Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge to environmental health stressors (polygon shapefile)

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Frequently anticipated questions:


What does this data set describe?

Title:
Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge to environmental health stressors (polygon shapefile)
Abstract:
Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays the exposure potential to environmental health stressors in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. Exposure potential is calculated with the Sediment-bound Contaminant Resiliency and Response (SCoRR) ranking system (Reilly and others, 2015) designed to define baseline and post-event sediment-bound environmental health stressors. Facilities obtained from the Environmental Protection Agency’s (EPA) Toxic Release Inventory (TRI) and Facility Registry Service (FRS) databases were ranked based on their potential contaminant hazard. Ranks were based in part on previous work by Olsen and others (2013), literature reviews, and an expert review panel. A 2000 meter search radius was used to identify nearby ranked facility locations.
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.
  1. How might this data set be cited?
    Defne, Zafer, Ganju, Neil K., Jones, Daniel K., Reilly, Timothy J., Aquino, Kimberly C., Carbo, Chelsea L., Kaufhold, Erika E., Benzel, William M., Fisher, Shawn C., Griffin, Dale W., Iwanowicz, Luke R., and Loftin, Keith A., 2017, Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge to environmental health stressors (polygon shapefile): data release DOI:10.5066/F7765CH1, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Defne, Zafer, Ganju, Neil K., Jones, Daniel K., Reilly, Timothy J., Aquino, Kimberly C., Carbo, Chelsea L., Kaufhold, Erika E., Benzel, William M., Fisher, Shawn C., Griffin, Dale W., Iwanowicz, Luke R., and Loftin, Keith A., 2017, Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey, to environmental health stressors: data release DOI:10.5066/F7765CH1, U.S. Geological Survey, Reston, Virginia.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Defne, Zafer, Ganju, N.K., Jones, D.K., Reilly, T.J, Aquino, K.C., Carbo, C.L, Kaufhold, E.E., Benzel, W.M., Fisher, S.C., Griffin, D.W., Iwanowicz, L.R., and Loftin, K.A., 2017, Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey, to environmental health stressors: U.S. Geological Survey data release, https://doi.org/10.5066/F7765CH1
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -74.487530855
    East_Bounding_Coordinate: -74.048254681
    North_Bounding_Coordinate: 40.055965831
    South_Bounding_Coordinate: 39.432668022
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/585b1298e4b01224f329b88b?name=mu_scorr_EBFNWRp.png (PNG)
    Graphic that shows the exposure potential to environmental health stressors in the EBFNWR salt marsh complex overlaying Esri Shaded World Relief Map.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2017
    Currentness_Reference:
    publication date
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Vector Digital Data Set (Polygon)
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set. It contains the following vector data types (SDTS terminology):
      • G-polygon (1338)
    2. What coordinate system is used to represent geographic features?
      The map projection used is WGS 1984 Web Mercator Auxiliary Sphere (ESRI Full Name: WGS_1984_Web_Mercator_Auxiliary_Sphere).
      Projection parameters:
      Standard_Parallel: 0.0
      Longitude_of_Central_Meridian: 0.0
      False_Easting: 0.0
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.6096
      Ordinates (y-coordinates) are specified to the nearest 0.6096
      Planar coordinates are specified in Meter
      The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    mu_scorr_EBFNWRp
    Attribute information associated with the SCoRR ranks projected on to the conceptual marsh units of EBFNWR salt marsh complex. (Source: U.S. Geological Survey)
    FID
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Shape
    Feature geometry. (Source: Esri) Coordinates defining the features.
    FID_CMU
    Sequential unique whole numbers that represents the identification number for each conceptual marsh units. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:1337
    MIN
    Minimum value of Sediment-bound Contaminant Resiliency and Response (SCoRR) rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:39.3574714661
    Units:None
    MAX
    Maximum value of SCoRR rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:55.6815910339
    Units:None
    MEAN
    Mean value of SCoRR rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:39.615655899
    Units:None
    STD
    Standard deviation of SCoRR rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:8.33737753934
    Units:None
    Entity_and_Attribute_Overview:
    In this dataset, exposure potential to environmental health stressors calculated with the SCoRR strategy is interpolated over the salt marsh units in EBFNWR. A higher SCoRR rank indicates higher exposure potential to environmental health stressors. Raster cells that do not have a facility within a 2000 meter proximity are assigned a SCoRR rank of zero prior to clipping to the processing extent. Decimal values in the attribute table are a result of double precision calculations while significant digits are considered in defining the attribute measurement resolutions. Therefore, the smallest unit increment for exposure potential (SCoRR) values can be assumed to be a practical value such as 0.01.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Zafer Defne
    • Neil K. Ganju
    • Daniel K. Jones
    • Timothy J. Reilly
    • Kimberly C. Aquino
    • Chelsea L. Carbo
    • Erika E. Kaufhold
    • William M. Benzel
    • Shawn C. Fisher
    • Dale W. Griffin
    • Luke R. Iwanowicz
    • Keith A. Loftin
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Zafer Defne
    Ocean Scientist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2254 (voice)
    508-457-2310 (FAX)
    zdefne@usgs.gov

Why was the data set created?

