Climate-change Induced Multi-hazard Risk Assessment of Himachal Pradesh in Western Himalayan Region Using IPCC-AR6 Framework and Multi-Attribute Decision-Making Approach

Climate-change Induced Multi-hazard Risk Assessment of Himachal Pradesh in Western Himalayan Region Using IPCC-AR6 Framework and Multi-Attribute Decision-Making Approach | 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 Climate-change Induced Multi-hazard Risk Assessment of Himachal Pradesh in Western Himalayan Region Using IPCC-AR6 Framework and Multi-Attribute Decision-Making Approach Shibu DAS, SANJEEV SHARMA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8565899/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 This study develops a composite multi-hazard risk index using 84 indicators across three components, i.e., hazard, vulnerability, and exposure. These indicators were selected based on the IPCC-AR6 Framework, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-Attribute Decision-Making (MADM) approach. Unlike earlier studies, present study uniquely applies this framework to the Western Himalayan state of Himachal Pradesh to measure climate-induced multi-hazard risk; identify spatially heterogeneous risk factors, and propose region-specific policy strategies. The findings of the study revealed significant inter-district spatial variation in risk levels. Five northern and northeastern districts, i.e., Lahaul-Spiti, Kullu, Kangra, Solan, and Kinnaur, exhibited high-risk status (> 0.375), while Chamba, Mandi, and Hamirpur showed moderate risk (0.200–0.375). The remaining districts, of the state including Una, Bilaspur, and Sirmaur, were categorised as low risk (< 0.200). One-way ANOVA identified 18 significant indicators with spatial differences: seven under hazard ( e.g., cold wave days, snowfall, lightning, extreme temperature, elevation ) and 11 under vulnerability and exposure ( e.g. , disability prevalence, Scheduled Caste population, health and education infrastructure, agricultural dependence, mobile access, population growth). The study also incorporates primary field investigation to validate the secondary findings. The results provide a robust evidence base for policymakers to formulate targeted climate risk mitigation strategies. This integrated approach offers a scalable model for assessing multi-hazard risks in other climate-vulnerable regions of the Himalaya. Himachal Pradesh IPCC-AR6 Framework Multi-Attribute Decision-Making Approach Multi-hazard Risk One-way ANOVA Western Himalayan region Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction In the 21st century, climate change has become a most important and burning issue throughout the world. Researchers from across the globe are dealing with different dimensions of climate change actively (Houghton & Woodwell, 2017 ; Jones et al., 2022 ; LI et al., 2021 ; Mikhaylov et al., 2020 ; Nashier & Lakra, 2020 ; North, 2014 ; Shah & Malakar, 2024 ). Based on the IPCC sixth assessment report, climate change is causing more landslides, floods, and other catastrophes in the world's mountainous regions (Chettri et al., 2018 ; Shah & Malakar, 2024 ). The Himalayas are a prime example of these warnings (Intergovernmental Panel on Climate Change (IPCC), 2021 ; Rusk et al., 2022 ). In order to explain the societal issues of climate change, the significance of risk, vulnerability, and adaptive capacity has been brought up more than once in recent years (Cantwell-Chavez, 2023 ; Etongo et al., 2022 ; Kumar et al., 2020 ; Thangjam et al., 2024 ; Thomas et al., 2019 ). Risk is calculated by multiplying the likelihood that harmful effects or trends will occur by the consequences of occurrence (Thangjam et al., 2024 ). This analysis adhered to the 2014 Intergovernmental Panel on Climate Change (IPCC) climate risk framework. Natural disasters, ecosystem structure, biodiversity, water availability, and ecological processes have all suffered as a result of climate change in the Highlands (Kumar et al., 2020 ; Thangjam et al., 2024 ). There are many scholars deals with climate change-induced vulnerability in different parts of Indian Himalayan region including eastern Himalaya (Banerjee et al., 2022 ; Bhadwal et al., 2019 ; Chettri et al., 2018 ; Debnath et al., 2024 ; Kaushik et al., 2024 ; Sharma et al., 2023 ); in Hindukush Himalaya (Dilshad et al., 2019 ; Elalem & Pal, 2015 ; Goodrich et al., 2019 ; Khalid et al., 2021 ; Rahman et al., 2022 ; Ren & Shrestha, 2017 ; Rusk et al., 2022 ); in Western Himalaya (Jha et al., 2022 ; Kumar et al., 2019 ; Pandey et al., 2022 ; Thakur et al., 2020 ; Upgupta et al., 2015 ); in entire Indian Himalayan region several scholars measure different aspects of vulnerability (Aggarwal & Saha, 2023 ; Alam et al., 2022 ; Sultan et al., 2022 ). Based on its usability and practicality, the paper constructs a risk index comprising three components, i.e. , hazard, vulnerability, and exposure—using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Attribute Decision-Making (MADM) approach. Majority of the studies earlier conducted in the Indian Himalayan Region have adopted Geographically Weighted Principal Component Analysis (GWPCA), and Analytical Hierarchy Process (AHP) to measure the vulnerability and risk (Roy, Bose, & Chowdhury, 2021 ; Roy, Bose, Singha, et al., 2021 ; Roy et al., 2022 ; A. Shah & Malakar, 2024 ; Shukla et al., 2016a ). However, because TOPSIS can handle a large number of criteria and alternatives, is logical and can be modified, relies less on subjective inputs, and consistently ranks the alternatives, it is advised over other MADM approaches (Shah & Malakar, 2024 ; Yadav et al., 2019 ). Several scholars adopted this approach to measure the risk index in different parts of the world, i.e., Shah & Malakar ( 2024 ) used this approach to measure the risk index in the entire Himalayan districts of India; Thangjam et al. ( 2024 ) measured risk index in the eastern part of the Himalayan region; Malakar et al. (2021) using this methodology to measure the risk index in the coastal region of India; Mondal et al. ( 2022 ) used this approach to measure the rural livelihood risk due to hydro-meteorological extreme events in the Indian Sundarban Biosphere Reserve; Zang et al. ( 2024 ) measured the flood risk assessment of the coastal cities in Shenzhen. However, no previous studies have considered the IPCC-AR6 Framework, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-Attribute Decision-Making (MADM) approach to measure multi-hazard risk in the Western Himalayan region, particularly in the state of Himachal Pradesh. The IPCC-AR6 Framework, integrated with TOPSIS and MADM, provides a comprehensive multi-hazard risk assessment by incorporating hazard, exposure, and vulnerability dimensions, unlike IPCC-AR4, which focused solely on vulnerability. By using quantitative decision-making tools like TOPSIS and MADM, the AR6-based approach ensures objective, indicator-based prioritization of risks, offering greater accuracy and policy relevance compared to the more descriptive and limited scope of the IPCC-AR4 framework. To fill the existing research gap, the present study makes a novel attempt to: (1) measure climate-change-induced multi-hazard risk using the IPCC-AR6 framework in Himachal Pradesh; (2) to identify the key spatially heterogeneous factors influencing risk assessment; and (3) to propose region-specific policy recommendations to mitigate the adverse effects of multi-hazard risk in the study area. The second section of the study deals with conceptual framework of the study, section three stated the central hypothesis of this study followed by a section deals with study area, fifth section deals with database and methodology, sixth section deals with results, seventh section deals with hypothesis testing, next section deals with discussion of the study, and reminder of the article deals with conclusion and policy recommendation. 2. Conceptual Framework The conceptual framework of this study is grounded in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC-AR6), which reconceptualizes climate risk as a function of three interrelated components: hazard, vulnerability, and exposure (Fig. 1 ). Anchored in this tripartite model, the present research constructs a composite Climate-Change-Induced Multi-Hazard Risk Index (CCHMRI) tailored to the Western Himalayan state of Himachal Pradesh. The framework integrates physical and socio-economic dimensions to capture climate-induced risks holistically across diverse terrains and districts. It operationalizes ‘84’ indicators across three dimensions—‘19’ for hazards ( e.g. , elevation, slope, cold wave days, precipitation variability), ‘57’ for vulnerability (subdivided into sensitivity and adaptive capacity indicators such as percentage of SC/ST population, disability prevalence, female literacy, and access to health infrastructure), and ‘8’ for exposure ( e.g. , population density, agricultural land use, and road density). The study employs the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under the Multi-Attribute Decision-Making (MADM) framework to generate normalized, weighted scores for each district under each risk component. These scores are then synthesized into a single risk index using the mathematical relationship R = H × V × E, where risk is a product of hazard (H), vulnerability (V), and exposure (E). The final risk values are standardized (scaled from 0 to 1) for inter-district comparability and categorized into high, moderate, and low-risk zones. The framework is validated through both secondary data analysis and primary field observations across vulnerable districts. Additionally, One-way ANOVA is applied to identify spatially heterogeneous risk determinants. The framework’s novelty lies in its unique integration of the IPCC-AR6 model with quantitative decision-making tools (TOPSIS and MADM) to provide an objective, scalable, and replicable method for multi-hazard risk assessment. Unlike, earlier studies in the Indian Himalayan Region primarily relied on Geographically Weighted Principal Component Analysis (GWPCA) or Analytical Hierarchy Process (AHP). The approach used in the present study reduces subjective bias and enhances policy relevance through clear risk ranking and indicator-based diagnostics. The framework thus offers a decision-support tool for policymakers to formulate targeted, evidence-based, and district-specific interventions aimed at climate risk reduction, adaptive capacity enhancement, and sustainable development planning in Himachal Pradesh and other similar mountainous regions globally. 3. Hypothesis H1 A higher probability of hazard occurrence is positively associated with an increase in the standardized multi-hazard risk score. 4. Study area The state of Himachal Pradesh occupies 55673 Square Kms and is situated in the northwest region of the Himalayas (Singh & Kumar, 2014 ). The geographical boundaries of this state lies between 75 0 45'55" east and 79 0 04'20" east and between 30 0 22'44" north and 33 0 12'40" north (Singh & Kumar, 2014 ). The state has twelve districts (Fig. 2 ). The hill state of Himachal Pradesh has a wide range of elevations ranges from 350 to 6975 meters ASL, from plains to the summits of mountains (Lokesh & Singh, 2014 ; Sharma, 2005 ; Singh & Kumar, 2014 ). Significant differences in temperature, rainfall, soil, and vegetation are caused by shifting elevations and aspects. This state is home to five majors Rivers System: the Satluj, Beas, Chenab, Yamuna, and Ravi. The main source of livelihood and economy is agriculture and directly employs about 62.85% of the state's principal workforce (Indian census, 2011). Another source of revenue for residents of the state is religious and adventure tourism (Singh & Kumar, 2014 ). The northern part of the state is relatively sparsely populated, while the southern plains are densely populated. Active plate tectonic edges and altered climate conditions make the state vulnerable to a range of natural disasters, such as landslides, earthquakes, flash floods, avalanches, glacial lake outburst floods (GLOFs), etc. 5. Database and Methodology 5.1. Database: The study is based on secondary data sources from different published reports and articles in the referred journals. The Himachal Pradesh Vulnerability Atlas, 2016 was used to get the data related to multi-hazard events, i.e. , earthquakes, landslides, and people with disability at risk. The Climate Hazard and Vulnerability Atlas of India was used to get data regarding cold waves, heat waves, floods, and snowfall. Hazard Atlas of India, 2022 was used to get the data regarding drought, fog, extreme wind speed, hailstorm, lightning flashes per km 2 , and thunderstorm. Different types of climatic variables related to extreme maximum temperature, extreme minimum temperature, average annual mean temperature, average annual cloud coverage from 1981 to 2022, and precipitation variability were collected from MEERA-2 and NASA Power data. Elevation and slope-related data were collected from the SRTM DEM and USGS Earth Explorer. To measure the sensitivity, adaptive capacity, and exposure, demographic-related variables, and agricultural variables were collected from the Census of India, 2011, and Statistical Abstract of Himachal Pradesh, 2021-22. To validate the secondary results, authors have visited the different vulnerable sites of Himachal Pradesh to present pictorial view of the events. 5.2. Methodology: The three components of risk—hazard, vulnerability (which is further subdivided into sensitivity and adaptive capacity), and exposure—are used to construct the risk index in this study. A composite score is then obtained by adding up these indicators. The study's methodology is shown as a flow chart in Fig. 3 , and the processes involved are explained in the subsections that follow. 5.2.1. Risk indicators: To calculate the risk index, three components of risk, i.e. , hazard, vulnerability (which is further subdivided into two categories, i.e., sensitivity, and adaptive capacity), and exposure, are considered in this study. A flow chart was used to visualize the adopted methodology (Fig. 3 ), and the processes involved are explained in the subsections that follow. This study uses the most recent sixth IPCC assessment report framework, which views risk as a consequence of three elements: exposure, vulnerability, and hazard (Malakar et al., 2021a ; Mondal et al., 2022 ; Shah & Malakar, 2024 ; Thangjam et al., 2024 ; Zang et al., 2024 ). The relationship of function is stated as R = H × V × E……………………………………………………………………………………(1) Where “R”, “H”, “V”, and “E” represent, respectively, risk, hazard, vulnerability, and exposure. Both physical and socio-economic characteristics are taken into account when creating the risk index. The socio-economic indicators point to the vulnerability and exposure constructions, whereas the physical indicators point to the hazard construct (Shah & Malakar, 2024 ). Detailed information on indicators is given in Table 1 . The rationale for each indicator selection is provided in the supplementary file (Appendix 1). The ensuing subsections contain a detailed discussion of the three elements of risk. Table 1 Components, sub-components, and indicators used for risk assessment of Himachal Pradesh along with their functional relationship and data source Component of risk Sub Components Indicators Functional Relationship Data source Hazard Percentage of state population Vulnerable to earthquake Positive (+) Himachal Pradesh Vulnerability Atlas, 2016 Percentage of sate population vulnerable to land slide (Severe to very high, high, and moderate Positive (+) Himachal Pradesh Vulnerability Atlas, 2016 Total number of disastrous cold wave days annually from 1969 to 2019 Positive (+) Climate hazard and Vulnerability Atlas of India, 2022 Total number of disastrous heat wave days annually from 1969 to 2019 Positive (+) Climate hazard and Vulnerability Atlas of India, 2022 Total occurrences of flood events from 1969 to 2019 Positive (+) Climate hazard and Vulnerability Atlas of India, 2022 All category (moderate, severe, and extreme) drought normalized vulnerability index (based on standardized precipitation index) Positive (+) Hazard Atlas of India, 2022 Average number of fog days annually from 1981 to 2010 Positive (+) Hazard Atlas of India, 2022 Extreme wind speed (in meters per second) annually Positive (+) Hazard Atlas of India, 2022 Total number of snowfall days annually from 1969 to 2019 Positive (+) Climate hazard and Vulnerability Atlas of India, 2022 Average number of hailstorm days annually from 1981 to 2010 Positive (+) Hazard Atlas of India, 2022 Average number of lightning flashes per km2 per day annually from 1983 to 2013 Positive (+) Hazard Atlas of India, 2022 Average number of thunderstorm days annually from 1981 to 2010 Positive (+) Hazard Atlas of India, 2022 Extreme maximum temperature from 1981 to 2022 Positive (+) MEERA-2, NASA Power data Extreme minimum temperature from 1981 to 2022 Positive (+) MEERA-2, NASA Power data Average annual mean temperature from 1981 to 2022 Positive (+) MEERA-2, NASA Power data Precipitation variability Positive (+) MEERA-2, NASA Power data Average annual cloud coverage from 1981 to 2022 Positive (+) MEERA-2, NASA Power data Elevation Positive (+) SRTM DEM, USGS Slope Positive (+) SRTM DEM, USGS Vulnerability Sensitivity Percentage of female population Positive (+) Census of India, 2011 Percentage of female headed household Positive (+) Census of India, 2011 Percentage of population below six years Positive (+) Census of India, 2011 Percentage of population above 60 years Positive (+) Census of India, 2011 Percentage of rural population to total population Positive (+) Census of India, 2011 Percentage of S.C. population Positive (+) Census of India, 2011 Percentage of S.T. population Positive (+) Census of India, 2011 People with disability at risk Positive (+) Himachal Pradesh Vulnerability Atlas Percentage of non-working population Positive (+) Census of India, 2011 No of marginal farmer Positive (+) Census of India, 2011 Illiteracy rate Positive (+) Census of India, 2011 Percentage of dilapidated households Positive (+) Census of India, 2011 Percentage of households with material of roof as grass/ bamboo / thatch / wood / mud / plastic / polythene Positive (+) Census of India, 2011 Percentage of households with material of wall as grass/ bamboo / thatch / wood / mud / plastic / polythene / un brunt brick / stones / non packed with mortrar Positive (+) Census of India, 2011 Percentage of households not having latrine facility within the premises Positive (+) Census of India, 2011 Adaptive capacity Total work participation rate Negative (-) Census of India, 2011 Female work participation rate Negative (-) Census of India, 2011 Male literacy rate Negative (-) Census of India, 2011 Female literacy rate Negative (-) Census of India, 2011 Rural literacy rate Negative (-) Census of India, 2011 S.T. literacy rate Negative (-) Census of India, 2011 Sex ratio Negative (-) Census of India, 2011 Percentage of households living owned house Negative (-) Census of India, 2011 Percentage of households having radio Negative (-) Census of India, 2011 Percentage of households having television Negative (-) Census of India, 2011 Percentage of households having computer / laptop Negative (-) Census of India, 2011 Percentage of households having internet Negative (-) Census of India, 2011 Percentage of households having landline only Negative (-) Census of India, 2011 Percentage of households having mobile only Negative (-) Census of India, 2011 Percentage of main worker to total population Negative (-) Census of India, 2011 Percentage of household with access to two wheeler Negative (-) Census of India, 2011 Percentage of households with access to four wheeler Negative (-) Census of India, 2011 Percentage of households availing banking service Negative (-) Census of India, 2011 Percentage of households using cleaner source of energy for cooking Negative (-) Census of India, 2011 Percentage of households with access to drinking water source within premise Negative (-) Census of India, 2011 Percentage of households with access to bathroom facility within premises Negative (-) Census of India, 2011 Percentage of households having covered drainage as against open drainage or no drainage Negative (-) Census of India, 2011 Percentage of households having electricity as the main source of lightning Negative (-) Census of India, 2011 Per capita net district domestic product 2015-16 Negative (-) District domestic product of Himachal Pradesh, 2011-12 to 2015-16 No of beds available in health institution per 1000 population Negative (-) Census of India, 2011 Percentage of cultivator to total worker Negative (-) Census of India, 2011 Percentage of agriculture laborers to total worker Positive (+) Census of India, 2011 Per cultivator net area sown Negative (-) Census of India, 2011 and Statistical abstract of Himachal Pradesh, 2021-22 Number of livestock per 10000 population Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Percentage of pucca house Negative (-) Census of India, 2011 Cropping intensity Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Irrigation intensity Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Crop diversification index Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Horticulture efficiency index Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Total food grain yield Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Per capita food grain production Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 Population served per bank Negative (-) Census of India, 2011 Density of school Negative (-) Census of India, 2011 Livestock density Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 No of medical institutions / 10000 population Negative (-) Statistical abstract of Himachal Pradesh, 2021-22 No. of family welfare Centre / 5000 population Negative (-) Census of India, 2011 Literacy rate Negative (-) Census of India, 2011 Exposure Space Population growth rate Positive (+) Census of India, 2011 Population density Positive (+) Census of India, 2011 Percentage of area under agriculture Positive (+) Statistical abstract of Himachal Pradesh, 2021-22 Percentage of permanent pastures & other grazing lands Positive (+) Statistical abstract of Himachal Pradesh, 2021-22 Percentage of households having no exclusive room Positive (+) Census of India, 2011 Road density Negative (-) Census of India, 2011 Agriculture dependent population Positive (+) Census of India, 2011 5.2.1.1. Hazard index A hazard is any physical event or trend that could occur, whether as a result of human activity or natural causes, and that carries the risk of resulting in fatalities, serious injuries, negative health effects, and damage to property, infrastructure, livelihoods, services, ecosystems, and natural resources (Shah & Malakar, 2024 ). The Himalayan region has face multi-hazard events i.e. , landslides, earthquakes, flash floods, fog, adverse effect of cold wave, drought, hailstorm, thunder, and lightning among others (Dikshit et al., 2020 ; Intergovernmental Panel on Climate Change (IPCC), 2021 ; Joshi & Kumar, 2006 ; Pandey et al., 2011 ; Rajeevan, 2017 ; Rusk et al., 2022 ; Scherler et al., 2011 ; Schneiderbauer et al., 2021 ; Shekhar et al., 2015 ; Wester et al., 2019 ; Xu et al., 2009 ), and due to the continuous climate change the occurrences have increased even more (Bhutiyani et al., 2007 ; Dash et al., 2007 ; Dimri & Dash, 2012 ; Jhajharia & Singh, 2011 ; Shrestha et al., 2012 ; Tewari et al., 2017 ; Xu et al., 2009 ). To represent hazard, present study utilize ‘19’ indicators i.e., percentage of state population vulnerable to earthquake, percentage of state population vulnerable to landslide, total number of disastrous cold wave days annually from 1969 to 2019, total number of heat wave days annually from 1969 to 2019, total occurrence of flood events from 1969 to 2019, all category of drought normalized vulnerability index, average number of fog days annually from 1981 to 2010, extreme wind speed annually, total number of snowfall days annually from 1969 to 2019, average number of hailstorm days annually from 1981 to 2010, average number of lightning flashes per sq km per day annually from 1983 to 2013, average number of thunderstorm days annually from 1981 to 2010, extreme minimum temperature from 1981 to 2022, extreme maximum temperature from 1981 to 2022, average annual mean temperature from 1981 to 2022, precipitation variability, average annual cloud coverage from 1981 to 2022, elevation, and slope. These occurrences are selected because they are linked to both economic shocks and the loss of human life, and other earlier research has also used these indicators (Malakar et al., 2021a ; Mondal et al., 2022 ; Shah & Malakar, 2024 ; Thangjam et al., 2024 ; Zang et al., 2024 ). The details of the indicators and their data sources are provided in Table 1 . 