30 meter Esri binary grids of predicted elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83)

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Frequently anticipated questions:


What does this data set describe?

Title:
30 meter Esri binary grids of predicted elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83)
Abstract:
The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.
Supplemental_Information:
These data layers area a model output produced as part of the U.S. Geological Survey Sea-level Rise and Decision Support Project (http://wh.er.usgs.gov/slr/), and contains the forecast of adjusted land elevation with respect to predicted sea-level rise in the Northeastern United States at specified time intervals.
  1. How might this data set be cited?
    U.S. Geological Survey, 2015, 30 meter Esri binary grids of predicted elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83): data release DOI:10.5066/F73J3B0B, 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.

    Lentz, E.E., Stippa, S.R., Thieler, E.R., Plant, N.G., Gesch, D.B., and Horton, R.M., 2015, Coastal landscape response to sea-level rise assessment for the northeastern United States: data release DOI:10.5066/F73J3B0B, U.S. Geological Survey, Reston, VA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -77.830618
    East_Bounding_Coordinate: -66.813170
    North_Bounding_Coordinate: 46.642941
    South_Bounding_Coordinate: 35.344738
  3. What does it look like?
    ne_ae2080 (JPEG)
    Map example showing Adjusted Elevation Predictions for the 2080s
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2015
    Currentness_Reference:
    publication
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: raster digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 37886 x 22017, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      The map projection used is Albers Conical Equal Area.
      Projection parameters:
      False_Easting: 0.0
      False_Northing: 0.0
      Latitude_of_Projection_Origin: 23.0
      Longitude_of_Central_Meridian: -96.0
      Standard_Parallel: 29.5
      Standard_Parallel: 45.5
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 30.0
      Ordinates (y-coordinates) are specified to the nearest 30.0
      Planar coordinates are specified in Meters
      The horizontal datum used is North American Datum 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?
    Entity_and_Attribute_Overview:
    Layer files (.lyr) have been created for each of the adjusted elevation (AE) time intervals: ne_ae2020, ne_ae2030, ne_ae2050 and ne_ae2080. The data are intended to be viewed using the unqiue values and labels as shown in the layer files. Label names reflect the adjusted elevation (AE) ranges as stated in the USGS Open-File Report 2014-1252. A color ramp has been chosen that best highlights the individual AE ranges.
    Entity_and_Attribute_Detail_Citation: Please refer to the USGS Open-File Report 2014-1252.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • U.S. Geological Survey
  2. Who also contributed to the data set?
    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 originator of the dataset.
  3. To whom should users address questions about the data?
    E. Robert Thieler
    U.S. Geological Survey
    Research Geologist
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 x2350 (voice)
    508-457-2310 (FAX)
    rthieler@usgs.gov

Why was the data set created?

These GIS layers provide a forecast of the adjusted land elevation (AE) with respect to predicted sea-level rise for the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the most likely of one of five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the likelihood layer (PAE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with an estimation of the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation ranges over a large spatial scale and should therefore be used qualitatively (see Horizontal Positional Accuracy Report).

How was the data set created?

  1. From what previous works were the data drawn?
    SC_SL (source 1 of 7)
    University, Columbia, Unpublished material, Sea-level.

    Type_of_Source_Media: Mathworks MATLAB matrix format
    Source_Contribution:
    Global sea-level projections (SL) generated using Representative Concentration Pathways (RCPs) scenarios 4.5 and 8.5 for the 2013 Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) are used as model inputs. Three components comprise sea-level projections: those related to oceans (both local ocean height and global thermal expansion), ice melt, and global land water storage. For each of these three components of sea-level change, set percentiles (10th, 25th, 75th, and 90th) of the distribution were estimated. The sum of all components at each percentile is assumed to give the aggregate sea-level rise projection. This method does not take into account potential correlation among components. Decadal projections for the 2020s, 2030s, 2050s, and 2080s, were generated by averaging over ten year intervals and subtracting average values for 2000-2004. Data were provided by collaborators at Columbia University in Mathworks MATLAB matrix format. See Open File Report 2014-1252 .
    SC_NED_19 (source 2 of 7)
    U.S. Geological Survey., 201207, National Elevation Dataset (NED) 1/9 arc-second DEM.