Analysis of environmental health stressors is part of a comprehensive assessment to identify the factors and their weights in determining the vulnerability and resiliency of salt marshes. This polygon dataset facilitates quantifying vulnerability to contaminant hazards in the EBFNWR salt marsh complex. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016).

How was the data set created?

  1. From what previous works were the data drawn?
    marshUnitsEBFp (source 1 of 1)
    Defne, Zafer, and Ganju, Neil K., 2016, Conceptual salt marsh units for wetland synthesis: Edwin B. Forsythe National Wildlife Refuge, New Jersey: data release DOI:10.5066/F7QV3JPG, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: online
    Source_Contribution:
    Downloaded and used the features in [marshUnitsEBFp.shp] to summarize the SCoRR ranks at each marsh unit
  2. How were the data generated, processed, and modified?
    Date: 2016 (process 1 of 8)
    This process step used ArcMap (ver. 10.3.1) using tools from ArcToolbox. Names of specific tools used are given in CAPITAL letters (any critical parameters used is given in parentheses, separated by a semicolon, immediately after the tool name). Input and output file names are provided in [square brackets] when necessary. Units for length and area calculations are meters (m) and square meters (m2) unless otherwise stated. Generate a point cloud of coordinates indicating calculation locations for the SCoRR ranks.
    a) CREATE CONSTANT RASTER(Constant value= 1; Output cell size: 0.003 degrees) that surpasses the EBFNWR boundaries.
    b) RASTER TO POINT to generate an array of points [scorr_pts.shp].
    c) Export point coordinates to an Excel table [contPts.xls]. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Zafer Defne
    Ocean Scientist
    384 Woods Hole Rd.
    Woods Hole, MA
    USA

    508-458-8700 x2254 (voice)
    508-457-2310 (FAX)
    zdefne@usgs.gov
    Data sources produced in this process:
    • contPts.xls
    Date: 2016 (process 2 of 8)
    This step was performed by Daniel K. Jones following the SCoRR strategy (Reilly and others, 2015). ArcMap (ver. 10.2) and tools from ArcToolbox were used for analysis unless otherwise stated. Names of specific tools used are given in CAPITAL letters (any critical parameters used is given in parentheses, separated by a semicolon, immediately after the tool name). Input and output file names are provided in [square brackets] when necessary. Apply SCoRR ranks to the gridded array of points [contPts.xls].
    a) Generate the list for facilities of concern from EPA's Toxic Release Inventory (TRI) data, and EPA's Facility Registry Service (FRS) data.
    TRI data was downloaded from https://www.epa.gov/toxics-release-inventory-tri-program/tri-basic-data-files-calendar-years-1987-2015 using the drop down menus for the year of interest and select the state. For this work 2013 data for the state of New Jersey was used. Each facility was assigned a rank value from 1 to 4 indicating its 'perceived contaminant hazard' based on i) a modified version of ranked contaminants from Olsen et al. 2013 (Reilly and others, 2015; Table 1), or ii) paneled expert opinion following methods outlined in Olsen et al. 2013.
    FRS data was downloaded from https://www.epa.gov/enviro/epa-frs-facilities-state-single-file-csv-download for New Jersey. Each facility was assigned a rank value from 1 to 4 based upon a questionnaire administered to the same expert panel mentioned above. Three attribute fields were utilized to assess the perceived contaminant hazard of each FRS facility: Environmental Program Type, Environmental Interest Type, and Site Type. Each of these fields provides details about regulations, uses, and potential hazards relevant to each facility.
    When relevant, historic storm vulnerabilities were used to then weight each ranked FRS and TRI facility. Storm vulnerability datasets included Federal Emergency Management Agency's (FEMA) 100-year flood zones, National Oceanic and Atmospheric Administration’ Sea, Lake, and Overland Surges from Hurricanes (NOAA SLOSH) model-based inundation extents for hurricanes of category 1 to 5 in magnitude, and probabilities of storm impacts based on National Weather Service’s International Best Track Archive for Climate Stewardship (NWS IBTrACS) data. Overlay analysis was used to identify all facilities falling within 1 or more of these storm vulnerabilities. Equation 1 in Reilly and others (2015) was used to then weight each facility's perceived contaminant hazard rank based on the number of intersected storm vulnerabilities.
    b) Create a shapefile from the point coordinates and DEFINE PROJECTION(Coordinate System=WGS 84) as World Geodetic System 1984.
    c) GENERATE NEAR TABLE(Input Features=IN_FID; Near Features=NEAR_FID; Search radius= 2000 m) to identify facilities of concern within 2000 m of each point. Separate tables were generated for the TRI and FRS facilities. These were then recombined later during the inverse distance weighting procedure to arrive at the final rank at each location.
    d) Tables are imported into R software package and joined using the fields 'IN_FID' and 'NEAR_FID' to link facility attributes with each location using near table output.
    e) SCoRR rank at each location is calculated using the inverse distance-weighted average rank equation (Reilly and others, 2015; Equation 2) within a search radius of 2000 meters, based on the storm vulnerability-weighted potential contaminant hazard rank for each facility, and the distance to the facility location. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Daniel K. Jones
    Geographer
    2329 Orton Circle
    Salt Lake City, UT
    USA

