Quantifying parameter uncertainty in reservoir operation associated with environmental flow management

Abstract Parameter uncertainty inherent in reservoir operation affects operation model robustness and has been considered in conventional operation focusing on improving hydropower generation. With more attention paid to ecological environment protection recently, riverine ecosystem protection requires environmental flow (e-flow) management to sustain a near-natural flow regime. Whether there is e-flow management in reservoir operation has an impact on the uncertainty of reservoir operation, but parameter uncertainty was rarely considered in reservoir operation with e-flow management. In this study, a framework is proposed for performing parameter uncertainty analysis in reservoir operation associated with e-flow management. Both e-flow requirements and hydropower generation are considered in reservoir operation to sustain the harmonious development between ecological environment and human society. To compare the effect of different e-flow managements on the uncertainty of reservoir operation, three e-flow management scenarios are set. The Metropolis-Hastings algorithm of Markov Chain Monte Carlo (MCMC) sampling approach was applied for parameter estimation and uncertainty quantification. We used this framework in a case study of Nuozhadu hydropower station on the Lancang River in southern China to test its effectiveness. The results demonstrated that parameter uncertainty greatly affects the robustness of reservoir operation model. The comparison of reservoir operation under different e-flow management scenarios shows that more detailed e-flow management can effectively reduce uncertainty in reservoir operation and sustain the near-natural flow regime in a river.

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