Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

• Published in: Stochastic environmental research and risk assessment 2022, Vol.36 (1), p.1-16
• Main Authors: Lu, Lu, Yuan, Wenlin, Su, Chengguo, Gao, Qianyu, Yan, Denghua, Wu, Zening
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Decision making; Distribution functions; Earth and Environmental Science; Earth Sciences; Environment; Flash floods; Flood forecasting; Floods; Forecasting; Hydrologic models; Hydrology; Math. Appl. in Environmental Science; Mathematical analysis; Mathematical models; Original Paper; Physics; Probability Theory and Stochastic Processes; Rainfall intensity; Rainfall-runoff relationships; Risk; Soil conditions; Soil moisture; Statistics for Engineering; Waste Water Technology; Water Management; Water Pollution Control; Watersheds
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-021-02059-0

Flash floods cause great harm to people’s lives and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. To carry out the effective flash flood early warning and forecasting, this paper proposes a novel rainfall pattern by coupling total rainfall and peak rainfall intensity based on copula functions, i.e., the rainfall pattern of risk probability combination (RPRPC). On this basis, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model is used to simulate the rainfall-runoff process, the trial algorithm is used to calculate the critical rainfall (CR), and the optimistic-general-pessimistic (O–G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern, Frank copula function is the best for determining the joint distribution function of total rainfall and peak rainfall intensity, and the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, both RPRPC and antecedent soil moisture condition (ASMC) have influence on CR, and the variation of RPRPC will change the influence of ASMC on CR. Finally, the effectiveness of O–G-P early warning mode is verified.