Pennsylvania Elevation Data underlying the analysis entitled "Maps of lands close to sea level along the middle Atlantic coast of the United States" by J.G. Titus and J. Wang, 2008.

Metadata:

Identification_Information:

Citation:

Citation_Information:

Originator: US Environmental Protection Agency

Publication_Date: February 2008

Title:

Pennsylvania Elevation Data underlying the analysis entitled "Maps of lands close to sea level along the middle Atlantic coast of the United States" by J.G. Titus and J. Wang, 2008.

Geospatial_Data_Presentation_Form: raster digital data

Other_Citation_Details:

Data underlying the analysis reported in J.G. Titus and J. Wang, 2008.

Online_Linkage: http://maps.risingsea.net/data.html

Larger_Work_Citation:

Citation_Information:

Originator: US Environmental Protection Agency

Publication_Date: February 2008

Title:

Maps of lands close to sea level along the middle Atlantic coast of the United States

Other_Citation_Details:

Full Citation: Titus, J.G. and J. Wang, 2008: Maps of lands close to sea level along the middle Atlantic coast of the United States: an elevation data set to use while waiting for LIDAR. In: Background Documents Supporting Climate Change Science Program Synthesis and Assessment Product 4.1: Coastal Elevations and Sensitivity to Sea Level Rise [J.G. Titus and E.M. Strange (eds.)]. EPA430R07004, U.S. Environmental Protection Agency, Washington, DC.

Description:

Abstract:

Coastal Pennsylvania Digital Elevation Model (Environmental Systems Research
Institute [ESRI] Grid format) represents an elevation map of the Pennsylvania coastal zone created for the purposes of analyzing vulnerability to rising sea level. The domain of the data set extends from the upper tidal wetland boundary up to the 40-foot NGVD29 contour, but the primary focus of the analytical approach and quality control has focused on land below the 10-foot contour. 
This data set has been derived from several sources of elevation data, including United States Geological
Survey (USGS) 1:24,000 Digital Line Graphs (DLG), US. Corps of Engineer Spot
Elevation data, Monmouth County elevation data,  as well as DGL's
created by Henan Institute of Geography from USGS 1:24,000 Digital Raster Graphs.
In addition, the analysis created a supplemental contour representing the
elevation of spring high water (SHW), which ranges from 1.2 to 5.8 feet
above NGVD29 in Pennsylvania.  We defined the horizontal position of that contour
by extracting the inland limit of the tidal wetland polygons in a separate Coastal
Wetlands data set we created for this project (based on the National Wetlands
Inventory) . We defined the vertical position of the supplemental contour by creating a
"tidal elevation surface" using the National Ocean Service's (NOS) estimated tide
ranges, NOS estimated sea level trends, NOS published benchmark sheets, and the
National Geodetic Survey North American Vertical Datum Conversion Utility
(VERTCON) program to convert the Mean Tide Level (MTL) relative to NAVD88 to
NGVD29. All elevation information was converted to a common vertical reference,
usually NGVD29, and the DEM was generated from that input data using ESRI's
interpolation algorithm TOPOGRID (within ArcGIS workstation GRID extension).
We converted the absolute elevation estimates (usually NGVD29) into elevations relative to SHW using the "tidal elevation surface."
For purposes of this data set, SHW is the upper boundary of tidal wetlands (including vegetated wetlands and
intertidal beaches). Elevation is expressed in cm.
The zip file associated with this data set should include:
 
1.       README_PA_Elevation.doc, which provides a brief overview of the relationship between this dataset and related data
 
2.       InterpolationMethods_MEMO.doc
 
3.       PA_Data_Quality.jpg
 
4.       DEM_LidarComparisonTable.doc
 
5.       DEM_Comparison_with_DLG_11_quads.xls
 
6.       Institute_of_Geography_DLG.xls
 
7.       Titus_and_Wang_2008.pdf
 
However, to speed download, in the online versions, (2) and (7) which are associated with all of the states may have been removed
and included in a file called “Common_supplemental_metadata.zip”
 

Purpose:

The Pennsylvania Digital Elevation Model provides a base map layer for assessing the possible influences of potential sea level rise on coast regions. We recommend against using this data to create maps with scales greater than 1:100,000, regardless of the level of vertical precision portrayed.  Moreover, if the purpose of using this data is to create graphical depictions of risk with contour intervals of 50-100 cm, we recommend a considerably smaller scale unless the audience is likely to understand the limitations of the data.

Supplemental_Information:

Elevations relative to year 2000.

Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2000

Currentness_Reference:

ground condition

Status:

Progress: Complete

Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Bounding_Coordinates:

West_Bounding_Coordinate: -75.938065

East_Bounding_Coordinate: -73.516493

North_Bounding_Coordinate: 41.360902

South_Bounding_Coordinate: 38.754772

Keywords:

Theme:

Theme_Keyword_Thesaurus: General

Theme_Keyword: Pennsylvania Elevation

Theme_Keyword: DEM

Theme_Keyword: Coastal Elevation

Place:

Place_Keyword: PA

Access_Constraints: None

Use_Constraints:

None

Point_of_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: James G. Titus

Contact_Organization: U.S. Environmental Protection Agency, Climate Change Division

Contact_Position: Project Manager

Contact_Address:

Address_Type: mailing address

Address:

Mailcode 6207J

City: Washington

State_or_Province: DC

Postal_Code: 20460

Contact_Voice_Telephone: 202-343-9307

Contact_Facsimile_Telephone: 202-343-2338

Contact_Electronic_Mail_Address: Titus.Jim@epamail.epa.gov

Hours_of_Service: 9:00 - 6:00 Eastern

Data_Set_Credit:

Jue Wang, GIS Practice, ICF Consulting, Inc.

