Poster: CERF 2025
SS Omogbehin, JC Dietrich. “Baroclinic 3D modeling of circulation patterns in the Pamlico–Albemarle Sound System.” Coastal and Estuarine Research Federation (CERF) Biennial Conference, Richmond, Virginia, 10 November 2025.
Baroclinic 3D modeling of circulation patterns in the Pamlico-Albemarle Sound System
Ranges of Peak Storm Tides Between Open‐Coast and Bay Locations
Storm tides — the combination of tides and storm surge — cause flooding in coastal regions, often with differences in magnitudes between the open coast and locations within water bodies like bays and estuaries. Previous studies have shown that storm surge is sensitive to the storm’s wind intensity, speed, and track; the coast’s geometry and relative position to the storm; and also to nonlinear interactions with tides. These sensitivities have been documented at either open coast or bay locations, but without comparing or quantifying the differences in behavior between them, even though these differences may have implications for risk management. This study examines the range of peak storm tides within the Lower Chesapeake Bay, which has vulnerable communities at the open coast, like Virginia Beach, and inside the bay near the James River, like Hampton and Norfolk. A high‐resolution model was developed for the region and validated against observations of water levels during Hurricane Irene in 2011. Storm parameters were perturbed to analyze the variation in storm tide ranges. It was found that the range of possible storm tides was greater at bay locations than at the open coast, by as much as 47%. This higher variability at the bay locations was due to sensitivities to storm parameters like the wind intensity and storm tracks, which led to storm tide peaks outside of the interquartile range. This finding highlights the importance of understanding the uncertainty in storm forecasts concerning future possible impacts in complex coastal regions.
Posters: Fall 2025 Conferences
ME McKenna, TA Cuevas López, DL Anderson, JC Dietrich. Deep Learning-Based Prediction of Coastal Storm Tide Flood Maps. Applied AI in Engineering & Computer Science Symposium, North Carolina State University, Raleigh, North Carolina, 27 Oct 2025.
Deep Learning-Based Prediction of Coastal Storm Tide Flood Maps
Conference: ASBPA 2025
Webinar: ADCIRC Coordination
Neural Network Predictions of Peak Storm Tides due to Tropical Cyclones
Storm-driven flooding is a hazard for coastal communities. Process-based models can predict the combined effects of tides, winds, and flooding due to tropical cyclones, including in real-time, but often with restrictions due to a model’s runtime. Researchers have developed neural networks (NN), trained on libraries of storm surge simulations, to predict flooding in seconds. However, previous NNs ignored interactions with astronomical tides, limited to storms of specific durations, and trained for extreme conditions.
In this study, a NN is developed to predict peak values for storm tides (storm surge and tides) at nine stations along the North Carolina coast. For training, a library of storm-tides was developed via process-based model simulations of 1,813 synthetic storms based on historical data in the north Atlantic Ocean, but with a specific focus on North Carolina, and then augmented by a factor of 50 via combinations with random tides. Unlike previous NN, this approach incorporates the astronomical tides in the training and uses data augmentation techniques for enhanced generalization. The NN performs well, with root-mean-square errors of about 6 cm and mean bias errors for the extreme storms of about 5 cm. For probabilistic predictions of historical storms, the model can predict for 100 ensemble members in 1 sec, and the ranges of peak storm tides are close to their true values.
Resolution Sensitivities for Subgrid Modeling of Coastal Flooding
Flooding due to storm surge can propagate through coastal regions to threaten the built and natural environments. This propagation is controlled by geographic features of varying scales, from the largest oceans to the smallest marsh channels and sandy dunes. Numerical models to predict coastal flooding have been improved via the use of subgrid corrections, which use information about the smallest-scale flow controls to provide corrections to coarser scale grids. Although previous studies have demonstrated the benefits of subgrid models, especially how coarser models can be more efficient without a trade-off in accuracy, this study systematically investigates subgrid corrections in storm surge models across large domains. Here, we apply the widely used ADVanced CIRCulation (ADCIRC) storm surge model with revised subgrid corrections to develop guidance for resolution of coastal regions. Recent hurricanes in the South Atlantic Bight are simulated with five models, each with varying resolution of coastal islands, estuaries, rivers, and floodplains. Model performance is quantified via comparisons with observed data and high-resolution simulations. Clear degradation is observed in the subgrid model performance as minimum mesh resolution becomes coarser than the width of channels conveying flow or the barrier islands blocking flow. Therefore, subgrid model mesh resolution should account for spatial scales of local flow pathways and barrier islands to maintain proper model mass and momentum transfer. However, with subgrid modeling this can be done at much coarser (and thus computationally faster) resolutions than with conventional models.