Simulation-Optimization of Reservoir Water Quality under Climate Change

Abstract

Increasing population and water use, rising pollution of water resources, and climate change affect the quantity and quality of water resources. Reservoir operation is an important tool for water supply that can be optimized by simulation-optimization considering the impact of climate change on water quality. This study presents a simulation-optimization approach linking the CE-QUAL-W2 hydrodynamic model with the firefly algorithm k-nearest neighbor (FA-KNN) model to obtain optimal reservoir discharges to achieve water quality objectives under climate change conditions. The developed algorithm overcomes the computational burden of CE-QUAL-W2. The FA-KNN hybrid algorithm is employed to optimize the total dissolved solids (TDS) while achieving computational efficiently beyond what could be achieved with CE-QUAL-W2 simulations alone. This paper’s approach is evaluated with the Aidoghmoush Reservoir (East Azerbaijan, Iran). Overall, 36 simulation-optimization scenarios for dry and wet years under baseline and climate change conditions are evaluated by considering three initial water levels for the reservoir (minimum, average, and normal) and three thresholds for assessing the hybrid algorithm. The TDS released from the reservoir in wet years would be acceptable for agricultural use; in dry years, on average, the TDS would not be acceptable for 24 days per year under climate change. The reservoir in winter undergoes complete mixing; it becomes stratified in spring and summer, and it is close to complete mixing in the autumn. The highest TDS in the reservoir would occur during the summer in dry years under climate change, reaching TDS of approximately 2,645 g/m3.