The NC C-CAPE: North Carolina Center for Coastal Algae, People, and Environment will investigate the health effects of various microcystin (MC) mixtures, and it will elucidate links between environmental and climatic drivers and harmful algal bloom (HAB) dynamics, MC congener composition, and toxin contamination in oysters and blue crabs. We will determine and the health effects of MC-mixtures on hepatic toxicity, NAFLD and hepatocellular carcinoma in model systems and humans. The Center’s Community Engagement Core will use the principles of data justice to address HAB exposure and prevention, where community members are experts, rather than objects of research, and have the capacity to conduct critical and systemic inquiry into their own lived experiences. The Administrative Core will provide efficient and effective fiscal and scientific leadership and promote interactions and collaborations across all Center components and beyond. Project 1 will advance our understanding of HAB dynamics and MC contamination in seafood, combining state-of-the-art in situ observing technologies and targeted field surveys. In addition, experimental work will elucidate trophic transfer of toxins in oysters and blue crabs. Project 2 will define how MC mixtures influence mechanisms of liver toxicity and resulting risk of adverse health outcomes in regulatory-relevant mammalian models as well as at-risk human populations. Project 3 will integrate highly diverse data sets and coastal circulation modeling within a probabilistic (Bayesian) modeling framework to elucidate environmental controls on MC distribution in water and seafood and assess MC exposure risk in a changing climate. NC C-CAPE will provide significant insight to guide efforts to implement effective monitoring approaches, inform guideline values for safe consumption of water and seafood, deliver predictive tools to assess emergent and future toxin exposure risk, and will leverage community engagement initiatives to fill data gaps and improve oceans and human health.
A Schnetzer, SM Belcher, BB Cutts, DR Obenour, T Ben-Horin, JC Dietrich, C Hoyo, NG Nelson, R Paerl. “North Carolina Center for Coastal Algae, People, and Environment (NC C-CAPE).” National Institutes of Health, National Institute of Environmental Health Sciences, Centers for Oceans and Human Health 4: Impacts of Climate Change on Oceans and Great Lakes, 2024/02/01 to 2029/01/31, $6,913,382 (Dietrich: $467,482).
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.