5.2.1.2. Vulnerability index Vulnerability is the term for the potential for harm during disasters as a result of inadequate response capabilities or poor recovery capacities (Das, 2024 ; Malakar et al., 2021a ; Shah & Malakar, 2024 ; Zang et al., 2024 ). According to earlier Indian Himalayan Region studies, the IHR population is extremely vulnerable and faces numerous socio-economic challenges (Alam et al., 2022 ; Biella et al., 2022 ; Chauhan et al., 2022 ; Dhanai, Rekha, 2014; Gerlitz et al., 2017 ; Gupta et al., 2019 ; Rawat et al., 2012 ; Shukla et al., 2016b , 2016a ). In this study to measure the vulnerability index, ‘57’ indicators were selected, and the vulnerability component is subdivided into two sub-component indicators, i.e., sensitivity index, and adaptive capacity index. Table 1 lists the indicators taken into account when creating the vulnerability index and the data source. A total of ‘15’ indicators, including percentage of female population, percentage of female headed household, percentage of population below six year age group, percentage of population above 60 years age, percentage of rural population to total population, percentage of S.C. population, percentage of S.T. population, people with disability at risk, percentage of non-working population, number of marginal farmers, illiteracy rate, percentage of dilapidated households, percentage of households with material of roof (grass /bamboo/thatch/wood/mud/plastic/polythene, percentage of households with material of wall as grass/bamboo/thatch/wood/mud/plastic/polythene/un brunt brick/stones / non packed) with mortrar, percentage of households not having latrine facility within the premises were selected under sensitivity sub components. On the other hand, ‘42’ indicators including total work participation rate, female work participation rate, male literacy rate, female literacy rate, rural literacy rate, S.T. literacy rate, sex ratio, percentage of households living owned house, percentage of households having radio, percentage of households having television, percentage of households having computer / laptop, percentage of households having internet, percentage of households having landline only, percentage of households having mobile only, percentage of main worker to total population, Percentage of household with access to two wheeler, percentage of households with access to four wheeler, percentage of households availing banking service, percentage of households using cleaner source of energy for cooking, percentage of households with access to drinking water source within premise, Percentage of households with access to bathroom facility within premises, Percentage of households having covered drainage as against open drainage or no drainage, Percentage of households having electricity as the primary source of lightning, Per capita net district domestic product 2015-16, No of beds available in health institution per 1000 population, Percentage of cultivator to total worker, percentage of agriculture laborers to total worker, Per cultivator net area sown, Number of livestock per 10000 population, Percentage of pucca house, Cropping intensity, Irrigation intensity, Crop diversification index, Horticulture efficiency index, Total food grain yield, Per capita food grain production, population served per bank, Density of school, Livestock density, No of medical institutions / 10000 population, No. of Family Welfare Centre / 5000 population, and Literacy rate were selected under the adaptive capacity sub-components. 5.2.1.3. Exposure index Exposure includes population and assets that could be affected by local hazards (Das et al., 2023 ; Das & Sharma, 2024 ; Malakar et al., 2021a ; Mondal et al., 2022 ; Shah & Malakar, 2024 ; Thangjam et al., 2024 ; Zang et al., 2024 ). Space is the only sub component under exposure used in this study. There are eight indicators, i.e., population growth rate, Population density, percentage of area under agriculture, Percentage of permanent pastures & other grazing lands, Percentage of households having no exclusive room, Road density, and agriculture-dependent population present under the exposure index. Details of the indicators and their sources are given in Table 1 . 5.2.2. Standardize Risk index The risk index in the study was computed using TOPSIS, or the Technique for Order of Preference by Similarity to Ideal Solution. Hwang and Yoon were the first to propose TOPSIS, a Multi-Attribute Decision Making (MADM) technique (Yadav et al., 2019 ). It is preferred in this study because of how easily it can be used to choose the best and worst options among the several indicators under each component. The option with the greatest distance from the negative ideal solution and the smallest distance from the positive ideal solution is the best one. The sum of all the worst values obtained for each attribute is the negative ideal solution, whereas the sum of all the best values realised for each attribute is the positive ideal solution (Shah & Malakar, 2024 ). TOPSIS measures an alternative's proximity to the ideal solution using the Euclidean distance. Taking into consideration proximity to both the positive and negative ideal solutions, TOPSIS determines the relative distance to the positive ideal solution. Lastly, a comparison of relative distances is used to arrive at different priority rankings. The risk index has been calculated using this method in several earlier studies (Malakar et al., 2021b ; Rahim et al., 2018 ; Shah & Malakar, 2024 ; Yadav et al., 2019 ). This investigation's hazard, vulnerability, and exposure components were obtained using TOPSIS in a process described below (Shah & Malakar, 2024 ). There are five steps present in this process, which are discussed below. Step 1 (Normalised decision matrix construction) : In this stage, the values of the indicators are normalised using the Eq. (2) formula. Comparing indicators measured on different scales is made easier by normalisation. V ij = \(\:\frac{X\text{i}\text{j}}{\sqrt{{\sum\:}_{i=1}^{n}{x}_{ij}^{2}}}\) Ɐj …………………………………………………………………………………..(2) where Vij indicates the values of the normalised matrix, xij stands for the ith district, and jth indicates the real score matrix. There are 12 districts in the current study, and there are 19, 57, and 8 indications (m) for exposure, vulnerability, and hazard, respectively. Step 2 (Building of weighted normalised decision matrix) : In this stage, the normalised decision matrix is given weights. This study, however, follows many other earlier studies (Malakar et al., 2021b ; Malakar & Mishra, 2017 ) in assigning equal weights and merely averaging the data to create the index, notwithstanding the subjectivity inherent in weight formulation. Step 3 (Measurement of Positive Ideal Solution and Negative Ideal Solution) : Each indicator determines the ideal best and ideal worst, or positive and negative ideal solutions for each district. The resulting index represents increased exposure, vulnerability, or hazard. The ideal worst ( \(\:{V}_{J}^{-}\) ) is the lowest value across all districts (n), and the ideal best ( \(\:{V}_{J}^{+}\) ) is the maximum value across all districts (n) when it comes to positively (+) contributing indicators. The ideal worst ( \(\:{V}_{J}^{-}\) ) value is the greatest value across all districts, while the ideal best ( \(\:{V}_{J}^{+}\) ) value is the lowest value across all districts for the negatively (−) contributing indicators. Step 4 (Measurement of separation distance) : Each district's Euclidean distance value is calculated, also known as the separation distance value between the ideal best (positive) and worst (negative) solutions. Eq. (3) expresses the Euclidean distance from the ideal best, or positive ideal solution, and Eq. (4) expresses the Euclidean distance from the ideal worst, or negative ideal solution. $$\:{S}_{i}^{+}\:=\:\sqrt{{\sum\:}_{j=1}^{m}(Vij-\:{V}_{j}^{+}})\text{Ɐ}\text{i}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:(3)$$ $$\:{S}_{i}^{-}\:=\:\sqrt{{\sum\:}_{j=1}^{m}(Vij-\:{V}_{j}^{+}})\text{Ɐ}\text{i}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:(4)$$ The optimal outcome of the TOPSIS is the one that is closest to the ideal best (positive ideal solution V+) and furthest from the ideal worst (negative ideal solution V–). Step 5 (Calculation of relative closeness to ideal solution score) : The performance score or relative proximity to the ideal solution score is calculated using Eq. (5). Each study component's index is equal to this performance score. The components, i.e., hazard, vulnerability, or exposure, increase with the performance score or index value. Pi = \(\:\frac{{S}_{i}^{+}}{{S}_{i}^{+}+\:{S}_{i}^{-}}\) ………………………………………………………………………………………(5) Where Pi represents the (i) individual risk (Ri) components of the n number of districts, such as exposure (Ei), vulnerability (Vi), or hazard (Hi). Equation (6) shows that the three components, i.e., hazard (Hi), vulnerability (Vi), and exposure (Ei) are multiplied to get the final risk index (Ri). Ri = Hi × Vi × Ei………………………………………………………………………………….(6) Using Eq. (7), the risk index (Ri) and the indices of its constituent parts, i.e., hazard (Hi), vulnerability (Vi), and exposure (Ei), are standardised. Higher values indicate greater risk and related components, and index values are thus produced in the interval [0, 1]. Standardisation makes it easier to compare the districts in a meaningful way. Zi = \(\:\frac{Yi-Ymin}{Y\text{max}-\:Ymin}\) …………………………………………………………………………………(7) Where Zi stands for the standardised values of risk (Ri), hazard (Hi), vulnerability (Vi), or exposure (Ei), Yi represents the indices of risk (Ri), hazard (Hi), vulnerability (Vi), or exposure (Ei). The risk index and its component elements' indices, i.e., hazard, vulnerability, and exposure, are obtained using the previously described process. Based on the results and using Arc GIS 10.3 software, four maps for hazard index, vulnerability index, exposure index, and standardised risk index were prepared. There were three zones, i.e., high, moderate and low, created to explore the spatial variation. One-way ANOVA : One-way ANOVA was adopted to identify the key spatially heterogeneous factors influencing risk assessment. 6. Results 6.1. Hazard index Table 2 revealed that the district Lahaul-Spiti in Himachal Pradesh was recorded as most hazard prone district with perfect score 1.00 due to higher level of elevation and slope, moderate level of drought vulnerability score, higher level of total number of disastrous cold wave days annually from 1969 to 2019. Total number of snowfall days annually from 1969 to 2019, and average annual cloud coverage from 1981 to 2022. Four districts, i.e., Lahaul-Spiti, Chamba, Kullu, and Kinnaur, were found to have a higher level of hazard status (> 0.300) (Table 2 and Fig. 4 ). Among these regions in Kullu district, the occurrence of floods from 1969 to 2019 was found to be highest. In Kinnaur and Chamba, the drought vulnerability score was found to be the highest. The total number of snowfalls was found to be highest in Chamba, Lahaul-Spiti, and Kinnaur. The district Chamba found the highest level of precipitation variability in this region. Among these high hazardous zones in Kullu, Lahaul-Spiti, and Kinnaur, the elevation and slope are very high, which is responsible for multi-hazard events in this region. Shimla district was found to have a moderate level of hazard status (0.100–0.300) due to higher levels of landslide occurrences from 1969 to 2019, higher number of disastrous cold wave days, and higher occurrences of flood events from 1969 to 2019, higher number of fogs days, snow fall days, thunder storm, and higher slope angle. On the other hand, a lower level of hazard status (< 0.100) was found in seven western districts, i.e, Kangra, Una, Hamirpur, Mandi, Bilaspur, Solan, and Sirmaur. Though this region fell into low hazard zone, the district Kangra faces higher levels of earthquake, landslide, flood, drought, and precipitation variability within this zone. Table 2 Standardize risk index and its component indices in Himachal Pradesh Sl. No. District Hazard index Rank Vulnerability index Rank Exposure index Rank Standardize Risk Index Rank 1 Bilaspur 0.000 12 0.651 4 0.708 3 0.000 12 2 Chamba 0.364 4 0.000 12 0.099 9 0.303 8 3 Hamirpur 0.020 10 0.683 2 1.000 1 0.326 7 4 Kangra 0.067 7 1.000 1 0.439 5 0.493 2 5 Kinnaur 0.797 2 0.423 8 0.080 10 1.000 1 6 Kullu 0.464 3 0.245 10 0.063 11 0.380 5 7 Lahaul-Spiti 1.000 1 0.485 6 0.000 12 0.384 4 8 Mandi 0.080 6 0.479 7 0.411 6 0.373 6 9 Shimla 0.219 5 0.564 5 0.162 8 0.473 3 10 Sirmaur 0.040 8 0.324 9 0.216 7 0.030 11 11 Solan 0.020 9 0.030 11 0.589 4 0.051 10 12 Una 0.017 11 0.666 3 0.741 2 0.179 9 6.2. Vulnerability index Figure 5 depicted that Kangra district was recorded as most vulnerable district with perfect score 1.00 due to higher level sensitivity aspects i.e. , percentage of female population, percentage of female headed household, percentage of population above 60 years age, percentage of rural population to total population, percentage of non-working population, no of marginal farmer, percentage of households with material of wall as grass/ bamboo / thatch / wood / mud / plastic / polythene / un brunt brick / stones / non packed with mortrar, and percentage of households not having latrine facility within the premises, and lower adaptive capacity aspects i.e., lower level of total work participation rate, female work participation rate, percentage of households living owned house, percentage of households having computer / laptop, percentage of main worker to total population, percentage of households with access to four wheeler, per capita net district domestic product 2015-16, percentage of cultivator to total worker, per cultivator net area sown, no of medical institutions / 10000 population, and no. of family welfare center / 5000 population. Five districts i.e., Kangra, Una, Hamirpur, Bilaspur, and Shimla were found to have a higher level of vulnerability status (> 0.500) (Table 2 , and Fig. 5 ). Four districts, i.e., Lahul-Spiti, Kinnaur, Mandi, and Sirmaur, were found to have a moderate level of vulnerability status (0.25–0.50). On the other hand, three districts, i.e., Kullu, Chamba, and Solan, were found to have a low level of vulnerability status (< 0.25). 6.3. Exposure index Table 2 found that the district Hamirpur in Himachal Pradesh is recorded as the highest exposure district with a perfect score 1.00 due to the highest level of population density, percentage of area under agriculture, and road density, higher level of agriculture dependent population, and moderate level of population growth rate. Six districts, i.e., Kangra, Una, Hamirpur, Mandi, and Solan, were found to have a higher level of exposure status (> 0.300) (Table 2 and Fig. 6 ). Among this region in Una district, the Population growth rate is found highest. In Solan, the percentage of permanent pastures & other grazing lands was found to be higher. In mandi, road density and agriculture-dependent population are found higher in the districts of Sirmaur and Shimla, a moderate level of exposure status (0.100 to 0.300) (Table 2 , and Fig. 6 ). Among these regions, the dependent population is higher, and the percentage of area under agriculture and road density is found at a moderate level. On the other hand, a lower level of exposure status (< 0.100) was found in Chamba, Lahual & Spiti, Kullu, and Kinnaur. Among this region, in Lahual & Spiti, population growth rate, population density, percentage of area under agriculture, and road density were found to be the lowest. In Kullu, the Percentage of permanent pastures & other grazing lands is found to be lower. In Kullu, Lahual & Spiti, the percentage of households having no exclusive room was found to be lower. 6.4. Standardize Risk index Inter-district spatial variation has been observed in the standardized risk status of Himachal Pradesh (Figs. 7 , 8 and Table 2 ). Kinnaur district was recorded as the most risk-prone district due to multi-hazard events with a perfect score of 1.00, where the hazard score is high, vulnerability score is moderate, and the exposure. On the contrary, district Bilaspur was the least risk-prone district due to the lowest hazard score and higher exposure and vulnerability scores. Five districts from north-to-north-eastern, i.e. , Lahaul-Spiti, Kullu, Kangra, Solan, and Kinnaur, were found to have a higher level of risk status (> 0.375), where three districts, i.e. , Chamba, Mandi, and Hamirpur, were found to have a moderate level of risk status (0.200 to 0.375). On the other hand, the four districts, i.e., Una, Bilaspur, Solan, and Sirmaur, have a lower level of risk status (< 0.200). 6.5. Major factors of spatial heterogeneity in risk assessment Table 3 revealed that among the 19 indicators under hazard component, six variables were found significant spatial difference. In hazard dimension significant spatial difference found in total number of disastrous cold wave days annually from 1969 to 2019 (F value = 3.95, p value < 0.1), Average number of fog days annually from 1981 to 2010 (F value = 3.1, p value < 0.1), Total number of snowfall days annually from 1969 to 2019 (F value = 3.89, p value < 0.1), Average number of lightning flashes per km 2 per day annually from1983 to 2013 (F value = 3.3, p value < 0.1), Extreme maximum temperature from 1981 to 2022 (F value = 3.96, p value < 0.1), Average annual cloud coverage from 1981 to 2022 (F value = 3.98, p value < 0.1), and elevation (F value = 3.39, p value < 0.1). Table 3 Major factors of Spatially heterogeneous risk assessment in Himachal Pradesh Component of risk Sub component Indicators F value Sig. level Hazard Total number of disastrous cold wave days annually from 1969 to 2019 3.95 0.059* Average number of fog days annually from 1981 to 2010 3.1 0.095* Total number of snowfall days annually from 1969 to 2019 3.89 0.061* Average number of lightning flashes per km2 per day annually from1983 to 2013 3.3 0.084* Extreme maximum temperature from 1981 to 2022 3.96 0.058* Average annual cloud coverage from 1981 to 2022 3.98 0.058* Elevation 3.39 0.080* Vulnerability Sensitivity Percentage of population below six years 3.22 0.088* Percentage of S.C. population 3.26 0.086* People with disability at risk 10.64 0.004*** Adaptive capacity Percentage of households having mobile only 4.31 0.049** Percentage of household with access to two-wheeler 3.34 0.082* No of beds available in health institution per 1000 population 3.87 0.061* Percentage of cultivator to total worker 3.1 0.095* Per cultivator net area sown 3.52 0.074* Per capita food grain production 3.13 0.093* Density of school 3.65 0.069* No of medical institutions / 10000 population 3.58 0.072* Exposure Space Population growth rate 3.99 0.058* Note: *p value < 0.1, **p value < 0.05, ***p value < 0.01 In the vulnerability dimension, there are ‘57’ indicators, and among them, ‘11’ variables have been found has significant spatial differences. The significant spatial difference found in percentage of population below six years (F value = 3.22, p value > 0.1), percentage of S.C. population (3.26, p value < 0.1), people with disability at risk (F value: 10.64, p value < 0.001, percentage of households having mobile only (F value = 4.31, p value < 0.05), percentage of household with access to two-wheeler (F value = 3.34, p value < 0.1), number of beds available in health institution per 1000 population (F value = 3.87, p value < 0.1), percentage of cultivator to total worker (F value = 3.1, p value < 0.1), per cultivator net area sown (F value = 3.52, p value < 0.1), per capita food grain production (F value = 3.13, p value < 0.1), density of school (F value = 3.65, p value < 0.1), and number of medical institutions per 10000 population (F value = 3.58, p value < 0.1). On the other hand, among the eight variables under the exposure dimension, one, population growth, has found a significant spatial difference (F value = 3.99, p value < 0.1). 7. Hypothesis test Table 4 revealed that the correlation coefficient between hazard index and standardized risk index is 0.617, and the p value for two tailed tests of significance is found 0.032 (p value < 0.05). These results indicated that there is a strong positive correlation between the hazard index and the standardized risk index. Therefore, this research hypothesis (A higher probability of hazard occurrence is positively associated with an increase in the standardized multi-hazard risk score) is accepted. Table 4 Hypothesis test Independent variable Standardize risk index Hazard index Pearson Correlation 0.617** Sig. (2-tailed) 0.032 N 12 Note: *p value < 0.1, **p value < 0.05, ***p value < 0.01 8. Discussion The Himalayan region is among the world's most ecologically sensitive and geologically fragile mountain systems across the globe (Ahmad et al., 1990 ; Singh, 2006 ; Khawas, 2007 ; Negi et al., 2012 ; Panwar, 2020 ; Shrestha, 2005 ). Climate change has exacerbated this fragility, triggering a rise in both the frequency and severity of multi-hazard events such as landslides, flash floods, droughts, extreme temperatures, snowfall anomalies, and glacial lake outburst floods (Shah et al., 2025 ). Particularly in the Western Himalayan region, the impacts of global warming are more pronounced due to its unique topographic and climatic conditions (Dimri et al., 2021 ; Hunt et al., 2020 ; Madhura et al., 2015 ; Negi et al., 2012 ; Negi et al., 2018 ; Pepin et al., 2022 ; Shafiq et al., 2019 ; Shekhar et al., 2010 ; Vedwan & Rhoades, 2001 ; Yadav et al., 2021 ). The findings from this study, conducted across the 12 districts of Himachal Pradesh, substantiate this growing threat. The interlinkage between climate-sensitive sectors such as agriculture, hydrology, and forestry and the increasing frequency of hazard events is clearly evident. For instance, districts like Kinnaur and Lahaul-Spiti—characterized by high elevation and steep slopes—demonstrate high hazard scores due to climate-induced snowfall variability, increased cold wave days, and precipitation variability, which directly impact agricultural cycles and forest ecosystems. The adverse effect on agriculture-dependent communities is further aggravated by the recurrence of drought events and flooding, which reduces agricultural productivity and disrupts local food systems, water availability, and biodiversity (Balgah et al., 2023 ). Likewise, snowfall and cold wave days significantly affect forest cover and downstream water availability, influencing livelihood and ecological balance (Berhanu et al., 2016 ; Lepcha et al., 2021 ; Negi et al., 2012 ). The integration of climate-sensitive indicators in this study offers clear empirical evidence that climatic hazards are not isolated incidents but rather deeply intertwined with the socio-ecological systems of the region (Dorkenoo et al., 2024 ; Dovie et al., 2017 ; Hackfort & Burchardt, 2018 ). Spatially, the study identifies considerable heterogeneity in the distribution and intensity of multi-hazard risks across Himachal Pradesh. The risk profiles vary from high-risk districts in the northern and northeastern belts to low-risk districts in the southern and western parts. According to the standardised risk index, five districts namely, Kinnaur, Lahaul-Spiti, Kullu, Kangra, and Solan—fall into the high-risk category, with risk scores exceeding 0.375. These regions are frequently exposed to extreme climatic conditions and geological vulnerabilities. For instance, Kinnaur records a perfect risk score of 1.00 due to its high hazard exposure, moderate vulnerability, and low adaptive capacity. This suggests that even regions with relatively low exposure can experience high composite risk if other dimensions, such as hazard frequency and vulnerability indicators, are substantial. This result is align with earlier findings of the research in different parts of the world (Birkmann, 2007 ; Brooks et al., 2005 ; Dandapat & Panda, 2017 ; Fekete, 2019 ; Garschagen et al., 2021 ; Highfield et al., 2014 ; Jha & Gundimeda, 2019 ; Koks et al., 2015 ; Menoni et al., 2012 ; Murthy et al., 2015 ; Peduzzi et al., 2009 ; Schmidt-Thomé, 2006 ; Zuzak et al., 2022 ). Conversely, districts like Bilaspur and Una, located in the southern belt, demonstrate low risk scores (< 0.200), owing to lower hazard occurrences and better infrastructure or adaptive capacities. These findings are supported by the earlier research findings (Balgah et al., 2023 ; Das et al., 2023 ; Das & Sharma, 2024 ). The spatial analysis using the IPCC-AR6 framework and TOPSIS-based multi-attribute decision-making approach illustrates that the northern districts face significantly higher hazard exposure due to elevation, geological instability, and climate variability. However, exposure alone does not define risk. For example, despite lower hazard occurrences in Hamirpur, its high exposure index—driven by population density and land use pressure—elevates its risk level to moderate. Such spatial differentiation underscores the importance of a composite and region-specific framework for risk assessment that considers the interaction between hazard, vulnerability, and exposure in a localized context. The study also highlights the importance of multi-hazard risk and its spatial variation across the state. Vulnerability is not uniformly distributed; instead, it is intricately shaped by demographic patterns, infrastructural gaps, and disparities in access to resources and services. Districts like Kangra, Una, and Hamirpur register high vulnerability scores (> 0.500), influenced by a combination of high sensitivity and limited adaptive capacity. Kangra, in particular, records a perfect vulnerability score of 1.00 due to factors such as a high percentage of elderly population, female-headed households, marginal farmers, and poor housing conditions. These conditions are further compounded by limited access to four-wheelers, computers, and health infrastructure. In contrast, districts such as Kullu, Chamba, and Solan show lower vulnerability scores (< 0.25), indicating a relatively higher adaptive capacity. The ANOVA results reinforce these spatial patterns, identifying key indicators like S.C. population percentage, prevalence of disability, and mobile access as significant determinants of spatial heterogeneity. In effect, vulnerability acts as a multiplier of hazard risk, and its interplay with exposure determines the ultimate risk profile. This complexity reiterates the inadequacy of hazard-only assessments and the necessity to integrate socio-economic parameters for a holistic understanding of climate risk. Addressing infrastructure, health, education, and livelihood indicators is essential for reducing multi-hazard risk and enhancing long-term resilience. The policy implications of these findings are manifold. First, the clear spatial heterogeneity of risks underscores the need for region-specific interventions rather than a one-size-fits-all approach, for high-risk districts like Kinnaur and Lahaul-Spiti, structural mitigation (e.g., slope stabilization, avalanche control), early warning systems, and climate-resilient infrastructure development should be prioritized. Policy efforts must focus on improving healthcare, education, housing, and rural connectivity in moderately exposed but highly vulnerable districts like Kangra and Hamirpur. Moreover, the study demonstrates the efficacy of integrating the IPCC-AR6 risk framework with a quantitative decision-making tool like TOPSIS, enabling objective prioritization of regions based on a multidimensional perspective. This methodological innovation ensures transparency and scientific robustness and enhances the model's scalability for other climate-sensitive regions. Additionally, the validation through field investigations affirms the accuracy of the secondary data-driven results and enriches the policy relevance of the study. This study provides practical evidence for policymakers, disaster risk managers, and local planners to develop tailored, adaptive, and inclusive strategies to mitigate the adverse impacts of climate-change-induced multi-hazard risks in the Western Himalayas. The focus should now move from relief-based responses to proactive, data-driven, and locally focused risk reduction strategies. To validate the secondary results, we also visited the different vulnerable sites across the Himachal Pradesh (Fig. 9 ). 