**Screening to Identify and Prevent Urban Storm Water Problems:**

**Estimating Impervious Area Accurately and Inexpensively**

## Sandra Bird, Jim Harrison, Linda Exum, Stephen Alberty and Christine Perkins

**Biographical Sketches**

Sandra Bird is an Environmental Engineer with the EPA Office of Research
and Development in Athens, GA. Her research interests include
forecasting impacts of land use and land cover changes on aquatic
resources, and modeling the off-site drift from agricultural pesticide
use.

Jim Harrison, the presenting author, has been an Environmental Scientist
with EPA's Region 4 in Atlanta, GA for 18 years. His responsibilities
include developing biological and nutrient criteria, monitoring, data
management, and water quality indicators for geographic planning and
community-based projects.

Linda Exum is a Geographer with the EPA Office of Research and
Development in Athens, GA and has worked for EPA for 25 years. Her
research interests include analysis of land use and land cover change
and interpretation of aerial photography.

Stephen Alberty is a Software Development Specialist with the Computer
Sciences Corporation, Athens, Georgia.

Christine Perkins is a Software Development Specialist with the Computer
Sciences Corporation, Athens, Georgia.

# Abstract

Complete identification and eventual prevention of urban water quality
problems pose significant monitoring, "smart growth" and water quality
management challenges. Uncontrolled increase of impervious surface area
(roads, buildings, and parking lots) causes detrimental hydrologic
changes, stream channel erosion, habitat degradation and severe
impairment of aquatic communities. Existing aerial photography (digital
orthophoto quarter quadrangles - DOQQ's), sampled statistically using
desktop GIS tools, was used to evaluate impervious area estimates based
on readily available landscape data including: categorized land-cover
data (National Land Cover Data -- NLCD); block-level census data; and
road networks. Models linking the photo interpretation and wide area
estimation techniques provided: 1) cheap estimates of impervious cover
with known accuracy at the watershed and sub-watershed scales; 2) a
comprehensive state-wide ranking of Georgia waters likely impaired or
threatened by urban storm water; and 3) characterization of change in
imperviousness over time. Multiple data source estimation of
imperviousness provides improved accuracy compared to the use of
land-use/land- cover alone, especially for the 5-10% impervious range
where prevention of storm water problems is critical. Estimated
imperviousness change from 1993 to 1999 revealed 51 Georgia watersheds
defined by 12-digit hydrological unit codes (HUCs) with substantial
impervious area increases (class changes) during this short, 6-year
period. For 1999, 92 HUCs were estimated to be more than 10% impervious
with potentially detrimental aquatic impacts, and 137 in the 5 to 10%
range with detrimental aquatic impacts likely with future growth unless
preventive actions are taken. Similar analyses will be expanded to the 8
Southeastern states of EPA Region 4. These screening results can guide
in-situ monitoring to confirm problems, aid listing of impaired waters
under Section 303(d) of the Clean Water Act and total maximum daily load
(TMDL) development, provide reliable scientific information to energize
sound local planning and land-use decisions, and promote protection
[and]{.underline} restoration of urban streams.

# Background/Introduction

Urban and suburban development threatens surface water quality in many
areas of the United States (USEPA 2000). This threat is rapidly
increasing as the U.S. population grows. Along with increased
development comes increased impervious surface\--areas preventing
infiltration of water into the underlying soil. Roadways, parking lots
and rooftops account for the majority of impervious area. Hydrologic
(Poff et al. 1997; Richter et al. 1996) and physical stresses (Gaff
2001), as well as chemical contamination, must be addressed to protect
and restore urban water resources. Screening techniques are needed to
assess imperviousness and related urban stresses to allow comprehensive
identification of water quality problem areas as part of systematic
water quality monitoring strategies (Harrison 1998).

The pace of urban growth in the Southeastern United States is
unprecedented. A recent National Geographic map (Mitchell and Leen 2001)
illustrates this extremely rapid urban/suburban expansion using
Department of Defense "city lights" data from two time periods, 1993 and
the "present." Huge areas of "sprawl" growth are particularly evident
throughout the Southeast and are most heavily concentrated in the area
between Atlanta, GA and Raleigh, NC. Based on National Resources
Inventory data, land in developed uses increased by over 25% in Georgia
between 1992 and 1997 (USDA 2000).

