1.2. Interpolating Elevations: Proposed Method
for Conducting Overlay Analysis of GIS Data on
Coastal Elevations, Shore Protection, and
Wetland Accretion
By Russell Jones, Stratus Consulting Inc., and
Jue Wang, Pyramid Systems, Inc.
This section should be cited as:
Jones, R. and J. Wang. 2008. Interpolating Elevations: Proposed Method for Conducting
Overlay Analysis of GIS Data on Coastal Elevations, Shore Protection, and Wetland
Accretion. Section 1.2 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.). EPA 430R07004. U.S. EPA, Washington,
DC.
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[ 46 INTERPOLATING ELEVATIONS ]
1.2.1 Introduction
Section 1.1 (by Titus and Wang) of this report
and the metadata provided with the elevation
Geographic Information System (GIS) data
document the methods used to generate state-
specific GIS data sets of elevation relative to
spring high water (Jones, 2008, Jones et al.,
2008).\ Titus and Hudgens (unpublished
analysis) generated data on the likelihood of
shoreline protection. In that analysis, the authors
attempted to divide all dry land below the 20-ft
(NGVD29) contour—as well as all land within
1,000 ft of the shore regardless of elevation—
into one of four categories representing the
likelihood of shore protection: shore protection
almost certain (PC), shore protection likely (PL),
shore protection unlikely (PU), and no protection
(NP). Using these two data sets, this section
shows the methods used to quantify the area of
land close to sea level by shore by various
elevation increments and protection category.
However, because the results of the shore
protection analysis are unpublished, we report
only the elevation statistics.
Using the elevation data discussed in Section
1.1, and wetland data compiled from a
combination of the U.S. Fish and Wildlife
Service National Wetlands Inventory (NWI) data
and state-specific wetlands data, we created
summary tables, which we explain in Section
1.2.2. Those tables provide the area of land
within 50 cm elevation increments at the state
level of aggregation and are provided in the
appendix to this section.2 The versions with 0.1-
1 Titus and Wang in Section 1.1 generated the DEM data
by interpolating elevations from a variety of source data
sets for the eight states covered by this report. To make the
elevations relative to SHW, they used the National Ocean
Service's (NOS) estimated tide ranges, NOS estimated sea
level trends, and the NOS published benchmark sheets
along with National Geodetic Survey North American
Vertical Datum Conversion Utility (VERTCON) program
to convert the mean tide level (MTL) above NAVD88 to
NGVD29. See "General Approach" of Section 1.1 for a
brief overview. Jones (2007) created a revised dataset for
North Carolina.
2 Additionally, subregional and regional low and high
estimates of land area are provided in Appendices B and C,
respectively, to Section 1.3.
ft increments were used by the uncertainty
analysis described in Section 1.3.3
Our analysis (as well that of Section 2.1) had to
confront the fact that the attempt to assign a
shore protection category to all dry land close to
sea level was not entirely successful. In some
cases, the state-specific studies failed to assign
land to one of these four categories because (for
example) land use data were unavailable. This
happened particularly at the seaward boundary of
their study areas. They called these areas "not
considered" (NC).
Section 1.2.3 discusses several supplemental
analyses. Using a tide range GIS surface
generated by Titus and Wang, along with the dry
land elevation and tidal wetlands data, we
generated additional sets of tables4. Some of
these tables estimate the area of dry land within
one-half tide range above spring high water.
Assuming that tidal wetlands are within one-half
tide range below spring high water (i.e., between
mean sea level and spring high water), these
tables give us the ratio of slopes above and
below spring high water, that is, the ratio of
existing wetlands to the potential for new
wetland creation. Other tables estimate the area
of potential tidal wetland loss by estimating the
portion of existing tidal wetlands that would fall
below mean sea level if sea level were to rise a
particular magnitude, with and without wetland
accretion.
1.2.2. Estimating Land Area by
Elevation Increment and Protection
Category
We estimated the land area by protection
category using several steps. First, to summarize
the protection data by elevation, it was necessary
3. Horizontal and vertical accuracy issues are addressed in
Section 1.3. An additional discussion on reporting data at
0.1 ft increments is provided here. The increments used
imperial rather than metric units because the interpolation
is facilitated when the contour interval (mostly in imperial
units as well) are an integer multiple of the increment.
4These tables are not provided as the likelihood of
shoreline protection data from which they were generated
are based on an unpublished analysis.
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[ SECTION 1.2 47 ]
to first convert the shore protection GIS data
from a vector format (i.e., polygons) into a raster
(or grid) format to match the digital elevation
model (DEM) data. As part of this step, we
developed a procedure to lessen the amount of
land classified as "not considered" (which would
otherwise be enhanced by the vector-to-raster
conversion process). Once this was done, we
were able to quantify the amount of land at
specific elevations by protection category. To
improve our elevation-specific area estimates,
we tailored our approach to the accuracy of the
source data—interpolating lower accuracy data
and using the area estimates directly from the
DEM for those with higher accuracies. We then
provided summary results in tables "rolled up"
by different elevations. The appendix to this
section provides county-by-county results for the
analysis we describe in this section. Section 1.3
provides additional information about variations
in data quality and the associated appendices also
provides results, by state, subregion, and region.
Converting shore protection polygons to
grid
General approach
In converting vector data into grid format,
several considerations need to be taken into
account. Spatially, the size of the raster cell
generated should be based on the estimated
accuracy or minimum mapping unit, as well as
whether the output raster data will be combined
with other data sets. We generated our raster
based on a 30-m cell size to match our DEM
data. In addition, this cell size was not
inappropriate given the source of the
information. Similarly, because the cell
boundaries will inevitably cross the vector
polygons (cell boundaries rarely coincide exactly
to vector polygon outlines of the input data),
different approaches can be taken to transfer the
attributes of a particular polygon to the output
raster cells. The attribute assignment can be
based on the centroid of the cell (i.e., the
attribute of the polygon is assigned to the raster
cell whose center it encapsulates), on the
polygon covering the majority of the cell (or the
combined area of multiple polygons with the
same attribute), or through attribute priority (i.e.,
if any portion of the polygon has a certain
attribute, the cell is assigned that attribute). We
used a combination of approaches in our
analysis. In our initial conversion, we used a
centroid approach. In subsequent reclassification,
we assigned attributes based on attribute values
(i.e., priority approach), and attributed remaining
cells based on proximity of neighboring cells.
The specific methods used are described below.
Approach for avoiding the "not considered"
designation
One of our main goals was to limit the amount of
land classified as "not considered." The original
shapefile dataset had numerous narrow polygons
along the shore classified as "not considered."
Usually, those polygons were not visible in the
county-scale maps that county officials and the
authors had closely examined, which the state-
specific chapters of this report display. Usually,
the polygons of "not considered" resulted
because the planning data used in the state-
specific analyses did not extend all the way out
to the wetland/dryland boundary defined by the
wetlands data set we were using. This occurred
for at least two reasons: In some cases, the
planning data were more precise than the old
NWI wetlands data we used; in other cases, the
planning study had used very coarse land use
data. Whenever the land use data extended
seaward of the wetland boundary, the use of
wetlands data as a "mask" resolved the data
conflict. But if the land use data did not extend
all the way to the wetlands or open water, we
were left with dry land with no protection
category (i.e., not considered).
A related problem was that the shore protection
polygons created by the state-specific studies
sometimes labeled lands as "wetlands" even
though that study ostensibly categorized dry land
by likelihood of shore protection and relied on a
wetlands data set to define wetlands. In several
cases—particularly the Hampton Roads area of
Virginia and some Maryland counties, local data
defined wetlands in areas that the statewide data
set classified as being dry. The study authors
wanted the maps to show those areas as
wetlands—a reasonable objective given that the
local planning data that form the basis of the
-------
[ 48 INTERPOLATING ELEVATIONS ]
studies treated it as wetlands. But we wanted our
results to be consistent with the Section 1.1
estimates of dry land and wetlands that relied on
the wetlands data set rather than local planning
data.
