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Risk assessment model of agricultural drought disaster based on grey matter-element analysis theory

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  • Huafeng Xu

    (Henan University of Urban Construction)

  • Kexin Xu

    (University of Glasgow)

  • Yingjie Yang

    (De Montfort University)

Abstract

Carrying out risk assessments of agricultural drought disasters is helpful to understanding agricultural drought quantitatively and scientifically guiding drought prevention and drought relief work. In this paper, the risk assessment system and evaluation index of drought disasters are constructed, and they are composed of a drought risk subsystem, drought exposure subsystem, disaster damage sensitivity subsystem and drought resistance subsystem. Based on the grey matter-element analysis method, the agricultural drought risk evaluation model was established. Grey matter-element analysis method was used to evaluate the risk of agricultural drought in 18 regions of Henan Province, China in 2019. The results validation showed that high drought disaster risk area in Henan province is located in the western, north and the central area. This study provides a new method for the risk assessment of agricultural drought disasters. Understanding the risk in the study area can improve agricultural system resilience. This model could be used to provide support for increasing agricultural drought disaster resilience and risk management efficiency.

Suggested Citation

  • Huafeng Xu & Kexin Xu & Yingjie Yang, 2021. "Risk assessment model of agricultural drought disaster based on grey matter-element analysis theory," 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. 107(3), pages 2693-2707, July.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:3:d:10.1007_s11069-021-04681-1
    DOI: 10.1007/s11069-021-04681-1
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    References listed on IDEAS

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    1. F. Todisco & F. Mannocchi & L. Vergni, 2013. "Severity–duration–frequency curves in the mitigation of drought impact: an agricultural case study," 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. 65(3), pages 1863-1881, February.
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    Cited by:

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    2. Wei Pei & Lei Hao & Qiang Fu & Yongtai Ren & Tianxiao Li, 2023. "Study on Agricultural Drought Risk Assessment Based on Information Entropy and a Cluster Projection Pursuit Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 619-638, January.
    3. Manman Zhang & Dang Luo & Yongqiang Su, 2022. "Drought monitoring and agricultural drought loss risk assessment based on multisource information fusion," 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. 111(1), pages 775-801, March.
    4. Babel, Mukand S. & Chawrua, Lapanploy & Khadka, Dibesh & Tingsanchali, Tawatchai & Shanmungam, Mohana Sundaram, 2024. "Agricultural drought risk and local adaptation measures in the Upper Mun River Basin, Thailand," Agricultural Water Management, Elsevier, vol. 292(C).
    5. Cui, Yi & Zhou, Yuliang & Jin, Juliang & Jiang, Shangming & Wu, Chengguo & Ning, Shaowei, 2023. "Spatiotemporal characteristics and obstacle factors identification of agricultural drought disaster risk: A case study across Anhui Province, China," Agricultural Water Management, Elsevier, vol. 289(C).
    6. Tan Huizhi & Lu Xiaoning & Yang Shiqi & Wang Yongqian & Li Feng & Liu Jinbao & Chen Jun & Huang Yue, 2022. "Drought risk assessment in the coupled spatial–temporal dimension of the Sichuan Basin, China," 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. 114(3), pages 3205-3233, December.

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