Simulating thin layer placement on a vulnerable Chesapeake Bay saltmarsh using the SLAMM model

Laplace, Alexander, Environmental Sciences, University of Virginia
Porter, John, AS-Environmental Sciences (ENVS), University of Virginia
Tidal marshes in the Chesapeake Bay are under increasing threat from accelerated sea-level rise, land subsidence, and declining sediment inputs. Thin Layer Placement (TLP), the targeted addition of sediment to marsh surfaces, has emerged over the past few decades as a promising strategy to enhance marsh resilience, but restoration prioritization remains a challenge in Virginia, where decentralized dredging limits coordinated and efficient reuse of dredged material. This study presents a two-part framework to evaluate the viability and potential impact of TLP treatment in the Virginia portion of the Chesapeake Bay. First, a GIS-based suitability analysis was developed to identify marshes most in need of elevation enhancement using two key factors: elevation relative to mean high water (MHW) and total suspended solids (TSS). Elevation was determined to be the strongest predictor of marsh vulnerability and was thus weighted higher. Second, the Sea Level Affecting Marshes Model (SLAMM) was applied to simulate marsh response to sea-level rise with and without a 10 cm TLP intervention starting in 2030. The study focused on Plum Tree Island National Wildlife Refuge, a high-priority marsh identified by the suitability model. Results showed that TLP treatment delayed submergence and preserved more regularly-flooded marsh area compared to the control, particularly in mid-century projections (2050–2060). Though differences in acreage were modest, the results suggest TLP could provide meaningful resilience benefits at targeted sites. This research offers a reworkable, targeted approach to prioritize restoration sites and simulate long-term ecological outcomes and supports broader efforts to integrate beneficial use of dredge material into coastal resilience planning.
BS (Bachelor of Science)
marsh, resilience, dredge, modeling
English
2025/05/14