    Online Links:

    Type_of_Source_Media: Esri binary grid format
    Source_Contribution:
    Elevation data (E) acquired from the National Elevation Dataset (NED) (http://ned.usgs.gov/). Partial coverage from light detection and ranging (lidar) is available at 1/9 arc-sec (approximately 3 m cells) from a culmination of bare earth lidar datasets. NED datasets are provided in the geographic coordinates of decimal degrees and are referenced to the North American Datum of 1983 (NAD83)and North American Vertical Datum of 1988 (NAVD88). Lidar data in the NED were collected by a variety of Federal, State, and local partners between 2001 and 2011. NED 1/9 are updated monthly and in conjunction with bi-monthly updates where possible. We use the available 1/9 arc-sec data from the NED June 1, 2012 release, supplemented with the 1/3 arc-sec data from the August 1, 2012 release, only where gaps in the 1/9 coverage exist (see citation below). Using the highest resolution elevation available allows detection of changes in low-lying coastal topography at increments small enough to correspond with near future (approximately 100 year) sea-level forecasts.
    SC_NED_13 (source 3 of 7)
    U.S. Geological Survey., 201208, National Elevation Dataset (NED) 1/3 arc-second DEM.

    Online Links:

    Type_of_Source_Media: Esri binary grid format
    Source_Contribution:
    Supplement elevation data (E) acquired from the National Elevation Dataset (NED) (http://ned.usgs.gov/). Complete regional coverage of the land (topography) for the northeast is available from the NED at 1/3 arc-second (approximately 9 m cells). The 1/3 arc-second data come from a combination of topographic maps, aerial photos, and lidar. NED datasets are provided in the geographic coordinates of decimal degrees and are referenced to the North American Datum of 1983 (NAD83)and North American Vertical Datum of 1988 (NAVD88). Lidar data in the NED were collected by a variety of Federal, State, and local partners between 2001 and 2011. NED 1/3 data are updated every other month to integrate improved source data. We use the available 1/9 arc-second data from the NED June 1, 2012 release (see citation above), supplemented with the 1/3 arc-sec data from the August 1, 2012 release, only where gaps in the 1/9 coverage exist.
    SC_CRM (source 4 of 7)
    National Oceanic and Atmospheric Administration., 19990101, Coastal Relief Model (CRM).

    Online Links:

    Type_of_Source_Media: ASCII grid format
    Source_Contribution:
    Bathymetric data from the Coastal Relief Model (CRM) are produced by the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center. These data are available at a 3 arc-second (~90 meter) resolution and are produced using hydrographic soundings data (1930s to present) from the National Ocean Service (NOS) and a number of academic institutions (http://www.ngdc.noaa.gov/mgg/coastal/model.html). CRM bathymetric data (E) are used where NED elevation data (E) are unavailable in submerged areas such as bays, estuaries, and open ocean coasts.
    SC_VLM_GPS (source 5 of 7)
    Sella, Giovanni F., Stein, Seth, Dixon, Timothy H., Craymer, Michael, James, Thomas S., Mazzotti, Stephane, and Dokka, Roy K., 20070126, Observation of glacial isostatic adjustment in "stable" North America with GPS.

    Online Links:

    Type_of_Source_Media: Plain Text Document (.txt)
    Source_Contribution:
    A combination of GPS data and long range tide data were used to estimate vertical land motion (VLM) rates due to glacial isostatic adjustment (GIA) for the conterminous United States and Canada. These point data represent vertical velocities recently processed to account for glacial isostatic adjustment from continuously recording GPS devices throughout North America at 362 locations. Used in combination with citation below.
    SC_VLM_TIDE (source 6 of 7)
    National Oceanic and Atmospheric Administration (NOAA), Zervas, Chris, Gill, Stephen, and Sweet, William, 201305, Estimating Vertical Land Motion from Long-Term Tide Gauge Records.

    Online Links:

    Type_of_Source_Media: Plain Text (.txt)
    Source_Contribution:
    A combination of GPS data and long range tide data were used to estimate vertical land motion (VLM) rates due to glacial isostatic adjustment (GIA) for the conterminous United States and Canada. Estimates generated using a methodology that extracts oceanographic effects from relative sea-level rates to determine local VLM rates from long range tide station records were incorporated at 69 locations along the Atlantic, Gulf, and Pacific coasts (Zervas and others, 2013). Used in combination with previous citation.
    SC_LC (source 7 of 7)
    Massachusetts., University of, Unpublished material, Ecological Systems Map (ESM) Plus.

