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Assessing climate-relevant vulnerability of the Indian Himalayan Region (IHR): a district-level analysis

Author

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  • Mir Khursheed Alam

    (Indian Institute of Technology Mandi)

  • Shyamasree Dasgupta

    (Indian Institute of Technology Mandi
    School of Humanities and Social Sciences, Indian Institute of Technology Mandi)

  • Anamika Barua

    (Indian Institute of Technology Guwahati)

  • N. H. Ravindranath

    (Indian Institute of Science Bangalore)

Abstract

Indian Himalayan Region (IHR) is highly vulnerable to climate change and its variability. The present study employs an indicator-based approach to assess the district-level (132 districts) vulnerability of the IHR, based on a modified risk assessment framework proposed by the Intergovernmental Panel on Climate Change (IPCC-AR5). Based on Moron's I and Local Geary, the study also carries out a geospatial analysis to identify the vulnerability hotspots. The Composite Vulnerability Indices (CVI) developed in this study, based on a set of 14 common indicators, suggest that Assam (in the eastern IHR), and Jammu and Kashmir (in the western IHR) are highly vulnerable states as the majority of their districts fall in the “highly vulnerable” cohort. The CVI ranges from 0.75 (for Goalpara in Assam) to 0.35 (for Kolasib in Mizoram). The hotspots are also found to be located in the hazard-prone regions of Jammu and Kashmir and the ‘char’ belt of the Brahmaputra River basin in Assam. These districts are economically poor with fragile agro-based livelihoods, and limited institutional and infrastructural provisions. The outcome of the study has significant potential to assist targeted policy interventions in the most vulnerable locations and rational allocation of adaptation resources through identification of vulnerability hotspots and the drivers of vulnerability.

Suggested Citation

  • Mir Khursheed Alam & Shyamasree Dasgupta & Anamika Barua & N. H. Ravindranath, 2022. "Assessing climate-relevant vulnerability of the Indian Himalayan Region (IHR): a district-level analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1395-1421, June.
    • Handle: RePEc:spr:nathaz:v:112:y:2022:i:2:d:10.1007_s11069-022-05233-x
    DOI: 10.1007/s11069-022-05233-x
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    2. Mohsin Fayaz & Gowhar Meraj & Sheik Abdul Khader & Majid Farooq & Shruti Kanga & Suraj Kumar Singh & Pankaj Kumar & Netrananda Sahu, 2022. "Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains," Land, MDPI, vol. 11(6), pages 1-27, June.

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