Shore Proximal Marsh Sediment Deposition and Ancillary Data From Grand Bay National Estuarine Research Reserve, Mississippi: grain size analysis

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Frequently anticipated questions:

What does this data set describe?

Title:
Shore Proximal Marsh Sediment Deposition and Ancillary Data From Grand Bay National Estuarine Research Reserve, Mississippi: grain size analysis
Abstract:
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.
Supplemental_Information:
Data was collected through quarterly field visits. Each field visit was assigned a field activity number (FAN) and sub-FAN by the U.S Geological Survey (USGS). This publication contains data for field activities from: 1) 2018-332-FA (18CCT01, July 2018), 2) 2018-358-FA (18CCT10, July 2018-October 2018), 3) 2019-303-FA (19CCT01, October 2018-February 2019; 19CCT02, February 2019-May 2019, 19CCT03, May 2019-August 2019; 19CCT04, August 2019-November 2019), 4) 2020-301-FA (20CCT01, November 2019-January 2020). NSTs were deployed on the start date and collected on the end date. The unedited output grain size files with statistics for each sample run along with averaged statistics for each sample generated by the USGS Average and Check Standard Deviation macro are available upon request. The averaged results for the subset of statistical parameters that apply to these data are summarized and published in this data release.
  1. How might this data set be cited?
    Smith, Kathryn E.L., Terrano, Joseph F., Atchia, Isabelle D., Vargas, Joseph M., and Everhart, Cheyenne S., 20230605, Shore Proximal Marsh Sediment Deposition and Ancillary Data From Grand Bay National Estuarine Research Reserve, Mississippi: grain size analysis:.

    This is part of the following larger work.

    Smith, Kathryn E.L., Terrano, Joseph F., Pitchford, Jonathan, Brochard, Michael, Vargas, Joseph M., Atchia, Isabelle D., Everhart, Cheyenne S., and Smith, Christopher G., 20200713, Shore Proximal Marsh Sediment Deposition and Ancillary Data From Grand Bay National Estuarine Research Reserve, Mississippi, From July 2018 to January 2020: U.S. Geological Survey data release doi:10.5066/P9ONI8PS, U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -88.449757
    East_Bounding_Coordinate: -88.396209
    North_Bounding_Coordinate: 30.429843
    South_Bounding_Coordinate: 30.335728
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 18-Jul-2018
    Ending_Date: 28-Jan-2020
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: tabular digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
    2. What coordinate system is used to represent geographic features?
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    The detailed attribute descriptions for the statistics summary workbooks are provided in the included data dictionary (Grain_Size_Data_Dictionary.docx). These metadata are not complete without this file.
    Entity_and_Attribute_Detail_Citation:
    The entity and attribute information were generated by the individual and/or agency identified as the originator of the dataset. Please review the rest of the metadata record for additional details and information.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Kathryn E.L. Smith
    • Joseph F. Terrano
    • Isabelle D. Atchia
    • Joseph M. Vargas
    • Cheyenne S. Everhart
  2. Who also contributed to the data set?
    Acknowledgment of the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, as a data source would be appreciated in products developed from these data, and such acknowledgment as is standard for citation and legal practices. Sharing of new data layers developed directly from these data would also be appreciated by the U.S. Geological Survey staff. Users should be aware that comparisons with other datasets for the same area from other time periods may be inaccurate due to inconsistencies in data collection methods. These data are not legal documents and are not to be used as such.
  3. To whom should users address questions about the data?
    U.S. Geological Survey - St Petersburg Coastal and Marine Science Center
    Attn: Joseph F. Terrano
    Physical Scientist
    600 4th Street South
    St. Petersburg, FL

    727-502-8000 (voice)
    727-502-8182 (FAX)
    jterrano@usgs.gov

Why was the data set created?

