentrate on lowering long-term exposure and vulnerability of people and assets in addition to structural protection efforts and hazardous process mitigation. In this context, this study will fulfil the objectives at the Gewog level in the following key areas: Access landslide risk using a dataset of variables that allow characterizing three dimensions of landslide risk: hazard, exposure, and vulnerability. Propose LRi for Bhutan at a Gewog level using three dimensions of landslide risk: landslide hazard index (LHi), landslide exposure index (LEi), and landslide vulnerability index (LVi). The elements (agricultural land, settlement/built-up, forest, schools, health centres, historical monuments, roads, and bridges) at risk are studied based on landslide susceptibility zones, and their degree of exposure is explored through the geographic information systems (GIS). Perform a cluster analysis and rank the Gewog landslide risk, identifying landslide risk profiles for different groups. 2. Study area Study area, Bhutan is a mountainous nation sandwiched between China in the north and India to the south, spanning 38,394 km 2 with challenging topography and steep mountains. Bhutan is located in the greater Himalayan range, one of the seismically active zones, particularly zones IV and V. Bhutan has just over 700,000 population, with more than 70% of people living in rural areas, hence there is a high chance of disaster. There are a total of 20 dzongkhags (districts) and 205 Gewogs in Bhutan. The region experiences an annual average temperature ranging from 12.30 -22.54°C with summer monsoon spanning from June to September, bringing an average annual rainfall of more than 1685.20 mm. The southern and central Bhutan is particularly vulnerable to landslides due to its distinct topography, steep mountainous terrain, monsoon-dominated climate, and growing development activity (e.g. road construction). Landslides are predicted to occur more frequently and with greater severity as climate change exacerbates rainfall variability and extreme weather events. These facts motivated the researchers to contribute original insights and develop a localized decision support system through improved landslide hazard mapping for Bhutan. 3. Materials and methods The methodology encompasses several sequential steps, including data collection, processing, and construction of LHi, LEi, LVi, and LRi including cluster analysis and cross-impact evaluation among others (Fig. 2 ). 3.1 Data The data from various primary and secondary sources were collected to meet the required objectives of the study. As a mandatory part of the study, the spatial data of landslide susceptibility and the land use/land cover (LULC) data of the study area were adopted from previous research. The population and household data were acquired from the Bhutan Statistical Database System of the National Statistics Bureau. The vector geodatabase of settlement/building and administrative units was obtained from the National Land Commission Secretariate (NLCS). The shapefiles of the road network were acquired from the Department of Surface Transport, Ministry of Infrastructure and Transport (MoIT). Similarly, the rainfall data were acquired from Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS). The input data used in the assessment of the landslide risk dimensions, adjusted to the gewog level are documented and presented in Table 1 . Table 1 Dataset used in the study and their sources. Data type Adopted/Acquired from Acquisition date Spatial Resolution 2021 land use/land cover (LULC) (Venter et al., 2022 ) Global 10 m InV landslide susceptibility map (Gyeltshen et al., 2024a ) 90 m Population and household data, Population and Housing Census of Bhutan (PHCB, 2017) Bhutan Statistical Database System, National Statistics Bureau ( https://www.nsb.gov.bt/ ) 25/08/2024 Settlement and administrative unit data National Land Commission Secretariate (NLCS), Bhutan Rainfall data CHIRPS Daily: Climate Hazards Center InfraRed Precipitation with Station data (Version 2.0 Final) https://earthengine.google.com/ 20/03/2025 Global 5000 m Road network and bridge location data Department of Surface Transport, (MoIT), Bhutan 3.2 Landslide hazard index A landslide hazard is an expected probability of occurrence of a potentially damaging landslide of a given intensity that takes place in a certain area within a given time (Catani et al., 2005 ; Pereira et al., 2020 ). This, essentially, means that landslide hazard assessment procedures must take into account both space and time prediction. In this study, the landslide susceptibility index (SUSCLi) and the extreme weather index as an indicator of the spatial and temporal dimension of hazard are taken into account in the landslide hazard assessment at the Gewog scale. A Landslide susceptibility from the national landslide susceptibility model (based on InV) obtained using geomorphological landslide inventory (1665 landslides) was adopted from the previous study by Gyeltshen et al. ( 2024a ). For this study, the most unstable regions in the study area were selected based on positive InV scores (InV > 0) from the national landslide susceptibility model. Subsequently, the SUSCLi of each Gewog is determined by computing the percentage of the municipality area with positive InV scores (InV > 0). Further, landslide-triggering conditions due to rainfall are used in the hazard model by computing the 10-year extreme precipitation susceptibility index (EPSi) proposed by Santos et al. ( 2017 ) aggregated by Gewogs. Further, both SUSCLi and EPSi are used as a proxy for the temporal component of the landslide hazard normalized to adjust to the interval [0, 1] using Eq. (1). \(x_{{i,norm}}^{m}=\frac{{x_{i}^{m} - {x_{i,\hbox{min} }}}}{{x_{{i,\hbox{max} }}^{m} - {x_{i,\hbox{min} }}}}{\text{ (1)}}\) Where: \(x_{i}^{m}\) is the data of the m-th Gewogs from i-th indicator’s dataset; \({x_{i,\hbox{min} }}\) is the minimum value of the i-th indicator’s dataset; \(x_{{i,\hbox{max} }}^{m}\) is the maximum value for i-th indicator’s dataset; and \(x_{{i,norm}}^{m}\) is the normalized data or the m-th Gewog from i-th indicator’s dataset. Now, the LHi for each Gewog is computed further using Eq. (2) proposed by Pereira et al. ( 2020 ). \(LHi=0.75 * SUSCLi+0.25*EPSi{\text{ (2)}}\) 3.3 Landslide exposure index The exposure elements are expressed into three themes: social, infrastructure, and environmental. The social and infrastructure theme includes elements such as population, settlement (no. of buildings), historical monuments, schools, bridges, and roads at risk. The environmental theme considered elements such as agricultural land, built-up, and forests as elements at risk for vulnerability assessment. The exposure maps were prepared for each element at risk using the landslide hazard (susceptibility) zones to measure the level of exposure of these elements explored through GIS. Each element at risk is categorized into five exposure groups such as very high, high, moderate, low, and very low exposure based on the susceptibility zones that each element falls. However, in this study, the assessment of the exposure index to landslides at the Gewog level is mainly based on two main exposure elements: population and buildings (settlements), expressed using population density (PD) and number of buildings (NB). This is mainly because, the country is characterized by the concentration of population and economic activities in the district capitals and along the southern, western, and central districts, which is satisfactorily represented by the population density (PD, no. of inhabitants/km 2 ) and the number of buildings per Gewog. The Kolmogorov Smirnov (K–S) (Crutcher, 1975 ; Filion, 2015 ) test was applied to check the best-fit probability distribution of population density and the number of buildings per municipality. Both distributions are then best fitted to an exponential distribution using a ln transformation to obtain a distribution closer to normal. The results were further normalized to the range [0, 1] using the max-min method, using Eq. (1). Then the landslide exposure index at a Gewog level was computed using the same weight for both variables using Eq. (3). \(LEi=PD * 0.5+NB*0.5{\text{ (3)}}\) 3.4 Landslide vulnerability index Landslide vulnerability was assessed by evaluating elements at risk and the probability of loss due to landslides. In this study, the physical vulnerability assessment of the infrastructure (buildings) of the built environment at a Gewog level was considered based on the building features such as construction technique and construction materials (CTCM); reinforced structure (RS); number of rooms (NR), and type of roof (TR). Each building feature is further divided into a set of building feature classes (BFC) and its weightage is adopted as indicated in Table 2 . The information about the construction materials and other associated information are gathered from the same population and housing census data (Table 1 ). The categorization of the feature classes is based on indigenous knowledge and expert opinions. For example, CTCM is categorized into four main classes from highest to lowest resistance to landslide: reinforced concrete, brick or stone walls, adobe, and other materials (e.g. wood and bamboo). The reinforced concrete also provides additional resistance to the buildings against landslides. Similarly, the number of floors is a proxy variable of the number of people living in the building, because the higher the number of rooms, the higher the number of people living and the higher the vulnerability. Similarly, the type of roof materials is used as indicators to calculate the resistance of the buildings against landslide hazards. For this study, the building feature and the corresponding building feature classes are presented in Table 2 . Table 2 Building features and corresponding building feature class vulnerability scores. Building feature (BF) Building feature class (BFC) Vulnerability score Adopted from Construction technique and construction materials (CTCM) Reinforced concrete 0.1 (Pereira et al., 2020 ; Silva & Pereira, 2014 ) Brick or stone walls 0.4 Adobe 0.8 Others 1 Reinforced structure (RS) With reinforced structure 0.1 (Pereira et al., 2020 ; Silva & Pereira, 2014 ) Without reinforced structure 0.8 Number of rooms (NR) 1 to 3 rooms 0.3 (Pereira et al., 2020 ; Silva & Pereira, 2014 ; Subasinghe & Kawasaki, 2021 ) > 3 rooms 0.8 Type of roof (TR) Concrete 0.1 (Pereira et al., 2020 ; Silva & Pereira, 2014 ; Singh et al., 2019 ; Subasinghe & Kawasaki, 2021 ) Metal sheet 0.4 Tiles/slates 0.3 Wood/planks/bamboo 0.5 Thatch 0.6 Tarpaulin 0.9 Others 1 The aggregate vulnerability of each building feature (AVBF) is computed for each Gewog using Eq. (4). \(AVBF=\sum {VBFC/NB} {\text{ (4)}}\) Where AVBF is a score representing the aggregate vulnerability of a particular building feature (e.g. construction material), VBFC is the vulnerability score of a building feature class x (see scores in Table 2 ) multiplied by the number of buildings within the building feature class x , and NB is the total number of buildings in each Gewog. Now, the LVi of each Gewog is computed based on the weighted average of the aggregate vulnerabilities of the building feature using Eq. (5), and the LVi layer is normalized to adjust to the interval [0, 1] using Eq. (1). \(LVi=\left( {CTCM * 0.6} \right){\text{ +}}\left( {RS * 0.2} \right){\text{ +}}\left( {NR * 0.2} \right){\text{ (5)}}\) 3.5 Landslide risk index Landslide risk is defined as the product of the landslide hazard, the vulnerability, and the exposure (Dikshit, Sarkar, Pradhan, Acharya, et al., 2020; Pereira et al., 2020 ; Rahman et al., 2022 ). The LRi is computed based on the combination of the preceding parameters following a numerical approach using Eq. (6). \(LRi=\left( {LH{i^{1/3}}} \right)*\left( {LE{i^{1/3}}} \right){\text{ *}}\left( {LV{i^{1/3}}} \right){\text{ (6)}}\) 4. Results and discussion 4.1 Landslide hazard index The landslide susceptibility map with InV scores (InV > 0) is considered to isolate the most unstable areas in the country (Fig. 1 ). This finding reinforces the previous research efforts indicating that a landslide is more likely to occur than usual when the InV value is greater than zero (positive) (Gyeltshen et al., 2024b ). The normalized Gewog level SUSCLi determined by computing the percentage of the municipality area with positive InV scores is shown in Fig. 3 . Further, the normalized EPSi by Gewog is shown in Fig. 4 . When the generalization of the LHi (Fig. 5 ) is carried out, maximum values (LHi > 0.60) of landslide susceptibility are registered in Gewogs located in the south, south-western and south-eastern regions of Bhutan. In these Gewogs, metasedimentary and palaeozoic rocks with luvisols and cambisols soils were predominantly found indicating medium to high landslide susceptibility which coincides with the study by Gyeltshen et al. (2024). The same region also records the highest total amount and extreme values of rainfall during the hydrological year, which frequently generates landslide disaster events (EPSi; Fig. 4 ). The dominant 10-year EPSi of each Gewog corresponds with higher LHi, indicating the spatial propensity to the occurrence of hydro-meteorological hazards, particularly landslides due to saturation and reduced shear strength during rainfall. The Gewogs located in east-central Bhutan also show higher LHi with Leucogranite, Orthogneiss, Miocene rocks, limestones, and low plasticity, recent alluvial, coarse-grained soil deposits as indicated by Gyeltshen et al. (2024). This result suggests that the influence of soil type has a direct relation with the subsequent rainfall triggering landslides in mountain landscapes. The maximum LHi (0.98) is recorded in Samphelling Gewog in Chhukha and the minimum LHi (0.03) is recorded in Lunana Gewog in Gasa. The percentage of Gewogs exposed to (LHi > 0.60) is 34.43% of the total Gewogs. Gewogs such as Yoeseltse, Ugyentse, Phuentshopelhri, Samtse, Tashicholing, Sangngagchholing, and Tading in Samtse, Tashiding, Sompangkha, Samtenling, Dekiling, Serzhong, Shompangkha, Tareythang, and Chhuzagang in Sarpang, Lhamoizingkha, Tsendagang, Trashiding, and Karmaling in Dagana, Barzhong in Tsirang, Yurung, Chhimung, Chongshing and Norbugang in Pemagatshel, Jurmey in Mongar, Sampheling, Phuentsholing, Logchina, Darla, and Samphelling in Chhukha, Ngangla in Zhemgang, Dewanthang, Pemathang, Phuentshothang, and Samrang in Samdrup Jongkhar have LHi (> 0.80) as compared to other Gewogs in Bhutan. All of these Gewogs also have recorded the highest number of roadblock events which indicates the prevalence of landslides along the road slope (Fig. 1 ). On the other hand, the lowest LHi (< 0.20) is found in Gewogs located in the northern, north-western, and west-central parts of the country, which corresponds to less than 30% of the total sets of 205 Gewogs, which is consistent with a low number of reported roadblock events. 