9. Conclusion and policy recommendation This study assesses inter-district spatial variation in climate-change-induced multi-hazard risk across Himachal Pradesh using the IPCC-AR6 framework and a Multi-Attribute Decision-Making approach. A composite index was developed using 84 indicators across three components: hazard, vulnerability, and exposure. The districts of Lahaul-Spiti, Chamba, Kullu, and Kinnaur exhibited high hazard levels (> 0.300) due to factors such as frequent flooding (1969–2019), severe droughts, heavy snowfall, precipitation variability, and steep terrain. Kangra, Una, Hamirpur, Bilaspur, and Shimla showed high vulnerability (> 0.500), driven by higher proportions of elderly, female-headed households, non-working and rural populations, inadequate housing, poor sanitation, and low adaptive capacity indicators like weak labor participation, low income, limited asset ownership, and inadequate health infrastructure. Exposure levels were highest in Kangra, Una, Hamirpur, Mandi, and Solan, attributed to rapid population growth (Una), dependence on agriculture (Mandi), and extensive grazing land (Solan). Overall risk was highest (> 0.375) in Lahaul-Spiti, Kullu, Kangra, Solan, and Kinnaur; moderate (0.200–0.375) in Chamba, Mandi, and Hamirpur; and lowest (< 0.200) in Una, Bilaspur, Solan, and Sirmaur. One-way ANOVA identified 18 key indicators with significant spatial variation, including seven under hazard (e.g., snowfall, fog, lightning, extreme temperatures) and eleven under vulnerability and exposure (e.g., SC population, disability, healthcare, agricultural dependence, and mobile/transport access). These findings offer critical insights for region-specific, data-driven climate risk mitigation planning in Himachal Pradesh. Policy recommendation Based on the findings, this study suggests some region-specific policy recommendations as: To reduce the high multi-hazard risk in Kinnaur located in high altitude with majority of tribal population, installing advanced early warning systems for snow, landslides, and flash floods is essential, and conducting regular village-level disaster preparedness drills in collaboration with local authorities. To address the high hazard risk in Lahaul-Spiti, investment should be directed toward developing climate-resilient road infrastructure with avalanche protection and constructing thermal-insulated shelters for vulnerable populations facing extreme cold events. To reduce climate risk in Kangra, targeted interventions should focus on enhancing female workforce participation through skill development and strengthening healthcare infrastructure for disabled and elderly populations, addressing the district's high vulnerability and exposure levels. Land-use zoning laws should be enforced to mitigate risk in Hamirpur, and vertical urban planning should be promoted to manage high exposure driven by dense population, agricultural dependence, and expanding road networks. To manage snow and rain-related hazards in Chamba, set up local snow forecasting systems and promote slope protection measures like contour trenching to prevent landslides. To reduce risk in Shimla, old buildings and public infrastructure should be retrofitted for landslide and earthquake safety, while promoting rainwater harvesting and green roofs to manage runoff and urban flooding. Climate-resilient crop varieties and agroforestry should be promoted to enhance resilience in Solan, along with crop insurance and irrigation support for farmers in high-risk areas. To reduce risk in Mandi, emergency road connectivity to remote villages should be improved, and mobile disaster response units with health and communication services should be established. Despite low hazard levels, Bilaspur should strengthen institutional capacity by integrating climate risk into local planning and building community-based disaster preparedness networks to address its moderate vulnerability and high exposure. To strengthen resilience in Sirmaur, access to health and education services should be expanded in rural and hilly areas, along with improved digital literacy and mobile coverage for effective risk communication. Limitations and future scope of the study The entire study is based on a quantitative approach. To reveal the actual ground reality in the future, in-depth field investigation with an extended period, participant observations, and a mixed method approach adaptation is highly recommended. This study is based on a cross-sectional approach. A longitudinal study is required to evaluate the spatio-temporal changes in multi-hazard risk assessment in the Western Himalayan region and any parts of the Himalayan belt, and formulate evidence-based policy. Particular attention must be paid to places that are more vulnerable to multi-hazard risk, and specific policy changes may be necessary to lessen the negative effects of multi-hazard risk. Future research must help us better understand these dynamics and offer recommendations for wise policy formulation. Declarations Declaration of competing interest The work described in this article has never been influenced by the author’s personal or financial interests. Funding The authors did not receive any funding for this study. Author Contribution Shibu Das: Conceptualization, methodology, investigation, validation, writing- original draft, review & editing.Sanjeev Sharma: Review & Editing Acknowledgement: None References Aggarwal Y, Saha SK (2023) An improved rapid visual screening method for seismic vulnerability assessment of reinforced concrete buildings in Indian Himalayan region. Bull Earthq Eng 21(1):319–347. https://doi.org/10.1007/s10518-022-01537-2 Ahmad A, Rawat JS, Rai SC (1990) An analysis of the himalayan environment and guidelines for its management and ecologically sustainable development. Environmentalist 10(4):281–298. https://doi.org/10.1007/BF02239722 Alam MK, Dasgupta S, Barua A, Ravindranath NH (2022) Assessing climate-relevant vulnerability of the Indian Himalayan Region (IHR): a district-level analysis. Nat Hazards 112(2):1395–1421. https://doi.org/10.1007/s11069-022-05233-x Balgah RA, Ngwa KA, Buchenrieder GR, Kimengsi JN (2023) Impacts of Floods on Agriculture-Dependent Livelihoods in Sub-Saharan Africa: An Assessment from Multiple. Land Banerjee S, Niyogi R, Sarkar MS, John R (2022) Assessing the vulnerability of protected areas in the eastern Himalayas based on their biological, anthropogenic, and environmental aspects. Trees Forests People 8(February):100228. https://doi.org/10.1016/j.tfp.2022.100228 Berhanu B, Seleshi Y, Amare M, Melesse AM (2016) Upstream–downstream linkages of hydrological processes in the Nile River basin. Springer Geogr 207–223. https://doi.org/10.1007/978-3-319-18787-7_11 Bhadwal S, Sharma G, Gorti G, Sen SM (2019) Livelihoods, gender and climate change in the Eastern himalayas. Environ Dev 31(April):68–77. https://doi.org/10.1016/j.envdev.2019.04.008 Bhutiyani MR, Kale VS, Pawar NJ (2007) Long-term trends in maximum, minimum and mean annual air temperatures across the Northwestern Himalaya during the twentieth century. Clim Change 85(1–2):159–177. https://doi.org/10.1007/s10584-006-9196-1 Biella R, Hoffmann R, Upadhyay H (2022) Climate, Agriculture, and Migration: Exploring the Vulnerability and Outmigration Nexus in the Indian Himalayan Region. Mt Res Dev 42(2). https://doi.org/10.1659/mrd-journal-d-21-00058.1 Birkmann J (2007) Risk and vulnerability indicators at different scales:. Applicability, usefulness and policy implications. Environ Hazards 7(1):20–31. https://doi.org/10.1016/j.envhaz.2007.04.002 Brooks N, Adger WN, Kelly PM (2005) The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Glob Environ Change 15(2):151–163. https://doi.org/10.1016/j.gloenvcha.2004.12.006 Cantwell-Chavez D (2023) Interlocal adaptations to climate in east and southeast Asia: sharing lessons of agriculture, disaster risk reduction, and resource management. In International Affairs (Vol. 99, Issue 3). https://doi.org/10.1093/ia/iiad074 Chauhan D, Thiyaharajan M, Pandey A, Singh N, Singh V, Sen S, Pandey R (2022) Climate change water vulnerability and adaptation mechanism in a Himalayan City, Nainital, India. Environ Sci Pollut Res 29(57):85904–85921. https://doi.org/10.1007/s11356-021-15713-5 Chettri N, Tsering K, Shrestha A, Sharma E (2018) Ecological Vulnerability to Climate Change in the Mountains: A Case Study from the Eastern Himalayas. Plant Diversity in the Himalaya Hotspot Region , January Dandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ 3(4):1627–1646. https://doi.org/10.1007/s40808-017-0388-7 Das S (2024) Determinants of livelihood vulnerability to climate change among the agrarian community: Empirical evidence from the coastal region of Indian Sundarban Biosphere Reserve. Reg Stud Mar Sci 78(September):103777. https://doi.org/10.1016/j.rsma.2024.103777 Das S, Majumder S, Sharma KK (2023) Assessing integrated agricultural livelihood vulnerability to climate change in the coastal region of West Bengal: Implication for spatial adaptation planning. Reg Stud Mar Sci 57:102748. https://doi.org/10.1016/j.rsma.2022.102748 Das S, Sharma KK (2024) Livelihood vulnerability assessment among the agrarian indigenous communities of Sundarban Biosphere Reserve in India for building resilience and adaptation. In Environmental Science and Pollution Research (Issue 0123456789). Springer Berlin Heidelberg. https://doi.org/10.1007/s11356-024-35560-4 Dash SK, Jenamani RK, Kalsi SR, Panda SK (2007) Some evidence of climate change in twentieth-century India. Clim Change 85(3–4):299–321. https://doi.org/10.1007/s10584-007-9305-9 Debnath J, Sahariah D, Nath N, Saikia A, Lahon D, Islam MN, Hashimoto S, Meraj G, Kumar P, Singh SK, Kanga S, Chand K (2024) Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques. Model Earth Syst Environ 10(2):2393–2419. https://doi.org/10.1007/s40808-023-01912-1 Dhanai R et al (2014) The Effects of Climate Change on Natural Resources and Socio-economic Condition of Himalayan Communities of. Int J Mod Communication Technol Res 2(11):21–25 Dikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM (2020) Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review. Applied Sciences , 10 , 1–24. https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4 Dilshad T, Mallick D, Udas PB, Goodrich CG, Prakash A, Gorti G, Bhadwal S, Anwar MZ, Khandekar N, Hassan SMT, Habib N, Abbasi SS, Syed MA, Rahman A (2019) Growing social vulnerability in the river basins: Evidence from the Hindu Kush Himalaya (HKH) Region. Environmental Development , 31 (December 2018), 19–33. https://doi.org/10.1016/j.envdev.2018.12.004 Dimri AP, Allen S, Huggel C, Mal S, Ballesteros-Cánovas JA, Rohrer M, Shukla A, Tiwari P, Maharana P, Bolch T, Thayyen RJ, Stoffel M, Pandey A (2021) Climate change, cryosphere and impacts in the Indian Himalayan Region. Curr Sci 120(6):1012–1012 Dimri AP, Dash SK (2012) Wintertime climatic trends in the western Himalayas. Clim Change 111(3):775–800. https://doi.org/10.1007/s10584-011-0201-y Dorkenoo K, Nong M, Persson J, Chea N, Scown M (2024) Climate-related loss and damage in contexts of agrarian change: differentiated sense of loss from extreme weather events in northeast Cambodia. Reg Envriron Chang 24(4):1–18. https://doi.org/10.1007/s10113-024-02314-4 Dovie DBK, Dzodzomenyo M, Ogunseitan OA (2017) Sensitivity of health sector indicators’ response to climate change in Ghana. Sci Total Environ 574:837–846. https://doi.org/10.1016/j.scitotenv.2016.09.066 Elalem S, Pal I (2015) Mapping the vulnerability hotspots over Hindu-Kush Himalaya region to flooding disasters. Weather Clim Extremes 8:46–58. https://doi.org/10.1016/j.wace.2014.12.001 Etongo D, Bandara A, Murugaiyan A, Bristol U, Nancy K, Petrousse B, Sinon S (2022) Risk perceptions, vulnerability and adaptation to climate change at farm level across four agricultural zones in Seychelles. World Dev Sustain 1(July):100025. https://doi.org/10.1016/j.wds.2022.100025 Fekete A (2019) Social Vulnerability (Re-)Assessment in Context to Natural Hazards: Review of the Usefulness of the Spatial Indicator Approach and Investigations of Validation Demands. Int J Disaster Risk Sci 10(2):220–232. https://doi.org/10.1007/s13753-019-0213-1 Garschagen M, Doshi D, Reith J, Hagenlocher M (2021) Global patterns of disaster and climate risk—an analysis of the consistency of leading index-based assessments and their results. Climatic Change , 169 (1–2). https://doi.org/10.1007/s10584-021-03209-7 Gerlitz JY, Macchi M, Brooks N, Pandey R, Banerjee S, Jha SK (2017) The Multidimensional Livelihood Vulnerability Index–an instrument to measure livelihood vulnerability to change in the Hindu Kush Himalayas. Climate Dev 9(2):124–140. https://doi.org/10.1080/17565529.2016.1145099 Goodrich CG, Prakash A, Udas PB (2019) Gendered vulnerability and adaptation in Hindu-Kush Himalayas: Research insights. Environmental Development , 31 (November 2018), 1–8. https://doi.org/10.1016/j.envdev.2019.01.001 Gupta AK, Negi M, Nandy S, Alatalo JM, Singh V, Pandey R (2019) Assessing the vulnerability of socio-environmental systems to climate change along an altitude gradient in the Indian Himalayas. Ecological Indicators , 106 (December 2018). https://doi.org/10.1016/j.ecolind.2019.105512 Hackfort S, Burchardt HJ (2018) Analyzing socio-ecological transformations–a relational approach to gender and climate adaptation. Crit Policy Stud 12(2):169–186. https://doi.org/10.1080/19460171.2016.1191363 Highfield WE, Peacock WG, Van Zandt S (2014) Mitigation Planning: Why Hazard Exposure, Structural Vulnerability, and Social Vulnerability Matter. J Plann Educ Res 34(3):287–300. https://doi.org/10.1177/0739456X14531828 Houghton RA, Woodwell GM (2017) Global Climatic Change. Sci Am 214(2):82–91. https://www.jstor.org/stable/24995913 Hunt KMR, Turner AG, Shaffrey LC (2020) The impacts of climate change on the winter water cycle of the western Himalaya. Clim Dyn 55(7–8):2287–2307. https://doi.org/10.1007/s00382-020-05383-3 Intergovernmental Panel on Climate Change (IPCC) (2021) IPCC Report. In Climate Change 2021 – The Physical Science Basis . https://doi.org/10.1017/9781009157896 Singh JS (2006) Sustainable development of the Indian Himalayan region: Linking ecological and. In Current science (Vol. 90, Issue 6, pp. 784–788) Jha RK, Gundimeda H (2019) An integrated assessment of vulnerability to floods using composite index – A district level analysis for Bihar, India. Int J Disaster Risk Reduct 35(January):101074. https://doi.org/10.1016/j.ijdrr.2019.101074 Jha SK, Negi AK, Alatalo JM, Prabhu V, Jha MB, Kumar H (2022) Forest Degradation Index: A Tool for Forest Vulnerability Assessment in Indian Western Himalaya. Sustainability 14(23):1–29. https://doi.org/10.3390/su142315655 Jhajharia D, Singh VP (2011) Trends in temperature, diurnal temperature range and sunshine duration in Northeast India. Int J Climatol 31(9):1353–1367. https://doi.org/10.1002/joc.2164 Jones MW, Abatzoglou JT, Veraverbeke S, Andela N, Lasslop G, Forkel M, Smith AJP, Burton C, Betts RA, van der Werf GR, Sitch S, Canadell JG, Santín C, Kolden C, Doerr SH, Le Quéré C (2022) Global and Regional Trends and Drivers of Fire Under Climate Change. Rev Geophys 60(3):1–76. https://doi.org/10.1029/2020RG000726 Joshi V, Kumar K (2006) Extreme rainfall events and associated natural hazards in Alaknanda valley, Indian Himalayan region. J Mt Sci 3(3):228–236. https://doi.org/10.1007/s11629-006-0228-0 Kaushik S, Rafiq M, Dharpure JK, Howat I, Moortgat J, Joshi PK, Singh T, Dietz AJ (2024) Increasing risk of glacial lake outburst flood in Sikkim, Eastern Himalaya under climate warming. Remote Sens Applications: Soc Environ 36:101286. https://doi.org/10.1016/j.rsase.2024.101286 Khalid Z, Meng X, Rana IA, Rehman M ur, Su X (2021) Holistic Multidimensional Vulnerability Assessment: An empirical investigation on rural communities of the Hindu Kush Himalayan region, Northern Pakistan. International Journal of Disaster Risk Reduction , 62 (May), 102413. https://doi.org/10.1016/j.ijdrr.2021.102413 Khawas V (2007) Environmental Challenges and Human Security in the Himalaya. Himalayas in the New Millennium: Environment, Society, Economy and PolityJanuary 11–12, 2007 Koks EE, Jongman B, Husby TG, Botzen WJW (2015) Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environ Sci Policy 47(November):42–52. https://doi.org/10.1016/j.envsci.2014.10.013 Kumar M, Savita, Singh H, Pandey R, Singh MP, Ravindranath NH, Kalra N (2019) Assessing vulnerability of forest ecosystem in the Indian Western Himalayan region using trends of net primary productivity. Biodivers Conserv 28(8–9):2163–2182. https://doi.org/10.1007/s10531-018-1663-2 Kumar S, Mishra AK, Pramanik S, Mamidanna S, Whitbread A (2020) Climate risk, vulnerability and resilience: Supporting livelihood of smallholders in semiarid India. Land Use Policy 97(May):104729. https://doi.org/10.1016/j.landusepol.2020.104729 Lepcha PT, Pandey PK, Ranjan P (2021) Hydrological significance of Himalayan surface water and its management considering anthropogenic and climate change aspects. IOP Conference Series: Materials Science and Engineering , 1020 (1). https://doi.org/10.1088/1757-899X/1020/1/012013 LI HM, WANG XC, ZHAO XF, QI Y (2021) Understanding systemic risk induced by climate change. Adv Clim Change Res 12(3):384–394. https://doi.org/10.1016/j.accre.2021.05.006 Lokesh, Singh H (2014) Horticultural Development in Himachal Pradesh: An Empirical Analysis. Eur Acad Res II(5):6633–6648 Madhura RK, Krishnan R, Revadekar JV, Mujumdar M, Goswami BN (2015) Changes in western disturbances over the Western Himalayas in a warming environment. Clim Dyn 44(3–4):1157–1168. https://doi.org/10.1007/s00382-014-2166-9 Malakar K, Mishra T (2017) Assessing socio-economic vulnerability to climate change: a city-level index-based approach. Climate Dev 9(4):348–363. https://doi.org/10.1080/17565529.2016.1154449 Malakar K, Mishra T, Hari V, Karmakar S (2021a) Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach. J Environ Manage, 294 (May) Malakar K, Mishra T, Hari V, Karmakar S (2021b) Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach. J Environ Manage 294(September). https://doi.org/10.1016/j.jenvman.2021.112948 Menoni S, Molinari D, Parker D, Ballio F, Tapsell S (2012) Assessing multifaceted vulnerability and resilience in order to design risk-mitigation strategies. Nat Hazards 64(3):2057–2082. https://doi.org/10.1007/s11069-012-0134-4 Mikhaylov A, Moiseev N, Aleshin K, Burkhardt T (2020) Global climate change and greenhouse effect. Entrepreneurship Sustain Issues 7(4):2897–2913. https://doi.org/10.9770/jesi.2020.7.4(21) Mondal M, Biswas A, Haldar S, Mandal S (2022) Rural livelihood risk to hydro-meteorological extreme events: Empirical evidence from Indian Sundarban applying IPCC-AR5 and DEMATEL methodology. Int J Disaster Risk Reduct 77(April):1–18 Murthy CS, Laxman B, Sesha Sai MVR (2015) Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. Int J Disaster Risk Reduct 12:163–171. https://doi.org/10.1016/j.ijdrr.2015.01.004 Nashier I, Lakra P (2020) Global Climate change and its effects. Integr J Social Sci Integr J Soc Sci 2020(1):14–23. http://pubs.iscience.in/ijss Negi GCS, Samal PK, Kuniyal JC, Kothyari BP, Sharma RK, Dhyani PP (2012) Impact of climate change on the western Himalayan mountain ecosystems: An overview. Trop Ecol 53(3):345–356 Negi HS, Kanda N, Shekhar MS, Ganju A (2018) Recent wintertime climatic variability over the North West Himalayan cryosphere. Curr Sci 114(4):760–770. https://doi.org/10.18520/cs/v114/i04/760-770 North GR (2014) Global climate change. J Geoscience Environ Prot 2(April):114–122. https://doi.org/10.1007/978-94-007-7353-0_3 Pandey AC, Ghosh S, Nathawat MS (2011) Evaluating patterns of temporal glacier changes in Greater Himalayan Range, Jammu & Kashmir, India. Geocarto Int 26(4):321–338. https://doi.org/10.1080/10106049.2011.554611 Pandey R, Sharma J, Singh R, Rawat M, Saklani H, Tomar PK, Tiwari L, Bhatt ID, Chand T, Bala N, Panwar VP, Semeraro T (2022) Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya. Forests 13(6):1–12. https://doi.org/10.3390/f13060848 Panwar S (2020) Vulnerability of Himalayan springs to climate change and anthropogenic impact: a review. J Mt Sci 17(1):117–132. https://doi.org/10.1007/s11629-018-5308-4 Peduzzi P, Dao H, Herold C, Mouton F (2009) Assessing global exposure and vulnerability towards natural hazards: The Disaster Risk Index. Nat Hazards Earth Syst Sci 9(4):1149–1159. https://doi.org/10.5194/nhess-9-1149-2009 Pepin NC, Arnone E, Gobiet A, Haslinger K, Kotlarski S, Notarnicola C, Palazzi E, Seibert P, Serafin S, Schöner W, Terzago S, Thornton JM, Vuille M, Adler C (2022) Climate Changes and Their Elevational Patterns in the Mountains of the World. Rev Geophys 60(1):1–40. https://doi.org/10.1029/2020RG000730 Rahim R, Supiyandi S, Siahaan APU, Listyorini T, Utomo AP, Triyanto WA, Irawan Y, Aisyah S, Khairani M, Sundari S, Khairunnisa K (2018) TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees. Journal of Physics: Conference Series , 1028 (1). https://doi.org/10.1088/1742-6596/1028/1/012052 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 Rajeevan MN (2017) M.N. Rajeevan Shailesh Nayak. In Observed Climate Variability and