    Type_of_Source_Media: Tagged Image File Format (TIFF)
    Source_Contribution:
    Land cover (LC) information was obtained from the Ecological Systems Map (ESM) Plus provided by the University of Massachusetts Designing Sustainable Landscapes (DSL) project Landscape Conservation and Design (LCAD) model (http://jamba.provost.ads.umass.edu/web/lcc/DSL_documentation_landscape_design.pdf). Because sea-level rise predictions will be integrated into the LCAD model, using a land cover layer identical to that of collaborators was an essential input for coastal response (CR) grids. As AE predictions are necessary for CR outputs, it was ideal to use the LC extent and horizontal resolution for AE processing as well. Additionally, the LC BEACH category (see processing steps below) is included as part of the methodology.
  2. How were the data generated, processed, and modified?
    Date: 2014 (process 1 of 11)
    Interpolate surfaces for sea-level projections specific to: 1) time step and 2) percentile using Esri ArcGIS (v. 10.2.1). Sea-level data were exported from MATLAB (v. R2013b) in XYZ ASCII format as a .CSV file. XY data were provided in geographic coordinates, and data were imported to ArcGIS > add xyz data in a geographic projection (World Geodetic System 1984 or WGS84). Point data were converted to a point feature class within a geodatabase in ArcCatalog by right-mouse clicking the point data file > Create Feature Class > From XY table. Point data were then interpolated to generate a surface of sea-level estimates using natural neighbor interpolation: ArcToolbox > Toolboxes > 3D Analyst Tools.tbx > Natural Neighbor (option: automatic densification). The resulting raster surface provided cells at 0.66 degrees (or approximately 74,000 m) resolution, and was imported into a geodatabase (with sea-level and VLM rasters). Sea-level grid values were used in calculations of adjusted land elevation ranges and associated probabilities. Extraction of sea-level values for use in modeling is described in metadata sections to follow. This process step and all subsequent process steps performed by Sawyer Stippa. Person who carried out this activity:
    Sawyer Stippa
    USGS
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 x2230 (voice)
    508-457-2310 (FAX)
    sstippa@usgs.gov
    Date: 2014 (process 2 of 11)
    Vertically converted elevation datasets from NAVD 88 to mean high water (MHW) were included to take into account tidal variation. The vertical datum was adjusted to MHW along the coastline using a one-step conversion grid generated through the National Oceanic and Atmospheric Administration (NOAA) Vertical Datum Transformation (VDatum, v. 3.1) tool which was added to the NED data in ArcGIS (v. 10.2.1) > Spatial Analyst Tools > Plus. Vertical conversion was not deemed necessary for the CRM data (see vertical accuracy). The conversion grid is provided at a resolution of 0.0017 degrees, which is the full resolution as supplied with VDatum. Vertical datum shifts to MHW were propagated landward via Euclidean allocation, which smoothes vertical ledges at edges of VDatum zones. Elevation grid values were used in calculations of adjusted land elevation ranges and associated probabilities. Extraction of elevation values for use in modeling is described in metadata sections to follow.
    Date: 2014 (process 3 of 11)
    Vertical land motion estimates in the form of point data (rates) were used to interpolate a surface to provide continuous VLM rate estimates across the region using ArcGIS v. 10.2.1. Point data (XYZ format) were downloaded from Sella and others, 2007 and Zervas and others, 2013 and imported to Esri ArcGIS (add XYZ data) and a feature class of the points was created. The points (geographic coordinates, projected in WGS84) were used to interpolate a raster surface using the multiquadratic radial basis function: ArcToolbox > Toolboxes > Geostatistical Analyst Tools.tbx > Radial Basis Functions (selected tool options: Multiquadratic Function). The resulting surface was gridded at a per cell resolution of 0.23 degrees (or approximately 24,000 m) and imported to the geodatabase containing sea-level projections and elevation. VLM grid values were used in calculations of adjusted land elevation ranges and associated probabilities. Extraction of VLM values for use in modeling is described in metadata sections to follow.
    Date: 2014 (process 4 of 11)
    Any land cover class, as determined from ESM Plus name and/or description, that included dune and swale/sandy beach (including bluffs) or marine and estuarine intertidal unconsolidated shore was assigned to 'Beach' category. All other classes were given "general" land cover categories. Assignments were made by joining an Excel table of the classified land cover categories with the attribute table of the raster land cover data using Esri ArcGIS (v. 10.2.1): ArcToolbox > Toolboxes > Data Management Tools.tbx > Add Join.
    Date: 2014 (process 5 of 11)
    Generate a database of points at land cover locations from which model inputs might be culled. An ESM Plus database (EPD) of points was created using the extent of the converted 1/3 arc-second DEM at -15 to 10 m. The 1/3 data were selected because they provided continuous regional coverage. The 1/3 DEM data was reclassified by converting all data within the stated ranges (-15 to 10) to 1 and all other to null: ArcToolbox > Toolboxes > Spatial Analyst Tools.tbx > Reclassify (options: min to -15 no data. 10 to max no data). The resulting raster was converted to a polygon to be used in clipping the ESM Plus raster: ArcToolbox > Toolboxes > Conversion Tools.tbx > Raster To Polygon (option: no simplify). The ESM Plus raster clip was converted to points using Esri ArcGIS software (v. 10.2.1) to form the EPD: ArcToolbox > Toolboxes > Conversion Tools.tbx > Raster to Point.
    Date: 2014 (process 6 of 11)
    Use elevation and land cover information to delineate study area extent. Elevation and land cover information was used to define the coastal extent as follows: a) at or below 10 m and at or above -10 m elevation for the BEACH land cover category and b) at or below 5 m and at or above -10 m for all other general land cover categories. The delineation of this area required the creation of several polygons generated from these raster inputs that were then merged together to define the full study area extent. Footprints of the NED and CRM datasets were created using polygon clips of the EPD. The NED footprint was created by reclassifying the 1/3 arc-second DEM within 0 to 5 m: ArcToolbox > Toolboxes > Spatial Analyst Tools.tbx > Reclassify (options: min to 0 no data. 5 to max no data). The resulting raster was converted to a polygon: ArcToolbox > Toolboxes > Conversion Tools.tbx > Raster To Polygon (option: no simplify). A polygon of the ESM Plus BEACH land cover category was created by reclassifying the ESM Plus to only show the beach classification: ArcToolbox > Toolboxes > Spatial Analyst Tools.tbx > Reclassify (options: 1 to 2 no data. 4 to 6 no data). The resulting raster was converted to a polygon: ArcToolbox > Toolboxes > Conversion Tools.tbx > Raster To Polygon (option: no simplify). The beach polygon was clipped further using a reclassified 1/3 arc-second DEM polygon at 5 to 10 m. The beach polygon was merged with the reclassified 1/3 arc-second DEM polygon at 0 to 5m: ArcToolbox > Toolboxes > Data Management Tools.tbx > Merge. A third polygon was merged with the NED footprint that included a reclassified segment of the NAVD88 1/3 arc-second DEM (prior to MHW conversion) at 0 to 3.5m: ArcToolbox > Toolboxes > Data Management Tools.tbx > Merge. This segment was used to provide continuous coverage starting slightly below MHW. The CRM footprint was created by reclassifying the CRM dataset within -10 to 1 m: ArcToolbox > Toolboxes > Spatial Analyst Tools.tbx > Reclassify (options: min to -10 no data. 1 to max no data). The resulting raster was converted to polygon: ArcToolbox > Toolboxes > Conversion Tools.tbx > Raster To Polygon (option: no simplify).CRM and NED footprints were used separately to allow for faster processing times, though future performance increases may allow for the combination of each footprint.
    Date: 2014 (process 7 of 11)
    Cull points from the EPD within the study area extent and extract grid values at coincident locations from VLM, elevation, and sea-level projection rasters. A NED point database was created by clipping the EPD with the NED footprint. The resulting dataset was consequently divided into 10 subregions to make processing more manageable: ArcToolbox > Toolboxes > Data Management Tools > Create Fishnet (options: 10 rows 1 column): ArcToolbox > Toolboxes > Analysis Tools.tbx > Clip. The CRM point database was created by clipping the EPD with the CRM footprint: ArcToolbox > Toolboxes > Data Management Tools.tbx > Clip. CRM database points that overlapped NED database points were removed: ArcToolbox > Toolboxes > Data Management Tools.tbx > Select Layer By Location (options: intersect new selection): ArcToolbox > Toolboxes > Data Management Tools.tbx > Delete Rows. The resulting CRM point database was divided into 59 regions based on increments of 500,000 points using a Python, v. 2.7 script. For each NED and CRM subregion, raster data from each sea-level projection year and percentile range, vertical land movement, and elevation layers were extracted at each point location: ArcToolbox > Toolboxes > Spatial Analyst Tools.tbx > Extract Multi Values To Points. A script (Python, v. 2.7) was developed to populate an elevation column with 1/3 arc-second data that were overwritten with 1/9 arc-second values if present for all NED subregions. An error column was also populated to distinguish 1/3 arc-second and 1/9 arc-second values for each NED subregion. The NED subregions were further truncated to the 0 to 10 m range for BEACH land cover category and 0 to 5 m range for all other land cover categories, this time based on the extent defined by the 1/9 arc second data where present. CRM points outside of the -10 to 1 m range were removed. Geographic coordinate information (latitude and longitude, WGS84) for each point was also added to the NED and CRM subregions before being exported to csv tables.
    Date: 2014 (process 8 of 11)
    The Bayesian network (BN) model was used to generate region-wide geospatial values for the probability of AE outputs using elevation data, sea-level projections for selected time intervals, and vertical land movement rates. A BN offers the capability to generate robust, spatially-explicit predictions and a corresponding confidence interval with every predicted outcome. Parameters that influence coastal change may all be included in the network design to make a prediction (an updated conditional probability) given a set of observations. The BN results in the probability of each adjusted elevation range (PAE) being reached given a range of sea-level rise scenarios. Full details of the BN, including conceptual diagrams and probabilistic equations used to describe the model may be found elsewhere (Lentz and others, 2015). Point data from each subregion were exported as tables and imported (sans headers, object ID and shape geometry fields) into MathWorks MATLAB software (v. R2013B) for processing in MATLAB and Netica. Input datasets consisted of 20 primary columns: SL1020, SL1030, SL1050, SL1080, SL2520, SL2530, SL2550, SL2580, SL7520, SL7530, SL7550, SL7580, SL9020, SL9030, SL9050, SL9080 (sea-level predictions where the first two digits indicate percentile range, the second two the prediction year in 20XX); VLM (vertical land motion); DEM (1/3 or 1/9 arc-second DEM values or CRM DEM values); latitude; and longitude.
    