4.2 Landslide exposure index Elements at risk were assessed as one of the core characteristics of exposure to see the conditions of elements or communities at risk. In Bhutan, agriculture is the mainstay of the economy as 16.52% of the national economy depends on agriculture, and about 57.20% of the Bhutanese population practices organic agriculture (Chhogyel & Kumar, 2018 ). It was found from the results that 36.21% and 41.08% of the total agricultural land is in high to very high exposure to the likelihood of landslide occurrence (Fig. 6 ). About 20.66% of agricultural land is moderately exposed, and only 2.06% of the agricultural land is in the category of very low exposure to the likelihood of landslide occurrence. In Bhutan, over 60% of the land has remained under forest cover thus meeting the commitment of Bhutan’s Constitution (Bruggeman et al., 2016 ). The forest is a main source of timber for house construction and wood for cooking and heating in winter for the local people and contributes equally to the country’s economy. Forest cover is a bioengineering solution for landslide hazards and soil erosion. The forests play important roles in reducing landslide risk through various mechanisms. Tree roots reinforce soil layers, anchor the soil to bedrock, and form buttresses against soil movement. Like agriculture, forest is also an important indicator in landslide vulnerability analysis which cannot be neglected. About 32.29% of the area under forest cover is highly exposed, 27.10% of the area is moderately exposed, 30.79% of the area is in low exposure category, and only 9.82% of the area under forest cover has very low exposure to the likelihood of landslide occurrence (Fig. 7 ). Table 3 Exposure of the principal elements (social and infrastructure) at risk. Exposure Class Bridges School/Education centers Health centers Settlements Historical monuments Overall percentage V High 160 283 178 41306 300 32.59 High 120 362 230 49263 675 39.10 Moderate 69 185 106 29461 608 23.49 Low 9 24 14 5433 169 4.36 V Low 4 1 5 569 17 0.46 Total 362 855 533 126032 1769 100.00 Table 4 Exposure of road infrastructure at risk (length in km). Exposure class Major Roads Other Roads Asian Highway (AH) Primary National Highway (PNH) Secondary National Highway (SNH) Dzongkhag Roads Thromde Roads Farm Roads Access Roads Very High 52.47 584.59 496.27 871.19 161.37 2563.16 197.80 High 66.49 600.69 351.72 1048.43 184.25 3913.92 242.70 Moderate 19.27 307.25 186.69 350.62 124.19 2251.45 369.57 Low 0.00 32.53 38.60 39.32 14.38 215.61 87.66 Very Low 0.00 0.00 0.00 1.33 0.13 5.15 0.41 Total 138.23 1525.06 1073.28 2310.89 484.33 8949.28 898.14 Similarly, built-up, settlements (no. of buildings), historical monuments, schools, bridges, and roads were also analyzed for exposure to landslide hazards. The results also suggest that, of the approximately (2700 sq.km) of built-up area in the country, 72.54% area falls under the moderate-to-high exposure category (Fig. 8 ). Out of 855 schools/education centers, 283 were found in very high, and 362 were found in high exposure zones, which make up 75% of the total schools categorized as highly exposed to landslide hazards (Fig. 9 ). There are a total of 533 health centers. Out of the total health centers, 178 in very high, 230 in high, and 106 in moderate exposure to the likelihood of landslide occurrence (Fig. 10 ). Of the total of 1769 historical monuments (Dzongs/Lhakhangs/Goenpa/Dratshang etc.), 300 were found in very high, 675 in high, and 608 in moderate exposure to landslides (Fig. 11 ). In Bhutan, there is a total of about 3000 km of road network in terms of length, out of which 2000 km falls in very high, 500 km in high, and 500 km in moderate exposure to landslide class (Fig. 12 and Fig. 13 ). Most of the roads follow the riverside, so the river lateral erosion and road construction itself pose a serious threat, whereas human intervention over the natural slope increases the vulnerability to landslide hazards. Similarly, bridges were also analyzed for landslide exposure. Out of 362 bridges, 160 bridges were very highly exposed to landslide, 120 were highly exposed, and 69 bridges were moderately exposed to the likelihood of landslide occurrence. Likewise, 9 bridges are located in a low-exposed area, and the remaining 4 bridges are in a zone of very low exposure to landslide hazards (Fig. 14 ). However, in this present study, two main exposure elements such as population and settlements were used for the assessment of the exposure index because population and economic activities are more concentrated in the district capitals and Gewog centers. Settlements include housing units, schools, health centers, historical monuments, and commercial units such as shops are considered. People living in these settlements and their vulnerability depend on the location of settlements, whether they are situated in hazard-free zones or hazard-prone areas. Figures 15 and 16 show the population density (PD, no. of inhabitants/km 2 ) and building density (BD, no. of buildings per Gewogs) normalized to the range [0, 1] using the max-min method respectively. Thus, the LEi map was prepared through weighted overlay analysis in a GIS environment using the element at-risk layers as shown in Fig. 17 . The high and very high (LEi > 0.60) exposure elements to landslide are mostly found in Gewgs with higher population and household density such as Phuentsholing (0.76) in Chhukha, Wangchang (0.73) in Paro, Bapisa (0.62) and Guma (0.64) in Punakha, Dewathang (0.60) in Samdrup Jongkhar, Gelegphu (0.71) in Sarpang, Kikhorthang (0.65) in Tsirang, and Thedtsho (0.73) in Wangdue Phodrang. In the analysis, it was also apparent that Thimphu Thromde shows the highest exposure index of 0.97, indicating that the region experiencing both urban and population growth demonstrating increased densification which aligns well with previous research in the region (Chhetri, 2023 ; Chhetri et al., 2024 ). The exposure variation is mainly characterized by the variation in the population inhabiting the area and the number of households in the Gewogs. 4.3 Landslide vulnerability index The indicator-based landslide vulnerability at the Gewog level is evaluated based on the physical vulnerability assessment of the built environment (estimated degree of loss of buildings) to landslide. The analysis considered building indicators or features such as CTCM, RS, NR, and TR (Table 2 ). The number of houses in each category of the resistance indicator was used for mapping the LVi of each Gewog. Figure 18 shows the building’s physical vulnerability of each Gewog computed as the weighted average of the aggregate vulnerabilities of the building features. The overall LVi value ranges from 0.02 to 0.98. The result shows that 54.8% of the Gewogs belong to the moderate to high category of vulnerability. About 41% of the gewogs have a low vulnerability index. Only 4% of the Gewogs (Phuentsholing in Chhukha, Saleng in Mongar, Dewathang in Samdrup Jongkhar, Tashicholing in Samtse, Gelegphu in Sarpang, Samkhar and Songphu in Trashigang, and Thimphu Thromde) have come to the very low category of vulnerability. These Gewogs have their houses have construction materials such as reinforced concrete with bricks or stone walls. Further, we observed that the Gewogs with (LVi > 0.6) have most of the houses constructed with a combination of stones, mud, wood, bamboo, planks, metal sheets, and tarpaulin. More than 40% of the houses have stone with mud wall materials. Most of the houses (90%) have metal sheet types of roofs. Overall, about 70% of the houses in the country are made without reinforced concrete. Only 30% of houses have reinforced concrete, about 37% of the houses have rooms greater than three, and negligible (1%) of the houses’ roofs are tarpaulin. The numbers and percentage of houses in the different categories of each resistant indicator are given in Table 5 . Table 5 The distribution of houses in the different categories of each resistant - indicator. Building feature/housing indicator (BF) Building feature class (BFC) Number of houses Percentage of houses Construction technique and construction materials (CTCM) Reinforced concrete 45823 28.11 Brick or stone walls 65298 40.06 Adobe 10236 6.28 Others 41644 25.55 Reinforced structure (RS) With reinforced structure 45823 28.11 Without reinforced structure 117178 71.89 Number of rooms (NR) 1 to 3 rooms 102579 62.93 > 3 rooms 60422 37.07 Type of roof (TR) Concrete 3200 1.96 Metal sheet 147468 90.47 Tiles/Slates 1160 0.71 Wood/planks/bamboo 7513 4.61 Thatch 1195 0.73 Tarpaulin 1465 0.90 Others 1000 0.61 The result also suggests that almost 55.8% of the houses belong to the high and very high category of vulnerability. About 19% of the houses have a moderate vulnerability index. Only 4% of the houses have come to low and very low categories of vulnerability. The spatial distribution of the household exposure classes to landslides is shown in Fig. 19 . 4.4 Landslide risk index The multiplication between hazard, exposure, and vulnerability indexes was used to determine the LRi. The hazard index, exposure index, and vulnerability index for each Gewog were combined through overlay analysis, and the final LRi map was prepared as shown in Fig. 20 . The results were classified into five groups: very high, high, moderate, low, and very low. The likelihood of landslides occurring and the potential for damage to elements at risk are high in both high and very-high landslide-risk zones, whereas the likelihood of landslides occurring and the potential for damage to elements at risk are low in both low or moderate-risk areas. According to Fig. 20 , in Bhutan, 19% of the total Gewogs fall in low-to very low-risk areas (LRi < 0.40), 33.5% of the Gewogs fall under the moderate category (LRi LRi 0.80). Most of the Gewogs with the highest LRi are located south, south-east, and south-western parts of the country. 4.5 Cluster analysis The strongest correlation, with a possible trend, is shown between LRi and the hazard as compared to exposure, and physical vulnerability of buildings (r 2 = 0.61), which indicates that the hazard dimension has the most contribution as shown in Fig. 21 . The Gewogs that belong to clusters 3 and 4 are predominantly the Gewogs that lie in the southern part of the country (Fig. 22 ). There are 65 Gewogs in cluster 3 with the highest average landslide hazard index (0.86), while cluster 4 is composed of 21 clusters presenting an average hazard index of 0.70. Gewogs in clusters 3 and 4 show similar average values of exposure and risk, but a different behaviour concerning the vulnerability of the buildings. Cluster 1 includes 95 Gewogs mostly located in the central part of the country characterized by average vulnerability and exposure index of 0.43 and 0.39 respectively. Cluster 5 includes 14 Gewogs located in the northern, and north-western mountains, and it is characterized by the high average vulnerability index (0.49), and the lowest average exposure index (0.20). Cluster 6 includes 2 Gewogs with the highest average vulnerability index (0.88) and the highest average landslide risk index (0.99), whereas Thimphu Thromde falls under cluster 7 with the highest average exposure index (0.99) and the lowest average vulnerability index (0.16). The building's physical vulnerability (clusters 1 and 5), exposure (clusters 2, 6, and 7), and hazard (clusters 3, and 4) all influence the likelihood of landslides in various Gewog groups. The detailed characteristics of the clusters of Gewogs and corresponding risk indices are presented in Table 6 . Table 6 Characteristics of the clusters of municipalities according to LHi, Lei, LVi, and LRi. Cluster No. of Gewogs/Thromde LVi LEi LHi LRi Min. Max. Mean Min. Max. Mean Min. Max. Mean Min. Max. Mean 1 95 0.26 0.70 0.43 0.23 0.58 0.39 0.00 0.42 0.18 0.17 0.64 0.46 2 8 0.00 0.43 0.24 0.37 0.73 0.58 0.14 0.32 0.23 0.00 0.63 0.44 3 65 0.31 0.63 0.45 0.17 0.54 0.40 0.43 0.98 0.70 0.56 0.92 0.73 4 21 0.12 0.37 0.28 0.36 0.76 0.53 0.72 0.97 0.86 0.63 0.81 0.73 5 14 0.30 0.65 0.49 0.05 0.31 0.20 0.00 0.18 0.06 0.12 0.46 0.28 6 2 0.77 0.98 0.88 0.47 0.60 0.53 0.65 0.69 0.67 0.97 0.98 0.99 7 1 0.16 0.16 0.16 0.99 0.98 0.99 0.36 0.36 0.36 0.58 0.58 0.58 5. Conclusion The present study discusses the landslide hazard, exposure, vulnerability, and risk indexes for every Gewog in Bhutan to assess local-level national landslide disaster risk. In this study, the selection of risk dimension i.e. the hazard is based on the previously published work about the national-level landslide susceptibility mapping. The landslide hazard assessment is based on two main interacting factors: (i) SUSCLi obtained from positive InV as a threshold; and (ii) EPSi calculated using 10-year dominant extreme rainfall events as a tiggering factor for landslides. On the other hand, exposure is very dependent on demographics and household variables. However, the physical vulnerability of buildings is exclusively used to express the vulnerability dimension. Through the development of LRi at a Gewog level, government/stakeholders should organize their efforts with future decisions in mind, as towns with high average values of hazard, the most static factor of LRi are susceptible to changes on the other dimensions. Planning tools should consider the adverse effects on LRi of policies that encourage the migration of people and economic activity to high-hazard areas. Regarding physical vulnerability, public policy should be cognizant of the fact that structures/buildings, especially those in historic districts and rural locations, are becoming more and more vulnerable as a result of deterioration and age. For instance, the above planning instruments must be considered for Gewogs of clusters 3, and 4 as these Gewogs already have a high level of hazard. Similarly, Gewogs belonging to clusters 2, 6, and 7 have high average values of exposure as these Gewogs superimpose to areas with high demographics and infrastructure, and Gewogs in clusters 1, and 5 have higher physical vulnerability of buildings. While the local-level LRi is a valuable contribution to the national and regional level decision-making, this study still possesses some limitations and assumptions. The hazard assessment was based on only positive InV and 10-year dominant extreme rainfall events of each Gewog. In the evaluation of the exposure dimension, the officially registered census population data ( PHCB 2017) is used instead of the dynamic dimension i.e. the de facto population which might not have given a more accurate picture of their current exposure level. Regarding landslide vulnerability, the building features such as construction technique and construction materials (CTCM); reinforced structure (RS); number of rooms (NR), and type of roof (TR) were considered, not taking into account the other attributes such as building age and foundation types due to unavailability of data. Despite the aforementioned limitations, the findings provide valuable information to government agencies/stakeholders for landslide risk management plans that balance the structural interventions that have been the most common interventions thus far with plans for the long-term mitigation of hazard, exposure, and building physical vulnerability. Moving forward, we aim to refine the exposure and vulnerability dimensions by considering more comprehensive indicators for their assessment. Further, we aim to develop a web-based landslide risk index decision support system to access real-time or near-real-time data from anywhere. These interactive maps and dashboards help the government, planners, or the public to visualize landslide-prone areas for better understanding and quick decision-making. Declarations Author Contributions : Mr. Indra Bahadur Chhetri conceived the core idea of the research, analyzed the spatial content, and wrote the manuscript. Mr. Mim Prasad Phuyel supported the cartographic design of all the maps. All the authors reviewed the manuscript and consented to the publication of this study. Funding details: The author declares that no funding has been provided for this research. Conflict of interest: The authors report that there are no competing interests to declare. Data Availability Declaration: The authors confirm that the data supporting the findings of this study shall be provided with a reasonable request from the corresponding author. References Baharvand S, Rahnamarad J, Soori S, Saadatkhah N (2020) Landslide susceptibility zoning in a catchment of Zagros Mountains using fuzzy logic and GIS. Environ Earth Sci 79:1–10 Barman J, Ali SS, Biswas B, Das J (2023) Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India. Nat Hazards Res 3(3):508–521 Bruggeman D, Meyfroidt P, Lambin EF (2016) Forest cover changes in Bhutan: Revisiting the forest transition. Appl Geogr 67:49–66. https://doi.org/https://doi.org/10.1016/j.apgeog.2015.11.019 Catani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2(4):329–342. https://doi.org/10.1007/s10346-005-0021-0 Cemiloglu A, Zhu L, Mohammednour AB, Azarafza M, Nanehkaran YA (2023) Landslide susceptibility assessment for Maragheh County, Iran, using the logistic regression algorithm. Land 12(7):1397 Chen T, Zhu L, Niu R, Trinder CJ, Peng L, Lei T (2020) Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. J Mt Sci 17(3):670–685 Chhetri IB, SPATIAL-TEMPORAL URBAN GROWTH AND LAND CONSUMPTION PATTERNS OF THIMPHU, BHUTAN USING MULTI-TEMPORAL SATELLITE IMAGES (2023) J Appl Eng Technol Manage, 3(1 SE-Articles), 32–38. https://doi.org/10.54417/jaetm.v3i1.107 Chhetri IB, Gyeltshen S, Dema K (2024) Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach. Environ Monit Assess 196(8). https://doi.org/10.1007/s10661-024-12882-x Chhogyel N, Kumar L (2018) Climate change and potential impacts on agriculture in Bhutan: A discussion of pertinent issues. Agric Food Secur 7(1):1–13. https://doi.org/10.1186/s40066-018-0229-6 Corominas J, Westen CJ, Frattini P, Cascini L, Jean-Philippe, Fotopoulou S, Catani F, Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter M, Pastor M, Ferlisi S, Tofani V, Hervàs J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8 Crutcher HL (1975) A Note on the Possible Misuse of the Kolmogorov-Smirnov Test. J Appl Meteorol (1962–1982) 14(8):1600–1603. http://www.jstor.org/stable/26177409 Dikshit A, Sarkar R, Pradhan B, Acharya S, Alamri AM (2020) Spatial landslide risk assessment at Phuentsholing. Bhutan Geosci 10(4):131 Dikshit A, Sarkar R, Pradhan B, Jena R, Drukpa D, Alamri AM (2020) Temporal probability assessment and its use in landslide susceptibility mapping for eastern Bhutan. Water 12(1):267 Emberson RA, Kirschbaum DB, Stanley T (2021) Landslide Hazard and Exposure Modelling in Data-Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh. Earth’s Future 9(2):1–22. https://doi.org/10.1029/2020EF001666 Filion GJ (2015) The signed Kolmogorov-Smirnov test: why it should not be used. GigaScience, 4(1), s13742-015-0048–7. https://doi.org/10.1186/s13742-015-0048-7 Garcia RAC, Oliveira SC, Zêzere JL (2016) Assessing population exposure for landslide risk analysis using dasymetric cartography. Nat Hazards Earth Syst Sci 16(12):2769–2782. https://doi.org/10.5194/nhess-16-2769-2016 Gyeltshen S, Chhetri IB, Dema K (2024a) Evaluation of statistical modeling (SM) approaches for landslide susceptibility mapping: geospatial insights for Bhutan. Environ Earth Sci 83(20). https://doi.org/10.1007/s12665-024-11897-4 Gyeltshen S, Chhetri IB, Dema K (2024b) Evaluation of statistical modeling (SM) approaches for landslide susceptibility mapping: geospatial insights for Bhutan. Environ Earth Sci 83(20):585. https://doi.org/10.1007/s12665-024-11897-4 Highland LM, Bobrowsky P (2008) The Landslide Handbook - A guide to understanding landslides. In US Geological Survey Circular (Issue 1325) Huang C-M, Lee C-T, Jian L-X, Wei L-W, Chu W-C, Lin H-H (2022) Using fuzzy neural networks to model landslide susceptibility at the Shihmen Reservoir catchment in Taiwan. Water 14(8):1196 Jin J, Chen G, Meng X, Zhang Y, Shi W, Li Y, Yang Y, Jiang W (2022) Prediction of river damming susceptibility by landslides based on a logistic regression model and InSAR techniques: A case study of the Bailong River Basin, China. Eng Geol 299:106562 Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855 Li L, Nahayo L, Habiyaremye G, Christophe M (2022) Applicability and performance of statistical index, certain factor and frequency ratio models in mapping landslides susceptibility in Rwanda. Geocarto Int 37(2):638–656 Lucchese LV, de Oliveira GG, Pedrollo OC (2021) Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping. Nat Hazards 106(3):2381–2405 Ministry of Health (2016) Health Emergency And Disaster Preparedness Plan Oleng M, Ozdemir Z, Pilakoutas K (2024) Co-seismic and rainfall-triggered landslide hazard susceptibility assessment for Uganda derived using fuzzy logic and geospatial modelling techniques. Natural Hazards, pp 1–34 Orejuela IP, Toulkeridis T (2020) Evaluation of the susceptibility to landslides through diffuse logic and analytical hierarchy process (AHP) between Macas and Riobamba in Central Ecuador. 2020 Seventh International Conference on EDemocracy & eGovernment (ICEDEG), 201–207 Ozdemir A (2020) A comparative study of the frequency ratio, analytical hierarchy process, artificial neural networks, and fuzzy logic methods for landslide susceptibility mapping: Taşkent (Konya), Turkey. Geotech Geol Eng 38:4129–4157 Panchal S, Shrivastava AK (2020) Application of analytic hierarchy process in landslide susceptibility mapping at regional scale in GIS environment. J Stat Manage Syst 23(2):199–206 Panchal S, Shrivastava AK (2022) Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India. Ain Shams Eng J 13(3):101626 Pasang S, Kubíček P (2020) Landslide susceptibility mapping using statistical methods along the asian highway, bhutan. Geosci (Switzerland) 10(11):1–26. https://doi.org/10.3390/geosciences10110430 Pereira S, Santos PP, Zêzere JL, Tavares AO, Garcia RAC, Oliveira SC (2020) A landslide risk index for municipal land use planning in Portugal. Sci Total Environ 735:139463. https://doi.org/10.1016/j.scitotenv.2020.139463 Rahman G, Bacha AS, Moazzam U, Rahman MF, Mahmood AU, Almohamad S, Al Dughairi H, Al-Mutiry AA, Alrasheedi M, M., Abdo HG (2022) Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush. Front Earth Sci 10(August):1–23. https://doi.org/10.3389/feart.2022.953627 Riegel RP, Alves DD, Schmidt BC, de Oliveira GG, Haetinger C, Osório DMM, Rodrigues MAS, de Quevedo DM (2020) Assessment of susceptibility to landslides through geographic information systems and the logistic regression model. Nat Hazards 103:497–511 Saha S, Sarkar R, Roy J, Hembram TK, Acharya S, Thapa G, Drukpa D (2021) Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms. Sci Rep 11(1):16374 Saha S, Sarkar R, Roy J, Saha TK, Bhardwaj D, Acharya S (2022) Predicting the landslide susceptibility using ensembles of bagging with RF and REPTree in Logchina, Bhutan. Impact of Climate Change, Land Use and Land Cover, and Socio-Economic Dynamics on Landslides, 275–298 Santos M, Fragoso M, Santos A, J (2017) Regionalization and susceptibility assessment to daily precipitation extremes in mainland Portugal. 86:128–138. https://doi.org/10.1016/j.apgeog.2017.06.020 Sarkar R, Saha S, Roy J, Bhardwaj D (2022) Measuring landslide susceptibility of Phuentsholling, Bhutan using novel ensemble machine learning methods. Impact of Climate Change, Land Use and Land Cover, and Socio-Economic Dynamics on Landslides, 197–220 Schneiderbauer S, Ehrlich D (2004) Risk, hazard and people’s vulnerability to Natural hazards: a review of definitions, concepts and data. European Commission Joint Research Centre, January, pp 01–40 Shahabi H, Ahmad B, Bin, Khezri S (2012) Application of satellite remote sensing for detailed landslide inventories using frequency ratio model and GIS. Int J Comput Sci 9:108–117 Silva M, Pereira S (2014) Assessment of physical vulnerability and potential losses of buildings due to shallow slides. Nat Hazards 72(2):1029–1050. https://doi.org/10.1007/s11069-014-1052-4 Singh A, Kanungo DP, Pal S (2019) Physical vulnerability assessment of buildings exposed to landslides in India. In Natural Hazards (Vol. 96, Issue 2). Springer Netherlands. https://doi.org/10.1007/s11069-018-03568-y Subasinghe CN, Kawasaki A (2021) Assessment of physical vulnerability of buildings and socio-economic vulnerability of residents to rainfall induced cut slope failures: A case study in central highlands, Sri Lanka. Int J Disaster Risk Reduct 65:102550. https://doi.org/10.1016/j.ijdrr.2021.102550 Sujatha ER, Sridhar V (2021) Landslide susceptibility analysis: A logistic regression model case study in Coonoor, India. Hydrology 8(1):41 Tang R-X, Yan E-C, Wen T, Yin X-M, Tang W (2021) Comparison of logistic regression, information value, and comprehensive evaluating model for landslide susceptibility mapping. Sustainability 13(7):3803 Tempa K, Peljor K, Wangdi S, Ghalley R, Jamtsho K, Ghalley S, Pradhan P (2021) UAV technique to localize landslide susceptibility and mitigation proposal: a case of Rinchending Goenpa landslide in Bhutan. Nat Hazards Res 1(4):171–186 Teshnizi ES, Golian M, Sadeghi S, Rastegarnia A (2022) Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran. Computers in Earth and Environmental Sciences. Elsevier, pp 55–95 Thambidurai P, Veerappan R, Beigh IH, Luitel KK (2023) Landslide susceptibility assessment using frequency ratio model in turung mamring, south district of Sikkim, India. Landslides: Detection, Prediction and Monitoring: Technological Developments. Springer, pp 285–305 Thongley T, Vansarochana C (2021a) Landslide Identification and Zonation Using the Index of Entropy Technique at Ossey Watershed Area in Bhutan. Appl Environ Res 43(1):102–115 Thongley T, Vansarochana C (2021b) Landslide susceptibility assessment using frequency ratio model at Ossey watershed area in Bhutan. Eng Appl Sci Res 48:56–64 1 SE-ORIGINAL RESEARCH). https://ph01.tci-thaijo.org/index.php/easr/article/view/240477 Venter ZS, Barton DN, Chakraborty T, Simensen T, Singh G (2022) Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sens 14(16). https://doi.org/10.3390/rs14164101 Wang Y, Nanehkaran YA (2024) GIS-based fuzzy logic technique for mapping landslide susceptibility analyzing in a coastal soft rock zone. Nat Hazards, 1–33 Wubalem A, Getahun B, Hailemariam Y, Mesele A, Tesfaw G, Dawit Z, Goshe E (2022) Landslide susceptibility modeling using the index of entropy and frequency ratio method from nefas-mewcha to weldiya road corridor, northwestern Ethiopia. Geotech Geol Eng 40(10):5249–5278 Wubalem A, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2:1–19 XU S, WANG LIUJ, ZHANG X, LIN Y, ZHANG R, LIU M, M., JIANG T (2020) Landslide susceptibility assessment method incorporating index of entropy based on support vector machine: A case study of Shaanxi Province. Geomatics Inform Sci Wuhan Univ 45(8):1214–1222 Zhao B, Ge Y, Chen H (2021) Landslide susceptibility assessment for a transmission line in Gansu Province, China by using a hybrid approach of fractal theory, information value, and random forest models. Environ Earth Sci 80(12):441 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6472965","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444495086,"identity":"5a71083e-6e6d-47d4-ae26-9e53b1885bdd","order_by":0,"name":"Indra Bahadur Chhetri","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0005-6864-2020","institution":"Jigme Namgyel Engineering College, Royal University of Bhutan","correspondingAuthor":true,"prefix":"","firstName":"Indra","middleName":"Bahadur","lastName":"Chhetri","suffix":""},{"id":444495087,"identity":"1d9bd88d-9d3e-4d0f-90cc-08512c4de510","order_by":1,"name":"Mim Prasad Phuyel","email":"","orcid":"","institution":"Jigme Namgyel Engineering College, Royal University of Bhutan","correspondingAuthor":false,"prefix":"","firstName":"Mim","middleName":"Prasad","lastName":"Phuyel","suffix":""}],"badges":[],"createdAt":"2025-04-17 15:09:01","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6472965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6472965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80868655,"identity":"c0e9cc0a-5b86-4081-b18a-826e5008aeb5","added_by":"auto","created_at":"2025-04-18 04:29:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3838319,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area showing Gewogs of Bhutan. The figure also shows the spatial distribution of the LANSLIDE \u0026nbsp;events recorded in the year 2020 and landslide-susceptible areas with positive information value (InV \u0026gt; 0).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/f0e90e664a915d85b682ada0.png"},{"id":80868621,"identity":"03df4d3a-c29c-4e83-9233-aeec618e39b8","added_by":"auto","created_at":"2025-04-18 04:29:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204513,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral approach and methodology framework.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/e6efa09aa7fe9f7b3ed158ce.png"},{"id":80868648,"identity":"364883ed-7af7-4f36-8627-dc463168e7bd","added_by":"auto","created_at":"2025-04-18 04:29:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2431479,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized SUSCLi by Gewog for Bhutan.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/2868ccbc188230f8eb80cea2.png"},{"id":80868631,"identity":"6c4c9f71-47a3-4e58-ada3-cd9faf1c188a","added_by":"auto","created_at":"2025-04-18 04:29:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3557888,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized 10-year dominant annual EPSi by Gewog for Bhutan.