Welcome to the CCHT! We develop computational models for wind waves and coastal circulation, and then apply these models to high-resolution simulations of ocean behavior. Our goals are to understand how coastlines are threatened during storms, how materials are transported in the coastal environment, and how to communicate these hazard risks for use in decision support. Our research spans the disciplines of coastal engineering, numerical methods, computational mathematics, and high-performance computing.
In this web site, we share our research progress, from development to application, and from coding to publishing. Learn more about What We Do and how to Join Our Team.
The accuracy of atmospheric forcing is a critical component for total water level (TWL) prediction and coastal flood management during hurricanes. In this study, three classes of atmospheric inputs, parametric (Holland Model; HM), physics-based models (ECMWF-ERA5, Global Forecast System (GFS), and North American Mesoscale Forecast System (NAM)), and blended (HM+Physics-based), were evaluated during Hurricane Michael (October 2018) in the northeast Gulf of America (GoA). Simulations with and without wave coupling were carried out using Delft3D FM to quantify the contribution of wave-induced forces to TWL and flooding at Tyndall Air Force Base (TAFB) and Mexico Beach. HM captures the hurricane core winds, but underpredicts the far-field winds, whereas the physics-based models underestimate winds >20 m/s by over 50%. Blended forcing rectifies these biases with a decreased normalized root-mean-square error in TWL to as low as 0.05, with NAM-based blending outperforming ERA5- and GFS-based approaches due to a finer (∼12 km) spatial resolution. The physics-based models underpredicted the peak significant wave height (SWH), while the HM model overpredicted peak and underpredicted smaller SWHs. The blended models balanced these errors, enabling Delft3D FM (D-Waves) to reproduce large and small SWHs and to capture the 1D/2D spectra observed west of the hurricane track, with minor energy bias linked to phase shift. Overall, blending high-resolution physics-based with parametric core winds improves surge-wave prediction accuracy. While spatial resolution primarily controls blending skill, efficiency is region-dependent and event-specific. The necessity of blending remains sensitive to the nonlinear interaction between storm intensity, size, track, and geographic location.
This thesis develops an artificial intelligence (AI) model to predict peak storm-tides as flood maps with sufficient detail to describe hazards at community scales. Unlike prior approaches that predict storm surge or storm tides at discrete points, this model generates continuous inundation maps that consider the geomorphic controls governing coastal flooding. This model is trained on a library of process-based model simulations spanning a realistic range of storm intensities, forward speeds, track geometries, and landfall locations. Storm-tide maps are predicted for coastal North Carolina, which has a complex coastal region with narrow and wide shelves, barrier islands, inlets, sounds, estuaries, and extensive low-lying floodplains, and which is threatened by landfalling tropical cyclones.
A central methodological contribution of this work is a physics‑aware tiling and data-augmentation framework that restructures each full-domain simulation into a set of local storm-tide map tiles. Rather than treating each storm as a single, domain‑wide field, the augmentation extracts 64 non-overlapping map tiles (0.25º longitude by 0.25° latitude) that retain geospatial reference, coastal morphology, and hydrodynamic structure. This transformation allows the model to learn how individual storms affect specific regions of the coastline, rather than attempting to generalize across the entire domain at once. The model can see diverse, meaningful examples without introducing distortions from naive image‑based augmentation, expanding the effective training dataset by more than an order of magnitude. The neural‑network architecture fuses two complementary data streams — temporal storm evolution (track, intensity, motion) and static elevation maps encoding pre-storm geomorphology — and is trained using a staged Huber-loss strategy that refines spatial accuracy. Together, these components enable the AI model to learn localized storm‑tide responses while maintaining consistency with the underlying coastal physics.
Model performance is evaluated across tens of thousands of test tiles spanning the full range of coastal settings and flood responses. The surrogate achieves an overall root-mean-square error (RMSE) = 0.2722 m and maintains strong accuracy across the critical 0 m to 3 m elevation range that governs inundation onset, shoreline overtopping, and inland flood propagation. Elevation-dependent analyses reveal predictable patterns: highest accuracy in persistently dry uplands, moderate accuracy in shallow-water transition zones where small vertical differences strongly influence flooding, and larger errors in sparsely sampled or highly nonlinear regions such as tidal channels. Tile‑level case studies further show how storm approach angle, intensity, and local geomorphology shape prediction fidelity — parallel approaching storms over barrier islands produce larger errors due to enhanced alongshore gradients, whereas perpendicular tracks yield cleaner cross‑shore forcing that the model captures more reliably. These results highlight both the model’s strengths in reproducing coherent surge patterns and its limitations in regions with complex hydrodynamics or limited training representation.
