Bian Jie, Li Shuanglin, He Jinhai. Risk assessment of flood disaster in the Mid-lower Reaches of the Yangtze. J Appl Meteor Sci, 2011, 22(5): 604-611. .
Citation: Bian Jie, Li Shuanglin, He Jinhai. Risk assessment of flood disaster in the Mid-lower Reaches of the Yangtze. J Appl Meteor Sci, 2011, 22(5): 604-611. .

Risk Assessment of Flood Disaster in the Mid-lower Reaches of the Yangtze

  • Flood disasters caused by heavy rain events occur frequently in the mid-lower reaches of the Yangtze in monsoonal rainy season. The risk of heavy rain events is an important topic in meteorological research in China. Therefore, risk rank of flood disaster in six provinces in the mid-lower reaches of the Yangtze (Hubei, Hunan, Anhui, Jiangxi, Jiangsu and Zhejiang) are assessed based on most recently updated meteorological disaster losses dataset collected by the Ministry of Civil Affairs, National Disaster Reduction Centre of China from 2000 to 2008.First, losses due to flood disasters are classified and quantified by using the method of grey association analysis. The results show that the grey association method is reasonable in disaster situation grading and loss ranking, and the results are basically in agreement with the actual situation. Then an integrated index series in the recent 9 years is established. In addition, the model is very practical and flexible because the numbers of the grade indexes are not limited. Second, since there are not enough integrated historical disaster indexes, an information diffusion based fuzzy method is introduced to optimize the historical disaster data and then the risk in each province is assessed individually. The risk rank results show substantial difference in these provinces. Although medium risk is universal in all the provinces with the occurrence probability of once in one to two years, high risk is relatively more frequent in three provinces, Anhui, Hubei and Hunan, which approximately tallies with the practical situation. This also suggests an efficiency of the present risk-assessment model in processing inadequately long records and deserves extensive use.
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