Rapid growth is expected to continue. Preliminary forecasts expect urban
land in the study area to increase from 20 million acres in 1992 to
roughly 52-55 million acres in 2020, and to 72-81 million acres in 2040
(Southern Forest Resource Assessment 2001 draft). This urban expansion
will likely come at the expense of both agricultural and forest areas.
Regions likely to be most affected by future growth are the Piedmont,
the Lower Atlantic and Gulf Coastal Plains and the Southern
Appalachians.

Fundamental social and economic forces govern conversion of land from
uses of less value to uses of greater value. Production of wealth drives
much economic activity and growth. In the Willamette River Basin
(Oregon, USA), the dollar value of developed land relative to its dollar
value for dry land (non-irrigated) agriculture was 59 times for land
prepared for homes, 253 times for land with single family homes, up to
552 times for land in commercial use, and 390 to 2535 times for
industrial use (Hulse and Ribe 2001). This tremendous increase in land
valuation places intense economic pressure promoting development of land
to urban use whenever the demand exists.

Urban growth produces many stresses on water quality. Some of these
stresses include: lack of maintenance of sanitary sewer infrastructure,
e.g. combined sewer overflows, sanitary sewer overflows, sewer leaks and
faulty septic systems; extensive hydrologic alteration of watersheds,
e.g., excessive runoff from impervious surfaces; riparian area
destruction or degradation; polluted runoff from impervious areas and
managed landscapes; sedimentation from construction activities;
inadequate control of point sources; and illicit discharges (Harrison et
al. 2001). In addition to extremely deleterious ecological and water
quality impacts, flooding is also an often devastating result of the
urban hydrologic alteration (Inman 2000; Inman 1995), a stress that is
only sporadically regulated at the local level.

Increased imperviousness causes a well-known cascade of damaging results
to streams (Wolman 1967). Detrimental hydrologic changes cause more
frequent, higher peak flows and lower base flows. Altered flow regimes
also increase stream bank erosion and channel enlargement producing
significant sedimentation from the stream channel itself. The resulting
unstable channel often evidences highly degraded aquatic habitat,
largely due to unstable substrates. Due to lowered base flows, streams
do not have the resilience to recover from drought conditions. The end
result of these stresses is usually severe biological impairment and
poor aquatic community integrity. Other stresses often compound
hydrologic impacts from imperviousness. Summer stream temperatures may
be elevated due to runoff from pavement and structures, placing
additional stress on the biological communities. Riparian alterations
regularly exacerbate stream channel erosion and increase stream
temperatures further. Additional habitat degradation often ensues from
reduced input of large woody debris (LWD), and from increased stream
crossings by roads, sewers and other structures that create barriers to
fish movement. Impervious surfaces channel pollutants directly into
waterways, preventing processing of these pollutants in soils. Higher
pollutant loads, particularly oils, other petroleum products and metals
are typically associated with roadways, while biocides (pesticides and
herbicides) are generally associated with managed landscapes (Center for
Watershed Protection 1998b).

Recent research has consistently shown strong relationships between the
percentage of impervious cover in a watershed and the health of the
receiving stream. Booth and Jackson (1994) suggest that 10% impervious
watershed area "typically yields demonstrable loss of aquatic system
function," and that lower levels may be significant to sensitive waters.
In a review of research on impervious cover, Schueler (1994) concluded
that, despite a range of different criteria for stream health, use of
widely varying methods and a range of geographic conditions, stream
degradation consistently occurred at relatively low levels of
imperviousness (10% or greater). May et al. (1997) found that indicators
of stream health in the Pudget Sound Lowlands declined most rapidly from
5 to 10 % impervious cover. A recent survey of Maryland streams (Boward
et al. 1999) found that brook trout (Salvelinus fontinalis), a species
very sensitive to water temperature, were not present in any streams
where the watershed was greater than 2% impervious cover.