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/69485b264de897e2d6c44fb4.png"},{"id":80868673,"identity":"64f76467-5168-4f02-b89d-6dfdc2b64ec9","added_by":"auto","created_at":"2025-04-18 04:29:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3684443,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized LHi by Gewog for Bhutan.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/302b4d6414a76a5cc354fd64.png"},{"id":80868622,"identity":"d160c2bd-831c-4be6-be70-7cc31ee23006","added_by":"auto","created_at":"2025-04-18 04:29:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5267533,"visible":true,"origin":"","legend":"\u003cp\u003eAgricultural land exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/01668090acd6ce4421bc7d81.png"},{"id":80868675,"identity":"d6a4d7f3-8d79-4cfd-89f0-80648b6dfbfb","added_by":"auto","created_at":"2025-04-18 04:29:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":18624918,"visible":true,"origin":"","legend":"\u003cp\u003eForest area exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/68547673ace6290dc480e964.png"},{"id":80869407,"identity":"44fd208f-6270-4be5-913e-15ffb7d44375","added_by":"auto","created_at":"2025-04-18 04:45:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3409807,"visible":true,"origin":"","legend":"\u003cp\u003eBuilt-up area exposure to the likelihood of landslide occurrence\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/bcd9e4f84dd8d390959dd244.png"},{"id":80868628,"identity":"21bc41e3-c75d-438f-90b5-4c24563684ec","added_by":"auto","created_at":"2025-04-18 04:29:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":6444782,"visible":true,"origin":"","legend":"\u003cp\u003eSchool/education centers exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/cfc0a2c7b4c2817d5e0cbe71.png"},{"id":80868850,"identity":"e81108a6-1f3f-44cf-adf5-f527acb57a4c","added_by":"auto","created_at":"2025-04-18 04:37:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":6306148,"visible":true,"origin":"","legend":"\u003cp\u003eHealth centers exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/0df44a0506835630b13385af.png"},{"id":80868669,"identity":"de4afc95-4822-4685-9a6b-a88cedf9ab2f","added_by":"auto","created_at":"2025-04-18 04:29:02","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":6993957,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical monuments' exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/9c87887fc32b6da098ea7e62.png"},{"id":80868636,"identity":"3046cd71-09e6-4013-b1f3-beaba77d9856","added_by":"auto","created_at":"2025-04-18 04:29:01","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":24263825,"visible":true,"origin":"","legend":"\u003cp\u003eAsian Highway (AH), Primary National Highway (PNH), and Secondary National Highway (SNH), \u0026nbsp;exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/e3fbe311233f55e384c6c314.png"},{"id":80868857,"identity":"da73e73a-ad78-47fb-9018-6332a65afd5b","added_by":"auto","created_at":"2025-04-18 04:37:02","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":26110124,"visible":true,"origin":"","legend":"\u003cp\u003eDzongkhag roads, Thromde roads, Farm roads, and Access roads exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/c7c2339a0af8ca064a80ee82.png"},{"id":80868633,"identity":"8535abc7-933f-4ddc-a722-fe86361d5dcb","added_by":"auto","created_at":"2025-04-18 04:29:01","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":6612823,"visible":true,"origin":"","legend":"\u003cp\u003eBridges exposure to the likelihood of landslide occurrence.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/8d86754ad19bc52441f0db55.png"},{"id":80868856,"identity":"b8c76268-d713-40ed-9865-b832c4a5bb6d","added_by":"auto","created_at":"2025-04-18 04:37:02","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":3521409,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation density by Gewogs for Bhutan.\u003c/p\u003e","description":"","filename":"Figure15.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/86ac9006cfc198a874cad240.png"},{"id":80868690,"identity":"c5cdfe31-bce3-4e7c-9f42-fc299cac54a8","added_by":"auto","created_at":"2025-04-18 04:29:03","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":3520720,"visible":true,"origin":"","legend":"\u003cp\u003eHousehold density by Gewogs for Bhutan.\u003c/p\u003e","description":"","filename":"Figure16.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/41f982cd49864256ac44085a.png"},{"id":80868686,"identity":"5ede3a43-0d3a-4a6c-9254-3fec86e294d3","added_by":"auto","created_at":"2025-04-18 04:29:03","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":3657752,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized LEi by Gewog for Bhutan.\u003c/p\u003e","description":"","filename":"Figure17.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/852215f0943f3a18b7f27c01.png"},{"id":80868650,"identity":"850f6bf6-4268-4e13-8b03-411a59e8a48c","added_by":"auto","created_at":"2025-04-18 04:29:01","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":3750229,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized LVi by Gewog for Bhutan.\u003c/p\u003e","description":"","filename":"Figure18.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/e758f0f46681d86c53ee6f6c.png"},{"id":80869408,"identity":"0209ddcc-d9c5-4501-9073-9148230493b5","added_by":"auto","created_at":"2025-04-18 04:45:01","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":10294953,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the households and their exposure classes to a landslide in Bhutan.\u003c/p\u003e","description":"","filename":"Figure19.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/c79de4c5e6ae7d960e489679.png"},{"id":80868859,"identity":"96d50f66-c1c9-49a1-8f61-10a9afccbf8c","added_by":"auto","created_at":"2025-04-18 04:37:02","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":3664139,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized LRi by Gewog for Bhutan.\u003c/p\u003e","description":"","filename":"Figure20.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/88eaaf15b506efc3c193a8b5.png"},{"id":80868867,"identity":"4b0cea47-ab2c-49c9-ae45-593a8a1491bc","added_by":"auto","created_at":"2025-04-18 04:37:03","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":192877,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot showing the correlation of LRi with LHi, LEi, and LVi.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/fb2573a4be3de1a7b02ea713.png"},{"id":80868660,"identity":"3ad5b001-b6b8-43b9-aa59-298a85523da3","added_by":"auto","created_at":"2025-04-18 04:29:02","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":2820837,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram showing the cluster of Gewogs according to their LHi, LEi, and LVi.\u003c/p\u003e","description":"","filename":"Figure22.png","url":"https://assets-eu.researchsquare.com/files/rs-6472965/v1/1fb035ad4607dff4c3079c73.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA National Level Landslide Risk Index for Land Use Planning in Bhutan: Towards Assessing Landslide Hazard, Exposure, and Vulnerability Indexes\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe term \u0026ldquo;landslides\u0026rdquo; refers to the downward and outward movement of slope-forming materials such as a mass of rock, earth, debris, or a combination of these (Highland \u0026amp; Bobrowsky, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). They are categorized based on the type of slope movement (fall, topple, spread, flow, slide, etc.), the material involved (rock, soil, debris, etc.), and the speed of movement. On the other hand, landslide risk is a measure of the probability and severity of an adverse effect on the property, or the environment as a result of landslides (Corominas et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pasang \u0026amp; Kub\u0026iacute;ček, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Generally, there are three dimensions of landslide risk: hazard, exposure, and vulnerability which must be specifically considered that can help understand the spatial distribution and trends of landslide disasters (Corominas et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Schneiderbauer \u0026amp; Ehrlich (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) define a hazard as \u0026ldquo;a potentially damaging physical event, phenomenon and/or human activity, which may cause loss of life or injury, property damage, social and economic disruption or environmental degradation\u0026rdquo;. The landslide hazard is a function of susceptibility (spatial propensity to landslide activity) and temporal frequency of landslide triggers, i.e. the probability of occurrence. This means that for landslides, geophysical methods of assessment at a very local level become less practical considering the spatial scales (in terms of area and volume) and the temporal frequency (Emberson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, to ascertain risk to society, infrastructure, and the environment, risk analysts often delineate where the geographic footprints of such elements intersect with a defined hazard footprint. This will help represent the proportion of the value (lives, buildings, and environment) of the element expected to be lost as a result of the hazard to obtain the estimated risk. Consequently, landslide exposure is the relationship between past evidence of landslides and the spatial arrangement of exposed elements that best characterize such a relationship (Emberson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, landslide vulnerability is the quantitative evaluation of vulnerability components (physical exposure and social characteristics) to identify the elements at risk. To understand the landslide risk i.e probability of occurrence or expected degree of loss, a multi-level analysis is required due to the range of potentially exposed elements (e.g., buildings, roads, people, and environment), or elements located in hazardous zones that are consequently prone to possible losses, and their different characteristics (Garcia et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Globally, 67,052 deaths were reported due to landslides and Asia alone has reported a total of 25,604 deaths which is about 38.2% between 1910 to 2020 (Thongley \u0026amp; Vansarochana, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). In Bhutan, a total of 1665 landslide events have been identified constituting 0.10% of the total geographical area of Bhutan (3,730.22 km\u003csup\u003e2\u003c/sup\u003e out of 38,394 km\u003csup\u003e2\u003c/sup\u003e). Over 380 individuals died and about 87,000 people were affected by natural disasters in Bhutan between 1994 and 2016 and the number is only expected to rise further (Ministry of Health, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The landslides in Bhutan are mainly rainfall-induced and further aggravated by the toe-cutting of slopes along the road corridor and deforestation emphasizing infrastructure development (Dikshit, Sarkar, Pradhan, Acharya, et al., 2020; Pasang \u0026amp; Kub\u0026iacute;ček, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWorldwide, several methodologies and models have been studied for prediction and susceptibility mapping. Some of the past research and the frequently used models are frequency ratio (FR) (Lee \u0026amp; Sambath, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shahabi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thambidurai et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the weight of evidence (WoE), information value (InV) (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wubalem \u0026amp; Meten, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), fuzzy neural network (FNN) (Huang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lucchese et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ozdemir, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), index of entropy (IoE) (Barman et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wubalem et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; XU et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), analytical hierarchy process (AHP) (Orejuela \u0026amp; Toulkeridis, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Panchal \u0026amp; Shrivastava, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Teshnizi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), logistic regression (LR) (Cemiloglu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jin et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Riegel et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sujatha \u0026amp; Sridhar, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and fuzzy logic (FL) (Baharvand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oleng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang \u0026amp; Nanehkaran, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, in Bhutan, all researchers have conducted studies regarding susceptibility mapping considering the small pockets of an area rather than across the entire nation (Dikshit, Sarkar, Pradhan, Acharya, et al., 2020; Dikshit, Sarkar, Pradhan, Jena, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pasang \u0026amp; Kub\u0026iacute;ček, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Saha et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sarkar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tempa et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thongley \u0026amp; Vansarochana, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). In their study, no researchers have conducted studies to explore the possible impact of landslides on humans, property, infrastructure, and the environment at a Gewog (smallest territorial units) level in the country making their assessment either generalized or incomplete. The present study would allow accessing social, infrastructure, and environmental exposure to landslides at a Gewog level across the entire nation. Further, the study will also propose a landslide risk index (LRi) for Bhutan at a Gewog level. This Gewog level landslide risk index correlates to the scale of strategic risk management and is a crucial step in defining the proper public policy measures for land use planning and civil protection management to mitigate disaster risk and reduce future losses. Furthermore, adaption methods for disaster risk reduction should concentrate on lowering long-term exposure and vulnerability of people and assets in addition to structural protection efforts and hazardous process mitigation. In this context, this study will fulfil the objectives at the Gewog level in the following key areas:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAccess landslide risk using a dataset of variables that allow characterizing three dimensions of landslide risk: hazard, exposure, and vulnerability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePropose LRi for Bhutan at a Gewog level using three dimensions of landslide risk: landslide hazard index (LHi), landslide exposure index (LEi), and landslide vulnerability index (LVi).