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.
A Elkut, F Shi, JS Knowles, JC Dietrich, JA Puleo (2025). “Sensitivity of Water Level and Flood Area Prediction to Hurricane Characteristics and Climate Change Impacts.” Ocean and Coastal Management, 262, 107573, DOI: 10.1016/j.ocecoaman.2025.107573.
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.
M.S. student Sarah Grace Lott was selected as a Global Change Research Fellow by the Southeast Climate Adaptation Science Center. Sarah Grace will participate in the 
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.