We converted the shapefile planning data
according to the general process shown in
Figure 1.2.1. Figure 1.2.2 shows an example of
the process using GIS data. Specifically, we
recoded any polygons designated as a wetland in
the source protection data as protection unlikely.
We then clipped the data to the extent of the
study area boundary and excluded any polygons
that overlapped with tidal wetland or tidal open
water as determined by the state-specific
wetlands layers. Additionally, we coded any
cells without an attribute as NC. We then
converted the protection data from a vector (i.e.,
polygon) format to raster (grid-based) format
with a cell size of 30 meters to match the
resolution of the elevation data.5 Attributes were
assigned to the cells based on whichever polygon
from the source vector data covered the centroid
of the output raster cell. This approach was
preferable over dominant category, because in
some cases there are narrow environmental
buffers along the shore. The buffers are PL or
PU along an area where the rest of the land is
PC. The buffers are too narrow to be the
dominant shore protection category in a cell.
Thus, using dominant category would create a
downward bias for that category, while picking
the centroid would be expected to yield area
estimates similar to the actual area estimate.
We then subset the raster layer to elevations less
than 20 feet and converted the NC cells back into
a vector format. The result was a vector polygon
layer of NC cells. The resulting polygons were
then overlaid with the original polygon vector
shoreline protection data, and the NC polygons
were assigned the same attribute as any
overlapping polygons. Only individual 30-m
Input Vector Shoreline
Protection Scenario
(polygons)
1
r
Reclass polygons originally coded as
wetland to "PU"
1
Clip data to study area and mask by
tidal wetland or open water from
wetland data set
1
r
Code cells without a shoreline
protection code to "NC"
Convert protection data from vector to
raster (based on centroid)
Select cells classified as "NC" and
subset to elevation <= 20 ft MSHW
Convert "NC" cells to vector format
(polygons)
Overlay "NC" polygons with original
shoreline protection data
Convert back to raster format and
attribute with overlapping polygon
based on priority
Attribute remaining "NC" cells with
attribute of cell within 50 meters of cell
centroid
Merge reclassed cell into original
raster shoreline protection data
Output Raster Shoreline
Protection Scenario
(reclassed)
Figure 1.2.1. Approach used to reclassify not-
considered shoreline protection scenario cells.
5. The conversion from vector to raster was conducted
using ArcGIS Spatial Analyst extension (ESRI, 2006).
-------
[ SECTION 1.2 49 ]
A
B
PU
NC
PL
Original shoreline protection polygons (vector) Original shoreline protection cells (raster)
D
NC raster cells converted to polygons (vector) Final reclassed shoreline protection cells (raster)
and overlaid with original shoreline protection
polygons
Figure 1.2.2. Graphical Representation Showing How Original Shoreline Protection Scenario
Data in Vector Format was Reclassified to Reduce the Amount of "Not Considered" (NC)
Lands.
cells of NC were recoded. Where multiple
polygons overlapped with the NC cells, and none
crossed the cell centroid, attribute assignment
was based on the following priority: NP, PU, PL,
and PC. We used this priority rule instead of
picking the category that accounted for the
greatest portion of the cell because such cells are
generally along the water or wetlands (and
assumed to be water or wetlands in the land use
data set that gave rise to the shore protection
classifications). If any of the overlapping cells
did not contain any of these categories, the cell
remained NC. Finally, any remaining NC cells
were assigned the attribute of any other non-NC
cells within a maximum distance of 50 meters
(centroid to centroid).6 All other NC cells
remained NC. Finally, we merged the
6. Given the cell size of 30 meters, this effectively means
that NC cells would be attributed the same as any adjacent
(including cells diagonal to the NC cell) non-NC cell. Note
also the cell shown by "z" (panel D) remained NC because
it fell entirely within tidal wetlands.
-------
[ 50 INTERPOLATING ELEVATIONS ]
reclassified NC layer with the original raster
version of the protection data.
Estimating area of land at specific
elevations by shore protection category
Combining elevation, protection, wetland,
quadrangle, and county data
Our first step was to segment the final DEM data
(see Section 1.1) by the source data from which
they were derived.7 We needed to do this for two
reasons. First, the interpolations (discussed in the
following section) depended on contour interval.
Second, one of the expected uses of our output
was the creation of high and low estimates; and
the uncertainty would be a function of the data
quality (see Section 1.3).
Using the same resolution and projection as the
elevation data, we generated raster data sets from
the following vector GIS layers: USGS 1:24K
quadrangles, county boundaries, and source data
extent polygons, as well as a nontidal open water
(NO) and nontidal wetlands (NW) layer
generated from wetlands data from each state
data set. We then combined these raster layers
with the elevation data and reclassified shore
protection data to generate a composite raster
layer with attributes from each source data set
(e.g., quadrangle, county, wetland type, source
data name, elevation, and shoreline protection
scenario). We calculated a final protection
scenario attribute field from the shore protection
category and NO/NW wetlands data, with
priority assigned to the wetlands data. The
resulting protection scenario field contained one
of the following categories: NO, NW, PC, PL,
PU, NP, orNC.
Areas with source elevations of 1-m contours or
worse
As noted in Section 1.1, the ESRI GRID
extension function TOPOGRID (ESRI, 2006)
that was used to interpolate contours into a DEM
was spatially biased toward each input contour.
The resultant DEM data therefore contained
"plateaus" on either side of the source contours.
Given our objective of estimating the area of
land within elevation increments of 50 cm, this
was not a significant problem for our source data
sets with contour intervals of 2 feet (60 cm) or
better. But it presented a significant bias in the
lower accuracy data sets. As in Section 1.1, we
corrected for this distortion in the lower accuracy
data sets by redistributing the land area evenly
into 0.1-ft elevation bins between each source
contour elevation interval (e.g., for each 5 feet
for data with a 5-ft contour interval) for each
combination of quadrangle, county, and
protection scenario.8 For the first contour, the
area between SHW and the first contour (e.g., 5-
ft NGVD) was used. We calculated the SHW
value (relative to the NGVD29 vertical datum)
by overlaying the SHW surface generated by
Titus and Wang9 with the quadrangle/county grid
and taking the average for all cells over each
quadrangle/county combination.
The process used for the lower accuracy source
areas is summarized in the following steps with
the tabular data shown in Figure 1.2.3 (for USGS
24K quadrangles in Sussex County, Delaware,
under the PC scenario):
8. This approach effectively generates a linear
interpolation of land area. Lacking site-specific
topographic information, the exact profile of the landscape
cannot be determined. Therefore, this linear interpolation
represents a conservative approach and differences in
coastal profiles at any specific locality could be thought to
average out over the broad areas where this was applied.
Certainly the reader may question any quantification of
land at the 0.1-ft increment; however, to assess
vulnerability of lands to inundation by small rates of SLR
over different time periods, the increment chosen is
necessary. Accuracy issues are discussed in Annex 3.
7. USGS data varied by 24K quadrangle, whereas other
data sets were provided by county or other boundary.
9. The SHW surface was derived by Titus and Wang
through interpolation of local tide gage point data that was
referenced to the NVGD29 vertical datum. See Section 1.1
for full processing details.
-------
[ SECTION 1.2 51 ]
1. Sum the area of land between SHW and
source contour interval or between
successive contour intervals (SHW Table in
Figure 1.2.3).
2. Determine the number of 0.1 -ft elevation
bins between the SITW/first contour or
successive contours.
3. Divide the sum in #1 by the number of bins
in #2.
4. Assign each 0.1 -ft bin the output value from
#3 (NGVD29 Area Distribution Table in
Figure 1.2.3).
