| Summary: | 2021 Spring. Includes bibliographical references. Coastal cities are exposed to multiple flood drivers including high tide, storm surge, extreme rainfall, and high river flows. The occurrence of these flood drivers, either in isolation or in combination, can cause significant risk to property and human life. Climate change is placing greater pressure on coastal communities by increasing frequency and intensity of flood events through sea level rise (SLR) and more extreme rainfall and storm events. Therefore, effective adaptation strategies are essential to reduce future flood risk in exposed communities. The planning and implementation of effective adaptation strategies require a comprehensive understanding of future flood hazards and risks under future climate conditions and adaptation options. The overarching goal of this dissertation is to improve the capacity to understand, estimate and mitigate future flood hazards and risks in coastal areas under uncertain climate change. To achieve this goal, first, a nonstationary mixture probability model was developed that enables simultaneous characterization of minor and major flood events under future sea level conditions. The probability model was used to estimate minor and major flooding frequency at 68 locations along the coasts of the Contiguous United States (CONUS). The results showed a significant increase in frequency of both minor and major flood events under future sea level conditions. However, the frequency amplification of minor and major flooding varied by coastal regions. While regions in the Pacific and southeast Atlantic coast are likely to be exposed to higher frequency amplification in major flooding, the Gulf and northeast Atlantic coastal regions should expect the highest minor flood frequency amplification. Second, the proposed mixture probability model was employed in a flood risk assessment framework to enable assessing future acute and chronic coastal flood risks under different SLR and adaptation levels. The HAZUS-MH flood loss ...
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