EPA/600/R-05/061

May 2005 

Estimating and Projecting


Impervious Cover in the 


Southeastern United States


by 

Linda R. Exum


Sandra L. Bird


James Harrison1


Christine A. Perkins2


published by 

Ecosystems Research Division


National Exposure Research Laboratory


U.S. Environmental Protection Agency

Athens, GA 30605-2700


1

 U.S. Environmental Protection Agency 


Region 4


Atlanta, Georgia


2

 Computer Sciences Corporation


Athens, Georgia 30605-2700


U.S. Environmental Protection Agency

Office of Research and Development


Washington, DC 20460

Notice 

The research described in this document was funded by the U.S. Environmental 

Protection Agency through the Office of Research and Development and was conducted at the 

Ecosystems Research Division of the U.S. Environmental Protection Agency, National Exposure 

Research Laboratory in Athens, Georgia. Mention of trade names or commercial products does 

not constitute endorsement or recommendation for use. 

ii 
Acknow ledgments 

We wish to thank Lourdes Prieto for her immense help in lending GIS technical 

assistance, including helping determine impervious cover during the test data set development. 

We also thank Stephen Alberty for his development of the GIS Cover Tool and helping 

determine impervious cover during the test data set development. 

Foreword 

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 areas (roads, buildings and parking lots) causes detrimental 

hydrologic changes, stream channel erosion, habitat degradation and severe impairment of 

aquatic communities. In conjunction with the U.S. Environmental Protection Agency, Region 4­

Atlanta, we provide a multiple data source estimation of imperviousness in the southeastern U.S. 

These estimates demonstrate an inexpensive method of determining impervious cover with 

known accuracy at the watershed and sub-watershed scales plus characterization of the change in 

imperviousness over time. In addition, this report estimates future impervious cover in the 

southeastern U.S. using the multiple data source technique. These estimates 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 

restoration of urban streams. 

Rosemarie C. Russo, Ph.D. 

Director 

Ecosystems Research Division 

Athens, Georgia 

"When we see land as a community to which we belong, we may begin to use it with love and 

respect." --Aldo Leopold 

iii 
Abstract 

Urban/suburban land use is the most rapidly growing land use class. Along with 

increased development inevitably comes increased impervious surface--areas preventing 

infiltration of water into the underlying soil. The extensive hydrological alteration of watersheds 

associated with increased impervious cover is very difficult to control and correct relative to the 

impact of urbanization on waterways. Development practices that reduce impervious area and 

include preventative strategies to protect water quality are more effective and less costly than 

remedial restoration efforts. Simple and reliable methods to estimate and project impervious 

cover can help identify areas where a watershed is at risk of changing rapidly from a system with 

relatively pristine streams to one with significant symptoms of degradation. In this study, a 

method for estimating and projecting impervious cover for 12 and 14 digit HUCs over a large 

area was developed and tested. These methods were then applied in EPA Region 4’s eight

southeastern states to provide the Region with a screening tool to guide monitoring and 

educational efforts. 

iv 
Table of C ontents 

Abstract ................................................................................... iv


1. Background and Introduction ................................................................ 1


1.1 Stream Biotic Response to Impervious Cover ............................................ 3


1.2 Using Impervious Cover as a Regional Indicator ......................................... 5


1.3 Study Objectives .................................................................. 8


2. Test Data Set Development ................................................................. 10


2.1 Digital Orthophoto Quarter Quadrangles (DOQQs) ...................................... 10


2.2 Data Collection System ............................................................ 12


2.3 Sampling System Design ........................................................... 14


2.4 Sample Size ..................................................................... 15


2.5 Analyst Variability ............................................................... 16


2.6 Final Sampling Scheme ............................................................ 18


2.7 Results ......................................................................... 18


3. Development of a Multiple Data Source Method for Regional Scale Estimates of Impervious Cover ........ 26


3.1 Population Density Relationships .................................................... 26


3.2 Use of Categorized Satellite Imagery ................................................. 27


3.3 Multiple Data Source Approach ..................................................... 34


3.4 Comparison of NLCD only and Multiple Data Source (MDS) Approach ..................... 37


4. Impervious Cover in the Southeastern United States ............................................. 42


4.1 Alabama ....................................................................... 45


4.2 Florida ......................................................................... 47


4.3 Georgia ........................................................................ 49


4.4 Kentucky ....................................................................... 51


4.5 Mississippi ..................................................................... 53


4.6 North Carolina .................................................................. 55


4.7 South Carolina .................................................................. 57


4.8 Tennessee ...................................................................... 59


5. Future Impervious Cover Projections for the Southeastern United States .............................. 61


5.1 The Nature of Errors in Population Projections ......................................... 61


5.2 Impervious Cover Projection Method ................................................. 63


5.2.1 Residential Component .................................................... 63


5.2.2 Commercial/Industrial Component ........................................... 65


5.2.3 Major Highway Component ................................................ 67


5.3 Impervious Cover Projections ....................................................... 67


5.3.1 Alabama ............................................................... 69


5.3.2 Florida ................................................................ 73


5.3.3 Georgia ................................................................ 77


5.3.4 Kentucky ............................................................... 81


5.3.5 Mississippi ............................................................. 85


5.3.6 North Carolina .......................................................... 89


5.3.7 South Carolina .......................................................... 93


5.3.8 Tennessee .............................................................. 97


5.4 Using the Impervious Cover Projections .............................................. 101


6. Conclusions and Recommendations ......................................................... 102


References ............................................................................... 104


Appendix ................................................................................ 113


v 
List of Figures 

Figure 1.1 Multiple Data Source Impervious Area for North Carolina Piedmont Benthic Site Watersheds ...... 6


Figure 1.2 Percent Degraded Piedmont Sites vs. Total Impervious Area ................ ................. 7


Figure 2.1 Examples of Features in DOQQ scenes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12


Figure 2.2 Captured Screen from the Cover Tool Extension Software ................ ................. 14


Figure 2.3 Example of Difference (Unassigned Points) Between Analyst 1 and Analyst 2. ................. 15


Figure 2.4 Sample Size and Deviation vs. Grid Spacing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16


Figure 2.5 Comparison of Impervious Cover by Analyst. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17


Figure 2.6 Impervious Cover Results from the DOQQ Interpretation for Frederick County, MD ............. 20


Figure 2.7 Impervious Cover Results from the DOQQ Interpretation of 13 Atlanta Area HUCs ............. 24


Figure 3.1 The three relationships between population density and %TIA ................ .............. 28


Figure 3.2 Land cover map of the eight Southeastern states using the NLCD92 ................ .......... 30


Figure 3.3 Total acreage categorized as residential (combined high and low density) in the NLCD92 data ..... 31


Figure 3.4 Impervious cover for Frederick County, MD watersheds measured from aerial photographs vs that


estimated from categorized satellite imagery ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32


Figure 3.5 Impervious cover for 13 Atlanta, GA area HUCs measured from aerial photographs vs that estimated


from categorized satellite imagery ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34


Figure 3.6 Impervious cover for Frederick County, MD watersheds measured from aerial photographs vs that


estimated from Multiple Data Sources ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36


Figure 3.7 Impervious cover for 13 Atlanta area HUCs measured from aerial photographs vs that estimated from


Multiple Data Sources ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37


Figure 3.8 Estimated 1993 %TIA for 1624 Georgia 12 digit HUCs ................ . . . . . . . . . . . . . . . . . . . 40


Figure 3.9 Estimated 1999 %TIA for 1624 Georgia 12 digit HUCs ................ . . . . . . . . . . . . . . . . . . . 41


Figure 4.1 Southeastern United States impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . 44


Figure 4.2 Alabama impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46


Figure 4.3 Florida impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48


Figure 4.4 Georgia impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50


Figure 4.5 Kentucky impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52


Figure 4.6 Mississippi impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54


Figure 4.7 North Carolina impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56


Figure 4.8 South Carolina impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58


Figure 4.9 Tennessee impervious cover for 2000 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60


Figure 5.1 High Intensity Commercial/Industrial area (meters) vs. population for North Carolina ........... 66


Figure 5.2 Alabama projected impervious cover out to 2025. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70


Figure 5.3 Alabama Projected %TIA as % of Area out to 2025 ................ . . . . . . . . . . . . . . . . . . . . . . 71


Figure 5.4 Total River Miles in Alabama by %TIA Category out to 2025 ................ .............. 72


Figure 5.5 Florida projected impervious cover out to 2025. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74


Figure 5.6 Florida Projected %TIA as % of Area out to 2025 ................ . . . . . . . . . . . . . . . . . . . . . . . . 75


Figure 5.7 Total River Miles in Florida by %TIA Category out to 2025 ................ ................ 76


Figure 5.8 Georgia impervious cover out to 2010. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78


Figure 5.9 Georgia Projected %TIA as % of Area out to 2010 ................ . . . . . . . . . . . . . . . . . . . . . . . 79


Figure 5.10 Total River Miles in Georgia by %TIA Category out to 2010 ................ ............... 80


Figure 5.11 Kentucky impervious cover out to 2030. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82


Figure 5.12 Kentucky Projected %TIA as % of Area out to 2030 ................ . . . . . . . . . . . . . . . . . . . . . . 83


Figure 5.13 Total River Miles in Kentucky by %TIA Category out to 2030 ................ .............. 84


Figure 5.14 Mississippi impervious cover out to 2015. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86


Figure 5.15 Mississippi Projected %TIA as % of Area out to 2015 ................ . . . . . . . . . . . . . . . . . . . . 87


Figure 5.16 Total River Miles in Mississippi by %TIA Category out to 2015 ................ ............ 88


Figure 5.17 North Carolina impervious cover out to 2030. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90


Figure 5.18 North Carolina Projected %TIA as % of Area out to 2030 ................ ................. 91


Figure 5.19 Total River Miles in North Carolina by %TIA Category out to 2030 ................ ......... 92


Figure 5.20 South Carolina impervious cover out to 2025 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . 94


Figure 5.21 South Carolina Projected %TIA as % of Area out to 2025 ................ ................. 95


Figure 5.22 Total River Miles in South Carolina by %TIA Category out to 2025 ................ ......... 96


Figure 5.23 Tennessee impervious cover out to 2020 ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98


Figure 5.24 Tennessee Projected %TIA as % of Area out to 2020 ................ . . . . . . . . . . . . . . . . . . . . . 99


Figure 5.25 Total River Miles in Tennessee by %TIA Category out to 2020 ................ ............ 100


vi 
List of Tables 

Table 2.1 Impervious Cover Interpretation of 1989 HUCs for Frederick County, Maryland ................ 21


Table 2.2 Impervious Cover Interpretation of 1993 (Black & White) DOQQs and 1999 (Color) DOQQs of 13 12


digit HUCs in the Atlanta, Georgia Area ................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25


Table 3.1 Empirical relationships between population density and impervious area ................ ...... 27


Table 3.2 Impervious Cover for Frederick County, Maryland NLCD92 Land Cover Categories ............ 33


Table 3.3 Percent Total Impervious Area (%TIA) Results for North Georgia Watersheds ................. 38


Table 3.4 Evaluation of Impervious Cover Status of Georgia Watersheds/HUC’s ................ ........ 39


Table 4.1 %TIA as a Percentage of the Total Land Area of Each Southeastern State Using the 2000 


Census and the Multiple Data Source Approach ................ . . . . . . . . . . . . . . . . . . . . . . . . . 43


Table 5.1 Sources and Dates of Population Projections for Each Southeastern State ................ ..... 64


Table 5.2 2000 U.S. Census and State Population Projections for the Southeastern United States ........... 68


Table 5.3 %TIA as a Percentage of the Total Land Area of Alabama out to 2025 ................ ........ 71


Table 5.4 Total River Miles in Alabama by %TIA category out to 2025 ................ ............... 72


Table 5.5 %TIA as a Percentage of the Total Land Area of Florida out to 2025 ................ ......... 75


Table 5.6 Total River Miles in Florida per TIA category out to 2025 ................ ................. 76


Table 5.7 %T IA as a Percentage of the Total Land Area of Georgia ................ .................. 79


Table 5.8 Total River Miles in Georgia per TIA category ................ . . . . . . . . . . . . . . . . . . . . . . . . . . 80


Table 5.9 %TIA as a Percentage of the Total Land Area of Kentucky out to 2030 ................ ....... 83


Table 5.10 Total River Miles in Kentucky per TIA category out to 2030 ................ ............... 84


Table 5.11 %TIA as a Percentage of the Total Land Area of Mississippi out to 2015 ................ ...... 87


Table 5.12 Total River Miles in Mississippi per TIA category ................ . . . . . . . . . . . . . . . . . . . . . . . 88


Table 5.13 %TIA as a Percentage of the Total Land Area of North Carolina out to 2030 ................ ... 91


Table 5.14 Total River Miles in North Carolina per TIA category out to 2030 ................ ........... 92


Table 5.15 %TIA as a Percentage of the Total Land Area of South Carolina ................ ............ 95


Table 5.16 Total River Miles in South Carolina per TIA category ................ . . . . . . . . . . . . . . . . . . . . . 96


Table 5.17 %TIA as a Percentage of the Total Land Area of Tennessee out to 2020 ................ ...... 99


Table 5.18 Total River Miles in Tennessee per TIA category ................ . . . . . . . . . . . . . . . . . . . . . . . 100


vii 
1. Background and Introduction 

Nonpoint source pollution (NPS), i.e., pollution from diffuse sources such as

urban/suburban areas and farmlands, is now recognized as the primary threat to water quality in 

the United States (U.S. Environmental Protection Agency 1994). The pressure on water

resources due to urbanization is rapidly increasing as the U.S. population grows. Urban area in 

the contiguous United States increased 26% and roads increased 2% from 1982 to 1992, while 

rangeland and cropland/pasture each reduced 2%, respectively (USDA 1997). Between 1992 

and 1997 the estimated urban area in the contiguous United States increased another 6 million 

acres, or 11%, while grassland pasture and rangeland decreased by 11 million acres or another 

2% (USDA 2003). In 1997 USDA identified a new subcategory named “rural residential” as 

part of its miscellaneous uses, a category that includes marshes, swamps, bare rock areas, deserts 

and transitional areas (USDA 2003). Miscellaneous uses also increased significantly between 

1992 and 1997, due in large part to the increase of rural residential. 

The U.S. population more than doubled from 133 million to 281 million people between

1945 and 2000, with the total households increasing to 106 million, a quarter of which consisted

of a single individual. Besides more land being converted to residential uses, especially for 

homes, new residential areas also require land for schools, office buildings, shopping sites, and

other supporting commercial and industrial uses. The amount of urban land in the U.S. has risen 

steadily from 15 million acres in 1945 to an estimated 66 million acres in 1997, converted 

mostly from pasture, range and forest land (USDA 2003). 

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, developed land increased between 

1992 and 1997 in the Southeast as follows: Alabama (16.2%); Florida (18.9%); Georgia 

(27.4%); Kentucky (12.8%); Mississippi (16.2%); North Carolina (15.1%); South Carolina

(20.8%); and Tennessee (20.4%) (USDA 2000). A probability sample of landscape trends for 

ecoregions of the mid-Atlantic and Southeastern United States documented an increase in urban 

area for the Southern Piedmont from 12% to over 16% between 1972 and 2000, the most rapid 

urban growth among the ecoregions sampled (Griffith, et al. 2003). 

Rapid growth is expected to continue. Preliminary forecasts expect urban land in the 

study area of the Southern Forest Resource Assessment to increase from 20 million acres in 

1992 to 55 million acres in 2020, and to 81 million acres in 2040 (Wear and Greis, 2002). This 

urban expansion will likely come at the expense of both agricultural and forest areas. Regions in 

the Southeast 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 2000). This tremendous increase in land

valuation places intense economic pressure promoting development of land to urban use 

whenever the demand exists. 

1 
Urban growth produces many stresses on water quality. Often sanitary sewer 

infrastructure is not properly maintained and capacity is insufficient. Combined and sanitary

sewer overflow, leaking sewer pipes and faulty septic systems lead to effluent inadvertently 

reaching waterways. Sedimentation from construction activities, inadequate control of point

sources, polluted runoff and illicit discharges lead to a decline in water quality (Harrison, et al. 

2001). 

Arguably, the most difficult to control and correct relative to the impact of urbanization

on water courses is the extensive hydrologic alteration of watersheds , i.e., excessive (as well as 

polluted) runoff from impervious surfaces and riparian area degradation. Along with increased

development inevitably 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. It is estimated that there are more than 105,200,000 parking spaces in the U.S., 

with a ratio of off-street spaces to on-street spaces roughly two-to-one (NCDENR 2002). 

Studies in some metropolitan areas indicate that there are seven times more parking spaces than 

there are vehicles. 

In addition to extremely deleterious ecological and water quality impacts, flooding is also 

a devastating result of the urban hydrologic alteration (Inman 2000; Inman 1995), a stress that is 

only sporadically regulated at the local level. 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. 

Increased imperviousness causes a well-known cascade of damaging results to streams 

(Wolman 1967 and Caraco , et al. 1998). Detrimental hydrologic changes cause more frequent, 

higher peak flows (Jennings and Jarnagin, 2002) and lower water tables and base flows which 

can influence both riparian (Groffman, et al. 2003) and aquatic communities. Due to lowered 

base flows, streams have reduced resilience to recover from drought conditions. Watershed 

runoff can increase by two to over five times normal for forested catchments as impervious area 

increases from the 10 to 20% range to 75 to100% respectively (Arnold and Gibbons 1996). 

Altered high flow regimes also increase stream bank erosion and channel enlargement producing 

significant sedimentation from the stream channel itself. The few available quantitative studies 

of channel changes due to urbanization indicate that from one-half (1/2) to three-quarters (3/4) of 

stream sediment load originates from channel erosion (Trimble 1997; Dartiguenave and

Maidment 1997; Corbett, et al. 1997) rather than upland sources. The resulting unstable channel 

often evidences highly degraded aquatic habitat, largely due to unstable substrates. The end 

result of these stresses is usually severe biological impairment and poor aquatic community 

integrity. (See both Paul and Meyer 2001, and Center for Watershed Protection 2003 for 

comprehensive reviews of impacts of impervious area on aquatic systems.)

 Often, other ecological stresses compound hydrologic impacts from imperviousness. 

Summer stream temperatures can 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 

(Paul and Meyer 2001). 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 2003). 

2 
Effective storm water management practices implemented in a watershed to control

runoff volumes, flow rates and pollutant concentration can partially mitigate the impacts of 

urbanization and increased imperviousness. Development practices that reduce effective 

impervious area (EIA) and include other strategies to protect water quality are more effective 

and less costly than remedial restoration efforts (Nichols, et al. 1999). EIA is that portion of the 

total impervious area (TIA) that is directly connected to the stream drainage system. The EIA 

includes streets, driveways, sidewalks adjacent to curbed streets, parking lots, and rooftops 

hydraulically connected to the curb or storm sewer system. Empirical relationships between EIA 

and TIA have been developed (Sutherland 1995). Rainfall on impervious areas that are not 

directly connected hydraulically to the drainage collection system does not always result in 

direct runoff and is not as damaging to the biotic integrity of the stream system. 

Parcel based analyses of hydrologic and other impacts of impervious area are needed to 

inform effective land use policies and local development regulations. Regression modeling

using six important aspects of parcel and street network design explained roughly 77% of 

residential impervious cover variation in the Madison, Wisconsin area. This work pointed to 

potentially effective policies to reduce imperviousness through zoning considerations such as lot 

size, frontage, and front yard setbacks; through street and subdivision design practices such as 

block size and intersection density; and through retrofit of existing residential driveways (~20% 

of impervious area of parcels) with porous paving materials over time as resurfacing is needed 

(Stone 2004). 

