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<p>Designing Nature-based Solutions for flood mitigation in catchments can benefit from integrated decision-support frameworks (DSFs) that analyse trade-offs effectively. This study presents a framework for evaluating catchment-scale solutions, particularly reforestation, for flood risk reduction. It combines a semi-distributed hydrological model, flood frequency analysis (FFA) using the Log-Pearson Type III distribution, and regression models within a multi-objective particle swarm optimization (MOPSO) approach. The framework leverages total annual average flood damage (TAAD), implementation costs, and carbon credits (CCU) in a Pareto front analysis. The proposed framework was applied to the Bremer catchment in Australia, a region vulnerable to downstream flood impacts. Case study results showed that even during extreme flood events—such as the 100-year flood (1% annual exceedance probability)—afforestation has a meaningful impact on flood damage reduction. Specifically, scenarios with significant forest restoration or preservation demonstrated peak discharge reductions exceeding 10%. A maximum afforestation scenario across the catchment can reduce Total Annual Average Damage by approximately 12%. As an example of trade-off analysis, under 60% afforestation of the catchment area with an initial investment of $400&#xa0;million, the DSF estimates $3&#xa0;million in reduced TAAD, while the total annual benefit—including TAAD reduction and CCU revenue—is around $10&#xa0;million annually. Despite challenges and limitations, including uncertainties in hydrological modelling and simplified treatment of the spatial distribution of afforestation, the framework offers a solid foundation for sustainable and resilient decision-making regarding catchment-scale nature-based flood mitigation strategies. Future research should focus on expanding the framework to capture broader social and ecological co-benefits.</p>

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A Decision Support Framework for Catchment-Scale Nature-Based Flood Solutions Using Multi-Objective Particle Swarm Optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

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  • Mahdi Sedighkia,
  • Roslyn Prinsley,
  • Barry Croke

摘要

Designing Nature-based Solutions for flood mitigation in catchments can benefit from integrated decision-support frameworks (DSFs) that analyse trade-offs effectively. This study presents a framework for evaluating catchment-scale solutions, particularly reforestation, for flood risk reduction. It combines a semi-distributed hydrological model, flood frequency analysis (FFA) using the Log-Pearson Type III distribution, and regression models within a multi-objective particle swarm optimization (MOPSO) approach. The framework leverages total annual average flood damage (TAAD), implementation costs, and carbon credits (CCU) in a Pareto front analysis. The proposed framework was applied to the Bremer catchment in Australia, a region vulnerable to downstream flood impacts. Case study results showed that even during extreme flood events—such as the 100-year flood (1% annual exceedance probability)—afforestation has a meaningful impact on flood damage reduction. Specifically, scenarios with significant forest restoration or preservation demonstrated peak discharge reductions exceeding 10%. A maximum afforestation scenario across the catchment can reduce Total Annual Average Damage by approximately 12%. As an example of trade-off analysis, under 60% afforestation of the catchment area with an initial investment of $400 million, the DSF estimates $3 million in reduced TAAD, while the total annual benefit—including TAAD reduction and CCU revenue—is around $10 million annually. Despite challenges and limitations, including uncertainties in hydrological modelling and simplified treatment of the spatial distribution of afforestation, the framework offers a solid foundation for sustainable and resilient decision-making regarding catchment-scale nature-based flood mitigation strategies. Future research should focus on expanding the framework to capture broader social and ecological co-benefits.