Abstract
Robust reservoir operation has long been considered a promising solution for addressing water allocation problems in the absence of reliable hydroclimatic forecasts. This study aims to evaluate the performance of this solution using a novel two-stage stochastic optimization model. The model maximizes economic benefits from reservoir deliveries while integrating stochastic inflows into a water allocation system with multiple demands and various constraints. The outcome of the model is a robust set of monthly reservoir releases that perform well under a wide range of hydroclimatic conditions. The model has been applied to the case of the Big Bend Reach of the Rio Grande/Bravo, a transboundary river basin of high importance for Mexico and the United States. The performance of the robust operation policy was assessed by comparing its outcome to those obtained under observed historical operations and an operation policy derived from a deterministic version of the optimization model that assumes perfect hydroclimatic knowledge. The results of this study indicate that the set of robust releases developed here outperforms historical reservoir operations and performs similarly to operations under perfect knowledge. These results show the effectiveness of robust reservoir operation and the usefulness of the proposed optimization model for decision-making under increasing hydroclimatic uncertainty.
Highlights
2.1 Optimizing Reservoir OperationSince the 1960s, the optimization of reservoir operation has gained importance as one of the main research areas in water resources management (Yeh 1985)
This study develops a novel two-stage linear stochastic optimization model that includes various water supply objectives with predefined safety levels
Similar to the models previously developed by Kim et al (2007), Eum et al (2010) and Macian-Sorribes et al (2017), our study presents an optimization model that explicitly includes the uncertainty related to water inflows into reservoirs
Summary
2.1 Optimizing Reservoir OperationSince the 1960s, the optimization of reservoir operation has gained importance as one of the main research areas in water resources management (Yeh 1985). Traditional deterministic optimization models are scenario-dependent, meaning that certain variables such as reservoir inflows, water demands, or system losses are parameters in the models. This is because only statistical descriptions of hydrologic variables or unreliable forecasts of long-term average conditions usually exist. Deterministic optimization models may fail to include the impacts of low probability but costly events such as floods or droughts (Farmer and Vogel 2016). Another approach to incorporating variability into the optimization model is to include stochasticity in model inputs. Choong and El-Shafie (2015) provide a comprehensive review of the use and application of these techniques for reservoir management
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