Native_Data_Set_Environment:

Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.3.0.1770

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Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

The underlying data used in the creation of this layer may contain errors or omissions. The accuracy of this data set generally corresponds to the source data used in the layer development. See "PA_Data_Quality.jpg" for  an index of the source data used (that accompanied this data set in the zip file.)
See the sections on Positional Accuracy for more detailed information.
Additional consideration: The vertical values and their associated positions were generated using the interpolation function "TOPOGRID" within the ESRI GRID module. TOPOGRID uses input elevation data such as contours and elevation point data along with supplemental information such as stream networks, lakes (of known elevation), and bounding areas to generate a hydrologically-correct DEM. Each state DEM was generated using TOPOGRID but the specific parameters were unique to the data sets available and issues related to each state.
 
There are known issues relating to the interpolation algorithm TOPOGRID.
 
TOPOGRID Plateau Problem. The TOPOGRID function generates disproportionately large areas with the same value of the input contour lines, e.g., if we have 5 and 10 foot contour lines, there would be substantially more areas with values between 4 to 6 and 9 to 11, than 6 to 9 feet. At the upper tips of narrow valleys, the cell values tend to be the same as the bounding contours so the valleys become plateaus. The TOPOGRID function within the ESRI GRID module tends to calculate a trend from neighboring contour lines. As a result, TOPOGRID frequently creates areas of erroneous depressions on the plains adjacent to steep slopes, often substantially below the contours between which those depressions lay. It also creates plateaus along contours, which can be problematic because they overstate the amount of land barely above the wetlands and right at the first contour, while understating the amount of land halfway between the wetlands and the first contour. To address these problems, we processed the areas above and below the first contour separately. However, this caused another problem. In narrow valleys in the area below the first contour, the output DEM values were similar or identical to those of the bounding contour lines due to the lack of elevation information that TOPOGRID needs to calculate trend. The most problematic regions occurred where there was a stream valley below the first contour (e.g. between two parallel 5 foot contours), neither open water nor tidal wetlands along most of the length of the valley, but open water or tidal wetlands at one end of the valley (e.g. a typical non-tidal stream flowing into tidal waters). In some cases, the trend from the wetlands or open water at the mouth toward the bounding first contour would provide values even higher than that first contour farther up the valley. And in general, TOPOGRID would be more likely to assume a flat area between the contours, than to characterize it as a valley.
 
We did not use stream data in constructing the DEM for Pennsylvania (See comparable DEMs for Delaware, Maryland, New York and North Carolina, where stream data was available.).
 
Evaluation of other interpolation methods. 
Several interpolation methods were evaluated before the TOPOGRID function was selected. Specifically, Spline, Inverse Distance Weighting (IDW), and Triangulated Irregular Network (TIN) methods were evaluated and compared to the TOPOGRID function. Statistics and graphical examples of cross sections specific to each interpolation method are presented in the accompanying "InterpolationMethods_MEMO.doc" memo included in the zip file associated with this data set.

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Explanation:

An accuracy assessment was made between the source  DLG's and the DEM for a select number of quads. See "DEM_Comparison_with_DLG_11_quads.xls"  which accompanies this data set in the zip file.
An additional assessment was made between the DEM and lidar data where it was available in Maryland and North Carolina. The results can be found in the "DEM_LidarComparisonTable.doc" that accompanied this data set in the zip file.
See the sections on Positional Accuracy (Horizontal and Vertical).

Logical_Consistency_Report:

Refer to the Titus and Wang 2008 technical report that documents this study for information on the publication date for data and procedures used in the development of this layer.
See the sections on Positional Accuracy (Horizontal and Vertical) for additional information.
 
Note that the discussions presented in the accuracy reports refer to contour intervals using two different systems of measurements (meters and feet). We use the two diffent systems to reflect the actual contour intervals used by USGS over the years, which vary on a quadrangle by quadrangle basis.

Completeness_Report:

This data set generally corresponds to that of the source data used in the layer 
development. See the sections on Positional Accuracy (Horizontal and Vertical) for 
additional information. 
 
The vertical values and their associated positions were generated using the interpolation 
function "TOPOGRID" within the ESRI GRID module. TOPOGRID uses input 
elevation data such as contours and elevation point data along with supplemental 
information such as stream networks, lakes (of known elevation), and bounding areas to 
generate a hydrologically correct DEM. Each state DEM was generated using 
TOPOGRID but the specific parameters were unique to the data sets available and 
issues related to each state. The specifics to each state DEM are described under 
positional accuracy section of the metadata and in process steps. 
 
