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.
Sedighkia et al. (Sun,) studied this question.