The change from a watershed with relatively pristine streams to one with significant 

symptoms of degradation can occur rapidly in high growth urban areas. Often this occurs before 

an awareness by local planners develops on the need to consciously manage storm water 

impacts. State storm water control mandates are often set well above the levels where instream 

biotic degradation occurs. Impervious area estimates and projections are a potentially effective 

tool for highlighting areas that are at-risk for aquatic resources degradation or where stream 

system integrity is likely to decline in the near future if effective planning and management 

programs are not implemented. These estimates and projections can also guide the selection of 

monitoring locations by state and regional EPA officials, focus educational efforts in at-risk 

areas, and aid wide-area planning. 

1.1 Stream Biotic Response to Impervious Cover

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 Puget 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. 

Fish IBI results for Ridge and Valley streams indicated poor or very poor fish 

communities for catchments with greater than 7% urban land use (Snyder, et al. 2003). Ohio 

urban gradient stream sites - excluding sites with allied stresses such as combined sewer 

overflows, waste water treatment plants, sewer line problems and other habitat alterations ­

3 
showed significant IBI declines with urban area greater than 13.8% and failed to meet Clean 

Water Act goals where urban area exceeded 27.1% (Miltner, et al. 2004). Extensive loss of 

mussel species (50 to 70%) occurred in Georgia streams experiencing impervious area expansion 

(Gillies, et al. 2003). Tidal creek ecosystems in South Carolina experienced adverse physical 

and chemical changes (hydrology, salinity, sediment, chemical contamination and fecal coliform 

loading) above 10 to 20% imperviousness, with significant biological changes above 20 to 30% 

impervious area (Holland, et al. 2004). For southeastern Wisconsin streams, fish communities 

declined sharply between 8 to 12% connected imperviousness and were consistently poor above 

12% impervious area (Wang, et al. 2001). Evaluation of 245 sites with biological data in 

Montgomery County, Maryland required less than 10% impervious and greater than 60% 

riparian tree cover to attain a stream health rating of good (Goetz, et al. 2003). 

Scientists recognize that fish assemblages in developed watersheds are affected primarily 

by nonpoint source anthropogenic stressors that result from land use development (Williams, et

al. 1989; Richter, et al. 1997; Wilcove, et al. 1998). Alteration of hydrologic regimes in terms of 

the amount and variability of flow affect all aspects of fish life history (e.g., Allan 1995). 

Sedimentation can increase fish movement, interfere with fish feeding by reducing reactive 

distance for sight-feeders and lower the abundance of insects available as food, and impair 

reproduction of fishes with specific spawning habitat requirements (Newcombe and MacDonald 

1991; Bergstedt and Bergersen 1997). Habitat destruction can isolate patches of suitable habitat 

within a stream which reduces species' survival. Habitat destruction also changes the natural 

mosaic of habitat conditions, thereby altering natural fish movement and migration patterns 

(Reeves, et al. 1995). 

This wide variety of stream response to imperviousness may likely be due to local slope, 

soils, geology, land and storm water management practices and other factors. For example,

higher gradient sites in the Ridge and Valley show larger decreases in fish IBI with increasing 

imperviousness than do lower gradient sites (Snyder, et al. 2003). Absent more specific local

models, Schueler’s (1994) three imperviousness classes of impact provide a useful initial guide 

to stream quality in the Southeastern United States: 

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. 

A more extensive and updated review of this classification of impact corroborated these 

original conclusions (Center for Watershed Protection 2003). While impervious cover alone is 

not the sole causative agent for the decline of aquatic health in urbanizing areas (Miltner, et al. 

2004), it contributes significantly to the decline and appears to serve as an integrative screening 

indicator of urban hydrologic stress (Arnold and Gibbons, 1996). 

While complete descriptions of the range of aquatic responses to imperviousness are not 

available for all areas of the Southeastern United States, extensive biological sampling of benthic 

macro invertebrates by the North Carolina Division of Water Quality covering the wide gradient 

of impervious area throughout the Southern Piedmont ecological region (Griffith, et al. 2002)

provides the best existing data to begin building such relationships. Cursory descriptive 

4 
examination of a portion of this data allows us to glimpse the potential for using existing and

new data to construct robust relationships valid for the entire Southeast. 

Benthic data for over 300 Piedmont sites were kindly provided by Trish MacPherson of 

the North Carolina Division of Water Quality (NCDWQ), along with point watersheds 

delineated for those sites graciously shared by Dr. Halil Cakir and Dr. James Gilliam of North 

Carolina State University. Their detailed, rigorous statistical examination of this data is 

currently in preparation. 

Figure 1.1 maps these North Carolina Piedmont watersheds by impervious class, in the 

context of satellite based land use/land cover for that area. For 159 of these sites with nonoverlapping watersheds, Multiple Data Source (MDS - described in Section 3.3 of this report) 

impervious area estimates were produced. The MDS imperviousness of these watersheds ranges 

from 1% to 60%. 

Figure 1.2 depicts simple box plots of the benthic biological condition response of

streams to increasing impervious area (using both 5% and 10% ranges) for that gradient of 

Piedmont sites based on the North Carolina Biotic Index (NCBI), a tolerance based metric used 

for benthic community assessments and aquatic life use support determinations by NCDWQ 

(North Carolina Department of Environment and Natural Resources 2003). Assuming NCBI 

scores above 6.54 (worse than “fair” on the state’s scale of: excellent, good, good-fair, fair, fairpoor and poor) indicate degraded conditions, progressively greater fractions of degraded sites are 

evident as impervious area increases. For watershed Total Impervious Area (TIA) greater than 

10%: 62% (32/52) of sites are degraded; for TIA > 15%: 78% (25/32) of sites are degraded; for 

TIA > 20%: 83% (19/23) of sites are degraded; and for TIA > 30%: 91% (10/11) of sites are 

degraded. In contrast, for watersheds with TIA<10%: 10% (11/107) of sites were degraded. The 

figure also provides percentages and numbers of sites for individual 5% and 10% ranges of 

impervious area. 

1.2 Using Impervious Cover as a Regional Indicator 

Impervious cover when used as an indicator of stream health is typically presented as a 

percentage of the total land in an area that contains the impervious surfaces, or percent total 

impervious area (%TIA). Several challenges exist in using impervious cover as a regional 

indicator. First is simply defining impervious cover since it is not a single, unambiguous 

quantity. Generally, paved surfaces and buildings fall unambiguously under the definition of 

impervious surfaces. Ambiguity can exist, however, even for these categories since there is now 

a pervious asphalt paving material that allows some infiltration. Other areas, such as dirt roads, 

railroad yards and construction areas that may not be coated with manmade impervious 

materials, are in many instances so heavily compacted as to be functionally impervious. Another 

important distinction concerning impervious cover and its impact on stream health is between 

connected and disconnected impervious surfaces. Connected impervious surfaces are networked 

impervious surfaces (parking lots, roads, sidewalks, etc.) that are physically interconnected and 

eventually flow directly into stream systems via storm sewers, ditches and culverts. 

Disconnected impervious surfaces, such as rooftops, often deposit runoff onto vegetated 

pervious areas. The water from these disconnected impervious surfaces flows through the 

subsurface before reaching stream channel networks, mitigating some of the negative impact on 

the receiving waters. 

5 
Figure 1.1 Multiple Data Source Impervious Area for North Carolina Piedmont Benthic Site Watersheds 

6

Figure 1.2 Percent Degraded Piedmont Sites vs. Total Impervious Area 

A second challenge in using impervious cover as a regional indicator is determining the 

appropriate land area delineation to use in a regional coverage. For any single point in a stream, 

the land area or watershed that drains water to that point in the stream affects the water quality at 

that point. Delineating watersheds and defining %TIA for every stream mile is not a practical 

approach. For this study, we have chosen to use 12 or 14 digit hydrological units (HUCs) based 

on the U.S. Geologic Survey (USGS) hierarchical system. 

The United States is divided hierarchically into successively smaller hydrologic units. 

The USGS has prepared a national coverage of four nested levels identified by two to eight digit 

codes (Seaber, et al. 1987). The first level of classification divides the U.S. into 21 major areas 

containing either the drainage area of a major river or a combination of rivers. The second level 

divides the nation into 222 subregions. The third level divides some of the subregions further 

into a total of 352 hydrologic accounting units that are equivalent to or nest within the subunits. 

The fourth level is the cataloging unit identified by an eight digit code. A total of 2150 

cataloging units form this finest layer of the national coverage. Generally a cataloging unit is a 

geographic area representing part or all of a surface drainage basin. These cataloging units are

typically referred to as eight digit hydrologic unit codes (HUCs). 

7 
Individual states, in collaboration with the USGS and U.S. Department of Agriculture 

(USDA) Natural Resources Conservation Service (NRCS), have delineated subunits of the 8 

digit cataloging units into 11 digit and 12 or 14 digit units (depending on the particular states) 

that are inappropriately referred to as “watersheds” and “subwatersheds”, respectively. The 

“subwatershed” delineations represent areas typically in the 5 to 50 sq mi range (although some 

are larger or smaller). These small-scale subdivisions are more effective units for evaluating 

potential impacts of impervious cover on small, perennial streams. They also provide decisionmakers with appropriate scale geographic frameworks of input for evaluating and managing 

water resources at the local level. 

There are, however, at least two major considerations in using these 12 and 14 digit HUC 

coverages. First, they do not provide a consistent coverage across a multiple state region. This 

problem is particularly obvious when discontinuities are observed along state boundaries. These 

12 and 14 digit HUC delineations are, however, what individual states use for their water 

resources planning and from that perspective are the appropriate mechanism for communication 

between EPA Regional personnel and individual state governments. 

A more subtle and insidious problem to keep in mind is that hydrologic units at any 

hierarchical level are not synonymous with true watersheds. Omernik (2003) points out that

while true watersheds are areas within which surface water drains to a particular point, generally, 

only 45 percent of HUCs meet this definition. In over half of the HUCs, the most downstream 

points have greater drainage areas than those defined by the boundaries of the HUCs and thus are 

not true watersheds. For such stream locations, impacts on instream resources occur due to 

activities beyond a single, delineated HUC. That is, impacts on the stream are influenced by 

activities in more than one of the HUCs. 

A final challenge in use of impervious cover as an effective screening tool for identifying 

at-risk streams is finding an easy and relatively accurate method for estimating it over a large 

area. In addition, the ability to identify at-risk areas also requires the development of approaches 

for estimating impervious cover that link projections of imperviousness to socioeconomic 

projections. 

1.3 Study Objectives

 The objective of this study was to develop and test a method for estimating and

projecting impervious cover for 12 and 14 digit HUCs over a large area. This method was then 

applied in EPA Region 4’s eight Southeastern states, providing the Region with a screening tool 

to guide monitoring and educational efforts. These techniques will not replace the detailed

impervious cover information needed for planning and management of small watersheds, but 

rather will give state and regional planners and managers an overview of potential areas of 

concern so efficient monitoring and mitigation efforts can be initiated. 

A major question then is with what degree of accuracy can impervious cover be 

estimated for subwatershed areas in a region from data available throughout that region. To 

answer this question, test data sets of impervious cover for Frederick County, Maryland and the 

Atlanta, Georgia area were produced using an ESRITM ArcView extension developed for use 

with USGS aerial photography. Details on development of these test data sets are provided in 

Section 2 of this report. 

Existing wide area methods for estimating impervious cover were reviewed and tested 

early in this effort. Multiple sources of data, including the U.S. Census Bureau 1990 and 2000 

Census data, 1992 National Land Cover Data (NLCD) data, and highway information, were all 

used to develop estimates of imperviousness. Section 3 discusses the media, methods and results 

8 
of estimating impervious cover for the HUCs where test data were collected. Estimates of 

impervious cover were then made for 12 or 14 digit HUCs for the eight Southeastern states in 

Region 4 – Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina

and Tennessee – and presented in Section 4. 

Finally, state population projections were added to the Multiple Data Source estimation

technique as the basis for projecting future impervious cover in the eight Southeastern states. 

Projection methods and resulting projections of impervious cover are presented in Section 5. 

9 
2. Test Data Set Development 

The overall goal of this study is the development and application of a simple, reliable 

method for estimating and projecting impervious cover in 12 and 14 digit HUCs for all the states 

in EPA’s Region 4. This task depends on the availability of a smaller area test data set to 

determine if the region-wide estimation techniques developed adequately reflect what is on the 

ground. Such a test data set should include watersheds with a range of %TIA from rural, 

relatively undeveloped areas to high density urban watersheds. Multiple examples of low,

moderate, and intensely developed watersheds should be included in the sample. Ideally, sample 

watershed data should be available from more than one geographic area. Section 2 describes the 

method for developing this test data, describes the areas where the test data was measured and 

results of the final measurements. 

A number of approaches are used for measuring impervious cover. The most accurate 

and costly are ground-based surveys. Ground-based methods are prohibitively expensive to use 

where developing a data base from numerous watersheds as required in this study. The use of 

manual interpretation of aerial photography is commonplace in accuracy assessments of 

automated interpretation remote sensing techniques (Slonecker, et al. 2001) and for other 

applications, including watershed management and tax assessment (Lee 1987; Kienegger 1992). 

Manual interpretation of aerial photography was chosen for development of our test data sets 

since it allows collection of data in a sufficient number of watersheds with an adequate degree of 

accuracy. 

Test data were collected from aerial photographs in two separate locations: 56, 14 digit 

HUCs in Frederick County, Maryland covering 1728 sq km, and in 13, 12 digit HUCs in the 

Atlanta, GA area covering 888 sq km. A data collection and storage system was developed that 

allowed relatively rapid collection of the required data, plus allowed us to meet our data quality 

objectives (DQO). In the quality assurance plan developed at the outset of the project, the DQO 

was stated as +/- 10% of the %TIA, i.e. a 10 %TIA would be measured in the 9 to 11% TIA 

range. In retrospect, for areas with a TIA of 10% and greater, this was an appropriate DQO. For 

low impervious areas, however, this was an objective that was not only unreachable, but also 

unnecessarily stringent given the use of the data, e.g. TIA data in the 1.6 to 2.2 % (about a +/­

20% variability) range is functionally indistinguishable. The final DQO was restated as +/- 10%

of the %TIA for areas with $10 %TIA and as +/- 1 %TIA for areas with <10 %TIA. 

An important decision in the initial phase of the study was whether to collect data in only 

two categories, i.e., impervious vs. pervious cover, or to differentiate between different types of 

impervious elements. While the multi-category data were not necessary to meet the most basic 

needs of the study, it would have added significantly to the information data base and allowed us 

to address additional research questions plus increased flexibility in the use of the data. A 

decision to collect binary data was ultimately made on the basis of our DQOs and resource 

constraints. The uncertainty associated with identifying types of impervious elements from the 

aerial photography was high and the attempt to collect this data required a substantial increase in 

analyst time. 

2.1 Digital Orthophoto Quarter Quadrangles (DOQQs) 

Manual analysis was done on digital orthophoto quarter quadrangles (DOQQs) obtained 

from the USGS. DOQQs are digital versions of aerial photographs that have been orthorectified 

so they represent true map distances and are available for any area of the country from the 

USGS. The DOQQs have 1 m2

 resolution, and their analysis can provide a high level of 

accuracy in the determination of impervious cover at a subwatershed scale (Zandbergen, et al. 

2000). The DOQQs for Frederick County, Maryland photographed in 1989 were single channel, 

10 
gray-scale images with a small total variation in spectral characteristics. For the Atlanta, Georgia 

area watershed, two sets of DOQQs were analyzed. The first, taken in 1993, was a black and 

white (gray-scale) set of DOQQs similar to those used in the Frederick County, Maryland 

analysis. The second set of DOQQs, taken in 1999, was color-infrared. The color-infrared 

photography covered the same geographic location with the same resolution and was also 

created by the USGS. An example of one of the Frederick County DOQQs illustrating several 

pervious and impervious features is shown in Figure 2.1. 

The proportion of area covered by a given type of surface feature can be estimated from 

digital imagery using spectral or visual feature identification methods. Spectral feature

identification uses GIS software to automatically classify features while visual feature 

identification involves classifying features manually by a human analyst. Spectral image 

analysis involves using specialized GIS software to characterize each pixel in an image to 

determine its spectral reflectance. Pixels with reflectance values within predefined ranges are 

grouped together to form feature classes. Spectral analysis software is configured or “trained” to 

recognize a surface feature based on the spectral characteristics it commonly exhibits. Image

analysis software allows the user to graphically select examples of each type of surface feature. 

The programs then analyze the examples and search the entire image for areas that exhibit the 

same spectral characteristics. Spectral analysis works well with multi-spectral color imagery and 

when the surface features of interest are distinct and can be clearly defined. Features such as 

roof tops can have a wide variety of spectral characteristics since roofing materials are available 

in a broad range of colors. Spectral methods cannot identify the fact that a building or road 

extends under tree canopy as can be done by a human analyst. While the spectral analysis 

approach can be very efficient in terms of speed, for our analysis we were not confident that we 

would be able to achieve an acceptable level of accuracy using automated methods. 

Ground features can be identified and categorized efficiently and accurately by a human 

analyst with the help of Geographic Information System (GIS) software. Overlaying ancillary 

point, line or polygon data on top of a photographic image provides extra information that might 

be useful in differentiating features. A user looking at a good quality photograph can 

differentiate features using shape, spatial relationships and geographic context. For example, a

human can reason that a large rectangular feature in a rural area is more likely to be an 

agricultural field than a parking lot (Figure 2.1). Even with the help of software tools and 

ancillary data, visually identifying and categorizing features on aerial photography can be very 

time intensive depending on the size of the area, the density of features, and the speed with 

which features can be categorized. Visual identification can also be subjective and vary from 

analyst to analyst. In addition, the possibility of missing very small impervious features, such as 

sidewalks or even driveways, is very real. While the visual analysis of DOQQs appeared to be 

our best option for developing the desired data base, software that allowed for efficient and 

accurate collection of data and clear guidelines to maintain consistency between analysts were 

important considerations for the success of this effort. 

At the initiation of the analysis it was also very important to clearly state which features 

we would categorize as impervious and pervious from the DOQQs. The features we designated 

as impervious cover were commercial structures, parking areas, industrial areas, quarries, 

constructions sites, railroad yards and railroads, residential structures, driveways, roads, paved 

streets, dirt roads, highways (but not grassed medians) and airport runways. The features we 

designated as pervious cover were vegetated or bare areas, agricultural fields, lawns, parks, 

forests, grassed highway medians, water features (including swimming pools), lakes, ponds, 

streams and swamps. 

11 
Figure 2.1 Exam ples of Feature s in DO QQ scenes. 

2.2 Data Collection System 

The amount of area covered by impervious surface can be measured directly by 

delineating the extent of each impervious feature found on the DOQQ with a polygon. Because 

of the spatial distribution, size and shape of impervious features, like roof tops and sidewalks, it 

is time consuming to draw polygons that accurately delineate each feature. While delineating

each feature allows generation of a complete measure of the impervious cover of an area 

including the location of the impervious cover within the watershed, our goal was to simply 

estimate the fraction of impervious cover in the entire 12 or 14 digit HUC areas. Rather than 

delineating individual impervious features for this study, we estimated impervious cover in HUC 

areas using a point sampling technique. A grid of points was overlaid on the HUC area and the 

%TIA (percent total impervious area) was estimated as the percentage of the points sampled in 

the HUC classified as impervious. The selected software, sampling and analysis systems yielded 

accurate and reproducible results and allowed efficient collection of data that was stored in a 

georeferenced data format. Ground features were identified and categorized by human analysts 

12 
with the help of Geographic Information System (GIS) software and with a “cover tool” 

extension designed specifically for this data collection effort. 

