Comparative Study of Flood Coincidence Risk Estimation Methods in the Mainstream and its Tributaries

Na Li, Shenglian Guo (), Feng Xiong, Jun Wang and Yuzuo Xie
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Na Li: Wuhan University
Shenglian Guo: Wuhan University
Feng Xiong: Bureau of Hydrology, Changjiang Water Resources Commission
Jun Wang: Wuhan University
Yuzuo Xie: Wuhan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 2, No 15, 683-698

Abstract: Abstract The coincidence of floods in the mainstream and its tributaries may lead to a large flooding in the downstream confluence area, and the flood coincidence risk analysis is very important for flood prevention and disaster reduction. In this study, the multiple regression model was used to establish the functional relationship among flood magnitudes in the mainstream and its tributaries. The mixed von Mises distribution and Pearson Type III distribution were selected to fit the probability distribution of the annual maximum flood occurrence dates and magnitudes, respectively. The joint distributions of the annual maximum flood occurrence dates and magnitudes were established using copula function, respectively. Fuhe River in the Poyang Lake region was selected as a study case. The joint probability, co-occurrence probability and conditional probability of flood magnitudes were quantitatively estimated and compared with the predicted flood coincidence risks. The results show that the selected marginal and joint distributions can fit observed flood dataset very well. The coincidence probabilities of flood occurrence dates in the upper mainstream and its tributaries mainly occur from May to early July. It is found that the conditional probability is the most consistent with the predicted flood coincidence risks in the mainstream and its tributaries, and is more reliable and rational in practice.

Keywords: Flood coincidence; Copula function; Conditional probability; Estimation methods; Comparative study; Fuhe river (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-021-03050-8

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