Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Event-driven backshore shoreline change

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Metadata:

Identification_Information:
Citation:
Citation_Information:
Originator: Kathryn E.L. Smith
Originator: Davina L. Passeri
Originator: Nathaniel G. Plant
Publication_Date: 20170412
Title:
Estuarine Back-barrier Shoreline and Beach Sandline Change Model Skill and Predicted Probabilities: Event-driven backshore shoreline change
Geospatial_Data_Presentation_Form: Tabular digital data
Series_Information:
Series_Name: U.S. Geological Survey Data Release
Issue_Identification: doi:10.5066/F7CZ35BC
Publication_Information:
Publication_Place: St. Petersburg, FL
Publisher: U.S. Geological Survey
Online_Linkage: https://doi.org/10.5066/F7CZ35BC
Description:
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 (for example, 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 event-driven estuarine back-barrier shoreline change model.
Purpose:
The estuarine back-barrier shoreline 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.
Supplemental_Information:
A free version of the Netica application is available for download at http://www.norsys.com/download.html.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20160606
Currentness_Reference: dataset creation
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -75.382739
East_Bounding_Coordinate: -73.974687
North_Bounding_Coordinate: 40.479022
South_Bounding_Coordinate: 37.862809
Keywords:
Theme:
Theme_Keyword_Thesaurus: USGS Metadata Identifier
Theme_Keyword: USGS:7604e220-dba1-484d-815f-d41b3c540560
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: geoscientificInformation
Theme_Keyword: oceans
Theme_Keyword: environment
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: Long-term shoreline change
Theme_Keyword: Storm-driven shoreline change
Theme_Keyword: Coastal
Theme_Keyword: Bayesian models
Theme_Keyword: Barrier Islands
Theme_Keyword: Storms
Theme_Keyword: Hurricanes
Theme:
Theme_Keyword_Thesaurus: USGS Thesaurus
Theme_Keyword: geomorphology
Theme_Keyword: ecology
Theme_Keyword: geology
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: New Jersey
Place_Keyword: USA
Place_Keyword: Mid-Atlantic Ocean
Place_Keyword: VA
Place_Keyword: Assateague Island
Place_Keyword: Maryland
Place_Keyword: Virginia
Place_Keyword: MD
Place_Keyword: NJ
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.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey Coastal and Marine Science Center
Contact_Person: Kathryn E.L. Smith
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 600 4th Street South
City: St. Petersburg
State_or_Province: Florida
Postal_Code: 33701
Country: US
Contact_Voice_Telephone: (727) 502-8073
Contact_Facsimile_Telephone: (727) 502-8001
Contact_Electronic_Mail_Address: kelsmith@usgs.gov
Hours_of_Service:
Monday through Friday, 9:00 a.m. to 5:00 p.m., Eastern Standard Time
Data_Set_Credit:
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.
Native_Data_Set_Environment:
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Microsoft Excel 2010 Version 14.0.7173.5000 (32-bit)
Cross_Reference:
Citation_Information:
Originator: Norsys
Publication_Date: 20000101
Title: Netica
Edition: 5.12 for MS Windows (2000 to 7)
Geospatial_Data_Presentation_Form: software
Online_Linkage: http://www.norsys.com/netica.html
Cross_Reference:
Citation_Information:
Originator: MathWorks
Publication_Date: 20030215
Title: MatLab
Edition: R2013a Version 8.1.0.604 (64-bit)
Geospatial_Data_Presentation_Form: software
Online_Linkage: https://www.mathworks.com/
Cross_Reference:
Citation_Information:
Originator: Plant, N. G., E. R. Thieler, and D. L. Passeri
Publication_Date: 20160502
Title:
Coupling centennial-scale shoreline change to sea-level rise and coastal morphology in the Gulf of Mexico using a Bayesian network
Series_Information:
Series_Name: Earth's Future
Issue_Identification: Volume 4, Issue 5, pages 143-158
Publication_Information:
Publication_Place: Hoboken, NJ
Publisher: Wiley Periodicals Inc.
Online_Linkage: https://doi.org/10.1002/2015EF000331
Data_Quality_Information:
Logical_Consistency_Report:
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 a negative or positive value; however, positive values show an improvement over the prior. The Bayesian mean and predicted most likely value should fall within a reasonable estimate of shoreline change parameters of the case (calibration) dataset.
Completeness_Report:
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.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Terrano, J.F. and Smith, K.E.L.
Publication_Date: 2015
Title:
Estuarine Shoreline and Barrier-Island Sandline Change Assessment
Geospatial_Data_Presentation_Form: tabular digital data
Online_Linkage: http://coastal.er.usgs.gov/data-release/doi-F71Z42HN/
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20150101
Source_Currentness_Reference: publication
Source_Citation_Abbreviation: calibration data
Source_Contribution: This data served as the calibration data for the Bayes Net.
Process_Step:
Process_Description:
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 data set.
Source_Used_Citation_Abbreviation: calibration data
Process_Date: 20160101
Process_Step:
Process_Description:
Added keywords section with USGS persistent identifier as theme keyword.