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe elements (agricultural land, settlement/built-up, forest, schools, health centres, historical monuments, roads, and bridges) at risk are studied based on landslide susceptibility zones, and their degree of exposure is explored through the geographic information systems (GIS).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePerform a cluster analysis and rank the Gewog landslide risk, identifying landslide risk profiles for different groups.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"2. Study area","content":"\u003cp\u003eStudy area, Bhutan is a mountainous nation sandwiched between China in the north and India to the south, spanning 38,394 km\u003csup\u003e2\u003c/sup\u003e with challenging topography and steep mountains. Bhutan is located in the greater Himalayan range, one of the seismically active zones, particularly zones IV and V. Bhutan has just over 700,000 population, with more than 70% of people living in rural areas, hence there is a high chance of disaster. There are a total of 20 dzongkhags (districts) and 205 Gewogs in Bhutan. The region experiences an annual average temperature ranging from 12.30 -22.54\u0026deg;C with summer monsoon spanning from June to September, bringing an average annual rainfall of more than 1685.20 mm. The southern and central Bhutan is particularly vulnerable to landslides due to its distinct topography, steep mountainous terrain, monsoon-dominated climate, and growing development activity (e.g. road construction). Landslides are predicted to occur more frequently and with greater severity as climate change exacerbates rainfall variability and extreme weather events. These facts motivated the researchers to contribute original insights and develop a localized decision support system through improved landslide hazard mapping for Bhutan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cp\u003eThe methodology encompasses several sequential steps, including data collection, processing, and construction of LHi, LEi, LVi, and LRi including cluster analysis and cross-impact evaluation among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eThe data from various primary and secondary sources were collected to meet the required objectives of the study. As a mandatory part of the study, the spatial data of landslide susceptibility and the land use/land cover (LULC) data of the study area were adopted from previous research. The population and household data were acquired from the Bhutan Statistical Database System of the National Statistics Bureau. The vector geodatabase of settlement/building and administrative units was obtained from the National Land Commission Secretariate (NLCS). The shapefiles of the road network were acquired from the Department of Surface Transport, Ministry of Infrastructure and Transport (MoIT). Similarly, the rainfall data were acquired from Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS). The input data used in the assessment of the landslide risk dimensions, adjusted to the gewog level are documented and presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \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\u003eDataset used in the study and their sources.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdopted/Acquired from\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcquisition date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021 land use/land cover (LULC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Venter et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlobal 10 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInV landslide susceptibility map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Gyeltshen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation and household data, Population and Housing Census of Bhutan (PHCB, 2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBhutan Statistical Database System, National Statistics Bureau (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nsb.gov.bt/\u003c/span\u003e\u003cspan address=\"https://www.nsb.gov.bt/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25/08/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement and administrative unit data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Land Commission Secretariate (NLCS), Bhutan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHIRPS Daily: Climate Hazards Center InfraRed Precipitation with Station data (Version 2.0 Final)\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthengine.google.com/\u003c/span\u003e\u003cspan address=\"https://earthengine.google.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/03/2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlobal 5000 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad network and bridge location data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepartment of Surface Transport, (MoIT), Bhutan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Landslide hazard index\u003c/h2\u003e \u003cp\u003eA landslide hazard is an expected probability of occurrence of a potentially damaging landslide of a given intensity that takes place in a certain area within a given time (Catani et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This, essentially, means that landslide hazard assessment procedures must take into account both space and time prediction. In this study, the landslide susceptibility index (SUSCLi) and the extreme weather index as an indicator of the spatial and temporal dimension of hazard are taken into account in the landslide hazard assessment at the Gewog scale. A Landslide susceptibility from the national landslide susceptibility model (based on InV) obtained using geomorphological landslide inventory (1665 landslides) was adopted from the previous study by Gyeltshen et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). For this study, the most unstable regions in the study area were selected based on positive InV scores (InV\u0026thinsp;\u0026gt;\u0026thinsp;0) from the national landslide susceptibility model. Subsequently, the SUSCLi of each Gewog is determined by computing the percentage of the municipality area with positive InV scores (InV\u0026thinsp;\u0026gt;\u0026thinsp;0). Further, landslide-triggering conditions due to rainfall are used in the hazard model by computing the 10-year extreme precipitation susceptibility index (EPSi) proposed by Santos et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) aggregated by Gewogs. Further, both SUSCLi and EPSi are used as a proxy for the temporal component of the landslide hazard normalized to adjust to the interval [0, 1] using Eq.\u0026nbsp;(1).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(x_{{i,norm}}^{m}=\\frac{{x_{i}^{m} - {x_{i,\\hbox{min} }}}}{{x_{{i,\\hbox{max} }}^{m} - {x_{i,\\hbox{min} }}}}{\\text{ (1)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{i}^{m}\\)\u003c/span\u003e\u003c/span\u003eis the data of the \u003cem\u003em-th\u003c/em\u003e Gewogs from \u003cem\u003ei-th\u003c/em\u003e indicator\u0026rsquo;s dataset; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x_{i,\\hbox{min} }}\\)\u003c/span\u003e\u003c/span\u003eis the minimum value of the \u003cem\u003ei-th\u003c/em\u003e indicator\u0026rsquo;s dataset; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{{i,\\hbox{max} }}^{m}\\)\u003c/span\u003e\u003c/span\u003eis the maximum value for \u003cem\u003ei-th\u003c/em\u003e indicator\u0026rsquo;s dataset; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{{i,norm}}^{m}\\)\u003c/span\u003e\u003c/span\u003eis the normalized data or the \u003cem\u003em-th\u003c/em\u003e Gewog from \u003cem\u003ei-th\u003c/em\u003e indicator\u0026rsquo;s dataset. Now, the LHi for each Gewog is computed further using Eq.\u0026nbsp;(2) proposed by Pereira et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(LHi=0.75 * SUSCLi+0.25*EPSi{\\text{ (2)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Landslide exposure index\u003c/h2\u003e \u003cp\u003eThe exposure elements are expressed into three themes: social, infrastructure, and environmental. The social and infrastructure theme includes elements such as population, settlement (no. of buildings), historical monuments, schools, bridges, and roads at risk. The environmental theme considered elements such as agricultural land, built-up, and forests as elements at risk for vulnerability assessment. The exposure maps were prepared for each element at risk using the landslide hazard (susceptibility) zones to measure the level of exposure of these elements explored through GIS. Each element at risk is categorized into five exposure groups such as very high, high, moderate, low, and very low exposure based on the susceptibility zones that each element falls. However, in this study, the assessment of the exposure index to landslides at the Gewog level is mainly based on two main exposure elements: population and buildings (settlements), expressed using population density (PD) and number of buildings (NB). This is mainly because, the country is characterized by the concentration of population and economic activities in the district capitals and along the southern, western, and central districts, which is satisfactorily represented by the population density (PD, no. of inhabitants/km\u003csup\u003e2\u003c/sup\u003e) and the number of buildings per Gewog.\u003c/p\u003e \u003cp\u003eThe Kolmogorov Smirnov (K\u0026ndash;S) (Crutcher, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Filion, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) test was applied to check the best-fit probability distribution of population density and the number of buildings per municipality. Both distributions are then best fitted to an exponential distribution using a \u003cem\u003eln\u003c/em\u003e transformation to obtain a distribution closer to normal. The results were further normalized to the range [0, 1] using the max-min method, using Eq.\u0026nbsp;(1). Then the landslide exposure index at a Gewog level was computed using the same weight for both variables using Eq.\u0026nbsp;(3).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(LEi=PD * 0.5+NB*0.5{\\text{ (3)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Landslide vulnerability index\u003c/h2\u003e \u003cp\u003eLandslide vulnerability was assessed by evaluating elements at risk and the probability of loss due to landslides. In this study, the physical vulnerability assessment of the infrastructure (buildings) of the built environment at a Gewog level was considered based on the building features such as construction technique and construction materials (CTCM); reinforced structure (RS); number of rooms (NR), and type of roof (TR). Each building feature is further divided into a set of building feature classes (BFC) and its weightage is adopted as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The information about the construction materials and other associated information are gathered from the same population and housing census data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The categorization of the feature classes is based on indigenous knowledge and expert opinions. For example, CTCM is categorized into four main classes from highest to lowest resistance to landslide: reinforced concrete, brick or stone walls, adobe, and other materials (e.g. wood and bamboo). The reinforced concrete also provides additional resistance to the buildings against landslides. Similarly, the number of floors is a proxy variable of the number of people living in the building, because the higher the number of rooms, the higher the number of people living and the higher the vulnerability. Similarly, the type of roof materials is used as indicators to calculate the resistance of the buildings against landslide hazards. For this study, the building feature and the corresponding building feature classes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBuilding features and corresponding building feature class vulnerability scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding feature (BF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilding feature class (BFC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVulnerability score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdopted from\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eConstruction technique and construction materials (CTCM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforced concrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silva \u0026amp; Pereira, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrick or stone walls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReinforced structure (RS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith reinforced structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silva \u0026amp; Pereira, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout reinforced structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of rooms (NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 to 3 rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silva \u0026amp; Pereira, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Subasinghe \u0026amp; Kawasaki, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3 rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eType of roof (TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e(Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silva \u0026amp; Pereira, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Subasinghe \u0026amp; Kawasaki, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetal sheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiles/slates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWood/planks/bamboo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarpaulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\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\u003eThe aggregate vulnerability of each building feature (AVBF) is computed for each Gewog using Eq.\u0026nbsp;(4).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(AVBF=\\sum {VBFC/NB} {\\text{ (4)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eWhere AVBF is a score representing the aggregate vulnerability of a particular building feature (e.g. construction material), VBFC is the vulnerability score of a building feature class \u003cem\u003ex\u003c/em\u003e (see scores in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) multiplied by the number of buildings within the building feature class \u003cem\u003ex\u003c/em\u003e, and NB is the total number of buildings in each Gewog.