Salinity management of reservoirs by linking hydrodynamic model, surrogate model, and evolutionary optimization

Abstract

This study proposes a combined system for salinity management of reservoirs in which the lake ecosystem simulation is integrated with the reservoir operation optimization. A finite volume-based depth-averaged model is applied for simulating salinity in the reservoir for a long-term period. Then, a surrogate model is developed by applying outputs of the fluid dynamic model using adaptive neuro-fuzzy inference system. The surrogate model is used in the structure of the optimization model to estimate the average salinity concentration in the reservoir. Two objectives are defined in the reservoir operation optimization including minimizing water supply loss and mitigating salinity impacts on the aquatic habitats in the lake ecosystem. According to case study results, the fluid dynamic model is reliable for simulating salinity distribution in the reservoir, which means it is recommendable for simulating salinity distribution of reservoirs. Moreover, The Nash–Sutcliff coefficient of surrogate model is 0.79, which implies it is reliable for applying in the optimization model as a surrogate model of salinity. Based on the environmental considerations, 0.55 ppt was defined as the average threshold of habitat suitability. Average optimal salinity during the simulated period is 0.52 ppt, which implies the optimization model is able to reduce salinity impacts properly. We recommend using the proposed method for the case studies in which increasing salinity is an environmental challenge for the aquatic species those living in the artificial lakes of large dams.