Stream response to imperviousness likely varies due to local soils,
geology, slope and land management practices. Absent more specific local
models, Schueler's (1994) three imperviousness classes of impact provide
a useful initial guide to stream quality in the Southeastern US:

> **Sensitive streams** have 0 to10% imperviousness and typically have
> good water quality, good habitat structure, and diverse biological
> communities if riparian zones are intact and other stresses are
> absent.
>
> **Impacted streams** have 10 to 25% imperviousness and show clear
> signs of degradation and only fair in-stream biological diversity.
>
> **Non-supporting streams** have \>25% impervious, a highly unstable
> channel and poor biological condition supporting only
> pollutant-tolerant fish and insects.

Although there are strong relationships between impervious cover and
stream health, the utility of imperviousness as an indicator of
potential stream degradation remains a function of the ease and accuracy
for estimating it. A number of approaches have been used for measuring
and estimating impervious cover. While ground based surveys can be
extremely accurate, these surveys are typically prohibitively expensive.
Readily available, high-resolution satellite imagery facilitates rapidly
expanding use of remote sensing techniques for impervious cover
estimation. The National Land Cover Data (NLCD circa 1993), developed
for the Multi Resolution Land Characteristics Consortium, provides
nationally consistent land-use/land-cover at 30-meter resolution. The
NLCD identifies three urban area classes: high-intensity
commercial/industrial, high-density residential and low-density
residential (Vogelmann et al. 2001). A number of relationships between
population density and impervious cover have also been developed
(Stankowski (1972); Graham et al. (1974); Hicks and Woods (2000)). City
planners often use land-use zoning for rapid estimates of total
impervious area. Both population density and land-use zoning based
estimation methods provide a means for projecting increase in impervious
cover in a watershed, using either population growth or build-out
scenarios as the forcing function (Arnold 1996). Population density is
available nationally from the U.S. Census Bureau, but comprehensive
land-use zoning data are not available throughout the Southeast.

In this study, aerial photography (digital orthophoto quarter
quadrangles - DOQQs), sampled statistically using desktop GIS tools, was
used to evaluate impervious area estimates based on available landscape
data including categorized land cover data (National Land Cover Data --
NLCD), block level census data, and road networks. Wide area estimation
techniques were used to identify Georgia watersheds or hydrologic unit
codes (HUCs) that may currently be impaired due to urbanization plus
those that are likely to show degradation in the near future based on
the current status and rate of growth of impervious cover. \[Note:
Hydrologic units established by the USGS (8, 11 and 12/14 digit HUCs )
are widely available and are often used as surrogates for watersheds.
However, many HUCs are not true watersheds (Omernik and Bailey 1997),
and this must be recognized when using HUCs for water quality or
landscape analyses.\] These screening results presented herein can guide
in-situ monitoring to confirm problems, aid listing of impaired waters
under Section 303(d) of the Clean Water Act and total maximum daily load
(TMDL) development, provide sound information to energize local decision
makers and promote protection [and]{.underline} restoration of urban
streams.

**Materials and Methods**

#### Test Data Set Development

An impervious cover test data set for 56, 14-digit HUCs in Frederick
County, MD was developed using DOQQs from the U.S. Geological Survey
(USGS) taken in 1989. DOQQs are computer-generated versions of aerial
photographs that have been ortho-rectified so they represent true map
distances and are available for any area of the country (USGS, 1996).
The DOQQs have a 1 m^2^ resolution and their analysis provides a high
level of accuracy in the determination of impervious cover at a
sub-watershed scale (Zandbergen,. A point-sampling method on a 200 m
regular grid was used to evaluate the impervious area; a detailed
description of the methodology and quality assurance assessment is
provided in Bird, et al. (2000). The DOQQ sampling yielded an average of
approximately 800 sample points per 14-digit HUC\--with a total of
43,816 points in the study area. Quality assurance objectives for these
data were to obtain a measure of the percent total impervious area
(%TIA) within +/- 1 for watersheds with a %TIA of less than 10% of the
total watershed area and within 10% of the %TIA for watersheds with a
%TIA greater than 10%.