\u003c/p\u003e \u003cp\u003eNow, the LVi of each Gewog is computed based on the weighted average of the aggregate vulnerabilities of the building feature using Eq.\u0026nbsp;(5), and the LVi layer is normalized to adjust to the interval [0, 1] using Eq.\u0026nbsp;(1).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(LVi=\\left( {CTCM * 0.6} \\right){\\text{ +}}\\left( {RS * 0.2} \\right){\\text{ +}}\\left( {NR * 0.2} \\right){\\text{ (5)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Landslide risk index\u003c/h2\u003e \u003cp\u003eLandslide risk is defined as the product of the landslide hazard, the vulnerability, and the exposure (Dikshit, Sarkar, Pradhan, Acharya, et al., 2020; Pereira et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The LRi is computed based on the combination of the preceding parameters following a numerical approach using Eq.\u0026nbsp;(6).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(LRi=\\left( {LH{i^{1/3}}} \\right)*\\left( {LE{i^{1/3}}} \\right){\\text{ *}}\\left( {LV{i^{1/3}}} \\right){\\text{ (6)}}\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Landslide hazard index\u003c/h2\u003e \u003cp\u003eThe landslide susceptibility map with InV scores (InV\u0026thinsp;\u0026gt;\u0026thinsp;0) is considered to isolate the most unstable areas in the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This finding reinforces the previous research efforts indicating that a landslide is more likely to occur than usual when the InV value is greater than zero (positive) (Gyeltshen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). The normalized Gewog level SUSCLi determined by computing the percentage of the municipality area with positive InV scores is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Further, the normalized EPSi by Gewog is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. When the generalization of the LHi (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) is carried out, maximum values (LHi\u0026thinsp;\u0026gt;\u0026thinsp;0.60) of landslide susceptibility are registered in Gewogs located in the south, south-western and south-eastern regions of Bhutan. In these Gewogs, metasedimentary and palaeozoic rocks with luvisols and cambisols soils were predominantly found indicating medium to high landslide susceptibility which coincides with the study by Gyeltshen et al. (2024). The same region also records the highest total amount and extreme values of rainfall during the hydrological year, which frequently generates landslide disaster events (EPSi; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The dominant 10-year EPSi of each Gewog corresponds with higher LHi, indicating the spatial propensity to the occurrence of hydro-meteorological hazards, particularly landslides due to saturation and reduced shear strength during rainfall. The Gewogs located in east-central Bhutan also show higher LHi with Leucogranite, Orthogneiss, Miocene rocks, limestones, and low plasticity, recent alluvial, coarse-grained soil deposits as indicated by Gyeltshen et al. (2024). This result suggests that the influence of soil type has a direct relation with the subsequent rainfall triggering landslides in mountain landscapes. The maximum LHi (0.98) is recorded in Samphelling Gewog in Chhukha and the minimum LHi (0.03) is recorded in Lunana Gewog in Gasa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe percentage of Gewogs exposed to (LHi\u0026thinsp;\u0026gt;\u0026thinsp;0.60) is 34.43% of the total Gewogs. Gewogs such as Yoeseltse, Ugyentse, Phuentshopelhri, Samtse, Tashicholing, Sangngagchholing, and Tading in Samtse, Tashiding, Sompangkha, Samtenling, Dekiling, Serzhong, Shompangkha, Tareythang, and Chhuzagang in Sarpang, Lhamoizingkha, Tsendagang, Trashiding, and Karmaling in Dagana, Barzhong in Tsirang, Yurung, Chhimung, Chongshing and Norbugang in Pemagatshel, Jurmey in Mongar, Sampheling, Phuentsholing, Logchina, Darla, and Samphelling in Chhukha, Ngangla in Zhemgang, Dewanthang, Pemathang, Phuentshothang, and Samrang in Samdrup Jongkhar have LHi (\u0026gt;\u0026thinsp;0.80) as compared to other Gewogs in Bhutan. All of these Gewogs also have recorded the highest number of roadblock events which indicates the prevalence of landslides along the road slope (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other hand, the lowest LHi (\u0026lt;\u0026thinsp;0.20) is found in Gewogs located in the northern, north-western, and west-central parts of the country, which corresponds to less than 30% of the total sets of 205 Gewogs, which is consistent with a low number of reported roadblock events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Landslide exposure index\u003c/h2\u003e \u003cp\u003eElements at risk were assessed as one of the core characteristics of exposure to see the conditions of elements or communities at risk. In Bhutan, agriculture is the mainstay of the economy as 16.52% of the national economy depends on agriculture, and about 57.20% of the Bhutanese population practices organic agriculture (Chhogyel \u0026amp; Kumar, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It was found from the results that 36.21% and 41.08% of the total agricultural land is in high to very high exposure to the likelihood of landslide occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). About 20.66% of agricultural land is moderately exposed, and only 2.06% of the agricultural land is in the category of very low exposure to the likelihood of landslide occurrence. In Bhutan, over 60% of the land has remained under forest cover thus meeting the commitment of Bhutan\u0026rsquo;s Constitution (Bruggeman et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The forest is a main source of timber for house construction and wood for cooking and heating in winter for the local people and contributes equally to the country\u0026rsquo;s economy. Forest cover is a bioengineering solution for landslide hazards and soil erosion. The forests play important roles in reducing landslide risk through various mechanisms. Tree roots reinforce soil layers, anchor the soil to bedrock, and form buttresses against soil movement. Like agriculture, forest is also an important indicator in landslide vulnerability analysis which cannot be neglected. About 32.29% of the area under forest cover is highly exposed, 27.10% of the area is moderately exposed, 30.79% of the area is in low exposure category, and only 9.82% of the area under forest cover has very low exposure to the likelihood of landslide occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExposure of the principal elements (social and infrastructure) at risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBridges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchool/Education centers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealth centers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSettlements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHistorical monuments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOverall percentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e362\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e855\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e533\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e126032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1769\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100.00\u003c/b\u003e\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExposure of road infrastructure at risk (length in km).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMajor Roads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eOther Roads\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian Highway (AH)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary National Highway (PNH)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecondary National Highway (SNH)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDzongkhag Roads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThromde Roads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFarm Roads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccess Roads\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVery High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e584.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e496.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e871.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e161.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2563.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e197.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e600.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e351.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1048.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e184.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3913.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e242.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e350.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e124.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2251.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e369.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e215.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVery Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e138.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1525.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1073.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2310.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e484.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8949.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e898.14\u003c/b\u003e\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\u003eSimilarly, built-up, settlements (no. of buildings), historical monuments, schools, bridges, and roads were also analyzed for exposure to landslide hazards. The results also suggest that, of the approximately (2700 sq.km) of built-up area in the country, 72.54% area falls under the moderate-to-high exposure category (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOut of 855 schools/education centers, 283 were found in very high, and 362 were found in high exposure zones, which make up 75% of the total schools categorized as highly exposed to landslide hazards (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). There are a total of 533 health centers. Out of the total health centers, 178 in very high, 230 in high, and 106 in moderate exposure to the likelihood of landslide occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Of the total of 1769 historical monuments (Dzongs/Lhakhangs/Goenpa/Dratshang etc.), 300 were found in very high, 675 in high, and 608 in moderate exposure to landslides (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). In Bhutan, there is a total of about 3000 km of road network in terms of length, out of which 2000 km falls in very high, 500 km in high, and 500 km in moderate exposure to landslide class (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). Most of the roads follow the riverside, so the river lateral erosion and road construction itself pose a serious threat, whereas human intervention over the natural slope increases the vulnerability to landslide hazards. Similarly, bridges were also analyzed for landslide exposure. Out of 362 bridges, 160 bridges were very highly exposed to landslide, 120 were highly exposed, and 69 bridges were moderately exposed to the likelihood of landslide occurrence. Likewise, 9 bridges are located in a low-exposed area, and the remaining 4 bridges are in a zone of very low exposure to landslide hazards (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, in this present study, two main exposure elements such as population and settlements were used for the assessment of the exposure index because population and economic activities are more concentrated in the district capitals and Gewog centers. Settlements include housing units, schools, health centers, historical monuments, and commercial units such as shops are considered. People living in these settlements and their vulnerability depend on the location of settlements, whether they are situated in hazard-free zones or hazard-prone areas. Figures\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e and \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e show the population density (PD, no. of inhabitants/km\u003csup\u003e2\u003c/sup\u003e) and building density (BD, no. of buildings per Gewogs) normalized to the range [0, 1] using the max-min method respectively. Thus, the LEi map was prepared through weighted overlay analysis in a GIS environment using the element at-risk layers as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e. The high and very high (LEi\u0026thinsp;\u0026gt;\u0026thinsp;0.60) exposure elements to landslide are mostly found in Gewgs with higher population and household density such as Phuentsholing (0.76) in Chhukha, Wangchang (0.73) in Paro, Bapisa (0.62) and Guma (0.64) in Punakha, Dewathang (0.60) in Samdrup Jongkhar, Gelegphu (0.71) in Sarpang, Kikhorthang (0.65) in Tsirang, and Thedtsho (0.73) in Wangdue Phodrang. In the analysis, it was also apparent that Thimphu Thromde shows the highest exposure index of 0.97, indicating that the region experiencing both urban and population growth demonstrating increased densification which aligns well with previous research in the region (Chhetri, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chhetri et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The exposure variation is mainly characterized by the variation in the population inhabiting the area and the number of households in the Gewogs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Landslide vulnerability index\u003c/h2\u003e \u003cp\u003eThe indicator-based landslide vulnerability at the Gewog level is evaluated based on the physical vulnerability assessment of the built environment (estimated degree of loss of buildings) to landslide. The analysis considered building indicators or features such as CTCM, RS, NR, and TR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of houses in each category of the resistance indicator was used for mapping the LVi of each Gewog. Figure\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e shows the building\u0026rsquo;s physical vulnerability of each Gewog computed as the weighted average of the aggregate vulnerabilities of the building features. The overall LVi value ranges from 0.02 to 0.98. The result shows that 54.8% of the Gewogs belong to the moderate to high category of vulnerability. About 41% of the gewogs have a low vulnerability index. Only 4% of the Gewogs (Phuentsholing in Chhukha, Saleng in Mongar, Dewathang in Samdrup Jongkhar, Tashicholing in Samtse, Gelegphu in Sarpang, Samkhar and Songphu in Trashigang, and Thimphu Thromde) have come to the very low category of vulnerability. These Gewogs have their houses have construction materials such as reinforced concrete with bricks or stone walls. Further, we observed that the Gewogs with (LVi\u0026thinsp;\u0026gt;\u0026thinsp;0.6) have most of the houses constructed with a combination of stones, mud, wood, bamboo, planks, metal sheets, and tarpaulin. More than 40% of the houses have stone with mud wall materials. Most of the houses (90%) have metal sheet types of roofs. Overall, about 70% of the houses in the country are made without reinforced concrete. Only 30% of houses have reinforced concrete, about 37% of the houses have rooms greater than three, and negligible (1%) of the houses\u0026rsquo; roofs are tarpaulin. The numbers and percentage of houses in the different categories of each resistant indicator are given in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution of houses in the different categories of each resistant - indicator.