Assessing the Effectiveness of Nature‐Based Solutions and Building‐Level Flood Risk Reduction Measures: An Open‐Source Coupled Model

Abstract Floods are expected to increase in frequency and severity due to climate change. Recent floods have shown that many catchments worldwide are vulnerable to floods, highlighting the need for additional adaptation measures. This study extends the Geographical, Environmental, and Behavioral (GEB) model by coupling it to a hydrodynamic and a flood risk model to assess the effects of dry‐proofing, wet‐proofing, retention ponds, reforestation, and the creation of natural grassland. A key innovation is the integration of all local‐scale models, thereby allowing for a catchment‐wide assessment of the impacts of various measures on interlinked hydrological conditions, flood extents and depths, damages, and risk. We apply our method to the Geul catchment (shared between the Netherlands, Belgium and Germany), which was heavily flooded in July 2021. Our results show that reforestation and creation of natural grassland (both 10 km 2 ) reduce flood extent by 12% and average water depth by 10%. Damage is decreased up to 38%. Larger retention ponds (1 m deeper) have a much smaller reduction in flood extent (3%), depth (0.5%) and damage (1.6%), due to limited storage capacity compared to excess rainfall. The building‐level adaptation scenarios outperform all nature‐based solutions, with dry‐proofing reducing more damage (up to 95%) than wet‐proofing (around 55%). A cost‐benefit analysis shows that several adaptation measures are economically attractive. Overall, our findings show a coupled model is essential for comparing the relative effectiveness of different flood adaptation measures and supporting informed risk management decisions. The open‐source model is transferable to other catchments worldwide to guide decision‐making.