    
    Equations which previously appeared in this process step (describing Bayes theorem) have been modified to provide more explanatory content specific to our Bayesian network function. The updated equations appear in the Open-File Report cross-reference Lentz and others, 2015, version 2. The modifications to the equations do not change the results, but just provide a bit more detail and specifics on the probabilistic approach being used. Because of special characters and other considerations, please reference the Open-File Report for these equations.
    Date: 2014 (process 9 of 11)
    PAE prediction matrices from completed model runs were combined with lat/long of input matrices. Additional MATLAB code was used to determine the most likely adjusted elevation range (AE) based on corresponding PAE values and convert outputs to shapefile format. The resulting point shapefiles were brought back into ArcGIS as rasters using a python script (v. 2.7). Point to raster conversion did not require interpolation because the horizontal spacing between input points retained the 30 m horizontal resolution of the original land cover grid. All corresponding rasters were then merged ArcToolbox > Toolboxes > Data Management Tools.tbx > Mosaic to New Raster to generate a single raster of region-wide coverage.
    Date: 03-Dec-2015 (process 10 of 11)
    The metadata was modfied to edit the 8th process step which contained equations describing the Bayes theorem specific to the Bayesian network function used in this work. The updated equations appear in the cross-reference Lentz and others, 2015, version 2. Person who carried out this activity:
    U.S. Geological Survey
    Attn: VeeAnn A. Cross
    Marine Geologist
    384 Woods Hole Rd.
    Woods Hole, MA

