Forecasting marsh resilience in the Plum Island Estuary: Integrating uncertainty to inform coastal management
Salt marshes provide critical ecosystem services yet face increasing threats from accelerated relative sea-level rise (SLR). The long-term persistence of these habitats depends on their ability to vertically accrete and laterally migrate into adjacent uplands. Predicting the location and viability of these future marsh corridors is therefore a primary challenge for coastal managers; one often hindered by uncertainties in future water levels and topographic data accuracy, particularly in vegetated wetlands where light detection and ranging (lidar) data systematically overestimate elevation. This study applies an uncertainty-informed modeling framework to forecast marsh resilience in the Plum Island Estuary, Massachusetts. We integrated the Sea Level Affecting Marshes Model (SLAMM) with a Monte Carlo error analysis, generating an ensemble of bias-corrected digital elevation models (DEMs) alongside three probabilistic SLR scenarios (Intermediate, Intermediate High, High) through 2100. We synthesized these simulations into a “marsh fate” classification map to distinguish between resilient core habitats, vulnerable zones requiring intervention, and likely migration corridors. While the marsh platform exhibits resilience under Intermediate scenarios, a critical tipping point emerges under the High SLR scenario. In this case, accelerating inundation overwhelms accretion capacities, driving a landscape-scale regime shift. By integrating the interacting uncertainties of SLR trajectories and initial marsh elevation, we project 2100 net marsh coverage outcomes ranging from a 10.5% gain to a 23.2% loss. These results demonstrate that incorporating elevation uncertainty is essential for detecting non-linear threshold behaviors that deterministic models often miss. The resulting framework empowers managers with a spatially explicit tool to prioritize land acquisition and restoration efforts in the face of climate uncertainty.