A second set of test data was developed for 13 12-digit HUCs around and
just North of Atlanta, GA for two separate time periods. Two sets of
digital aerial photography existed for the study area. The first, taken
in 1993, was a black and white (gray-scale) set of DOQQs similar to
those used in the Frederick County evaluation. The second set of DOQQs,
taken in 1999, was color-infrared. The color-infrared photography
covered the same geographic location and was also created by the USGS.
These data allowed us to evaluate the ability to do wide area estimates
of the change in impervious cover over relatively short time periods. A
200 m regular grid was used for sampling and the analysis method was the
same as used in the Frederick County, MD study and described in Bird, et
al. (2000). There were a total of 23,176 points sampled averaging 1783
points per 12-digit HUC.

The greatest potential introduction of error identified in the quality
assurance assessment was from an individual analyst's interpretation of
the images. In order to control this error, sampling points overlaid on
the DOQQs were characterized by two independent analysts as either
pervious or impervious. A third individual served as a quality assurance
checker. The quality assurance checker imported the results of the first
two analysts into a program that compared the two grids on a
point-by-point basis. Points with discrepancies in categorization of
results by the first two analysts were reviewed by the quality assurance
checker who made the final determination of assignment of categories.
The black and white aerial photography for the Atlanta area were of poor
quality relative to the color-infrared photography during the study time
period, and were expected to have a higher interpretation error rate.

Impervious cover is not a single homogenous quantity. Generally, paved
surfaces and buildings fall unambiguously under the definition of
impervious surfaces. However, ambiguity can exist even for these
categories since there is now isolated use of pervious paving materials,
allowing some infiltration. Other areas, such as dirt and gravel roads
and parking lots, railroad yards, and quarry areas that may not be
coated with manmade impervious materials are in many instances so
heavily compacted as to be functionally impervious. Actual surface
material in these latter cases is often hard to determine from the
aerial photography. These features were categorized as impervious in the
interpretation of the photography in our study.

#### Wide Area Estimation Techniques for Impervious Surfaces

####  

Two different approaches were used to estimate impervious surfaces over
a large area, i.e., 1624 12-digit HUCs wholly contained within Georgia.
First, three different data types\--population density from block-level
census data, commercial/industrial and quarrying/mining land-cover
category from the National Land Cover Data, NLCD, (Vogelman, et al.
2001), and interstates and major US highway coverage\--were combined to
estimate impervious cover. Population density served as an indicator of
impervious cover generated by residential development. This residential
contribution was estimated from a relationship developed by Hicks and
Woods (2000) between population density and %TIA. The two NLCD
categories provide information on contributions from major
manufacturing, commercial and quarrying areas, which can be more
reliably detected by satellite imagery. These areas were assumed to be
90% impervious (the NLCD defines the commercial/manufacturing category
as 80% or greater impervious cover in a 30 m cell). The highway
coverages provided information on impervious cover contributed by major
highways (interstate and other US highways) that aren't necessarily
related to local residential development. The highway contribution was
calculated based on the length of the roadways and number of lanes,
assuming a 12 ft lane width.

Second, for purposes of comparison, simple class-based imperviousness
assumptions were applied to the National Land Cover Data (1993) for 1624
Georgia 12-digit HUCs using the ATTILA landscape factor extension tool
for ArcView (Ebert and Wade 2000). Imperviousness cover assumptions
were: High-Intensity Commercial/Industrial -- 90%, High-Density
Residential -- 60%, Low-Density Residential -- 40%, and Other Grasses
(primarily parks and golf courses) -- 10% (Center for Watershed
Protection 1998b).