For example, using the Assawoman Bay
quadrangle in Sussex County, Delaware, as an
example (highlighted in Figure 1.2.3), the source
data is 5-ft USGS, the SHW value is 2.7-ft
NGVD29, and the total area between SHW and
the 5-ft contour under the PC scenario is 370.53
hectares (ha). The land area was redistributed as
follows:
1. Sum of land between 2.7 and 5 feet (NGVD)
= 370.53 ha
2. Number of 0.1 ft bins: round (5—2.7) / 0.1)
= 23
3. Land area reported in each 0.1 ft bin: 370.53
/ 23 = 26.1 ha
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Figure 1.2.3. Example Tabular Summary Output of Land Elevation for Shore Protection Certain (PC)
Scenario for USGS 24K Quadrangles in Sussex County, Delaware. SHW Table shows land area (in
hectares) of PC between SHW relative to NGVD29 vertical datum and the 5-ft USGS contour.
NGVD29 Area Distribution Table shows how land area in SHW Table was distributed evenly into 0.1-
ft elevation bins. The SHW Area Distribution Table shows the re-distributed NGVD29 Table data
adjusted relative to SHW elevations. The highlighted row pertains to an example in the text.
-------
[ 52 INTERPOLATING ELEVATIONS ]
Figure 1.2.3 for the Assawoman Bay quadrangle
shows that 16.1 ha was input into each 0.1 -ft bin
between 2.7 feet (SHW) and 5 feet. The same
procedure was used for each successive 5-ft
contour.
Areas with source elevations better than a 1-m
contour
For the higher accuracy data sources, the land
area was summarized by larger elevation
increments (e.g., 50 cm and 1 foot) and output
directly from the DEM without any reallocation.
Final output
We subsequently output the land areas by
elevation bin into individual Excel workbooks
for each elevation data source. Individual sheets
within the workbooks were divided by protection
scenario and contained the area of land (in
hectares) within each elevation increment—50
cm and 1 foot for both low and higher resolution
data sets and 0.1 -ft increments where the source
data was 1-m contour or worse.10 Area estimates
were reported from 0 to 20 feet for English unit
tables and from 0 to 7 meters for metric tables. A
second set of Excel workbooks was generated
relative to SHW by subtracting the SHW-
NGVD29 elevation bin reported from each
quadrangle/county record within the
spreadsheets. An example of the output is shown
in the SHW Area Distribution Table in Figure
1.2.3. Therefore, relative to SHW, the 16.1-ha
bins are distributed between 0 and 2.3 feet (after
conversion from 2.7 to 5.0 feet relative to
NGVD29).
Finally, we added two additional sheets to each
Excel workbook: "All Land" and "Dry Land."
The first worksheet summarized all the other
shoreline protection scenario worksheets with the
exception of the NO sheet, and the "Dry Land"
worksheet represented the summary of all
worksheets except NO and NW.
10. In subsequent elevation rollups, to make the data
compatible with the lower accuracy data, we divide the
area of 1-fit increments evenly into 0.1-ft elevation bins.
This differs from the method used for the lower accuracy
data in that the redistribution occurred at 1-ft increments
instead of over the entire contour interval.
Once the individual source, quadrangle, county,
and protection scenario tables were generated,
we were able to summarize total areas for each
scenario or groups of scenarios by various
groupings, including state, county, or various
region (e.g., Chesapeake Bay) where each
quadrangle/county combination could be
assigned to the appropriate region.
In addition to the tables just described, we also
generated land area summaries for each shoreline
protection scenario by elevation taking into
account the uncertainty associated with different
source data sets. This was accomplished by
creating a lookup table of the root mean squared
error (RMSE) associated with each source data
set. By reporting the RMSE by individual
quadrangle, county, and source combination, we
were able to make low and high estimates of land
area similar to the tables generated using the
central estimate. The methods used to generate
the uncertainty tables are in Section 1.3.
1.2.3. Other Products—
Summarizing Land Area Vulnerable
to Inundation
General approach
In addition to the summaries described, we
generated another set of tables showing the area
of tidal wetlands at risk of inundation from SLR
and area of potentially new wetlands resulting
from inundation of lands above SHW under
alternative SLR and protection scenarios.11 To
derive this information we used the summary
statistics tables described and combined them
with lookup tables we developed. The lookup
tables were created for dry land and tidal
wetlands (TW) and provide the following
information: the mean (arithmetic) of full tide
range, the mean of the reciprocal of the tide
range (harmonic mean), the mean SLR rate, the
dominant accretion code, and the percentage of
wetland area with a specific accretion code of the
total wetlands for each quadrangle/county
combination. The sections that follow describe
11 These tables are not provided because the likelihood of
shoreline protection data from which they were generated
are based on an unpublished analysis.
-------
[ SECTION 1.2 53 ]
the methods we used to calculate the values in
the lookup tables.
Calculating average and average reciprocal
spring tide range values
To derive the mean spring tide range (STR) for
each quadrangle/county combination for the dry
land, we overlaid a raster layer of the
combination of quadrangle and county with a
raster surface of spring tide range developed
from interpolation of tide gauge data.12 We then
calculated the average STR using the ESRI
GRID extension function "ZONALSTATS"
(ESRI, 2006), which calculates the mean of the
values of all raster cells in the STR surface that
spatially coincide with the same
quadrangle/county combination. Similarly, we
calculated the reciprocal mean of STR by first
generating the raster layer of the inverse of the
STR surface (1/STR surface) and then
calculating the mean using the inverse layer as
an input into the ZONALSTATS function.
To calculate the average STR and average
reciprocal STR for the tidal wetlands, we first
overlaid the tidal wetland layer for each state13
with a GIS raster layer of accretion data
developed by Titus, Jones, and Streeter (in
Section 2.2) (based on a science panel
assessment and hand-annotated maps delineated
by Reed et al. [in Section 2.1]). We then
calculated the average STR values (mean and
reciprocal mean) using the same procedure that
was followed for the dry land data, but limiting
12. Titus and Wang (Section 1.1) generated vertical
elevations for the tide points using the National Ocean
Service's (NOS) estimated tide ranges, NOS estimated sea
level trends, and the NOS published benchmark sheets
along with National Geodetic Survey North American
Vertical Datum Conversion Utility (VERTCON) program
to convert the mean tide level (MTL) above NAVD88 to
NGVD29.
13. For all states except Pennsylvania, the wetland layer
that was generated by Titus and Wang was used. Titus and
Wang did not include mudflats in the tidal wetlands
classification for Pennsylvania. Because mudflats
represent a significant portion of tidal wetlands in
Pennsylvania, we extracted mudflats from the NWI source
data and added them to the final Pennsylvania wetlands
layer.
our averages to only the wetland/accretion code
combination within a quadrangle/county instead
of using the entire quadrangle/county that was
used in the dry land analysis.
Calculating the dominant accretion code for
tidal wetlands
Because the minimum mapping unit of analysis
(minimum unit of analysis) for dry land was the
quadrangle/county combination, we needed to
have a single accretion code for each
quadrangle/county combination. In addition,
because the accretion potential defined by Reed
et al. (2008) was categorical rather than
representing an average, we needed to use the
dominant accretion code instead of taking an
average. To determine the dominant accretion
code for wetlands within a quadrangle/county,
we first summed the area of tidal wetlands by
accretion code within a quadrangle/county and
divided it by the total area of tidal wetlands for
all accretion codes within a quadrangle/county.
The percentage of each tidal wetlands/accretion
code of the total wetlands within the
quadrangle/county was calculated as % TW
accretion = (Area specific TW accretion total
TW area) * 100.
The accretion code that accounted for the most
tidal wetlands was classified as the dominant
code.
