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Abstract <p>Climate change and anthropogenic activities have increasingly challenged the validity of the stationarity assumption in hydrology. Traditional reservoir operation schemes inadequately account for non-stationary factors, exhibiting limitations in utilizing contemporary and prospective hydrological information while demonstrating insufficient preparedness for extreme events. This necessitates the development of dynamic reservoir operation strategies that adaptively incorporate emerging information over time to address climate-induced uncertainties. In this context, we propose a two-stage risk-hedging adaptive reservoir operation model based on forecast-informed adaptive management. The model leverages real-time inflow forecasts to formulate operational strategies, achieving flood control and benefit enhancement during flood seasons while optimizing economic benefits in non-flood periods. The framework systematically quantifies forecast uncertainty through normal distribution modeling, Taylor series expansion, and mathematical expectation analysis, while implementing risk-hedging mechanisms to mitigate systemic operational risks across temporal stages. Applied to the upper Baoji Gorge Irrigation District, the model demonstrates enhanced adaptive capability through simulation of annual operations under six extreme hydrological scenarios, with comparative analysis against standard operating policy (SOP). Results indicate that the model proactively manages flood risks through preemptive reservoir drawdown during flood seasons while maintaining optimal storage levels during low-risk periods to maximize benefits. In non-flood periods, it achieves superior economic performance through rational water allocation without compromising supply reliability, thereby enhancing both water utilization efficiency and operational stability.</p> Highlights <p>• Forecast-Decision Dynamic Coupling Model.</p> <p>• Phased Risk Hedging Optimization.</p> <p>• Adaptive Dual-Mode Switching for Flood Season/Non-Flood Season.</p> <p>• Uncertainty Quantification and Benefit Trade-off.</p> <p>• Globally-Locally Coordinated Dispatch Framework.</p>

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Adaptive Reservoir Operation Considering Real-time Forecasting: Application of Two-stage Coordinated Control Risk Hedging Principle

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Jingzhe Liu,
  • Jiaqi Zhai,
  • Jingjing Duan,
  • Chenhui Jiang,
  • Zhaohan Zhang,
  • Qingming Wang,
  • Jing Zhao,
  • Tao Wang

摘要

Abstract

Climate change and anthropogenic activities have increasingly challenged the validity of the stationarity assumption in hydrology. Traditional reservoir operation schemes inadequately account for non-stationary factors, exhibiting limitations in utilizing contemporary and prospective hydrological information while demonstrating insufficient preparedness for extreme events. This necessitates the development of dynamic reservoir operation strategies that adaptively incorporate emerging information over time to address climate-induced uncertainties. In this context, we propose a two-stage risk-hedging adaptive reservoir operation model based on forecast-informed adaptive management. The model leverages real-time inflow forecasts to formulate operational strategies, achieving flood control and benefit enhancement during flood seasons while optimizing economic benefits in non-flood periods. The framework systematically quantifies forecast uncertainty through normal distribution modeling, Taylor series expansion, and mathematical expectation analysis, while implementing risk-hedging mechanisms to mitigate systemic operational risks across temporal stages. Applied to the upper Baoji Gorge Irrigation District, the model demonstrates enhanced adaptive capability through simulation of annual operations under six extreme hydrological scenarios, with comparative analysis against standard operating policy (SOP). Results indicate that the model proactively manages flood risks through preemptive reservoir drawdown during flood seasons while maintaining optimal storage levels during low-risk periods to maximize benefits. In non-flood periods, it achieves superior economic performance through rational water allocation without compromising supply reliability, thereby enhancing both water utilization efficiency and operational stability.

Highlights

• Forecast-Decision Dynamic Coupling Model.

• Phased Risk Hedging Optimization.

• Adaptive Dual-Mode Switching for Flood Season/Non-Flood Season.

• Uncertainty Quantification and Benefit Trade-off.

• Globally-Locally Coordinated Dispatch Framework.