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<p>Urban stormwater management faces deep uncertainty under climate change, which demands flexible and data-efficient decision tools. This study introduces an integrated Reality in Options (RIO) framework to prioritize hybrid green and blue infrastructures, including infiltration trenches, green and blue swales, retention ponds, rainwater harvesting basins, and permeable pavements, by combining consequence severity with scenario likelihood. We generated 27 probabilistic rainfall paths for 2025–2100 using a geometric Brownian motion model, chosen for its ability to capture log-normal variability and support multi-period scenario trees. Climate projections from MRI-ESM2 were validated against 1980–2010 observations at the Gorgan meteorological stations to ensure regional applicability. Each infrastructure mix was then evaluated through the RIO model over three future periods, revealing adaptive portfolios such as infiltration-focused strategies for low-rainfall conditions and transmission-based options for extreme events. This research lies in adapting the Reality in Options (RIO) framework originally developed in economic contexts to urban stormwater management under climate uncertainty. Unlike Robust Decision-Making (RDM), which requires thousands of simulations, or Adaptive Pathways, which focus on long-term sequences of actions, RIO provides a computationally simple yet scientifically rigorous approach that directly balances severity and likelihood of scenarios. This is the first study to operationalize RIO in hydrology, demonstrating its capacity to generate practical, transparent, and adaptive strategies for cities facing data scarcity and urgent climate adaptation needs, The main novelty of this study lies in operationalizing the RIO framework for urban hydrology and demonstrating that infiltration-based GBI consistently performs as the most robust strategy across uncertain rainfall futures. The novelty of this study lies in applying the Reality in Options (RIO) framework originally designed for financial and economic contexts to urban runoff management under climate uncertainty. Unlike previous works that either focus on Robust Decision-Making (RDM) requiring thousands of simulations or Real Options Analysis (ROA) emphasizing monetary valuation, our approach explicitly integrates severity likelihood trade-offs in a computationally simple tool tailored to data-scarce urban environments. This represents the first operationalization of RIO in hydrology, offering cities a practical, transparent, and adaptive method for prioritizing green and blue infrastructures.</p> Graphical Abstract <p></p>

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Adaptation Strategies for Urban Runoff Systems under Severe Uncertainty of Climate Change Using Green and Blue Infrastructure

  • 期刊论文

  • 发表日期：

    2026年2月21日

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  • Hossein Hojati Sayah,
  • Babak Omidvar,
  • Fereydoun Ghazban,
  • Mohammad Hossein Niksokhan

摘要

Urban stormwater management faces deep uncertainty under climate change, which demands flexible and data-efficient decision tools. This study introduces an integrated Reality in Options (RIO) framework to prioritize hybrid green and blue infrastructures, including infiltration trenches, green and blue swales, retention ponds, rainwater harvesting basins, and permeable pavements, by combining consequence severity with scenario likelihood. We generated 27 probabilistic rainfall paths for 2025–2100 using a geometric Brownian motion model, chosen for its ability to capture log-normal variability and support multi-period scenario trees. Climate projections from MRI-ESM2 were validated against 1980–2010 observations at the Gorgan meteorological stations to ensure regional applicability. Each infrastructure mix was then evaluated through the RIO model over three future periods, revealing adaptive portfolios such as infiltration-focused strategies for low-rainfall conditions and transmission-based options for extreme events. This research lies in adapting the Reality in Options (RIO) framework originally developed in economic contexts to urban stormwater management under climate uncertainty. Unlike Robust Decision-Making (RDM), which requires thousands of simulations, or Adaptive Pathways, which focus on long-term sequences of actions, RIO provides a computationally simple yet scientifically rigorous approach that directly balances severity and likelihood of scenarios. This is the first study to operationalize RIO in hydrology, demonstrating its capacity to generate practical, transparent, and adaptive strategies for cities facing data scarcity and urgent climate adaptation needs, The main novelty of this study lies in operationalizing the RIO framework for urban hydrology and demonstrating that infiltration-based GBI consistently performs as the most robust strategy across uncertain rainfall futures. The novelty of this study lies in applying the Reality in Options (RIO) framework originally designed for financial and economic contexts to urban runoff management under climate uncertainty. Unlike previous works that either focus on Robust Decision-Making (RDM) requiring thousands of simulations or Real Options Analysis (ROA) emphasizing monetary valuation, our approach explicitly integrates severity likelihood trade-offs in a computationally simple tool tailored to data-scarce urban environments. This represents the first operationalization of RIO in hydrology, offering cities a practical, transparent, and adaptive method for prioritizing green and blue infrastructures.

Graphical Abstract