These datasets provide information on soil properties, plant species cover, and soil surface elevation in a tidal wetland and a managed impounded wetland in northern Suisun Marsh, California, USA.

Soil cores were collected across the marshes of Don Edwards San Francisco Bay National Wildlife Refuge in California to assess belowground properties and for radioisotope dating. Data from the soil cores include dry bulk density, organic matter proportion, carbon, and accretion rates. These data were then used to calibrate a wetland model (WARMER3) to assess future elevation and vegetation trajectories with sea-level rise.

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Vegetation delineations based on photo interpretation and formal vegetation classification plus change detection.

This data release documents the spatial and temporal variability of nutrients and related water quality parameters at high spatial resolution in Suisun Marsh and Suisun Bay in the San Francisco Estuary of California, USA. The data set includes nitrate, ammonium, phosphate, dissolved organic carbon, temperature, conductivity, dissolved oxygen, turbidity, chlorophyll, blue-green algal pigments, and phytoplankton community structure. Data-collection cruises were conducted under three different environmental/flow conditions between July - September 2018 that coincided with conditions prior to, during, and following the Suisun Marsh Salinity Control Gates Summer Action. The action is part of the Delta Smelt Resiliency Strategy and organized by the California Department of Water Resources and California Natural Resources Agency.

This data release documents the spatial and temporal variability of nutrients and related water quality parameters at high spatial resolution in Suisun Marsh and Suisun Bay in the San Francisco Estuary of California, USA. The data set includes nitrate, ammonium, phosphate, dissolved organic carbon, temperature, conductivity, dissolved oxygen, turbidity, chlorophyll, blue-green algal pigments, and phytoplankton community structure. Data-collection cruises were conducted under three different environmental/flow conditions between July - September 2018 that coincided with conditions prior to, during, and following the Suisun Marsh Salinity Control Gates Summer Action. The action is part of the Delta Smelt Resiliency Strategy and organized by the California Department of Water Resources and California Natural Resources Agency.

Vegetation delineations based on photo interpretation and formal vegetation classification plus change detection.

Vegetation delineations based on photo interpretation and formal vegetation classification plus change detection.

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, Mississippi (GNDNERR). 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 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 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, turbidity, and wave data) are also provided in this data release. Data were collected during 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 from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found at https://coastal.er.usgs.gov/data-release/doi-P9BFR2US/. Please read the full metadata for details on data collection, dataset variables, and data quality.

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 five marsh study sites (Sites 5, 6, 7, 8, and 9) and four nearshore estuarine study sites (Sites 8S, 8D, 9S, 9D) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. This study builds on Smith and others (2020b) and includes datasets collected after the installation of a living shoreline (a subtidal sill [artificial reef]) that was completed in May 2021. Each marsh 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 three to 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 August 2021 to January 2023. 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 (via loss-on-ignition [LOI]). For select sampling periods, ancillary data (water level, elevation, and vegetation) are also provided in this data release. The estuarine sites consisted of Site 8S (shallow; landward of the subtidal sill), 8D (deep; seaward side of the subtidal sill), as well as 9S and 9D (both sites established adjacent to 8S/8D and the subtidal sill). During select trips water parameters (e.g., waves, water level, and turbidity) were collected at each estuarine site to collect turbidity and wave data. Data presented in this data release were collected under three USGS Field Activities Numbers (FAN; one FAN per year) during thirteen sampling trips (alternate FAN; each sampling trip was assigned a unique alt FAN under the main FAN): 2021-320-FA (21CCT01, 21CCT03, 21CCT04, 21CCT05), 2022-302-FA (22CCT01, 22CCT02, 22CCT03, 22CCT04, 22CCT05, 22CCT06, 22CCT07, 22CCT08) and 2023-301-FA (23CCT01), however, every trip does not contain all data types. This data release also contains data for 2020-323-FA (20CCT02) that was collected as a special collection trip for Hurricane Delta. This data was collected before the subtidal sill was installed and is being published with this data release since pre-subtidal sill data was already published. 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/. Users can look up USGS FANs in the CMGDS by replacing the FAN in the following url: https://cmgds.marine.usgs.gov/services/activity.php?fan=2022-302-FA. Data from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found in Smith and others (2020a). Data collected from before the living shoreline (subtidal sill) installation can be found in Smith and others (2020b). For additional information on data processing and analysis, refer to the accompanying journal publication Smith and others (2025). Please read the full metadata for details on data collection, dataset variables, and data quality.