Change over the Indian Region Rawat PK, Tiwari PC, Pant CC (2012) Climate change accelerating land use dynamic and its environmental and socio-economic risks in the Himalayas: Mitigation through sustainable land use. Int J Clim Change Strateg Manag 4(4):452–471. https://doi.org/10.1108/17568691211277764 Ren GY, Shrestha AB (2017) Climate change in the Hindu Kush Himalaya. Adv Clim Change Res 8(3):137–140. https://doi.org/10.1016/j.accre.2017.09.001 Roy S, Bose A, Chowdhury IR (2021) Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India. Arab J Geosci 14(11). https://doi.org/10.1007/s12517-021-07324-8 Roy S, Bose A, Mandal G (2022) Modeling and mapping geospatial distribution of groundwater potential zones in Darjeeling Himalayan region of India using analytical hierarchy process and GIS technique. Model Earth Syst Environ 8(2):1563–1584. https://doi.org/10.1007/s40808-021-01174-9 Roy S, Bose A, Singha N, Basak D, Chowdhury IR (2021) Urban waterlogging risk as an undervalued environmental challenge: An Integrated MCDA-GIS based modeling approach. Environ Challenges 4(June):100194. https://doi.org/10.1016/j.envc.2021.100194 Rusk J, Maharjan A, Tiwari P, Chen THK, Shneiderman S, Turin M, Seto KC (2022) Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya. Sci Total Environ 804:150039. https://doi.org/10.1016/j.scitotenv.2021.150039 Scherler D, Bookhagen B, Strecker MR (2011) Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nat Geosci 4(3):156–159. https://doi.org/10.1038/ngeo1068 Schmidt-Thomé P (2006) Natural and technological hazards and risks affecting the spatial development of European regions. Special Paper Geol Surv Finland 42:6 Schneiderbauer S, Pisa F, Delves P, Pedoth JL, Rufat L, Erschbamer S, Thaler M, Carnelli T, F., Granados-Chahin S (2021) Risk perception of climate change and natural hazards in global mountain regions: A critical review. Sci Total Environ 784:146957. https://doi.org/10.1016/j.scitotenv.2021.146957 Shafiq MU, Rasool R, Ahmed P, Dimri AP (2019) Temperature and Precipitation trends in Kashmir valley, North Western Himalayas. Theoret Appl Climatol 135(1–2):293–304. https://doi.org/10.1007/s00704-018-2377-9 Shah A, Malakar K (2024) Climate-change-induced risk mapping of the Indian Himalayan districts using the latest IPCC framework. International Journal of Disaster Risk Reduction , 102 (October 2023) Shah NA, Shafique M, Owen LA, AI-Mulla Y, Ullah Y (2025) Morphometric analysis of debris flow hazard and risk assessment in the mountain terrains of northern Pakistan using remote sensing and field data. Earth Sci Inf 18(3). https://doi.org/10.1007/s12145-025-01807-y Sharma D, Sreekesh S, Sinha B, Lunyolo LD, Aich V (2023) Flood and drought susceptibility zonation in the mountain environment: a case study of Upper Siang district, Eastern Himalayas, India. Environ Earth Sci 82(17):1–20. https://doi.org/10.1007/s12665-023-11084-x Sharma HR (2005) Agricultural Development and Crop Diversification in Himachal Pradesh: Understanding the Patterns, Processes, Determinants and Lessons. Indian J Agric Econ 60(1):18. file:///F: /Spec 2/Traffic Delay Model.pdf Shekhar MS, Chand H, Kumar S, Srinivasan K, Ganju A (2010) Climate-change studies in the western Himalaya. Ann Glaciol 51(54):105–112. https://doi.org/10.3189/172756410791386508 Shekhar MS, Pattanayak S, Mohanty UC, Paul S, Sravana Kumar M (2015) A study on the heavy rainfall event around Kedarnath area (Uttarakhand) on 16 June 2013. J Earth Syst Sci 124(7):1531–1544. https://doi.org/10.1007/s12040-015-0621-6 Shrestha KL (2005) Global Change Impact Assessment for Himalayan Mountain Regions for Environmental Management and Sustainable Development. Global Environ Res ©2005 AIRIES 9(1):69–81 Shrestha UB, Gautam S, Bawa KS (2012) Widespread climate change in the Himalayas and associated changes in local ecosystems. PLoS ONE 7(5):1–10. https://doi.org/10.1371/journal.pone.0036741 Shukla R, Sachdeva K, Joshi PK (2016a) An indicator-based approach to assess village-level social and biophysical vulnerability of agriculture communities in Uttarakhand, India. J Mt Sci 13(12):2260–2271. https://doi.org/10.1007/s11629-016-4058-4 Shukla R, Sachdeva K, Joshi PK (2016b) Inherent vulnerability of agricultural communities in Himalaya: A village-level hotspot analysis in the Uttarakhand state of India. Appl Geogr 74:182–198. https://doi.org/10.1016/j.apgeog.2016.07.013 Singh RB, Kumar P (2014) Geographic and Socio-Economic Realities of Himachal Pradesh, Northwestern Himalaya. In Livelihood Security in Northwestern Himalaya: Case Studies from Changing Socio-economic Environments in Himachal Pradesh, India (pp. 11–26). https://doi.org/10.1007/978-4-431-54868-3_2 Sultan H, Zhan J, Rashid W, Chu X, Bohnett E (2022) Systematic Review of Multi-Dimensional Vulnerabilities in the Himalayas. Int J Environ Res Public Health 19(19). https://doi.org/10.3390/ijerph191912177 Tewari VP, Verma RK, von Gadow K (2017) Climate change effects in the Western Himalayan ecosystems of India: evidence and strategies. For Ecosyst 4(1). https://doi.org/10.1186/s40663-017-0100-4 Thakur S, Negi VS, Pathak R, Dhyani R, Durgapal K, Rawal RS (2020) Indicator based integrated vulnerability assessment of community forests in Indian west Himalaya. For Ecol Manag 457(May). https://doi.org/10.1016/j.foreco.2019.117674 Thangjam U, Thong P, Sahoo SS, Kumar U (2024) Risk assessment of the jhumias in eastern Himalayan region: An IPCC framework approach. International Journal of Disaster Risk Reduction , 108 (December 2022) Thomas K, Hardy RD, Lazrus H, Mendez M, Orlove B, Rivera-Collazo I, Roberts JT, Rockman M, Warner BP, Winthrop R (2019) Explaining differential vulnerability to climate change: A social science review. Wiley Interdisciplinary Reviews: Clim Change 10(2):1–18. https://doi.org/10.1002/wcc.565 Upgupta S, Sharma J, Jayaraman M, Kumar V, Ravindranath NH (2015) Climate change impact and vulnerability assessment of forests in the Indian Western Himalayan region: A case study of Himachal Pradesh, India. Clim Risk Manage 10:63–76. https://doi.org/10.1016/j.crm.2015.08.002 Vedwan N, Rhoades RE (2001) Climate change in the western Himalayas of India: A study of local perception and response. Climate Res 19(2):109–117. https://doi.org/10.3354/cr019109 Wester P, Mishra A, Mukherji A, Shrestha AB, Change C (2019) The Hindu Kush Himalaya Assessment. In The Hindu Kush Himalaya Assessment . https://doi.org/10.1007/978-3-319-92288-1 Xu J, Grumbine RE, Shrestha A, Eriksson M, Yang X, Wang Y, Wilkes A (2009) The melting Himalayas: Cascading effects of climate change on water, biodiversity, and livelihoods. Conserv Biol 23(3):520–530. https://doi.org/10.1111/j.1523-1739.2009.01237.x Yadav JS, Tiwari SK, Misra A, Rai SK, Yadav RK (2021) High-altitude meteorology of Indian Himalayan Region: complexities, effects, and resolutions. Environ Monit Assess 193(10). https://doi.org/10.1007/s10661-021-09418-y Yadav V, Karmakar S, Kalbar PP, Dikshit AK (2019) PyTOPS: A Python based tool for TOPSIS. SoftwareX 9:217–222. https://doi.org/10.1016/j.softx.2019.02.004 Zang Y, Wang H, Liu Z, Huang J (2024) Flood risk assessment of coastal cities based on GCW _ ISODATA and explainable artificial intelligence methods. Int J Disaster Risk Reduct, 115 (February) Zuzak C, Mowrer M, Goodenough E, Burns J, Ranalli N, Rozelle J (2022) The national risk index: establishing a nationwide baseline for natural hazard risk in the US. Nat Hazards 114(2):2331–2355. https://doi.org/10.1007/s11069-022-05474-w Additional Declarations No competing interests reported. Supplementary Files Supplimentaryfile.docx 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. 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09:39:03","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":295313,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/e51ccf6731c9ec73638b6c64.html"},{"id":100971815,"identity":"efc99fb2-4d3d-4dfa-86ec-be722d4ee1fc","added_by":"auto","created_at":"2026-01-23 10:14:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1510275,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/e6ad262a9429f9c2f903bf50.png"},{"id":100971839,"identity":"161c1d9e-b651-4fc2-aa31-1108f951db32","added_by":"auto","created_at":"2026-01-23 10:14:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2067597,"visible":true,"origin":"","legend":"\u003cp\u003eLocation Map\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/ef90591c930ca32042853949.png"},{"id":100971816,"identity":"7d2f0ff9-cb03-482c-868c-486d3da73eeb","added_by":"auto","created_at":"2026-01-23 10:14:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1066940,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flow chart of multi-hazard risk assessment and its components, i.e., hazard, vulnerability, and exposure.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/abc428689a5cc3602af724f6.png"},{"id":101202818,"identity":"c81fdc9c-1762-4e1d-a57c-0addb4eafd4c","added_by":"auto","created_at":"2026-01-27 09:37:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1069614,"visible":true,"origin":"","legend":"\u003cp\u003eStatus of Hazard index in Himachal Pradesh\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/8bf957ea0c3b40794bbbba9a.png"},{"id":101202908,"identity":"cc2b5717-3e81-4cc4-8c6c-f14516cd351c","added_by":"auto","created_at":"2026-01-27 09:38:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":961673,"visible":true,"origin":"","legend":"\u003cp\u003eStatus of Vulnerability index in Himachal Pradesh\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/96b186fd7f06c0af39e007f9.png"},{"id":100971818,"identity":"a2c75034-c125-41f0-8b04-142ad44086bf","added_by":"auto","created_at":"2026-01-23 10:14:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1065502,"visible":true,"origin":"","legend":"\u003cp\u003eStatus of Exposure index in Himachal Pradesh\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/54ffe1575ae79053c51dd0a8.png"},{"id":101202716,"identity":"5512fd4e-f74b-4b41-911d-090b7bbc2612","added_by":"auto","created_at":"2026-01-27 09:37:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1044582,"visible":true,"origin":"","legend":"\u003cp\u003eStatus of Standardize Risk Index in Himachal Pradesh\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/aa5fbee7db6bd3184a1c62eb.png"},{"id":101203047,"identity":"50137885-ed55-4018-b565-e30b114716f2","added_by":"auto","created_at":"2026-01-27 09:38:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":261400,"visible":true,"origin":"","legend":"\u003cp\u003eRadar diagram showing the scores of standardize risk and its components in Himachal Pradesh\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/9a14b8976cb77e4f5485805b.png"},{"id":101202768,"identity":"a7729077-a1ca-46a1-8a2a-42e3d137f4e2","added_by":"auto","created_at":"2026-01-27 09:37:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":9517367,"visible":true,"origin":"","legend":"\u003cp\u003eEvidence of multi-hazard events in different parts of Himachal Pradesh: (a) Landslide in Chamba-Bharmour Road (2020), (b) Muck disposal during Partivati Stage II hydropower construction in Parvati valley, Kullu district (2008), (c) Land submergence in Sainj Valley (2008), (d \u0026amp; e) Drought condition of land during lean period near pong Dam (2020), (f) Landslides in Garsa Valley, Kullu District (2009).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/fbdc693850de74ab378def39.png"},{"id":102398809,"identity":"7410b075-9e49-4008-83f9-c9f8ac7d6518","added_by":"auto","created_at":"2026-02-11 10:29:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18479207,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/ab7317fc-076e-4645-a234-0cfe95df5be1.pdf"},{"id":101202979,"identity":"68094597-0881-455f-b138-33a71bc9b88a","added_by":"auto","created_at":"2026-01-27 09:38:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45087,"visible":true,"origin":"","legend":"","description":"","filename":"Supplimentaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8565899/v1/93656090382ef5cfc150d908.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate-change Induced Multi-hazard Risk Assessment of Himachal Pradesh in Western Himalayan Region Using IPCC-AR6 Framework and Multi-Attribute Decision-Making Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the 21st century, climate change has become a most important and burning issue throughout the world. Researchers from across the globe are dealing with different dimensions of climate change actively (Houghton \u0026amp; Woodwell, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; LI et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mikhaylov et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nashier \u0026amp; Lakra, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; North, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on the IPCC sixth assessment report, climate change is causing more landslides, floods, and other catastrophes in the world's mountainous regions (Chettri et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Himalayas are a prime example of these warnings (Intergovernmental Panel on Climate Change (IPCC), \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rusk et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In order to explain the societal issues of climate change, the significance of risk, vulnerability, and adaptive capacity has been brought up more than once in recent years (Cantwell-Chavez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Etongo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thangjam et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRisk is calculated by multiplying the likelihood that harmful effects or trends will occur by the consequences of occurrence (Thangjam et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This analysis adhered to the 2014 Intergovernmental Panel on Climate Change (IPCC) climate risk framework. Natural disasters, ecosystem structure, biodiversity, water availability, and ecological processes have all suffered as a result of climate change in the Highlands (Kumar et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thangjam et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There are many scholars deals with climate change-induced vulnerability in different parts of Indian Himalayan region including eastern Himalaya (Banerjee et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bhadwal et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chettri et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Debnath et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kaushik et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); in Hindukush Himalaya (Dilshad et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Elalem \u0026amp; Pal, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Goodrich et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khalid et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ren \u0026amp; Shrestha, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rusk et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); in Western Himalaya (Jha et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pandey et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thakur et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Upgupta et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); in entire Indian Himalayan region several scholars measure different aspects of vulnerability (Aggarwal \u0026amp; Saha, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sultan et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on its usability and practicality, the paper constructs a risk index comprising three components, \u003cem\u003ei.e.\u003c/em\u003e, hazard, vulnerability, and exposure\u0026mdash;using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Attribute Decision-Making (MADM) approach. Majority of the studies earlier conducted in the Indian Himalayan Region have adopted Geographically Weighted Principal Component Analysis (GWPCA), and Analytical Hierarchy Process (AHP) to measure the vulnerability and risk (Roy, Bose, \u0026amp; Chowdhury, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Roy, Bose, Singha, et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Roy et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; A. Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e). However, because TOPSIS can handle a large number of criteria and alternatives, is logical and can be modified, relies less on subjective inputs, and consistently ranks the alternatives, it is advised over other MADM approaches (Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Several scholars adopted this approach to measure the risk index in different parts of the world, i.e., Shah \u0026amp; Malakar (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used this approach to measure the risk index in the entire Himalayan districts of India; Thangjam et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) measured risk index in the eastern part of the Himalayan region; Malakar et al. (2021) using this methodology to measure the risk index in the coastal region of India; Mondal et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used this approach to measure the rural livelihood risk due to hydro-meteorological extreme events in the Indian Sundarban Biosphere Reserve; Zang et al. (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) measured the flood risk assessment of the coastal cities in Shenzhen. However, no previous studies have considered the IPCC-AR6 Framework, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-Attribute Decision-Making (MADM) approach to measure multi-hazard risk in the Western Himalayan region, particularly in the state of Himachal Pradesh. The IPCC-AR6 Framework, integrated with TOPSIS and MADM, provides a comprehensive multi-hazard risk assessment by incorporating hazard, exposure, and vulnerability dimensions, unlike IPCC-AR4, which focused solely on vulnerability. By using quantitative decision-making tools like TOPSIS and MADM, the AR6-based approach ensures objective, indicator-based prioritization of risks, offering greater accuracy and policy relevance compared to the more descriptive and limited scope of the IPCC-AR4 framework.\u003c/p\u003e \u003cp\u003eTo fill the existing research gap, the present study makes a novel attempt to: (1) measure climate-change-induced multi-hazard risk using the IPCC-AR6 framework in Himachal Pradesh; (2) to identify the key spatially heterogeneous factors influencing risk assessment; and (3) to propose region-specific policy recommendations to mitigate the adverse effects of multi-hazard risk in the study area. The second section of the study deals with conceptual framework of the study, section three stated the central hypothesis of this study followed by a section deals with study area, fifth section deals with database and methodology, sixth section deals with results, seventh section deals with hypothesis testing, next section deals with discussion of the study, and reminder of the article deals with conclusion and policy recommendation.\u003c/p\u003e"},{"header":"2. Conceptual Framework","content":"\u003cp\u003eThe conceptual framework of this study is grounded in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC-AR6), which reconceptualizes climate risk as a function of three interrelated components: hazard, vulnerability, and exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Anchored in this tripartite model, the present research constructs a composite Climate-Change-Induced Multi-Hazard Risk Index (CCHMRI) tailored to the Western Himalayan state of Himachal Pradesh. The framework integrates physical and socio-economic dimensions to capture climate-induced risks holistically across diverse terrains and districts. It operationalizes \u0026lsquo;84\u0026rsquo; indicators across three dimensions\u0026mdash;\u0026lsquo;19\u0026rsquo; for hazards (\u003cem\u003ee.g.\u003c/em\u003e, elevation, slope, cold wave days, precipitation variability), \u0026lsquo;57\u0026rsquo; for vulnerability (subdivided into sensitivity and adaptive capacity indicators such as percentage of SC/ST population, disability prevalence, female literacy, and access to health infrastructure), and \u0026lsquo;8\u0026rsquo; for exposure (\u003cem\u003ee.g.\u003c/em\u003e, population density, agricultural land use, and road density). The study employs the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under the Multi-Attribute Decision-Making (MADM) framework to generate normalized, weighted scores for each district under each risk component. These scores are then synthesized into a single risk index using the mathematical relationship R\u0026thinsp;=\u0026thinsp;H \u0026times; V \u0026times; E, where risk is a product of hazard (H), vulnerability (V), and exposure (E). The final risk values are standardized (scaled from 0 to 1) for inter-district comparability and categorized into high, moderate, and low-risk zones. The framework is validated through both secondary data analysis and primary field observations across vulnerable districts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, One-way ANOVA is applied to identify spatially heterogeneous risk determinants. The framework\u0026rsquo;s novelty lies in its unique integration of the IPCC-AR6 model with quantitative decision-making tools (TOPSIS and MADM) to provide an objective, scalable, and replicable method for multi-hazard risk assessment. Unlike, earlier studies in the Indian Himalayan Region primarily relied on Geographically Weighted Principal Component Analysis (GWPCA) or Analytical Hierarchy Process (AHP). The approach used in the present study reduces subjective bias and enhances policy relevance through clear risk ranking and indicator-based diagnostics. The framework thus offers a decision-support tool for policymakers to formulate targeted, evidence-based, and district-specific interventions aimed at climate risk reduction, adaptive capacity enhancement, and sustainable development planning in Himachal Pradesh and other similar mountainous regions globally.\u003c/p\u003e"},{"header":"3. Hypothesis","content":"\u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eA higher probability of hazard occurrence is positively associated with an increase in the standardized multi-hazard risk score.\u003c/p\u003e \u003c/p\u003e"},{"header":"4. Study area","content":"\u003cp\u003eThe state of Himachal Pradesh occupies 55673 Square Kms and is situated in the northwest region of the Himalayas (Singh \u0026amp; Kumar, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The geographical boundaries of this state lies between 75\u003csup\u003e0\u003c/sup\u003e45'55\" east and 79\u003csup\u003e0\u003c/sup\u003e04'20\" east and between 30\u003csup\u003e0\u003c/sup\u003e22'44\" north and 33\u003csup\u003e0\u003c/sup\u003e12'40\" north (Singh \u0026amp; Kumar, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The state has twelve districts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The hill state of Himachal Pradesh has a wide range of elevations ranges from 350 to 6975 meters ASL, from plains to the summits of mountains (Lokesh \u0026amp; Singh, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Singh \u0026amp; Kumar, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Significant differences in temperature, rainfall, soil, and vegetation are caused by shifting elevations and aspects. This state is home to five majors Rivers System: the Satluj, Beas, Chenab, Yamuna, and Ravi. The main source of livelihood and economy is agriculture and directly employs about 62.85% of the state's principal workforce (Indian census, 2011). Another source of revenue for residents of the state is religious and adventure tourism (Singh \u0026amp; Kumar, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The northern part of the state is relatively sparsely populated, while the southern plains are densely populated. Active plate tectonic edges and altered climate conditions make the state vulnerable to a range of natural disasters, such as landslides, earthquakes, flash floods, avalanches, glacial lake outburst floods (GLOFs), etc.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Database and Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Database:\u003c/h2\u003e \u003cp\u003eThe study is based on secondary data sources from different published reports and articles in the referred journals. The Himachal Pradesh Vulnerability Atlas, 2016 was used to get the data related to multi-hazard events, \u003cem\u003ei.e.