**Evaluation of Estimation Methods**

The accuracy of wide area estimates of impervious cover based on
combining the Hicks and Woods (2000) population-density method results
with estimates of industrial and commercial contributions from the NLCD
and contributions from highways (Interstates and other major U.S.
highways) were compared to the data sampled from two different areas --
Frederick County, MD and selected watersheds in Georgia in a region
North of Atlanta. Figure 1 compares the estimated impervious cover to
the measured values for Frederick County. The straight line indicates a
one-to-one match between the estimated and measured values. Overall,
this technique underestimated impervious cover by 0.8% TIA, with an
average absolute error of 1.4% TIA. This estimate was obtained without
fitting to the test data set. For Frederick County as a whole, the
residential area calculated from population density contributed 65% of
the imperviousness, commercial/industrial land cover from the NLCD
contributed 25%, the major highways contributed 6%, and quarrying and
mining contributed 4%. Fifty-six percent of the points categorized as
impervious from the aerial photography interpretation were in grid cells
categorized as agricultural class in the NLCD, indicating limitations of
using satellite land cover alone to estimate imperviousness (Bird et al
2001). Many of these points fell in very low density residential areas
which fell below the threshold for categorization as developed by the
NLCD criteria.

An additional set of test DOQQ measurements were made for thirteen
watersheds in midtown and north Atlanta and the Etowah River basin north
of Atlanta. Impervious cover was evaluated at two time periods, 1993 and
1999. Since the NLCD is based on 1993 data, the 1993 set of aerial
photography was excellent for evaluating the estimation methods. The two
time windows were informative for change detection since two of the
North Atlanta watersheds doubled in impervious cover during this time
period. Table 1 shows the results of estimated and measured impervious
cover in the 13 watersheds. Results for 1993 estimated and measured
values are shown in Figure 2. Both the NLCD only and the multiple data
source (MDS) approach provided reasonable estimates for urbanized
watersheds. For low impervious area watersheds, the MDS approach
underestimated the impervious area somewhat, as seen in the Frederick
County results. The NLCD only method underestimates impervious area even
more significantly than the MDS method. The NLCD does not identify
low-density residential development areas where lots are typically
greater than ¼ acre with impervious area under 30% in a 30 m x 30 m grid
cell. Since the MDS method relied on updated population data for the
1999 estimates, but only 1993 commercial/industrial area land cover
contribution, there was a greater underestimate for 1999 using MDS
method. By contrast, the MDS approach appeared to slightly overestimate
the imperviousness in the very-developed mid-town Atlanta watersheds.

Table 1. Percent Total Impervious Area (%TIA) Results from North Georgia
Watersheds

+-----------+---------+-----------+-----------+---------+-----------+
| HUC       | DOQQ    | NLCD      | Multiple  | DOQQ    | Multiple  |
| number    |         |           | Data      |         | Data      |
|           | 1993    | 1993      |           | 1999    |           |
|           |         |           | Sou       |         | Sou       |
|           |         |           | rces-1993 |         | rces-1999 |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 52.1    | 44.9      | 54.9      | 49.1    | 58.1      |
| 300011204 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 35.8    | 31.6      | 36.6      | 32.3    | 38.0      |
| 300011202 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 33.8    | 31.8      | 41.0      | 34.1    | 44.8      |
| 300011201 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 8.6     | 6.5       | 9.7       | 15.8    | 13.8      |
| 300011002 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 6.1     | 3.4       | 6.2       | 9.5     | 7.9       |
| 300011001 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 21.0    | 20.7      | 24.6      | 24.4    | 27.6      |
| 300010907 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 10.5    | 11.3      | 14.9      | 22.4    | 23.9      |
| 300010906 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 1.6     | 1.9       | 1.6       | 2.0     | 1.7       |
| 501040301 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 3.4     | 1.9       | 1.7       | 5.1     | 1.9       |
| 501040302 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 3.7     | 1.9       | 2.5       | 5.5     | 2.9       |
| 501040303 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 3.6     | 2.0       | 3.6       | 7.9     | 4.4       |
| 501040304 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 5.4     | 2.0       | 3.8       | 8.4     | 4.9       |
| 501040305 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+
| 031       | 2.0     | 1.8       | 1.7       | 3.9     | 1.7       |
| 501040306 |         |           |           |         |           |
+-----------+---------+-----------+-----------+---------+-----------+

