This study proposes a reservoir operation optimization framework to maximize the regional agricultural profit under the constraints of downstream environmental flow requirements and climate change. Three climate change models—CanESM2, MIROC5, and NorESM1-M—and the soil and water assessment tool (SWAT) were used to simulate the reservoir inflow in future periods under uncertainty. Minimum and ideal environmental flow regimes were embedded in the structure of the reservoir operation model to optimize the environmental flow needs and water supply and assess their tradeoffs. Cropping pattern optimization was used to maximize farmer profit. Particle swarm optimization was applied in the optimization processes. The method was applied to a case study in the Tajan River basin, Iran, with the results showing the environmental flow regime considerably reduces irrigation supply and has significant impacts on farmer profits. The results showed that cropping pattern optimization was not an effective strategy to mitigate the economic impacts of climate change under environmental flow constraints, but this assessment may not be generalized to other areas. Uncertainties related to the climate change models are a notable weakness of the approach and should be considered in future studies.

The present study proposes and evaluates an integrated optimization framework for agricultural planning in which an environmental flow model, drought analysis, cropping pattern model, and deficit irrigation functions are linked. Fuzzy physical habitat simulation was used to assess the environmental flow regime. A regression model was applied to develop the deficit irrigation functions. Average river flow time series in three hydrological conditions (dry, normal, and wet) were obtained using drought analysis. The environmental flow model, cropping pattern model, deficit irrigation functions, and river flow time series were then used in the structure of the optimization model. The goal of the optimization model is to provide an agricultural plan, including optimal cropping patterns and irrigation supply that minimizes ecological impacts on the river ecosystem. A genetic algorithm was used in the optimization process. Based on case study results, the proposed model is able to minimize ecological impacts on the river ecosystem in all hydrological conditions and propose an optimal plan for cropping patterns and irrigation supply. The difference between average revenue in the optimal plan and current conditions in all simulated hydrological conditions is less than 10%, which means the optimization system provides a sustainable plan for agricultural and environmental management.

Abstract Thermal pollution is an environmental impact of large dams altering the natural temperature regime of downstream river ecosystems. The present study proposes a simulation–optimization framework to reduce thermal pollution downstream from reservoirs and tests it on a real-world case study. This framework attempts to simultaneously minimize the environmental impacts as well as losses to reservoir objectives for water supply. A hybrid machine-learning model is applied to simulate water temperature downstream of the reservoir under various operation scenarios. This model is shown to be robust and achieves acceptable predictive accuracy. The results of simulation–optimization indicate that the reservoir could be operated in such a way that the natural temperature regime is reasonably preserved to protect downstream habitats. Doing so, however, would result in significant trade-offs for reservoir storage and water supply objectives. Such trade-offs can undermine the benefits of reservoirs and need to be carefully considered in reservoir design and operation.