Calculating the accretion code for dry land
To determine the accretion code for each
quadrangle/county combination for dry land, we
overlaid the raster accretion layer with the
quadrangle/county raster layer and assigned the
accretion code based on whichever accretion
code covered the majority of the
quadrangle/county. Where the accretion layer did
not extend far enough inland to cover all nontidal
lands being evaluated, the accretion code nearest
the quadrangle/county dry land being evaluated
was used. Figure 1.2.4 shows an example of the
output in the lookup tables (dry land and tidal
wetland) for Delaware. This table was then used
with the summary elevation statistics tables to
roll up elevations at various increments to
estimate the loss of tidal wetlands as well as the
-------
[ 54 INTERPOLATING ELEVATIONS ]
generation of new wetlands from inundation of
dry lands (these tables are not provided because
the likelihood of shoreline protection data from
which this was generated is based on an
unpublished analysis).
Generating tabular summaries of potential
wetland creation and loss
After we generated the lookup tables, we were
able to summarize the elevation data into tables
that provide information on the potential tidal
wetland creation and loss. For example, using
the elevation by protection scenario data along
with the tide range data in the lookup table, we
were able to calculate the area of tidal wetlands
and the area of dry land within 1 meter or one-
half tide range above spring high water by
protection scenario (results are part of an
ongoing analysis). Similarly, we calculated the
amount of land available for wetland migration
by shore protection likelihood by looking at the
amount of land between mean sea level and
spring high water if the sea level rises 1 meter
(results are part of ongoing analysis).
Additionally, other modifications included
summarizing the area of wetlands below a
particular elevation assuming uniform elevation
distribution, and subdividing quadrangle-specific
estimates by dominant accretion code that was
assigned to both wetlands and drylands.
References
ESRI. 2006. ArcGIS Arclnfo Workstation GRID
Extension and ArcGIS Desktop Spatial Analyst
Extension, v. 9.1 and 9.2. Environmental
Systems Research Institute, Inc., Redlands, CA.
Jones, R. 2007. Accuracy Assessment of EPA
Digital Elevation Model Results. Memorandum
and attached spreadsheets prepared for the U.S.
EPA under Work Assignment 409 of EPA
Contract #68-W-02-027.
Jones, R., J. Titus, and J. Wang. 2008. Metadata
for Elevations of Lands Close to Sea Level in the
Middle Atlantic Region of the United States.
Metadata accompanying Digital Elevation Model
data set. Distributed with the elevation data.
-------
[ SECTION 1 .2 55
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County
Accretion
TW Ha
Total TW Ha % Accretion of Total
Arith Mean STR
Harmonic Mean
Mean SLR Rate
23
Mispillion
Sussex
2
2136.87
2136.87
100
171.53
0.0058
3.21
:
24
Newark East
New Castle
8
225.36
225.36
100
176.8
0.0057
3.21
jj
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Rehoboth
Sussex
2
764.37
764.37
100
143.27
0.0070
3.21:
III
26
Saint Georges
New Castle
2
42.66
42.66
100
184.02
0.0054
3.21
27
Seaford East
Sussex
8 90.09
90.09
100
88.21
0.0113
3.21
III
28
Seaford West
Sussex
8 0.99
0.99
100
88.82
0.0113
3.21
iii
29
Selbyville
Sussex
2
17.46
17.46
100
15
0.0667
3.21
30
Sharptown
Sussex
8
561.06
561.06
100
88.21
0.0113
3.21
31
Smyrna
Kent
0
913.05
1579.77
57.8
196.43
0.0051
3.21
32
Smyrna
Kent
2
666.72
1579.77
42.2
196.43
0.0051
3.21
33
Smyrna
New Castle
2
864.18
864.18
100
194.47
0.0051
3.21
3 4
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New Castle
2
3815.37
3815.37
100
193.74
0.0052
3.21
III:
35
Wilmington_S
New Castle
2
254.43
511.02
49.79
176.24
0.0057
3.21
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County
Accretion Arith Mean STR Harmonic Mean STR
Mean SLR Rate
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Georgetown
Sussex
8
157.78
0.0063
3.21
3
Greenwood
Sussex
8
174.77
0.0057
3.21
A
Hickman
Sussex
8
147.98
0.0075
3.21
5
Laurel
Sussex
8
104.05
0.0098
3.21
6
Marcus Hook
New Castle
8
181.17
0.0055
3.21
7
Marydel
Kent
8
188.28
0.0053
3.21
8
Newark East
New Castle
8
177.75
0.0056
3.21
9
Penns Grove
New Castle
8
180.53
0.0055
3.21
10
Seaford East
Sussex
8
149.74
0.007
3.21
11
Seaford West
Sussex
8
100
0.0104
3.21
12
Sharptown
Sussex
8
87.34
0.0115
3.21
13
Trap Pond
Sussex
8
117.82
0.0085
3.21
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New Castle
8
178.8
0.0056
3.21
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Figure 1.2.4. Example of Lookup Tables. Top table: tidal wetland (TW) areas by
quadrangle/county/accretion code, total TW for quadrangle/county, percentage of accretion-specific
area to total, arithmetic mean of STR, harmonic mean (mean of reciprocal) of STR, and mean SLR
rate. Bottom table: dominant accretion code, and arithmetic and harmonic STR means and mean of
SLR rate.
-------
Section 1.2 Appendix
Area of Land Close to Sea Level, by State
By James G. Titus, U.S. Environmental Protection Agency
Russell Jones, Stratus Consulting Inc.
Richard Streeter, Stratus Consulting Inc.
-------
[ SECTION 1.2 57 ]
Table A1. New York (square kilometers)
Meters above Spring High Water
County
0.5 1.0
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Dry Land, by half meter elevation increment3
Bronx
2.3
2.3
2.3
2.6
2.8
2.8
2.8
2.8
2.8
1.4
Brooklyn
7.4
6.0
6.0
6.7
9.2
9.2
8.4
5.4
5.4
4.9
Manhattan
1.6
1.6
1.6
1.6
1.7
1.7
1.7
1.7
1.7
1.7
Nassau
13.2
17.8
21.2
21.2
13.3
8.8
8.8
8.6
8.1
7.4
Queens
13.2
8.9
8.9
9.6
9.3
9.3
7.4
5.0
5.0
3.1
Staten Island
5.7
5.7
5.7
4.9
2.7
2.7
2.7
2.7
2.7
2.4
Suffolk
36.8
37.0
38.0
37.6
37.6
34.3
33.9
33.4
30.3
29.5
Westchester
2.1
2.1
2.1
2.2
1.9
1.8
1.8
1.8
1.8
1.3
Ellis & Liberty Islands
0.04
0.04
0.04
0.03
0.00
0.00
0.00
0.00
0.00
0.00
Statewide
82.4
81.5
85.9
86.4
78.5
70.6
67.5
61.4
57.8
51.7
Wetlands
Tidal
¦Nontidal Wetlands, by half meter elevation increment
Brooklyn
3.5
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Nassau
43.4
0.3
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.3
0.3
Queens
7.6
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
Staten Island
5.4
0.3
0.3
0.3
0.3
0.1
0.1
0.1
0.1
0.1
0.1
Suffolk
82.3
4.1
4.0
2.5
2.4
2.3
1.5
1.5
1.4
1.4
1.3
Otherb
6.9
0.06
0.06
0.06
0.05
0.05
0.04
0.04
0.04
0.04
0.05
Statewide
149.1
5.0
4.8
3.4
3.2
2.8
2.0
1.9
1.9
1.9
1.8
Cumulative (total) amount of land below a given elevation0
Dry Land
82
164
250
336
415
485
553
614
672
724
Nontidal Wetlands
5
10
13
16
19
21
23
25
27
29
All Land
149
236
323
412
502
583
655
725
788
848
901
a For example, Bronx has 2.3 square kilometers of dry land between 0.5 and 1.0 meters above spring high
water.
b Includes Bronx, Dutchess, Manhattan, Orange, Putnam, Rockland, and Westchester counties.
c For example, New York State has 164 square kilometers of dry land less than 1 meter above spring high
water.
