Tropical Storm Marco Assessment of Potential Coastal-Change Impacts: NHC Advisory 16, 0400 AM CDT MON AUG 24 2020

Metadata also available as - [Outline] - [Parseable text] - [XML]

Frequently anticipated questions:

What does this data set describe?

Title:
Tropical Storm Marco Assessment of Potential Coastal-Change Impacts: NHC Advisory 16, 0400 AM CDT MON AUG 24 2020
Abstract:
This dataset defines storm-induced coastal erosion hazards for the Texas, Louisiana, Mississippi, and Alabama coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Marco in August 2020. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.
  1. How might this data set be cited?
    Birchler, Justin J., and Doran, Kara S., 20251117, Tropical Storm Marco Assessment of Potential Coastal-Change Impacts: NHC Advisory 16, 0400 AM CDT MON AUG 24 2020:.

    This is part of the following larger work.

    Doran, Kara S., Birchler, Justin J., Schreppel, Heather A., Stockdon, Hilary F., and Thompson, David M., 20190619, Storm-Induced Coastal Change Forecasts: Archive of Individual Storm Events: U.S. Geological Survey data release doi:10.5066/P9Z362BC, U.S. Geological Survey, St. Petersburg, Florida.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -95.3017
    East_Bounding_Coordinate: -87.5014
    North_Bounding_Coordinate: 30.2832
    South_Bounding_Coordinate: 28.9319
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 24-Aug-2020
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: vector digital data
  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):
      • String (2043)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.01975663976. Longitudes are given to the nearest 0.0249972312. Latitude and longitude values are specified in Decimal Degrees. 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?
    Marco_PCOI_line
    Probabilities of hurricane-induced coastal erosion, dune morphology, and hurricane hydrodynamic data (Source: USGS)
    FID
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Shape
    Feature geometry. (Source: Esri) Coordinates defining the features.
    DHIGH
    Elevation of dune crest, in meters, using the North American Vertical Datum of 1988 (NAVD88). Extracted from lidar surveys collected from May 2010 to January 2020. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.57875
    Maximum:8.5768
    Units:meters
    DLOW
    Elevation of the dune toe, in meters NAVD88. Extracted from lidar surveys collected from May 2010 to January 2020. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.69933
    Maximum:3.1321
    Units:meters
    DHIrms
    Root mean square error of dune crest elevation measurements. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.1109
    Maximum:1.7674
    Units:meters
    DLOrms
    Root mean square error of dune toe elevation measurements. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.1356
    Maximum:0.9804
    Units:meters
    SURGE
    Storm surge water level. (Source: NOAA)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.396
    Maximum:0.80932
    Units:meters
    RUNUP
    Wave runup water level. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.59716
    Maximum:4.9473
    Units:meters
    SETUP
    Wave setup water level. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.024713
    Maximum:2.0022
    Units:meters
    PCOL
    Probability of collision. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:6.921
    Maximum:100
    Units:percent
    POVW
    Probability of overwash. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:8.71e-05
    Maximum:99.9997
    Units:percent
    PIND
    Probability of inundation. (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0
    Maximum:89.7413
    Units:percent
    MEAN
    Mean water level (surge + setup). (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:0.45369
    Maximum:2.6422
    Units:meters
    EXTREME
    Extreme water level (surge + runup). (Source: USGS)
    ValueDefinition
    -999Null value
    Range of values
    Minimum:1.0242
    Maximum:5.5873
    Units:meters
    TIDE
    Predicted tide water level. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:0
    Units:meters

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Justin J. Birchler
    • Kara S. Doran
  2. Who also contributed to the data set?
    The predicted elevations of storm surge were extracted from the National Oceanic and Atmospheric Administration's (NOAA) Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model, which has been employed by NOAA in inundation risk studies and operational storm surge forecasting. Wave runup and setup conditions were generated using NOAA's WaveWatch III model.
  3. To whom should users address questions about the data?
    Kara Doran
    U.S. Geological Survey
    Physical Scientist
    600 4th Street South
    Saint Petersburg, FL
    UNITED STATES

    727-502-8117 (voice)
    kdoran@usgs.gov

Why was the data set created?

To provide data on the probability of storm-induced coastal erosion hazards for the Texas and Louisiana, Mississippi, Alabama coast for Tropical Storm Marco.

How was the data set created?