\u003c/em\u003e, earthquakes, landslides, and people with disability at risk. The Climate Hazard and Vulnerability Atlas of India was used to get data regarding cold waves, heat waves, floods, and snowfall. Hazard Atlas of India, 2022 was used to get the data regarding drought, fog, extreme wind speed, hailstorm, lightning flashes per km\u003csup\u003e2\u003c/sup\u003e, and thunderstorm. Different types of climatic variables related to extreme maximum temperature, extreme minimum temperature, average annual mean temperature, average annual cloud coverage from 1981 to 2022, and precipitation variability were collected from MEERA-2 and NASA Power data. Elevation and slope-related data were collected from the SRTM DEM and USGS Earth Explorer. To measure the sensitivity, adaptive capacity, and exposure, demographic-related variables, and agricultural variables were collected from the Census of India, 2011, and Statistical Abstract of Himachal Pradesh, 2021-22. To validate the secondary results, authors have visited the different vulnerable sites of Himachal Pradesh to present pictorial view of the events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Methodology:\u003c/h2\u003e \u003cp\u003eThe three components of risk\u0026mdash;hazard, vulnerability (which is further subdivided into sensitivity and adaptive capacity), and exposure\u0026mdash;are used to construct the risk index in this study. A composite score is then obtained by adding up these indicators. The study's methodology is shown as a flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and the processes involved are explained in the subsections that follow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1. Risk indicators:\u003c/h2\u003e \u003cp\u003eTo calculate the risk index, three components of risk, \u003cem\u003ei.e.\u003c/em\u003e, hazard, vulnerability (which is further subdivided into two categories, i.e., sensitivity, and adaptive capacity), and exposure, are considered in this study. A flow chart was used to visualize the adopted methodology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the processes involved are explained in the subsections that follow.\u003c/p\u003e \u003cp\u003eThis study uses the most recent sixth IPCC assessment report framework, which views risk as a consequence of three elements: exposure, vulnerability, and hazard (Malakar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Mondal et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thangjam et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship of function is stated as\u003c/p\u003e \u003cp\u003eR\u0026thinsp;=\u0026thinsp;H \u0026times; V \u0026times; E\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;(1)\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;R\u0026rdquo;, \u0026ldquo;H\u0026rdquo;, \u0026ldquo;V\u0026rdquo;, and \u0026ldquo;E\u0026rdquo; represent, respectively, risk, hazard, vulnerability, and exposure.\u003c/p\u003e \u003cp\u003eBoth physical and socio-economic characteristics are taken into account when creating the risk index. The socio-economic indicators point to the vulnerability and exposure constructions, whereas the physical indicators point to the hazard construct (Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Detailed information on indicators is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The rationale for each indicator selection is provided in the supplementary file (Appendix 1). The ensuing subsections contain a detailed discussion of the three elements of risk.\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\u003eComponents, sub-components, and indicators used for risk assessment of Himachal Pradesh along with their functional relationship and data source\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent of risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub Components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunctional Relationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"18\" rowspan=\"19\"\u003e \u003cp\u003eHazard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of state population Vulnerable to earthquake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHimachal Pradesh Vulnerability Atlas, 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of sate population vulnerable to land slide (Severe to very high, high, and moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHimachal Pradesh Vulnerability Atlas, 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of disastrous cold wave days annually from 1969 to 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate hazard and Vulnerability Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of disastrous heat wave days annually from 1969 to 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate hazard and Vulnerability Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal occurrences of flood events from 1969 to 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate hazard and Vulnerability Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll category (moderate, severe, and extreme) drought normalized vulnerability index (based on standardized precipitation index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage number of fog days annually from 1981 to 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtreme wind speed (in meters per second) annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of snowfall days annually from 1969 to 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate hazard and Vulnerability Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage number of hailstorm days annually from 1981 to 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage number of lightning flashes per km2 per day annually from 1983 to 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage number of thunderstorm days annually from 1981 to 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard Atlas of India, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtreme maximum temperature from 1981 to 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMEERA-2, NASA Power data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtreme minimum temperature from 1981 to 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMEERA-2, NASA Power data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual mean temperature from 1981 to 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMEERA-2, NASA Power data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation variability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMEERA-2, NASA Power data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual cloud coverage from 1981 to 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMEERA-2, NASA Power data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSRTM DEM, USGS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSRTM DEM, USGS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"56\" rowspan=\"57\"\u003e \u003cp\u003eVulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of female population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of female headed household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of population below six years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of population above 60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of rural population to total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of S.C. population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of S.T. population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople with disability at risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHimachal Pradesh Vulnerability Atlas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of non-working population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of marginal farmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIlliteracy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of dilapidated households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households with material of roof as grass/ bamboo / thatch / wood / mud / plastic / polythene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households with material of wall as grass/ bamboo / thatch / wood / mud / plastic / polythene / un brunt brick / stones / non packed with mortrar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households not having latrine facility within the premises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"41\" rowspan=\"42\"\u003e \u003cp\u003eAdaptive capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal work participation rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale work participation rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale literacy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale literacy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRural literacy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.T. literacy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households living owned house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having radio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having television\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having computer / laptop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having internet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having landline only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having mobile only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of main worker to total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of household with access to two wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households with access to four wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households availing banking service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households using cleaner source of energy for cooking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households with access to drinking water source within premise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households with access to bathroom facility within premises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having covered drainage as against open drainage or no drainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having electricity as the main source of lightning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita net district domestic product 2015-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistrict domestic product of Himachal Pradesh, 2011-12 to 2015-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of beds available in health institution per 1000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of cultivator to total worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of agriculture laborers to total worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer cultivator net area sown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011 and Statistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of livestock per 10000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of pucca house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCropping intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIrrigation intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrop diversification index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHorticulture efficiency index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal food grain yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita food grain production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation served per bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDensity of school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLivestock density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of medical institutions / 10000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of family welfare Centre / 5000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiteracy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eSpace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of area under agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of permanent pastures \u0026amp; other grazing lands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical abstract of Himachal Pradesh, 2021-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having no exclusive room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoad density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgriculture dependent population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositive (+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCensus of India, 2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e5.2.1.1. Hazard index\u003c/h2\u003e \u003cp\u003eA hazard is any physical event or trend that could occur, whether as a result of human activity or natural causes, and that carries the risk of resulting in fatalities, serious injuries, negative health effects, and damage to property, infrastructure, livelihoods, services, ecosystems, and natural resources (Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Himalayan region has face multi-hazard events \u003cem\u003ei.e.\u003c/em\u003e, landslides, earthquakes, flash floods, fog, adverse effect of cold wave, drought, hailstorm, thunder, and lightning among others (Dikshit et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Intergovernmental Panel on Climate Change (IPCC), \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Joshi \u0026amp; Kumar, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pandey et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rajeevan, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rusk et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Scherler et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schneiderbauer et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shekhar et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wester et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and due to the continuous climate change the occurrences have increased even more (Bhutiyani et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dash et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dimri \u0026amp; Dash, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jhajharia \u0026amp; Singh, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Shrestha et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tewari et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To represent hazard, present study utilize \u0026lsquo;19\u0026rsquo; indicators i.e., percentage of state population vulnerable to earthquake, percentage of state population vulnerable to landslide, total number of disastrous cold wave days annually from 1969 to 2019, total number of heat wave days annually from 1969 to 2019, total occurrence of flood events from 1969 to 2019, all category of drought normalized vulnerability index, average number of fog days annually from 1981 to 2010, extreme wind speed annually, total number of snowfall days annually from 1969 to 2019, average number of hailstorm days annually from 1981 to 2010, average number of lightning flashes per sq km per day annually from 1983 to 2013, average number of thunderstorm days annually from 1981 to 2010, extreme minimum temperature from 1981 to 2022, extreme maximum temperature from 1981 to 2022, average annual mean temperature from 1981 to 2022, precipitation variability, average annual cloud coverage from 1981 to 2022, elevation, and slope. These occurrences are selected because they are linked to both economic shocks and the loss of human life, and other earlier research has also used these indicators (Malakar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Mondal et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thangjam et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The details of the indicators and their data sources are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e5.2.1.2. Vulnerability index\u003c/h2\u003e \u003cp\u003eVulnerability is the term for the potential for harm during disasters as a result of inadequate response capabilities or poor recovery capacities (Das, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Malakar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to earlier Indian Himalayan Region studies, the IHR population is extremely vulnerable and faces numerous socio-economic challenges (Alam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Biella et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chauhan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dhanai, Rekha, 2014; Gerlitz et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rawat et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shukla et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2016b\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2016a\u003c/span\u003e). In this study to measure the vulnerability index, \u0026lsquo;57\u0026rsquo; indicators were selected, and the vulnerability component is subdivided into two sub-component indicators, i.e., sensitivity index, and adaptive capacity index. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the indicators taken into account when creating the vulnerability index and the data source. A total of \u0026lsquo;15\u0026rsquo; indicators, including percentage of female population, percentage of female headed household, percentage of population below six year age group, percentage of population above 60 years age, percentage of rural population to total population, percentage of S.C. population, percentage of S.T. population, people with disability at risk, percentage of non-working population, number of marginal farmers, illiteracy rate, percentage of dilapidated households, percentage of households with material of roof (grass /bamboo/thatch/wood/mud/plastic/polythene, percentage of households with material of wall as grass/bamboo/thatch/wood/mud/plastic/polythene/un brunt brick/stones / non packed) with mortrar, percentage of households not having latrine facility within the premises were selected under sensitivity sub components. On the other hand, \u0026lsquo;42\u0026rsquo; indicators including total work participation rate, female work participation rate, male literacy rate, female literacy rate, rural literacy rate, S.T. literacy rate, sex ratio, percentage of households living owned house, percentage of households having radio, percentage of households having television, percentage of households having computer / laptop, percentage of households having internet, percentage of households having landline only, percentage of households having mobile only, percentage of main worker to total population, Percentage of household with access to two wheeler, percentage of households with access to four wheeler, percentage of households availing banking service, percentage of households using cleaner source of energy for cooking, percentage of households with access to drinking water source within premise, Percentage of households with access to bathroom facility within premises, Percentage of households having covered drainage as against open drainage or no drainage, Percentage of households having electricity as the primary source of lightning, Per capita net district domestic product 2015-16, No of beds available in health institution per 1000 population, Percentage of cultivator to total worker, percentage of agriculture laborers to total worker, Per cultivator net area sown, Number of livestock per 10000 population, Percentage of pucca house, Cropping intensity, Irrigation intensity, Crop diversification index, Horticulture efficiency index, Total food grain yield, Per capita food grain production, population served per bank, Density of school, Livestock density, No of medical institutions / 10000 population, No. of Family Welfare Centre / 5000 population, and Literacy rate were selected under the adaptive capacity sub-components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e5.2.1.3. Exposure index\u003c/h2\u003e \u003cp\u003eExposure includes population and assets that could be affected by local hazards (Das et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Das \u0026amp; Sharma, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Malakar et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Mondal et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Thangjam et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Space is the only sub component under exposure used in this study. There are eight indicators, i.e., population growth rate, Population density, percentage of area under agriculture, Percentage of permanent pastures \u0026amp; other grazing lands, Percentage of households having no exclusive room, Road density, and agriculture-dependent population present under the exposure index. Details of the indicators and their sources are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2. Standardize Risk index\u003c/h2\u003e \u003cp\u003eThe risk index in the study was computed using TOPSIS, or the Technique for Order of Preference by Similarity to Ideal Solution. Hwang and Yoon were the first to propose TOPSIS, a Multi-Attribute Decision Making (MADM) technique (Yadav et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is preferred in this study because of how easily it can be used to choose the best and worst options among the several indicators under each component.\u003c/p\u003e \u003cp\u003eThe option with the greatest distance from the negative ideal solution and the smallest distance from the positive ideal solution is the best one. The sum of all the worst values obtained for each attribute is the negative ideal solution, whereas the sum of all the best values realised for each attribute is the positive ideal solution (Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). TOPSIS measures an alternative's proximity to the ideal solution using the Euclidean distance. Taking into consideration proximity to both the positive and negative ideal solutions, TOPSIS determines the relative distance to the positive ideal solution. Lastly, a comparison of relative distances is used to arrive at different priority rankings. The risk index has been calculated using this method in several earlier studies (Malakar et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Rahim et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis investigation's hazard, vulnerability, and exposure components were obtained using TOPSIS in a process described below (Shah \u0026amp; Malakar, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). There are five steps present in this process, which are discussed below.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1 (Normalised decision matrix construction)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eIn this stage, the values of the indicators are normalised using the Eq.\u0026nbsp;(2) formula. Comparing indicators measured on different scales is made easier by normalisation.\u003c/p\u003e \u003cp\u003eV\u003csub\u003eij\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{X\\text{i}\\text{j}}{\\sqrt{{\\sum\\:}_{i=1}^{n}{x}_{ij}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003eⱯj\u003c/sup\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..(2)\u003c/p\u003e \u003cp\u003ewhere Vij indicates the values of the normalised matrix, xij stands for the ith district, and jth indicates the real score matrix. There are 12 districts in the current study, and there are 19, 57, and 8 indications (m) for exposure, vulnerability, and hazard, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2 (Building of weighted normalised decision matrix)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eIn this stage, the normalised decision matrix is given weights. This study, however, follows many other earlier studies (Malakar et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Malakar \u0026amp; Mishra, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in assigning equal weights and merely averaging the data to create the index, notwithstanding the subjectivity inherent in weight formulation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3 (Measurement of Positive Ideal Solution and Negative Ideal Solution)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eEach indicator determines the ideal best and ideal worst, or positive and negative ideal solutions for each district. The resulting index represents increased exposure, vulnerability, or hazard. The ideal worst (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{J}^{-}\\)\u003c/span\u003e\u003c/span\u003e) is the lowest value across all districts (n), and the ideal best (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{J}^{+}\\)\u003c/span\u003e\u003c/span\u003e) is the maximum value across all districts (n) when it comes to positively (+) contributing indicators. The ideal worst (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{J}^{-}\\)\u003c/span\u003e\u003c/span\u003e) value is the greatest value across all districts, while the ideal best (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{J}^{+}\\)\u003c/span\u003e\u003c/span\u003e) value is the lowest value across all districts for the negatively (\u0026minus;) contributing indicators.