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding feature/housing indicator (BF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilding feature class (BFC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of houses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage of houses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eConstruction technique and construction materials (CTCM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReinforced concrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrick or stone walls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReinforced structure (RS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith reinforced structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout reinforced structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of rooms (NR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 to 3 rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3 rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eType of roof (TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcrete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetal sheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiles/Slates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWood/planks/bamboo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarpaulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\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\u003eThe result also suggests that almost 55.8% of the houses belong to the high and very high category of vulnerability. About 19% of the houses have a moderate vulnerability index. Only 4% of the houses have come to low and very low categories of vulnerability. The spatial distribution of the household exposure classes to landslides is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e19\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Landslide risk index\u003c/h2\u003e \u003cp\u003eThe multiplication between hazard, exposure, and vulnerability indexes was used to determine the LRi. The hazard index, exposure index, and vulnerability index for each Gewog were combined through overlay analysis, and the final LRi map was prepared as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e20\u003c/span\u003e. The results were classified into five groups: very high, high, moderate, low, and very low. The likelihood of landslides occurring and the potential for damage to elements at risk are high in both high and very-high landslide-risk zones, whereas the likelihood of landslides occurring and the potential for damage to elements at risk are low in both low or moderate-risk areas. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e20\u003c/span\u003e, in Bhutan, 19% of the total Gewogs fall in low-to very low-risk areas (LRi\u0026thinsp;\u0026lt;\u0026thinsp;0.40), 33.5% of the Gewogs fall under the moderate category (LRi\u0026thinsp;\u0026lt;\u0026thinsp;0.60), while the majority (40%) of the Gewogs is in a high landslide risk zone (0.60\u0026thinsp;\u0026gt;\u0026thinsp;LRi\u0026thinsp;\u0026lt;\u0026thinsp;0.80). The remaining 7.5% of the Gewogs is at very high landslide risk zone (LRi\u0026thinsp;\u0026gt;\u0026thinsp;0.80). Most of the Gewogs with the highest LRi are located south, south-east, and south-western parts of the country.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Cluster analysis\u003c/h2\u003e \u003cp\u003eThe strongest correlation, with a possible trend, is shown between LRi and the hazard as compared to exposure, and physical vulnerability of buildings (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.61), which indicates that the hazard dimension has the most contribution as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e21\u003c/span\u003e. The Gewogs that belong to clusters 3 and 4 are predominantly the Gewogs that lie in the southern part of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e22\u003c/span\u003e). There are 65 Gewogs in cluster 3 with the highest average landslide hazard index (0.86), while cluster 4 is composed of 21 clusters presenting an average hazard index of 0.70. Gewogs in clusters 3 and 4 show similar average values of exposure and risk, but a different behaviour concerning the vulnerability of the buildings. Cluster 1 includes 95 Gewogs mostly located in the central part of the country characterized by average vulnerability and exposure index of 0.43 and 0.39 respectively. Cluster 5 includes 14 Gewogs located in the northern, and north-western mountains, and it is characterized by the high average vulnerability index (0.49), and the lowest average exposure index (0.20). Cluster 6 includes 2 Gewogs with the highest average vulnerability index (0.88) and the highest average landslide risk index (0.99), whereas Thimphu Thromde falls under cluster 7 with the highest average exposure index (0.99) and the lowest average vulnerability index (0.16). The building's physical vulnerability (clusters 1 and 5), exposure (clusters 2, 6, and 7), and hazard (clusters 3, and 4) all influence the likelihood of landslides in various Gewog groups. The detailed characteristics of the clusters of Gewogs and corresponding risk indices are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the clusters of municipalities according to LHi, Lei, LVi, and LRi.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of Gewogs/Thromde\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eLVi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eLEi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eLHi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eLRi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.58\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\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study discusses the landslide hazard, exposure, vulnerability, and risk indexes for every Gewog in Bhutan to assess local-level national landslide disaster risk. In this study, the selection of risk dimension i.e. the hazard is based on the previously published work about the national-level landslide susceptibility mapping. The landslide hazard assessment is based on two main interacting factors: (i) SUSCLi obtained from positive InV as a threshold; and (ii) EPSi calculated using 10-year dominant extreme rainfall events as a tiggering factor for landslides. On the other hand, exposure is very dependent on demographics and household variables. However, the physical vulnerability of buildings is exclusively used to express the vulnerability dimension. Through the development of LRi at a Gewog level, government/stakeholders should organize their efforts with future decisions in mind, as towns with high average values of hazard, the most static factor of LRi are susceptible to changes on the other dimensions. Planning tools should consider the adverse effects on LRi of policies that encourage the migration of people and economic activity to high-hazard areas. Regarding physical vulnerability, public policy should be cognizant of the fact that structures/buildings, especially those in historic districts and rural locations, are becoming more and more vulnerable as a result of deterioration and age. For instance, the above planning instruments must be considered for Gewogs of clusters 3, and 4 as these Gewogs already have a high level of hazard. Similarly, Gewogs belonging to clusters 2, 6, and 7 have high average values of exposure as these Gewogs superimpose to areas with high demographics and infrastructure, and Gewogs in clusters 1, and 5 have higher physical vulnerability of buildings. While the local-level LRi is a valuable contribution to the national and regional level decision-making, this study still possesses some limitations and assumptions.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe hazard assessment was based on only positive InV and 10-year dominant extreme rainfall events of each Gewog.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn the evaluation of the exposure dimension, the officially registered census population data ( PHCB 2017) is used instead of the dynamic dimension i.e. the de facto population which might not have given a more accurate picture of their current exposure level.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRegarding landslide vulnerability, the building features such as construction technique and construction materials (CTCM); reinforced structure (RS); number of rooms (NR), and type of roof (TR) were considered, not taking into account the other attributes such as building age and foundation types due to unavailability of data.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDespite the aforementioned limitations, the findings provide valuable information to government agencies/stakeholders for landslide risk management plans that balance the structural interventions that have been the most common interventions thus far with plans for the long-term mitigation of hazard, exposure, and building physical vulnerability. Moving forward, we aim to refine the exposure and vulnerability dimensions by considering more comprehensive indicators for their assessment. Further, we aim to develop a web-based landslide risk index decision support system to access real-time or near-real-time data from anywhere. These interactive maps and dashboards help the government, planners, or the public to visualize landslide-prone areas for better understanding and quick decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: Mr. Indra Bahadur Chhetri conceived the core idea of the research, analyzed the spatial content, and wrote the manuscript. Mr. Mim Prasad Phuyel supported the cartographic design of all the maps. All the authors reviewed the manuscript and consented to the publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding details:\u0026nbsp;\u003c/strong\u003eThe author declares that no funding has been provided for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors report that there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Declaration:\u0026nbsp;\u003c/strong\u003eThe authors confirm that the data supporting the findings of this study shall be provided with a reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaharvand S, Rahnamarad J, Soori S, Saadatkhah N (2020) Landslide susceptibility zoning in a catchment of Zagros Mountains using fuzzy logic and GIS. Environ Earth Sci 79:1\u0026ndash;10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarman J, Ali SS, Biswas B, Das J (2023) Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India. Nat Hazards Res 3(3):508\u0026ndash;521\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruggeman D, Meyfroidt P, Lambin EF (2016) Forest cover changes in Bhutan: Revisiting the forest transition. Appl Geogr 67:49\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.apgeog.2015.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeog.2015.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatani F, Casagli N, Ermini L, Righini G, Menduni G (2005) Landslide hazard and risk mapping at catchment scale in the Arno River basin. Landslides 2(4):329\u0026ndash;342. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-005-0021-0\u003c/span\u003e\u003cspan address=\"10.1007/s10346-005-0021-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCemiloglu A, Zhu L, Mohammednour AB, Azarafza M, Nanehkaran YA (2023) Landslide susceptibility assessment for Maragheh County, Iran, using the logistic regression algorithm. Land 12(7):1397\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Zhu L, Niu R, Trinder CJ, Peng L, Lei T (2020) Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. J Mt Sci 17(3):670\u0026ndash;685\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChhetri IB, SPATIAL-TEMPORAL URBAN GROWTH AND LAND CONSUMPTION PATTERNS OF THIMPHU, BHUTAN USING MULTI-TEMPORAL SATELLITE IMAGES (2023) J Appl Eng Technol Manage, 3(1 SE-Articles), 32\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54417/jaetm.v3i1.107\u003c/span\u003e\u003cspan address=\"10.54417/jaetm.v3i1.107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChhetri IB, Gyeltshen S, Dema K (2024) Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach. Environ Monit Assess 196(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-024-12882-x\u003c/span\u003e\u003cspan address=\"10.1007/s10661-024-12882-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChhogyel N, Kumar L (2018) Climate change and potential impacts on agriculture in Bhutan: A discussion of pertinent issues. Agric Food Secur 7(1):1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40066-018-0229-6\u003c/span\u003e\u003cspan address=\"10.1186/s40066-018-0229-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorominas J, Westen CJ, Frattini P, Cascini L, Jean-Philippe, Fotopoulou S, Catani F, Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter M, Pastor M, Ferlisi S, Tofani V, Herv\u0026agrave;s J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209\u0026ndash;263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10064-013-0538-8\u003c/span\u003e\u003cspan address=\"10.1007/s10064-013-0538-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrutcher HL (1975) A Note on the Possible Misuse of the Kolmogorov-Smirnov Test. J Appl Meteorol (1962\u0026ndash;1982) 14(8):1600\u0026ndash;1603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jstor.org/stable/26177409\u003c/span\u003e\u003cspan address=\"http://www.jstor.org/stable/26177409\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDikshit A, Sarkar R, Pradhan B, Acharya S, Alamri AM (2020) Spatial landslide risk assessment at Phuentsholing. Bhutan Geosci 10(4):131\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDikshit A, Sarkar R, Pradhan B, Jena R, Drukpa D, Alamri AM (2020) Temporal probability assessment and its use in landslide susceptibility mapping for eastern Bhutan. Water 12(1):267\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmberson RA, Kirschbaum DB, Stanley T (2021) Landslide Hazard and Exposure Modelling in Data-Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh. Earth\u0026rsquo;s Future 9(2):1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020EF001666\u003c/span\u003e\u003cspan address=\"10.1029/2020EF001666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilion GJ (2015) The signed Kolmogorov-Smirnov test: why it should not be used. GigaScience, 4(1), s13742-015-0048\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13742-015-0048-7\u003c/span\u003e\u003cspan address=\"10.1186/s13742-015-0048-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia RAC, Oliveira SC, Z\u0026ecirc;zere JL (2016) Assessing population exposure for landslide risk analysis using dasymetric cartography. Nat Hazards Earth Syst Sci 16(12):2769\u0026ndash;2782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-16-2769-2016\u003c/span\u003e\u003cspan address=\"10.5194/nhess-16-2769-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGyeltshen S, Chhetri IB, Dema K (2024a) Evaluation of statistical modeling (SM) approaches for landslide susceptibility mapping: geospatial insights for Bhutan. Environ Earth Sci 83(20). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-024-11897-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-024-11897-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGyeltshen S, Chhetri IB, Dema K (2024b) Evaluation of statistical modeling (SM) approaches for landslide susceptibility mapping: geospatial insights for Bhutan. Environ Earth Sci 83(20):585. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-024-11897-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-024-11897-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHighland LM, Bobrowsky P (2008) The Landslide Handbook - A guide to understanding landslides. In US Geological Survey Circular (Issue 1325)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang C-M, Lee C-T, Jian L-X, Wei L-W, Chu W-C, Lin H-H (2022) Using fuzzy neural networks to model landslide susceptibility at the Shihmen Reservoir catchment in Taiwan. Water 14(8):1196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin J, Chen G, Meng X, Zhang Y, Shi W, Li Y, Yang Y, Jiang W (2022) Prediction of river damming susceptibility by landslides based on a logistic regression model and InSAR techniques: A case study of the Bailong River Basin, China. Eng Geol 299:106562\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847\u0026ndash;855\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Nahayo L, Habiyaremye G, Christophe M (2022) Applicability and performance of statistical index, certain factor and frequency ratio models in mapping landslides susceptibility in Rwanda. Geocarto Int 37(2):638\u0026ndash;656\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucchese LV, de Oliveira GG, Pedrollo OC (2021) Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping. Nat Hazards 106(3):2381\u0026ndash;2405\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health (2016) Health Emergency And Disaster Preparedness Plan\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOleng M, Ozdemir Z, Pilakoutas K (2024) Co-seismic and rainfall-triggered landslide hazard susceptibility assessment for Uganda derived using fuzzy logic and geospatial modelling techniques. Natural Hazards, pp 1\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrejuela IP, Toulkeridis T (2020) Evaluation of the susceptibility to landslides through diffuse logic and analytical hierarchy process (AHP) between Macas and Riobamba in Central Ecuador. 2020 Seventh International Conference on EDemocracy \u0026amp; eGovernment (ICEDEG), 201\u0026ndash;207\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzdemir A (2020) A comparative study of the frequency ratio, analytical hierarchy process, artificial neural networks, and fuzzy logic methods for landslide susceptibility mapping: Taşkent (Konya), Turkey. Geotech Geol Eng 38:4129\u0026ndash;4157\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanchal S, Shrivastava AK (2020) Application of analytic hierarchy process in landslide susceptibility mapping at regional scale in GIS environment. J Stat Manage Syst 23(2):199\u0026ndash;206\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanchal S, Shrivastava AK (2022) Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India. Ain Shams Eng J 13(3):101626\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasang S, Kub\u0026iacute;ček P (2020) Landslide susceptibility mapping using statistical methods along the asian highway, bhutan. Geosci (Switzerland) 10(11):1\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/geosciences10110430\u003c/span\u003e\u003cspan address=\"10.3390/geosciences10110430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira S, Santos PP, Z\u0026ecirc;zere JL, Tavares AO, Garcia RAC, Oliveira SC (2020) A landslide risk index for municipal land use planning in Portugal. Sci Total Environ 735:139463. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.139463\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2020.139463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman G, Bacha AS, Moazzam U, Rahman MF, Mahmood AU, Almohamad S, Al Dughairi H, Al-Mutiry AA, Alrasheedi M, M., Abdo HG (2022) Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush. Front Earth Sci 10(August):1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2022.953627\u003c/span\u003e\u003cspan address=\"10.3389/feart.2022.953627\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiegel RP, Alves DD, Schmidt BC, de Oliveira GG, Haetinger C, Os\u0026oacute;rio DMM, Rodrigues MAS, de Quevedo DM (2020) Assessment of susceptibility to landslides through geographic information systems and the logistic regression model. Nat Hazards 103:497\u0026ndash;511\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha S, Sarkar R, Roy J, Hembram TK, Acharya S, Thapa G, Drukpa D (2021) Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms. Sci Rep 11(1):16374\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha S, Sarkar R, Roy J, Saha TK, Bhardwaj D, Acharya S (2022) Predicting the landslide susceptibility using ensembles of bagging with RF and REPTree in Logchina, Bhutan. Impact of Climate Change, Land Use and Land Cover, and Socio-Economic Dynamics on Landslides, 275\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos M, Fragoso M, Santos A, J (2017) Regionalization and susceptibility assessment to daily precipitation extremes in mainland Portugal. 86:128\u0026ndash;138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apgeog.2017.06.020\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeog.2017.06.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar R, Saha S, Roy J, Bhardwaj D (2022) Measuring landslide susceptibility of Phuentsholling, Bhutan using novel ensemble machine learning methods. Impact of Climate Change, Land Use and Land Cover, and Socio-Economic Dynamics on Landslides, 197\u0026ndash;220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneiderbauer S, Ehrlich D (2004) Risk, hazard and people\u0026rsquo;s vulnerability to Natural hazards: a review of definitions, concepts and data. European Commission Joint Research Centre, January, pp 01\u0026ndash;40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahabi H, Ahmad B, Bin, Khezri S (2012) Application of satellite remote sensing for detailed landslide inventories using frequency ratio model and GIS. Int J Comput Sci 9:108\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva M, Pereira S (2014) Assessment of physical vulnerability and potential losses of buildings due to shallow slides. Nat Hazards 72(2):1029\u0026ndash;1050. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-014-1052-4\u003c/span\u003e\u003cspan address=\"10.1007/s11069-014-1052-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Kanungo DP, Pal S (2019) Physical vulnerability assessment of buildings exposed to landslides in India. In Natural Hazards (Vol. 96, Issue 2). Springer Netherlands. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-018-03568-y\u003c/span\u003e\u003cspan address=\"10.1007/s11069-018-03568-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubasinghe CN, Kawasaki A (2021) Assessment of physical vulnerability of buildings and socio-economic vulnerability of residents to rainfall induced cut slope failures: A case study in central highlands, Sri Lanka. Int J Disaster Risk Reduct 65:102550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijdrr.2021.102550\u003c/span\u003e\u003cspan address=\"10.1016/j.ijdrr.2021.102550\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSujatha ER, Sridhar V (2021) Landslide susceptibility analysis: A logistic regression model case study in Coonoor, India. Hydrology 8(1):41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang R-X, Yan E-C, Wen T, Yin X-M, Tang W (2021) Comparison of logistic regression, information value, and comprehensive evaluating model for landslide susceptibility mapping. Sustainability 13(7):3803\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTempa K, Peljor K, Wangdi S, Ghalley R, Jamtsho K, Ghalley S, Pradhan P (2021) UAV technique to localize landslide susceptibility and mitigation proposal: a case of Rinchending Goenpa landslide in Bhutan. Nat Hazards Res 1(4):171\u0026ndash;186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeshnizi ES, Golian M, Sadeghi S, Rastegarnia A (2022) Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran. Computers in Earth and Environmental Sciences. Elsevier, pp 55\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThambidurai P, Veerappan R, Beigh IH, Luitel KK (2023) Landslide susceptibility assessment using frequency ratio model in turung mamring, south district of Sikkim, India. Landslides: Detection, Prediction and Monitoring: Technological Developments. Springer, pp 285\u0026ndash;305\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThongley T, Vansarochana C (2021a) Landslide Identification and Zonation Using the Index of Entropy Technique at Ossey Watershed Area in Bhutan. Appl Environ Res 43(1):102\u0026ndash;115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThongley T, Vansarochana C (2021b) Landslide susceptibility assessment using frequency ratio model at Ossey watershed area in Bhutan. Eng Appl Sci Res 48:56\u0026ndash;64 1 SE-ORIGINAL RESEARCH). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ph01.tci-thaijo.org/index.php/easr/article/view/240477\u003c/span\u003e\u003cspan address=\"https://ph01.tci-thaijo.org/index.php/easr/article/view/240477\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenter ZS, Barton DN, Chakraborty T, Simensen T, Singh G (2022) Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sens 14(16). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs14164101\u003c/span\u003e\u003cspan address=\"10.3390/rs14164101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Nanehkaran YA (2024) GIS-based fuzzy logic technique for mapping landslide susceptibility analyzing in a coastal soft rock zone. Nat Hazards, 1\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWubalem A, Getahun B, Hailemariam Y, Mesele A, Tesfaw G, Dawit Z, Goshe E (2022) Landslide susceptibility modeling using the index of entropy and frequency ratio method from nefas-mewcha to weldiya road corridor, northwestern Ethiopia. Geotech Geol Eng 40(10):5249\u0026ndash;5278\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWubalem A, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2:1\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXU S, WANG LIUJ, ZHANG X, LIN Y, ZHANG R, LIU M, M., JIANG T (2020) Landslide susceptibility assessment method incorporating index of entropy based on support vector machine: A case study of Shaanxi Province. Geomatics Inform Sci Wuhan Univ 45(8):1214\u0026ndash;1222\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao B, Ge Y, Chen H (2021) Landslide susceptibility assessment for a transmission line in Gansu Province, China by using a hybrid approach of fractal theory, information value, and random forest models. Environ Earth Sci 80(12):441\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Landslide, landslide hazard index, landslide exposure index, landslide vulnerability index, landslide risk index, Bhutan","lastPublishedDoi":"10.21203/rs.3.rs-6472965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6472965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn Bhutan, landslides are a common natural hazard posing a greater impact on human settlements, infrastructure, and the environment. However, studies on the landslide risk to understand these impacts at a Gewog (smallest territorial units) level are limited. This study proposes an indicator-based approach to assessing the three risk dimensions; hazard, exposure, and physical vulnerability of buildings. The hazard component integrates a national landslide susceptibility index and an extreme precipitation susceptibility index. Exposure is assessed through population and building density, while vulnerability is determined by construction features such as construction technique and materials; number of rooms, and type of roofing, all weighted empirically. The final landslide risk index is derived by multiplying these risk dimensions. Cluster analysis further identifies key risk drivers across Gewogs. Results indicate that 47.5% of Gewogs (96) are at high to very high landslide risk, while only 19% (41) are at low to very low risk. High-risk areas are often rural Gewogs with dense populations and structurally vulnerable buildings. Additionally, 56% of houses nationwide fall into high or very high vulnerability categories. This integrative, localized risk assessment supports more targeted and context-sensitive landslide risk management strategies and offers a model adaptable to other regions for improved disaster risk reduction and land use planning.\u003c/p\u003e","manuscriptTitle":"A National Level Landslide Risk Index for Land Use Planning in Bhutan: Towards Assessing Landslide Hazard, Exposure, and Vulnerability Indexes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 04:28:55","doi":"10.21203/rs.3.rs-6472965/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c611902a-8edb-4d97-b58b-4983c898dfb2","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47324936,"name":"Environmental Engineering"},{"id":47324937,"name":"Climatology"},{"id":47324938,"name":"Civil Engineering"},{"id":47324939,"name":"Geographic Information Systems"}],"tags":[],"updatedAt":"2025-04-18T04:28:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-18 04:28:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6472965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6472965","identity":"rs-6472965","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}