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).
Compound flooding, the concurrence of multiple flooding mechanisms such as storm surge, heavy rainfall, and riverine flooding, poses a significant threat to coastal communities. To mitigate the impacts of compound flooding, forecasts must represent the variability of flooding drivers over a wide range of spatial scales while remaining timely. One approach to develop these forecasts is through subgrid corrections, which utilize information at smaller scales to “correct” water levels and current velocities averaged over the model scale. Recent studies have shown that subgrid models can improve both accuracy and efficiency; however, existing models are not able to account for the dynamic interactions of hydrologic and hydrodynamic drivers and their contributions to flooding along the smallest flow pathways when using a coarse resolution. Here, we have developed a solver called CoaSToRM (Coastal Subgrid Topography Research Model) with subgrid corrections to compute compound flooding in coastal systems resulting from fluvial, pluvial, tidal, and wind-driven processes. A key contribution is the model’s ability to enforce all flood drivers and use the subgrid corrections to improve the accuracy of the coarse-resolution simulation. The model is validated for Hurricane Eta 2020 in Tampa Bay, showing improved prediction accuracy with subgrid corrections at 42 locations. Subgrid models with coarse resolutions (R2 = 0.70, 0.73, 0.77 for 3-, 1.5-, 0.75-km grids) outperform standard counterparts (R2 = 0.03, 0.14, 0.26). A 3-km subgrid simulation runs roughly 50 times faster than a 0.75-km subgrid simulation, with similar accuracy.