**Application to Georgia**

Impervious cover was estimated for 1624 12-digit watersheds (HUCs)
wholly contained within the state of Georgia, using both the simple
NLCD-only approach and the multiple data source (MDS) approach. The use
of NLDC data with the ATTILA landscape factor extension tool provided a
very rapid analysis and identified most of the potentially degraded
watersheds (Table 2). The NLCD-only method identified 69 watersheds as
having over 10% TIA whereas the MDS approach identified 80. The
NLCD-only method under-estimated the number of watersheds in the
at-risk, 5 to 10 % TIA, range. For 1993, the MDS approach identified 117
HUCs in the 5 to 10% impervious class versus 76 for the NLCD only
approach\--35% fewer.

Thus, the NLCD-only approach appears to have limitations for identifying
imperviousness in the 5 to 10% range. This range, particularly in areas
with significant growth, likely incorporates the most critical areas
where prevention of storm water problems might be most effective. Figure
3, for 1993, identifies the specific Georgia HUCs categorized by MDS as
of concern (i.e. great than 5 %TIA) that were not identified by the
NLCD-only. It is important to remember that the MDS approach may
underestimate these HUCs somewhat as well.

Table 2. Evaluation of Impervious Cover Status of Georgia
watersheds/HUC's.

+---------+---------+------------+------------+----------+------------+
| Imp     | NLCD    | Multiple   | Multiple   | Change   | High       |
| ervious | Data    | Data       | Data       |          | Growth     |
|         |         |            |            | (19      |            |
| Cover   | Only    | Sources    | Sources    | 93-1999) | \> 0.2 %   |
| Class   | (1993)  | (1993)     | (1999)     |          | TIA/yr     |
|         |         |            |            | from     |            |
| (% TIA) | (Number | (Number of | (Number of | lower to | (Number of |
|         | of      | w          | w          | higher   | w          |
|         | wate    | atersheds) | atersheds) | class    | atersheds) |
|         | rsheds) |            |            |          |            |
+---------+---------+------------+------------+----------+------------+
| 0 -5    | 1479    | 1427       | 1395       | -32      | 12         |
+---------+---------+------------+------------+----------+------------+
| 5 - 10  | 76      | 117        | 137        | +32      | 19         |
+---------+---------+------------+------------+----------+------------+
| 10 - 25 | 58      | 62         | 67         | +12      | 36         |
+---------+---------+------------+------------+----------+------------+
| \> 25   | 11      | 18         | 25         | +7       | 13         |
+---------+---------+------------+------------+----------+------------+

Between 1993 and 1999, we estimated that a total of 51 HUCs changed to a
higher risk impervious cover category. Figure 4 shows that the majority
of these watersheds were in the Atlanta area. The largest change was 32
HUCs moving from the 0 to 5% class to the 5 to 10% class. Appreciable
imperviousness changes were also evident in the higher impervious
classes with 12 HUCs moving from the 5 to 10% range to the 10 to 25%
range and 7 HUCs from the 10 to 25% to the \>25% ranges. For 1999, we
estimated that there were a total of 229 HUCs of concern, i.e. HUCs that
are currently impaired or likely to be in the near future (14% of 1624):
92 (\~6%) for likely existing impairment (imperviousness above 10%), and
137 (\~8%) for impairment in the near future (5 to 10% impervious range)
if appropriate planning and management is not undertaken. Since there is
likely an underestimate of impervious area for 1999 using the MDS
approach, even more watersheds than we estimated are likely changing
categories. The expected result is increasing storm water stress on the
streams in these areas.