Both polygon and point sampling of impervious cover are limited in accuracy by the 

ability to properly identify and resolve ground features. The limitations of this sampling are

variable based on both the clarity of the photographs and the nature of the ground cover. 

Imperviousness in newly developed areas where photographic quality is high and landscaping 

has not developed to obscure ground features can be identified with confidence. In older 

neighbors where tree cover can obscure much of what is on the ground and mature shrubbery can 

often obscure sidewalk and driveway edges, accuracy will inevitably be lower. Thus the 

accuracy of our sampling system is limited by the characteristics of the media we are sampling. 

The goal, however, was to develop an efficient sampling system that gave us accurate and

reproducible results within the limitations of the media being sampled. Lack of “ground truth” 

data limited our ability to totally quantify the accuracy of our “air truth” data set. 

The primary software design goal was to develop an efficient, flexible tool that provided 

a framework for accurate and efficient land cover analysis. ArcView® GIS from Environmental 

Systems Research Institute, Inc. (ESRI) was chosen as the development platform because it was 

the U.S. EPA standard GIS software, was available and familiar to the analysts and provided an 

object-oriented programming and development environment called Avenue® (ESRI 1996).

Avenue® scripts were written to add several new functions and controls for characterizing 

impervious cover to the existing ArcView® user interface. Collectively, these new functions are 

referred-to as the “Cover Tool.” The Cover Tool functions fall into three categories: 1) sample 

point generation, 2) land cover type assignment, and 3) quality assessment. 

The sample point generation feature constructs a point coverage grid in ArcView at a 

user-specified density overlaying a DOQQ. This feature was designed so the analyst could 

configure the sampling density of a regular sampling grid by choosing the spacing between 

points in both the vertical and horizontal directions. Alternately, the user can generate a random 

coverage containing a specified number of points. A user-configurable sample point generator 

was one of the original software requirements. It allows the analyst to test a range of grid 

densities and configurations to find the configuration that minimizes the amount of time required 

to analyze impervious cover while assuring that data quality objectives are met. Sample size 

determination and sampling system design will be discussed subsequently. 

Fast and accurate assignment of the land cover type was the primary requirement in the 

design of the data collection software. An integrated point selection and cover type assignment 

tool was designed to make this operation as efficient as possible. Analysts can select one or 

more similar points and use function keys to rapidly assign a land cover type class to the selected 

sample point(s). Alternatively, the analyst can click their secondary mouse button to display a 

context-sensitive “popup” menu to change the cover type classification. Users can choose the 

classification method that best suits their style, allowing them to work most efficiently. A 

significant amount of an analyst’s time during on-screen analysis is spent navigating across the 

coverage. In order to navigate around an image, a control was designed to allow seamless 

panning (i.e., changing the geographic display area). The pan control (Figure 2.2) provides 

movement across a screen view width in the horizontal, vertical and diagonal directions and, 

thus, provides a systematic way for analysts to locate and analyze sample points. As an added 

benefit, the pan control allows the analysts to orient themselves and move efficiently across the 

image in either rows or columns. 

13 
Figure 2.2 Captured Screen from the Cover Tool Extension Software Showing the Pan Button 

and Grid Method 

To help ensure complete and reliable results, the cover tool includes reporting and 

comparison features. The report feature calculates the percentage of pervious, impervious or 

unassigned (i.e., not yet sampled) points, and lists preliminary and/or final analysis results. This 

feature quickly summarizes land cover type percentages and helps the analyst determine if any 

unclassified points remain. The comparison feature analyzes results from two independent 

analysts and identifies individual points that are classified differently. After applying the 

comparison tool, any sample point that is classified as “impervious” by one analyst and 

“pervious” by a second analyst will be reclassified by the software as “unassigned” and reported 

to the screen as shown in Figure 2.3. This allows a third, independent analyst to reclassify these 

conflicting points to obtain the final results for the DOQQ. 

2.3 Sampling System Design

After completion of a prototype version of the Cover Tool, a series of exercises to test the 

software and refine the sampling system were conducted. The purpose of the exercises was to 

identify potential sources of error and ensure the methods were efficient and reliable. 

Two popular schemes for placing the point sample locations are random and systematic 

point distribution. A GIS can employ the simple random sampling technique by placing a given 

number of points at random locations within a specified geographic study area. Properly 

designed random sampling schemes effectively reduce errors that can arise due to regular, 

repeating features on the landscape and provide defensible results. 

14 
Figure 2.3 Example of Difference (Unassigned Points) Between Analyst 1 and Analyst 2. 

P

Systematic point distribution can be an attractive alternative in cases where random 

sampling is more difficult or time-consuming. With the systematic technique, a Cartesian grid 

system with equally spaced points in the x and y dimensions (i.e., in rows and columns) is 

applied to the study area. When using the systematic approach, it is important that the origin of 

the grid be positioned randomly (Borgman and Quimby, 1988) to avoid personal bias. Lee (Lee, 

1987) observed no systematic bias using regular versus random grids for sampling impervious 

cover. During software testing, users found that a systematic sampling system in conjunction 

with the pan tool provided a very efficient means of locating and classifying sample points. The 

pan tool was used to move the photograph to the left and right along rows of sample points, or up 

and down along columns of sample points. This helped orient users and seemed to increase 

analysis speed. Both randomly and systematically spaced points were used and results compared 

for two different DOQQs. One DOQQ was located in a rural area (Catoctin_se) while the other 

was more urban (Fred_sw). Impervious cover results for random (4.81%) and systematic 

(4.56%) point placement analyses on the Catoctin_se DOQQ were not significantly different, 

2

(1,N=4697) = 0.289, p=0.60, and were well within the data quality objectives. Impervious cover

estimated with random point placement on Fred_sw (13.1%) was slightly different from that 

using systematic point placement (14.6%), P2

(1,N=4774) = 3.94, p<0.05. Analyst time required to 

categorize the randomly spaced layout was greater than that with the regularly spaced grid, and 

the analysts expressed a greater sense of fatigue categorizing the randomly spaced grid as well. 

2.4 Sample Size

The primary factors used to determine an appropriate sampling point density are: 1) the

time available for sampling, and 2) the quality objectives. The optimal sampling density, 

therefore, is the one that provides acceptable precision with the least effort. At the limit of an 

infinite number of points, the point sampling becomes a continuous cover similar to the polygon 

15 
delineation. Our goal was to find a sampling grid density that at a minimum would meet our 

data quality objectives. Our goal was not to just minimally meet these quality objectives, 

however, but would also exceed this minimal number and build in a margin of safety. 

Impervious cover was analyzed on two representative DOQQs using a regular grid system. As a 

test, sample points were positioned 50, 100, 200 and 400 m apart in both the x and y dimensions

on the Catoctin_se and Fred_sw DOQQs. Analysts then estimated the cover conditions on each 

DOQQ. The deviations estimated in impervious percent cover relative to their 50 m estimates 

were calculated for the two DOQQs and plotted against sample size to aid in determining the 

optimal sampling density (Figure 2.4). 

Figure 2.4 Sample Size and Deviation vs. Grid Spacing. 

The estimated percent impervious cover varied little over the four sampling densities. 

Even up to a 400 m spacing (~ 275 points per test DOQQ) variation was within the specified 

data quality objectives. A 200 m grid spacing was ultimately chosen for the analysis–a fourfold 

increase in the number of points over the 400 m spacing. 

2.5 Analyst Variability 

The greatest potential introduction of error identified in the quality assurance assessment 

was from an individual analyst’s interpretation of the images. Visual feature analysis relies on 

interpretation of aerial photographs by human analysts and can be subjective. Because 

impervious cover is not a single, homogenous quantity uncertainty can exist even with paved 

surfaces because of the aforementioned pervious asphalt. Paved surfaces and buildings in our 

study were deemed impervious surfaces. Dirt and gravel roads, parking lots, railroad yards and 

16 
quarries were deemed imperious as well due to their heavily compacted nature. Actual surface 

material and nature in these cases is often hard to determine from the aerial photography. In 

addition, trees can interfere with the interpretation of ground features under the canopy, and the 

analyst must interpolate what is under the canopy from surrounding features. 

To quantify variation in cover type results by analyst, the same DOQQ was characterized 

by six individuals (Figure 2.5). Each analyst used an identical sampling grid composed of 1,178

points spaced 200 m apart. The results were compared to determine if substantial bias existed 

between analysts. Some analysts tended to interpret more area as pervious while others tended 

toward impervious. Estimates of impervious cover for the test DOQQ ranged from 11% to 18%, 

with an average estimated value of 14%. This range of results was outside that required to meet 

our quality objectives (12.6% to 15.4%). The subjective judgement required and the resulting 

analyst to analyst variability in the results appeared to be the area in the data collection most 

likely to compromise our data quality standards. In the final development of our sampling 

protocols, reducing these latter errors was the primary focus for our resource investment. 

Figure 2.5 C omp arison of Imp ervio us Co ver b y Ana lyst. 

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 Cover Tool utility that automatically compared the two grids on a point-by-point 

basis. Points with discrepancies in the categorization by the first two analysts were reviewed by 

the quality assurance checker, who made the final determinations of assignment for these 

contested points. 

17 
2.6 Final Sampling Scheme 

A regular sampling grid was used in the final analysis because test analysis found 

categorization of the random grid much more time consuming and tiring than the regular grid. 

The difference in results generated by the two sampling schemes was much lower than the 

analyst to analyst variability. The time and energy saved was better spent on multiple analyses. 

The final method selected relied on the three-analyst scheme described in the previous section. 

Based on the sampling grid size results, a grid spacing of 200 m by 200 m was chosen. This 

density yielded an average of nearly 800 sample points per 14 digit HUC in Frederick County, 

Maryland. This sample size did not compromise our ability to meet our data quality 

requirements. The number of sample points within the entire Frederick County study area at this 

resolution totaled 43,816. At this resolution, approximately 3 to 4 hours was required per 

analyst to categorize each DOQQ. For the 13 Atlanta, Georgia area HUCs, an average of 

approximately 1700 points per HUC was sampled. A total of 22,206 points were sampled in the 

Atlanta area from DOQQs taken at two different time periods. 

Analysis procedures were developed to simplify land cover type assignment and ensure 

the quality of estimates. Because analyst interpretation was identified as the major source of 

sampling variability, training and validation procedures were designed to promote comparable 

results. Each analyst received training in photographic interpretation that included graphic 

examples of common pervious and impervious features. As a general rule, all analyses were 

conducted at a scale appropriate for the resolution of the photographs. Analysts were 

encouraged to zoom closer (i.e., increase the scale), however, to help classify hard-todifferentiate points. Analysts were instructed to characterize each point as absolutely inside the 

feature shown on the photograph. For example, analysts were warned against classifying a point 

as impervious just because it fell “close” to a house. 

To ensure the most reliable impervious cover estimates, two independent analysts 

characterized each of the DOQQs. The DOQQs were randomly assigned to analysts so that no 

individual analyzed a large, contiguous geographic region. A third individual served as a quality 

assurance checker. This final individual imported the results of the first two analysts and 

compared them on a point-by-point basis. This was accomplished using the Cover Tool’s

custom comparison function to identify any classification discrepancies between the two 

analysts. 

Figure 2.3 illustrates results of the comparison function analysis of themes created by 

analyst 1 and analyst 2. The Tool generates a third theme called “Cover Type” that shows points 

highlighted as discrepancies between the two analysts. These discrepancies, symbolized by a 

“X” in the screen view and labeled as unassigned, occurred when one analyst assigned the point 

as pervious while the other assigned it as impervious. Figure 2.3 shows that on a point-by-point 

basis for that DOQQ, the difference between analyst 1 and analyst 2 is 5.9% unassigned points. 

Despite the difference, the total impervious cover estimates were 16.8% vs.16.1% between the 

first two analysts. A third quality assurance analyst examined only the points where there was a 

discrepancy between analyst 1 and analyst 2. The final impervious cover for the DOQQ in 

Figure 2.3 after the quality analyst review was 15.4%. A more detailed discussion of quality 

assurance levels associated with this data collection scheme can be found in Bird, et al. (2000) 

2.7 Results 

The impervious cover for Frederick County, Maryland HUCs ranged from less than 1% 

to 35% as illustrated in Figure 2.6. The highest intensity impervious area centered on the town

of Frederick, with the HUC containing most of the town having 23% TIA. Only three of the 

Frederick County watersheds had impervious cover greater than 10%. The County mean value 

18 
was 5.1% TIA, the median 4.6 % TIA. Table 2.1 contains the final %TIA interpretation data, 

and lists all the HUCs completely or partially contained in Frederick County.

 An ideal data set for testing the use of estimated impervious cover as an environmental 

indicator would have more data points greater than10 %TIA where stream impairment is 

observed, than such data points contained in the Frederick County data. Atlanta, Georgia was 

chosen for an additional data set with the chance of considerably more impervious cover. The 

Atlanta area HUCs are in midtown, north Atlanta and in the Etowah River basin north of Atlanta. 

Six of the thirteen Atlanta area HUCs shown in Figure 2.7 contained greater than 10 %TIA, 

including one midtown watershed with a 50 %TIA. Data from both the 1993 and 1999 

photography are summarized in Table 2.2. North Atlanta is a very high growth area, with one of 

the HUCs there more than doubling in %TIA during that six year period. 

19 
Figure 2.6 Impervious Cover Results from the DOQQ Interpretation for Frederick County, MD 

20

Table 2.1 Impervious Cover Interpretation of 1989 HUCs for Frederick County, Maryland 

14 digit HUC Impervious Cover

 (% TIA) 

Area 

(Sq Mi) 

HU C within County 

02070009040124 1.6 0.9 Completely 

02070009040128 3.4 4.4 Partially 

02070009030101 2.1 11.0 Completely 

02070009030104 4.7 13.2 Completely 

02070009030102 7.8 5.5 Completely 

02070009040127 2.6 5.8 Completely 

02070009060176 2.5 21.6 Completely 

02070009060177 3.7 18.0 Completely 

02070009060201 3.3 4.7 Completely 

02070009060202 2.8 6.6 Partially 

02070008010026 2.1 10.8 Partially 

02070009060227 7.8 16.5 Completely 

02070009060226 3.5 6.9 Completely 

02070008010028 2.6 15.2 Completely 

02070009060228 2.6 18.3 Completely 

02070009050171 4.2 8.0 Partially 

02070009060204 5.2 9.4 Completely 

02070009060203 4.3 3.5 Completely 

02070009060205 3.7 18.3 Completely 

21 
Table 2.1 Impervious Cover Interpretation of 1989 HUCs for Frederick County, Maryland 

14 digit HUC Impervious Cover

 (% TIA) 

Area 

(Sq Mi) 

HU C within County 

02070008010027 3.5 7.3 Completely 

02070009050170 2.3 7.3 Completely 

02070009060251 6.0 14.0 Completely 

02070009050169 6.6 8.3 Partially 

02070009060206 6.4 12.3 Completely 

02070009050168 4.9 3.7 Partially 

02070008010029 5.4 17.3 Completely 

02070008010030 4.5 12.8 Completely 

02070009060208 7.6 8.1 Completely 

02070009060252 7.3 19.1 Completely 

02070009060209 7.0 17.8 Completely 

02070009070280 4.6 21.1 Completely 

02070009070276 2.7 16.3 Partially 

02070008010032 8.0 10.2 Completely 

02070008010031 4.6 14.1 Completely 

02070009060210 23.0 28.3 Completely 

02070009070278 3.9 15.1 Partially 

02070008010036 3.7 15.9 Partially 

02070009070286 5.6 12.3 Completely 

22 
Table 2.1 Impervious Cover Interpretation of 1989 HUCs for Frederick County, Maryland 

14 digit HUC Impervious Cover

 (% TIA) 

Area 

(Sq Mi) 

HU C within County 

02070009070283 3.0 15.8 Completely 

02070009080301 12.0 20.0 Completely 

02070009080302 14.8 5.1 Completely 

02070008010035 6.1 6.5 Completely 

02070009080305 4.9 19.4 Completely 

02070009080303 9.0 13.6 Completely 

02070009080306 5.0 17.1 Completely 

02070008010037 8.8 17.4 Partially 

02070008010052 5.2 24.0 Completely 

02070008010038 3.6 10.6 Completely 

02070009080326 5.6 7.1 Partially 

02070009080330 3.3 17.3 Completely 

02070009080327 7.2 6.6 Partially 

02070008010039 3.5 4.0 Completely 

02070008010051 4.9 5.7 Completely 

02070009080328 1.8 4.2 Partially 

02070009080308 1.5 11.5 Partially 

02070008020076 0.0 0.7 Partially 

23 
Figure 2.7 Impervious Cover Results from the DOQQ Interpretation of 13 Atlanta Area HUCs 

24

Table 2.2 Impervious Cover Interpretation of 1993 (Black & White) DOQQs and 

1999 (Color) DOQQs of 13 12 digit HUCs in the Atlanta, Georgia Area 

12 digit HUC 1993 Impervious 1999 Impervious Area 

Cover (%TIA) Cover (%TIA) (Sq Km) 

031300010906 10.5 22.4 34.0 

031300010907 21.0 24.4 111.9 

031300011001 6.1 9.5 91.8 

031300011002 8.6 15.8 83.7 

031300011201 32.9 35.1 101.4 

031300011202 33.2 34.3 77.6 

031300011204 50.5 49.7 62.5 

031501040301 0.7 1.6 38.7 

031501040302 2.6 4.2 43.0 

031501040303 3.6 4.4 37.9 

031501040304 4.2 7.1 54.5 

031501040305 4.4 6.8 76.8 

031501040306 1.8 3.0 74.4 

25 
3. Development of a Multiple Data Source Method for 

Regional Scale Estimates of Impervious Cover

 Regional (multi-state) scale estimates of impervious cover are not feasible using the 

labor intensive methods discussed in Section 2. Regional scale estimates need to be based on 

automated methodologies that are relatively rapid to implement. In order to achieve an 

acceptable and consistent level of quality throughout the region, calculations should be based on 

regionally available data of known and consistent quality. In this study an important feature of 

the method to estimate current levels of impervious cover was the ability to be able to use the 

same method as a basis to project future scenarios of impervious cover. Generally, the method 

of choice should not be dependent on calibrations and preferably would provide a linkage to 

demographics and other socioeconomic parameters to use as the basis for projections. 

Our study considered three different approaches for performing wide-area estimates of 

impervious cover. The first was based on the relationship of population density to impervious 

cover. The second looked at the potential of using categorized satellite imagery as an estimation 

approach. The third approach, and the one we adopted for the estimation and projection of

impervious cover throughout EPA Region 4 as detailed in the final chapters of this report, was

based on the use of Multiple Data Sources–block level census data, categorized land use/land 

cover data and road networks. This section details each of these three approaches considered 

and provides our evaluation of each. 

