Tidal marshes in the Grand Bay National Estuarine Research Reserve are critical ecosystems that provide storm surge protection and vital habitat. These marshes face significant threats from accelerating sea-level rise (SLR) and subsidence, making their long-term survival dependent on their ability to both maintain vertical sediment capital and migrate into adjacent low-lying uplands. Coastal managers need predictions of short- and long-term marsh conditions to effectively plan for intervention areas, land acquisition, and migration corridors. This is a primary challenge, as key uncertainties in SLR rates and current marsh elevations often produce divergent results, creating deep uncertainty for long-term conservation planning.

This study applies a replicable probabilistic modeling approach to forecast changes in marsh coverage for the Grand Bay estuary. We employ the Sea Level Affecting Marsh Model (SLAMM), integrating these key uncertainties in both probabilistic SLR projections and high-resolution topobathymetric data (i.e., Digital Elevation Model, DEM). Our framework will analyze a representative subset of DEMs and SLR projections across multiple scenarios to the year 2100. The goal is to translate these probabilistic outputs into a "marsh fate classification map," a spatially explicit tool that identifies resilient core areas, vulnerable zones requiring intervention, and high-priority migration corridors.

This presentation will introduce the probabilistic framework and its application to Grand Bay. This approach moves beyond single, deterministic predictions to reveal the complex, non-linear responses of the marsh to different sources of uncertainty. The resulting maps will provide an actionable, scientific tool for coastal managers to strategically prioritize land protection and restoration efforts, thereby enhancing the long-term sustainability of the Grand Bay ecosystem.