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<p>This study develops a composite multi-hazard risk index by integrating 84 indicators across three components, i.e. hazard, vulnerability, and exposure. Indicators were selected based on the IPCC-AR6 framework and operationalised through the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a Multi-Attribute Decision-Making (MADM) approach. This study applies the framework to the Western Himalayan state of Himachal Pradesh to quantify climate-induced multi-hazard risks, identify spatial heterogeneity, and propose region-specific strategies. Findings of the study revealed notable inter-district variation in risk levels. Five northern and northeastern districts—Lahaul-Spiti, Kullu, Kangra, Shimla, and Kinnaur—were identified as high-risk zones (&gt; 0.375), while Chamba, Mandi, and Hamirpur recorded moderate risk (0.200–0.375). The remaining districts, including Una, Bilaspur, and Sirmaur, exhibited low risk (&lt; 0.200). One-way ANOVA highlighted 18 significant indicators: seven related to hazards (e.g., cold wave days, snowfall, lightning, extreme temperature, elevation) and 11 linked to vulnerability and exposure (e.g., disability prevalence, Scheduled Caste population, education and health infrastructure, agricultural dependence, mobile access, population growth). The secondary data analysis of the study was validated with the help of the field observation. This integrated approach provides robust evidence for policymakers and offers a scalable model for assessing multi-hazard risks in climate-vulnerable Himalayan regions.</p>

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Climate-change induced multi-hazard risk assessment of Himachal Pradesh in Western Himalayan Region using IPCC-AR6 framework and multi-attribute decision-making approach

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Shibu Das,
  • Sanjeev Sharma

摘要

This study develops a composite multi-hazard risk index by integrating 84 indicators across three components, i.e. hazard, vulnerability, and exposure. Indicators were selected based on the IPCC-AR6 framework and operationalised through the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a Multi-Attribute Decision-Making (MADM) approach. This study applies the framework to the Western Himalayan state of Himachal Pradesh to quantify climate-induced multi-hazard risks, identify spatial heterogeneity, and propose region-specific strategies. Findings of the study revealed notable inter-district variation in risk levels. Five northern and northeastern districts—Lahaul-Spiti, Kullu, Kangra, Shimla, and Kinnaur—were identified as high-risk zones (> 0.375), while Chamba, Mandi, and Hamirpur recorded moderate risk (0.200–0.375). The remaining districts, including Una, Bilaspur, and Sirmaur, exhibited low risk (< 0.200). One-way ANOVA highlighted 18 significant indicators: seven related to hazards (e.g., cold wave days, snowfall, lightning, extreme temperature, elevation) and 11 linked to vulnerability and exposure (e.g., disability prevalence, Scheduled Caste population, education and health infrastructure, agricultural dependence, mobile access, population growth). The secondary data analysis of the study was validated with the help of the field observation. This integrated approach provides robust evidence for policymakers and offers a scalable model for assessing multi-hazard risks in climate-vulnerable Himalayan regions.