3.1 Population Density Relationships 

A number of relationships between population density and impervious cover have been 

developed. 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 an increase in impervious cover in a watershed, using either population growth or 

build-out scenarios as the forcing function (Arnold and Gibbons 1996). Comprehensive land-use 

zoning data is not available regionally, but population density is available from the U.S. Census 

Bureau. Impervious cover is a result of human settlement, and thus, population density should 

be a reasonable predictor of impervious cover arising from residential development and the 

commercial areas that directly support them. Use of population density as a means to estimate 

impervious cover is attractive since it provides a rapid technique for generating a quantitative 

estimation of both present and projected land surface cover. 

Stankowski (1972), Graham, et al. (1974) and the Greater Vancouver Sewerage and 

Drainage District (GVS&DD 1999, Hicks and Woods 2000) developed empirical relationships 

with different functional forms to relate population density (persons/mi2) to percent impervious

cover (%TIA). Table 3.1 shows %TIA as a function of population density developed in each of 

these three studies. Stankowski developed his relationship using county scale data from New 

Jersey with population densities ranging from 120 to 13,800 persons/mi2. The impervious cover

was estimated from land use data available from the state planning office. Graham, et al. 

evaluated selected census tracts for the Washington, DC metropolitan region. Population

densities ranged from 350 to 53,300 persons/mi2. They developed impervious cover estimates at 

the block level ranging from 14% to 98%. Test data of %TIA was developed using 1:50,000 

aerial photography. GVS&DD developed their relationship based on data for the greater 

Vancouver, British Columbia area using impervious cover estimated from land use zoning 

categories. 

26 
Table 3.1 Empirical relationships between population density and impervious area 

Source Relationship 

Stankowski (1972) 

Graham et al. (1974) 

GVS&DD (1999) 

Figure 3.1a shows the %TIA predicted by the relationships developed by Stankowski, 

Graham, et al. and GVS&DD. Data measured from aerial photographs are also included for the 

Frederick County, Maryland watersheds as described in Section 2 and the census tract level data 

from Washington, DC collected by Graham, et al. Whereas the Stankowski relationship 

seriously under predicts %TIA at population densities greater than 1000 persons/mi2, the 

Graham et al. relationship seriously over predicts %TIA for population densities under 500 

persons/mi2. Although the GVS&DD relationship appears to provide the best fit overall, closer 

inspection of the data for population densities under 2000 person/mi2 (Figure 3.1b) indicates that

this function actually underestimates %TIA in this range. Not surprisingly, the greatest under 

prediction occurred in watersheds (HUCs) with significant amounts of intensive 

commercial/industrial and mining/quarrying land cover types. For the Frederick County data, 

the most extreme error was for a watershed with 15 %TIA that was predicted by the GVS&DD 

relationship to have only 4 %. The occurrence of this magnitude of potential error emphasizes 

the limitation of relying solely on population data as an indicator of percent impervious surface 

area. On average, the GVS&DD relationship underestimated impervious cover for the 

Frederick County watersheds by 2 %TIA (sd = 2 %). 

These three population-based approaches do not account for commercial, industrial or 

mining contributions to impervious cover. In addition, they do not account for development 

styles that can alter the per household level contribution to impervious cover. However, 

population density is a good basis for screening level estimation of the residential contribution to 

impervious cover. The exponential relationship of GVS&DD captures the general shape of the 

relationship between population density and impervious cover, but generally underestimates the 

impervious cover. This underestimate is not unexpected since commercial, mining, 

manufacturing and some transportation contributions are not necessarily directly related to 

population density. 

3.2 Use of Categorized Satellite Imagery

While processing and categorization of satellite imagery is expensive and timeconsuming, use of categorized imagery is a rapid and relatively inexpensive method of 

estimating impervious cover. Categorized land use and land cover systems derived from remote 

sensing data define developed land cover classes based on the fraction of impervious cover in a 

specified area (Anderson, et al. 1976; Vogelmann, et al. 1998a). Sleavin, et al. (2000) generated 

percent impervious coefficients for generalized land use and land cover classes developed from 

30 m Landsat Thematic Mapper imagery. While subpixel classification methods show promise

in the quantification of impervious cover (Ji and Jensen, 1999; Slonecker, et al. 2001; Yang, et

al. 2003), data sets developed using these methods are not yet available over large areas and 

these methods generally do not attempt to estimate imperviousness in pixels with less than 20% 

impervious cover. 

27 
Figure 3.1 The three relationships between population density and %TIA 

presented in Table 3.1 are shown in Part a (top figure above) along with data 

collected for this study in watersheds in Frederick County, Maryland and by 

Graham (1974) for census tracts in Washington, DC. Part b (bottom figure above) 

shows the response of the GVS&DD (GVS&DD 1999, Hicks and Woods 2000) 

relationship for population densities less than 2000 persons/sq mi compared to 

data presented on a linear scale. 

28 
 The 1992 National Land Cover Data (NLCD 92) is a categorized land cover data set for 

the continental United States developed for the Multi Resolution Land Characteristics 

Consortium (Vogelmann, et al. 2001) that can be downloaded at no cost. It provides nationally 

consistent land-use/land-cover based on 30 m Thematic Mapper data from the early 1990s plus a 

variety of auxiliary data sources. A land cover map based on NLCD 92 is shown for the eight 

Southeastern states in Figure 3.2. Once watershed boundaries and categorized imagery are 

available in the same geographic projection, software such as ATtILA (Ebert and Wade, 2000) 

make watershed estimates of impervious cover using categorized imagery a very rapid 

operation. For example, estimates of the imperviousness of several hundred watersheds can be 

made within a day’s time. 

One difficulty with using the categorized land cover data for impervious cover estimation 

is the fact that for a pixel to be categorized as even low density developed, it must be at least 

30% impervious cover. Figure 3.3 shows the amount of developed residential land in different 

lot size categories in Frederick County, Maryland based on property tax records (Maryland 

Office of Planning 1999), and the total land area in the two residential cover classes from the 

NLCD 92. The total amount of residential land identified by the NLCD 92 is consistent with 

the acreage in residences on lots less than about 1⁄2 acre. As noted previously, to be classified as 

low density residential in the NLCD92, a 30 m cell must include at least 30% impervious cover. 

Residential development with houses on lots greater than 1⁄2 acre typically have less than 30% of 

the area in impervious cover. Larger lot developments, consistent with these definitions of

developed land cover, are classified into one of the undeveloped categories in the NLCD 92. 

The Frederick County, Maryland test data set was used to estimate the percentage of 

impervious cover in each NLCD92 land cover category as well as the total area of impervious 

cover in Frederick County that was in each land cover category. These estimates are shown in 

Table 3.2. The percentage of the category estimated as impervious in Frederick County is the 

percentage of the points sampled from the DOQQs falling in that land cover class that were 

categorized by analysts as impervious. The sample size is the number of the DOQQ sampling

points that were located within the specific land cover class in the NLCD 92 coverage. The final 

column of Table 3.2 is the percentage of the impervious cover points sampled from the DOQQs 

in Frederick County that are located in the cells of each land cover type. Only 23% of the 

DOQQ sampling points categorized as impervious in Frederick County are located in an area 

categorized by NLCD 92 as developed. Over 50% are located in the agricultural categories. 

While impervious cover certainly exists in rural agricultural areas, a significant portion of the 

land classified as agricultural by the NLCD 92 is, in fact, low density residential development. 

Frederick County is a suburban county, but the land cover data classifies much of the low 

density development as agricultural land. 

The percentage of imperviousness in each land cover class was then used with the 

NLCD92 data for each of the Frederick County HUCs to estimate the percent impervious area in 

each watershed. Since the impervious surface coefficients were derived from the data for the 

whole county, on average the impervious cover estimates were expected to closely match the 

measured data. The mean error between estimated and measured values was 0.2 %TIA (sd = 2). 

The best fit regression line for the data plotted in Figure 3.4 has a slope of 0.522 and a yintercept of 2.74. Ideally, for the perfect model, the regression line would have a slope of 1.0 

with an intercept of 0.0. 

29
Figure 3.2 Land cover map of the eight Southeastern states using the NLCD92 

30

Figure 3.3 Total acreage categorized as residential (combined high and low density) in the NLCD92 data (NLCD 

residential) and by residential lot size category from property tax records for Frederick County, M D. The labels 

for data from the property tax records indicate all the residential lots that are less than the indicated number of 

acres per residence, e.g., <5 ac is the sum of all properties in the tax records that are on lots of less than 5 acres 

per housing unit. 

31 
Figure 3.4 Impervious cover for Frederick County, MD watersheds measured from aerial 

photographs vs that estimated from categorized satellite imagery and categorized coefficients 

developed from county-wide data. This approach systemically over predicts imperviousness in 

relatively underdeveloped watersheds and under predicts imperviousness in developed watersheds. 

32 
Table 3.2 Impervious Cover for Frederick County, Maryland NLCD92 Land Cover Categories 

Land Cover Category Percentage

of Total 

Area in 

Land Cover 

Class 

Sample

Size 

Percentage

of the 

Category

Impervious 

Percent of 

Impervious Area

in Frederick 

County Accounted

for by Category 

low density residential 2.2 990 42 17 

high density residential 0.2 76 77 2 

commercial/industrial 0.4 156 57 4 

quarries/mines/gravel 0.3 117 62 3 

transitional barren 0.1 29 17 0 

deciduous forest 25.5 11159 2 9 

evergreen forest 1.6 697 4 1 

mixed forest 7.6 3400 5 7 

hay/pasture 53.8 23497 5 48 

row crops 6.0 2663 8 9 

other grasses 0.1 33 9 0 

woody wetland 0.9 368 3 0 

herbaceous wetland 0.3 138 1 0 

The impervious surface coefficients derived from the Frederick County, Maryland data 

were also used to estimate impervious area in the 13 Atlanta area HUCs. Figure 3.5 shows the

measured vs estimated %TIA for the Atlanta area HUCs. Once again, the mean error is low (0.6 

%TIA; sd = 3.4). In this case, the regression line slope is still less than 1.0, 0.812, although not 

as flat as the slope through the Frederick County data, with a y intercept of 1.96. The Atlanta 

area data set is more heavily influenced by more developed watersheds, areas where the satellite 

imagery is expected to perform best. Nevertheless, there is still an underestimate of 

imperviousness at the lower end and over estimate of impervious cover in the more developed 

watersheds although not as pronounced as for Frederick County. 

33 
Figure 3.5 Impervious cover for 13 Atlanta, GA area HUCs measured from aerial photographs vs that 

estima ted fro m ca tegor ized sa tellite imag ery and catego ry coe fficients de velop ed fro m Fr ede rick C ounty, 

Maryland data. This approach systematically over predicts imperviousness in relatively underdeveloped 

watersheds a nd und er pred icts imperviousne ss in develop ed watershe ds. 

Jennings et al. (2004) addressed this issue by developing three sets of coefficients of 

imperviousness for each NLCD92 land cover category (a total of 42 coefficients ) based on the 

percent of developed land in a watershed area. This multiple coefficient approach resulted in an 

approximately 2% absolute %TIA error, but does not have the systematic over and under 

prediction bias that using single coefficients per land cover category produced. 

3.3 Multiple Data Source Approach 

In the Multiple Data Source (MDS) approach, three different data types--population 

density from block level census data, the commercial-industrial and quarrying-mining land cover 

category from NLCD 92 and interstates and major US highway coverages--were combined to 

estimate impervious cover. The MDS uses the different data types to represent components of 

34 
imperviousness most appropriate to the specific data source. In the MDS approach, population 

density is used as an indicator of impervious cover generated by residential development. 

Categorized satellite imagery from the NLCD 92 is used to evaluate the contribution of 

commercial and industrial areas–areas that are clearly identified from satellite imagery. Road 

networks from the National Transportation Atlas (USDOT, 2001) data are used as a source for 

major highways to estimate impervious cover contributed by major highways that are not related 

to local residential development. 

The residential contribution to imperviousness was estimated based on population

density using the GVS&DD method (Hicks and Woods 2000, GVS&DD 1999) discussed in 

detail in Section 3.1. We used U.S. Census 2000 block level data to estimate population density 

in individual HUCs. Both population data and vacant housing was used to develop an effective 

population density in the watershed. Many areas of the Southeast, specifically the coastal and 

mountain areas, have high rates of vacation and seasonal housing which is not reflected in the 

resident census count. The number of vacant dwellings multiplied by the average persons per

household for the state in the 2000 Census was added to the residential population for each HUC 

to calculate an effective population. The effective population divided by the HUC area was used 

in the GVS&DD formula to calculate the residential %TIA. 

The categories pulled from the NLCD 92 for the MDS approach were #23/Commercial/ 

Industrial/Transportation and #32/Quarries/Strip Mines/Gravel Pits. The two NLCD 92 

categories add information on the contributions to imperviousness from major manufacturing, 

commercial and quarrying areas that can be detected by satellite imagery. These latter categories 

are assumed to be 90% impervious (Caraco, et al. 1998). By definition the commercialmanufacturing category is 80% or greater impervious in the NLCD 92 classification. 

Impervious area due to major highways was calculated based on the total length of 

interstate and other major US highways arcs (USDOT, 2001) in a watershed (HUC), times the

number of lanes for an individual road arc multiplied by an assumed lane width of 12 ft. Where 

highway arcs overlap with the NLCD categories we extracted, the road arcs were removed to 

prevent double-accounting. 

Total %TIA for the HUC was calculated by summing the impervious area contributed by 

major highways, commercial and mining in each HUC, dividing by the total HUC area and 

multiplying by 100 to convert to percentage, and adding to the %TIA calculated for the 

residential component from the GVS&DD equation. Calculations were performed using 

ArcView 3.2 and a detailed step-by-step procedure and Avenue scripts used in the computations 

are included as an Appendix in this report. 

Estimates of impervious cover based on combining Multiple Data Sources are illustrated 

in Figure 3.6 that compares the estimated impervious cover using the combined data set to the 

measured values for Frederick County, Maryland. The straight line indicates a one-to-one match 

between the estimated and measured %TIA values. Overall, this technique underestimated

impervious cover by 1 %TIA with an average, absolute error of 1 %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% of the calculated imperviousness, the 

major highways contributed 6%, and quarrying and mining contributed 4%. 

35 
Figure 3.6 Impervious cover for Frederick County, MD watersheds measured from aerial photographs vs 

that estimated from Multiple Data Sources, including U.S. Census population density, manufacturing and 

industrial areas from categorized satellite imagery, and major highway networks from U.S. Department of 

Transportation. Overall, this approach under estimated impervious cover by 1 %TIA. 

The Multiple Data Source approach was then applied to the 13 North Georgia HUCs 

(mentioned previously in Section 2). Comparison of estimated and measured %TIA values are 

shown in Figure 3.7 with the straight line once again showing the one-to-one match. Overall, the 

impervious area showed an overestimate of 2 %TIA (sd = 2). The greatest overestimate was 8 

%TIA in one of the central Atlanta watersheds. Impervious cover was generally over estimated 

somewhat greater in the higher impervious area watersheds (HUCs). The Multiple Data Source 

approach was generally able to accurately reflect the wide range of %TIA values in this data set. 

36 
Figure 3.7 Impervious cover for 13 Atlanta area HUCs measured from aerial photographs vs that estimated 

from Multiple Data Sources, including U.S. Census population density, manufacturing and industrial areas 

from derive d satellite im agery a nd m ajor highwa y netwo rks from U.S . Dep artme nt of T ransp ortatio n. For this 

data set, impervious cover was over estimated on average by 2 %TIA. 

3.4 Comparison of NLCD only and Multiple Data Source (MDS) Approach

 Table 3.3 contains the measured impervious cover in the 13 Atlanta area watersheds 

from both the 1993 and 1999 DOQQs, along with estimations from NLCD only data for 1993 

and from the MDS method for 1993 and 1999. Since the NLCD is based on 1993 data, the 1993 

set of aerial photography was excellent for evaluating and comparing the two estimation 

methods. The two time windows were informative relative to change detection, since two of the 

North Atlanta watersheds doubled in impervious cover during this time period. Both the NLCD 

only and the MDS approach provided reasonable %TIA estimates for urbanized watersheds 

(HUCs). For low impervious area watersheds, the MDS approach underestimated the 

impervious area somewhat, similar to the Frederick County results. The NLCD data only 

method underestimated impervious area even more significantly than the MDS method. Since 

the MDS method relied on updated population data for the 1999 estimates, but only the 1993 

37 
commercial/industrial area land cover contribution, there was a somewhat greater underestimate 

for 1999 using the MDS method. By contrast, the MDS approach appeared to slightly overestimate the imperviousness in the very-developed, mid-town Atlanta watersheds (HUCs). 

Table 3.3 Percent Total Impervious Area (%TIA) Results for North Georgia Watersheds 

HUC number DOQQ 

1993 

NLCD 

1993 

Multiple Data 

Sources-1993 

DOQQ 

1999 

Multiple Data 

Sources-1999 

031300011204 52.1 44.9 54.9 49.1 58.1 

031300011202 35.8 31.6 36.6 32.3 38.0 

031300011201 33.8 31.8 41.0 34.1 44.8 

031300011002 8.6 6.5 9.7 15.8 13.8 

031300011001 6.1 3.4 6.2 9.5 7.9 

031300010907 21.0 20.7 24.6 24.4 27.6 

031300010906 10.5 11.3 14.9 22.4 23.9 

031501040301 1.6 1.9 1.6 2.0 1.7 

031501040302 3.4 1.9 1.7 5.1 1.9 

031501040303 3.7 1.9 2.5 5.5 2.9 

031501040304 3.6 2.0 3.6 7.9 4.4 

031501040305 5.4 2.0 3.8 8.4 4.9 

031501040306 2.0 1.8 1.7 3.9 1.7 

Impervious cover was subsequently estimated for 1624, 12 digit watersheds (HUCs) 

wholly contained within the state of Georgia, using both the simple NLCD-only approach and 

the 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 

3.4). 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. 

38 
Table 3.4 Evaluation of Impervious Cover Status of Georgia Watersheds/HUC’s. 

Impervious

Cover 

Class 

(% TIA) 

NLCD Data 

Only (1993)

(# of

watersheds) 

Multiple Data 

Sources 

(1993)

(# of

watersheds) 

Multiple Data 

Sources 

(1999)

(# of

watersheds) 

Change

(1993-1999)

from lower to 

higher class* 

High %TIA 

Growth Rate 

> 0.2 %TIA/

year (# of 

watersheds) 

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 

* Note: Since 7 HUC's moved from the 10-25 class to the >25 class between 1993 and 1999, this would leave 55 

HU C's (62 - 7) in the 10 to 25 class; how ever, during the sam e time period 12 H UC 's moved from the 5 to 10 class 

to the 10 to 25 class for a total of 67 (1999 MDS). The calculation for movement from the 0 to 5 class to the 5 to 10 

class is sim ilar. 

Thus, the NLCD-only approach appears to have the most serious 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 3.8 identifies for 1993 the specific Georgia HUCs 

categorized by MDS as ‘of concern’ (i.e. >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. 

Between 1993 and 1999, we estimated that a total of 51 HUCs changed to a higher risk 

impervious cover category. Figure 3.9 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% range. 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 likely 

impairment in the near future (5 to 10% impervious range) if appropriate planning and 

management is not undertaken. The expected result is increasing storm water hydrologic, 

pollutant and habitat degradation stress on the streams in these areas. 

39 
Figure 3.8 Estimated 1993 %TIA for 1624 Georgia 12 digit HUCs. Fifty-two (52) HUCs 

identified as at-risk (5-10% impervious) or potentially degraded (>10% impervious) using M ultiple 

Data Sources (MDS), but not identified using the land cover data alone, are outlined. 

