We used WARMER, a 1-D cohort model of wetland accretion (Swanson et al. 2014), which is based on Callaway et al. (1996), to examine SLR projections across each study site. Each cohort in the model represents the total organic and inorganic matter added to the soil column each year. WARMER calculates elevation changes relative to MSL based on projected changes in relative sea level, subsidence, inorganic sediment accumulation, aboveground and belowground organic matter productivity, compaction, and decay for a representative marsh area. Each cohort provides the mass of inorganic and organic matter accumulated at the surface in a single year as well as any subsequent belowground organic matter productivity (root growth) minus decay. Cohort density, a function of mineral, organic, and water content, is calculated at each time step to account for the decay of organic material and auto-compaction of the soil column. The change in relative elevation is then calculated as the difference between the change in modeled sea level and the change in height of the soil column, which was estimated as the sum of the volume of all cohorts over the unit area model domain. The total volume of an individual cohort is estimated as the sum of the mass of pore space water, sediment, and organic matter, divided by the cohort bulk density for each annual time step. Elevation is adjusted relative to sea level rise after each year of organic and inorganic input, compaction, and decomposition. We parameterized WARMER from the elevation, vegetation, and water level data collected at each site. We evaluated model outputs between 2010 and 2110 using marsh elevation zones defined above.Model inputs Sea-level rise scenariosIn WARMER, we incorporated a recent forecast for the Pacific coast which projects low, mid, and high SLR scenarios of 12, 64 and 142 cm by 2110, respectively (NRC 2012). We used the average annual SLR curve as the input function for the WARMER model. We assumed the difference between the maximum tidal height and minimum tidal height (tide range) remained constant through time, with only MSL changing annually.Inorganic matterThe annual sediment accretion rate is a function of inundation frequency and the mineral accumulation rates measured from 137Csdating of soil cores sampled across each site. For each site, we developed a continuous model of water level from the major harmonic constituents of a nearby NOAA tide gauge. This allowed a more accurate characterization of the full tidal regime as our water loggers were located above MLLW. Following Swanson et al. (2014), we assumed that inundation frequency was directly related to sediment mass accumulation; this simplifying assumption does not account for the potential feedback between biomass and sediment deposition and holds suspended sediment concentration and settling velocity constant. Sediment accretion, Ms,at a given elevation, z, is equal to, where f(z) is dimensionless inundation frequency as a function of elevation (z), and Sis the annual sediment accumulation rate in g cm-2 y-1.Organic matterWe used a unimodal functional shape to describe the relationship between elevation and organic matter (Morris et al. 2002), based on Atlantic coast work on Spartina alterniflora. Given that Pacific Northwest tidal marshes are dominated by other plant species, we developed site-specific, asymmetric unimodal relationships to characterize elevation-productivity relationships. We used Bezier curves to draw a unimodal parabola, anchored on the low elevation by MTL at the high elevation by the maximum observed water level from a nearby NOAA tide gauge. We determined the elevation of peak productivity by analyzing the Normalized Difference Vegetation Index (NDVI; (NIR - Red)/(NIR + Red)) from 2011 NAIP imagery (4 spectral bands, 1 m resolution; Tucker 1979) and our interpolated DEM. We then calibrated the amplitude of the unimodal function to the organic matter input rates (determined from sediment

We used WARMER, a 1-D cohort model of wetland accretion (Swanson et al. 2014), which is based on Callaway et al. (1996), to examine SLR projections across each study site. Each cohort in the model represents the total organic and inorganic matter added to the soil column each year. WARMER calculates elevation changes relative to MSL based on projected changes in relative sea level, subsidence, inorganic sediment accumulation, aboveground and belowground organic matter productivity, compaction, and decay for a representative marsh area. Each cohort provides the mass of inorganic and organic matter accumulated at the surface in a single year as well as any subsequent belowground organic matter productivity (root growth) minus decay. Cohort density, a function of mineral, organic, and water content, is calculated at each time step to account for the decay of organic material and auto-compaction of the soil column. The change in relative elevation is then calculated as the difference between the change in modeled sea level and the change in height of the soil column, which was estimated as the sum of the volume of all cohorts over the unit area model domain. The total volume of an individual cohort is estimated as the sum of the mass of pore space water, sediment, and organic matter, divided by the cohort bulk density for each annual time step. Elevation is adjusted relative to sea level rise after each year of organic and inorganic input, compaction, and decomposition. We parameterized WARMER from the elevation, vegetation, and water level data collected at each site. We evaluated model outputs between 2010 and 2110 using marsh elevation zones defined above.Model inputs Sea-level rise scenariosIn WARMER, we incorporated a recent forecast for the Pacific coast which projects low, mid, and high SLR scenarios of 12, 64 and 142 cm by 2110, respectively (NRC 2012). We used the average annual SLR curve as the input function for the WARMER model. We assumed the difference between the maximum tidal height and minimum tidal height (tide range) remained constant through time, with only MSL changing annually.Inorganic matterThe annual sediment accretion rate is a function of inundation frequency and the mineral accumulation rates measured from 137Csdating of soil cores sampled across each site. For each site, we developed a continuous model of water level from the major harmonic constituents of a nearby NOAA tide gauge. This allowed a more accurate characterization of the full tidal regime as our water loggers were located above MLLW. Following Swanson et al. (2014), we assumed that inundation frequency was directly related to sediment mass accumulation; this simplifying assumption does not account for the potential feedback between biomass and sediment deposition and holds suspended sediment concentration and settling velocity constant. Sediment accretion, Ms,at a given elevation, z, is equal to, where f(z) is dimensionless inundation frequency as a function of elevation (z), and Sis the annual sediment accumulation rate in g cm-2 y-1.Organic matterWe used a unimodal functional shape to describe the relationship between elevation and organic matter (Morris et al. 2002), based on Atlantic coast work on Spartina alterniflora. Given that Pacific Northwest tidal marshes are dominated by other plant species, we developed site-specific, asymmetric unimodal relationships to characterize elevation-productivity relationships. We used Bezier curves to draw a unimodal parabola, anchored on the low elevation by MTL at the high elevation by the maximum observed water level from a nearby NOAA tide gauge. We determined the elevation of peak productivity by analyzing the Normalized Difference Vegetation Index (NDVI; (NIR - Red)/(NIR + Red)) from 2011 NAIP imagery (4 spectral bands, 1 m resolution; Tucker 1979) and our interpolated DEM. We then calibrated the amplitude of the unimodal function to the organic matter input rates (determined from sediment

