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China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model

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  • Naiming Xie
  • Jianghui Xin
  • Sifeng Liu

Abstract

To evaluate the regional meteorological disaster loss of China, this paper analyzed the different types of meteorological disasters, including droughts, floods, tropical storms, snowstorms and hail disasters. Based on the analysis about Chinese geographical features, the historical characteristics of different meteorological disasters are analyzed. In particular, these meteorological disasters influence to agriculture production are discussed. According to the analysis of data from 2004 to 2010, we know that the distribution characteristics are very different about different disasters. Provinces like Heilongjiang, Liaoning, Jilin, Inner Mongolia, Gansu, Shanxi and Yunnan are serious affected areas of drought influence. And Anhui, Shandong, Jiangsu, Henan, Jiangxi, Hubei, Hunan, Guangxi, Sichuan and Heilongjiang are serious affected areas by floods and heavy rain. While Guangdong, Fujian, Zhejiang, Shanghai, Jiangsu and Shandong are mainly affected by tropical storms, Henan, Hebei, Hunan and Hubei are serious affected by snowstorms and hail disasters. Then, a novel method based on grey cluster model is constructed and combined with the regional meteorological disaster loss evaluation index system. A total of 31 provinces are considered to evaluate the integrated meteorological disaster losses. The results indicated that Beijing, Tianjin, Shanghai, Xizang, Qinghai and Ningxia belong to the lighter loss grey class. Jiangxi, Hubei, Hunan, Hainan, Sichuan and Gansu belong to the serious loss grey class. Other regions belong to the general loss grey class that the influence caused by meteorological disasters not better than the lighter loss grey class and not worst than the serious loss grey class. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Naiming Xie & Jianghui Xin & Sifeng Liu, 2014. "China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model," 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. 71(2), pages 1067-1089, March.
    • Handle: RePEc:spr:nathaz:v:71:y:2014:i:2:p:1067-1089
    DOI: 10.1007/s11069-013-0662-6
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    4. Xiaoli Li & Zhiqiang Li & Jiansi Yang & Yaohui Liu & Bo Fu & Wenhua Qi & Xiwei Fan, 2018. "Spatiotemporal characteristics of earthquake disaster losses in China from 1993 to 2016," 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. 94(2), pages 843-865, November.
    5. Yanxu Liu & Shuangshuang Li & Yanglin Wang & Tian Zhang & Jian Peng & Tianyi Li, 2015. "Identification of multiple climatic extremes in metropolis: a comparison of Guangzhou and Shenzhen, 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. 79(2), pages 939-953, November.
    6. Lihong Wang & Zaiwu Gong, 2017. "Priority of a Hesitant Fuzzy Linguistic Preference Relation with a Normal Distribution in Meteorological Disaster Risk Assessment," IJERPH, MDPI, vol. 14(10), pages 1-16, October.
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