40 
Figure 3.9 Estimated 1999 %TIA for 1624 Georgia 12 digit HUCs. The 51 HUCs that changed to a higher 

risk impervious class between 1993 and 1999 are outlined. 

41 
4. Impervious Cover in the Southeastern United States 

This section contains the estimated impervious cover results for U.S. EPA Region 4 in 

2000 using the Multiple Data Source approach described in Section 3. Headquartered in 

Atlanta, Georgia, U.S. EPA Region 4 includes Alabama, Florida, Georgia, Kentucky, 

Mississippi, North Carolina, South Carolina and Tennessee. The Southeastern U.S. is one of the 

fastest growing areas of the country, with Florida, Georgia and North Carolina in the top ten 

growth states. 

The major centers of population in the South are expanding, putting stress on all its 

ecosystems, but this sprawl is especially harmful to coastal areas, wetlands and mountains. Just 

10% of the earth’s land surface holds the overwhelming majority of the earth’s population along 

or near coasts, and the United States is no exception. The Southeastern United States is growing 

rapidly along its coasts. Over 20 million people live in 99 coastal counties along the U.S. Gulf 

Coast (some of which are outside our study area). Florida’s burgeoning population is clearly 

evident, with the highest density along its eastern coast, but with significant population 

expansion from Tampa southward along the western coast. 

Using the Multiple Data Source Approach, Figure 4.1 shows the estimated impervious 

cover by 12 or 14 digit HUC (depending on the individual states) for the year 2000 for the eight 

Southeastern states. It is easy to see the growth around the cities and interstate corridors, 

especially the Interstate 85 corridor from Atlanta to the Raleigh-Durham-Chapel Hill, North 

Carolina area. The urban intensity along the Florida east coast is also particularly evident. 

Table 4.1 summarizes estimated %TIA for 2000 by state for five %TIA categories, 

providing a quick reference for each category for each Southeastern state. Streams in watersheds 

with > 20 %TIA are seriously degraded, and even the most intensive remediation efforts are 

likely to only partially restore functionality of those water bodies. While streams in the 10 to 20 

%TIA category are also likely to suffer significant degradation from urbanization, remediation 

efforts can potentially restore functionality to these streams. Streams in watersheds with 5 to 10 

%TIA suffer only modest degradation due to urbanization and can benefit substantially if careful 

planning and management of water resources is undertaken at that point in the development of 

the watershed. Table 4.1 clearly illustrates the extremes in the extent in urbanization in the 

Southeast. Whereas, only 0.1% of Mississippi’s land area is contained in HUCs with >20 %TIA, 

7.0% of Florida’s HUCs are in this largely degraded category. 

The maps for individual states (Figures 4.2 to 4.9) include labels of the Metropolitan 

Statistical Areas (MSAs) in the state. An MSA is a statistical definition by the U.S. Census 

Bureau to account for decentralized settlement and economic activity. It not only includes 

urbanized areas and outlying urban places, but also surrounding counties that are integrated with 

these urban centers as measured by substantial amounts of daily commuting, even if many of 

these surrounding areas have densities far too low to be classified as urban. A MSA must 

include at least one city with 50,000 inhabitants and a total metropolitan population of 100,000 

or more. Approximately 82% of the U.S. population is contained in these MSAs (Kaiser 

statehealthfacts.org, 2004). 

42 
Table 4.1 % TIA as a Percentage of the Total Land Area of Each Southeastern State Using the 2000 

Census and the Multiple Data Source Approach 

State 

Total # 

of 

watersheds 

Area 

in sq 

miles

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# watersheds / 

sq miles 

%area 

# watersheds / 

sq miles 

%area 

# watersheds / 

sq miles 

% area 

# watersheds / 

sq miles 

% area 

# watersheds / 

sq miles 

% area 

Alabama 1414 52197 12 / 

402 0.8 29 / 

990 1.9 76 / 3112 6.0 397 / 15426 29.6 900 / 32267 61.8 

Florida 1365 58373 116 / 

4071 7.0 104 / 4174 7.2 160 / 7053 12.1 337 / 

13818 23.7 648 / 29257 50.1 

Georgia 1865 58754 49 / 

1381 2.4 61 / 

2369 4.0 157 / 

4614 7.9 559 / 

17660 30.1 1039 / 

32730 55.7 

Kentucky 1241 40407 11 / 

315 0.8 24 / 905 2.2 91 / 2827 7.0 470 / 16244 40.2 645 / 

20116 49.8 

Mississippi 1114 49409 2 / 

67 0.1 15 / 

612 1.2 37 / 

1427 2.9 222 / 

10138 

20.5 838 / 

37165 

75.2 

North 

Carolina 1601 52662 44 / 1008 1.9 101 / 2720 5.2 177 / 

5769 11.0 628 / 20479 38.9 651 / 22686 43.1 

South 

Carolina 1031 31145 39 / 

513 1.7 48 / 

1232 4.0 106 / 

3181 10.1 362 / 

11740 37.7 476 / 

14547 46.7 

Tennessee 1093 42139 28 / 

811 1.9 40 / 1287 3.1 76 / 

2917 6.9 383 / 

15885 37.7 566 / 21239 50.4 

(Figures may not add up to 100% due to rounding up or down.) 

43 
Figure 4.1 Southeastern United States impervious cover for 2000. Impervious cover as %TIA (percent total 

impervious area) by 12- and 14 digit HUC using the Multiple Data Source approach. Data sources used in the 

calculation include 1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT data for interstates 

and other major highways. 

44 
4.1 Alabama 

The population of Alabama in 2000 was 4.4 million people, ranking it 23rd most 

populous among the 50 states (U.S. Census Bureau, 2004). The state’s population increased 

10.1% in the decade 1990 to 2000, below the U.S. average of 13.1%. (U.S. Census Bureau, 

2004). The metropolitan population for Alabama is 71%, below the national rate of 82% (Kaiser 

statehealthfacts.org, 2004). 

Figure 4.2 illustrates the impervious cover estimated by 12 digit HUC for Alabama in 

2000. Impervious cover calculations used the Multiple Data Source approach described in 

Section 3. The state’s most extensive impervious cover is in and around its largest city and a 

long-time industrial center in the south, Birmingham. The cities of Huntsville, Montgomery (the 

capital), Mobile and to a lesser degree Tuscaloosa, Florence, Gadsen and Anniston also 

contribute to urbanization in the state. For the most part, Alabama is very rural and contains less 

impervious cover than many of the other Southeastern states. As in every state, there is more 

impervious cover around the interstate highways, but the intensity is less than other Southeastern 

states. 

Alabama has 1414, 12 digit HUCs, 12 of which are >20% TIA, or 0.8% of its total land 

area. Five of those > 20% are >30%, and are in the Birmingham and Mobile MSAs. Only one

watershed, in Birmingham, is greater than 40%TIA, at 44.7%. Alabama has 29 watersheds in 

the 10 to 20% TIA category, or 1.9% of its area; 76 watersheds in the 5 to 10% TIA range, 6% 

of its area; 397 watersheds in the 2 to 5% TIA range, or 29.6% of its area; and 900 watersheds < 

2% TIA, 61.8% of its area. Although Alabama has one of the lowest %TIA in the Southeast 

with only 41 watersheds and 8.7% of the state >10% TIA, the fragile coastal area around Mobile 

has 10 HUCs greater than 10%. 

45 
Figure 4.2 Alabama impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) 

by 12 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation 

include 1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT data for interstates and other 

major highways. 

46 
4.2 Florida 

Florida is the fourth most populous state in the union with the official estimate as of

April 1, 2003 at approximately 17.1 million, a 6.8% increase in the first three years of this 

century. The U.S. Census Bureau ranked nine counties in Florida among the 100 fastest growing

counties in the nation during April 1, 2000 to July 1, 2003 (U.S. Census Bureau, 2004). During

the 1990's Florida’s population grew by 23.5%, during the 1980's by 32.7%. Net migration

continues to be the predominate pattern of growth for Florida, with 10.8% of the state’s growth 

due to natural increase and 89.2% due to net migration (Florida OCEDR 2004). 

Although one of the largest and fastest growing populations in the United States, Florida 

still contains low density areas of rural land dominated by agricultural uses and vast areas of 

wetlands. The majority of the population crowds in and around the biggest cities and along the 

coastal areas, providing typical sprawl problems and concerns. The metropolitan population

(percentage of the population located in MSAs) for Florida is 96%, the third largest of the 50

states and well above the U.S. average of 82% (Kaiser statehealthfacts.org, 2004). Florida is the 

only state in the Southeast with a metropolitan population greater than the U.S. average. 

Figure 4.3 illustrates the impervious cover by 12 digit HUC estimated for Florida in 2000 

by the Multiple Data Source approach. Florida has 1365 12 digit HUCs, 116 of which are >20 

%TIA, or 7% of its area. Twenty-nine of those >20 %TIA HUCs are >40%, accounting for 

~1.6% of Florida’s area, with 20 watersheds in the Miami-Ft. Lauderdale area between 45.2 and 

89.7 %TIA. Out of the 87 remaining HUCs in the >20 to 40 %TIA range, all but 21 are in 

coastal areas or sprawl from a coastal area, accounting for ~5.3% of Florida’s area. In the 10 to 

20 %TIA range, Florida has 104 HUCs, accounting for 7.2% of its area. In the 5 to 10 %TIA 

range, Florida has 160 HUCs, accounting for12.1% of its area. Florida has the highest total of 

impervious cover in the top 3 categories %TIA of our study, accounting for 26.3% of its area. In 

the 2 to 5 %TIA range, Florida has 337 HUCs, a vast majority in the interior, accounting for 

23.7% of its area. Florida has 648 watersheds <2 %TIA, the vast majority in Northern Florida, 

the Everglades and the interior area just north of the Everglades that accounts for 50.1% of its 

area. There are no watersheds below 2 %TIA on the eastern coast of Florida. Much of 

Florida’s coastal cities are becoming interconnected as the population grows, endangering the 

fragile wetlands, coastal areas and various other water ecosystems around the state. 

47 
Figure 4.3 Florida impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) by 12 digit HUC calculated 

using the Multiple Data Source approach. Data sources used in the calculation include 1993 NLCD commercial and industrial, 2000 Census 

data and U.S. DOT data for interstates and other major highways. 

48 
4.3 Georgia 

Georgia’s 2000 population at 8.2 million ranks 10th in the nation. Twenty counties in 

Georgia ranked in the 100 fastest growing counties in the nation during April 1, 2000 to July 1, 

2003. Five counties were in the national top ten with growth rates above 20% in that three year 

period (U.S. Census Bureau, 2004). The state’s population increased 26.4% in the decade 1990 

to 2000, more than double the U.S. average of 13.1%. (U.S. Census Bureau, 2004). The 

population for Georgia in MSAs is 72%, below the national rate of 82% (Kaiser 

statehealthfacts.org, 2004). Most of Georgia’s urban population is in the 28 county metropolitan 

statistical area (MSA) of Atlanta. This huge MSA spreads out in all directions, making 

congestion and environmental concerns serious issues for the state. 

Figure 4.4 illustrates the impervious cover by 12 digit HUC estimated for Georgia in 

2000 by the Multiple Data Source approach. There are 1865, 12 digit HUCs in Georgia, with 49 

watersheds with %TIA >20% that account for 2.4% of the area of the state. Forty of these 49 

HUCs are in the Atlanta MSA. The incredible sprawl facing Atlanta area residents is readily 

evident in Figure 4.4. Ten of the 49 >20 %TIA HUCs have >40 %TIA. There are 61 watersheds 

in the 10 to 20 %TIA category, accounting for 4% of Georgia’s area. The 5 to 10 %TIA range 

contains 157 watersheds, accounting for 7.9% of Georgia’s area. Georgia has 14.3% of its area 

in the top three %TIA categories of this study. Scattered throughout the state are 559 watersheds 

in the 2 to 5 %TIA range, accounting for 30.1% of Georgia’s area. The remaining 1039 

watersheds are in the <2 %TIA range, the majority of these are in the rural south and eastern 

Georgia accounting for 55.7% of its area. 

49 
Figure 4.4 Georgia impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) by 

12 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation include 

1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT data for interstates and other major 

highways. 

50 
4.4 Kentucky 

The 2000 Census lists Kentucky’s population at slightly more than 4 million with a 9.6% 

increase in the decade 1990 to 2000, well below the U.S. average of 13.1%. Migration is the key

component of growth in the Kentucky population, affecting the composition as well as the size 

of the population. Kentucky has an aging population, with the rate of decadal natural increase of 

it’s population decreasing by 70% since 1960 to a current value of only 6% (Price,, et al. 2004). 

The population in MSAs for Kentucky is 45%, nearly half the national rate of 82% (Kaiser 

statehealthfacts.org, 2004). Kentucky’s population is centered in the north central part of the 

state, with the cities of Louisville, Lexington and suburban overflow population of Cincinnati, 

Ohio, accounting for the majority of the metropolitan population. 

Huge tracks of national forest and the eastern edge of the Eastern Kentucky Coal Field 

(and Cumberland Plateau), called the Pottsville or Cumberland Escarpment and formed from 

weathering of resistant sandstones and conglomerates, dominate the eastern portion of the state. 

The escarpment is stepped in south-central Kentucky because several thick, resistant sandstone 

layers are separated by less resistant shales. The manner in which the sandstones weather and 

are eroded along the escarpment results in sheer cliffs, steep-walled gorges, rock shelters, 

waterfalls, natural bridges and arches. The Eastern Kentucky topography and the karst 

topography of middle Kentucky make intensive urban development difficult. 

 Figure 4.5 illustrates the impervious cover by 12 digit HUC estimated for Kentucky in 

2000 by the Multiple Data Source approach. There are 1241, 12 digit HUCs in Kentucky, only 

11 of which are >20% TIA and account for only 0.8% of the state’s area. These 11 urban HUCs 

are found exclusively in Louisville and Hamilton (Cincinnati, Ohio suburb). The 10 to 20 %TIA 

range contains 24 watersheds and accounts for 2.2% of Kentucky’s area. There are 91 

watersheds in the 5 to 10 TIA% range, accounting for 7% of Kentucky’s area. Only 10% of 

Kentucky land area is in our top three %TIA categories where water quality impacts due to 

urbanization are a concern. Scattered throughout the state are 470 watersheds in the 2 to 5 %TIA 

range, accounting for 40.2% of Kentucky’s area. The remaining 645 watersheds in Kentucky are 

in the <2 %TIA range, accounting for 49.8% of its area. 

51 
Figure 4.5 Kentucky impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) by 12 digit HUC calculated using the 

Multiple Data Source approach. Data sources used in the calculation include 1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT 

data for interstates and other major highways. 

52 
4.5 Mississippi 

The 2000 Mississippi population is listed at approximately 2.8 million, making it the

least populated state in the Southeastern region. A 10.5% increase in population in the decade

1990 to 2000 puts Mississippi’s growth rate below the U.S. average of 13.1% (U.S. Census 

Bureau, 2004). The metropolitan population (percentage of population in MSAs) for Mississippi

is 34%, making it fourth lowest in the nation (Kaiser statehealthfacts.org, 2004). Mississippi’s

urban population is centered around it’s largest city, Jackson, the overflow from Memphis along 

its northern border, and the booming coastal area of Gulfport and Biloxi.

 Figure 4.5 illustrates the impervious cover by 12 digit HUC estimated for Mississippi in 

2000 by the Multiple Data Source approach. Of the 1114, 12 digit HUCs in Mississippi, only 2 

are >20% TIA, accounting for only 0.1% of its area. One of these >20 %TIA watersheds is 

located in the Jackson MSA with 38.6 %TIA, and the other is located along the Gulf Coast with 

22.1 %TIA. Even the next category, the 10 to 20 %TIA range, only contains 15 watersheds, 

accounting for 1.2 % of Mississippi’s area. There are 37 watersheds in the 5 to 10 %TIA range, 

accounting for 2.9 % of Mississippi’s area. In the 2 to 5 %TIA range, Mississippi has 222 

watersheds, accounting for 20.5% of Mississippi’s area. The remaining 838 watersheds in 

Mississippi are in the <2 %TIA range, accounting for 75.2% of its area. Mississippi has the

lowest impervious cover of all the Southeastern states, with only 4.2% of its area in the top three 

categories of %TIA. Unfortunately, approximately 90% of the area in the fragile ecosystems of 

the Gulf Coast are in the top three categories of impervious cover (>5%) of this study. 

53 
Figure 4.6 Mississippi impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) 

by 12 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation 

include 1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT data for interstates and other 

major highways. 

54 
4.6 North Carolina 

The North Carolina 2000 population was slightly more than 8 million, with a 21.4% 

increase in the decade 1990 to 2000, well above the U.S. average of 13.1%. Five counties in 

North Carolina ranked in the 100 fastest growing counties in the nation during April 1, 2000 to 

July 1, 2003 (U.S. Census Bureau, 2004). The metropolitan population (percentage of the

population living in MSAs) for North Carolina is 70%, below the national rate of 82% (Kaiser 

statehealthfacts.org, 2004). The majority of the North Carolina population is concentrated in the

middle of the state, in clusters around their major metropolitan statistical areas–CharlotteGastonia-Rock Hill, Raleigh-Durham-Chapel Hill, Greensboro-Winston-Salem-High Point and 

Fayetteville. The western portion of North Carolina is mountainous and the eastern portion is 

coastal, making North Carolina attractive for second homes, retirement and seasonal housing. 

North Carolina was fourth in the nation in adding persons 65 and older to its numbers April 1, 

2000 to July 1, 2003 (U.S. Census Bureau, 2004). 

Figure 4.7 illustrates the state’s impervious cover by 12 digit HUC estimated for 2000 

using the Multiple Data Source approach. This figure clearly shows the extensive development 

around its MSAs in the central part of the state and along Interstates 85 and 40. There are 1601, 

14 digit HUCs in North Carolina, 44 of which are >20 %TIA, accounting for 1.9% of its area. 

There are 101 watersheds 10 to 20 %TIA accounting for 5.2% of North Carolina’s area. The 5 

to 10 %TIA range contains 177 watersheds, accounting for 11% of its area. There are 628 

watersheds in the 2 to 5 %TIA range, accounting for 38.9% of North Carolina’s area. The 

remaining 651 watersheds are in the <2 %TIA range, accounting for 43.1% of its area, the lowest 

amount for any Southeastern state. Although North Carolina does not have an extremely high 

number of watersheds in the highest %TIA category, it does have a relatively high percentage of 

its land area, 18.1%, in the top three %TIA categories, making it second highest in the Southeast. 

55 
Figure 4.7 North Carolina impervious cover for 2000. Impervious cover as %T IA (percent total impervious area) by 14 digit HUC calculated using 

the Multiple Data Source approach. Data sources used in the calculation include 1993 NLCD commercial and industrial, 2000 Census data and U.S. 

DOT data for interstates and other major highways. 

56 
4.7 South Carolina 

The South Carolina 2000 population was 4 million, with a 15.1% increase in the decade

1990 to 2000, just slightly above the U.S. average of 13.1%. The metropolitan population

(percentage of the population in MSAs) for South Carolina is 75%, below the national rate of 

82% (Kaiser statehealthfacts.org, 2004). South Carolina’s population is much like the other

Southern states, expanding out from its biggest metropolitan area, the Anderson-GreenvilleSpartanburg MSA, along the Interstate 85 corridor. Other areas of growth in South Carolina 

include its capitol, Columbia, and the coastal port of Charleston. 

