Reservoir operation under influence of the joint uncertainty of inflow and evaporation

Omid Bozorg-Haddad (), Pouria Yari (), Mohammad Delpasand () and Xuefeng Chu ()
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Omid Bozorg-Haddad: University of Tehran
Pouria Yari: University of Tehran
Mohammad Delpasand: University of Tehran
Xuefeng Chu: North Dakota State Univ., Dept 2470

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 2, No 58, 2914-2940

Abstract: Abstract Reservoirs play a major role as an essential source of surface water, especially in arid and semi-arid regions. To optimize the operation of a reservoir and determine its storage, which varies in time, the uncertainties of major influencing factors such as its inflow and evaporation should be considered. The objective of this study is to examine the effects of joint uncertainties of the inflow and evaporation of Durudzan reservoir on its performance for the first time. The Monte Carlo simulation is used for uncertainty assessment. Specifically, the monthly time series of inflow and evaporation were generated by using artificial neural networks and the standard operation policy was used for reservoir operation. Furthermore, the probabilistic distributions of four performance indices, including time-based reliability, volumetric reliability, vulnerability, and resiliency were calculated to assess the effects of the joint uncertainties of inflow and evaporation as well as the physical parameters on the reservoir variables (e.g., water release, storage, and spill). The results showed that the highest and lowest uncertainties of the reservoir water release occurred in July and May, respectively. In addition, the highest and lowest uncertainties were, respectively, observed in March and October for the reservoir storage, and in March and May for the water spill. The results also showed that the volumetric reliability had the highest uncertainty with a coefficient of variation (CV) of 0.158, while the resiliency had the lowest uncertainty with a CV of 0.020.

Keywords: Reservoir operation management; Artificial neural network (ANN); Probability distribution; Uncertainty analysis; Monte Carlo simulation; Reservoir performance indices (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10668-021-01560-4

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