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

Metadata:

• Identification_Information
• Data_Quality_Information
• Spatial_Data_Organization_Information
• Spatial_Reference_Information
• Entity_and_Attribute_Information
• Distribution_Information
• Metadata_Reference_Information

Identification_Information:

Citation:

Citation_Information:

Originator: US Environmental Protection Agency

Publication_Date: February 2008

Title:

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

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:

The District of Columbia Digital Elevation Model

(Environmental Systems Research Institute [ESRI] Grid

format) represents an elevation map of the District of

Columbia coastal zone created for the purposes of analyzing

vulnerability to rising sea level.

The domain of the data includes all of the District of

Columbia, 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 National

Capital Planning Commission and District of Columbia

Department of Public Works Ground Elevation Data. In

addition, the analysis created a supplemental contour

representing the elevation of spring high water

(SHW), which is generally between 1.5 and 3 feet above

NGVD29 in the District of Columbia. We defined the horizontal

position of that contour using polygon tidal wetlands

data from the US Fish and Wildlife Service National

Wetlands Inventory (NWI). 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, the NOS

estimated sea level trends, the 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 a

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_DC_Elevation.doc, which provides a brief overview of the

relationship between this dataset and related data

2.   InterpolationMethods_MEMO.doc

3.   MD_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, (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 District of Columbia 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: -77.189819

East_Bounding_Coordinate: -76.845663

North_Bounding_Coordinate: 39.036284

South_Bounding_Coordinate: 38.718194

Keywords:

Theme:

Theme_Keyword_Thesaurus: General

Theme_Keyword: Washington District of Columbia Elevation

Theme_Keyword: DEM

Theme_Keyword: Coastal Elevation

Place:

Place_Keyword: DC

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

Back to Top

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 "DC_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 the District of Columbia. (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.

Attribute_Accuracy:

Quantitative_Attribute_Accuracy_Assessment:

Attribute_Accuracy_Explanation:

Vertical values were rounded to nearest whole cm. 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 in multiple units

(meters and feet). This was done purposefully to

reflect the actual contour intervals used by USGS over

the years and which vary on a quadrangle by quadrangle

basis.

Completeness_Report:

This elevation 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  areas of

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.

Omissions: Neither stream networks nor lake features

were used as inputs into the TOPOGRID function in

creation of this data set. The absence of a hydrologic

network explains a significant proportion of the greatest

errors in the vicinity of non-tidal 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 the omission of non-tidal streams were 

absent in areas where tidal streams or tidal wetlands were present, 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.

Also see the sections on Positional Accuracy

(Horizontal and Vertical) and process steps.

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Quantitative_Horizontal_Positional_Accuracy_Assessment:

Horizontal_Positional_Accuracy_Value: 40-42.5 meters

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Quantitative_Horizontal_Positional_Accuracy_Assessment:

Horizontal_Positional_Accuracy_Value: 40-42.5 meters

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

The source data generally were 1:1000 scale. 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:

In the event the horizontal error is as great as the width of a cell, the diagonal would be 42.4 m.

Vertical_Positional_Accuracy:

Vertical_Positional_Accuracy_Report:

The vertical accuracy of 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 the District of 

Columbia, one would expect an RMS error of approximately

50 cm (i.e. similar to the areas in the Eastern Shore

of Maryland where the DEM also relied on input DLG's

with a one-meter 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, none

of which were in the District of Columbia.   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).

Two quads in Maryland.were particularly problematic:

Broomes and South River.  For South River, the DEM 

underestimates the amount of land below 5 ft by 50%.

However, it is within 5% and 1% for the areas between 5-

10 and 10-15 ft, 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 5 ft, 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 the 

District of Columbia, our results may be 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. However, 

the availability of a one-meter contour interval 

substantially mitigates this concern.

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: National Capital Planning Commission and District of Columbia Department of Public Works

Publication_Date: 2001

Title:

Rooftop Elevation and Ground Elevation

Source_Scale_Denominator: 1,000

Type_of_Source_Media: Digital data

Source_Time_Period_of_Content:

Source_Currentness_Reference:

2001

Source_Contribution:

Elevation contours. Source contours are 1 meter interval.