Figure 4.8 illustrates the South Carolina impervious cover by 14 digit HUC estimated for 

2000 using the Multiple Data Source approach. South Carolina has 1031, 14 digit HUCs, 39 of 

which are >20% TIA, representing 1.7% of its land area. Eighteen of those >20 %TIA are >30 

%TIA and are located in Columbia, the Greenville-Spartanburg-Anderson MSA and in the 

Atlantic coastal area. South Carolina has 48 watersheds in the 10 to 20% TIA range, 

representing 4% of its land area; 106 watersheds in the 5 to 10% TIA range, or 10.1% of its area. 

South Carolina’s total area in our top three %TIA categories is 15.8%, third highest in the 

Southeast. South Carolina’s 362 watersheds in the 2 to 5% TIA range account for 37.7% of its 

area, and the 476 watersheds <2 %TIA account for 46.7% of its area. 

57 
Figure 4.8 South Carolina impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) 

by 14 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation include 

1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT data for interstates and other major 

highways. 

58 
4.8 Tennessee

The Tennessee 2000 population was 5.7 million, with a 21.4% increase in the decade 

1990 to 2000, about 60% greater than the U.S. average of 13.1%. The metropolitan population

(percentage of the population residing in MSAs) for Tennessee was 69%, below the national rate 

of 82% (Kaiser statehealthfacts.org, 2004). The majority of the population in Tennessee is 

centered around Memphis in the southwestern corner of the state, Nashville in central Tennessee, 

and in the eastern portion of the state from Chattanooga to Knoxville and along a corridor 

stretching along Interstates 75, 40 and 81 to Bristol, Tennessee at the Virginia border. 

Figure 4.9 illustrates the estimated Tennessee impervious cover by 12 digit HUC for 

2000 using the Multiple Data Source approach. Tennessee has 1093, 12 digit HUCs, 28 of which 

are >20% TIA, or 1.9% of its area; 40 watersheds 10 to 20% TIA, or 3.1% of its area; 76 

watersheds 5 to 10% TIA, or 6.9% of its area; 383 watersheds 2 to 5% TIA, or 37.7% of its area; 

and 566 watersheds < 2% TIA, or 50.4% of its area. Tennessee has 11.9% of its total land area 

in the top three %TIA categories of this study. 

59 
Figure 4.9 Tennessee impervious cover for 2000. Impervious cover as %TIA (percent total impervious area) by 12 digit HUC calculated using the 

Multiple Data Source approach. Data sources used in the calculation include 1993 NLCD commercial and industrial, 2000 Census data and U.S. DOT 

data for interstates and other major highways. 

60 
5. Future Impervious Cover Projections 

for the Southeastern United States 

According to the Census Bureau population projections, several states in the Southeastern 

United States will be among the fastest growing of the country in the next three decades. EPA’s 

Region 4 contains two states, Florida and Georgia, that are expected to have the third and fourth 

largest population increase between 1995 and 2025 in the nation (Campbell, 1997). Florida is 

expected to add over six million additional residents while Georgia and North Carolina are

expected to add over two million. Mississippi and Kentucky are projected to show the slowest 

net population growth, adding just under 1⁄2 million individuals. Coastal areas will generally 

show the greatest growth, with slower growth in the inland areas. 

Along with this population growth will inevitably come an increase in impervious cover. 

In this section projections of impervious cover in the12 and 14 digit HUCs are made based on 

county scale population projections obtained from the individual states combined with the 

Multiple Data Source impervious cover estimation method discussed in Sections 3 and 4. These 

projections represent a single scenario of a possible distribution of impervious cover in the eight

Southeastern states and should be used in this context. Included is a discussion of error 

estimates of population projections upon which the imperviousness is based to give the reader a 

sense of the magnitude of error associated with these projections. 

5.1 The Nature of Errors in Population Projections 

Estimates of potential error for population projections are not done routinely among the 

community of demographers (Smith 1987). Such estimates are particularly desirable for this 

impervious estimation study, however, since population is the key driving component of the 

impervious area projections and estimates. Several existing population projection error studies 

were surveyed to establish a basis for potential errors associated with impervious estimates and 

projections. These studies spanned several spatial scales including: state, county and subcounty areas. Population projection error estimates at all these scales are summarized here and 

used in to illustrate the likely bounds of potential impervious area estimates based largely on 

population projections. 

Smith and Sincich (1992) carried out a comprehensive retrospective evaluation of state 

level population projection errors using an array of simple extrapolation approaches compared to 

each other and to more complex models. They evaluated the accuracy of state population 

projections for the 1960’s, 70’s and 80’s using: linear extrapolation (LINE); exponential 

extrapolation (EXPO); a moving average time series model (ARIMA); ratio techniques including 

shift-share (SHIFT), which assumes that a state’s share of national population changes “...by the 

same annual amount during the projection horizon as the average annual change during the base 

period,” and share of growth (SHARE), which assumes that “...each state’s share of national 

population growth during the projection horizon is the same as during the base period.” Other

more complex models evaluated included those of the Census bureau, the National Planning

Association, and the Bureau of Economic Analysis. The measures of error that they evaluated 

included: mean absolute percent error (MAPE), root mean square percent error (RMSPE), 90th 

percentile of absolute percent errors (90PE), Mean Algebraic Percent Error (MALPE), 

Percentage of positive errors (%POS) 

The two best simple models were LINE and SHARE, with SHARE performing slightly 

better. The simple models performed as well or better than the more complex approaches (Smith 

& Sincich 1992). Typical MAPE ranges for these techniques were: 5 to 8% for 10 year 

61 
projections, and 10 to 12% for 20 year projections. Thirty (30) year projections were not 

evaluated. Here we assume that a reasonable MAPE for 30-year projections at the State level 

might be in the range 15 to 18%. This “guess”, however, is based only on limited experience 

and best professional judgement and no hard data. Clearly, additional research is needed to 

establish potential error for long term population projections over 20 years. 

The U.S. Bureau of the Census and others, especially users of population projections, are 

becoming increasingly interested in consistent, regular, sound evaluation of population 

projection error. Campbell and others evaluated error in state population projections using 

Census 2000 counts. They found that short term projections up to 5 years might expect MAPE’s 

of about 0.5% per year at the state level (Campbell, et al. 2002). These values are consistent 

with the results of Smith and Sincich (1992) discussed above. 

At the county scale a similar evaluation process found “...mean absolute errors of around 

15% for 10-year projections and around 30% for 20-year projections” (Smith 1987). Smith 

evaluated projections vs. census enumerations for 2,971 counties over the years 1950 through 

1980. He observed that migration patterns dominated both increases and decreases in population 

and that extremely high growth rates tend to moderate over time. He also established several 

general characteristics of population projection accuracy: 

• forecast errors increase with length of projection horizon, 

• larger errors are expected for places with high growth rates, 

• larger errors are expected for small (low population) places, and 

• there is no way to predict whether errors will be positive or negative. 

Since the errors in population projections depend on population (size of place) and on the

rate of population growth, “... population forecast errors are frequently going to be large, 

especially for small and/or rapidly growing places” (Smith 1987). Recognizing this, Smith 

suggested that population projection refinements could be based on separating counties by rate 

of population growth (during the base period), by size of place (population) or both. 

At the census tract scale a study of three diverse Florida counties found a MAPE in the 

range of 17 to 20% for 10-year projections (Smith and Shahidullah 1995). These authors found 

larger errors for smaller tracts, ~ 30 to 35% for <2,500 population over 10 years, and larger 

errors for larger absolute growth rates. They also tested and found the smallest errors for a 

composite (COMP) method where the estimation approach was tailored to population growth 

characteristics of each census tract. The COMP errors for census tracts by growth rate for a 10­

year period were as follows: 

Growth Rate COMP error 

<10% ~20%


10 to 25% ~10%


25 to 50% ~20%


>50% ~30%


Using over 40,000 sub-county areas (municipalities, townships, etc.) nationwide, Harper 

and others evaluated 10 year errors for projections using sub-county housing unit data to 

distribute county populations to sub-county areas (Harper, et al. 2003). They found that error 

(MAPE and MAPLE) depended on both sub-county area size (population) and growth rate. 

62 
MAPE for all areas was 12.4% ranging from 4.0% (population 50,000 to 100,000) to 35.1% 

(population < 100). 

Projections of urban expansion tailored to specific locales can incorporate considerable 

additional complexity and potentially more accuracy relative to the simpler approaches based on 

population projections taken here. One example of a more detailed local projection of urban 

growth is that for the Charleston region of South Carolina (Allen and Lu 2003) which predicts a 

change in urban area from 250 square miles in 1994 to 868 square miles in 2030. This effort 

utilized multiple approaches including logistic regression, rule based suitability (for transition 

probabilities) and focus groups as the basis for an integrated future urban growth model. 

Comprehensive accuracy assessments of these complex localized approaches have not been done 

and no good guidelines exist to evaluate improvement in projection accurate as a function of this 

increased complexity. 

5.2 Impervious Cover Projection Method 

Impervious cover projections based on an Multiple Data Source approach are analogous 

to those described for current condition, but incorporate projected population growth as 

described below. Additional details of calculation and data processing can be found in the 

Appendix. 

5.2.1 Residential Component 

Although the U.S. Census Bureau prepares state level population projections for the 

entire nation, individual states prepare projections of population at the county scale level. Both 

methodology and available time period for projection vary from state to state. Some states in 

Region 4 have official population projections until 2030, while other states have them only until 

2010 or 2015. Population projections by county for the eight Southeastern states in Region 4 

were obtained from each individual state. Table 5.1 summarizes information on county scale 

projections for each state including the projection time period, date projections were made and

organization from which projections were obtained. 

Since we are interested in projections for the 12 and 14 digit HUCs, the environmentally 

significant subdivision, the coarser political (county) scale projections need to be apportioned to 

the finer HUC scale. The first step was to apportion the growth to the 2000 census block level. 

With this done the population could be apportioned to the individual blocks as described

previously for current condition estimation of impervious cover. Two different approaches were 

considered to distribute county scale projections to the block level: (1) based on the most recent

growth at the block level, i.e. proportional to block level population change between 1990 and 

2000, or (2) based on the most recent block level population density. Both of these approaches

have arguable advantages and disadvantages. The first approach assumes that the recent 

historical level of growth will continue over the next 30 years while the second assumes that 

population growth will be proportional to the current population. Neither of these methods can 

be considered the “right” way to apportion the growth, but both can form the basis for making 

reasonable projections. 

Basing the distribution on growth patterns in the past ten years identifies recent boom 

areas of a county. This method is likely to overestimate future growth for the very rapidly 

growing areas of the counties and to underestimate growth for areas that are currently 

experiencing slower growth and to continue contraction of populations unrealistically in some 

63 
Table 5.1 Sources and Dates of Population Projections for Each Southeastern State 

State Source Date of 

Projections

 Years A vailab le 

2005 2010 2015 2020 2025 2030 

Alabama Univ. of 

Alabama 

August

2001 x x x x x 

Florida Univ. of 

Florida July 2003 x x x x x 

Georgia 

Gov’s. Ofc. 

of Planning

& Budget 

June 2002 x 

Kentucky Univ. of 

Louisville July 2003 x x x x x x 

Mississippi 

MS 

Institutes of 

Higher

Learning 

March 

2002 x x x 

North 

Carolina 

NC Ofc. of 

State Budget

& Mgmt. 

June 2003 x x x x x x 

South 

Carolina 

SC State 

Budget & 

Control Bd. 

October 

2002 x x x x x 

Tennessee Univ. of 

Tennessee 

March 

1999 x x x x 

blocks that have had declines in population. Local peak growth or contraction areas will shift 

over less than the 25 year period for most of the areas. A technical difficulty in trying to use this 

approach is that census blocks were realigned between the 1990 and 2000 Census so determining

the growth individual blocks presents difficulties. A commercial product which offers 1990 

Census data mapped onto the 2000 block configuration was evaluated for use in this process. 

Unfortunately, this product shows some unrealistic expansion and contractions in some areas. 

Blocks around large military bases seemed to be particularly problematic but other pockets of 

problems also seem to exist with this data set. Some block populations are zeroed out and other

show unrealistically high increases in population between the two census periods. Doing

adequate quality control with this approach for a multiple state area did not appear feasible. This 

approach may be possible to use for smaller multiple county areas. Due to the quality control 

issues with method one, projected growth in a county was distributed based on the 2000 

population in the blocks. Distributing growth proportional to the 2000 population will tend to 

underestimate growth in rural areas of a county and overestimate growth in urban areas. 

64 
The population projection errors at the HUC level due to their relatively small size will 

be larger than county scale projection errors no matter how the population is distributed. For 

example, the average of the HUC population in Georgia in 2000 (based on 8.2 million people 

and 1865 HUCs) is only 4400 and there are an average of 12 HUCs per county in the state. 

Based on the error in population projection analysis [Smith (1987), Smith and Shahidullah

(1995)] discussed in the previous section, smaller high growth rate HUCs could easily have 

projection errors in the 50% range even for a 10 year projection horizon. 

The projected population density in each HUC was calculated for the projection time 

periods available from individual states. The projection window ranged from Georgia with 

projections out only to 2010, to Kentucky and North Carolina with projections out to 2030. 

Projections of HUC populations were made at five year intervals based on the individual state 

projections. The residential component of impervious cover for each time period was then 

calculated based on the Hicks equation as described in Section 3. 

5.2.2 Commercial/Industrial Component 

The critical assumption for the High Intensity Commerial/Industrial (HICI) area future 

projections was to maintain in a particular HUC the HICI (in square miles) /10,000 population 

ratio constant for future periods. This assumption would provide at least a reasonable estimate 

of future HICI change due to population growth since the commercial/industrial contribution to 

%TIA contains a major component that is proportional to the population density in the nearby

area, i.e. commercial areas that serve the residents of an area. Figure 5.1 illustrates the 

relationship between population and the High Intensity Commercial/Industrial class for North 

Carolina HUC’s for the single time period -- 1993 -- when the categorized land cover data was 

available. 

The above assumption reflects both the linear nature and the dispersion of the relationship between HICI area and population. The implications of this assumption for the projection

are that the historical pattern of commercial growth with respect to population for any particular 

area (HUC) will continue into the projection periods. Thus, areas with high HICI/population 

ratios are projected to experience high commercial growth with population increases, areas with 

low HICI/population ratios are projected to experience low commercial growth with population 

increases, and those areas with intermediate ratios will be in between. Actual future change of 

HICI area with population for any individual HUC could differ from this assumption, but lacking

historical change information for the HICI/population ratio, we consider this “space for time” 

substitution approach for the assumption to be objective and sensible based on the available data. 

As future coincident commercial/industrial area and population data become available, including 

an updated NLCD for 2000, future censuses and other appropriate data, the actual reliability of 

this assumption should be tested and evaluated. 

65 
Figure 5.1 H igh Inten sity Com merc ial/Indu strial area (mete rs) vs. po pulatio n for N orth C arolina 12 d igit 

HU C’s 

For the majority of HUCs, the projection was made as follows: 

ComTIA%[period] = ((Comm_area[1993] * Bpop[period]) / HUC_area) * 0.90 * 100 

Bpop_93

Where: 

Comm_area[1993] = NLCD HICI of HUC for 1993


Bpop_93 = population of HUC for 1993


(interpolated between 1990 & 2000)


Bpop[period] = population of HUC for projected period


(2000, 2010, 2020 & 2030)


HUC_area = area of HUC in square miles


0.90 = assumed fraction of TIA for HICI class of NLCD 

and ComTIA%[period] = commercial/industrial component of TIA for projected period 

However, a major industrial component in this category can be relatively independent of the 

surrounding residential population, i.e., large manufacturing or transportation facilities may be 

located at a distance from local population centers. In addition, if population declines in an area 

the impervious area does not tend to decline. 

66 
Two constraints were implemented to maintain realistic ranges and distribution of 

variability in future projections of the HICI (High Intensity Commercial/Industrial) TIA 

component. First, decreases of HICI (High Intensity Commercial/Industrial area) for future time 

periods are not considered likely and are not allowed, i.e.: 

IF HICI(future period) < HICI(1993) THEN HICI(future period) = HICI(1993) 

Second, increases of HICI were also limited to constrain potential large increases in 

future periods for outliers (HUCs with large HICI/population ratios) that could unrealistically 

increase HICI area with only small population increases. North Carolina was used as a case 

study to establish a reasonable range for this ratio. We identified 57 HUC’s: 3.6 % of the 1601 

HUC’s statewide, that had HICI/population ratios above 3 square miles per 10,000 population. 

We consider these to be out of the typical range. The unusual high ratio areas examined 

evidenced a wide array of circumstances including, for example: low population with a coastal 

causeway or rural interstate classified as commercial/industrial, low population with a large rural 

industrial facility, airport, mine or other facility classified as commercial/industrial. 

This constraint was implemented with the IF-THEN construction:


IF HICI(in square miles for 1993)/10Kpopulation(1993) > 3.0


....THEN HICI(future period) = HICI(1993) + Median HICI Change 

Where: 	 Median HICI Change 

= 0.6 square mile * ((Population(period2) - Population(period1))/10,000 

This constant is based on the median HICI area per 10,000 population ratio of all HUCs 

statewide in North Carolina. (The mean ratio was 0.7.) 

5.2.3 Major Highway Component 

The only roadways included in this component were interstate highways and major US 

highways. This is a minor component in the impervious cover estimation and very little new 

connector highway construction is proposed. Projected construction information is in a variety 

of formats and not easily obtainable from individual states. Since updated values will have a

negligible effects on projections, estimates of current status described in Chapter 4 was used in 

projection estimates. 

5.3 Impervious Cover Projections 

The West and the South are projected to have the greatest net population change over the 

next three decades in the nation. All of the states in the Southeast are expected to increase in 

population in the following decades with Florida and Georgia leading the growth. Florida is 

expected to replace New York as the third (behind California and Texas) most populous state by 

2020 (Campbell, 1997). Population projections from each of the eight Southeastern states in 

EPA Region 4 are show in Table 5.2 and form the basis for the impervious cover projections for 

each state of the eight states detailed below. 

67 
--

--

-- -- -- -- --

-- -- --

--

-- --

Table 5.2 2000 U.S. Census and State Population Projections for the Southeastern United States

in Thousands 

State 2000 

Population 

2005 

Population 

2010 

Population 

2015 

Population 

2020 

Population 

2025 

Population 

2030 

Population 

Alabama 4,447 4,645 4,839 5,028 5,211 5,386 

Florida 15,982 17,499 18,978 20,387 21,807 23,178 

Georgia 8,186 9,592 

Kentucky 4,042 4,183 4,321 4,447 4,563 4,663 4,744 

Mississippi 2,845 2,991 3,118 3,227 

North 

Carolina 

8,049 8,784 9,491 10,227 10,966 11,712 12,448 

South 

Carolina 

4,012 4,155 4,388 4,618 4,850 5,077 

Tennessee 5,689 5,798 6,063 6,327 6,593 

68 
5.3.1 Alabama 

In the 2000 Census Alabama was the 22nd most populous state with 4.4 million people 

and is not expected to change its ranking significantly among the 50 states and District of 

Columbia in the next 25 years (U.S. Census Bureau, 2001a and b). Overall, growth will be

moderate with the population projected to grow by just under 1 million people reaching 5.4 

million people by 2025, an increase of 21% over a 25 year period (Campbell, 1997). 