Table A2. New York jurisdictions not included in shore protection study (hectares)
Meters above Spring High Water
County
0.5
1.0
1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Tidal Nontidal Wetlands, by half meter elevation increment
Dutchess
7.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Orange
24.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Putnam
126.6
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Rockland
228.6
1.5
1.5
1.5
1.5
0.9
0.6
0.6
0.6
0.6
0.6
Note: The analysis found no dry land below 5 meters for these jurisdictions.
-------
[ 58 AREA OF LAND CLOSE TO SEA LEVEL, BY STATE ]
Table A3. New Jersey (square kilometers)
Meters above Spring High Water
County
0.5
1.0
1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Dry Land, by half meter elevation increment3
Atlantic
8.1
13.7
14.2
10.9
9.3
8.1
7.8
8.1
7.8
7.8
Bergen
11.4
11.4
11.4
7.5
2.2
2.1
2.1
2.1
2.1
2.1
Burlington
4.6
4.6
4.6
4.5
5.6
5.9
5.9
5.9
5.9
7.3
Cape May
16.2
23.0
20.0
16.3
23.0
21.8
20.6
20.7
19.6
18.1
Cumberland
11.8
10.0
10.0
10.1
11.1
11.1
10.6
9.9
9.9
9.6
Gloucester
6.8
6.7
6.7
6.6
6.0
6.0
6.0
6.0
6.0
5.8
Hudson
11.9
11.9
11.9
9.4
3.5
3.5
3.5
3.5
3.5
3.0
Middlesex
6.5
6.5
6.5
5.7
5.2
5.2
5.2
5.2
5.2
4.9
Monmouth
7.3
7.8
9.9
10.4
9.2
9.0
8.1
7.3
8.2
8.0
Ocean
10.1
22.4
25.2
16.6
12.7
12.9
12.3
11.1
10.0
9.0
Salem
20.0
17.3
17.3
16.7
14.2
14.2
13.7
12.1
12.1
11.8
Otherb
12.4
12.4
12.4
10.8
8.5
8.5
8.5
8.5
8.5
7.7
Statewide
127.2
148.0
150.2
125.5
110.5
108.4
104.5
100.5
98.8
95.0
Wetlands
Tidal
Nontidal Wetlands, by half meter elevation increment-
Atlantic
204.0
14.3
9.1
9.1
9.1
8.7
8.6
8.5
8.4
8.3
8.3
Burlington
42.8
7.6
7.5
7.3
7.3
4.7
4.4
4.4
4.4
4.4
4.6
Cape May
201.4
20.5
15.4
14.9
13.7
10.1
9.8
9.5
7.2
7.0
6.6
Cumberland
212.6
18.1
14.1
14.1
12.0
7.2
7.2
6.8
6.3
6.3
6.1
Gloucester
18.0
6.5
6.3
6.3
5.3
1.3
1.3
1.3
1.3
1.3
1.3
Ocean
124.8
7.9
9.2
8.3
7.4
6.6
5.2
4.7
4.3
4.0
3.8
Salem
110.1
21.8
8.5
8.5
7.5
3.1
3.1
3.0
2.7
2.7
2.7
Other0
66.7
2.8
2.5
2.5
2.1
1.5
1.4
1.4
1.5
1.6
1.5
Statewide
980.4
99.5
72.6
70.9
64.4
43.2
41.0
39.8
36.0
35.5
35.0
Cumulative (total) amount of land below a given elevation0
Dry Land
127
275
425
551
661
770
874
975
1073
1169
Nontidal Wetlands
99
172
243
307
351
392
431
467
503
538
All Land
980
1207
1428
1649
1839
1992
2142
2286
2422
2557
2687
a For example, Atlantic County has 13.7 square kilometers of dry land between 0.5 and 1.0 meters above
spring high water.
b Includes Camden, Essex, Mercer, Passaic, Union, and Somerset above 4.5m.
c Includes Camden, Essex, Mercer, Passaic, Union, Somerset above 4.5m, Bergen, Hudson, Middlesex,
and Monmouth.
d For example, New Jersey has 275 square kilometers of dry land less than 1 meter above spring high
water.
-------
[ SECTION 1.2 59 ]
Table A4. New Jersey jurisdictions not included in shore protection study (hectares)
Meters above Spring High Water
County
0.5
1.0
1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment
Mercer1
4.5
4.5
4.5 4.5 4.5 4.5 4.5 4.5
3.5
0.3
Passaic
11.7
11.7
11.7 14.4 17.7 17.7 17.7 17.7
17.7
18.1
Somerset
0.0
0.0
o
o
o
o
o
o
o
o
o
o
o
o
0.0
2.9
Wetlands Tidal
-
Nontidal Wetlands, by half meter elevation increment-
Mercer3 178
0.4
0.4
o
4^
o
4^
O
4^
O
4^
O
4^
O
4^
0.3
0.0
Passaic 0
1.2
1.2
1.2 0.7 0.1 0.1 0.1 0.1
0.1
0.3
Somerset 0
0.0
0.0
O
o
o
o
o
o
o
o
o
o
o
o
0.0
0.6
a The "not considered"
category includes Mercer County because we calculated these statistics before the
Mercer County results had been incorporated into our data set.
Table A5. Pennsylvania (square kilometers)
Meters above Spring High Water
County
0.5
1.0 1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment3
Bucks
3.2
CD
CO
CD
CO
CD
CO
CD
CO
CD
CO
CO
CO
C\J
CO
3.5
3.4
Delaware
4.4
CO
CO
CO
CO
CO
CO
1.3
1.2
Philadelphia
4.9°
3.5 7.2 6.5 6.4 6.4 5.0 4.3
4.6
4.4
Statewide
12.6
11.1 15.0 13.4 11.3 11.3 9.8 9.2
9.3
9.1
Wetlands
Tidal -¦
Nontidal Wetlands, by half meter elevation increment-
Bucks
1.9
0.7
0.7 0.8 0.9 0.9 0.9 0.9 0.9
0.7
0.3
Delaware
0.6
0.6
o
o
o
o
o
o
o
o
o
CD
o
CD
O
0.0
0.0
Philadelphia
3.6
0.5C
0.2 0.3 0.3 0.2 0.1 0.1 0.1
0.1
0.0
Statewide
6.1
1.9
1.5 1.7 1.6 1.1 1.0 1.0 1.0
0.8
0.3
Cumulative (total) amount of land below a given elevationd
Dry Land
13
24 39 52 63 75 85 94
103
112
Nontidal Wetlands
2
3 5 7 8 9 10 11
11
12
All Land
6
21
33 50 65 77 89 100 110
121
130
a For example, Philadelphia has 3.5 square kilometers of dry land between 0.5 and 1.0 meters above spring
high water.
bThis value includes 2.4 square kilometers of dry land below spring high water in Philadelphia, of which
0.87, 0.054, and 0.005 are at least 1, 2, and 3 meters below spring high water, respectively. Most of this
land is near Philadelphia International airport.
cThis value includes 39 hectares below spring high water, of which 3.8 are at least 1 meter below spring
high water. Most of this land is near Philadelphia International airport.
d For example, Pennsylvania has 24 square kilometers of dry land less than 1 meter above spring high
water.
