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.