Although overall population increase is projected to be less than 1% per year over the 

next two decades, some counties are projected to grow much more rapidly while some will show 

a decline in population. Shelby County, located in the Southeastern portion of the Birmingham

MSA is projected to add over 120,000 people, a percentage increase of 85% or over 3% annual 

growth rate. Baldwin County located on the Gulf Coast on the eastern side of the Mobile MSA 

is projected to add over a 100,000 people with a 77% increase over the 25 year period. Five 

other counties (Autauga, Blount, Elmore, Lee, and St.Clair) are expected to show an increase in 

population of over 50% by 2025. These counties are located on the outskirts of existing

population centers including Birmingham, Montgomery and Auburn. At the other end of the 

spectrum, several rural counties distant from major population centers are projected to decline in 

population. The population projections by county for the state of Alabama were obtained from 

the Center for Business and Economic Research, University of Alabama. The projections, 

available out to 2025, were published in August 2001. Projections in this series are based on 

trends between the 1990 and 2000 censuses. 

Figure 5.2 shows the impervious cover projections for Alabama watersheds calculated 

using the Multiple Data Source approach described in Section 4 and Section 5.2. The number of 

HUCs and percentage land area in each impervious cover class are summarized in Table 5.3 with 

the pattern of percent area by TIA class shown in Figure 5.3 The number of stream miles in 

each %TIA category is summarized in Table 5.4 with the pattern of stream miles by TIA class 

shown in Figure 5.4. 

The increase in impervious cover between 2000 and 2025 can be seen in the far south 

Gulf Coast and around the other urban centers. Figure 5.3 shows a drop in the area of HUCs 

with <2% impervious and increases in the other four classes. In 2000, 2.7 % of the land area 

(1409 mi2) was in HUCs with %TIA > 10% (areas where stream quality is likely degraded). By

2025, 3.5% of the land area (1827 mi2) was in HUCs with %TIA > 10%. By 2025, 3.0 % (2294 

mi) of Alabama streams are projected to be in HUCs with %TIA > 10% while 91 % (70,116 mi) 

are in areas not immediately threatened by urbanization with < 5% impervious area. 

69 
Figure 5.2 Alabama projected impervious cover out to 2025. Impervious cover as 

%TIA (percent total impervious area) by 12 digit HUC calculated using the Multiple Data 

Source approach. Data sources used in the calculation include 1993 NLCD commercial and 

industrial cover, 2000 Census data, county level population projections from University of 

Alabama and U.S. DOT data for interstates and other major highways. 

70 
Table 5.3 %TIA as a Percentage of the Total Land Area of Alabama out to 2025 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs % area 

2000 12 0.8 29 1.9 76 6.0 397 29.6 900 61.8 

2010 15 1.0 31 2.0 78 6.2 430 31.8 860 59.0 

2020 19 1.3 31 2.1 90 7.0 435 32.2 839 57.5 

2025 21 1.4 31 2.1 92 7.0 443 32.7 827 56.8 

Total number of HUCs: 1414 

Total area: 52,197.5 sq mi 

Figu re 5.3 Alabama Projected %TIA as % of Area out to 2025 

71 
Table 5.4 Total River Miles in Alabama by %TIA category out to 2025 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 12 420 29 1225 76 4320 397 21654 900 49769 

2010 15 579 31 1314 78 4452 430 23472 860 47572 

2020 19 783 31 1329 90 5049 435 23815 839 46411 

2025 21 882 31 1412 92 4977 443 24216 827 45900 

Total number of HUCs: 1414 

Total river miles: 77,389 

Figure 5.4 Total River Miles in Alabama by %TIA Category out to 2025 

72 
5.3.2 Florida 

Florida is currently the 4th most populous state in the U.S. with nearly 16 million people 

counted in the 2000 census (U.S. Censure Bureau, 2001). According to the U.S. Census Bureau, 

Florida is projected to replace New York as the 3rd most populous state in the union and by 2025

the population is projected to grow to 20.7 million people (Campbell, 1997). The University of 

Florida Population Program, on the other hand, projects even more rapid growth for the state 

with a total of 23.2 million people by 2025 (Smith and Nogle, 2003). These projections refer

solely to permanent residents and do not include tourists or seasonal residents, a major category 

in some areas of Florida. Florida is a highly diverse state and growth is not distributed evenly. 

While some areas have grown rapidly, others have grown slowly or even declined in population. 

Rapid growth is expected along the northeastern Atlantic coast with St. Johns (St. 

Augustine area) and Flagler County (south of St. Augustine) doubling in population between 

2000 and 2025. Sumter County (southwest of Ocala) is projected to double in population while 

Marion County located west of Ocala will nearly double as well. Collier County on the Gulf 

Coast south of Fort Meyers is also expected to double in population. Miami-Dade is expected to 

add 800,000 more people, with its population reaching 3.1 million by 2025. Counties 

surrounding Orlando are expected to grow rapidly as well. Some rural areas of the state such as 

the area south and east of Tallahassee will experience slow or no growth. 

The population projections by county for the state of Florida were obtained from the 

Bureau of Economic and Business Research, Warrington College of Business Administration, 

University of Florida, Gainesville, Florida. The projections, available out to 2025, were

published July 2003. These county scale projections served as the basis for the projection of 

future impervious cover for the state of Florida 

Figure 5.5 shows the impervious cover estimations and projections for Florida 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.5 with the pattern of percent area by TIA class shown in Figure 5.6. The 

number of stream miles in each %TIA category is summarized in Table 5.6 with the pattern of 

stream miles by TIA class shown in Figure 5.7. 

In 2000, 14.2 % of the land area (8289 mi2) was in the 220 HUCs with %TIA > 10%

(areas where stream quality is likely degraded). By 2025, 21.3% of the land area (12434 mi2)

was projected to be in the 298 HUCs with %TIA > 10%. By 2025, 26% (15341 mi) of Florida 

streams are projected to be in HUCs with %TIA > 10% while 63% (36598 mi) are in areas not 

immediately threatened by urbanization with < 5% impervious area. Between 2000 and 2025, 

4900 more miles of streams, a 45% increase, will be located in watersheds likely to suffer 

serious degradation due to development (>10 %TIA) unless advanced planning and mitigation 

efforts are undertaken soon. These watersheds are located along both the Atlantic and Gulf 

coastal areas and in the central Florida expanding east and southwest of Orlando. 

73 
Figure 5.5 Florida projected impervious cover out to 2025. Impervious cover as %TIA (percent total impervious 

area) by 12 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation 

include 1993 NLCD commercial and industrial cover, 2000 Census data, county level population projections from 

University of Florida and U.S. DOT data for interstates and other major highways. 

74 
Table 5.5 % TIA as a Percentage of the Total Land Area of Florida out to 2025 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs % area 

2000 116 7.0 104 7.2 160 12.1 337 23.7 648 50.0 

2010 133 7.9 127 8.6 142 11.4 351 25.2 612 47.0 

2020 142 8.5 144 10.1 142 11.4 352 25.0 585 45.1 

2025 152 9.6 146 11.7 147 9.9 342 24.1 578 44.7 

Total number of HUCs: 1365 

Total area: 58,376.2 sq mi 

Figure 5.6 Florida Projected %TIA as % of Area out to 2025 

75 
Table 5.6 Total River Miles in Florida per TIA category out to 2025 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 116 5346 104 5101 160 8034 337 12008 648 27462 

2010 133 5787 127 6398 142 7025 351 13357 612 25384 

2020 142 6090 144 6885 142 7870 352 12536 585 24569 

2025 152 7048 146 8293 147 6011 342 12209 578 24389 

Total number of HUCs: 1365 

Total river miles: 57,953 

Figure 5.7 Total River Miles in Florida by %TIA Category out to 2025 

76 
5.3.3 Georgia 

According to the U.S. Census Bureau (Campbell, 1997), by 2025 Georgia is projected to 

be the 9th most populous state (it was ranked 10th in the 2000 census) and it is projected to rank

4th largest in net growth between 1995 and 2025. Between 1990 and 2000, the Georgia 

population increased from 6.5 million to 8.2 million (U.S. Census Bureau, 2001). The Atlanta 

MSA added 1.2 million people between 1990 and 2000 (U.S. Census Bureau, 2003) accounting 

for approximately 70% of the net population growth in the state. The Atlanta MSA experienced

a 38.4% rate of growth while the state as a whole grew by 26.4%. 

The State of Georgia, Governor’s Office of Planning and Budget estimates the state’s 

population will grow to 9.6 million by 2010, an addition of 1.4 million people between 2000 and 

2010. Counties in the Atlanta MSA will continue to dominate the growth in the state in this ten

year period. While population growth will plateau in the central Atlanta counties of Fulton and

DeKalb, counties in the outer ring are projected to grow rapidly with several counties including 

Cherokee, Forsyth, Henry and Newton projected to grow by more than 50% in a ten year period. 

The population projections by county for the state of Georgia were obtained from the 

Planning, Research, & Evaluation Division, Governor’s Office of Planning and Budget, Atlanta, 

Georgia. The projections, currently available only to 2010, were published June 2002. These 

county scale projections served as the basis for the projection of future impervious cover for the 

state of Georgia. 

Figure 5.8 shows the impervious cover estimations and projections for Georgia 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.7 with the pattern of percent area by TIA class shown in Figure 5.9. The 

number of stream miles in each %TIA category is summarized in Table 5.8 with the pattern of 

stream miles by TIA class shown in Figure 5.10. 

Watersheds in Georgia with increasing impervious cover will be located primarily in the 

greater Atlanta metropolitan area. In 2000, 6.2% of the land area (3760 mi2) was in the 110

HUCs with %TIA >10% (areas where stream quality is likely degraded). By 2010, 7.5% of the 

land area (4407 mi2) was projected to be in the 133 HUCs with %TIA >10%. In Georgia, 

HUCs in the 5 to 10 %TIA category will increase from 157 to 169 between 2000 and 2010. 

Streams in watersheds in the 5 to 10 %TIA category are vulnerable to degradation if any 

additional growth occurs, although not necessarily severely degraded at that level of %TIA. 

By 2010, 7.1% (5026 mi) of Georgia streams are projected to be in HUCs with %TIA > 

10% while 84% of the streams (59590 mi) are in areas not immediately threatened by 

urbanization with < 5% impervious area. Between 2000 and 2010, 799 more miles of streams 

will be located in watersheds likely to suffer serious degradation due to development (>10 

%TIA) unless advanced planning and mitigation efforts are undertaken. 

77 
Figure 5.8 Georgia impervious cover out to 2010. Impervious cover as %TIA (percent total 

impervious area) by 12 digit HUC calculated using the Multiple Data Source approach. Data sources 

used in the calculation include 1993 NLCD commercial and industrial cover, 2000 Census data, 

county level population projections from Georgia Governor’s Office of Planning & Budget and U.S. 

DOT data for interstates and other major highways. 

78 
Table 5.7 % TIA as a Percentage of the Total Land Area of Georgia 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

2000 49 2.4 61 4.0 157 7.9 559 30.1 1039 55.7 

2010 58 2.8 75 4.7 169 8.3 579 31.2 984 53.0 

Total number of HUCs: 1865 

Total area: 58,754.1 

Figure 5.9 Georgia Projected %TIA as % of Area out to 2010 

79 
Table 5.8 Total River Miles in Georgia per TIA category 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 49 1464 61 2763 157 5833 559 22671 1039 38235 

2010 58 1735 75 3291 169 6350 579 23599 984 35991 

Total number of HUCs: 1865 

Total river miles: 70,966 

Figure 5.10 Total River Miles in Georgia by %TIA Category out to 2010 

80 
5.3.4 Kentucky 

According to the U.S. Census Bureau (Campbell, 1997), Kentucky is projected to be one 

of the slowest growing states in the nation between 1995 and 2025. A landlocked state at the 

northern edge of Region 4, Kentucky’s growth patterns are likely to be more similar to its 

midwestern neighbors to the north and west than the booming South Atlantic states. The 

University of Louisville, Urban Studies Institute (2003) projects an increase in Kentucky’s 

population from 4.0 million in 2000 to 4.7 million in 2030. Growth in the state will occur 

primarily around Lexington, Louisville, and in the suburban areas south of Cincinnati, OH. 

Population projections by county for the state of Kentucky were obtained from the 

Kentucky State Data Center, University of Louisville, Urban Studies Institute, Louisville, 

Kentucky http://ksdc.louisville.edu/kpr/pro/pro2002.htm. The projections, available out to 2030,

were published July 2003. 

Figure 5.11 shows the impervious cover estimations and projections for Kentucky 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.9 with the pattern of percent area by TIA class shown in Figure 5.12. 

The number of stream miles in each %TIA category is summarized in Table 5.10 with the 

pattern of stream miles by TIA class shown in Figure 5.13. 

Inspection of Figure 5.11 shows relatively little change between 2000 and 2030. In 2000, 

3.0 % of the land area (1212 mi2

) were in the 35 HUCs with %TIA >10% (areas where stream 

quality is likely degraded). By 2030, 3.7% of the land area (1495 mi2) was projected to be in the

42 HUCs with %TIA >10%. HUCs in the 5 to 10 %TIA category will increase from 91 to 105 

between 2000 and 2030. Streams in watersheds in the 5 to 10 %TIA category are vulnerable to 

degradation if any additional growth occurs, although not necessarily severely degraded at that 

level of %TIA. 

By 2030, 2.9% (1464 mi) of Kentucky streams are projected to be in HUCs with %TIA > 

10% while 89% of the streams (43828 mi) are in areas not immediately threatened by 

urbanization with < 5% impervious area. In 2030, only 273 more miles of streams than in 2000 

are likely to be located in watersheds where they will suffer serious degradation due to 

development (>10 %TIA) unless advanced planning and mitigation efforts are undertaken. An 

additional 3877 stream miles are projected to be in areas with 5 to 10% TIA. Streams in 

watersheds in the 5 to 10 %TIA category are vulnerable to degradation if any additional growth 

occurs although not necessarily severely degraded at that level of %TIA. 

81 
Figure 5.11 Kentucky impervious cover out to 2030. Impervious cover as %TIA (percent total 

impervious area) by 12 digit HUC calculated using the Multiple Data Source approach. Data sources used 

in the calculation include 1993 NLCD commercial and industrial cover, 2000 Census data, county level 

population projections from University of Louisville and U.S. DOT data for interstates and other major 

highways. 

82 
Table 5.9 % TIA as a Percentage of the Total Land Area of Kentucky out to 2030 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs % area 

2000 11 0.8 24 2.2 91 7.0 470 40.2 645 49.8 

2010 11 0.8 26 2.4 96 8.2 483 40.5 625 48.1 

2020 12 0.9 29 2.6 99 8.2 499 42.4 602 45.9 

2030 13 1.0 29 2.7 105 9.1 506 42.4 588 44.9 

Total number HUCs: 1241 

Total area: 40,407.4 

Figure 5.12 Kentucky Projected %TIA as % of Area out to 2030 

83 
Table 5.10 Total River Miles in Kentucky per TIA category out to 2030 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 11 302 24 889 91 3230 470 18904 645 25846 

2010 11 302 26 970 96 3483 483 19374 625 25041 

2020 12 356 29 1070 99 3509 499 20011 602 24223 

2030 13 371 29 1093 105 3877 506 20075 588 23753 

Total number of HUCs: 1241 

Total river miles: 49,169 

Figure 5.13 Total River Miles in Kentucky by %TIA Category out to 2030 

84 
5.3.5 Mississippi 

Mississippi is currently the 31st most populous state with a population of 2.8 million in 

the 2000 Census. During the period 1990 to 2000 Mississippi was ranked 33rd by numeric 

population change and was ranked 24th by percentage population growth (U.S. Census Bureau, 

2001). According to U.S. Census Bureau projections for the period 1995 to 2025, Mississippi

will be one of the slowest growing states in Region 4 (only Kentucky is projected to grow 

slower) and is ranked 34th nationally by percentage population change (Campbell, 1997). 

The population projections by county for the state of Mississippi were obtained from the 

Center for Policy Research and Planning, Mississippi Institutions of Higher Learning, Jackson, 

Mississippi, www.ihl.state.ms.us. The projections, available out to 2015, were published March

2002. While overall growth in Mississippi is projected to be moderate, growth in two counties is 

projected to have an annual average growth of approximately 2%. In the northeast corner of the 

state, Desoto County located in the Memphis MSA is projected to have the highest growth rate 

in the state. Madison County, a suburban county in the Jackson MSA, has the second highest 

projected growth rate. Moderate growth is also projected for the counties along the Gulf Coast 

(Hancock, Harrison, and Jackson). Declining populations are projected along the western border 

of the state south of the influence of Memphis (Washington, Issaquena, Sharkey, Humphreys). 

Figure 5.14 shows the impervious cover estimations and projections for Mississippi 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.11 with the pattern of percent area by TIA class shown in Figure 5.15. 

The number of stream miles in each %TIA category is summarized in Table 5.12 with the 

pattern of stream miles by TIA class shown in Figure 5.16. 

Inspection of Figure 5.14 shows little change between 2000 and 2015. The number of 

HUCs with >20% TIA is projected to increase from 2 to 4 HUCs while the number in the 10 to 

20 %TIA category is projected to increase to 17 from 15 (Table 5.11). These changes are 

located along the Gulf Coast, south of Memphis, and near Jackson. 

In 2000, 1.3 % of the land area (642 mi2

) was in the 17 HUCs with %TIA >10% (areas 

where stream quality is likely degraded). By 2015, 1.9% of the land area (938mi2) was projected

to be in the 21 HUCs with %TIA >10%. HUCs in the 5 to 10 %TIA category will increase from 

37 to 42 between 2000 and 2015. Streams in watersheds in the 5 to 10 %TIA category are 

vulnerable to degradation if any additional growth occurs, although not necessarily severely 

degraded at that level of %TIA. 

By 2015, 1.7% (1494 mi) of Mississippi streams are projected to be in HUCs with %TIA 

>10% while 89% of the streams (81610 mi) are in areas not immediately threatened by 

urbanization with < 5% impervious area. In 2015, 376 more miles of streams than in 2000 are 

likely to be located in watersheds where they will suffer serious degradation due to development 

(>10 %TIA) unless advanced planning and mitigation efforts are undertaken. By 2015, a total 

of 2652 stream miles are projected to be in areas with 5 to 10% TIA. 

85 
Figure 5.14 Mississippi impervious cover out to 2015. Impervious cover as %TIA (percent total impervious 

area) by 12 digit HUC calculated using the Multiple Data Source approach. Data sources used in the 

calculation include 1993 NLCD commercial and industrial cover, 2000 Census data, county level population 

projections from Mississippi Institutes of Higher Learning and U.S. DOT data for interstates and other major 

highways. 

86 
Table 5.11 % TIA as a Percentage of the Total Land Area of Mississippi out to 2015 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs % area 

2000 2 0.1 15 1.2 37 2.9 222 20.5 838 75.2 

2010 3 0.3 18 1.6 40 3.0 244 23.7 809 71.6 

2015 4 0.4 17 1.5 42 3.2 249 24.0 802 71.1 

Total number of HUCs: 1114 

Total area: 49,409.3 

Figure 5.15 Mississippi Projected %TIA as % of Area out to 2015 

87 
Table 5.12 Total River Miles in Mississippi per TIA category 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 2 92 15 1026 37 2428 222 17605 838 64604 

2010 3 165 18 1328 40 2497 244 20671 809 61094 

2015 4 259 17 1235 42 2652 249 20988 802 60622 

Total number of HUCs: 1114 

Total river miles: 85,756 

Figure 5.16 Total River Miles in Mississippi by %TIA Category out to 2015 

88 
5.3.6 North Carolina 

North Carolina is currently the 11th most populous state with just over 8.0 million people 

in the 2000 census. Between 1990 and 2000 North Carolina’s population increased 21.4 %, 

adding 1.4 million and making it the 9th fasting growing state in the nation on a percentage basis 

and 6th ranked by numeric population change (U.S. Census Bureau, 2001). Between 1995 and 

2025 the U.S. Census Bureau projects North Carolina to be ranked 7th of the 50 states in net 

increase in population (Campbell, 1997). 

