Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Long-term sandline change

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Frequently anticipated questions:

What does this data set describe?

Title:
Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Long-term sandline change
Abstract:
The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (i.e., Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the long-term beach sandline change model.
Supplemental_Information:
A free version of the Netica application is available for download at http://www.norsys.com/download.html.
  1. How might this data set be cited?
    Smith, Kathryn E.L., Passeri, Davina L., and Plant, Nathaniel G., 20170421, Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Long-term sandline change: U.S. Geological Survey Data Release doi:10.5066/F7CZ35BC, U.S. Geological Survey, St. Petersburg, FL.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -75.382739
    East_Bounding_Coordinate: -73.974687
    North_Bounding_Coordinate: 40.479022
    South_Bounding_Coordinate: 37.862809
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 06-Jun-2016
    Currentness_Reference:
    dataset creation
  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?
      This is a Point data set. It contains the following vector data types (SDTS terminology):
      • Entity point (3458)
    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 0.0197519519. Longitudes are given to the nearest 0.0253592358. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is D North American 1983.
      The ellipsoid used is GRS 1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222101.
  7. How does the data set describe geographic features?
    ASI_Predict_LT_Sandline, NJ_Predict_LT_Sandline
    Model prediction probabilities and skill metrics in CSV format (Source: USGS)
    objectid
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    X_long
    Longitude location in geographic coordinate system (World Geodetic System 1984) decimal degrees (Source: USGS)
    Range of values
    Minimum:-75.364603
    Maximum:-73.974687
    Units:decimal degrees
    Y_lat
    Latitude location in geographic coordinate system (World Geodetic System 1984) decimal degrees (Source: USGS)
    Range of values
    Minimum:37.8652809
    Maximum:40.479022
    Units:decimal degrees
    P-56.0
    Predicted probability of the net long-term sandline change falling between (-56.0) and (-0.5 m) (Source: USGS)
    Range of values
    Minimum:0
    Maximum:1
    P-0.5
    Predicted probability of the net long-term sandline change falling between (-0.5 m) and (+0.5 m) (Source: USGS)
    Range of values
    Minimum:0
    Maximum:1
    P0.5
    Predicted probability of the net long-term sandline change falling between (0.5 m) and (+30.0 m) (Source: USGS)
    Range of values
    Minimum:0
    Maximum:1
    LR
    Log-transform of the ratio of predicted probability in the bin corresponding to the observed net sandline change to the prior probability in that bin, where LR>0 means the prediction is an improvement over the prior (Source: USGS)
    Range of values
    Minimum:-1.132
    Maximum:0.758
    mean
    Bayesian-mean value (sum of p(x)*x over all bins) of the predicted sandline change (meters/year) (Source: USGS)
    Range of values
    Minimum:-27.8
    Maximum:60.2
    mostProb
    Predicted-most-likely value (center of bin with highest probability) of the predicted sandline change (meters/year) (Source: USGS)
    Range of values
    Minimum:-28.3
    Maximum:60.3
    PmostProb
    Probability value at the predicted-most likely value (Source: USGS)
    Range of values
    Minimum:0.35
    Maximum:1

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
    • Davina L. Passeri
    • Nathaniel G. Plant
  2. Who also contributed to the data set?
    This project was funded by the USGS Coastal and Marine Geology Program. 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 resulting from changes in photointerpretation, mapping conventions, and digital processes over time. 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 Coastal and Marine Science Center
    Attn: Kathryn E.L. Smith
    Ecologist
    600 4th Street South
    St. Petersburg, Florida
    US

    (727) 502-8073 (voice)
    (727) 502-8001 (FAX)
    kelsmith@usgs.gov
    Hours_of_Service:
    Monday through Friday, 9:00 a.m. to 5:00 p.m., Eastern Standard Time

Why was the data set created?

The beach sandline change model will be used to examine how geophysical and hydrodynamic variables influence both long-term and storm-driven dynamics of barrier islands overwash and erosion. This information is critical to understanding and predicting the implications of future sea-level and storm impacts on barrier islands and estuaries.

How was the data set created?

  1. From what previous works were the data drawn?
    calibration data (source 1 of 1)
    Terrano, J.F. and Smith, K.E.L., 2015, Estuarine Shoreline and Barrier-Island Sandline Change Assessment.