-------
[ 60 AREA OF LAND CLOSE TO SEA LEVEL, BY STATE ]
Table A6. Delaware (square kilometers)
Meters above Spring High Water
County
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment3
Kent
19.2
13.0
13.0
16.2
20.5
20.5
22.0
24.3
24.3
22.2
New Castle
15.4
9.0
9.0
9.6
11.1
11.1
11.3
11.3
11.3
10.7
Sussex: Chesapeake Bay
1.1
1.3
1.6
1.6
2.3
3.4
3.4
4.6
5.7
5.7
Sussex: Delaware Bay
13.7
10.9
10.7
10.8
11.8
11.7
11.6
10.2
10.1
10.2
Sussex: Atlantic Coast
22.7
19.9
18.1
18.1
20.7
22.3
22.3
23.5
24.0
24.0
Statewide
72.2
53.9
52.4
56.3
66.4
68.9
70.5
73.8
75.5
72.9
Wetlands
Tidal
Nontidal Wetlands, by half meter elevation increment
Kent
168.7
9.6
4.3
4.3
4.0
3.1
3.1
3.2
3.3
3.3
3.2
New Castle
73.5
3.5
0.8
0.8
0.8
0.9
0.9
0.8
0.7
0.7
0.7
Sussex: Chesapeake Bay
6.6
1.4
0.9
0.7
0.7
0.9
1.0
1.0
1.5
1.7
1.7
Sussex: Delaware Bay
67.5
4.3
1.2
1.1
1.1
1.0
1.0
1.0
0.7
0.7
0.7
Sussex: Atlantic Coast
40.9
3.5
2.6
2.2
2.2
2.0
1.8
1.8
1.4
1.2
1.2
Statewide
357.1
22.2
9.8
9.2
8.9
7.9
7.8
7.9
7.6
7.5
7.4
Cumulative (total) amount of land below a given elevation
b
Dry Land
72
126
178
235
301
370
441
514
590
663
Nontidal Wetlands
22
32
41
50
58
66
74
81
89
96
All Land
357
452
515
577
642
716
793
871
953
1036
1116
a For example, Kent County has 13 square kilometers of dry land between 0.5 and 1.0 meters above spring
high water.
b For example, Delaware has 126 square kilometers of dry land less than 1 meter above spring high water.
-------
[ SECTION 1.2 61 ]
Table A7. Maryland (square kilometers)
Meters above Spring High Water
County
0.5
1.0
1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment3
Anne Arundel
5.3
5.3
7.4
11.7
11.7
10.9
8.9
8.9
8.9
8.7
Baltimore County
4.8
5.5
6
7.3
8.9
10.1
10.2
7.8
8.7
8.7
Calvert
1.9
1.9
1.6
1.5
1.6
3.4
3.6
3.6
4.6
4.7
Cecil
1.2
1.5
2.1
2.1
2.6
4.2
4.2
4.3
4.6
4.6
Charles
5.8
5.7
7.5
7.5
7.6
12.7
13.1
13.1
8.2
7.8
Dorchester
74
114.3
62.3
48.1
36.9
37
34
25
19.1
17.4
Harford
9.1
8.9
6.3
6.2
6.3
8.4
8.5
8.4
5.2
5.1
Kent
4
6
6.7
6.8
6.4
11.2
11.2
11.2
12.5
12.9
Queen Anne's
1.9
6.5
9.5
11.2
13.5
16.8
19.3
19.3
18.6
18
Somerset
39.2
47
45.5
52.5
19.9
18.5
27.8
28.4
28.7
29.3
St. Mary's
8.2
8.2
11
11.2
11.2
20.9
21.4
21.4
11.4
10.3
Talbot
4.2
12.2
23.2
41.7
44.1
37.1
35
32.3
23.4
19.5
Wicomico
10
13.1
14.7
15
14.6
13.7
14.3
14.3
14.5
13.5
Worcester
11.5
24.1
31.6
36.7
35
32
27.5
25.7
26
26.6
Other6
4.3
4.9
5.4
5.7
6.1
7.1
7.1
7.4
8.4
8.5
Statewide 185.3 265.1 240.7 265.1 226.3 243.8 246.0 231.2 202.8 195.3
Wetlands Tidal Nontidal Wetlands, by half meter elevation increment
Charles
24.2
1.9
1.9
2.2
2.2
2.2
2.4
2.4
2.4
1.5
1.4
Dorchester
424.8
32.5
30.1
20.6
16.2
10.3
6.9
10.1
6.8
4.8
3.1
Harford
29.4
1.4
1.3
1.0
1.0
1.0
1.4
1.4
1.4
0.7
0.6
Somerset
265.4
12.3
7.0
7.2
11.9
3.5
6.0
10.1
7.0
9.3
10.9
St. Mary's
18.7
1.5
1.6
2.1
2.1
2.1
3.9
3.9
3.9
3.0
2.9
Talbot
26.1
0.1
0.6
0.9
1.7
2.2
2.1
2.6
3.8
2.6
2.0
Wicomico
67.0
8.4
3.4
7.3
7.7
5.2
8.9
9.4
8.0
5.5
4.8
Worcester
142.2
2.8
5.4
5.2
6.1
6.1
7.2
6.8
6.4
5.3
5.0
Other0
118.0
3.5
5.9
7.2
8.7
8.1
8.5
7.0
7.2
8.6
8.7
Statewide
1115.8
64.5
57.2
53.8
57.6
40.8
47.2
53.7
47.0
41.3
39.5
Cumulative (total) amount of land below a given elevation0
Dry Land 185 450 691 956 1182 1426 1672 1904 2106 2302
Nontidal Wetlands
64
122
175
233
274
321
375
422
463
503
All Land
1116 1366
1688
1982
2305
2572
2863
3163
3441
3685
3920
a For example, Anne Arundel County has 5.3 square kilometers of dry land between 0.5 and 1.0 meters
above spring high water.
b Includes Baltimore City, Caroline, and Prince George's Counties.
c Includes Baltimore City, Caroline, Prince George's, Anne Arundel, Baltimore County, Calvert, Cecil, Kent,
and Queen Anne's Counties.
d For example, Maryland has 450 square kilometers of dry land less than 1 meter above spring high water.
-------
[ 62 AREA OF LAND CLOSE TO SEA LEVEL, BY STATE ]
Table A8. Washington, D.C. (square kilometers)
0.5
Meters above Spring High Water
1.0 1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment3
Washington, D.C.
2.43
1.16 1.40 1.42 1.81 1.84 1.83 1.80
1.68
1.65
Wetlands
Tidal
-Nontidal Wetlands, by half meter elevation increment
Washington, D.C.
0.79
0.04
0.02 0.03 0.02 0.02 0.02 0.03 0.03
0.05
0.05
Cumulative (total) amount of land below a given elevationb
Dry Land
2.43
3.59 4.98 6.40 8.22 10.06 11.88 13.69 15.37
17.01
Nontidal Wetlands
0.04
0.06 0.09 0.11 0.13 0.14 0.17 0.21
0.26
0.31
All Land 0.79 3.26 4.44 5.86 7.31 9.13 10.99 12.85 14.68 16.41 18.12
a For example, DC has 1.16 square kilometers of dry land between 0.5 and 1.0 meters above spring high
water.
b For example, DC has 3.59 square kilometers of dry land less than 1 meter above spring high water.
