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.
TA Cuevas López, BJ Tucker, JC Dietrich, DL Anderson, E Lobaton, JS Mariegaard (2025). “Neural Network Predictions of Peak Storm Tides due to Tropical Cyclones.” Ocean Modelling, 197, 102588, DOI: 10.1016/j.ocemod.2025.102588.
An increase in commercial shipping has led to an increase in hazards for ship strikes on bridges, to which we refer as allisions. There is a need for a better understanding of how ships are affected by local flows as they approach an allision. We couple region- and local-scale models to simulate the allision of the container ship Dali with the Key Bridge. Simulations are forced with real tides, river inflows, and atmospheric conditions, and then the ship’s motion is predicted as it drifted and then allided with the bridge’s south pier. The trajectory is a close match to observations, and the allision timing is matched within 70 seconds of the real event. The ship’s southward turn was driven by a cross-channel gradient of 0.22 cm/s in the currents. Perturbations show the trajectory sensitivity to ship and environmental conditions, with many scenarios showing ship motion away from the bridge pier, as much as 500-m down-channel or 200-m to the north side. Simulations with wreckage show the depth-averaged currents may have increased by 10 to 20 cm/s in the temporary alternate channels around the bridge. Our findings can inform models for ship motion and management of navigation channels.
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.
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.
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.
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.
The combined impact of hurricanes and climate change can affect the total water level leading to severe impacts on coastal zones such as flooding. Accurate prediction and evaluation of water levels are essential for predicting the impact on military readiness and resilience for coastal facilities. This study uses D-Flow Flexible Mesh to evaluate the sensitivity of water level and flood area prediction to the impact of climate change and hurricane activity with application to the Naval Station Norfolk, Virginia, USA.
With sea-level rise, flooding in coastal communities is now common during the highest high tides. Floods also occur at normal tidal levels when rainfall overcomes stormwater infrastructure that is partially submerged by tides. Data describing this type of compound flooding is scarce and, therefore, it is unclear how often these floods occur and the extent to which non-tidal factors contribute to flooding. We combine measurements of flooding on roads and within storm drains with a numerical model to examine processes that contribute to flooding in Carolina Beach, NC, USA — a community that chronically floods outside of extreme storms despite flood mitigation infrastructure to combat tidal flooding. Of the 43 non-storm floods we measured during a year-long study period, one-third were unexpected based on the tidal threshold used by the community for flood monitoring. We introduce a novel model coupling between an ocean-scale hydrodynamic model (ADCIRC) and a community-scale surface water and pipe flow model (3Di) to quantify contributions from multiple flood drivers. Accounting for the compounding effects of tides, wind, and rain increases flood water levels by up to 0.4 m compared to simulations that include only tides. Setup from sustained (non-storm) regional winds causes deeper, longer, more extensive flooding during the highest high tides and can cause floods on days when flooding would not have occurred due to tides alone. Rainfall also contributes to unexpected floods; because tides submerge stormwater outfalls on a daily basis, even minor rainstorms lead to flooding as runoff has nowhere to drain. As a particularly low-lying coastal community, Carolina Beach provides a glimpse into future challenges that coastal communities worldwide will face in predicting, preparing for, and adapting to increasingly frequent flooding from compounding tidal and non-tidal drivers atop sea-level rise.