A dataset of modeled nearshore water levels (WLs) was developed for the North and South Carolina coastlines. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields -. Water level outputs were extracted from the global grid at approximately 20 km resolution along the coastlines. These data were then statistically downscaled using a signal-specific set of corrections to improve skill in comparison to tide gauge observations (Parker and others, 2023). Hindcast water levels were forced by ERA5 atmospheric forcing provided by the dataset of Hersbach and others (2020). ERA5 is a reanalysis product, incorporating observations and data assimilation to best represent the experienced climate. Therefore, data from this version of the dataset are comparable to observed WLs along the study region.

Projected water elevations from compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for the U.S. Atlantic coast for three states (Florida, Georgia, and Virginia). Projections were made using a system of numerical models driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a tropical cyclone database from U.S. Army Corps of Engineers. The resulting data are water elevations of projected flood hazards along the U.S. Atlantic coast due to sea-level rise and plausible future storm conditions that consider the changing climate, hurricanes, and natural variability. The resulting data products include water elevations that are consistent with coastal flood projections, also available in this dataset (Barnard, and others, 2023); see Nederhoff and others (2024) for a full explanation of data and methods. In addition to sea-level rise, flood simulations run by these numerical models included dynamic contributions from tide, storm surge, wind, waves, river discharge, precipitation, and seasonal sea-level fluctuations. Outputs include impacts from combinations of SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, and 3.0 m), storm conditions including 1-year, 20-year, and 100-year return interval storms, and a background condition (no storm - astronomic tide and average atmospheric conditions). Similar projections for North Carolina and South Carolina are available from Barnard and others, 2023, at https://doi.org/10.5066/P9W91314

Projected water elevations from compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for the U.S. Atlantic coast for three states (Florida, Georgia, and Virginia). Projections were made using a system of numerical models driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a tropical cyclone database from U.S. Army Corps of Engineers. The resulting data are water elevations of projected flood hazards along the U.S. Atlantic coast due to sea-level rise and plausible future storm conditions that consider the changing climate, hurricanes, and natural variability. The resulting data products include water elevations that are consistent with coastal flood projections, also available in this dataset (Barnard, and others, 2023); see Nederhoff and others (2024) for a full explanation of data and methods. In addition to sea-level rise, flood simulations run by these numerical models included dynamic contributions from tide, storm surge, wind, waves, river discharge, precipitation, and seasonal sea-level fluctuations. Outputs include impacts from combinations of SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, and 3.0 m), storm conditions including 1-year, 20-year, and 100-year return interval storms, and a background condition (no storm - astronomic tide and average atmospheric conditions). Similar projections for North Carolina and South Carolina are available from Barnard and others, 2023, at https://doi.org/10.5066/P9W91314

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).

To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, 20, and 25 m from the shoreline). Each plot contained six net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three month sampling periods, measuring sediment deposition from July 2018 to January 2020, with one set of NST being deployed for six months. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, grain size, and organic content (loss-on-ignition [LOI]). For select sampling periods, ancillary data (water level, elevation, and wave data) are also provided in this data release. Data were collected during USGS Field Activities Numbers (FAN) 2018-332-FA (18CCT01), 2018-358-FA (18CCT10), 2019-303-FA (19CCT01, 19CCT02, 19CCT03, and 19CCT04, respectively), and 2020-301-FA (20CCT01). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Data collected between 2016 and 2017 from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found at https://doi.org/10.5066/P9BFR2US. Please read the full metadata for details on data collection, dataset variables, and data quality.

This data release is comprised of tidal marsh biomass data and spatial predictions of peak biomass and Julian day of peak biomass using data from the Landsat archive. Aboveground biomass dry weight of mixed-species plots (25x50 cm) at a tidal marsh in Willapa Bay, Washington were used to establish a relationship between biomass and tasseled cap greeness (TCG). The julian day of annual peak greenness and the value of annual peak greenness for 32 years at Bandon National Wildlife Refuge (NWR), Grays Harbor NWR, and Nisqually NWR was calculated by fitting a Gaussian function to the TCG values for a given year. The value of each 30 meter pixel is the Julian day of maximum predicted TCG or the maximum predicted TCG. There are 32 layers in the raster, each corresponding to the the years 1984-2015 (layer 1 is 1984, layer 32 is 2015). These data support the following publication: Buffington, K. J., Dugger, B. D., and Thorne, K. M. (2018). Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes. Estuarine, Coastal and Shelf Science, 202, 212-221. https://doi.org/10.1016/j.ecss.2018.01.006

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, data set variables, and data quality.

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, data set variables, and data quality.