暴雨灾害 (Feb 2023)

The relationship model between flood-hit population and rainstorm waterlogging risk index

  • Xiaofang ZHAO,
  • Ruiqin SHI,
  • Guoping HONG,
  • Yuehua ZHOU,
  • Zhihong XIA,
  • Liangmin DU,
  • Hua XIANG

DOI
https://doi.org/10.12406/byzh.2022-223
Journal volume & issue
Vol. 42, no. 1
pp. 79 – 87

Abstract

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Establishing the relationship model between the rainstorm waterlogging and flood-hit population is of great significance for the rapid assessment of the situation before, during, and after the disaster of rainstorm. Taking Hubei as an example, we established the rainstorm waterlogging risk index by using the meteorological data such as hourly precipitation, daily maximum precipitation, cumulative precipitation, and the continuous days of rainstorm, as well as the environmental data such as water system and elevation. Combined with the historical flood-hit population, we also established the disaster loss curve of the flood-hit population based on the waterlogging risk index. Then, we calculated the flood-hit population by using the threshold of waterlogging risk index according to the return period level. The results show that (1) the rainstorm waterlogging high-risk area in Hubei mainly locates in the eastern Jianghan Plain, followed by the south of southwestern Hubei and the southern Jianghan Plain. (2) By inspecting the disaster situation of historical rainstorm process, taking into account the rainstorm waterlogging risk index of favoring environment, can reflect the extent of disaster. (3) The flood-hit population was in a power function relationship with the rainstorm waterlogging risk index, and the correlation coefficients between the actual and the fitted flood-hit population in the year and during the rainstorm process reached 0.800 and 0.891, respectively. (4) The 5 a, 10 a and 20 a return periods were used to divide the rainstorm waterlogging risk index into the three levels. When the rainstorm waterlogging risk index exceeds the return period of 20 a, it is estimated that the number of affected population will reach more than 120 000.

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