-------
[ SECTION 1.2 63 ]
Table A9. Virginia (square kilometers)
Meters above Spring High Water
County
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Dry Land, by half meter elevation increment3
Eastern Shore
45.5
39.8
42.9
43.1
42.6
37.1
36.4
35.6
33.5
33.5
Accomack
29.5
29.1
32.7
32.9
31.3
20.7
20.0
19.1
15.3
15.0
Northampton
15.9
10.7
10.2
10.2
11.3
16.4
16.5
16.6
18.1
18.5
Northern Virginia
2.7
2.7
2.7
2.7
2.9
3.3
3.3
3.3
3.3
3.3
Rappahannock Area
3.5
3.5
3.5
3.5
3.5
6.8
6.8
6.8
6.8
6.8
Northern Neck
16.2
16.2
16.5
16.5
16.7
42.4
46.9
46.9
47.0
47.0
Middle Peninsula
30.6
32.5
42.3
42.5
42.7
37.3
37.4
36.7
26.6
26.4
Gloucester
11.3
12.4
15.1
15.1
13.5
8.5
8.5
7.9
5.6
5.6
Mathews
10.7
11.5
18.2
18.3
17.8
11.4
11.4
11.2
3.7
3.6
Otherb
8.5
8.5
9.0
9.1
11.5
17.4
17.6
17.6
17.4
17.3
Hampton Roads0
65.5
74.0
105.9
119.3
134.1
188.7
198.7
191.9
138.4
116.3
Virginia Beach
24.0
25.2
35.0
44.0
45.3
56.3
54.4
53.6
35.7
25.3
Chesapeake
8.4
10.7
20.2
24.6
29.7
55.7
67.5
68.4
59.9
48.1
Portsmouth
2.7
3.7
5.2
5.2
7.4
11.5
11.5
9.6
4.8
4.8
Hampton
4.1
6.4
12.2
12.2
13.1
14.3
14.3
12.4
4.8
4.8
Norfolk
4.1
6.3
11.3
11.3
14.5
24.5
24.5
20.5
4.2
4.2
York
4.3
5.0
6.5
6.5
6.0
4.8
4.8
4.3
2.7
2.7
Newport News
4.9
4.3
3.2
3.2
3.2
3.5
3.5
3.8
4.7
4.7
Poquoson
3.2
3.4
3.6
3.6
2.7
0.1
0.1
0.1
0.0
0.0
Suffolk
3.4
3.0
2.8
2.8
5.4
8.6
8.6
9.6
11.7
11.8
James City
2.8
2.7
2.5
2.5
2.7
3.8
3.8
3.8
3.9
3.9
Isle of Wight
2.6
2.4
2.4
2.4
3.1
4.9
4.9
5.0
5.2
5.2
Surry
1.0
1.0
1.0
1.0
1.0
0.9
0.9
0.9
0.9
0.9
Other Jurisdictions'1
8.1
8.1
9.3
9.3
11.0
16.5
16.6
16.7
19.4
19.7
Statewide
172.1
176.8
223.0
236.9
253.4
332.1
346.2
337.9
275.0
253.0
Table continued on following page
-------
[ 64 AREA OF LAND CLOSE TO SEA LEVEL, BY STATE ]
Table A9. Virginia (square kilometers) continued
Meters above Spring High Water
County
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Wetlands
Tidal -
—Nontidal Wetlands, by half meter elevation increment
Eastern Shore
945.5
15.8
18.2
24.3
24.5
21.8
12.2
11.7
11.3
7.9
7.6
Accomack
483.5
15.0
17.0
22.0
22.2
20.0
10.6
10.1
9.7
6.9
6.6
Northampton
462.0
0.8
1.2
2.2
2.3
1.9
1.6
1.6
1.6
1.1
1.0
Northern Virginia
10.2
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
Rappahannock
Area
26.7
0.8
0.8
0.8
0.8
0.8
0.9
0.9
0.9
0.9
0.9
Northern Neck
57.3
1.8
1.8
1.8
1.8
1.8
3.5
3.9
3.9
3.9
3.9
Middle Peninsula
164.4
8.7
9.4
12.5
12.5
11.9
12.0
11.9
11.7
7.7
7.6
Gloucester
43.5
3.9
4.5
5.7
5.7
5.1
2.9
2.9
2.7
1.7
1.7
Mathews
27.1
2.8
3.0
4.8
4.8
4.9
7.5
7.5
7.5
4.5
4.4
Other8
93.9
2.0
2.0
2.0
2.0
1.9
1.5
1.5
1.5
1.5
1.5
Hampton Roads'
330.2
32.6
31.4
22.6
20.7
28.9
39.3
38.8
39.9
39.8
37.9
Virginia Beach
112.4
10.5
10.0
7.0
7.5
7.3
4.6
3.4
3.3
2.5
1.8
Chesapeake
39.7
12.2
12.7
10.1
7.7
16.1
30.1
30.7
31.8
32.2
31.0
Portsmouth
3.7
5.3
3.5
0.2
0.2
0.3
0.4
0.4
0.3
0.2
0.2
Hampton
14.4
0.1
0.2
0.2
0.2
0.3
0.7
0.7
0.8
1.1
1.1
Norfolk
4.7
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.0
0.0
York
17.0
0.6
1.0
1.9
1.9
1.5
0.6
0.6
0.6
0.4
0.4
Newport News
15.1
0.2
0.3
0.3
0.3
0.2
0.0
0.0
0.0
0.1
0.1
Poquoson
23.7
0.0
0.1
0.3
0.3
0.2
0.0
0.0
0.0
0.0
0.0
Suffolk
26.3
1.5
1.5
0.7
0.7
0.9
1.0
1.0
1.1
1.6
1.6
James City
32.8
0.6
0.6
0.6
0.6
0.5
0.4
0.4
0.4
0.4
0.4
Isle of Wght
28.9
0.9
0.9
0.7
0.7
0.8
1.1
1.1
1.1
1.2
1.2
Surry
11.5
0.5
0.5
0.5
0.5
0.4
0.2
0.2
0.2
0.2
0.2
Other
Jurisdictions9
84.5
13.1
13.1
8.1
8.0
7.1
6.3
6.3
6.3
6.0
6.0
Virginia
1618.9
73.1
75.0
70.4
68.6
72.6
74.3
73.7
74.1
66.5
64.1
Cumulative (total) amount of land below a given elevation"
Dry Land
172
349
572
809
1062
1394
1741
2079
2354
2606
Nontidal Wetlands
73
148
218
287
360
434
508
582
648
713
All Land
1619
1864
2116
2409
2715
3041
3447
3867
4279
4621
4938
a For example, Gloucester has 12.4 km2 of dry land between 0.5 and 1.0 meters above spring high water.
b Includes Essex, King and Queen, King William, and Middlesex Counties.
cExcludes Southampton, Franklin, and Williamsburg.
includes Charles City, Chesterfield, Hanover, Henrico, New Kent, Prince George, Southampton, and Sussex
Counties and the cities of Colonial Heights, Franklin, Hopewell, Petersburg, and Wlliamsburg.
includes Essex, King and Queen, King Wlliam, and Middlesex Counties.
fExcludes Southampton, Franklin, and Wlliamsburg.
includes Charles City, Chesterfield, Hanover, Henrico, New Kent, Prince George, Southampton, and Sussex
Counties and the cities of Colonial Heights, Franklin, Hopewell, Petersburg, and Wlliamsburg.
hFor example, Virginia has a total of 349 square kilometers of dry land less than 1 meter above spring high
water.
