The U.S. Atlantic and Gulf coasts are vulnerable to storms, which can cause significant erosion of beaches and dunes that otherwise protect coastal communities. One example is Hurricane Ian (2022), which impacted Florida’s Gulf coast and then again the southeast U.S. Atlantic coast, resulting in significant beach and dune scarping and breaches in multiple locations. Models can be used for real-time forecasts of storm-driven erosion, which can support decision-making, but are limited due to demands for computational resources and uncertainties in dynamic coastal systems. Current methods for erosion forecasts are based on empirical equations for wave run-up, which do not represent sediment transport during the storm, and on surrogate models, which also must rely on simplified representations of the system. However, with continued advancements in high-resolution geospatial data and computational efficiencies, there is an opportunity to apply morphodynamic models for deterministic forecasts of beach and dune erosion as a stormapproaches the coast. Real-time morphodynamic model implementation is challenging because the framework must be accurate and efficient while maintaining versatility to account for forecast uncertainties. Additionally, the evaluation and post-processing for the model needs to effectively communicate the results, including the timing and scale of coastal change during an extreme event when temporal observations are unavailable.

In this study, we apply the state-of-art model eXtreme Beach (XBeach) to predict coastal erosion due to Hurricanes Michael (2018) and Ian (2022). Sandy beaches along the U.S. Atlantic and Gulf coasts are represented with thousands of one-dimensional transects, which are sampled for real-time forecasts based on the storms’ tracks and projected landfall locations. The morphodynamic model is initialized with high-resolution digital elevation models of the present-day conditions and forced with hydrodynamics from high-resolution wave and circulation models, and its predictions are categorized based on impacts to the primary dune. A key contribution of this study is the semi-automation of the modeling system, so the modeling framework can be applied to different regions of the coast as the landfall location shifts.

To demonstrate this, forecasts for Ian (2022) were initiated several days before the initial landfall location in Punta Gorda, Florida, and continued as the track made a secondary landfall near Georgetown, South Carolina. About 1800 transects are selected for each of the 25 advisories. The simulations are monitored, evaluated, and visualized to communicate the XBeach predictions of coastal change. The framework produces results in less than an hour and then publishes visualizations in less than 10 minutes. Results are compared spatially and temporally to qualitative post-Ian observations and total water level predictions. XBeach can predict dune impact compared to an established coastal change forecasting model while providing additional morphodynamic information not typically available, such as timing and magnitude of volume change. The addition of fully resolved ground surface information and morphodynamics in the model makes it possible to better understand the storm evolution and how that translates into erosion of beaches and dunes.

JF Gorski (2023). “Deterministic, Dynamic Model Forecasts of Storm-Driven Erosion,” North Carolina State University.