There are known issues relating to the interpolation algorithm TOPOGRID. 
 
TOPOGRID Plateau Problem. 
 
The TOPOGRID function generates disproportionately large areas with the same value of the input contour lines, e.g., if we 
have 5 and 10 foot contour lines, there would be substantially more areas with values 
between 4 to 6 and 9 to 11, than 6 to 9 feet.. At the upper tips of narrow valleys, the 
cell values tend to be the same as the bounding contours so the valleys become 
plateaus. 
 
The TOPOGRID function within the ESRI GRID module tends to calculate a trend 
from neighboring contour lines.  As a result, TOPOGRID frequently creates erroneous 
depressions on the plains adjacent to steep slopes, often substantially below the 
contours between which those depressions lie. It also creates plateaus along contours, 
which can be problematic because they overstate the amount of land barely above the 
wetlands and right at the first contour, while understating the amount of land halfway 
between the wetlands and the first contour. To address these problems, we processed 
the areas above and below the first contour separately. However, this caused another 
problem. In narrow valleys in the area below the first contour, the output DEM values 
were similar or identical to those of the bounding contour lines due to the lack of 
elevation information that TOPOGRID needs to calculate trend. The most problematic 
regions occurred where there was a stream valley below the first contour (e.g. between 
two parallel 5 foot contours), no open water or tidal wetlands along most of the length 
of the valley, but open water or tidal wetlands at one end of the valley (e.g. a typical 
nontidal stream flowing into tidal waters). In some cases, the trend from the wetlands or 
open water at the mouth toward the bounding first contour, would provide values even 
higher than that first contour farther up the valley. And in general, TOPOGRID would 
be more likely to assume a flat area between the contours, than to characterize it as a 
valley--except for when stream data was used. 
 
Omissions: Neither stream networks nor lake features were used as inputs into the 
TOPOGRID function in creation of the DEM for Pennsylvania. The EPA project 
manager discovered this omission as a result of questions raised by Russ Jones of 
Stratus Consulting, who provided EPA with assistance in preparing this metadata.   ICF 
has asked EPA to omit further discussion while it investigates the cause of this omission.  
(See comparable data sets for New York, Delaware, Maryland, and North Carolina, 
where we did use stream data.)
 
The absence of a hydrologic network may explain a significant proportion of the 
greatest errors in the vicinity of nontidal streams at elevations below the first contour. 
TOPOGRID infers stream valleys above the first contour by the pattern of a valley 
without the stream data. The decision to split the data into land below and above the 
first contour (discussed in process step #4 "Interpolation of Digital Elevation Model ") 
had the effect of increasing the potential errors resulting from the absence of stream 
data. However, the magnitude of any errors induced by this problem was limited by the 
"first contour truncating" (also discussed in process step #4). The net effect was to put 
back some of the plateaus eliminated by dividing the data, but not all of those plateaus.  
Moreover, errors introduced by of omitting stream were avoided in areas with tidal 
streams or tidal wetlands, by the use of tidal wetlands data, which served the same 
function.  
 
Other Issues: In addition to excluding streams, the decision to process 3 elevation areas 
separately within TOPOGRID (as described in the process step #4 - "Interpolation of 
Digital Elevation Model") and then combine them into a single DEM removes the 
algorithm from its theoretical underpinning, because it separates each elevation zone 
from the context of the overall environment that TOPOGRID uses to generate a 
hydrologically-correct DEM. Because the objective of this DEM is to estimate 
elevations of lands close to sea level, rather than characterize drainage correctly, the ad 
hoc response to the TOPOGRID plateau problem is not as unreasonable as would have 
been the case were this data to be used for analyzing hydrology. Nevertheless, the 
inclusion of an accurate stream network, modification of the tolerance values and other 
parameters within TOPOGRID, and inclusion of additional vertical data in areas of 
known errors (determined through the use of diagnostic outputs within the TOPOGRID 
function), probably could have substantially diminished the plateau problem in the 
vicinity of the first topographic contour. Because the plateau problem around the edge 
of tidal wetlands was often caused largely by the relative complexity of the wetland 
supplemental contour compared with other contours, and because the tidal wetlands 
and open water data which we used in effect provide the stream data, the case for 
dividing the data as we did is probably greater along the wetland boundary than along 
the first contour. 
 
We did not divide the data  into the separate elevation classes in those areas where we 
had two-foot contours or spot elevation data. Because we had 2-foot contours for 
Philadelphia, this issue is inapplicable there.    (See PA_Elevation_Data_Quality.jpg , 
included in the zip file in which this data set was distributed.)  
Also see the sections on Positional Accuracy (Horizontal and Vertical) and process 
steps.

Positional_Accuracy:

Positional_Accuracy:

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The source data generally were 1:24,000 scale or better. Therefore our use of 30-meter cells deteriorated the horizontal accuracy.  Assuming that 90% of well defined points are within 30 meters of the indicated location would imply a scale of 1:60,000 under National Map Accuracy Standards. (That assumption may be conservative because 100% of the points in a 30 meter cell are less than 21.2 meters of the center of the cell. If the input map has 1:24,000 scale (well defined points within 12.2 meters)  and errors are random, then more than 90% of the points will be within 24.5 meters of the indicated location, which would imply a scale of 1:50,000.)  However, our interpolation program may further deteriorate the horizontal accuracy.  Under some circumstances, the horizontal error appears to be as great as the width of a cell.  Given that the diagonal in this case would be 42.4 m, if errors are random, then the scale might be as poor as 1:86,000 in areas where those 1-cell errors are common.
 
