Exposure potential of marsh units to environmental health stressors in eastern Long Island salt marsh complex, New York

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Frequently anticipated questions:


What does this data set describe?

Title:
Exposure potential of marsh units to environmental health stressors in eastern Long Island salt marsh complex, New York
Abstract:
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. 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.

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 eastern Long Island salt marsh complex. 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.
  1. How might this data set be cited?
    Welk, Robert, Defne, Zafer, and Ganju, Neil K., 20200331, Exposure potential of marsh units to environmental health stressors in eastern Long Island salt marsh complex, New York: data release DOI:10.5066/P91H426U, 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.

    Welk, Robert, Defne, Zafer, and Ganju, Neil, 2020, Coastal wetlands of eastern Long Island, New York: data release DOI:10.5066/P91H426U, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Welk, R., Defne, Z., and Ganju, N.K., 2020, Coastal wetlands of eastern Long Island, New York: U.S. Geological Survey data release, https://doi.org/10.5066/P91H426U
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -72.748198
    East_Bounding_Coordinate: 71.885316
    North_Bounding_Coordinate: 41.291362
    South_Bounding_Coordinate: 40.766433
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/5ddd98cde4b04a30051ac211?name=SCoRR_ELI_browse.png (PNG)
    Graphic that shows the exposure potential to environmental health stressors overlaying Esri basemap.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2020
    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 (2828)
    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_ELI
    Attribute information associated with the SCoRR ranks projected on to the conceptual marsh units of the eastern Long Island 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 unit. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:3455
    MIN
    Minimum value of Sediment-bound Contaminant Resiliency and Response (SCoRR) rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:14.654953
    Units:None
    MAX
    Maximum value of SCoRR rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:43.309837
    Units:None
    MEAN
    Mean value of SCoRR rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:15.520519
    Units:None
    STD
    Standard deviation of SCoRR rank within a marsh unit (Source: USGS)
    Range of values
    Minimum:0
    Maximum:5.089573
    Units:None
    FLG
    Marsh unit flag indicating anomalous marsh units based on elevation, surface area, and absence of vegetation. FLG values indicate: (-1) no vegetated area; (-10) marsh unit elevation higher than the 99.8 percentile; (-100) marsh unit elevation less than 0.2 percentile; (-200) greater than 25% of marsh unit has hydroflattened area (and has -9999 as its elevation value; (-1000) marsh unit surface area less than 900 m2; and (0) no flag. Combination of negative values indicate combination of flags. For example, -1001 indicates no vegetated area and area less than 900 m2. (Source: USGS)
    Range of values
    Minimum:-11200
    Maximum:0
    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 the eastern Long Island salt marsh complex. 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)
    • Robert Welk
    • Zafer Defne
    • Neil K. Ganju
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Robert J Welk
    Hydrologic Technician
    2045 Route 112
    Coram, NY
    US

    631-736-0783 x141 (voice)
    rwelk@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 eastern Long Island salt marsh complex.

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: 2019 (process 1 of 8)
    This process step and all subsequent process steps were performed in ArcMap (ver. 10.7.1) using tools from ArcToolbox, unless otherwise stated. For complex operations, names of specific tools used are given in CAPITAL letters (any critical parameters used are given in parentheses, separated by a semicolon, immediately after the tool name). The 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.
    a) CREATE CONSTANT RASTER(Constant value= 1; Output cell size: 0.003 degrees) that surpasses eastern Long Island boundaries, as defined in the conceptual marsh units layer.
    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 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 were downloaded from https://www.epa.gov/toxics-release-inventory-tri-program/tri-basic-data-files-calendar-years-1987-2015 using the drop down menu for the year and state of interest. For this work, 2013 data for the state of New York were 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 were downloaded from https://www.epa.gov/frs/frs-query for New York. 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 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’s 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 Robert Welk, in ArcMap (ver. 10.7.1) 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: Robert Welk
    Hydrologic Technician
    2045 NY 112
    Coram, NY
    USA

    631-736-0783 x141 (voice)
    welk@usgs.gov
    Data sources used in this process:
    • CMU_ELI
    Data sources produced in this process:
    • mu
    Date: 2019 (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: 2019 (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_ELI.tif]. Data sources used in this process:
    • mu
    • scorr_idw.tif
    Data sources produced in this process:
    • scorr_ELI.tif
    Date: 2019 (process 6 of 8)
    PROJECT RASTER(Input raster= scorr_ELI.tif; Output raster= scorr_ELI.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_ELI.tif]. Data sources used in this process:
    • scorr_ELI.tif
    Data sources produced in this process:
    • scorr_ELI.tif
    Date: 2019 (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_ELI.shp] and keep the 'MIN", 'MAX', "MEAN" and "STD" fields from the join operation. Data sources used in this process:
    • mu
    • scorr_ELI.tif
    Data sources produced in this process:
    • mu_scorr_ELI.shp
    Date: 06-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:

    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:

    NOAA, 2016, VDatum (v3.5): NOAA, Silver Spring, MD.

    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 is considered to be +/- 6 meters. The point cloud used for calculating tidal range with VDatum had a 0.0005 arc degree (~50-meter) resolution that was interpolated over a 0.0001 arc degree raster prior to clipping and calculating the mean value for each marsh unit.
  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. 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_MN_SCoRR.shp (and other shapefile components), browse graphic (SCoRR.png), and CSDGM metadata in XML format.
  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: 06-Aug-2020
Metadata author:
U.S. Geological Survey, Northeast Region
Attn: Robert J Welk
Hydrologic Technician
2045 Route 112
Coram, NY
US

631-736-0783 x141 (voice)
rwelk@usgs.gov
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

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