    508-548-8700 x2251 (voice)
    508-457-2310 (FAX)
    vatnipp@usgs.gov
    Date: 20-Jul-2018 (process 11 of 11)
    USGS Thesaurus keywords added to the keyword section. 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?
    Lentz, E.E., Stippa, S.R., Thieler, E.R., Plant, N.G., Gesch, D.B., and Horton, R.M., 2015, Evaluating coastal landscape response to sea-level rise in the northeastern United States—Approach and Methods: Open File Report 2014-1252, 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?
    These grids represent point data that have been previously attributed. They have gone through a series of QA/QC procedures, and is therefore believed to accurately reflect the modeling process and underlying grids used to attribute the point data.
  2. How accurate are the geographic locations?
    A probabilistic model (Bayesian Network) is used to generate the forecast of adjusted elevation ranges shown in this data layer. Because the overall horizontal accuracy of the dataset depends on the accuracy of the model, the forcing values used, expert knowledge, the underlying inputs (i.e., sea-level projections, elevation, vertical land movement rates) and so forth, the spatial accuracy of this dataset cannot be meaningfully quantified. These maps are intended to provide a qualitative and relative regional assessment of sea-level impacts to the landscape at the 30 m horizontal resolution displayed. Users are advised not to use the dataset to determine specific values quantitatively at any particular geographic location.
  3. How accurate are the heights or depths?
    Data from the National Elevation Dataset (NED) were referenced to MHW and contained a vertical accuracy specification as follows: NED 1/9 Arc-Second DEM +/- 0.42 m. NED 1/3 Arc-Second DEM +/- 1.25 m. A small subset of the NED dataset (0 to 3.5 meters) remained referenced to NAVD88. Coastal Relief Model (CRM) DEM data were not referenced to MHW but remained primarily MLW with a vertical accuracy of +/- 1.0 m. Because the model uses initial elevation ranges (-10 to -1, -1 to 0, 0 to 1, 1 to 5, 5 to 10 m) as opposed to discrete values, a significant portion of the CRM DEM (~80%) remained within the same elevation range when applying MHW conversion. Furthermore, CRM vertical datum conversion to MHW did not cover the full extent of the study area (~9% loss) and an additional percentage (~10%) of the MHW conversion exceeded the vertical accuracy (+/- 1.0 m). Depending on the elevation dataset extracted to each point, it can be expected that the vertical datum of the output at each point remained the same.
  4. Where are the gaps in the data? What is missing?
    Data from approximately 42,000,000 coastal locations throughout the Northeast from Maine to Virginia were used to make sea-level rise probability forecasts. Model inputs (raster format) were either upscaled or downscaled to provide inputs at the 30 m horizontal resolution of the land cover data. Each cell in this data layer displays the most probable adjusted elevation range of five possible outcomes: -12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 meters. Forecast values were calculated for the time period of 2020 to 2080, and will vary if performed on a different time period if different models are used or if different model inputs (such as the year of elevation data, vertical land movement rate estimates, revised sea-level estimates), discretization of such inputs, or parameterizations were chosen.
  5. How consistent are the relationships among the observations, including topology?
    Point data were converted from grid cells covering the full extent of the study area. This grid represents the conversion of these points back to their corresponding grid cell with no interpolation.