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 4 (Measurement of separation distance)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eEach district's Euclidean distance value is calculated, also known as the separation distance value between the ideal best (positive) and worst (negative) solutions. Eq.\u0026nbsp;(3) expresses the Euclidean distance from the ideal best, or positive ideal solution, and Eq.\u0026nbsp;(4) expresses the Euclidean distance from the ideal worst, or negative ideal solution.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{S}_{i}^{+}\\:=\\:\\sqrt{{\\sum\\:}_{j=1}^{m}(Vij-\\:{V}_{j}^{+}})\\text{Ɐ}\\text{i}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:(3)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{S}_{i}^{-}\\:=\\:\\sqrt{{\\sum\\:}_{j=1}^{m}(Vij-\\:{V}_{j}^{+}})\\text{Ɐ}\\text{i}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:(4)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe optimal outcome of the TOPSIS is the one that is closest to the ideal best (positive ideal solution V+) and furthest from the ideal worst (negative ideal solution V\u0026ndash;).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 5 (Calculation of relative closeness to ideal solution score)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe performance score or relative proximity to the ideal solution score is calculated using Eq.\u0026nbsp;(5). Each study component's index is equal to this performance score. The components, i.e., hazard, vulnerability, or exposure, increase with the performance score or index value.\u003c/p\u003e \u003cp\u003ePi = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{S}_{i}^{+}}{{S}_{i}^{+}+\\:{S}_{i}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;(5)\u003c/p\u003e \u003cp\u003eWhere Pi represents the (i) individual risk (Ri) components of the n number of districts, such as exposure (Ei), vulnerability (Vi), or hazard (Hi).\u003c/p\u003e \u003cp\u003eEquation (6) shows that the three components, i.e., hazard (Hi), vulnerability (Vi), and exposure (Ei) are multiplied to get the final risk index (Ri).\u003c/p\u003e \u003cp\u003eRi\u0026thinsp;=\u0026thinsp;Hi \u0026times; Vi \u0026times; Ei\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.(6)\u003c/p\u003e \u003cp\u003eUsing Eq.\u0026nbsp;(7), the risk index (Ri) and the indices of its constituent parts, i.e., hazard (Hi), vulnerability (Vi), and exposure (Ei), are standardised. Higher values indicate greater risk and related components, and index values are thus produced in the interval [0, 1]. Standardisation makes it easier to compare the districts in a meaningful way.\u003c/p\u003e \u003cp\u003eZi = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Yi-Ymin}{Y\\text{max}-\\:Ymin}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;(7)\u003c/p\u003e \u003cp\u003eWhere Zi stands for the standardised values of risk (Ri), hazard (Hi), vulnerability (Vi), or exposure (Ei), Yi represents the indices of risk (Ri), hazard (Hi), vulnerability (Vi), or exposure (Ei). The risk index and its component elements' indices, i.e., hazard, vulnerability, and exposure, are obtained using the previously described process.\u003c/p\u003e \u003cp\u003eBased on the results and using Arc GIS 10.3 software, four maps for hazard index, vulnerability index, exposure index, and standardised risk index were prepared. There were three zones, i.e., high, moderate and low, created to explore the spatial variation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOne-way ANOVA\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eOne-way ANOVA was adopted to identify the key spatially heterogeneous factors influencing risk assessment.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Hazard index\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e revealed that the district Lahaul-Spiti in Himachal Pradesh was recorded as most hazard prone district with perfect score 1.00 due to higher level of elevation and slope, moderate level of drought vulnerability score, higher level of total number of disastrous cold wave days annually from 1969 to 2019. Total number of snowfall days annually from 1969 to 2019, and average annual cloud coverage from 1981 to 2022. Four districts, i.e., Lahaul-Spiti, Chamba, Kullu, and Kinnaur, were found to have a higher level of hazard status (\u0026gt;\u0026thinsp;0.300) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among these regions in Kullu district, the occurrence of floods from 1969 to 2019 was found to be highest. In Kinnaur and Chamba, the drought vulnerability score was found to be the highest. The total number of snowfalls was found to be highest in Chamba, Lahaul-Spiti, and Kinnaur. The district Chamba found the highest level of precipitation variability in this region. Among these high hazardous zones in Kullu, Lahaul-Spiti, and Kinnaur, the elevation and slope are very high, which is responsible for multi-hazard events in this region. Shimla district was found to have a moderate level of hazard status (0.100\u0026ndash;0.300) due to higher levels of landslide occurrences from 1969 to 2019, higher number of disastrous cold wave days, and higher occurrences of flood events from 1969 to 2019, higher number of fogs days, snow fall days, thunder storm, and higher slope angle. On the other hand, a lower level of hazard status (\u0026lt;\u0026thinsp;0.100) was found in seven western districts, i.e, Kangra, Una, Hamirpur, Mandi, Bilaspur, Solan, and Sirmaur. Though this region fell into low hazard zone, the district Kangra faces higher levels of earthquake, landslide, flood, drought, and precipitation variability within this zone.\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\u003eStandardize risk index and its component indices in Himachal Pradesh\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl.\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard\u003c/p\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVulnerability\u003c/p\u003e \u003cp\u003eindex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExposure index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStandardize Risk Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRank\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilaspur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eChamba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eHamirpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eKangra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eKinnaur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eKullu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eLahaul-Spiti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShimla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.219\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\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSirmaur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\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 \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Vulnerability index\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicted that Kangra district was recorded as most vulnerable district with perfect score 1.00 due to higher level sensitivity aspects \u003cem\u003ei.e.\u003c/em\u003e, percentage of female population, percentage of female headed household, percentage of population above 60 years age, percentage of rural population to total population, percentage of non-working population, no of marginal farmer, percentage of households with material of wall as grass/ bamboo / thatch / wood / mud / plastic / polythene / un brunt brick / stones / non packed with mortrar, and percentage of households not having latrine facility within the premises, and lower adaptive capacity aspects i.e., lower level of total work participation rate, female work participation rate, percentage of households living owned house, percentage of households having computer / laptop, percentage of main worker to total population, percentage of households with access to four wheeler, per capita net district domestic product 2015-16, percentage of cultivator to total worker, per cultivator net area sown, no of medical institutions / 10000 population, and no. of family welfare center / 5000 population. Five districts i.e., Kangra, Una, Hamirpur, Bilaspur, and Shimla were found to have a higher level of vulnerability status (\u0026gt;\u0026thinsp;0.500) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Four districts, i.e., Lahul-Spiti, Kinnaur, Mandi, and Sirmaur, were found to have a moderate level of vulnerability status (0.25\u0026ndash;0.50). On the other hand, three districts, i.e., Kullu, Chamba, and Solan, were found to have a low level of vulnerability status (\u0026lt;\u0026thinsp;0.25).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e6.3. Exposure index\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e found that the district Hamirpur in Himachal Pradesh is recorded as the highest exposure district with a perfect score 1.00 due to the highest level of population density, percentage of area under agriculture, and road density, higher level of agriculture dependent population, and moderate level of population growth rate. Six districts, i.e., Kangra, Una, Hamirpur, Mandi, and Solan, were found to have a higher level of exposure status (\u0026gt;\u0026thinsp;0.300) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among this region in Una district, the Population growth rate is found highest. In Solan, the percentage of permanent pastures \u0026amp; other grazing lands was found to be higher. In mandi, road density and agriculture-dependent population are found higher in the districts of Sirmaur and Shimla, a moderate level of exposure status (0.100 to 0.300) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among these regions, the dependent population is higher, and the percentage of area under agriculture and road density is found at a moderate level. On the other hand, a lower level of exposure status (\u0026lt;\u0026thinsp;0.100) was found in Chamba, Lahual \u0026amp; Spiti, Kullu, and Kinnaur. Among this region, in Lahual \u0026amp; Spiti, population growth rate, population density, percentage of area under agriculture, and road density were found to be the lowest. In Kullu, the Percentage of permanent pastures \u0026amp; other grazing lands is found to be lower. In Kullu, Lahual \u0026amp; Spiti, the percentage of households having no exclusive room was found to be lower.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e6.4. Standardize Risk index\u003c/h2\u003e \u003cp\u003eInter-district spatial variation has been observed in the standardized risk status of Himachal Pradesh (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Kinnaur district was recorded as the most risk-prone district due to multi-hazard events with a perfect score of 1.00, where the hazard score is high, vulnerability score is moderate, and the exposure. On the contrary, district Bilaspur was the least risk-prone district due to the lowest hazard score and higher exposure and vulnerability scores. Five districts from north-to-north-eastern, \u003cem\u003ei.e.\u003c/em\u003e, Lahaul-Spiti, Kullu, Kangra, Solan, and Kinnaur, were found to have a higher level of risk status (\u0026gt;\u0026thinsp;0.375), where three districts, \u003cem\u003ei.e.\u003c/em\u003e, Chamba, Mandi, and Hamirpur, were found to have a moderate level of risk status (0.200 to 0.375). On the other hand, the four districts, i.e., Una, Bilaspur, Solan, and Sirmaur, have a lower level of risk status (\u0026lt;\u0026thinsp;0.200).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.5. Major factors of spatial heterogeneity in risk assessment\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed that among the 19 indicators under hazard component, six variables were found significant spatial difference. In hazard dimension significant spatial difference found in total number of disastrous cold wave days annually from 1969 to 2019 (F value\u0026thinsp;=\u0026thinsp;3.95, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), Average number of fog days annually from 1981 to 2010 (F value\u0026thinsp;=\u0026thinsp;3.1, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), Total number of snowfall days annually from 1969 to 2019 (F value\u0026thinsp;=\u0026thinsp;3.89, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), Average number of lightning flashes per km\u003csup\u003e2\u003c/sup\u003e per day annually from1983 to 2013 (F value\u0026thinsp;=\u0026thinsp;3.3, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), Extreme maximum temperature from 1981 to 2022 (F value\u0026thinsp;=\u0026thinsp;3.96, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), Average annual cloud coverage from 1981 to 2022 (F value\u0026thinsp;=\u0026thinsp;3.98, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), and elevation (F value\u0026thinsp;=\u0026thinsp;3.39, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\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\u003eMajor factors of Spatially heterogeneous risk assessment in Himachal Pradesh\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent of risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSub component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig. level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eHazard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of disastrous cold wave days annually from 1969 to 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage number of fog days annually from 1981 to 2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of snowfall days annually from 1969 to 2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage number of lightning flashes per km2 per day annually from1983 to 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtreme maximum temperature from 1981 to 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage annual cloud coverage from 1981 to 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eVulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of population below six years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of S.C. population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople with disability at risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAdaptive capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of households having mobile only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of household with access to two-wheeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of beds available in health institution per 1000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of cultivator to total worker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer cultivator net area sown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita food grain production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDensity of school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of medical institutions / 10000 population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the vulnerability dimension, there are \u0026lsquo;57\u0026rsquo; indicators, and among them, \u0026lsquo;11\u0026rsquo; variables have been found has significant spatial differences. The significant spatial difference found in percentage of population below six years (F value\u0026thinsp;=\u0026thinsp;3.22, p value\u0026thinsp;\u0026gt;\u0026thinsp;0.1), percentage of S.C. population (3.26, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), people with disability at risk (F value: 10.64, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, percentage of households having mobile only (F value\u0026thinsp;=\u0026thinsp;4.31, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), percentage of household with access to two-wheeler (F value\u0026thinsp;=\u0026thinsp;3.34, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), number of beds available in health institution per 1000 population (F value\u0026thinsp;=\u0026thinsp;3.87, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), percentage of cultivator to total worker (F value\u0026thinsp;=\u0026thinsp;3.1, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), per cultivator net area sown (F value\u0026thinsp;=\u0026thinsp;3.52, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), per capita food grain production (F value\u0026thinsp;=\u0026thinsp;3.13, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), density of school (F value\u0026thinsp;=\u0026thinsp;3.65, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1), and number of medical institutions per 10000 population (F value\u0026thinsp;=\u0026thinsp;3.58, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1). On the other hand, among the eight variables under the exposure dimension, one, population growth, has found a significant spatial difference (F value\u0026thinsp;=\u0026thinsp;3.99, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Hypothesis test","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e revealed that the correlation coefficient between hazard index and standardized risk index is 0.617, and the p value for two tailed tests of significance is found 0.032 (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results indicated that there is a strong positive correlation between the hazard index and the standardized risk index. Therefore, this research hypothesis (A higher probability of hazard occurrence is positively associated with an increase in the standardized multi-hazard risk score) is accepted.\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\u003eHypothesis test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIndependent\u003c/p\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardize\u003c/p\u003e \u003cp\u003erisk index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.617**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSig. (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: *p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1, **p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"8. Discussion","content":"\u003cp\u003eThe Himalayan region is among the world's most ecologically sensitive and geologically fragile mountain systems across the globe (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Singh, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Khawas, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Negi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Panwar, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shrestha, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Climate change has exacerbated this fragility, triggering a rise in both the frequency and severity of multi-hazard events such as landslides, flash floods, droughts, extreme temperatures, snowfall anomalies, and glacial lake outburst floods (Shah et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Particularly in the Western Himalayan region, the impacts of global warming are more pronounced due to its unique topographic and climatic conditions (Dimri et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hunt et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Madhura et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Negi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Negi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pepin et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shafiq et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shekhar et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Vedwan \u0026amp; Rhoades, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The findings from this study, conducted across the 12 districts of Himachal Pradesh, substantiate this growing threat. The interlinkage between climate-sensitive sectors such as agriculture, hydrology, and forestry and the increasing frequency of hazard events is clearly evident. For instance, districts like Kinnaur and Lahaul-Spiti\u0026mdash;characterized by high elevation and steep slopes\u0026mdash;demonstrate high hazard scores due to climate-induced snowfall variability, increased cold wave days, and precipitation variability, which directly impact agricultural cycles and forest ecosystems. The adverse effect on agriculture-dependent communities is further aggravated by the recurrence of drought events and flooding, which reduces agricultural productivity and disrupts local food systems, water availability, and biodiversity (Balgah et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Likewise, snowfall and cold wave days significantly affect forest cover and downstream water availability, influencing livelihood and ecological balance (Berhanu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lepcha et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Negi et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The integration of climate-sensitive indicators in this study offers clear empirical evidence that climatic hazards are not isolated incidents but rather deeply intertwined with the socio-ecological systems of the region (Dorkenoo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dovie et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hackfort \u0026amp; Burchardt, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatially, the study identifies considerable heterogeneity in the distribution and intensity of multi-hazard risks across Himachal Pradesh. The risk profiles vary from high-risk districts in the northern and northeastern belts to low-risk districts in the southern and western parts. According to the standardised risk index, five districts namely, Kinnaur, Lahaul-Spiti, Kullu, Kangra, and Solan\u0026mdash;fall into the high-risk category, with risk scores exceeding 0.375. These regions are frequently exposed to extreme climatic conditions and geological vulnerabilities. For instance, Kinnaur records a perfect risk score of 1.00 due to its high hazard exposure, moderate vulnerability, and low adaptive capacity. This suggests that even regions with relatively low exposure can experience high composite risk if other dimensions, such as hazard frequency and vulnerability indicators, are substantial. This result is align with earlier findings of the research in different parts of the world (Birkmann, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Brooks et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dandapat \u0026amp; Panda, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fekete, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Garschagen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Highfield et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jha \u0026amp; Gundimeda, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Koks et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Menoni et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Murthy et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Peduzzi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Schmidt-Thom\u0026eacute;, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zuzak et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, districts like Bilaspur and Una, located in the southern belt, demonstrate low risk scores (\u0026lt;\u0026thinsp;0.200), owing to lower hazard occurrences and better infrastructure or adaptive capacities. These findings are supported by the earlier research findings (Balgah et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Das et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Das \u0026amp; Sharma, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The spatial analysis using the IPCC-AR6 framework and TOPSIS-based multi-attribute decision-making approach illustrates that the northern districts face significantly higher hazard exposure due to elevation, geological instability, and climate variability. However, exposure alone does not define risk. For example, despite lower hazard occurrences in Hamirpur, its high exposure index\u0026mdash;driven by population density and land use pressure\u0026mdash;elevates its risk level to moderate. Such spatial differentiation underscores the importance of a composite and region-specific framework for risk assessment that considers the interaction between hazard, vulnerability, and exposure in a localized context.