Neither stream networks nor lake features were used as inputs into the TOPOGRID function in creation of this data set for reasons discussed in the completeness report. The addition of a hydrologic network would increase the accuracy of the resulting
DEM.

Quantitative_Horizontal_Positional_Accuracy_Assessment:

Horizontal_Positional_Accuracy_Value: 40-42.5 meters

Horizontal_Positional_Accuracy_Explanation:

Under some circumstances the horizontal error appears can be as great as the width of a cell.  Given that the diagonal would be 42.4 m.

Vertical_Positional_Accuracy:

Vertical_Positional_Accuracy_Report:

This data set generally corresponds to that of the source data (described below) used in the layer development, plus errors induced through the various processing steps. The most important processing errors probably concern the procedures used to interpolate between contours, which do not necessarily correspond to the actual geometry of the land surfaces. Therefore, points that are near a contour have greater accuracy than points that are farther away from a contour.
 
In order to assess the vertical accuracy of  DEMs generated by ICF Consulting, Russ Jones of Stratus Consulting Inc. compared DEMs with LIDAR data in two areas: 1) an area south of Rock Hall along the eastern shore of Maryland, and 2) portions of North Carolina. Table 1 within DEM_LidarComparisonTable.doc summarizes the comparison. The analysis suggests a Root Mean Square (RMS) discrepancy between LIDAR and this DEM approximately one-half of the input contour interval in cases where the contour interval was 1 meter, 5 feet, or 2 meters.  
 
In areas where the USGS contour interval was 20 feet and we used MD DNR data for supplemental contours, the mean discrepancy (LIDAR-DEM) was -2.4 feet with a RMS discrepancy of 6 feet for DEM observations less than 10 feet. The error was much less (mean -1.1 feet, RMS 3.9 feet) for DEM values between 10 and 20 feet NGVD29. Most of the errors appear to be centered in Caroline County, where the Maryland DNR data incorrectly showed a large area below 5 feet NGVD29.  If the LIDAR comparison is applicable to Pennsylvania, one would expect RMS errors of approximately one-half the USGS contour interval, with a mean error less than 1/10 the contour interval.
 
