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The research on coastal hazards predicts substantial adverse impacts of chronic and episodic flooding on populated coastal areas. Despite the growing evidence about anticipated flood risks, many coastal communities are still not adapting. The observed disconnect between science on physical impacts and adaptation decisionmaking in part reflects stakeholders’ inability to envision the implications of these impacts on socioeconomic systems and the built environment in their jurisdictions. This inertia is particularly apparent in the discourse on flood-driven displacement and relocation. There is a lack of knowledge about direct and indirect flood impacts on community attributes and services that contribute to relocation decision-making. This study holistically evaluates the flood exposure on municipal features vital for socioeconomic stability, livelihoods, and quality of life across spatiotemporal scales. As such, it uses a more nuanced approach to relocation risk assessment than those solely focused on direct inundation impacts. It measures flood exposure of land use, land cover, and sociocultural and economic dimensions that are important drivers of relocation in selected rural and urban areas. The approach uses a 50-year floodplain to delineate populated coastal locations exposed to 2% Annual Exceedance Probability (AEP) storm surge projections adjusted for 2030, 2060, and 2090 sea level rise (SLR) scenarios. It then evaluates the potential impacts of this flood exposure on different types of land uses and critical socioeconomic assets in rural (Dorchester and Talbot Counties, Maryland, USA) and urban (Cities of Hampton, Norfolk, Portsmouth, and Virginia Beach, Virginia, USA) settings. The results show that some urban land uses, such as open space, military and mixed-use, and rural residential and commercial areas, might experience significantly more flooding. There are also notable differences in the baseline flood exposure and the anticipated rate and acceleration in the future among selected communities with significant implications for relocation planning. 

Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the Joint Probability Method with Response Surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise. 

Coastal populations are facing increasing environmental stress from coastal hazards including sea level rise, increasing tidal ranges, and storm surges from hurricanes. The East and Gulf Coasts of the United States (U.S.) are projected to face high rates of sea level rise and include many of the U.S.’s largest urban populations. This study proposes modelling land-use change and coastal change between 1996-2019 to project the impacts of intensifying coastal hazards on the U.S. Gulf and East Coast populations and to estimate how coastal populations are growing or retreating from high-risk areas. The primary objective is to develop a multifaceted spatial-temporal (MuST) framework to model coastal change through land-use projections and thorough analysis of the indicators of coastal urban growth or retreat. While urban growth models exist, one that presents an interdisciplinary evaluation of potential growth and retreat due to geographic factors and coastal hazards has not been released. This study proposes modelling urban growth using geospatial metrics including topographic slope, topographic elevation, distance to existing urban areas, distance to existing roads, and distance to the coast. The model will also use historic hurricane data, including storm track and footprint for named storms between 1996-2019 and the associated flood claims data from Federal Emergency Management Agency (FEMA), to account for existing impacts from coastal storms. Additionally, climate change data including sea level rise projections and future tidal ranges will be incorporated to project the impacts of future coastal hazards on urban expansion over the next 30 years (2020-2050). The basis of the urban growth model compares land-use change between 1996-2019 to complete a geospatial analysis of both the areas shifting from rural (agricultural, forest, wetlands) to urban, indicating growth and population data from 2000-2020, to evaluate coastal retreat or abandonment over the next 30 years.