Net sedimentation tiles (also called sediment plates, tiles, or traps) are an inexpensive tool to assess sediment deposition in coastal zones. Each of the four study sites consisted of five NST plots at 5, 10, 15, 20, and 25 m from the shoreline. Multiple NSTs per distance were used to assess the spatial variation within the marsh and across the shoreline to the marsh interior. To begin this study, textured NST were used to assess the temporal variability in the sediment depositional flux by varying the deployment duration (two consecutive 3-month versus one 6-month deployment period). The initial design (textured NST) used the textured bottom of a 4.25-inch (in) square glazed tile commonly found at local hardware stores glued to a PVC stem and pushed flush into the marsh surface. Six textured NST were deployed in each plot in July 2018 (30 per site or 120 total). In October 2018, three of the six NST (60 total NST) were collected, and three new textured NST were then placed in the same locations. In February 2019, all 120 NST (60 three-month NST and 60 six-month NST) were collected. After the textured NST were collected, three NST of both designs (textured and untextured) were deployed within the same NST plot (120 total NST deployed) to determine if there was a statistical difference in sediment retention. Subsequent NST (untextured) were constructed using a 4.25-in. square fully unglazed and untextured tile, which can be ordered at specialty tile or art supply stores, glued to a PVC stem and inserted until the top of the NST was flush with the marsh surface. From February to August 2019, all 120 NST were collected and redeployed, every three months. From August 2019 to January 2020, only three untextured NST were deployed in each plot (60 total NST deployed) and were collected and redeployed every three months. This data release includes spreadsheets summarizing particle size analysis results for select NST and surface samples collected from Grand Bay.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 2020 (process 1 of 3)
    In July 2018, NSTs (4.25 in x 4.25 in textured ceramic floor tiles that were glued to a PVC end cap and attached to a short PVC pole) were deployed at four study sites (Sites 5, 6, 7, and 8) within the Grand Bay National Estuarine Research Reserve, Mississippi. NSTs were deployed at 5, 10, 15, 20 and 25 m inland and perpendicular to the shoreline. At each distance, six NSTs were placed in randomly selected locations within a 1-m quadrat, referred to as the "NST plot". Each NST was gently pushed down into the marsh surface until the NST was flush with the marsh surface. For more information on NST construction and deployment, refer to Terrano (2018). In October 2018, three of the six NST within each plot (60 total NST) were collected, and three new NST were then placed in the same locations. In February 2019, all 120 NST (60 three-month NST and 60 six-month NST) were collected. After the NST were collected, new NST consisting of two designs (three with a textured surface and three untextured) were deployed within the same NST plots (120 total NST deployed) to determine if there was a statistical difference in sediment retention over various surfaces. From February 2019 to August 2019, all 120 NST were collected and redeployed, every three months. From August 2019 to January 2020, only three untextured NST were deployed in each plot (60 total NST deployed) and were collected and redeployed every three months. Person who carried out this activity:
    U.S. Geological Survey St. Petersburg Coastal and Marine Science Center
    Attn: Kathryn E.L. Smith
    Research Ecologist
    600 4th Street South
    St. Petersburg, FL
    U.S.

    (727) 502-8000 (voice)
    kelsmith@usgs.gov
    Date: 2021 (process 2 of 3)
    Sediment samples were collected from the top of NST or collected from the top 1 cm of sediment (surface samples). Prior to particle size analysis, sediment samples which appeared to consist of greater than 5 percent organic material had the organic matter chemically removed from the samples using approximately 8 milliliters (mL) of 30 percent hydrogen peroxide (H2O2) in 50 mL beakers overnight. The H2O2 was then evaporated through slow heating on a hot plate, and the sediment was washed and centrifuged twice with deionized water. Samples which appeared to consist of less than 5 percent organic material were dried at 60 degrees Celsius (°C). To prevent shell fragments from damaging the Coulter LS200, particles greater than 1 millimeter (mm) in diameter were separated from all samples prior to analysis with a number 18 (1000 micrometer [µm]) U.S. standard sieve, which meets the American Society for Testing and Materials (ASTM) E11 standard specifications for determining particle size with woven-wire test sieves. The samples were washed through the sieve with filtered municipal water and a few milliliters of sodium hexametaphosphate solution to act as a deflocculant. The sediment slurry was sonicated with a wand sonicator for 30–60 seconds before being introduced into the Coulter LS200 to break down aggregated particles. Two subsamples from each sample were processed through the Coulter LS200 with a minimum of four runs apiece. The Coulter LS200 measures the particle-size distribution of each sample by passing sediment suspended in solution between two narrow panes of glass in front of a laser. The particles scatter light into characteristic refraction patterns that are measured by an array of photodetectors as intensity per unit area and recorded as relative volume for 92 size-classification channels, or bins. The size-classification boundaries for each bin were based on the ATSM E11 standard specification for wire cloth and sieves for testing purposes. Person who carried out this activity:
    U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center
    Attn: Kathryn E.L. Smith
    Physical Scientist
    600 4th Street South
    St. Petersburg, FL
    U.S.

    (727) 502-8000 (voice)
    jterrano@usgs.gov
    Date: 2021 (process 3 of 3)
    The raw grain-size data were then run through the free software program, GRADISTAT version 8, (Blott and Pye, 2001; kpal.co.uk/gradistat), which calculates the mean, median, sorting, skewness, and kurtosis of each sample geometrically in metric units and logarithmically in phi units (Krumbein, 1934) using the Folk and Ward (1957) method. GRADISTAT also calculates the fraction of sediment from each sample by size category (for example, clay, coarse silt, fine sand) based on Friedman and Saunders (1978), a modified Wentworth (1922) size scale. A macro function in Microsoft Excel, developed by the USGS SPCMSC, was applied to the data to calculate the average and standard deviation for each sample set (7-8 runs per sample), and highlight runs that varied from the set average by more than ± 1.5 standard deviations. Excessive deviations from the mean are likely the result of equipment error or extraneous organic material in the sample and are not considered representative of the sample. The highlighted runs were removed from the results, and the sample average was recalculated using the remaining runs. The raw, unaltered, individual run statistics and class size distribution as produced by GRADISTAT version 8 (Blott and Pye, 2001), as well as the averaged run statistics and class size distributions, are available upon request. The averaged results for all samples, including the number of runs included and the standard deviation of the averaged results were summarized in an Excel workbook. Person who carried out this activity:
    U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center
    Attn: Kathryn E.L. Smith
    Research Ecologist
    600 4th Street South
    St. Petersburg, FL
    U.S.