  1. From what previous works were the data drawn?
    P-Surge (source 1 of 9)
    National Hurricane Center, National Oceanic and Atmospheric Administration, 20200824, Probabilistic Tropical Storm Surge.

    Online Links:

    Type_of_Source_Media: Online digital data
    Source_Contribution:
    Data provides water levels that have a 1 in 10 chance of being exceeded within the next 102 hours.
    WW3 (source 2 of 9)
    NOAA National Weather Service Environmental Modeling Center, 20200824, NOAA Wavewatch III.

    Online Links:

    Type_of_Source_Media: Online digital data
    Source_Contribution:
    Wave model that was used to estimate wave setup and runup conditions at the shoreline within the next 180 hours.
    USGS TX 2018 (source 3 of 9)
    Quantum Spatial, Inc., United States Geological Survey (USGS), 20191011, 2018 USGS Lidar: South Texas.

    Online Links:

    Other_Citation_Details: Geographic Coverage: South Texas (through Matagorda Is.)
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
    USACE NCMP Gulf Coast 2016 (source 4 of 9)
    United States Army Corps of Engineers (USACE), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX), 20170913, 2016 USACE NCMP Topobathy Lidar: Gulf Coast (AL, FL, MS, TX).

    Online Links:

    Other_Citation_Details: Geographic Coverage: AL, FL, MS, TX
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
    USGS LA 2017 (source 5 of 9)
    Woolpert, Inc., United States Geological Survey (USGS), 20171212, 2017 USGS Lidar: Chenier Plain, LA.

    Online Links:

    Other_Citation_Details: Geographic Coverage: LA Chenier Plain
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
    USGS LA 2015 (source 6 of 9)
    Digital Aerial Solutions, LLC, United States Geological Survey (USGS), 20161018, South Terrebonne and Gulf Islands QL2 LiDAR.

    Online Links:

    Other_Citation_Details: Geographic Coverage: South Terrebonne and Gulf Islands
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
    USGS DS839 LA 2012 (source 7 of 9)
    Guy, K.K., 20140602, Topographic Lidar Survey of the Alabama, Mississippi, and Southeast Louisiana Barrier Islands, from September 5 to October 11, 2012: USGS Data Series 839.

    Online Links:

    Other_Citation_Details: Geographic Coverage: LA, MS
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
    USGS LA AL 2018 (source 8 of 9)
    Quantum Spatial, Inc., United States Geological Survey (USGS), 20190329, Florida Gulf Coast Topobathy LiDAR 2018 D19 GDD.

    Online Links:

    Other_Citation_Details:
    Geographic Coverage: LA Chandeleurs, AL Dauphin Island, FL Gulf Islands
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
    USACE NCMP MS 2018 (source 9 of 9)
    U.S. Army Corps of Engineers (USACE), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX), 20181108, 2018 USACE NCMP Topobathy Lidar: Gulf Coast (AL, MS).

    Online Links:

    Other_Citation_Details: Geographic Coverage: AL, MS Islands
    Type_of_Source_Media: Online digital data
    Source_Contribution:
    A lidar survey that was used to estimate dune morphology variables.
  2. How were the data generated, processed, and modified?
    Date: 2020 (process 1 of 3)
    For dune morphology data: Elevation data from lidar surveys were interpolated in MATLAB (version 2017a) to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 m in the longshore direction and 2.5 m in the cross-shore direction. The interpolation method applied spatial filtering with a Hanning window that was twice as wide as the grid resolution. Dune morphology data were extracted from the elevation grid in MATLAB. Dune morphology data were then summarized to 1 km sections. Sections with greater than 75 percent of data missing were flagged with the invalid number of -999. The 1-km smoothed dune crest (DHIGH), toe (DLOW) and root mean square (RMS) error for each (DHIrms and DLOrms) were written to line shapefiles using MATLAB's shapewrite.m script. Person who carried out this activity:
    Kara Doran
    U.S. Geological Survey
    Physical Scientist
    600 4th Street South
    Saint Petersburg, FL
    UNITED STATES

