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Vulnerability Analysis to Drought Based on Remote Sensing Indexes

Author

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  • Huicong Jia

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing 100875, China
    Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China)

  • Fang Chen

    (Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Beijing 100094, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jing Zhang

    (Department of Geography, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing 100875, China)

  • Enyu Du

    (College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

A vulnerability curve is an important tool for the rapid assessment of drought losses, and it can provide a scientific basis for drought risk prevention and post-disaster relief. Those populations with difficulty in accessing drinking water because of drought (hereon “drought at risk populations”, abbreviated as DRP) were selected as the target of the analysis, which examined factors contributing to their risk status. Here, after the standardization of disaster data from the middle and lower reaches of the Yangtze River in 2013, the parameter estimation method was used to determine the probability distribution of drought perturbations data. The results showed that, at the significant level of α = 0.05, the DRP followed the Weibull distribution, whose parameters were optimal. According to the statistical characteristics of the probability density function and cumulative distribution function, the bulk of the standardized DRP is concentrated in the range of 0 to 0.2, with a cumulative probability of about 75%, of which 17% is the cumulative probability from 0.2 to 0.4, and that greater than 0.4 amounts to only 8%. From the perspective of the vulnerability curve, when the variance ratio of the normalized vegetation index (NDVI) is between 0.65 and 0.85, the DRP will increase at a faster rate; when it is greater than 0.85, the growth rate of DRP will be relatively slow, and the disaster losses will stabilize. When the variance ratio of the enhanced vegetation index (EVI) is between 0.5 and 0.85, the growth rate of DRP accelerates, but when it is greater than 0.85, the disaster losses tend to stabilize. By comparing the coefficient of determination (R 2 ) values fitted for the vulnerability curve, in the same situation, EVI is more suitable to indicate drought vulnerability than NDVI for estimating the DRP.

Suggested Citation

  • Huicong Jia & Fang Chen & Jing Zhang & Enyu Du, 2020. "Vulnerability Analysis to Drought Based on Remote Sensing Indexes," IJERPH, MDPI, vol. 17(20), pages 1-20, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7660-:d:432030
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    References listed on IDEAS

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    1. Huicong Jia & Donghua Pan & Jing-ai Wang & Wan-chang Zhang, 2016. "Risk mapping of integrated natural disasters in 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. 80(3), pages 2023-2035, February.
    2. Donald Wilhite & Mark Svoboda & Michael Hayes, 2007. "Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 763-774, May.
    3. Huicong Jia & Donghua Pan & Jing-ai Wang & Wan-chang Zhang, 2016. "Risk mapping of integrated natural disasters in 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. 80(3), pages 2023-2035, February.
    4. Hongjian Zhou & Jing’ai Wang & Jinhong Wan & Huicong Jia, 2010. "Resilience to natural hazards: a geographic perspective," 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. 53(1), pages 21-41, April.
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