Shore proximal sediment deposition in coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi: net sedimentation tile datasets from October 2016 to October 2017

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, dataset variables, and data quality.