\u003c/p\u003e \u003cp\u003eThe study also highlights the importance of multi-hazard risk and its spatial variation across the state. Vulnerability is not uniformly distributed; instead, it is intricately shaped by demographic patterns, infrastructural gaps, and disparities in access to resources and services. Districts like Kangra, Una, and Hamirpur register high vulnerability scores (\u0026gt;\u0026thinsp;0.500), influenced by a combination of high sensitivity and limited adaptive capacity. Kangra, in particular, records a perfect vulnerability score of 1.00 due to factors such as a high percentage of elderly population, female-headed households, marginal farmers, and poor housing conditions. These conditions are further compounded by limited access to four-wheelers, computers, and health infrastructure. In contrast, districts such as Kullu, Chamba, and Solan show lower vulnerability scores (\u0026lt;\u0026thinsp;0.25), indicating a relatively higher adaptive capacity. The ANOVA results reinforce these spatial patterns, identifying key indicators like S.C. population percentage, prevalence of disability, and mobile access as significant determinants of spatial heterogeneity. In effect, vulnerability acts as a multiplier of hazard risk, and its interplay with exposure determines the ultimate risk profile. This complexity reiterates the inadequacy of hazard-only assessments and the necessity to integrate socio-economic parameters for a holistic understanding of climate risk. Addressing infrastructure, health, education, and livelihood indicators is essential for reducing multi-hazard risk and enhancing long-term resilience.\u003c/p\u003e \u003cp\u003eThe policy implications of these findings are manifold. First, the clear spatial heterogeneity of risks underscores the need for region-specific interventions rather than a one-size-fits-all approach, for high-risk districts like Kinnaur and Lahaul-Spiti, structural mitigation (e.g., slope stabilization, avalanche control), early warning systems, and climate-resilient infrastructure development should be prioritized. Policy efforts must focus on improving healthcare, education, housing, and rural connectivity in moderately exposed but highly vulnerable districts like Kangra and Hamirpur. Moreover, the study demonstrates the efficacy of integrating the IPCC-AR6 risk framework with a quantitative decision-making tool like TOPSIS, enabling objective prioritization of regions based on a multidimensional perspective. This methodological innovation ensures transparency and scientific robustness and enhances the model's scalability for other climate-sensitive regions. Additionally, the validation through field investigations affirms the accuracy of the secondary data-driven results and enriches the policy relevance of the study. This study provides practical evidence for policymakers, disaster risk managers, and local planners to develop tailored, adaptive, and inclusive strategies to mitigate the adverse impacts of climate-change-induced multi-hazard risks in the Western Himalayas. The focus should now move from relief-based responses to proactive, data-driven, and locally focused risk reduction strategies. To validate the secondary results, we also visited the different vulnerable sites across the Himachal Pradesh (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"9. Conclusion and policy recommendation","content":"\u003cp\u003eThis study assesses inter-district spatial variation in climate-change-induced multi-hazard risk across Himachal Pradesh using the IPCC-AR6 framework and a Multi-Attribute Decision-Making approach. A composite index was developed using 84 indicators across three components: hazard, vulnerability, and exposure. The districts of Lahaul-Spiti, Chamba, Kullu, and Kinnaur exhibited high hazard levels (\u0026gt;\u0026thinsp;0.300) due to factors such as frequent flooding (1969\u0026ndash;2019), severe droughts, heavy snowfall, precipitation variability, and steep terrain. Kangra, Una, Hamirpur, Bilaspur, and Shimla showed high vulnerability (\u0026gt;\u0026thinsp;0.500), driven by higher proportions of elderly, female-headed households, non-working and rural populations, inadequate housing, poor sanitation, and low adaptive capacity indicators like weak labor participation, low income, limited asset ownership, and inadequate health infrastructure. Exposure levels were highest in Kangra, Una, Hamirpur, Mandi, and Solan, attributed to rapid population growth (Una), dependence on agriculture (Mandi), and extensive grazing land (Solan). Overall risk was highest (\u0026gt;\u0026thinsp;0.375) in Lahaul-Spiti, Kullu, Kangra, Solan, and Kinnaur; moderate (0.200\u0026ndash;0.375) in Chamba, Mandi, and Hamirpur; and lowest (\u0026lt;\u0026thinsp;0.200) in Una, Bilaspur, Solan, and Sirmaur. One-way ANOVA identified 18 key indicators with significant spatial variation, including seven under hazard (e.g., snowfall, fog, lightning, extreme temperatures) and eleven under vulnerability and exposure (e.g., SC population, disability, healthcare, agricultural dependence, and mobile/transport access). These findings offer critical insights for region-specific, data-driven climate risk mitigation planning in Himachal Pradesh.\u003cb\u003ePolicy recommendation\u003c/b\u003eBased on the findings, this study suggests some region-specific policy recommendations as:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo reduce the high multi-hazard risk in Kinnaur located in high altitude with majority of tribal population, installing advanced early warning systems for snow, landslides, and flash floods is essential, and conducting regular village-level disaster preparedness drills in collaboration with local authorities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo address the high hazard risk in Lahaul-Spiti, investment should be directed toward developing climate-resilient road infrastructure with avalanche protection and constructing thermal-insulated shelters for vulnerable populations facing extreme cold events.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo reduce climate risk in Kangra, targeted interventions should focus on enhancing female workforce participation through skill development and strengthening healthcare infrastructure for disabled and elderly populations, addressing the district's high vulnerability and exposure levels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLand-use zoning laws should be enforced to mitigate risk in Hamirpur, and vertical urban planning should be promoted to manage high exposure driven by dense population, agricultural dependence, and expanding road networks.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo manage snow and rain-related hazards in Chamba, set up local snow forecasting systems and promote slope protection measures like contour trenching to prevent landslides.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo reduce risk in Shimla, old buildings and public infrastructure should be retrofitted for landslide and earthquake safety, while promoting rainwater harvesting and green roofs to manage runoff and urban flooding.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eClimate-resilient crop varieties and agroforestry should be promoted to enhance resilience in Solan, along with crop insurance and irrigation support for farmers in high-risk areas.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo reduce risk in Mandi, emergency road connectivity to remote villages should be improved, and mobile disaster response units with health and communication services should be established.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDespite low hazard levels, Bilaspur should strengthen institutional capacity by integrating climate risk into local planning and building community-based disaster preparedness networks to address its moderate vulnerability and high exposure.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo strengthen resilience in Sirmaur, access to health and education services should be expanded in rural and hilly areas, along with improved digital literacy and mobile coverage for effective risk communication.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eLimitations and future scope of the study\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe entire study is based on a quantitative approach. To reveal the actual ground reality in the future, in-depth field investigation with an extended period, participant observations, and a mixed method approach adaptation is highly recommended.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThis study is based on a cross-sectional approach. A longitudinal study is required to evaluate the spatio-temporal changes in multi-hazard risk assessment in the Western Himalayan region and any parts of the Himalayan belt, and formulate evidence-based policy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eParticular attention must be paid to places that are more vulnerable to multi-hazard risk, and specific policy changes may be necessary to lessen the negative effects of multi-hazard risk. Future research must help us better understand these dynamics and offer recommendations for wise policy formulation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe work described in this article has never been influenced by the author\u0026rsquo;s personal or financial interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not receive any funding for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShibu Das: Conceptualization, methodology, investigation, validation, writing- original draft, review \u0026amp; editing.Sanjeev Sharma: Review \u0026amp; Editing\u003c/p\u003e\u003ch2\u003eAcknowledgement:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal Y, Saha SK (2023) An improved rapid visual screening method for seismic vulnerability assessment of reinforced concrete buildings in Indian Himalayan region. Bull Earthq Eng 21(1):319\u0026ndash;347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10518-022-01537-2\u003c/span\u003e\u003cspan address=\"10.1007/s10518-022-01537-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad A, Rawat JS, Rai SC (1990) An analysis of the himalayan environment and guidelines for its management and ecologically sustainable development. Environmentalist 10(4):281\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02239722\u003c/span\u003e\u003cspan address=\"10.1007/BF02239722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam MK, Dasgupta S, Barua A, Ravindranath NH (2022) Assessing climate-relevant vulnerability of the Indian Himalayan Region (IHR): a district-level analysis. Nat Hazards 112(2):1395\u0026ndash;1421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-022-05233-x\u003c/span\u003e\u003cspan address=\"10.1007/s11069-022-05233-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalgah RA, Ngwa KA, Buchenrieder GR, Kimengsi JN (2023) Impacts of Floods on Agriculture-Dependent Livelihoods in Sub-Saharan Africa: An Assessment from Multiple. \u003cem\u003eLand\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee S, Niyogi R, Sarkar MS, John R (2022) Assessing the vulnerability of protected areas in the eastern Himalayas based on their biological, anthropogenic, and environmental aspects. Trees Forests People 8(February):100228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tfp.2022.100228\u003c/span\u003e\u003cspan address=\"10.1016/j.tfp.2022.100228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerhanu B, Seleshi Y, Amare M, Melesse AM (2016) Upstream\u0026ndash;downstream linkages of hydrological processes in the Nile River basin. Springer Geogr 207\u0026ndash;223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-18787-7_11\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-18787-7_11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhadwal S, Sharma G, Gorti G, Sen SM (2019) Livelihoods, gender and climate change in the Eastern himalayas. Environ Dev 31(April):68\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envdev.2019.04.008\u003c/span\u003e\u003cspan address=\"10.1016/j.envdev.2019.04.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhutiyani MR, Kale VS, Pawar NJ (2007) Long-term trends in maximum, minimum and mean annual air temperatures across the Northwestern Himalaya during the twentieth century. Clim Change 85(1\u0026ndash;2):159\u0026ndash;177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-006-9196-1\u003c/span\u003e\u003cspan address=\"10.1007/s10584-006-9196-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiella R, Hoffmann R, Upadhyay H (2022) Climate, Agriculture, and Migration: Exploring the Vulnerability and Outmigration Nexus in the Indian Himalayan Region. Mt Res Dev 42(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1659/mrd-journal-d-21-00058.1\u003c/span\u003e\u003cspan address=\"10.1659/mrd-journal-d-21-00058.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirkmann J (2007) Risk and vulnerability indicators at different scales:. Applicability, usefulness and policy implications. Environ Hazards 7(1):20\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envhaz.2007.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.envhaz.2007.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks N, Adger WN, Kelly PM (2005) The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Glob Environ Change 15(2):151\u0026ndash;163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gloenvcha.2004.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.gloenvcha.2004.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantwell-Chavez D (2023) Interlocal adaptations to climate in east and southeast Asia: sharing lessons of agriculture, disaster risk reduction, and resource management. In \u003cem\u003eInternational Affairs\u003c/em\u003e (Vol. 99, Issue 3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ia/iiad074\u003c/span\u003e\u003cspan address=\"10.1093/ia/iiad074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChauhan D, Thiyaharajan M, Pandey A, Singh N, Singh V, Sen S, Pandey R (2022) Climate change water vulnerability and adaptation mechanism in a Himalayan City, Nainital, India. Environ Sci Pollut Res 29(57):85904\u0026ndash;85921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-021-15713-5\u003c/span\u003e\u003cspan address=\"10.1007/s11356-021-15713-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChettri N, Tsering K, Shrestha A, Sharma E (2018) Ecological Vulnerability to Climate Change in the Mountains: A Case Study from the Eastern Himalayas. \u003cem\u003ePlant Diversity in the Himalaya Hotspot Region\u003c/em\u003e, \u003cem\u003eJanuary\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ 3(4):1627\u0026ndash;1646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-017-0388-7\u003c/span\u003e\u003cspan address=\"10.1007/s40808-017-0388-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S (2024) Determinants of livelihood vulnerability to climate change among the agrarian community: Empirical evidence from the coastal region of Indian Sundarban Biosphere Reserve. Reg Stud Mar Sci 78(September):103777. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsma.2024.103777\u003c/span\u003e\u003cspan address=\"10.1016/j.rsma.2024.103777\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Majumder S, Sharma KK (2023) Assessing integrated agricultural livelihood vulnerability to climate change in the coastal region of West Bengal: Implication for spatial adaptation planning. Reg Stud Mar Sci 57:102748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsma.2022.102748\u003c/span\u003e\u003cspan address=\"10.1016/j.rsma.2022.102748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas S, Sharma KK (2024) Livelihood vulnerability assessment among the agrarian indigenous communities of Sundarban Biosphere Reserve in India for building resilience and adaptation. In \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e (Issue 0123456789). Springer Berlin Heidelberg. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-024-35560-4\u003c/span\u003e\u003cspan address=\"10.1007/s11356-024-35560-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDash SK, Jenamani RK, Kalsi SR, Panda SK (2007) Some evidence of climate change in twentieth-century India. Clim Change 85(3\u0026ndash;4):299\u0026ndash;321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-007-9305-9\u003c/span\u003e\u003cspan address=\"10.1007/s10584-007-9305-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebnath J, Sahariah D, Nath N, Saikia A, Lahon D, Islam MN, Hashimoto S, Meraj G, Kumar P, Singh SK, Kanga S, Chand K (2024) Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques. Model Earth Syst Environ 10(2):2393\u0026ndash;2419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-023-01912-1\u003c/span\u003e\u003cspan address=\"10.1007/s40808-023-01912-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhanai R et al (2014) The Effects of Climate Change on Natural Resources and Socio-economic Condition of Himalayan Communities of. Int J Mod Communication Technol Res 2(11):21\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM (2020) Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review. \u003cem\u003eApplied Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4\u003c/span\u003e\u003cspan address=\"https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilshad T, Mallick D, Udas PB, Goodrich CG, Prakash A, Gorti G, Bhadwal S, Anwar MZ, Khandekar N, Hassan SMT, Habib N, Abbasi SS, Syed MA, Rahman A (2019) Growing social vulnerability in the river basins: Evidence from the Hindu Kush Himalaya (HKH) Region. \u003cem\u003eEnvironmental Development\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(December 2018), 19\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envdev.2018.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.envdev.2018.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimri AP, Allen S, Huggel C, Mal S, Ballesteros-C\u0026aacute;novas JA, Rohrer M, Shukla A, Tiwari P, Maharana P, Bolch T, Thayyen RJ, Stoffel M, Pandey A (2021) Climate change, cryosphere and impacts in the Indian Himalayan Region. Curr Sci 120(6):1012\u0026ndash;1012\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimri AP, Dash SK (2012) Wintertime climatic trends in the western Himalayas. Clim Change 111(3):775\u0026ndash;800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-011-0201-y\u003c/span\u003e\u003cspan address=\"10.1007/s10584-011-0201-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDorkenoo K, Nong M, Persson J, Chea N, Scown M (2024) Climate-related loss and damage in contexts of agrarian change: differentiated sense of loss from extreme weather events in northeast Cambodia. Reg Envriron Chang 24(4):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10113-024-02314-4\u003c/span\u003e\u003cspan address=\"10.1007/s10113-024-02314-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDovie DBK, Dzodzomenyo M, Ogunseitan OA (2017) Sensitivity of health sector indicators\u0026rsquo; response to climate change in Ghana. Sci Total Environ 574:837\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2016.09.066\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2016.09.066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElalem S, Pal I (2015) Mapping the vulnerability hotspots over Hindu-Kush Himalaya region to flooding disasters. Weather Clim Extremes 8:46\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wace.2014.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.wace.2014.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEtongo D, Bandara A, Murugaiyan A, Bristol U, Nancy K, Petrousse B, Sinon S (2022) Risk perceptions, vulnerability and adaptation to climate change at farm level across four agricultural zones in Seychelles. World Dev Sustain 1(July):100025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wds.2022.100025\u003c/span\u003e\u003cspan address=\"10.1016/j.wds.2022.100025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFekete A (2019) Social Vulnerability (Re-)Assessment in Context to Natural Hazards: Review of the Usefulness of the Spatial Indicator Approach and Investigations of Validation Demands. Int J Disaster Risk Sci 10(2):220\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13753-019-0213-1\u003c/span\u003e\u003cspan address=\"10.1007/s13753-019-0213-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarschagen M, Doshi D, Reith J, Hagenlocher M (2021) Global patterns of disaster and climate risk\u0026mdash;an analysis of the consistency of leading index-based assessments and their results. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e169\u003c/em\u003e(1\u0026ndash;2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-021-03209-7\u003c/span\u003e\u003cspan address=\"10.1007/s10584-021-03209-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerlitz JY, Macchi M, Brooks N, Pandey R, Banerjee S, Jha SK (2017) The Multidimensional Livelihood Vulnerability Index\u0026ndash;an instrument to measure livelihood vulnerability to change in the Hindu Kush Himalayas. Climate Dev 9(2):124\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17565529.2016.1145099\u003c/span\u003e\u003cspan address=\"10.1080/17565529.2016.1145099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodrich CG, Prakash A, Udas PB (2019) Gendered vulnerability and adaptation in Hindu-Kush Himalayas: Research insights. \u003cem\u003eEnvironmental Development\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(November 2018), 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envdev.2019.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.envdev.2019.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta AK, Negi M, Nandy S, Alatalo JM, Singh V, Pandey R (2019) Assessing the vulnerability of socio-environmental systems to climate change along an altitude gradient in the Indian Himalayas. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(December 2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2019.105512\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2019.105512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackfort S, Burchardt HJ (2018) Analyzing socio-ecological transformations\u0026ndash;a relational approach to gender and climate adaptation. Crit Policy Stud 12(2):169\u0026ndash;186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19460171.2016.1191363\u003c/span\u003e\u003cspan address=\"10.1080/19460171.2016.1191363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHighfield WE, Peacock WG, Van Zandt S (2014) Mitigation Planning: Why Hazard Exposure, Structural Vulnerability, and Social Vulnerability Matter. J Plann Educ Res 34(3):287\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0739456X14531828\u003c/span\u003e\u003cspan address=\"10.1177/0739456X14531828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoughton RA, Woodwell GM (2017) Global Climatic Change. Sci Am 214(2):82\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jstor.org/stable/24995913\u003c/span\u003e\u003cspan address=\"https://www.jstor.org/stable/24995913\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunt KMR, Turner AG, Shaffrey LC (2020) The impacts of climate change on the winter water cycle of the western Himalaya. Clim Dyn 55(7\u0026ndash;8):2287\u0026ndash;2307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-020-05383-3\u003c/span\u003e\u003cspan address=\"10.1007/s00382-020-05383-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntergovernmental Panel on Climate Change (IPCC) (2021) IPCC Report. In \u003cem\u003eClimate Change 2021 \u0026ndash; The Physical Science Basis\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/9781009157896\u003c/span\u003e\u003cspan address=\"10.1017/9781009157896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh JS (2006) Sustainable development of the Indian Himalayan region: Linking ecological and. In \u003cem\u003eCurrent science\u003c/em\u003e (Vol. 90, Issue 6, pp. 784\u0026ndash;788)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha RK, Gundimeda H (2019) An integrated assessment of vulnerability to floods using composite index \u0026ndash; A district level analysis for Bihar, India. Int J Disaster Risk Reduct 35(January):101074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijdrr.2019.101074\u003c/span\u003e\u003cspan address=\"10.1016/j.ijdrr.2019.101074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha SK, Negi AK, Alatalo JM, Prabhu V, Jha MB, Kumar H (2022) Forest Degradation Index: A Tool for Forest Vulnerability Assessment in Indian Western Himalaya. Sustainability 14(23):1\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su142315655\u003c/span\u003e\u003cspan address=\"10.3390/su142315655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJhajharia D, Singh VP (2011) Trends in temperature, diurnal temperature range and sunshine duration in Northeast India. Int J Climatol 31(9):1353\u0026ndash;1367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.2164\u003c/span\u003e\u003cspan address=\"10.1002/joc.2164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones MW, Abatzoglou JT, Veraverbeke S, Andela N, Lasslop G, Forkel M, Smith AJP, Burton C, Betts RA, van der Werf GR, Sitch S, Canadell JG, Sant\u0026iacute;n C, Kolden C, Doerr SH, Le Qu\u0026eacute;r\u0026eacute; C (2022) Global and Regional Trends and Drivers of Fire Under Climate Change. Rev Geophys 60(3):1\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020RG000726\u003c/span\u003e\u003cspan address=\"10.1029/2020RG000726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi V, Kumar K (2006) Extreme rainfall events and associated natural hazards in Alaknanda valley, Indian Himalayan region. J Mt Sci 3(3):228\u0026ndash;236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11629-006-0228-0\u003c/span\u003e\u003cspan address=\"10.1007/s11629-006-0228-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaushik S, Rafiq M, Dharpure JK, Howat I, Moortgat J, Joshi PK, Singh T, Dietz AJ (2024) Increasing risk of glacial lake outburst flood in Sikkim, Eastern Himalaya under climate warming. Remote Sens Applications: Soc Environ 36:101286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2024.101286\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2024.101286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalid Z, Meng X, Rana IA, Rehman M ur, Su X (2021) Holistic Multidimensional Vulnerability Assessment: An empirical investigation on rural communities of the Hindu Kush Himalayan region, Northern Pakistan. \u003cem\u003eInternational Journal of Disaster Risk Reduction\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(May), 102413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijdrr.2021.102413\u003c/span\u003e\u003cspan address=\"10.1016/j.ijdrr.2021.102413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhawas V (2007) Environmental Challenges and Human Security in the Himalaya. \u003cem\u003eHimalayas in the New Millennium: Environment, Society, Economy and PolityJanuary 11\u0026ndash;12, 2007\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoks EE, Jongman B, Husby TG, Botzen WJW (2015) Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environ Sci Policy 47(November):42\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsci.2014.