Barrier-island communities face flooding due to rising sea levels and stronger storms, and typical adaptations (protect, accommodate, retreat) may not keep up with increasing risks. Communities are now considering extreme adaptations, such as allowing an island section to ‘return to nature’ by removing roadways and other infrastructure. But these extreme adaptations transform the natural processes of and the community’s relationship with barriers. The effects on flood risks at nearby communities are not well understood, and it is not clear whether communities will ‘welcome the water’ or reject it as opposing their sense of place. This Disaster Resilience Research Grant (DRRG) project explores participatory transformation of barriers. Stakeholders will provide insights on place meanings across the barrier island and how floods affect these places and their connections to the community. The project also quantifies how flooding at a natural island section may change the hazard at neighboring communities, and whether these locations can be selected to minimize the risks while maximizing community attachments. These activities provide a framework for participatory transformation, as well as advance technologies for flood risk modeling that can be expanded to improve disaster resilience for communities along the U.S. Gulf and Atlantic coasts. This research also supports an immersive experience for students to collaborate across engineering and social-science disciplines to tackle the challenges of climate change.
JC Dietrich, EL Seekamp. “Enhancing Coastal Resilience through Participatory Transformation of Barrier Islands.” National Science Foundation, Directorate for Engineering, Division of Civil, Mechanical and Manufacturing Innovation, Disaster Resilience Research Grants, 2024/01/01 to 2026/12/31, $398,891 (Dietrich: $199,117).
In this dissertation, subgrid corrections have been added to the ADvanced CIRCulation (ADCIRC) model, a widely used, continuous-Galerkin finite-element based, shallow water flow model. This includes the full derivation of averaged governing equations, closure approximations, and subgrid implementation into the source code. Testing of this new model was first performed on 3 domains: an idealized winding channel, a tidally influenced bay in Massachusetts, and a regional storm surge model covering Calcasieu Lake in Southwestern Louisiana with forcing from Rita (2005). By pre-computing the averaged variables from high-resolution bathy/topo data sets, the model can represent hydraulic connectivity at smaller scales. This allows for a coarsening of the model and thus faster predictions of flooding, while also improving accuracy. The implementation permits changing a logic-based wetting and drying algorithm to a more desirable logic-less algorithm, and requires averaging correction factors on both an elemental and vertex basis. This new framework further increases efficiency of the model, and is general enough to be used in other Galerkin-based, finite-element, hydrodynamic models. It is shown that the flooding model with subgrid corrections can match the accuracy of the conventional model, while offering a 10 to 50 times increase in speed.
Next, higher level corrections to bottom friction and advection were incorporated into the subgrid model, and the framework was expanded and tested at the ocean-scale. It was hypothesized that by adding higher-level corrections to the model and applying them to ocean-scale domains, accurate predictions of storm surge at the smallest coastal scales can be obtained. To accomplish this, higher-level corrections were derived and implemented into the governing equations and extensive elevation and landcover data sets were curated to cover the South Atlantic Bight region of the U.S. Atlantic Coast. From there, the subgrid model was tested on an ocean-scale domain with tidal and meteorological forcing from Matthew (2016). The improvements in water level prediction accuracy due to subgrid corrections are evaluated at 218 observation locations throughout 1500 km of coast along the South Atlantic Bight. The accuracy of the subgrid model with relatively coarse spatial resolution (RMSE = 0.41 m) is better than that of a conventional model with relatively fine spatial resolution (RMSE = 0.67 m).By running on the coarsened subgrid model, we improved the accuracy over efficiency curve for the model, and as a result the computational expense of the simulation was decreased by a factor of 13.
Finally, subgrid corrections were systematically tested on a series of five ocean-scale meshes with minimum nearshore resolutions ranging from around 60 m on the highest resolution mesh to 1000 m on the coarsest mesh. This study aimed to find the mesh resolution that offered the best trade-off between accuracy and efficiency. The limitations of the subgrid model were explored and guidelines for future users were established. In all, it was found that the primary limitation to the subgrid model came from the aliasing of important flow-blocking features such as barrier islands in the coarsest resolution meshes. However, in areas without these features subgrid corrections can offer tremendous advantages while running on very coarse meshes.