Jones also provided histograms showing the relationship between
input contour intervals and the DEM values, for 11 USGS 7.5'
topographical quadrangles in the study area from New York to North
Carolina, including Marcus Hook.  The technical paper by Titus and Wang (2008, listed in the citation section above)  analyzes the
results of that comparison. Note that this comparison was conducted on the initial DEM generated with TOPOGRID. As a result of this analysis, the minimum and maximum elevation limits were constrained to ensure that the resulting elevations were in accordance with the input data. (See "First-contour truncating" in the process step on interpolation of Digital Elevation Model). That paper compares the area of land below the first, second, and third contour according to the DEM,
with the area of the input polygons.  That error can be considered
both in terms of the difference in area estimates, and as a
vertical error.  As a measure of the vertical error, Titus and
Wang consider the effective elevation of the DLG contour that the
DEM estimates, that is, at what elevation does the DEM find the
same amount of land that the DLG polygons show to be below the
first contour.
The technical paper also calculates a plateau exaggeration factor:
The ratio of the area (according to the DEM) within 0.1 feet above
or below a contour, to the area that one would expect if
elevations were uniformly distributed between the contour above
and (if it exists) the contour below.  Suppose for example, 
spring high water is 2 ft NGVD, the contour interval is 5 feet,
there are 3 ha between spring high water and the 5 ft
contour, and 5 ha between the 5- and 10-foot contours, and the DEM
finds 2 ha between 4.9 and 5.1 ft.  The plateau exaggeration factor
would be 10, because a uniform elevation distribution would imply
1 ha per foot of elevation change; but around the plateau we have
2 ha in a 0.2 ft elevation increment.
This metadata discusses only the one quad in Pennsylvania,  plus two quads in
Maryland that were particularly problematic.
For the Marcus Hook quad, the DEM underestimated the low land (below
10ft) implied by the DLG by 20%.   Examining an overlay of the DEM and the DLG's, the discrepancy for land below the 10-foot contour appears to be explained by a couple of factors. First, in some places the land is sufficiently steep that particular cells will have  more than one contour crossing them. The DEM assigns an average elevation to the cell.   Assuming that the contours cross the centers of cells randomly, one would normally expect that the amount of higher and lower ground being "averaged in" would approximately offset each other, so that the DEM should find the same amount of land within a given elevation range 
as the input DLG's. For land below 10 feet, however, there is higher ground but no lower ground  to be "averaged in."  Therefore, an upward bias is created for the lowest areas. Second, in some areas that were completely enclosed by the 10 foot contour, TOPOGRID interpolates the elevation within the contour to elevations just above 10 feet while the contour data does not have sufficient resolution to depict elevations between 10 and 20 feet. The plateau exaggeration factors were 0.8, 12, and 3.5 for the tidal wetland, 10-ft, and 20-ft contours, respectively. This represents an area twice the size of the 20% discrepancy between the polygon input and the grid output.  Thus, in effect, the DEM estimates the
DLG's 10-ft contour to be between 10 and 10.1 feet.  The discrepancy for lands
between 10-20 and 20-30 feet are less than 2%, which suggests that part of the
problem for the lowest land may be explained by areas where the dry land below 10 ft
is too narrow to take up an entire cell (see discussion of South River, Maryland,
below).
The discrepancies were more serious, however, for two quads in Maryland:  Broomes
and South River.  For South River the DEM under estimates the amount of land below
5 ft by 50%.  However, it is within 5% and 1% for the areas between 5-10 and 10-
15ft, respectively.  This error appears to have resulted because the land is sufficiently
steep that particular cells will have more than one contour crossing them.  The DEM
assigns an average elevation to the cell.  Assuming that the contours cross the centers of
cells randomly, one would normally expect that the amount of higher and lower ground
being "averaged in" would approximately offset each other, so that the DEM should find
the same amount of land within a given elevation range as the input DLG's.  Indeed, this
appears to be the case for land at 10-15 ft.  For land below 5ft, however, there is
higher ground but no lower ground to be "averaged in."  Therefore, an upward bias is
created for the lowest areas.  Such an upward bias in the lowest contour range could
have been avoided with an algorithm that calculated elevations for points rather than
cells or using a much smaller cell size.  Doing so, however, would have increased the
costs of this study several fold The net impact was that the DEM, in effect, estimated
the 5-ft contour to be approximately 7.3 ft above the vertical datum for South River.
The Broomes quad had a similar upward bias, effectively treating the 5-ft contour as a
5.8-ft contour.  The experience with those two quads in Maryland should serve as a
caution that in Pennsylvania, our results may be much less accurate in areas with slopes
steep enough to have two contours within 30 meters (e.g. slopes greater than 6% with
5-ft contours, or 12% with 10ft contours), especially in the area above the lowest
contour.  (Our first-contour truncating mitigates this upward bias below the first contour;
for reasons explained in the metadata for the Maryland study (dem_MD_shw_cm). In Pensylvania, that caution may be applicable to much of Bucks County. The results of the "11 quadrangle" analysis are shown in DEM_Comparison_with_DLG_11_quads.xls., which is included in the zip file distributed with this dataset.

Quantitative_Vertical_Positional_Accuracy_Assessment:

Vertical_Positional_Accuracy_Explanation:

See vertical accuracy report.

Lineage:

Source_Information:

Source_Citation:

Citation_Information:

Originator: US Geological Survey

Publication_Date: Multiple

Title:

Large Scale USGS Digital Line Graph (DLG) Other_Citation_Details: Source contours are 10 feet, and 20 feet depending on the 7.5' topographic quadrangle used. The zip file with which this metadata is distributed will include a graphic defining the contour intervals of the input data, PA_Elevation_Data_Quality.jpg

Online_Linkage: <http://edc.usgs.gov/geodata/dlg_large/states/PA.html>

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Digital data and paper

Source_Time_Period_of_Content:

Source_Currentness_Reference:

Multiple

Source_Contribution:

Elevation Contours used in Spatial data and attributes. Some contours digitized from
Digital Raster Graphics (DRGs) and hardcopy. The zip file with which this metadata is distributed includes a the file "PA_Data_Quality.jpg" which defines the contour intervals of the input data.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Henan Institute of Geograophy

Publication_Date: Multiple

Title:

Coastal Digital Line Graphs

Online_Linkage: <http://edc.usgs.gov/geodata/dlg_large/states/PA.html>

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Digital data

Source_Time_Period_of_Content:

Source_Currentness_Reference:

Multiple

Source_Contribution:

Elevation Contours used in Spatial data and attributes for USGS quads where USGS
DLG was unavailable.  See "Institute_of_Geography_DLG.doc" for a list of quads
where we used these DLG's. Source contours are 10 feet and 20 feet depending on the 7.5' topographic quadrangle
used. The zip file with which this metadata is distributed will include a graphic defining
contour intervals of the input data: PA_Elevation_Data_Quality.jpg

Source_Information:

Source_Citation:

Citation_Information:

Originator: National Oceanic Service

Publication_Date: Unknown

Publication_Time: Unknown

Title:

NOS Tide Observation Data

Online_Linkage: <http://co-ops.nos.noaa.gov/bench.html>

Source_Scale_Denominator: 24000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Range_of_Dates/Times:

Beginning_Date: 1960

Ending_Date: 1978

Source_Currentness_Reference:

Relative to the 1960-1978 Tidal Epoch

Source_Contribution:

Spatial coordinates and elevations of MTL relative to mean lower low water (MLLW),
the elevation of MLLW relative to several benchmarks nearby, and the elevations of
these benchmarks above NAVD88 for 12 tide gages in or around Pennsylvania

Source_Information:

Source_Citation:

Citation_Information:

Originator: National Oceanic Service

Publication_Date: 2000

Title:

NOS Tide Estimation Data. Tide Tables 2000, High and Low Water Predictions, East Coast of North and South America including Greenland

Geospatial_Data_Presentation_Form: document

Source_Scale_Denominator: NA

Type_of_Source_Media: paper report, whose tables contained latitude, longitude, and tide range.

