As sea levels continue to rise, coastal communities are searching for strategies to reduce flooding of low-lying roads, property, and stormwater drainage networks. Here we focus on the development of adaptation strategies for communities that experience flooding outside of extreme storms like hurricanes due to sea level rise (SLR). Processes that contribute to these floods can include tides, rainfall, wind setup, groundwater, and infrastructure failure (Gold et al., 2023). Here we present a framework to test the effectiveness of adaptation strategies in reducing multi-driver chronic flooding at both current and future sea levels. This framework integrates coastal engineering and stakeholder input to 1) identify adaptation strategies that are preferred by a community that frequently floods and 2) test the effectiveness of these strategies with a numerical model under both current and future conditions.
Community-Engaged Coastal Flood Modeling to Evaluate Sea Level Rise Adaptation Strategies
As sea levels continue to rise, coastal communities are searching for strategies to reduce flooding of low-lying roads, property, and stormwater drainage networks. Here we focus on the development of adaptation strategies for communities that experience flooding outside of extreme storms like hurricanes due to sea level rise (SLR). Processes that contribute to these floods can include tides, rainfall, wind setup, groundwater, and infrastructure failure (Gold et al., 2023). Here we present a framework to test the effectiveness of adaptation strategies in reducing multi-driver chronic flooding at both current and future sea levels. This framework integrates coastal engineering and stakeholder input to 1) identify adaptation strategies that are preferred by a community that frequently floods and 2) test the effectiveness of these strategies with a numerical model under both current and future conditions.
M.S. student Sarah Grace Lott was selected as a Global Change Research Fellow by the Southeast Climate Adaptation Science Center. Sarah Grace will participate in the 
The U.S. Atlantic and Gulf of Mexico coasts are vulnerable to storms, which can cause significant erosion of beaches and dunes that protect coastal communities. Real-time forecasts of storm-driven erosion are useful for decision support, but they are limited due to demands for computational resources and uncertainties in dynamic coastal systems and storm forcings. Current methods for coastal change forecasts are based on empirical calculations for wave run-up and conceptual models for erosion, which do not represent sediment transport and morphological change during the storm. However, with continued advancements in high-resolution geospatial data and computational efficiencies, there is an opportunity to apply morphodynamic models for forecasts of beach and dune erosion as a storm approaches the coast. In this study, we implement a forecast system based on a deterministic, dynamic model. The morphodynamic model is initialized with digital elevation models of the most up-to-date conditions and forced with hydrodynamics from wave and circulation model forecasts, and its predictions are categorized based on impact to the primary dune, defined in this study as the first ridge of sand landward of the beach. Results are compared spatially to the observed post-storm topography using changes to dune crest elevations and volumes, and temporally to the predicted total water level at the forecasted moment of dune impact.
