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<p>This study presents the development of a novel Stochastic Demand-Inflow Dynamic Programming (SDIDP), designed to improve reservoir operation under fluctuating conditions. A new approach for calculating transition probabilities based on bivariate normal distribution was introduced, replacing traditional discretization of inflows and demands. The Soil and Water Assessment Tool (SWAT) was applied to assess reservoir inflow under CMIP6 scenarios corrected by quantile mapping (QM) and quantile delta mapping (QDM). The QM represented more challenging future. SDIDP demonstrates superior performance, with decreasing deficits between 10 to 60 MCMs across scenarios, compared to SDP. Reliability and resiliency also show marked improvements with SDIDP; However, they are not perfect measures because an operating system which use hedging may has a lower resiliency than a not hedging system. Although vulnerability increases under severe conditions, SDIDP consistently outperforms SDP, indicating that it offers a more robust and adaptive approach for managing reservoir operations under climate change scenarios.</p>

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An Adaptive Reservoir Operation Model for Stochastic Demand-Inflow Planning

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Hossein Yousefi,
  • Saeed Alimohammadi,
  • Ali Moridi

摘要

This study presents the development of a novel Stochastic Demand-Inflow Dynamic Programming (SDIDP), designed to improve reservoir operation under fluctuating conditions. A new approach for calculating transition probabilities based on bivariate normal distribution was introduced, replacing traditional discretization of inflows and demands. The Soil and Water Assessment Tool (SWAT) was applied to assess reservoir inflow under CMIP6 scenarios corrected by quantile mapping (QM) and quantile delta mapping (QDM). The QM represented more challenging future. SDIDP demonstrates superior performance, with decreasing deficits between 10 to 60 MCMs across scenarios, compared to SDP. Reliability and resiliency also show marked improvements with SDIDP; However, they are not perfect measures because an operating system which use hedging may has a lower resiliency than a not hedging system. Although vulnerability increases under severe conditions, SDIDP consistently outperforms SDP, indicating that it offers a more robust and adaptive approach for managing reservoir operations under climate change scenarios.