    240-620-7822 (voice)
    801-908-5001 (FAX)
    dkjones@usgs.gov
    Data sources used in this process:
    • contPts.xls
    Data sources produced in this process:
    • contPts.csv
    Date: 2016 (process 3 of 8)
    Remaining process steps were performed by Zafer Defne, in ArcMap (ver. 10.3.1) using tools from ArcToolbox, unless otherwise stated. Names of specific tools used are given in CAPITAL letters (any critical parameters used is given in parentheses, separated by a semicolon, immediately after the tool name). Input and output file names are provided in [square brackets] when necessary. Units for length and area calculations are meters (m) and square meters (m2) unless otherwise stated.
    Establish marsh unit boundaries. DISSOLVE(Dissolve Fields= FID_CMU) features in marsh units layer based on the attribute identification numbers. FID_CMU is the unique identifier for each marsh unit. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Zafer Defne
    Ocean Scientist
    384 Woods Hole Rd.
    Woods Hole, MA
    USA

    508-458-8700 x2254 (voice)
    508-457-2310 (FAX)
    zdefne@usgs.gov
    Data sources used in this process:
    • marshUnitsEBFp
    Data sources produced in this process:
    • mu
    Date: 2016 (process 4 of 8)
    Create a raster surface dataset from the feature points.
    a) Import points to ArcMap using MAKE XY EVENT LAYER [scorr.shp]. NoData values are converted to zero at this step.
    b) Create a raster surface with inverse distance weighting in Spatial Analyst toolbox. IDW.SpatialAnalyst(Output Cell Size=0.0003 degrees; ; Power=2; Search Radius=Variable; Number of Points=12) to obtain [scorr_idw.tif]. Data sources used in this process:
    • contPts.csv
    Data sources produced in this process:
    • scorr_idw.tif
    Date: 2016 (process 5 of 8)
    Clip the raster to marsh boundaries. A buffer zone is created around the marsh boundaries to facilitate alternative interpolation and clipping options in future studies.
    a) Create 250 m buffer around the marsh polygon [mu.shp] to create a clipping mask [mu_buff250m.shp]. BUFFER(Side type= Full; Method =Geodesic; Dissolve type= All).
    b) Clip the raster clipping mask. EXTRACT BY MASK to obtain [scorr_EBFNWR.tif]. Data sources used in this process:
    • mu
    • scorr_idw.tif
    Data sources produced in this process:
    • scorr_EBFNWR.tif
    Date: 2016 (process 6 of 8)
    PROJECT RASTER(Input raster= scorr_EBFNWR.tif; Output raster= scorr_EBFNWRp.tif; Input coordinate system= GCS_WGS_1984; Output coordinate system= WGS_1984_Web_Mercator_Auxiliary_Sphere; Resampling technique= Nearest; Output cell size= 30 meters) to obtain [scorr_EBFNWRp.tif]. Data sources used in this process:
    • scorr_EBFNWR.tif
    Data sources produced in this process:
    • scorr_EBFNWRp.tif
    Date: 2016 (process 7 of 8)
    Calculate values for each marsh unit. a) Clip the raster with the marsh units polygon by using EXTRACT BY MASK(Cell Size= 3 m) to obtain [mu_scorr_idw.tif]. Cell size for raster analysis was reduced to 3 m to have smoother boundaries.
    b) ZONAL STATISTICS AS TABLE(Feature Zone Data=[mu]; Value Raster=[mu_scorr_idw.tif]; Output Table=mu_scorr_table; Statistics type=ALL)
    c) ADD JOIN(Layer name=[mu]; Input join field=FID_CMU; Join Table=mu_scorr_table; Output join field=FID_CMU) to marsh units layer based on the "FID_CMU" field
    d)Export dataset as a shapefile [mu_scorr_EBFNWR.shp] and keep the 'MIN", 'MAX', "MEAN" and "STD" fields from the join operation. Data sources used in this process:
    • mu
    • scorr_EBFNWRp.tif
    Data sources produced in this process:
    • mu_scorr_EBFNWRp.shp
    Date: 07-Aug-2020 (process 8 of 8)
    Added keywords section with USGS persistent identifier as theme keyword. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Road
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
  3. What similar or related data should the user be aware of?
    Reilly, Timothy J., Jones, Daniel K., Focazio, Michael J., Aquino, Kimberly C., Carbo, Chelsea L., Kaufhold, Erika E., Zinecker, Elizabeth K., Benzel, William M., Fisher, Shawn C., Griffin, Dale W., Iwanowicz, Luke R., Loftin, Keith A., and Schill, William B., 2015, Strategy to evaluate persistent contaminant hazards resulting from sea-level rise and storm-derived disturbances-Study design and methodology for station prioritization: Open-File Report 2015-1188-A, U.S. Geological Survey, Reston, VA.