    727-502-8117 (voice)
    kdoran@usgs.gov
    Data sources used in this process:
    • USGS TX 2018
    • USACE NCMP Gulf Coast 2016
    • USGS LA 2017
    • USGS LA 2015
    • USGS DS839 LA 2012
    • USGS LA AL 2018
    • USACE NCMP MS 2018
    Data sources produced in this process:
    • Dune morphology (DHIGH, DLOW, DHIrms, DLOrms)
    Date: 24-Aug-2020 (process 2 of 3)
    For hydrodynamic data: Water level was computed in MATLAB by adding storm surge from NOAA's Probabilistic Tropical Storm Surge (P- Surge) model (https://slosh.nws.noaa.gov/psurge2.0/) to wave setup and runup. The wave height and period used for calculating wave runup and setup came from the Wavewatch III model. Hydrodynamic parameters were calculated in MATLAB and exported into ArcGIS shapefile format. For details on modeling parameterization, see Stockdon and others (2012). Person who carried out this activity:
    Kara Doran
    U.S. Geological Survey
    Physical Scientist
    600 4th Street South
    Saint Petersburg, FL
    UNITED STATES

    727-502-8117 (voice)
    kdoran@usgs.gov
    Data sources used in this process:
    • P-Surge
    • WW3
    Data sources produced in this process:
    • Hydrodynamics (SURGE, SETUP, RUNUP)
    Date: 24-Aug-2020 (process 3 of 3)
    Probabilities of coastal erosion hazards were based on estimating the likelihood that the beach system would experience erosion and deposition patterns consistent with collision (PCOL), overwash (POVW), or inundation (PIND) regimes. The regimes were calculated by using values of dune morphology and mean and extreme water levels for each 1 km section, such that the probability of collision (PCOL) occurs when extreme water levels reach the dune toe; overwash (POVW) when extreme water levels reach the dune crest; and inundation (PIND) when mean water levels reach the dune crest. In cases where DLOW is null (-999) PCOL is also null (-999) due to the inability to compute water level elevation reaching dune toe. Probabilities were calculated in MATLAB and exported using MATLAB's shapewrite.m script. For details on modeling parameterization, see Stockdon and others (2012). Person who carried out this activity:
    Kara Doran
    U.S. Geological Survey
    Physical Scientist
    600 4th Street South
    Saint Petersburg, FL
    UNITED STATES

    727-502-8117 (voice)
    kdoran@usgs.gov
    Data sources used in this process:
    • Dune morphology
    • Hydrodynamics
    Data sources produced in this process:
    • Probabilities (PCOL, POVW, PIND)
  3. What similar or related data should the user be aware of?
    Stockdon, H.F., Doran, K.J., Thompson, D.M., Sopkin, K.L., Plant, N.G., and Sallenger, A.H., 2012, National Assessment of Hurricane-Induced Coastal Erosion Hazards: Gulf of Mexico: U.S. Geological Survey Open-File Report doi:10.3133/ofr20121084, 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?
  2. How accurate are the geographic locations?
    Horizontal accuracy was not estimated.
  3. How accurate are the heights or depths?
    Vertical accuracy for hydrodynamic measurements (surge, setup, and runup) is dependent on input data. SLOSH model accuracy is estimated to be +/- 20 percent of the calculated value. No other accuracy checks were performed. Vertical accuracy for dune morphology (dune crest and toe elevation) data is dependent on the positional accuracy of the lidar data. Estimated accuracy of lidar surveys are +/- 15 centimeters. However, vertical accuracies may vary based on the type of terrain (for example, inaccuracies may increase as slope increases or with the presence of extremely dense vegetation), the accuracy of the global positioning system (GPS), and aircraft-attitude measurements.
  4. Where are the gaps in the data? What is missing?
    This dataset includes dune morphology and hurricane hydrodynamic data used to generate probabilities of hurricane-induced erosion, elevation data from lidar surveys are not included. Measurements were collected approximately every 10 meters (m) and summarized to 1-kilometer (km) segments.
  5. How consistent are the relationships among the observations, including topology?
    No additional checks for consistency were performed on this data.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints No access constraints. Please see 'Distribution Information' for details.
Use_Constraints These data are marked with a Creative Commons CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
    Attn: USGS SPCMSC Data Management
    600 4th Street South
    Saint Petersburg, FL

    727-502-8000 (voice)
    gs-g-spcmsc_data_inquiries@usgs.gov
  2. What's the catalog number I need to order this data set?
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 17-Nov-2025
Metadata author:
U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
Attn: USGS SPCMSC Data Management
600 4th Street South
Saint Petersburg, FL

727-502-8000 (voice)
gs-g-spcmsc_data_inquiries@usgs.gov
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/Marco_PCOI_line_metadata.faq.html>
Generated by mp version 2.9.51 on Mon Nov 17 14:47:01 2025