Justin J. Birchler
Kara S. Doran
Joseph W. Long
Heather A. Schreppel
Hilary F. Stockdon
20190619
Subtropical Storm Alberto Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0800 AM EDT SUN MAY 27 2018
vector digital data
U.S. Geological Survey Data Release
doi:10.5066/P9Z362BC
St. Petersburg, FL
U.S. Geological Survey
https://doi.org/10.5066/P9Z362BC
U.S. Geological Survey
2019
USGS Coastal Change Hazards Portal
https://marine.usgs.gov/coastalchangehazardsportal
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida 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 Subtropical Storm Alberto in May 2018. 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.
To provide data on the probability of storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coast post-Subtropical Storm Alberto.
20180527
ground condition
None planned, however future updates and post-storm analyses are anticipated.
-90.962989
-84.334191
30.397062
29.045053
USGS Metadata Identifier
USGS:217c3e2c-b4b5-4c2d-bdad-d473bfe0a355
None
U.S. Geological Survey
USGS
St. Petersburg Coastal and Marine Science Center
Coastal and Marine Geology Program
CMGP
SPCMSC
Subtropical Storm Alberto
coastal
ISO 19115 Topic Category
oceans
elevation
environment
geoscientificInformation
USGS Thesaurus
hazards
marine geology
ocean sciences
coastal processes
erosion
Data Categories for Marine Planning
distributions
bathymetry and elevation
physical habitats and geomorphology
Marine Realms Information Bank (MRIB) Keywords
effects of coastal change
shoreline accretion
shoreline erosion
storm erosion
topographic mapping
hurricanes and typhoons
Geographic Names Information System
United States of America
Louisiana
Mississippi
Alabama
Florida
None
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 originators of the data in future products or derivative research.
U.S. Geological Survey
Hilary Stockdon
mailing and physical
600 4th Street South
Saint Petersburg
FL
33701
UNITED STATES
727-502-8074
727-502-8182
hstockdon@usgs.gov
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.
Microsoft Windows 7 Version 6.1 (Build 7600); Esri ArcGIS 10.0.4.4000
No additional checks for consistency were performed on this data.
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.
Horizontal accuracy was not estimated.
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.
National Weather Service, National Oceanic and Atmospheric Administration
20180527
Extratropical Surge and Tide Operational Forecast System
https://www.opc.ncep.noaa.gov/estofs/estofs_surge_info.shtml
Online digital data
20180527
The date when web page was last modified.
ESTOFS
Data provides anticipated water levels during the next 4 days.
NOAA National Weather Service Environmental Modeling Center
20180527
NOAA Wavewatch III
https://polar.ncep.noaa.gov/waves/
Online digital data
20180527
The date when the model was run.
WW3
Model that was used to estimate wave setup and runup conditions for Subtropical Storm Alberto.
Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX)
20160523
2009 US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center for Expertise (JALBTCX) Topographic Lidar: Post Hurricane Gustav and Post Hurricane Ike
Geographic Coverage: TX, mainland LA
https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/1061/
https://coast.noaa.gov/htdata/lidar1_z/geoid12a/data/1063/
Online digital data
20090203
20090423
The date when lidar surveys were collected.
JALBTCX TX/LA 2009
A lidar survey that was used to estimate dune morphology variables.
Guy, K.K.
20140602
Topographic Lidar Survey of the Alabama, Mississippi, and Southeast Louisiana Barrier Islands, from September 5 to October 11, 2012
U.S. Geological Survey Data Series
839
Geographic Coverage: LA, MS
https://doi.org/10.3133/ds839
Online digital data
20120905
20121011
The date when lidar surveys were collected.
DS 839 LA 2012
A lidar survey that was used to estimate dune morphology variables.
Guy, K.K.
20140602
Topographic Lidar Survey of Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana, July 12-14, 2013
U.S. Geological Survey Data Series
838
Geographic Coverage: Chandeleur Islands, LA
https://doi.org/10.3133/ds838
Online digital data
20130712
20130714
The date when lidar surveys were collected.
DS 838 LA 2013
A lidar survey that was used to estimate dune morphology variables.
U.S. Geological Survey
20161018
USGS Lidar Point Cloud LA SoTerrebonne-GI 2015
Geographic Coverage: Mississippi Barrier Islands
https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map
Online digital data
20150118
20150213
The date when lidar surveys were collected.
USGS MS 2015
A lidar survey that was used to estimate dune morphology variables.
United States Army Corps of Engineers (USACE)
20180514
2016 USACE NCMP Topobathy Lidar: Gulf Coast (AL, FL, MS, TX)
Geographic Coverage: Alabama
https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=5186
Online digital data
20160723
20161010
The date when lidar surveys were collected.
USACE AL 2016
A lidar survey that was used to estimate dune morphology variables.
