Evaluating the Impacts of a Large-Scale Multi-Reservoir System on Flooding: Case of the Huai River in China

Wan, Xinyu; Hua, Lijuan; Yang, Shutan; Gupta, Hoshin; Zhong, Ping‐an

doi:10.1007/s11269-017-1852-x

Cited by 11 publications

(1 citation statement)

References 21 publications

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“…The Shi river feeds into the Huai river, which is one of the largest rivers in China. The river basin is in the north-south climate transboundary zone, and thus the reservoir inflow varies greatly from year to year and is concentrated in the flood season, from June to September [39,40]. The average precipitation at the Shi river basin is 1320 mm per year.…”

Section: Study Sitementioning

confidence: 99%

Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation

Zhou

Jin

et al. 2019

Water

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Flood frequency analysis plays a fundamental role in dam planning, reservoir operation, and risk assessment. However, conventional univariate flood frequency analysis carried out by flood peak inflow or volume does not account for the dependence between flood properties. In this paper, we proposed an integrated approach to estimate reservoir risk by combining the copula-based bivariate flood frequency (peak and volume) and reservoir routing. Through investigating the chain reaction of “flood frequency—reservoir operation-flood risk”, this paper demonstrated how to simulate flood hydrographs using different frequency definitions (copula “Or” and “And” scenario), and how these definitions affect flood risks. The approach was applied to the Meishan reservoir in central China. A set of flood hydrographs with 0.01 frequency under copula “Or” and “And” definitions were constructed, respectively. Upstream and downstream flood risks incorporating reservoir operation were calculated for each scenario. Comparisons between flood risks from univariate and bivariate flood frequency analysis showed that bivariate flood frequency analysis produced less diversity in the results, and thus the results are more reliable in risk assessment. More importantly, the peak-volume combinations in a bivariate approach can be adjusted according to certain prediction accuracy, providing a flexible estimation of real-time flood risk under different prediction accuracies and safety requirements.

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Section: Study Sitementioning

confidence: 99%