-------
[ SECTION 1.2 65 ]
Table A10. Virginia jurisdictions not included in shore protection study (hectares)
Meters above Spring High Water
County
0.5
1.0 1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment
Charles City
237.9
237.9
237.9
237.9
296.2
445.5
445.5
445.5
445.5
445.5
Chesterfield
97.5
97.5
97.5
97.5
78.0
67.2
67.2
67.2
67.2
67.2
Colonial Heights
2.6
2.6
2.6
2.6
1.7
1.1
1.1
1.1
1.1
1.1
Franklin
5.1
5.1
19.5
19.7
19.7
24.2
24.5
24.5
35.1
36.4
Hanover
1.8
1.8
1.8
1.8
7.6
9.9
9.9
9.9
9.9
9.9
Henrico
57.0
57.0
57.0
57.0
47.4
40.8
40.8
40.8
40.8
40.8
Hopewell
28.1
28.1
28.1
28.1
18.0
10.9
10.9
10.9
10.9
10.9
New Kent
154.0
154.0
154.0
154.0
257.5
372.5
372.5
372.5
372.5
372.5
Petersburg
0.0
0.0
0.0
0.0
0.2
0.3
0.3
0.3
0.3
0.3
Prince George
140.5
140.5
140.5
140.5
178.4
287.8
287.8
287.8
287.8
287.8
Southampton
82.3
82.3
184.4
185.7
185.7
379.0
391.6
391.6
653.7
686.0
Williamsburg
3.7
3.7
3.7
3.7
4.0
5.7
5.7
5.7
5.7
5.7
Wetlands
Tidal
Nontidal Wetlands, by half meter elevation increment
Charles City
2215.5
138.8
138.8
138.8
138.8
108.2
57.9
57.9
57.9
57.9
57.9
Chesterfield
1052.3
26.1
26.1
26.1
26.1
11.2
2.7
2.7
2.7
2.7
2.7
Colonial Heights
52.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Franklin
0.0
67.6
67.6
23.2
22.7
22.7
2.9
1.6
1.6
0.8
0.7
Hanover
114.2
0.0
0.0
0.0
0.0
0.1
0.2
0.2
0.2
0.2
0.2
Henrico
422.5
3.0
3.0
3.0
3.0
3.4
3.8
3.8
3.8
3.8
3.8
Hopewell
73.1
7.4
7.4
7.4
7.4
3.9
1.3
1.3
1.3
1.3
1.3
New Kent
3390.9
169.5
169.5
169.5
169.5
120.1
55.6
55.6
55.6
55.6
55.6
Petersburg
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Prince George
1091.1
57.6
57.6
57.6
57.6
59.3
76.2
76.2
76.2
76.2
76.2
Southampton
0.0
835.8
835.8
383.9
378.3
378.3
421.1
423.9
423.9
399.9
396.9
Williamsburg
39.7
1.1
1.1
1.1
1.1
1.1
1.1
1.1
1.1
1.1
1.1
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[ 66 AREA OF LAND CLOSE TO SEA LEVEL, BY STATE ]
Table A11. North Carolina (square kilometers)
Meters above Spring High Water
County
0.5a
1.0
1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment11
Beaufort
50.4
61.0
66.2
81.9
84.7
80.9
83.3
96.7
68.9
48.8
Camden
16.8
11.3
50.0
39.0
46.5
52.8
26.4
23.1
35.8
22.3
Carteret
51.2
69.8
90.0
107.5
79.1
21.7
15.1
16.5
17.4
13.3
Currituck
19.8
26.4
36.6
57.4
57.2
51.8
32.7
21.6
9.1
5.4
Dare
45.4
22.2
17.9
15.2
15.2
11.7
8.8
5.3
3.3
2.1
Hyde
295.7
141.3
56.4
52.9
51.6
39.5
25.2
18.4
12.0
5.7
Onslow
24.6
10.1
9.9
11.5
14.7
11.6
15.5
17.9
13.6
21.8
Pamlico
24.2
35.4
52.2
53.4
38.6
34.8
30.7
22.7
15.7
9.2
Pasquotank
10.6
28.8
43.4
48.7
47.3
40.6
71.8
93.7
47.8
25.3
Tyrrell
139.9
143.4
49.6
26.1
12.6
3.5
3.2
1.3
0.5
0.0
Other0
60.3
73.7
105.6
138.2
177.8
213.7
292.6
380.4
319.8
227.9
Not Considered01
3.0
2.7
3.8
5.1
7.1
9.4
12.9
18.0
22.5
30.5
Statewide
741.9
626.1
581.6
636.9
632.5
572
618.2
715.6
566.4
412.3
Wetlands
Tidal -
Nontidal Wetlands, by half meter elevation increment-
Beaufort
35.1
68.0
40.9
32.3
32.4
44.6
37.0
24.2
16.4
15.3
12.7
Brunswick
109.2
38.5
8.7
7.4
6.1
6.3
6.2
5.7
5.9
5.0
4.8
Camden
7.1
142.5
7.5
10.6
7.6
10.2
11.8
7.2
7.4
12.5
30.1
Carteret
334.3
34.3
53.0
48.1
44.7
36.2
20.5
10.6
10.9
15.6
12.7
Currituck
124.6
131.8
18.3
13.2
14.6
9.7
8.9
4.2
3.3
4.4
10.6
Dare
167.8
402.2
162.2
61.4
33.8
5.0
1.1
0.4
0.2
0.1
0.1
Hyde
199.3
345.6
153.3
52.9
27.5
19.7
22.1
18.0
22.4
13.7
10.2
Pamlico
111.6
52.8
20.8
12.1
20.8
25.6
16.4
22.5
22.1
13.0
15.2
Pender
38.2
87.2
28.2
18.0
17.5
14.6
14.3
13.6
13.1
13.9
12.2
Tyrrell
3.8
433.4
95.7
32.3
10.7
11.4
10.6
12.8
9.7
5.0
1.1
Other8
137.5
605.1
119.8
96.1
93.4
98.3
94.6
95.7
105.4
100.8
98.7
Not Considered01
3.5
30.9
10.2
10.0
11.7
14.2
15.8
18.7
21.2
19.6
26.3
Statewide
1272.0
2372.3
718.6
394.4
320.8
295.8
259.3
233.6
238
218.9
234.7
Cumulative (total) amount of land below a given elevation'
Dry Land
742
1368
1950
2587
3219
3791
4410
5125
5692
6104
Nontidal Wetlands
2372
3091
3485
3806
4102
4361
4595
4833
5052
5286
All Land
1272
4386
5731
6707
7665
8593
9425
10276
11230
12016
12662
a Includes land below spring high water.
b For example, Beaufort County has 61 square kilometers of dry land between 0.5 and 1.0 meters above
spring high water.
c Includes Bertie, Brunswick, Chowan, Craven, Gates, Hertford, Martin, New Hanover, Pender, Perquimans,
and Washington Counties.
d Includes Bladen, Columbus, Duplin, Jones, Lenoir, Northampton, Pitt and Sampson Counties.
8 Includes Bertie, Chowan, Craven, Gates, Hertford, Martin, New Hanover, Onslow, Pasquotank, Perquimans,
and Washington Counties.
f For example, North Carolina has 1368 square kilometers of dry land less than 1 meter above spring high
water.
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[ SECTION 1.2 67 ]
Table A12. North Carolina jurisdictions not included in shore protection study (hectares)
Meters above Spring High Water
County
0.5
1.0
1.5 2.0 2.5 3.0 3.5 4.0
4.5
5.0
Dry Land, by half meter elevation increment
Bladen
0.0
0.0
0.1
1.7
6.8
12.2
33.7
112.2
225.0
691.0
Columbus
0.2
2.1
2.8
8.8
13.9
18.5
21.2
22.9
32.9
39.3
Duplin
0.2
0.1
0.1
0.0
0.5
2.3
6.2
13.7
19.3
55.2
Jones
190.4
116.3
140.3
178.4
224.2
312.0
388.4
525.8
676.4
762.9
Lenoir
0.0
0.0
0.0
0.0
0.5
5.1
11.3
21.2
50.9
96.2
Northampton
6.5
10.4
11.1
19.8
47.7
83.2
114.2
124.7
131.6
140.1
Pitt
105.8
137.0
230.2
303.5
421.4
508.0
710.1
973.0
1106.3
1233.4
Sampson
0.0
0.0
0.0
0.0
0.0
2.5
5.0
8.2
11.4
34.1
Wetlands
Tidal
Nontidal Wetlands,
, by half
meter elevation increment
Bladen
0.0
0.3
20.3
70.1
125.9
214.1
277.6
432.4
644.7
461.4
895.1
Columbus
0.0
20.1
58.2
104.9
134.7
126.8
108.1
86.3
58.1
47.3
143.5
Duplin
0.0
0.0
0.0
0.0
0.0
5.0
9.5
65.3
134.6
112.4
221.9
Jones
350.8
811.1
332.6
246.7
263.8
244.8
251.8
241.0
271.4
242.4
220.7
Lenoir
0.0
0.0
0.0
13.6
40.3
108.4
168.4
246.9
205.3
361.9
405.4
Northampton
0.0
119.8
85.7
73.5
125.2
224.1
192.9
194.0
133.7
82.8
80.3
Pitt
0.0
2142.9
526.3
490.1
479.3
497.3
497.0
500.9
557.6
550.0
456.0
Sampson
0.0
0.0
0.0
0.0
0.0
0.1
70.1
99.5
115.9
100.5
202.1
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