Process_Date: 20201013
Process_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey
Contact_Person: VeeAnn A. Cross
Contact_Position: Marine Geologist
Contact_Address:
Address_Type: Mailing and Physical
Address: 384 Woods Hole Road
City: Woods Hole
State_or_Province: MA
Postal_Code: 02543-1598
Contact_Voice_Telephone: 508-548-8700 x2251
Contact_Facsimile_Telephone: 508-457-2310
Contact_Electronic_Mail_Address: vatnipp@usgs.gov
Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Point
Point_and_Vector_Object_Information:
SDTS_Terms_Description:
SDTS_Point_and_Vector_Object_Type: Entity point
Point_and_Vector_Object_Count: 3458
Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Geographic:
Latitude_Resolution: 0.0197519519
Longitude_Resolution: 0.0253592358
Geographic_Coordinate_Units: Decimal degrees
Geodetic_Model:
Horizontal_Datum_Name: D North American 1983
Ellipsoid_Name: GRS 1980
Semi-major_Axis: 6378137.0
Denominator_of_Flattening_Ratio: 298.257222101
Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: ASI_Predict_HS_Backshore, NJ_Predict_HS_Backshore
Entity_Type_Definition: Model prediction probabilities and skill metrics in CSV format
Entity_Type_Definition_Source: USGS
Attribute:
Attribute_Label: objectid
Attribute_Definition: Internal feature number
Attribute_Definition_Source: Esri
Attribute_Domain_Values:
Unrepresentable_Domain:
Sequential unique whole numbers that are automatically generated.
Attribute:
Attribute_Label: X_long
Attribute_Definition:
Longitude location in geographic coordinate system (World Geodetic System 1984) decimal degrees
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: -75.3827
Range_Domain_Maximum: -73.9769
Attribute_Units_of_Measure: decimal degrees
Attribute:
Attribute_Label: Y_lat
Attribute_Definition:
Latitude location in geographic coordinate system (World Geodetic System 1984) decimal degrees
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 37.86545
Range_Domain_Maximum: 40.47719
Attribute_Units_of_Measure: decimal degrees
Attribute:
Attribute_Label: P-310.0
Attribute_Definition:
Predicted probability of the net backshore shoreline change from before to after Hurricane Sandy falling between (-310 m) and (-10 m)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0
Range_Domain_Maximum: 1
Attribute:
Attribute_Label: P-10.0
Attribute_Definition:
Predicted probability of the net backshore shoreline change from before to after Hurricane Sandy falling between (-10 m) and (-0.5 m)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0
Range_Domain_Maximum: 1
Attribute:
Attribute_Label: P-0.5
Attribute_Definition:
Predicted probability of the net backshore shoreline change from before to after Hurricane Sandy falling between (-0.5 m) and (+0.5 m)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0
Range_Domain_Maximum: 1
Attribute:
Attribute_Label: P0.5
Attribute_Definition:
Predicted probability of the net backshore shoreline change from before to after Hurricane Sandy falling between (+0.5 m) and (+10 m)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0
Range_Domain_Maximum: 1
Attribute:
Attribute_Label: P10.0
Attribute_Definition:
Predicted probability of the net backshore shoreline change from before to after Hurricane Sandy falling between (+10 m) and (+308 m)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0
Range_Domain_Maximum: 1
Attribute:
Attribute_Label: LR
Attribute_Definition:
Log-transform of the ratio of predicted probability in the bin corresponding to the observed net backshore change to the prior probability in that bin, where LR>0 means the prediction is an improvement over the prior
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: -0.96
Range_Domain_Maximum: 0.81
Attribute:
Attribute_Label: mean
Attribute_Definition:
Bayesian-mean value (sum of p(x)*x over all bins) of the predicted backshore shoreline change (meters)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: -161.7
Range_Domain_Maximum: 159.7
Attribute:
Attribute_Label: mostProb
Attribute_Definition:
Predicted-most-likely value (center of bin with highest probability) of the predicted backshore shoreline change (meters)
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: -160
Range_Domain_Maximum: 159
Attribute:
Attribute_Label: PmostProb
Attribute_Definition: Probability value at the predicted-most likely value
Attribute_Definition_Source: USGS
Attribute_Domain_Values:
Range_Domain:
Range_Domain_Minimum: 0.2
Range_Domain_Maximum: 1
Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey
Contact_Person: Kathryn E.L. Smith
Contact_Address:
Address_Type: mailing and physical
Address: 600 4th Street South
City: St. Petersburg
State_or_Province: Florida
Postal_Code: 33701
Country: US
Contact_Voice_Telephone: (727) 502-8073
Contact_Facsimile_Telephone: (727) 502-8001
Contact_Electronic_Mail_Address: 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.
Resource_Description: ASI_Predict_HS_Backshore.csv, NJ_Predict_HS_Backshore.csv
Distribution_Liability:
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.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: Comma Separated Values (CSV)
Format_Information_Content:
CSV files containing prediction probabilities and skill metrics for Bayes Net models.
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name:
http://coastal.er.usgs.gov/data-release/doi-F7CZ35BC/data/Predict_HS_Backshore.zip
Fees: None
Metadata_Reference_Information:
Metadata_Date: 20201013
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: U.S. Geological Survey
Contact_Person: Kathryn E.L. Smith
Contact_Address:
Address_Type: mailing and physical
Address: 600 4th Street South
City: St. Petersburg
State_or_Province: Florida
Postal_Code: 33701
Country: US
Contact_Voice_Telephone: (727) 502-8073
Contact_Facsimile_Telephone: (727) 502-8001
Contact_Electronic_Mail_Address: kelsmith@usgs.gov
Hours_of_Service:
Monday through Friday, 9:00 a.m. to 5:00 p.m., Eastern Standard Time
Metadata_Standard_Name: Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Access_Constraints:
Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. The U.S. Geological Survey requests to be acknowledged as originator of the data in future products or derivative research.
This page is <https://cmgds.marine.usgs.gov/catalog/spcmsc/Predict_HS_Backshore_metadata.html>
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