Abstract
Water shortages during dry periods can be successfully mitigated by managing reservoirs in real-time to conserve water as floods recede. The inherent uncertainty in inflow forecasts however means that it remains a challenge to balance the risks of flooding against those of water shortages. Few studies have examined how the risks of floods and water shortages can be managed using reservoir operation rules. In this study, a two-phase stochastic optimization model was built to determine the optimal conservation level for flood water by minimizing the risks from both floods and water shortages. For the optimal condition, hedging rules were analytically derived as a quasi-linear function of the storage capability and the expected water shortage. The rules indicate that the flood water conservation was achieved when the marginal upstream flood risk was equal to the marginal water shortage risk, and that the limits of three operation zones divided by the expected water availability should be used when determining the water release. The results from testing the model with data from the Xianghongdian Reservoir (China) showed that the hedging rules outperformed the capacity-constrained pre-release rules for conserving flood water without increasing the flood risk. This proposed methodology will inform the process for making decisions about how to operate reservoirs to ensure optimal real-time flood water conservation.
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References
Bayazit M, Unal NE (1990) Effects of hedging on reservoir performance. Water Resour Res 26(4):713–719
Chou FNF, Wu C (2013) Expected shortage based pre-release strategy for reservoir flood control. J Hydrol 497:1–14. https://doi.org/10.1016/j.jhydrol.2013.05.039
Ding W, Zhang C, Peng Y, Zeng R, Zhou H, Cai X (2015) An analytical framework for flood water conservation considering forecast uncertainty and acceptable risk. Water Resour Res 51(6):4702–4726. https://doi.org/10.1002/2015WR017127
Draper AJ, Lund JR (2004) Optimal hedging and carryover storage value. J Water Res Plan Man 130(1):83–87
Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour Res 18(1):14–20
Hua L, Wan X, Wang X, Zhao F, Zhong PA, Liu M, Yang Q (2020) Floodwater utilization based on reservoir pre-release strategy considering the worst-case scenario. Water-Sui 12(3):892. https://doi.org/10.3390/w12030892
Hui R, Lund J, Zhao J, Zhao T (2016) Optimal pre-storm flood hedging releases for a single reservoir. Water Resour Manag 30:5113–5129. https://doi.org/10.1007/s11269-016-1472-x
Labadie JW (2004) Optimal operation of multireservoir systems: state-of-the-art review. J Water Res Plan Man 130(2):93–111
Li X, Guo S, Liu P, Chen G (2010) Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty. J Hydrol 391(1–2):126–134. https://doi.org/10.1016/j.jhydrol.2010.07.011
Liu S, Shi H (2019) A recursive approach to long-term prediction of monthly precipitation using genetic programming. Water Resour Manag 33(3):1103–1121. https://doi.org/10.1007/s11269-018-2169-0
Lund JR, Guzman J (1999) Derived operating rules for reservoirs in series or in parallel. J Water Res Plan Man 125(3):143–153. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:3(143)
Mao J, Tian M, Hu T, Ji K, Dai L, Dai H (2019) Shuffled complex evolution coupled with stochastic ranking for reservoir scheduling problems. Wat Sci Eng 12(4):307–318
Moridi A, Yazdi J (2017) Optimal allocation of flood control capacity for multi-reservoir systems using multi-objective optimization approach. Water Resour Manag 31(14):4521–4538. https://doi.org/10.1007/s11269-017-1763-x
Nayak MA, Herman JD, Steinschneider S (2018) Balancing flood risk and water supply in California: policy search integrating short-term forecast ensembles with conjunctive use. Water Resour Res 54(10):7557–7576. https://doi.org/10.1029/2018WR023177
Shenava N, Shourian M (2018) Optimal reservoir operation with water supply enhancement and flood mitigation objectives using an optimization-simulation approach. Water Resour Manag 32(13):4393–4407. https://doi.org/10.1007/s11269-018-2068-4
Shiau JT (2011) Analytical optimal hedging with explicit incorporation of reservoir release and carryover storage targets. Water Resour Res 47(1):W1515. https://doi.org/10.1029/2010WR009166
Wan W, Zhao J, Lund JR, Zhao T, Lei X, Wang H (2016) Optimal hedging rule for reservoir refill. J Water Res Plan Man 142(11)
Wen X, Liu Z, Lei X, Lin R, Fang G, Tan Q, Wang C, Tian Y, Quan J (2018) Future changes in Yuan River ecohydrology: individual and cumulative impacts of climates change and cascade hydropower development on runoff and aquatic habitat quality. Sci Total Environ 633:1403–1417. https://doi.org/10.1016/j.scitotenv.2018.03.309
Xu B, Boyce SE, Zhang Y, Liu Q, Guo L, Zhong P (2017) Stochastic programming with a joint chance constraint model for reservoir refill operation considering flood risk. J Water Res Plan Man 143(1):4016067. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000715
Xu B, Zhu F, Zhong P, Chen J, Liu W, Ma Y, Guo L, Deng X (2019) Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming. Appl Energ 253:113535. https://doi.org/10.1016/j.apenergy.2019.113535
Yang W, Norrlund P, Saarinen L, Witt A, Smith B, Yang J, Lundin U (2018) Burden on hydropower units for short-term balancing of renewable power systems. Nat Commun 9(1). https://doi.org/10.1038/s41467-018-05060-4
Yeh WW (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818. https://doi.org/10.1029/WR021i012p01797
You J, Cai X (2008) Hedging rule for reservoir operations: 1. A theoretical analysis. Water Resour Res 44(1):1–9. https://doi.org/10.1029/2006WR005481
Zhang X, Liu P, Xu C, Gong Y, Cheng L, He S (2019) Real-time reservoir flood control operation for cascade reservoirs using a two-stage flood risk analysis method. J Hydrol 577:123954. https://doi.org/10.1016/j.jhydrol.2019.123954
Zhao J, Cai X, Wang Z (2011) Optimality conditions for a two-stage reservoir operation problem. Water Resour Res 47(8):1–16. https://doi.org/10.1029/2010WR009971
Zhao T, Zhao J, Lund JR, Yang D (2014) Optimal hedging rules for reservoir flood operation from forecast uncertainties. J Water Res Plan Man 140(12):4014041. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000432
Zhou Y, Guo S, Liu P, Xu C (2014) Joint operation and dynamic control of flood limiting water levels for mixed cascade reservoir systems. J Hydrol 519:248–257. https://doi.org/10.1016/j.jhydrol.2014.07.029
Zhu F, Zhong P, Xu B, Chen J, Sun Y, Liu W, Li T (2020) Stochastic multi-criteria decision making based on stepwise weight information for real-time reservoir operation. J Clean Prod 257:120554. https://doi.org/10.1016/j.jclepro.2020.120554
Acknowledgements
We would like to thank three anonymous reviewers for their in-depth reviews and constructive suggestions. The remarks and summary of reviewer comments provided by the Editor and Associate Editor are also greatly appreciated, which have facilitated major improvements in this paper.
Funding
This study is supported by National Key Technologies R&D Program of China (Grant No. 2017YFC0405604), the Fundamental Research Funds for the Central Universities (B200202028, B200202032), and the China Postdoctoral Science Foundation Funded Project (Grant No. 2018 T110525).
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Xu, B., Huang, X., Zhong, Pa. et al. Two-Phase Risk Hedging Rules for Informing Conservation of Flood Resources in Reservoir Operation Considering Inflow Forecast Uncertainty. Water Resour Manage 34, 2731–2752 (2020). https://doi.org/10.1007/s11269-020-02571-y
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DOI: https://doi.org/10.1007/s11269-020-02571-y