**Conclusions and Recommendations**

### Monitoring and Priority Storm Water Management Areas

This study demonstrates the utility of using inexpensive landscape
screening tools to identify areas for priority monitoring for urban or
urbanizing watersheds. The use of the NLCD only with the ATTILA tool
identifies most watersheds that are likely suffering impairment from
urbanization and allows a very rapid assessment. Unfortunately, this
tool is not as useful in identifying watersheds whose condition may be
in a borderline category and vulnerable to impairment in the near
future. The statistical approach to air photo interpretation of
imperviousness supplies an essential, cost-effective, independent
accuracy assessment for both the MDS and the NLCD only approaches and
allows their use for wide areas with known accuracy. The promising pilot
results of the multiple data source (MDS) approach in identifying
watersheds vulnerable to degradation from increasing impervious area for
Georgia improve on the NLCD only approach and encourage application of
the MDS approach to all eight of the Southeastern states of EPA Region
4. These analyses are now underway, to the extent that comparable
watershed/HUC mapping is available for the other states, through
cooperative efforts of EPA/ORD-Athens and EPA Region 4-Atlanta. Results
of these analyses will be provided to state water quality agencies to
aid their water quality monitoring efforts.

Accurate, inexpensive impervious area estimates constitute an important
landscape screening tool for designing water quality monitoring
programs. State monitoring programs have limited resources, and thus
cannot sample everywhere. Scientifically sound landscape screening
processes provide workable, defensible methods to: extrapolate condition
estimates to waters lacking in-stream data; identify suspected problem
areas (likely impaired waters); identify candidate reference areas
(least impaired waters); target additional monitoring to confirm
problems; target prevention activities to specific threatened areas;
prioritize TMDL development and restoration planning efforts; and,
evaluate landscape stresses and causes of water quality problems for
large areas (Harrison et al 2000).

Some urban streams are listed as impaired through Section 303(d) of the
Clean Water Act and are subject to TMDL development. However, many are
not yet listed, primarily due to a lack of systematic monitoring
approaches to identify urban water quality problems. Specific
recommendations for the results presented here are:

1)  HUCs with imperviousness exceeding 10% that are not already listed
    under the 303(d) impaired waters listing process should be monitored
    to ascertain if they are in fact impaired;

2)  Jurisdictions encompassing HUCs within the imperviousness range 5 to
    10% should undertake proactive storm water management actions to
    prevent water quality degradation; and,

3)  Jurisdictions encompassing HUCs with imperviousness exceeding 10%
    and confirmed impairment should quickly adopt effective storm water
    management ordinances and provide necessary funding to address
    existing problems and institute expanded prevention activities.

### Further Research

Additional research will be needed to account for differences in
sensitivity to hydrologic storm water stress in different areas. A
number of geographic frameworks should be tested to evaluate the
variation in response to hydrologic stress from impervious areas
including: ecoregions and subecoregions (McMahon and others 2001);
hydrologic landscapes (Winter 2001); and average hydrologic response
(Woodruff and Hewlett 1970). In addition, the USGS is undertaking a
series of "urban gradient" studies that will gather both landscape and
in-stream data for a variety of urban areas around the nation. These
efforts could provide valuable information to help understand variations
in response to imperviousness and other urban stresses. Since some
impervious areas are not directly connected to streams and other waters,
work is also needed to incorporate cost-effective estimates of effective
impervious area into storm water planning (Sutherland 1995 and Alley and
Veenhuis 1983).

Tools to estimate impervious area and in-stream response attack just one
of many stresses associated with urban expansion (Karr 1999). Practical
screening tools are also needed for nutrient and upland sediment loading
(Jones and others 2001, and Wickham and others 2002), bacterial
contamination (Mallin and others 2000) and for pesticide/herbicide
contamination.

#### Approaches for Storm Water Management

The known severity and growing extent of urban storm water problems
strongly argues for comprehensive local approaches to storm water
management, including both prevention of problems in growing areas and
restoration/retrofit of existing problem areas (Center for Watershed
Protection 1998a & b). Current approaches to municipal/county storm
water permitting, including the new round of MS 4 permits that now
include smaller urban areas, have traditionally focused on chemical
monitoring rather than the impacts of storm water volumes coming from
impervious surfaces. While there are a growing number of examples of
local governments who are tackling storm water management head on, such
as Griffin, GA and Charlotte, NC, effective, adequately funded local
storm water programs are the exception rather than the rule.

