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Discussion papers
https://doi.org/10.5194/hess-2019-42
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-42
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 19 Mar 2019

Research article | 19 Mar 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).

Assessing the impacts of reservoirs on the downstream flood frequency by coupling the effect of the scheduling-related multivariate rainfall into an indicator of reservoir effects

Bin Xiong1, Lihua Xiong1, Jun Xia1, Chong-Yu Xu1,3, Cong Jiang2, and Tao Du4 Bin Xiong et al.
  • 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. China
  • 2School of Environmental Studies, China University of Geosciences (Wuhan), Wuhan 430074, China
  • 3Department of Geosciences, University of Oslo, P.O. Box 1022 Blindern, N-0315 Oslo, Norway
  • 4Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, China

Abstract. Many studies have shown that the downstream flood regimes have been significantly altered by upstream reservoir operation. Reservoir effects on the downstream flow regime are normally carried out by comparing the pre-dam and post-dam frequencies of some streamflow indicators such as floods and droughts. In this paper, a rainfall-reservoir composite index (RRCI) is developed to precisely quantify reservoir impacts on downstream flood frequency under the framework of covariate-based flood frequency analysis. The RRCI is derived from both the reservoir index (RI) of the previous study and the joint cumulative probability (JCP) of multiple rainfall variables (i.e., the maximum, intensity, volume and timing) of multiday rainfall input (MRI), and is calculated by a c-vine copula model. Then, using RI or RRCI as covariate, a nonstationary generalized extreme value (NGEV) distribution model with time-varying location and/or scale parameters is developed and used to analyze the annual maximum daily flow (AMDF) of Ankang, Huangjiagang and Huangzhuang gauging stations of the Hanjiang River, China with the Bayesian estimation method. The results show that regardless of using RRCI or RI, nonstationary flood frequency analysis demonstrates that the overall flood risk of the basin has been significantly reduced by reservoirs, and the reduction increases with the reservoir capacity. What’s more, compared with RI, RRCI through incorporating the effect of the scheduling-related multivariate MRI can better explain the alteration of AMDF. And for a given reservoir capacity (i.e., a specific RI), the flood risk (e.g., the Huangzhuang station) increases with the JCP of rainfall variables and gradually approaches the risk of no reservoir (i.e., RI = 0). This analysis, combining the reservoir index with the scheduling-related multivariate MRI to account for the alteration in flood frequency, provides a comprehensive approach and knowledge for downstream flood risk management under the impacts of reservoirs.

Bin Xiong et al.
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Short summary
We develop a new indicator of reservoir effects called rainfall-reservoir composite index (RRCI). RRCI, coupled by the effects of static reservoir capacity and scheduling-related multivariate rainfall (SRMR), has better performance than the previous indicator in terms of explaining the variation of the downstream floods affected by reservoir operation. Covariate-based flood frequency analysis using RRCI can provide more reliable downstream flood risk estimation.
We develop a new indicator of reservoir effects called rainfall-reservoir composite index...
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