Coastal environments are expected to respond to rising sea levels through migration inland. This process is limited by the availability of corridors of sufficiently flat, undeveloped land to be converted to wetland. Developed land protected from tidal influence through the construction of bulkheads and levies and natural areas of elevated land will constrain the area of marsh and wetland forest over time, leading to a loss of biodiversity as habitat area for species requiring tidal influence is decreased. Because these processes depend strongly on the spatial configuration of vegetationelevation relationships, they must be modeled within a framework that accounts for the specific elevation ranges over which different vegetation classes persist, and elevation change due to accretion net of settling and compaction across the full range of affected elevations. For the National Parks along the Potomac River Estuary, models must operate at both a high spatial resolution and over broad spatial extents to capture changes at the fine-grain variation needed by park resource managers. This project produced a spatially explicit computational model (termed the Marsh Accretion and Inundation Model (MAIM)) and model results predicting the impact of user-defined sea level rise scenarios on vegetation. The model takes as input detailed (1-m resolution) map layers representing elevation (generated from LiDAR) and initial vegetation classification. Vegetation classes are constrained to individual elevation ranges, predetermined based on NPS vegetation maps, plot inventory data, and digitalization of aerial photography. Uncertainty in these elevation ranges is represented through 100 Monte Carlo permutations of the vegetation class elevation boundaries. As sea level rise progresses, the model identifies where vegetation classes will become unsuitable for their location, and adjusts the map accordingly. At each time step accretion net of settling and compaction is modeled using an empirical model based on real time kinematic GPS surveys spanning 20 years of historic sea level rise. The accretion sub-model is a non-linear function of elevation, but is not separately parameterized for each vegetation class, thus maintaining smooth topography gradients between vegetation classes. The model also takes into account current species distributions that are likely to persist longer under saturated soil conditions, thus realistically modeling elements of landscape change important for resource conservation. MAIM is not a dynamic model and therefore does not alter accretion net of settling in response to accelerated sea level rise or any other environmental conditions; MAIM assumes that the relationship between accretion and elevation observed in historical data is adequate for modeling future conditions. It also does not account for hydrologic interaction with the watershed that might be expected to increase inundation times in areas of high flow accumulation area. MAIM is not a sediment dynamics model, and therefore cannot respond to changes in suspended sediment concentration in estuarine waters. Finally, MAIM does not model shoreline erosion, as this was not found to be a dominant predictable process over the majority of the study area.