Some large cities are initiating storm water management, and known
problems will be very expensive to solve. Atlanta, with a metropolitan
population of over 3 million, has begun this process. The Metro Atlanta
Urban Watershed Initiative and other local watershed studies revealed
that most area streams are already degraded (MAUWI 1998). The Clean
Water Initiative of the Metro Atlanta Chamber of Commerce led to the
creation of the North Metro Atlanta Water Authority--a coalition of
local governments focusing on integrated planning for water supply,
waste water treatment and storm water management. Storm water management
needs for the next 20 years are estimated at over \$10 billion for the
Atlanta metro area (Clean Water Initiative 2000). Early products of the
Authority, required by the enabling legislation, include model storm
water ordinances to be implemented and funded through individual local
governments. Effective development practices that protect water quality
are perhaps the most critical element for storm water management, and
should be adopted by all local governments (Nichols and others 1997 and
1999).

Storm water utilities are one increasingly attractive option to fund and
to focus planning and implementation to prevent and correct storm water
problems. About 350 municipal governments in the United States have
begun fee-based storm water utilities, most within the past 10 years
(Walker 2001). Other options to explore for balanced, equitable funding
include supplemental road use fees (which might be collected through gas
tax mechanisms) since roads comprise roughly 2/3 of the impervious area
in many urban watersheds, and bonds for low interest loans and grants.
Additional continued private and government support for pilot storm
water efforts remains essential while long term funding for effective
planning, implementation and maintenance is structured.

#### Prevention and Restoration

Prevention is critical. Stream channels de-stablized by excessive urban
storm water runoff from impervious surfaces continue to erode for many
decades (or longer) (Hammer 1972), have little potential to recover
naturally and can be restored only with great difficulty and expense
(Rosgen 1994). Combining storm water management options, e.g., reduction
of impervious surfaces through smart design, watershed retrofits using
infiltration, extended detention and on site practices, with geomorphic
stream channel restoration can help many urban streams regain some of
their natural integrity. Successful restoration should follow the
sequence of 1) hydrology, 2) channel and habitat, 3) riparian zones and
4) aquatic biological communities (National Research Council 1992, and
Brosnan and others 1999). Total Maximum Daily Load (TMDL) implementation
should consider using this same sequence. Restoration should be pursued
to the maximum extent possible, but will be expensive.

A three to four fold increase of urban area in the Southeast over the
next 40 years need not result in the widespread destruction of our
streams, a resource vital to every community's quality of life. If we
get serious now about the importance of imperviousness, we can avoid
totally unnecessary storm water degradation of streams, and put those
waters already impacted back on the road to recovery.

**Disclaimer**

# 

This paper has been reviewed in accordance with the U.S. Environmental
Protection Agency's peer and administrative review policies and approved
for presentation and publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.

# 

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![](media/image1.wmf)

Figure 1. Impervious cover for 56 Frederick County, MD watersheds
measured from aerial photographs and estimated from multiple data
sources (MDS), including U.S. Census population density, manufacturing
and industrial areas from derived satellite imagery and major highway
networks from U.S. Department of Transportation. The straight line would
be the one-to-one match of measured data and the estimated.

![](media/image2.wmf)

Figure 2. Impervious cover for 13 North Georgia HUCs measured from 1993
aerial photographs and estimated from multiple data sources (MDS),
including U.S. Census population density, manufacturing and industrial
areas from derived satellite imagery, and major highway networks from
U.S. Department of Transportation along with estimates based on National
Land Cover Data only (NLCD). The straight line would be a one-to-one
match of measured and the estimated.

![](media/image3.wmf){width="6.675in" height="8.629166666666666in"}

Figure 3. Estimated 1993 Percent Impervious Area for 1624 Georgia
12-digit HUCs. Fifty-two (52) HUCs identified as at-risk (5-10%
impervious) or potentially degraded (\>10% impervious) using Multiple
Data Sources (MDS), but not identified using the land cover data alone,
are cross hatched.

![](media/image4.wmf){width="7.371527777777778in"
height="8.651388888888889in"}

Figure 4. Estimated 1999 Percent Impervious Area in 1624 Georgia
12-digit HUCs. The 51 HUCs which changed to a higher risk impervious
class between 1993 and 1999 are cross hatched.