United States Army Corps of Engineers (USACE)
20150508
2010 US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) Topobathy Lidar: Alabama Coast and Florida Gulf Coast
Geographic Coverage: AL (Fort Morgan to AL/FL border), FL (AL/FL border to Bald Point State Park)
https://coast.noaa.gov/dataviewer/index.html?action=advsearch&qType=in&qFld=ID&qVal=1064
Online digital data
201001
201003
The date when lidar surveys were collected.
USACE FL 2010
A lidar survey that was used to estimate dune morphology variables.
Process_Description: 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.
JALBTCX TX/LA 2009
DS 839 LA 2012
DS 838 LA 2013
USGS MS 2015
USACE AL 2016
USACE FL PH 2010
2018
Dune morphology (DHIGH, DLOW, DHIrms, DLOrms)
U.S. Geological Survey
Justin J. Birchler
mailing and physical
600 4th Street South
Saint Petersburg
FL
33701
UNITED STATES
727-502-8019
727-502-8182
jbirchler@usgs.gov
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, 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 2012-1084, 51 p. https://doi.org/10.3133/ofr20121084
ESTOFS
WW3
20180527
Hydrodynamics (SURGE, SETUP, RUNUP)
U.S. Geological Survey
Justin J. Birchler
mailing and physical
600 4th Street South
Saint Petersburg
FL
33701
UNITED STATES
727-502-8019
727-502-8182
jbirchler@usgs.gov
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. Probabilities were calculated in MATLAB and exported using MATLAB's shapewrite.m script.
For details on modeling parameterization, see:
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 2012-1084, 51 p. . https://doi.org/10.3133/ofr20121084
Dune morphology
Hydrodynamics
20180527
Probabilities (PCOL, POVW, PIND)
U.S. Geological Survey
Justin J. Birchler
mailing and physical
600 4th Street South
Saint Petersburg
FL
33701
UNITED STATES
727-502-8019
727-502-8182
jbirchler@usgs.gov
Added keywords section with USGS persistent identifier as theme keyword.
20201013
U.S. Geological Survey
VeeAnn A. Cross
Marine Geologist
Mailing and Physical
384 Woods Hole Road
Woods Hole
MA
02543-1598
508-548-8700 x2251
508-457-2310
vatnipp@usgs.gov
Vector
String
883
8.9831528411952133e-009
8.9831528411952133e-009
Decimal Degrees
D_WGS_1984
WGS_1984
6378137.0
298.257223563
Alberto_PCOI_line
Probabilities of hurricane-induced coastal erosion, dune morphology, and hurricane hydrodynamic data
USGS
FID
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Feature geometry.
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 February 2009 to October 2016.
USGS
0.809263
8.993615
meters NAVD88
-999
Null value
USGS
DLOW
Elevation of the dune toe, in meters NAVD88. Extracted from lidar surveys collected February 2009 to October 2016.
USGS
0.673475
4.341412
meters NAVD88
-999
Null value
USGS
DHIrms
Root mean squared error of dune crest elevation measurements (square meters).
USGS
0.109599
1.718375
square meters
-999
Null value
USGS
DLOrms
Root mean square error of dune toe elevation measurements (square meters).
USGS
0
1.115019
square meters
-999
Null value
USGS
SURGE
Storm surge water level
NOAA
0
1.227408
meters NAVD88
RUNUP
Wave runup water level
USGS
0.316245
2.026379
meters NAVD88
-999
Null value
USGS
SETUP
Wave setup water level
USGS
0.007964
0.7767766
meters NAVD88
-999
Null value
USGS
PCOL
Probability of collision
USGS
0.016232
95.07805
percent
-999
Null value
USGS
POVW
Probability of overwash
USGS
0
96.611361
percent
-999
Null value
USGS
PIND
Probability of inundation
USGS
0
76.756885
percent
-999
Null value
USGS
MEAN
Mean water level (surge + setup)
USGS
0.404473
1.807431
meters NAVD88
-999
Null value
USGS
EXTREME
Extreme water level (surge + runup).
USGS
0.748464
3.028131
meters NAVD88
-999
Null value
USGS
TIDE
Predicted tide water level
USGS
0
0
meters NAVD88
U.S. Geological Survey
Justin J. Birchler
mailing and physical
600 4th Street South
Saint Petersburg
FL
33701
727-502-8019
727-502-8182
jbirchler@usgs.gov
Although these data have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The USGS shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
shapefile
zip
https://coastal.er.usgs.gov/data-release/doi-P9Z362BC/data/Alberto_2018.zip
None, if obtained online.
20201013
U.S. Geological Survey
Justin J. Birchler
mailing and physical
600 4th Street South
Saint Petersburg
FL
33701
UNITED STATES
727-502-8019
727-502-8182
jbirchler@usgs.gov
Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998
local time