    (727) 502-8000 (voice)
    kelsmith@usgs.gov
    Data sources produced in this process:
    • Grand_Bay_Grain_Size_Data.zip
    • Grand_Bay_Grain_Size_Data.csv
  3. What similar or related data should the user be aware of?
    Wentworth, C.K., 1922, A scale of grade and class terms for clastic sediments: Journal of Geology Volume 30, No. 5.

    Online Links:

    Other_Citation_Details: Pages 377-392
    Krumbein, W.C., 19340801, Size frequency distributions of sediments: SEPM Journal of Sedimentary Petrology Volume 4, No.2.

    Online Links:

    Other_Citation_Details: Pages 65-77
    Folk, R.L., and Ward, W.C., 19570301, Brazos River bar: A study in the significance of grain size parameters: SEPM Journal of Sedimentary Petrology Volume 27, No. 1.

    Online Links:

    Other_Citation_Details: Pages 3-26
    Friedman, G.M., and Sanders, J.E., 1978, Principles of Sedimentology.

    Other_Citation_Details: 792 Pages
    Blott, S.J., and Pye, K., 2001, Gradistat: A grain size distribution and statistics package for the analysis of unconsolidated sediments: Earth Surface Processes and Landforms Volume 26.

    Online Links:

    Other_Citation_Details: Pages 1237-1248
    Terrano, Joseph F., 2018, An evaluation of marsh shoreline erosion and sediment deposition in the Grand Bay National Estuarine Research Reserve, Mississippi, USA: University of South Florida Scholar Commons, Tampa, Florida.

    Online Links:

    Other_Citation_Details:
    Contains detailed process descriptions for NST construction, deployment, collection, and laboratory processing.
    Smith, Kathryn E.L., Terrano, Joseph F., Khan, Nicole S., and Stalk, Chelsea A., 20200206, Shore proximal sediment deposition, elevation, turbidity, and water level data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017: U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida.

    Online Links:

    Other_Citation_Details:
    Contains data from a related NST study in North Rigolets and Middle Bay areas of the GNDNERR.

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    The physical parameters reported in the table were analyzed at the USGS-SPCMSC sedimentology lab. This sediment dataset has not been independently verified for accuracy. The grain size data presented here represent the sample averages for a subset of the statistical parameters calculated by GRADISTAT. The number of runs included in the averaged results are also reported, and the standard deviation of the averaged results are reported for most parameters.
  2. How accurate are the geographic locations?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    This is a complete grain size dataset that includes results for 115 NST samples, 4 surface sediment samples, and 1 estuarine seafloor sediment sample collected from the Grand Bay National Estuarine Research Reserve, Mississippi.
  5. How consistent are the relationships among the observations, including topology?
    The complete grain size dataset including all GRADISTAT parameters, results of individual sample runs, and standard deviations for all sample averages are available upon request.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None
Use_Constraints Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. The U.S. Geological Survey requests that it be acknowledged as the originator of this dataset in any future products or research derived from these data. This metadata record should be reviewed in its entirety.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center
    Attn: Joseph F. Terrano
    Physical Scientist
    600 4th Street South
    St. Petersburg, FL
    U.S.

    727-502-8000 (voice)
    jterrano@usgs.gov
  2. What's the catalog number I need to order this data set? Grand_Bay_NST_Grain_Size.csv
  3. What legal disclaimers am I supposed to read?
    Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, nor shall the act of distribution imply any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and (or) contained herein. Any use of trade, firm, or product name is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?
  5. Is there some other way to get the data?
    None
  6. What hardware or software do I need in order to use the data set?
    The spreadsheet included in this .zip file were created for use with Microsoft Excel 365. The file may also be viewed with the Microsoft Excel viewer or by downloading a Microsoft Compatibility Pack to view with older versions of Microsoft Excel.

Who wrote the metadata?

Dates:
Last modified: 05-Jun-2023
Metadata author:
U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center
Attn: Joseph F. Terrano
Physical Scientist
600 4th Street South
St. Petersburg, FL
U.S.

(727) 502-8047 (voice)
jterrano@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/Grand_Bay_Grain_Size_Metadata.faq.html>
Generated by mp version 2.9.51 on Tue Jun 6 10:02:37 2023