Spatial and temporal controls within a coupled spectral wave and circulation model.
Community-Informed modeling of storm surge adaptations on barrier islands.
Baroclinic 3D modeling of circulation patterns in the Pamlico-Albemarle Sound System
Identifying the Extreme Scenario of Storm Tides from Tropical Cyclones in Coastal Communities.
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.
Spatial controls and efficiency gains within a spectral wave model.
Analyzing Dune Maintenance effects on Storm Surge at Tyndall Air Force Base.
Prediction of Dune Erosion and Inlet Formation during Hurricanes Helene and Milton.
Neural Network Predictions of Flood Maps
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.
The water level (tide and surge) was simulated and the potential flooding resulting from historical hurricanes (Irene and Isabel) in Norfolk, VA was evaluated. The model was forced using the parametric Holland Model and various perturbations in the hurricane characteristics were evaluated. In addition, projected relative sea level rise up to the year 2150 was investigated.
D-Flow can accurately simulate the water level with an average correlation coefficient and root-mean-square-error of 0.974 and 0.17 m, respectively. Water level prediction showed high sensitivity to climate change impacts and inaccuracies in hurricane track and lower sensitivity to changes in hurricane central pressure and radius of maximum wind. A mesh resolution that reflects accurate topographical depiction is required to estimate the flood area accurately. Willoughby Spit (a narrow peninsula north of the naval base extending into Chesapeake Bay) was the most susceptible area to flooding. Significant parts of the base were found to be vulnerable to flooding under the considered scenarios, with flood areas ranging from 0.28 km2 to 5.94 km2 (1.3%–43% of the base area), with the largest predicted flooding for the sea level rise and wind speed scenarios. The insights of the sensitivity of flood predictions to various factors could enable targeted adaptation measures and resource allocation, for enhanced resilience and sustainable development in vulnerable coastal areas.
