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<p>In this research, a new approach is proposed to determine an optimal discrete and continuous hedging rule (HR) for the Marun Dam reservoir in Iran, considering climate change conditions. In the proposed method, hydrological modeling, optimization approaches, and climate projections are combined to determine the reservoir operation policies under drought conditions. In the optimization approaches, the set of HR coefficient variables and starting and ending storage volumes for the discrete HR are determined using a genetic algorithm (GA). Here, climate change is simulated using the CanESM2 model under the RCP 8.5 scenario. In addition, artificial neural network (ANN) and genetic programming (GP) models are applied to predict reservoir inflow values. The performance of the proposed approaches is evaluated using reliability (time and volume), vulnerability, and sustainability indices. Results show that discrete HR leads to higher reservoir storage value at the end of the operation period (655 MCM) compared with standard operation policy (SOP) (285.48 MCM) and continuous HR (469.12 MCM). Furthermore, the discrete HR also reduces the maximum monthly shortage volume, from 424 to 338 MCM. Nevertheless, when compared to SOP, its performance is inferior in certain indices, including maximizing reservoir storage and minimizing severe shortages. This study demonstrates that discrete HR offers a more robust strategy for reservoir management under climate change in which it is improved by considering drought condition in modeling. It provides valuable insights for water resources planning and policy-making.</p>

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Optimal Hedging Rules Determination for Dam Reservoir Operation Under Climate Change

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Pegah Hoseingholi,
  • Ramtin Moeini,
  • Mehry Akbary

摘要

In this research, a new approach is proposed to determine an optimal discrete and continuous hedging rule (HR) for the Marun Dam reservoir in Iran, considering climate change conditions. In the proposed method, hydrological modeling, optimization approaches, and climate projections are combined to determine the reservoir operation policies under drought conditions. In the optimization approaches, the set of HR coefficient variables and starting and ending storage volumes for the discrete HR are determined using a genetic algorithm (GA). Here, climate change is simulated using the CanESM2 model under the RCP 8.5 scenario. In addition, artificial neural network (ANN) and genetic programming (GP) models are applied to predict reservoir inflow values. The performance of the proposed approaches is evaluated using reliability (time and volume), vulnerability, and sustainability indices. Results show that discrete HR leads to higher reservoir storage value at the end of the operation period (655 MCM) compared with standard operation policy (SOP) (285.48 MCM) and continuous HR (469.12 MCM). Furthermore, the discrete HR also reduces the maximum monthly shortage volume, from 424 to 338 MCM. Nevertheless, when compared to SOP, its performance is inferior in certain indices, including maximizing reservoir storage and minimizing severe shortages. This study demonstrates that discrete HR offers a more robust strategy for reservoir management under climate change in which it is improved by considering drought condition in modeling. It provides valuable insights for water resources planning and policy-making.