Towards nature-based and hybrid adaptation to coastal flood risk: a global perspective

In the coming century, people in low-lying coastal urban areas are projected to face an increase in coastal flood risk due to increases in, for example, urban development, sea-level rise, subsidence, and degradation of foreshore vegetation. To prevent this increase, or even to reduce risk below today’s levels, adaptation measures must be implemented. Nature-based adaptation in coastal areas, such as vegetation on the foreshore, is showing potential to mitigate the impacts of climate change. However, to our knowledge no studies have been carried out to assess and quantify the potential effectiveness of these nature-based or hybrid measures on a global-scale. Therefore, this study aims to provide a global scale assessment of nature-based (i.e. mangrove restoration and conservation of foreshore vegetation), structural (i.e. grey infrastructure) and hybrid (a combination of the latter two) adaptation measures to quantify its potential effectiveness. Although quantifying nature-based or hybrid adaptation can be challenging, it is critical to document such efforts to better understand their effectiveness. To achieve this, we develop a methodology to assess these adaptation measures in a global coastal flood risk modelling cascade. We will use this modelling framework to assess the benefits and costs of nature-based, structural and hybrid adaptation measures at the global scale. To address uncertainty of future conditions, we include a range of socioeconomic change projections and sea-level rise projections including uncertainties. The results of this study have major policy implications as it highlights the potential effectiveness of hybrid strategies at the global scale and shows where it is beneficial from an economic point of view to implement which strategies when assessing the direct benefits of the adaptation measure through flood risk reduction. This study will therefore be crucial to highlight regions where these adaptation measures might benefit for further research at the regional/local scale.