Abstract Coastal wetland predictions typically depend on biophysical models that incorporate geomorphological, hydrological, and vegetation dynamics. However, such models are rooted in scientific knowledge (SK), and can often exclude specific local environmental, cultural, and generational contexts. These aspects are better captured in traditional ecological knowledge (TEK), passed down through generations via oral histories. As TEK is context-specific and place-based, its comparison with SK can provide a more comprehensive understanding, especially to better predict coastal wetland vulnerability to relative sea-level rise (RSLR). In this study, we applied a biophysical mechanistic model to predict the impact of RSLR on Louisiana’s Terrebonne Bay wetlands, which are important for the subsistence and commercial livelihoods of the Pointe-au-Chien Indian Tribe (PACIT), but have experienced some of the highest rates of RSLR in the United States. The mechanistic model generated wetland predictions through 2100, using field measurements (vegetation productivity, soil pore-water salinity, total suspended solids, accretion rates), historic and current National Wetlands Inventory (NWI) wetland maps, and elevation data. We derived RSLR thresholds (beyond which wetland is lost dramatically) in 2050 and 2100 and applied the 2050 threshold to predict wetland change, which we then used to analyze the spatial vulnerability of coastal wetlands. Results indicate most wetland loss (~ 93%) will occur by 2075 under a high acceleration scenario, and were compared with previously co-produced TEK assessments to guide restoration prioritization, creating a GIS tool with broader insight than the SK or TEK data alone and providing a potential model for Indigenous-led climate adaptation planning.
Antonio et al. (Fri,) studied this question.