Uncertainty Analysis of Flood Control Design Under Multiple Floods

• Published in: Water resources management 2022-03, Vol.36 (4), p.1175-1189
• Main Authors: Huang, Yuming, Li, Yanjie, Liu, Min, Xiao, Liang, Gan, Fuwan, Jiao, Jian
• Format: Article
• Language: English
• Subjects: Analysis; Atmospheric Sciences; Case studies; Civil Engineering; Confidence intervals; Design; Design analysis; Design standards; Earth and Environmental Science; Earth Sciences; Environment; Flood control; Flood management; Floods; Fluxes; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology/Water Resources; Runoff; Shipping; Streams; Tributaries; Uncertainty; Uncertainty analysis
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-022-03066-8

Hydraulic engineerings built on tributaries at the confluence of main and branch streams are significant to river management and runoff regulation. The Flood Control Design Level (FCDL) calculations for these works are directly influenced by tributary floods and supporting effects from the mainstream. However, the determination of design level under main and tributary floods has not been well investigated. To address this issue, the authors proposed a Copula-based approach to analyze the design level under multiple runoff discharge with a case study of the Guiping Shipping Hub(GPSH). The proposed method is compared with the conventional multivariate hydrological elements analysis approach, and the sampling uncertainty is also studied. The results showed that the joint distribution of main and tributary floods is well modeled by Clayton Copula, with PE3s as the best-fit marginal distributions. Furthermore, the different roles of main and branch fluxes in design level calculation can be identified by the offered Flood Control return period(FCRP). And the design levels conducted by the FCRP can avoid the situation over-or-under performed by the OR or AND RP. Moreover, flood combinations uncertainty analysis indicates that the uncertainty of the joint design combinations decreases with the increase of sample size n but increases with the rise of the design T . Finally, the 95% confidence interval and standard deviation of the design level calculated by FCRP are smaller than that of OR RP, which means the FCRP can reduce uncertainty under multiple floods. These results suggest that the proposed FCRP provides an appropriate approach for determining the design level under combined fluxes and serves as a reference for engineering practice.