    Online Links:

    Defne, Zafer, and Ganju, Neil K., 2016, Conceptual salt marsh units for wetland synthesis: Edwin B. Forsythe National Wildlife Refuge, New Jersey: data release DOI:10.5066/F7QV3JPG, U.S. Geological Survey, Reston, Virginia.

    Online Links:

    Olsen, Lisa D., Valder, Joshua F., Carter, Janet M., and Zogorski, John S., 2013, Prioritization of Constituents for National- and Regional-Scale Ambient Monitoring of Water and Sediment in the United States: Scientific Investigations Report 2012-5218, U.S. Geological Survey, Reston, VA.

    Online Links:


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

  1. How well have the observations been checked?
    Water-land boundary and marsh unit boundaries inherit from accuracy of source data, conceptual marsh units. Exposure potential to environmental health stressors is calculated with the SCoRR strategy for a uniform grid with 0.003 degrees (~300 meters) resolution and resampled at a higher resolution of 0.0003 degrees (~30 meters) prior to averaging over each marsh unit.
  2. How accurate are the geographic locations?
    Horizontal accuracy for the polygon boundaries is inherited from the source layer, conceptual marsh units, and considered to be greater than 3 meters. The point cloud used for calculating exposure potential had ~300-meter resolution that was interpolated over a 30-meter raster. The raster dataset was resampled at 3 meter resolution to enhance the conformity with the marsh unit boundaries prior to calculating zonal statistics.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    The boundaries of this dataset were created by expanding the outline boundaries of polygons created with a 250 meter exterior buffer around the source data, conceptual marsh units, which include: 1) Marsh Salt, 2) Wetland Scrub Shrub Salt, and 3) channels of Open Water Salt that are not wider than 50 meters. Any polygons with an area smaller than 50 square meters were omitted in the source dataset.
  5. How consistent are the relationships among the observations, including topology?
    By definition SCoRR ranks cannot be a negative real number. Final values for each marsh unit were verified to be either zero or a positive real number.

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 The SCoRR ranks in this dataset are defined for scientific research purposes and should not be used as a sole source of reference for any regulations and policy making. Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the source of this information.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? mu_scorr_EBFNWRp.shp (together with shapefile components, browse graphic, and associated FGDC CSDGM metadata in XML, TEXT, FAQ, and HTML formats with the same file name).
  3. What legal disclaimers am I supposed to read?
    Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Not for navigational use.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 19-Mar-2024
Metadata author:
U.S. Geological Survey
Attn: Zafer Defne
Ocean Scientist
384 Woods Hole Road
Woods Hole, MA

508-548-8700 x2254 (voice)
508-457-2310 (FAX)
whsc_data_contact@usgs.gov
Contact_Instructions:
The metadata contact email address is a generic address in the event the person is no longer with USGS. (updated on 20240319)
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

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