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.

Understanding how ecological and cultural resources may change in the future is an important component of conservation planning and for the implementation of long-term environmental monitoring. We modeled six future scenarios of urbanization and sea level rise to investigate their potential effects on the Peninsular Florida Landscape Conservation Cooperative's Priority Resources (PFLCC 2016), which were identified as important for conservation through a cooperative multi-partner effort to prioritize conservation efforts on a state-wide scale. These data represent conservation targets for the Coastal Uplands at present, and under six future scenarios of sea level rise and urbanization.

Understanding how ecological and cultural resources may change in the future is an important component of conservation planning and for the implementation of long-term environmental monitoring. We modeled six future scenarios of urbanization and sea level rise to investigate their potential effects on the Peninsular Florida Landscape Conservation Cooperative's Priority Resources (PFLCC 2016), which were identified as important for conservation through a cooperative multi-partner effort to prioritize conservation efforts on a state-wide scale. These data represent conservation targets for the Coastal Uplands at present, and under six future scenarios of sea level rise and urbanization.

These elevation data (in meters) in Apalachee Bay, Florida, have been systematically and variably lowered, mitigating the bias in the lidar DEM and improving its spot elevation accuracy by approximately 69% in Apalachee Bay, Florida. These data span the big bend region of Florida’s gulf coast consisting of Gulf, Franklin, Wakulla, Jefferson, and Taylor counties. The data are in GIS raster format. These adjusted data are now suitable for modeling salt marsh evolution and flood inundation under sea-level rise (SLR) scenarios. Lidar data used in this adjustment were collected on March 01, 2018.

Using the Hydro-MEM (Hydrodynamic-Marsh Equilibrium Model) (Alizad and others, 2016a; 2016b), the wetlands system within the Apalachicola-Big-Bend (ABB) region of Florida (FL) was assessed using initial and three sea-level rise (SLR) scenarios from the National Oceanic and Atmospheric Administration (NOAA) (Sweet and others, 2017). The initial (init) scenario represents the present conditions in the year 2020. The intermediate-low (int-low) scenario projects 50 centimeters (cm) of SLR by 2100, the intermediate (int) scenario projects 1 meter (m) of SLR by 2100, and the intermediate-high (int-high) scenario projects 1.5 m of SLR by 2100. Hydro-MEM input data includes elevation, tidal forcings, river inflow, and field-collected parameters and couples a hydrodynamic and biological model to capture feedback processes in the wetland system. The model incorporates a spatially-varying marsh parabola parametrization and considers SLR-induced salinity intrusion proxy in the system (Alizad and others, 2022b). This data release (Alizad and others, 2022a) includes the initial and future conditions under three SLR scenarios and model outputs of marsh vegetation type. For further information regarding model input generation and visualization of model output, refer to Alizad and others (2016a).

Using the Hydro-MEM (Hydrodynamic-Marsh Equilibrium Model) (Alizad and others, 2016a; 2016b), the wetlands system within the Apalachicola-Big-Bend (ABB) region of Florida (FL) was assessed using initial and three sea-level rise (SLR) scenarios from the National Oceanic and Atmospheric Administration (NOAA) (Sweet and others, 2017). The initial (init) scenario represents the present conditions in the year 2020. The intermediate-low (int-low) scenario projects 50 centimeters (cm) of SLR by 2100, the intermediate (int) scenario projects 1 meter (m) of SLR by 2100, and the intermediate-high (int-high) scenario projects 1.5 m of SLR by 2100. Hydro-MEM input data includes elevation, tidal forcings, river inflow, and field-collected parameters and couples a hydrodynamic and biological model to capture feedback processes in the wetland system. The model incorporates a spatially-varying marsh parabola parametrization and considers SLR-induced salinity intrusion proxy in the system (Alizad and others, 2022b). This data release (Alizad and others, 2022a) includes the initial and future conditions under three SLR scenarios and model outputs of marsh vegetation type. For further information regarding model input generation and visualization of model output, refer to Alizad and others (2016a).

Sand beaches are dynamic, making it difficult to accurately predict habitat loss from sea level rise (SLR). We mapped sand beach habitat based on two datasets - a remotely sensed land cover and hand-digitized aerial imagery collected concurrently with digital elevation data. Data include the predicted area of beach habitat still above water (units = number of pixels) from SLR per decade, for 3 SLR scenarios, based on two mapping methods, for 12 barrier island beaches and 4 control islands off Florida's Gulf Coast.

Sand beaches are dynamic, making it difficult to accurately predict habitat loss from sea level rise (SLR). We mapped sand beach habitat based on two datasets - a remotely sensed land cover and hand-digitized aerial imagery collected concurrently with digital elevation data. Data include the predicted area of beach habitat still above water (units = number of pixels) from SLR per decade, for 3 SLR scenarios, based on two mapping methods, for 12 barrier island beaches and 4 control islands off Florida's Gulf Coast.

This data release presents an update to the Coastal Response Likelihood (CRL) model (Lentz and others 2015); a spatially explicit, probabilistic model that evaluates coastal response for the Northeastern U.S. under various sea-level scenarios. The model considers the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Updated model results provide higher spatial resolution predictions (from 30 meters (m) to 10 m) of adjusted land elevation ranges (AE) with respect to projected relative sea-level scenarios, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static (inundated) or dynamic (maintaining or changing state). The predictions span the coastal zone vertically from 10 m below to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 10 meters for four decades (2030, 2050, 2080 and 2100) and two possible sea-level change scenarios (Intermediate Low (IL), Intermediate High (IH)) as defined by Sweet and others 2022. Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of relative sea-level scenarios and current elevation data. Coastal response outcomes are determined by combining adjusted elevation outputs with land cover data and expert judgment (Lentz and others 2015) to assess whether an area is likely to maintain its existing land class, or transition to a new one (dynamic), or become submerged (static). The intended users of these data include scientific researchers, coastal planners, and natural resource managers.

This data release presents an update to the Coastal Response Likelihood (CRL) model (Lentz and others 2015); a spatially explicit, probabilistic model that evaluates coastal response for the Northeastern U.S. under various sea-level scenarios. The model considers the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Updated model results provide higher spatial resolution predictions (from 30 meters (m) to 10 m) of adjusted land elevation ranges (AE) with respect to projected relative sea-level scenarios, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static (inundated) or dynamic (maintaining or changing state). The predictions span the coastal zone vertically from 10 m below to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 10 meters for four decades (2030, 2050, 2080 and 2100) and two possible sea-level change scenarios (Intermediate Low (IL), Intermediate High (IH)) as defined by Sweet and others 2022. Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of relative sea-level scenarios and current elevation data. Coastal response outcomes are determined by combining adjusted elevation outputs with land cover data and expert judgment (Lentz and others 2015) to assess whether an area is likely to maintain its existing land class, or transition to a new one (dynamic), or become submerged (static). The intended users of these data include scientific researchers, coastal planners, and natural resource managers.