Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson's correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters.

Coastal inundation causes considerable impacts on communities and economies. Sea level rise due to climate change increases the occurrence of coastal flood events, creating more challenges to coastal societies. Here we intend to draw the understanding of coastal inundation from our early studies, and provide a silhouette of our approaches in assessing climate change impacts as well as developing risk-based climate adaptation. As a result, we impart a distinctive view of the adaption towards the integration of asset design, coastal planning and policy development, which reflect multiscale approaches crossing individual systems to regions and then nation. Having the approaches, we also discussed the constraints that would be faced in adaptation implementation. In this regard, we initially follow the risk approach by illustrating hazards, exposure and vulnerability in relation to coastal inundation, and manifest the impact and risk assessment by considering an urban environment pertinent to built, natural, and socioeconomic systems. We then extend the scope and recommend the general approaches in developing adaptation to coastal inundation under climate change towards ameliorating overall risks, practically, by the reduction in exposure and vulnerability in virtue of the integration of design, planning and polices. In more details, a resilience design is introduced, to effectively enhance the capacity of built assets to resist coastal inundation impact. We then emphasize on the cost-effective adaptation for coastal planning, which delineates the problem of under-adaptation that leaves some potential benefits unrealized or over-adaptation that potentially consumes an excessive amount of resources. Finally, we specifically explore the issues in planning and policies in mitigating climate change risks, and put forward some emerging constraints in adaptation implementation. It suggests further requirements of harmonizing while transforming national policies into the contents aligned with provincial and local governments, communities, and households.