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: None
  1. Who distributes the data set? (Distributor 1 of 1)
    E. Robert Thieler
    U.S. Geological Survey
    Research Geologist
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 x2350 (voice)
    508-457-2310 (FAX)
    rthieler@usgs.gov
  2. What's the catalog number I need to order this data set? NE_region_AE.zip: contains four subfolders with the time interval grids and layer files (.lyr). The subfolder ne_ae2020 contains the ne_ae2020 Esri binary grid folder, info folder and layer file, ne_ae2030 contains the ne_ae2030 Esri binary grid folder, info folder and layer file, ne_ae2050 contains the ne_ae2050 Esri binary grid folder, info folder and layer file, and ne_ae2080 contains the ne_ae2080 Esri binary grid folder, info folder and layer file. Additionally the FGDC comliant metadata describing the grids is contained in the zip file, along with a browse graphic image.
  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. Any use of trade, product, or firm 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?
  5. What hardware or software do I need in order to use the data set?
    These data are available in Environmental Systems Research Institute (Esri) raster format. The user must have software capable of reading an Esri binary grid format. Each individual grid is over 3.1 GB and can be extracted individually from the zip file.

Who wrote the metadata?

Dates:
Last modified: 20-Jul-2018
Metadata author:
Sawyer Stippa
U.S. Geological Survey
Geologist
384 Woods Hole Road
Woods Hole, MA
USA

508-458-8700 x2230 (voice)
508-457-2310 (FAX)
sstippa@usgs.gov
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/data_release/DR_F73J3B0B/ne_AEmeta.faq.html>
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