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2000

Source_Currentness_Reference:

publication date

Source_Contribution:

Horizontal location, and spring high tide ranges for 32 tide gages in or around
Pennsylvania

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Environmental Protection Agency

Publication_Date: 2006

Title:

Coastal Wetlands Data: Pennsylvania

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1980

Source_Currentness_Reference:

__________

Source_Contribution:

Horizontal location of upper and lower limits of tidal wetlands. See Readme.doc (distributed in the zip file with this data set), for directions on how to
download the Coastal Wetlands Data .  See also the technical report for this project J.G. Titus and J. Wang. Titus, J.G. and J. Wang, 2008: Maps of lands close to sea level along the middle Atlantic coast of the United States: an elevation data set to use while waiting for LIDAR. In: Background Documents Supporting Climate Change Science Program
Synthesis and Assessment Product 4.1: Coastal Elevations and Sensitivity to Sea
Level Rise [J.G. Titus and E.M. Strange (eds.)]. EPA430R07004, U.S. Environmental Protection Agency, Washington, DC.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Proudman Oceanographic Laboratory (POL)

Publication_Date: 2000

Title:

Permanent Service for Mean Sea Level (PSMSL)-- Sea Level Rise Trend Data

Other_Citation_Details:

The PSMSL is a member of the Federation of Astronomical and Geophysical Data Analysis Services (FAGS) established by the International Council of Scientific Unions (ICSU).

Online_Linkage: <http://www.nbi.ac.uk/psmsl/datainfo/rlr.trends>

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: unknown

Source_Currentness_Reference:

publication date

Source_Contribution:

Spatial location and estimates of the rate of sea level rise.

Source_Information:

Source_Citation:

Citation_Information:

Originator: City of Philadelphia Water Department - Information Systems and Technology

Publication_Date: 1997

Title:

Topographic Contours, 2-foot

Online_Linkage: <http://www.pasda.psu.edu/philacity/philadownload.cgi/phila-topocontours2ft.zip>

Source_Scale_Denominator: 2,400

Type_of_Source_Media: vector digital data

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 1997

Source_Currentness_Reference:

ground condition

Source_Contribution:

2 foot contour interval elevation map was used to generate a DEM for Philadelphia. The elevation of this data set is based on Philadelphia Vertical
Datum  which is 5.71 feet above NGVD29.

Process_Step:

Process_Description:

Process of Input Elevation Data
 
1) Input elevation contour lines were either 1:24,000 USGS DLGs or 1:24,000 DLG's
created by the Institute of Geography (using USGS DRG's). They were appended and
then projected into Albers projection except for the City of Philadelphia, where we had
2-foot contours.
 
2) City of Philadelphia. The City's 2 foot-contour interval elevation map was used to
generate the DEM. First, the contours was projected into Albers projection. A value of
5.71 was added to the elevation value of contour lines to convert from the city datum to
NGVD29. Then the upper limit of tidal wetlands was moved seawards where it
overlaps with the elevation contour lines.

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Jue Wang

Contact_Organization: GIS Practice, ICF Consulting, Inc.

Contact_Position: Senior GIS Analyst

Contact_Address:

Address_Type: mailing and physical address

Address:

9300 Lee Highway

City: Fairfax

State_or_Province: VA

Postal_Code: 22031

Contact_Voice_Telephone: 703-218-2766

Contact_Facsimile_Telephone: 703-934-3974

Contact_Electronic_Mail_Address: jwang@icfconsulting.com

Hours_of_Service: 9:30 - 5:30 EST

Process_Step:

Process_Description:

Process of Tidal Record: Calculating the Elevation of Spring High Water
Supplemental Contour
 
1) Creation of Mean Tide Level Surface. National Oceanic Service (NOS) tide
observation data, i.e., the latitudes and longitudes, the elevations of mean tide level
(MTL) above mean lower low water (MLLW), the elevations of MLLW relative to
several benchmarks, and the elevations above NAVD88 of these benchmarks of 12
tide gages in or near Pennsylvania were downloaded from the NOS website. The
elevations of mean tide level relative to NAVD88 were calculated and then converted to
a NGVD29 using USGS VERTCON program. A point coverage was then
created with such data and projected into Albers projection. Using the point coverage
as a reference, artificial contour lines were created with consideration of shorelines via
heads-up digitizing and then used to interpolate the mean tide level surface using a
Triangular Irregular Network (TIN).  As the tidal epoch used for the MTL data was
1960-1978, a separate sea level rise rate surface was created by interpolating actual
sea level rise data (trend) from the Proudman Oceanographic Laboratory website and
was used to adjust the mean tide level to years corresponding to other data sets, such
as NWI data, so that the wetland boundary would represent spring high tide for the
year the map imagery was taken.
 