Source_Information:

Source_Citation:

Citation_Information:

Originator: National Oceanic Service

Publication_Date: Unknown

Publication_Time: Unknown

Title:

NOS Tide Observation Data

Other_Citation_Details:

Accessed December 2005

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: 2000

Source_Currentness_Reference:

Relative to 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 1 tide

gauge in the District of Columbia and 1 nearby tide gauge in

Maryland.

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

more than 9 tide gauges in and around the District of Columbia.

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Environmental Protection Agency

Publication_Date: 2006

Title:

Coastal Wetlands Data: District of Columbia

Source_Scale_Denominator: 24,000

Type_of_Source_Media: online

Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Source_Contribution:

Horizontal location of upper and lower limits of tidal

wetlands. See README_DC_Elevation.doc (distributed in the zip file with this

data set), for directions on how to download the

Coastal Wetlands Data .  See also J.G. Titus 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." Section 1.1 in: Background Documents Supporting Climate Change Science Program Synthesis and Assessment Product 4.1, J.G. Titus and E.M. Strange (eds.). EPA 430R07004. U.S. EPA, 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) (accessed December 2005).

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: Henan Institute of Geography, China

Publication_Date: Unpublished Material

Publication_Time: Unknown

Title:

Coastal Digital Line Graphs (DLG) created from USGS Digital Raster Graphs

Source_Scale_Denominator: 24,000

Type_of_Source_Media: Vector digital data

Source_Contribution:

Spatial data and attributes. Contours digitized from USGS 7.5 minute Digital Raster Graphics (DRGs). See Institute_of_Geography_DLG.doc, included in the zip file with which this data is distributed, for a list of the 7.5-minute quads where we used the Institute of Geography DLG's.

Process_Step:

Process_Description:

Process of Input Elevation Data

Because there was no vertical datum information

associated with the National Capital Planning

Commission and District of Columbia Department of

Public Works Rooftop Elevation and Ground Elevation

dataset, we plotted the data against USGS contour lines

and determined that vertical datum to be NAVD88. We

projected the dataset into Albers projection and

converted the elevation unit from meters to feet.

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 2

tide gages in the District of Columbia and nearby Maryland were

downloaded from the NOS website. The elevations of mean

tide level above NAVD88 were calculated and then

converted to above NGVD29 using USGS VERTCON program. 

A point coverage was then created with this 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 more than 150 tide gages 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

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: 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:

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: District of Columbia" 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) The study area was divided into lowland and upland.

"Tidal Wetlands" represents the tidal wetlands as classified from the wetland layer.

Lowland represents the area under the 1 meter contour

and upland represents area from 1 meter contour line up

to the political boundary of the District of Columbia.

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 gridded (and a

polygon) wetland dataset so that the user can

distinguish tidal wetlands from open water for cells

with no data..

2) The DEMs were interpolated with a predetermined

cellsize of 30 meters for the lowland and upland areas

using contour lines described in Process Step 1

("Process of Input Elevation Data") and 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" (#4) below. 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_up_ft 30

contour dlg_mtr_alb elev_feet

xyzlimits 1610096.5  # 1907771 # 3.2809

boundary topo_bnd_up

iterations 40

tolerances # 2

enforce on

end

3) The interpolated DEMs were displayed against input

elevation data and visually checked. These visual

checks showed 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 equal or

greater than the first contour within the "lowland", we

reset the value to 0.001 lower than the first contour

and whenever TOPOGRID calculated a value equal or less

than the first contour within the upland, 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 upland and lowland. 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 upland

values being outside of their appropriate ranges.

5) To be consistent with the other states, we converted

the DEMs into NGVD29. We generated a point coverage

with 130 randomly distributed points over the entire

study area and calculated the difference values between

the two data sets for each point using National Geodetic

Survey North American Vertical Datum Conversion Utility

(VERTCON) program. We projected the coverage into

Albers projection and interpolated a grid of 30 meter

resolution by TOPOGRID.  We then used this grid to

convert the DEM from NAVD88 to NGVD29.

6) The DEMs of  upland and lowland were eventually

merged into the final DEM with the MERGE function

within the ESRI GRID module.

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

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: 1052

Column_Count: 793

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: dc_dem_sph_cm.vat

Entity_Type_Definition:

DEM

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@epamail.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: 0.511

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: 303-381-8000 (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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