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.envsci.2014.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar M, Savita, Singh H, Pandey R, Singh MP, Ravindranath NH, Kalra N (2019) Assessing vulnerability of forest ecosystem in the Indian Western Himalayan region using trends of net primary productivity. Biodivers Conserv 28(8\u0026ndash;9):2163\u0026ndash;2182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10531-018-1663-2\u003c/span\u003e\u003cspan address=\"10.1007/s10531-018-1663-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar S, Mishra AK, Pramanik S, Mamidanna S, Whitbread A (2020) Climate risk, vulnerability and resilience: Supporting livelihood of smallholders in semiarid India. Land Use Policy 97(May):104729. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landusepol.2020.104729\u003c/span\u003e\u003cspan address=\"10.1016/j.landusepol.2020.104729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLepcha PT, Pandey PK, Ranjan P (2021) Hydrological significance of Himalayan surface water and its management considering anthropogenic and climate change aspects. \u003cem\u003eIOP Conference Series: Materials Science and Engineering\u003c/em\u003e, \u003cem\u003e1020\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1757-899X/1020/1/012013\u003c/span\u003e\u003cspan address=\"10.1088/1757-899X/1020/1/012013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI HM, WANG XC, ZHAO XF, QI Y (2021) Understanding systemic risk induced by climate change. Adv Clim Change Res 12(3):384\u0026ndash;394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.accre.2021.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.accre.2021.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLokesh, Singh H (2014) Horticultural Development in Himachal Pradesh: An Empirical Analysis. Eur Acad Res II(5):6633\u0026ndash;6648\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadhura RK, Krishnan R, Revadekar JV, Mujumdar M, Goswami BN (2015) Changes in western disturbances over the Western Himalayas in a warming environment. Clim Dyn 44(3\u0026ndash;4):1157\u0026ndash;1168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-014-2166-9\u003c/span\u003e\u003cspan address=\"10.1007/s00382-014-2166-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalakar K, Mishra T (2017) Assessing socio-economic vulnerability to climate change: a city-level index-based approach. Climate Dev 9(4):348\u0026ndash;363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17565529.2016.1154449\u003c/span\u003e\u003cspan address=\"10.1080/17565529.2016.1154449\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalakar K, Mishra T, Hari V, Karmakar S (2021a) Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach. J Environ Manage, \u003cem\u003e294\u003c/em\u003e(May)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalakar K, Mishra T, Hari V, Karmakar S (2021b) Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach. J Environ Manage 294(September). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jenvman.2021.112948\u003c/span\u003e\u003cspan address=\"10.1016/j.jenvman.2021.112948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMenoni S, Molinari D, Parker D, Ballio F, Tapsell S (2012) Assessing multifaceted vulnerability and resilience in order to design risk-mitigation strategies. Nat Hazards 64(3):2057\u0026ndash;2082. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-012-0134-4\u003c/span\u003e\u003cspan address=\"10.1007/s11069-012-0134-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikhaylov A, Moiseev N, Aleshin K, Burkhardt T (2020) Global climate change and greenhouse effect. Entrepreneurship Sustain Issues 7(4):2897\u0026ndash;2913. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9770/jesi.2020.7.4(21)\u003c/span\u003e\u003cspan address=\"10.9770/jesi.2020.7.4(21)\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMondal M, Biswas A, Haldar S, Mandal S (2022) Rural livelihood risk to hydro-meteorological extreme events: Empirical evidence from Indian Sundarban applying IPCC-AR5 and DEMATEL methodology. Int J Disaster Risk Reduct 77(April):1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurthy CS, Laxman B, Sesha Sai MVR (2015) Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. Int J Disaster Risk Reduct 12:163\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijdrr.2015.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ijdrr.2015.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNashier I, Lakra P (2020) Global Climate change and its effects. Integr J Social Sci Integr J Soc Sci 2020(1):14\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pubs.iscience.in/ijss\u003c/span\u003e\u003cspan address=\"http://pubs.iscience.in/ijss\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegi GCS, Samal PK, Kuniyal JC, Kothyari BP, Sharma RK, Dhyani PP (2012) Impact of climate change on the western Himalayan mountain ecosystems: An overview. Trop Ecol 53(3):345\u0026ndash;356\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegi HS, Kanda N, Shekhar MS, Ganju A (2018) Recent wintertime climatic variability over the North West Himalayan cryosphere. Curr Sci 114(4):760\u0026ndash;770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18520/cs/v114/i04/760-770\u003c/span\u003e\u003cspan address=\"10.18520/cs/v114/i04/760-770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorth GR (2014) Global climate change. J Geoscience Environ Prot 2(April):114\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-007-7353-0_3\u003c/span\u003e\u003cspan address=\"10.1007/978-94-007-7353-0_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey AC, Ghosh S, Nathawat MS (2011) Evaluating patterns of temporal glacier changes in Greater Himalayan Range, Jammu \u0026amp; Kashmir, India. Geocarto Int 26(4):321\u0026ndash;338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10106049.2011.554611\u003c/span\u003e\u003cspan address=\"10.1080/10106049.2011.554611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandey R, Sharma J, Singh R, Rawat M, Saklani H, Tomar PK, Tiwari L, Bhatt ID, Chand T, Bala N, Panwar VP, Semeraro T (2022) Vegetation Characteristics Based Climate Change Vulnerability Assessment of Temperate Forests of Western Himalaya. Forests 13(6):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/f13060848\u003c/span\u003e\u003cspan address=\"10.3390/f13060848\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanwar S (2020) Vulnerability of Himalayan springs to climate change and anthropogenic impact: a review. J Mt Sci 17(1):117\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11629-018-5308-4\u003c/span\u003e\u003cspan address=\"10.1007/s11629-018-5308-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeduzzi P, Dao H, Herold C, Mouton F (2009) Assessing global exposure and vulnerability towards natural hazards: The Disaster Risk Index. Nat Hazards Earth Syst Sci 9(4):1149\u0026ndash;1159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-9-1149-2009\u003c/span\u003e\u003cspan address=\"10.5194/nhess-9-1149-2009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePepin NC, Arnone E, Gobiet A, Haslinger K, Kotlarski S, Notarnicola C, Palazzi E, Seibert P, Serafin S, Sch\u0026ouml;ner W, Terzago S, Thornton JM, Vuille M, Adler C (2022) Climate Changes and Their Elevational Patterns in the Mountains of the World. Rev Geophys 60(1):1\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020RG000730\u003c/span\u003e\u003cspan address=\"10.1029/2020RG000730\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahim R, Supiyandi S, Siahaan APU, Listyorini T, Utomo AP, Triyanto WA, Irawan Y, Aisyah S, Khairani M, Sundari S, Khairunnisa K (2018) TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees. \u003cem\u003eJournal of Physics: Conference Series\u003c/em\u003e, \u003cem\u003e1028\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1742-6596/1028/1/012052\u003c/span\u003e\u003cspan address=\"10.1088/1742-6596/1028/1/012052\" 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\u003eRajeevan MN (2017) M.N. Rajeevan Shailesh Nayak. In \u003cem\u003eObserved Climate Variability and Change over the Indian Region\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawat PK, Tiwari PC, Pant CC (2012) Climate change accelerating land use dynamic and its environmental and socio-economic risks in the Himalayas: Mitigation through sustainable land use. Int J Clim Change Strateg Manag 4(4):452\u0026ndash;471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/17568691211277764\u003c/span\u003e\u003cspan address=\"10.1108/17568691211277764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen GY, Shrestha AB (2017) Climate change in the Hindu Kush Himalaya. Adv Clim Change Res 8(3):137\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.accre.2017.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.accre.2017.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy S, Bose A, Chowdhury IR (2021) Flood risk assessment using geospatial data and multi-criteria decision approach: a study from historically active flood-prone region of Himalayan foothill, India. Arab J Geosci 14(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12517-021-07324-8\u003c/span\u003e\u003cspan address=\"10.1007/s12517-021-07324-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy S, Bose A, Mandal G (2022) Modeling and mapping geospatial distribution of groundwater potential zones in Darjeeling Himalayan region of India using analytical hierarchy process and GIS technique. Model Earth Syst Environ 8(2):1563\u0026ndash;1584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-021-01174-9\u003c/span\u003e\u003cspan address=\"10.1007/s40808-021-01174-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoy S, Bose A, Singha N, Basak D, Chowdhury IR (2021) Urban waterlogging risk as an undervalued environmental challenge: An Integrated MCDA-GIS based modeling approach. Environ Challenges 4(June):100194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envc.2021.100194\u003c/span\u003e\u003cspan address=\"10.1016/j.envc.2021.100194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusk J, Maharjan A, Tiwari P, Chen THK, Shneiderman S, Turin M, Seto KC (2022) Multi-hazard susceptibility and exposure assessment of the Hindu Kush Himalaya. Sci Total Environ 804:150039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.150039\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.150039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherler D, Bookhagen B, Strecker MR (2011) Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Nat Geosci 4(3):156\u0026ndash;159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ngeo1068\u003c/span\u003e\u003cspan address=\"10.1038/ngeo1068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt-Thom\u0026eacute; P (2006) Natural and technological hazards and risks affecting the spatial development of European regions. Special Paper Geol Surv Finland 42:6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneiderbauer S, Pisa F, Delves P, Pedoth JL, Rufat L, Erschbamer S, Thaler M, Carnelli T, F., Granados-Chahin S (2021) Risk perception of climate change and natural hazards in global mountain regions: A critical review. Sci Total Environ 784:146957. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2021.146957\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.146957\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafiq MU, Rasool R, Ahmed P, Dimri AP (2019) Temperature and Precipitation trends in Kashmir valley, North Western Himalayas. Theoret Appl Climatol 135(1\u0026ndash;2):293\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-018-2377-9\u003c/span\u003e\u003cspan address=\"10.1007/s00704-018-2377-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah A, Malakar K (2024) Climate-change-induced risk mapping of the Indian Himalayan districts using the latest IPCC framework. \u003cem\u003eInternational Journal of Disaster Risk Reduction\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e(October 2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah NA, Shafique M, Owen LA, AI-Mulla Y, Ullah Y (2025) Morphometric analysis of debris flow hazard and risk assessment in the mountain terrains of northern Pakistan using remote sensing and field data. Earth Sci Inf 18(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12145-025-01807-y\u003c/span\u003e\u003cspan address=\"10.1007/s12145-025-01807-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma D, Sreekesh S, Sinha B, Lunyolo LD, Aich V (2023) Flood and drought susceptibility zonation in the mountain environment: a case study of Upper Siang district, Eastern Himalayas, India. Environ Earth Sci 82(17):1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-023-11084-x\u003c/span\u003e\u003cspan address=\"10.1007/s12665-023-11084-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma HR (2005) Agricultural Development and Crop Diversification in Himachal Pradesh: Understanding the Patterns, Processes, Determinants and Lessons. Indian J Agric Econ 60(1):18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003efile:///F:\u003c/span\u003e\u003cspan address=\"http://file:///F:\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e /Spec 2/Traffic Delay Model.pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShekhar MS, Chand H, Kumar S, Srinivasan K, Ganju A (2010) Climate-change studies in the western Himalaya. Ann Glaciol 51(54):105\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3189/172756410791386508\u003c/span\u003e\u003cspan address=\"10.3189/172756410791386508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShekhar MS, Pattanayak S, Mohanty UC, Paul S, Sravana Kumar M (2015) A study on the heavy rainfall event around Kedarnath area (Uttarakhand) on 16 June 2013. J Earth Syst Sci 124(7):1531\u0026ndash;1544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12040-015-0621-6\u003c/span\u003e\u003cspan address=\"10.1007/s12040-015-0621-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha KL (2005) Global Change Impact Assessment for Himalayan Mountain Regions for Environmental Management and Sustainable Development. Global Environ Res \u0026copy;2005 AIRIES 9(1):69\u0026ndash;81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha UB, Gautam S, Bawa KS (2012) Widespread climate change in the Himalayas and associated changes in local ecosystems. PLoS ONE 7(5):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0036741\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0036741\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShukla R, Sachdeva K, Joshi PK (2016a) An indicator-based approach to assess village-level social and biophysical vulnerability of agriculture communities in Uttarakhand, India. J Mt Sci 13(12):2260\u0026ndash;2271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11629-016-4058-4\u003c/span\u003e\u003cspan address=\"10.1007/s11629-016-4058-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShukla R, Sachdeva K, Joshi PK (2016b) Inherent vulnerability of agricultural communities in Himalaya: A village-level hotspot analysis in the Uttarakhand state of India. Appl Geogr 74:182\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.apgeog.2016.07.013\u003c/span\u003e\u003cspan address=\"10.1016/j.apgeog.2016.07.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh RB, Kumar P (2014) Geographic and Socio-Economic Realities of Himachal Pradesh, Northwestern Himalaya. In \u003cem\u003eLivelihood Security in Northwestern Himalaya: Case Studies from Changing Socio-economic Environments in Himachal Pradesh, India\u003c/em\u003e (pp. 11\u0026ndash;26). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-4-431-54868-3_2\u003c/span\u003e\u003cspan address=\"10.1007/978-4-431-54868-3_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultan H, Zhan J, Rashid W, Chu X, Bohnett E (2022) Systematic Review of Multi-Dimensional Vulnerabilities in the Himalayas. Int J Environ Res Public Health 19(19). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph191912177\u003c/span\u003e\u003cspan address=\"10.3390/ijerph191912177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTewari VP, Verma RK, von Gadow K (2017) Climate change effects in the Western Himalayan ecosystems of India: evidence and strategies. For Ecosyst 4(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40663-017-0100-4\u003c/span\u003e\u003cspan address=\"10.1186/s40663-017-0100-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur S, Negi VS, Pathak R, Dhyani R, Durgapal K, Rawal RS (2020) Indicator based integrated vulnerability assessment of community forests in Indian west Himalaya. For Ecol Manag 457(May). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2019.117674\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2019.117674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThangjam U, Thong P, Sahoo SS, Kumar U (2024) Risk assessment of the jhumias in eastern Himalayan region: An IPCC framework approach. \u003cem\u003eInternational Journal of Disaster Risk Reduction\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e(December 2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas K, Hardy RD, Lazrus H, Mendez M, Orlove B, Rivera-Collazo I, Roberts JT, Rockman M, Warner BP, Winthrop R (2019) Explaining differential vulnerability to climate change: A social science review. Wiley Interdisciplinary Reviews: Clim Change 10(2):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wcc.565\u003c/span\u003e\u003cspan address=\"10.1002/wcc.565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUpgupta S, Sharma J, Jayaraman M, Kumar V, Ravindranath NH (2015) Climate change impact and vulnerability assessment of forests in the Indian Western Himalayan region: A case study of Himachal Pradesh, India. Clim Risk Manage 10:63\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crm.2015.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.crm.2015.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVedwan N, Rhoades RE (2001) Climate change in the western Himalayas of India: A study of local perception and response. Climate Res 19(2):109\u0026ndash;117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3354/cr019109\u003c/span\u003e\u003cspan address=\"10.3354/cr019109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWester P, Mishra A, Mukherji A, Shrestha AB, Change C (2019) The Hindu Kush Himalaya Assessment. In \u003cem\u003eThe Hindu Kush Himalaya Assessment\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-92288-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-92288-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Grumbine RE, Shrestha A, Eriksson M, Yang X, Wang Y, Wilkes A (2009) The melting Himalayas: Cascading effects of climate change on water, biodiversity, and livelihoods. Conserv Biol 23(3):520\u0026ndash;530. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1739.2009.01237.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1739.2009.01237.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav JS, Tiwari SK, Misra A, Rai SK, Yadav RK (2021) High-altitude meteorology of Indian Himalayan Region: complexities, effects, and resolutions. Environ Monit Assess 193(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-021-09418-y\u003c/span\u003e\u003cspan address=\"10.1007/s10661-021-09418-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav V, Karmakar S, Kalbar PP, Dikshit AK (2019) PyTOPS: A Python based tool for TOPSIS. SoftwareX 9:217\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.softx.2019.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.softx.2019.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZang Y, Wang H, Liu Z, Huang J (2024) Flood risk assessment of coastal cities based on GCW _ ISODATA and explainable artificial intelligence methods. Int J Disaster Risk Reduct, \u003cem\u003e115\u003c/em\u003e(February)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuzak C, Mowrer M, Goodenough E, Burns J, Ranalli N, Rozelle J (2022) The national risk index: establishing a nationwide baseline for natural hazard risk in the US. Nat Hazards 114(2):2331\u0026ndash;2355. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-022-05474-w\u003c/span\u003e\u003cspan address=\"10.1007/s11069-022-05474-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"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":"Himachal Pradesh, IPCC-AR6 Framework, Multi-Attribute Decision-Making Approach, Multi-hazard Risk, One-way ANOVA, Western Himalayan region","lastPublishedDoi":"10.21203/rs.3.rs-8565899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8565899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops a composite multi-hazard risk index using 84 indicators across three components, i.e., hazard, vulnerability, and exposure. These indicators were selected based on the IPCC-AR6 Framework, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Multi-Attribute Decision-Making (MADM) approach. Unlike earlier studies, present study uniquely applies this framework to the Western Himalayan state of Himachal Pradesh to measure climate-induced multi-hazard risk; identify spatially heterogeneous risk factors, and propose region-specific policy strategies. The findings of the study revealed significant inter-district spatial variation in risk levels. Five northern and northeastern districts, i.e., Lahaul-Spiti, Kullu, Kangra, Solan, and Kinnaur, exhibited high-risk status (\u0026gt;\u0026thinsp;0.375), while Chamba, Mandi, and Hamirpur showed moderate risk (0.200\u0026ndash;0.375). The remaining districts, of the state including Una, Bilaspur, and Sirmaur, were categorised as low risk (\u0026lt;\u0026thinsp;0.200). One-way ANOVA identified 18 significant indicators with spatial differences: seven under hazard (\u003cem\u003ee.g., cold wave days, snowfall, lightning, extreme temperature, elevation\u003c/em\u003e) and 11 under vulnerability and exposure (\u003cem\u003ee.g.\u003c/em\u003e, disability prevalence, Scheduled Caste population, health and education infrastructure, agricultural dependence, mobile access, population growth). The study also incorporates primary field investigation to validate the secondary findings. The results provide a robust evidence base for policymakers to formulate targeted climate risk mitigation strategies. This integrated approach offers a scalable model for assessing multi-hazard risks in other climate-vulnerable regions of the Himalaya.\u003c/p\u003e","manuscriptTitle":"Climate-change Induced Multi-hazard Risk Assessment of Himachal Pradesh in Western Himalayan Region Using IPCC-AR6 Framework and Multi-Attribute Decision-Making Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 10:14:49","doi":"10.21203/rs.3.rs-8565899/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"a06d18b8-4b20-4345-8597-f69e791a8174","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T11:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 10:14:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8565899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8565899","identity":"rs-8565899","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}