Abstract
In this study, a generalized extreme value (GEV) distribution–based statistical model has been developed and proposed to simulate historical and future precipitation extremes in the Xin’an River basin, and the vertical mixed runoff model was driven by future precipitation extremes to simulate the hydrological response to extreme flood events. An adaptive flood control operation model has been established and solved using genetic algorithm in order to ensure the safety of dam and downstream areas under precipitation extremes. In view of the precipitation events for the period 1951–2017, the monthly extreme precipitation events are expected to rise in the period 2020–2100 by 10.4%, 11.0%, and 11.4% at a 10-, 20-, and 50-year return period, respectively. After optimal regulation, the maximum release is reduced by 60.8%, 43.6%, and 42.7%, while the average reservoir water level is reduced by 0.13 m, 0.14 m, and 0.11 m in extreme flood events with a 10-, 20-, and 50-year return period, respectively. In conclusion, the adaptive flood control operation can ensure the safety of dam and downstream areas and mitigate possible impacts of extreme flood events under climate change.
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Acknowledgments
Thanks are also due to the referees and the editor for their valuable comments and suggestions on improvement of this study and to Guohua Fang for her constructive opinion and linguistic assistance during the preparation and revision of this manuscript.
Funding
This research is funded by the National Key Research and Development Program of China (2018YFC0407902), the National Natural Science Foundation of China (U1765201, 51609061), the Fundamental Research Funds for the Central Universities (2018B11314), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (SKL2018ZY06).
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Zhu, X., Wen, X., Sun, C. et al. Adaptive flood control operation of the Xin’an Reservoir in future precipitation extremes under climate change. Arab J Geosci 13, 720 (2020). https://doi.org/10.1007/s12517-020-05711-1
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DOI: https://doi.org/10.1007/s12517-020-05711-1