    Online Links:

    Type_of_Source_Media: online
    Source_Contribution: This data served as the calibration data for the Bayes Net.
  2. How were the data generated, processed, and modified?
    Date: 01-Jan-2016 (process 1 of 2)
    The Bayesian network (BN) for back-barrier shoreline (backshore) change was constructed in Netica using the following variables: barrier island height, barrier island width, estuary depth, estuary width, maximum storm-driven wave height, long-term and storm-driven (Hurricane Sandy) ocean shoreline change, and long-term and storm-driven backshore change. Calibration (case) data were generated using a transect method in Esri ArcGIS (Version 10.3.1.4959) from pre-Hurricane Sandy lidar, imagery, and hydrodynamic models. The BN was constrained using the case data to produce a prediction of a conditional probability of back-barrier shoreline (backshore) change. These case data and methods were previously published in Terrano, J.F. and Smith, K.E.L., 2015, Estuarine Shoreline and Barrier-Island Sandline Change Assessment: U.S. Geological Survey data release, https://doi.org/10.5066/F71Z42HN. The BN predictions are produced at the same spatial scale as the input variables. At locations where some or all of the data are missing (e.g., due to differences in the spatial resolution of the datasets) predictions can still be made since missing data are taken into account with prediction uncertainty. For example, at worst, with no specific inputs, the BN prediction returns the prior distribution of the output variable, which reflects the spatial variability of that variable over the entire study area. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, and log likelihood ratio are utilized. The BN was loaded into MatLab and hindcast to predict the best possible skill for back-barrier shoreline or beach sandline change. The equation used to compute the skill and likelihood ratios are given in equations 4 and 5 in Plant and others (2016). Predicted probabilities and skill metrics were output from Matlab into tabular form to produce this summary dataset. Data sources used in this process:
    • calibration data
    Date: 13-Oct-2020 (process 2 of 2)
    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?
    Norsys, 20000101, Netica.

    Online Links:

    MathWorks, 20030215, MatLab.

    Online Links:

    Plant, N. G., E. R. Thieler, and D. L. Passeri, 20160502, Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network: Earth's Future Volume 4, Issue 5, pages 143-158, Wiley Periodicals Inc., Hoboken, NJ.

    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?
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    Data are complete. In some cases, all modeled variables were not available for each observation; however, Bayesian Networks can learn from a file of cases, relying on complete observations to create a maximum likelihood Bayes net. For this model, the expectation-maximization (EM) learning algorithm in Netica was used. At locations where some or all of the data are missing (e.g., due to differences in the spatial resolution of the datasets) predictions can still be made since missing data are taken into account with prediction uncertainty. For example, at worst, with no specific inputs, the BN prediction returns the prior distribution of the output variable, which reflects the spatial variability of that variable over the entire study area.
  5. How consistent are the relationships among the observations, including topology?
    Data values were checked for logical consistency. Predicted probabilities range from 0 to 1, where 0 is least likely and 1 is most likely to fall within the identified bin. The log-transform of the likelihood ratio can be negative or positive values; however, positive values show an improvement over the prior. The Bayesian mean and predicted most likely value should fall within a reasonable estimate of sandline change parameters of the case (calibration) dataset.

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 U.S. Geological Survey requests to be acknowledged as originator of the data in future products or derivative research.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey
    Attn: Kathryn E.L. Smith
    600 4th Street South
    St. Petersburg, Florida
    US

    (727) 502-8073 (voice)
    (727) 502-8001 (FAX)
    kelsmith@usgs.gov
    Hours_of_Service:
    Monday through Friday, 9:00 a.m. to 5:00 p.m., Eastern Standard Time
    Contact_Instructions: All of this report is available online.
  2. What's the catalog number I need to order this data set? ASI_Predict_LT_Sandline.csv, NJ_Predict_LT_Sandline.csv
  3. What legal disclaimers am I supposed to read?
    This digital publication was prepared by an agency of the United States Government. 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. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 13-Oct-2020
Metadata author:
U.S. Geological Survey
Attn: Kathryn E.L. Smith
600 4th Street South
St. Petersburg, Florida
US

(727) 502-8073 (voice)
(727) 502-8001 (FAX)
kelsmith@usgs.gov
Hours_of_Service:
Monday through Friday, 9:00 a.m. to 5:00 p.m., Eastern Standard Time
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/Predict_LT_Sandline_metadata.faq.html>
Generated by mp version 2.9.50 on Tue Sep 21 18:18:51 2021