2) Creation of Spring Tide Range Surface. From Table 2 of "Tide Tables 2000, High
and Low Water Predictions, East Coast of North and South America including
Greenland", the latitudes, longitudes and spring high tide ranges of 32 tide gauges in or
near Pennsylvania were obtained and used to create a point coverage. Using the point
coverage as a reference, artificial contour lines were created with consideration of
shorelines and then used to interpolate the spring high tide range surface with the TIN
method.
 
3) Creation of a Spring High Water Level Surface. A spring high water level surface
was created by adding half of spring tide range onto mean tide level surface.

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Jue Wang

Contact_Organization: GIS Practice, ICF Consulting, Inc.

Contact_Position: Senior GIS Analyst

Contact_Voice_Telephone: 703-218-2766

Contact_Facsimile_Telephone: 703-934-3974

Contact_Electronic_Mail_Address: jwang@icfconsulting.com

Process_Step:

Process_Description:

Processing Tidal Wetlands:  Calculating the Horizontal Position
of Spring High Water Supplemental Contour
 
(1)We identified the upper limit of tidal wetland by extracting the boundaries between
tidal polygons (consisting of tidal wetland and tidal open water) and non-tidal polygons
(consisting of dry land, non-tidal wetland, and non-tidal open water).
 
(2) We used the upper and lower limits of tidal wetlands to generate supplemental
contours. We assigned these contours elevations derived from spring high tide level and
mean tide level surface grids respectively. The upper wetland boundary was used as a
supplemental contour. The lower boundary was used for reporting the area of wetlands
but not for elevations, because the project manager decided not to report wetland
elevations.
See also the metadata accompanying the Coastal Wetlands Data: Pennsylvania
polygon  dataset

Process_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Jue Wang

Contact_Organization: ICF Consulting, Inc., GIS Practice

Contact_Position: Senior GIS Analyst

Contact_Voice_Telephone: 703-218-2766

Contact_Facsimile_Telephone: 703-934-3974

Contact_Electronic_Mail_Address: jwang@icfconsulting.com

Process_Step:

Process_Description:

Interpolation of Digital Elevation Model
 
1) With the exception of those areas with two-foot contour intervals (i.e. Philadelphia),
the study area was divided into four parts (tidal wetlands, lowland, midland, and
upland). "Tidal Wetlands" represents the tidal wetlands as classified from the wetland layer. 
Lowland represents the area between the tidal wetlands and the lowest USGS
contour available, which is generally above the tidal wetlands. Depending on the contour
interval of the input data, this lowest contour may have been 5 feet, 10 feet, or 20 feet -
- in all cases relative to NGVD29.
Midland represents the area above the lowest contour we used and below the highest
USGS contour lines (40 foot NGVD29) used.
Upland represents land above the midland contour (i.e., above the 40 foot NGVD29
contour).
Boundary coverages were created for tidal wetlands, lowland, and midland using
appropriate elevation contours or upper and lower limits of tidal wetland. However, we
are not making the tidal wetland interpolations available in this dataset due to the lack of
a theoretical justification for believing that interpolation to have any information content.
(At best, the wetland elevation interpolations might be used for graphical representations
of the impact of sea level rise.) We will retain a companion dataset with elevations
stored as floating point double precision, which may be made available for the sole
purpose of evaluating any graphical representations that use wetland elevations. We
provide a  a polygon wetland dataset so that the user can  distinguish tidal wetlands
from open water for cells with no data.
In the case Philadelphia, where the underlying topographic information had a two-foot
contour interval, the lowland and midland were treated together as a single category.
 
2) The DEMs were interpolated with a predetermined cell size of 30 meters for the 
lowland, midland, and upland areas using contour lines described in Process step 1 (the 
section "Process of Input Elevation Data") and the supplemental contours explained in 
process step 3. A common starting coordinate was set for all three DEM interpolation 
processes to ensure alignment of the separate layers after processing. The minimum and 
maximum limits were set for each process according to the input elevation data to 
ensure the resulting elevations were in accordance with the input data. See "first contour 
truncating", paragraph 5, below. This means whenever  TOPOGRID calculated a value greater than the first contour within the "lowland", we reset the value to 0.001 less than the first contour. Whenever TOPOGRID calculated a midland value less than the first contour, we reset the value to 0.001 greater than the first contour. The iteration was set to 40, the 
horizontal standard error tolerance was set to 2 to minimize the depression caused by 
inappropriate tend calculation, and the drainage enforcement option was turned on to 
remove isolated depressions. 
 
The following represents the options used in TOPOGRID: 
 
topogrid dem_mid_ft 30 
 
contour topo_cntr elevation 
 
xyzlimits 1712800 # 1960000 # 5.0001 
 
boundary topo_bnd_mid 
 
iterations 40 
 
tolerances # 2 
 
enforce on 
 
end 
 
3) The interpolated DEMs were displayed against input elevation data and visually 
checked. These visual checks would show gross errors, but not necessarily errors in 
which the amount of low land is over- or underestimated by 10-40 percent 
(additionally, see Data Quality, Positional Accuracy section). If obvious errors such as 
artificial depressions occurred, supplemental elevation lines were added by heads-up 
digitizing to the input contour lines and the interpolation was repeated. 
 
4) First-contour truncating. As a result of the comparison between the initial DEM and the source contours for 11 USGS quadrangles (see vertical accuracy report), we decided to reset the DEM values to coincide with source contours. Our general approach was that whenever TOPOGRID 
calculated a value greater than the first contour within the "lowland", we reset the value 
to 0.001 less than the first contour. Whenever TOPOGRID calculated a midland value 
less than the first contour, we reset the value to 0.001 greater than the first contour. 
Given the rounding of this integer dataset, all such values are effectively rounded to the 
bounding contour value between midland and lowland (e.g. 5 feet). Although this 
approach leaves us with some plateaus, we have fewer plateaus than we had when we 
did not divide the data; and dividing the data left us with fewer cases of midland and 
lowland values being outside of their appropriate ranges.
 
5) The DEMs of each separate part were eventually merged into the final DEM with the 
MERGE function within the ESRI GRID module.

Cloud_Cover: NA

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Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster

Raster_Object_Information:

Raster_Object_Type: Grid Cell

Row_Count: 8847

Column_Count: 4986

Vertical_Count: 1

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Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: Albers Conical Equal Area

Albers_Conical_Equal_Area:

Standard_Parallel: 29.500000

Standard_Parallel: 45.500000

Longitude_of_Central_Meridian: -96.000000

Latitude_of_Projection_Origin: 23.000000

False_Easting: 0.000000

False_Northing: 0.000000

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column

Coordinate_Representation:

Abscissa_Resolution: 30.000000

Ordinate_Resolution: 30.000000

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: North American Datum of 1983

Ellipsoid_Name: Geodetic Reference System 80

Semi-major_Axis: 6378137.000000

Denominator_of_Flattening_Ratio: 298.257222

Vertical_Coordinate_System_Definition:

Altitude_System_Definition:

Altitude_Datum_Name: SHW

Altitude_Resolution: 1 cm

Altitude_Distance_Units: cm

Altitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinates

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Entity_and_Attribute_Information:

Detailed_Description:

Entity_Type:

Entity_Type_Label: dem_pa_shw_cm.vat

Attribute:

Attribute_Label: Rowid

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: VALUE

Attribute:

Attribute_Label: Value

Attribute_Definition:

Elevation

Attribute_Definition_Source:

Interpolated from input data sets

Attribute_Value_Accuracy_Information:

Attribute_Value_Accuracy: 1 cm

Attribute_Value_Accuracy_Explanation:

Interpolated from source data sets and rounded to nearest 1 cm

Attribute:

Attribute_Label: COUNT

Attribute_Definition:

Count of cells with common elevation

Attribute_Definition_Source:

ESRI

Overview_Description:

Entity_and_Attribute_Overview:

Elevations generated from input data sets (contours and spot elevations) and
interpolated into a raster DEM and rounded to nearest cm.

Entity_and_Attribute_Detail_Citation:

See process steps.

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Distribution_Information:

Distributor:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: US Environmental Protection Agency, Climate Change Division

Contact_Address:

Address_Type: mailing address

Address:

USEPA (6207-J)

Address:

1200 Pennsylvania Ave. NW

City: Washington

State_or_Province: DC

Postal_Code: 20460

Contact_Voice_Telephone: 202-343-9990

Contact_Facsimile_Telephone: 202-343-2338

Contact_Electronic_Mail_Address: climatechange@epa.gov

Resource_Description: The dataset is being distributed by the US Environmental Protection Agency.

Distribution_Liability:

Although this data was created under the direction of the EPA, no warranty expressed
or implied is made regarding the accuracy or utility of the data. Neither EPA nor the
data developers shall be held liable for any use of the data and
information described and/or contained herein.

Standard_Order_Process:

Digital_Form:

Digital_Transfer_Information:

Transfer_Size: 1.413

Custom_Order_Process:

Data available to CCSP collaborators from Alan Cohn at 202-343-9814.

Technical_Prerequisites:

Requires software capable of displaying raster data.

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Metadata_Reference_Information:

Metadata_Date: 20080902

Metadata_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: Russ Jones, Jim Titus, and Jue Wang

Contact_Organization: Stratus Consulting Inc. (Jones)

Contact_Position: Managing Analyst (Jones)

Contact_Address:

Address_Type: mailing and physical address

Address:

1881 9th St. Suite 201 (Jones)

City: Boulder

State_or_Province: CO

Postal_Code: 80306

Contact_Voice_Telephone: 303-381-80000 (Jones)

Contact_Voice_Telephone: 202-343-9307 (Titus)

Contact_Facsimile_Telephone: 303-381-8200 (Jones)

Contact_Electronic_Mail_Address: rjones@stratusconsulting.com

Contact_Electronic_Mail_Address: Titus.Jim@epamail.epa.gov

Hours_of_Service: 9:00 - 5:00 MST

Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata

Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Time_Convention: local time

Metadata_Extensions:

Online_Linkage: http://www.esri.com/metadata/esriprof80.html

Profile_Name: ESRI Metadata Profile

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