The population projections by county for the state of North Carolina were obtained from 

North Carolina Office of State Budget and Management, Raleigh, North Carolina 

http://www.osbm.state.nc.us/osbm/index.html. The projections, available out to 2030, were

published June 2003. Overall, North Carolina is predicted to have a net gain of 4.4 million 

people between 2000 and 2030, a 55% increase in population over the 30 year period. Several 

counties located in three areas of the state will show an increase in population greater then 70% 

during the 30 year period. A set of counties north, south and east of Raleigh (Franklin, Wake, 

Johnston, Harnett, Hoke and Sampson) are projected to show growth rates from 74% to 123%. 

Four high growth counties surround Charlotte (Mecklenburg, Cabarrus, Iredell and Union), all 

with a projected population increase in excess of 80%. Southern coastal counties of Pender and 

Brunswick are predicted to show growth rates of 85% and 78%, respectively. Declining populations are projected in a few counties in northeastern North Carolina including Hertford, Bertie, 

Edgecombe and Washington with other counties in the area showing lower than average growth. 

Figure 5.17 shows the impervious cover estimations and projections for North Carolina 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.13 with the pattern of percent area by TIA class shown in Figure 5.18. 

The number of stream miles in each %TIA category is summarized in Table 5.14 with the 

pattern of stream miles by TIA class shown in Figure 5.19. Inspection of Figure 5.19 shows 

watersheds with increasing impervious cover located throughout the state with the greatest 

increase located around Raleigh and Charlotte, along the I-85 corridor between these two cities, 

and along a corridor between Raleigh and Fayetteville. Watersheds increasing in impervious

cover are also in evidence along the southern coastline. 

Between 2000 and 2030 the number of HUCs with >20% TIA is projected to double 

from 44 to 89 HUCs, while the number in the 10 to 20 %TIA category is projected to increase to 

130 from 101. In 2000, 7.1% of the land area (3739 mi2) was in the 145 HUCs with %TIA >

10% (areas where stream quality is likely degraded). By 2030, 11.7% of the land area (6161 

mi2

) was projected to be in the 219 HUCs with %TIA >10%. HUCs in the 5 to 10 %TIA 

category will increase from 177 to 231 between 2000 and 2030. Streams in watersheds in the 5 

to 10 %TIA category are vulnerable to degradation if any additional growth occurs, although not 

necessarily severely degraded at that level of %TIA.

 By 2030, 11.6% (7931mi) of North Carolina streams are projected to be in HUCs with 

%TIA >10%, while 73% of the streams (50096 mi) are in areas not immediately threatened by 

urbanization with < 5% impervious area. In 2030, 3272 more miles of streams than in 2000 are 

likely to be located in watersheds where they will suffer serious degradation due to development 

(>10 %TIA) unless advanced planning and mitigation efforts are undertaken. By 2030, a total 

of 10251 stream miles are projected to be in areas with 5 to 10% TIA. Streams in watersheds in 

the 5 to 10 %TIA category are vulnerable to degradation if any additional growth occurs, 

although not necessarily severely degraded at that level of %TIA. 

89 
Figure 5.17 North Carolina impervious cover out to 2030. Impervious cover as %TIA (percent 

total impervious area) by 14 digit HUC calculated using the M ultiple Data Source approach. Data 

sources used in the calculation include 1993 N LCD commercial and industrial cover, 2000 Census 

data, county level population projections from North Carolina Office of State Budget & Control 

Board and U.S. DOT data for interstates and other major highways. 

90 
Table 5.13 % TIA as a Percentage of the Total Land Area of North Carolina out to 2030 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

2000 44 1.9 101 5.2 177 11.0 628 38.9 651 43.1 

2010 62 2.9 115 6.1 204 13.0 642 39.3 578 38.7 

2020 78 3.9 120 6.6 226 14.2 638 39.6 539 35.8 

2030 89 4.4 130 7.3 231 14.7 642 39.8 509 33.8 

Total number of HUCs: 1601 

Total area: 52,662.7 

Figure 5.18 North Carolina Projected %TIA as % of Area out to 2030 

91 
Table 5.14 Total River Miles in North Carolina per TIA category out to 2030 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 44 1213 101 3446 177 7684 628 27845 651 28090 

2010 62 1800 115 4139 204 9259 642 28123 578 24957 

2020 78 2442 120 4574 226 9846 638 28515 539 22901 

2030 89 2864 130 5067 231 10251 642 28668 509 21428 

Total number of HUCs: 1601 

Total river miles: 68,278 

Figure 5.19 Total River Miles in North Carolina by %TIA Category out to 2030 

92 
5.3.7 South Carolina 

In the 2000 census, South Carolina was ranked 26th by population and between 1990 and 

2000 was ranked 19th by numeric population change and had a 15.1% growth in the population 

(U.S. Census, 2001). Between 1995 and 2025, the U.S. Census Bureau projected South Carolina 

to be the 20th ranked by percent increase in population (Campbell, 1997). 

The population projections by county for the state of South Carolina were obtained from 

South Carolina State Budget and Control Board, Office of Research and Statistics, Health and 

Demographics Division, http://www.ors2.state.sc.us/population/projections.asp. The 

projections, available out to 2025, were published October 2002. The state of South Carolina 

projects an increase of approximately 1.0 million people between 2000 and 2025 with a total

population of 5.1 million projected by 2025. The highest growth in the state is in the coastal 

counties with Horry (Myrtle Beach) and Beaufort showing the briskest pace of growth with a 

projected increase of 58% and 61%, respectively, over the 25 year period. Growth in inland 

areas is primarily in counties along the I-20 and I-85 corridors. 

Figure 5.20 shows the impervious cover estimations and projections for South Carolina 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.15 with the pattern of percent area by TIA class shown in Figure 5.21. 

The number of stream miles in each %TIA category is summarized in Table 5.16 with the 

pattern of stream miles by TIA class shown in Figure 5.22. 

Inspection of Figure 5.20 shows watersheds with increasing impervious cover located 

throughout the state with the greatest increase located along the coast, particularly in the 

northern coastal areas near Myrtle Beach. In the northern inland area increased impervious 

cover can be seen along the I-85 corridor and in the area south of Charlotte, NC. Increases are 

also evident in the central part of the state in Columbia area. 

Between 2000 and 2025, the number of HUCs with >20% TIA is projected to increase 

from 39 to 59 HUCs while the number in the 10 to 20 %TIA category is projected to increase to 

56 from 48. In 2000, 5.7 % of the land area (1775 mi2

) were in the 87 HUCs with %TIA >10% 

(areas where stream quality is likely degraded). By 2025, 8.2% of the land area (2554 mi2) was

projected to be in the 115 HUCs with %TIA >10%. HUCs in the 5 to 10 %TIA category will 

increase from 106 to 114 between 2000 and 2025. Streams in watersheds in the 5 to 10 %TIA 

category are vulnerable to degradation if any additional growth occurs, although not necessarily 

severely degraded at that level of %TIA.

 By 2025, 8.3% (2926 mi) of South Carolina streams are projected to be in HUCs with 

%TIA >10% while 80% of the streams (28284 mi) are in areas not immediately threatened by 

urbanization with < 5% impervious area. In 2025, 866 more miles of streams than in 2000 are 

likely to be located in watersheds where they will suffer serious degradation due to development 

(>10 %TIA) unless advanced planning and mitigation efforts are undertaken. By 2025, a total of 

4122 stream miles are projected to be in areas with 5 to 10% TIA. 

93 
Figure 5.20 South Carolina impervious cover out to 2025. Impervious cover as %T IA (percent total impervious 

area) by 14 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation 

include 1993 NLCD commercial and industrial cover, 2000 Census data, county level population projections from 

South Carolina State Budget & Control Board and U.S. DOT data for interstates and other major highways. 

94 
Table 5.15 % TIA as a Percentage of the Total Land Area of South Carolina 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs % area 

2000 39 1.7 48 4.0 106 10.1 362 37.7 476 46.7 

2010 45 2.0 52 4.5 107 10.4 366 38.8 461 44.4 

2020 54 2.6 54 4.8 108 10.8 380 39.6 435 42.3 

2025 59 2.9 56 5.3 114 11.5 378 39.1 424 41.4 

Total number of HUCs: 1031 

Total area: 31,144.8 

Figure 5.21 South Carolina Projected %TIA as % of Area out to 2025 

95 
Table 5.16 Total River Miles in South Carolina per TIA category 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 39 553 48 1507 106 3734 362 13697 476 15841 

2010 45 653 52 1714 107 3819 366 14230 461 14916 

2020 54 906 54 1811 108 3952 380 14438 435 14226 

2025 59 986 56 1940 114 4122 378 14190 424 14094 

Total number of HUCs: 1031 

Total river miles: 35,332 

Figure 5.22 Total River Miles in South Carolina by %TIA Category out to 2025 

96 
5.3.8 Tennessee

In the 2000 Census, Tennessee was ranked 16th in the nation based on total population

with 5.7 million people. Tennessee was ranked 12th in the nation based on total population

increase between 1990 and 2000 adding 0.8 million people, a 16.7% increase in population (U.S. 

Census Bureau, 2001). According to the U.S. Census Bureau, Tennessee is expected to be 13th 

in the nation ranked by net increase in population between 1995 and 2005 and 19th in the nation 

based on percent population increase for this time period (Campbell, 1997). 

The population projections by county for the state of Tennessee were prepared by the 

Center for Business and Economic Research, College of Business Administration, The 

University of Tennessee, Knoxville, Tennessee http://bus.utk.edu/cber/census/tnpopdat.htm. The projections, available out to 2020, were published March 1999. Based on these state 

projections, Tennessee’s population will increase to 6.6 million by 2020. Rapid growth is 

projected to focus primarily in the north central portion of the state in counties surrounding 

Nashville. Nashville and eight surrounding counties are projected to account for over 40% of 

the increase in population in Tennessee during the projection period. 

Figure 5.23 shows the impervious cover estimations and projections for Tennessee 

watersheds calculated using the Multiple Data Source approach described in Section 4 and 

Section 5.2. The number of HUCs and percentage land area in each impervious cover class are 

summarized in Table 5.17 with the pattern of percent area by TIA class shown in Figure 5.24. 

The number of stream miles in each %TIA category is summarized in Table 5.18 with the 

pattern of stream miles by TIA class shown in Figure 5.25. 

Inspection of Figure 5.23 shows watersheds with increasing impervious cover located 

primarily in counties surrounding Nashville. Increases in impervious cover are also evident in 

the eastern part of the state along the I-40 corridor from Chattanooga to Knoxville and Johnson 

City. 

Between 2000 and 2020, the number of HUCs with >20% TIA is projected to increase 

from 28 to 36 HUCs, while the number in the 10 to 20 %TIA category is projected to increase to 

48 from 40. In 2000, 5.0 % of the land area (2106 mi2) was in the 68 HUCs with %TIA >10%

(areas where stream quality is likely degraded). By 2020, 6.5% of the land area (2739 mi2) was

projected to be in the 84 HUCs with %TIA >10%. HUCs in the 5 to 10 %TIA category will 

increase from 76 to 98 between 2000 and 2020. Streams in watersheds in the 5 to 10 %TIA 

category are vulnerable to degradation if any additional growth occurs, although not necessarily 

severely degraded at that level of %TIA.

 By 2020, 5.6% (3610 mi) of Tennessee streams are projected to be in HUCs with %TIA 

>10% while 85.6% of the streams (54743 mi) are in areas not immediately threatened by 

urbanization with < 5% impervious area. In 2020, 867 more miles of streams than in 2000 are 

likely to be located in watersheds where they will suffer serious degradation due to development 

(>10 %TIA) unless advanced planning and mitigation efforts are undertaken. By 2020, a total of 

5628 stream miles are projected to be in areas with 5 to 10% TIA. 

97 
Figure 5.23 Tennessee impervious cover out to 2020. Impervious cover as %TIA (percent total impervious area) by 

12 digit HUC calculated using the Multiple Data Source approach. Data sources used in the calculation include 1993 

NLCD commercial and industrial cover, 2000 Census data, county level population projections from University of 

Tennessee and U.S. DOT data for interstates and other major highways. 

98 
Table 5.17 % TIA as a Percentage of the Total Land Area of Tennessee out to 2020 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

% 

area 

# 

HU Cs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs 

% 

area 

# 

HUCs % area 

2000 28 1.9 40 3.1 76 6.9 383 37.7 566 50.4 

2010 32 2.2 43 3.5 94 8.8 392 38.2 532 47.2 

2020 36 2.4 48 4.1 98 9.3 412 40.0 499 44.0 

Total number of HUCs: 1093 

Total area: 42,139.4 

Figure 5.24 Tennessee Projected %TIA as % of Area out to 2020 

99 
Table 5.18 Total River Miles in Tennessee per TIA category 

Year

 >20% TIA 10-20% TIA 5-10% TIA 2-5% TIA <2% TIA 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

# 

HUCs 

river 

miles 

2000 28 994 40 1740 76 4151 383 24025 566 33072 

2010 32 1143 43 1995 94 5392 392 24280 532 31173 

2020 36 1226 48 2384 98 5628 412 25492 499 29251 

Total number of HUCs: 1093 

Total river miles: 63,981 

Figure 5.25 Total River Miles in Tennessee by %TIA Category out to 2020 

100 
5.4 Using the Impervious Cover Projections 

As discussed in Section 5.1, population projections for small (12 or 14 digit HUC scale)

and rapidly growing places will be quite large. The population projections alone in HUCs in high 

growth areas can have errors in the 30 to 50% range for a 20 year time horizon. Impervious cover

calculations for future periods will add additional errors in addition to the very substantial errors 

associated with small scale population projections. A good assessment of impervious cover 

projections can only come as a retrospective analysis similar to what has been done at multiple 

scales for population projections discussed in Section 5.1. 

The projections of impervious cover are not meant to be used as definitive forecasts of the 

future state of a specific watershed, but rather as a plausible scenario to identify where to look for 

potential impairments and begin timely prevention efforts. These projections can give state and 

local planners and resource managers a reasonable assessment of the magnitude of the problems 

the states need to prepare for in the upcoming decades and can be used to guide monitoring to 

identify problems as they begin to surface. Stream remediation is very expensive. The North 

Carolina Ecosystem Enhancement Program stream reported costs for restoration/rehabilitation of

urban streams in 2004 to be $201.00 per foot of stream length (Jurek 2004). Well-timed and 

targeted prevention and management actions can avoid the need for at least some of these very 

expensive remediation expenditures in the future. Spatial tools, including the impervious cover

projections for the Southeastern states presented in this section, can aid in targeting these 

prevention and management activities. 

101 
6. Conclusions and Recommendations

 Complete identification and eventual prevention of urban water quality problems pose 

significant monitoring and water quality management challenges. The purpose of the methods

and analyses discussed in this report was to provide tools to assist decision makers in meeting

these challenges. The Multiple Data Source (MDS) wide area impervious estimation and

projection techniques can assist in meeting these challenges by providing: 1) cheap estimates of

impervious cover at the watershed and sub-watershed scales; 2) a region-wide approach to 

screening for waters likely impaired or threatened by urban storm water; and 3) projections of 

change in imperviousness over time. 

The point sampling approach to aerial photo interpretation of imperviousness supplied an 

essential, cost-effective, independent assessment for both the MDS and the NLCD only 

estimation techniques and identified the appropriate uses for these two approaches. The use of 

the NLCD data with the ATtILA tool identifies most watersheds that are likely suffering severe 

impairment from urbanization and allows a very rapid assessment. Unfortunately, this tool is not 

as robust in identifying watersheds whose condition may be in a borderline category and 

vulnerable to impairment in the near future. The MDS technique provides a more reliable 

method for identifying watersheds impaired by urbanization compared to the use of 

land-use/land-cover alone, especially for watersheds in the 5 to 10% impervious range where 

prevention of storm water problems is critical. 

The region-wide MDS impervious area estimates provide a screening tool for designing 

water quality monitoring programs by identifying areas for priority monitoring for urban and 

urbanizing watersheds. State monitoring programs have limited resources and thus cannot sample 

everywhere. This landscape screening process provides workable, defensible methods to:

extrapolate condition estimates to waters lacking in-stream data; identify suspected problem areas 

(likely impaired waters); and efficiently target additional monitoring to confirm problems. 

The current (year 2000) impervious area estimates of this study identify specific 

watersheds/HUC’s where existing adverse impacts due to impervious surfaces are likely (the 10 

to 20% and >20% impervious classes). Some urban streams in these watersheds are listed as 

impaired through Section 303(d) of the Clean Water Act and are subject to TMDL development. 

Many potentially degraded waters are not yet listed, however, primarily due to a lack of 

systematic monitoring approaches to identify urban water quality problems. Using the results 

presented in this study, streams in watersheds/HUCs with imperviousness exceeding 10% that are 

not already listed under the 303(d) impaired waters listing process for sediment and biological 

integrity impairment should be prioritized for monitoring to ascertain if they are in fact impaired. 

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). Successful rehabilitation and restoration of streams in urbanized watersheds will 

require complex, holistic approaches and should follow the sequence of: 1) hydrology, 2) 

channel and habitat, 3) riparian zones, and 4) aquatic biological communities, recommended by

the National Research Council (National Research Council 1992, and Brosnan, et al. 1999). The 

future impervious area projections of this study highlight the high growth areas of the Southeast, 

and the specific watersheds/HUC’s where this growth will be most likely to occur. These are the 

very areas where effective storm water management and prevention of urban storm water impacts 

are likely to be most cost effective. These same areas should be carefully considered to receive 

increased attention for storm water education for local leaders and the public, and to institute 

state-of-the-art storm water management practices. HUCs currently within the 5 to 10% 

102 
imperviousness range and projected to experience growth in the next decade need to be the 

highest priority focus of educational efforts and proactive storm water management actions to 

prevent water quality degradation. 

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 focus now on the importance of imperviousness to future stream health, we can 

avoid totally unnecessary storm water degradation of streams, and put those waters already 

impacted back on the road to recovery. 

Additional research will be needed to describe and explain differences in sensitivity to 

impervious cover and 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, et al. 2001); hydrologic 

landscapes (Winter 2001); and average hydrologic response (Woodruff and Hewlett 1970). 

Relationships to in-stream response need to be refined for the Piedmont ecological region and 

developed using existing and new data for other areas of the Southeast. This might be done

region by region (such as the Blue Ridge, Piedmont, and upper/lower coastal plains in North 

Carolina) using appropriate biological data sets encompassing the full gradient of imperviousness, 

or by using more generalized techniques such as multi-variate analyses incorporating critical 

physical factors likely to drive stream channel and sediment processes (and thus influence habitat

and biological responses) such as relief, slope, soil properties (erodability in particular) and 

riparian vegetation. A potential advantage of the latter approach is that resulting response models 

might be applicable to much broader regions. The USGS’s series of “urban gradient” studies, 

which are gathering both landscape and in-stream data for a variety of urban areas around the 

nation, as well as existing state biological data networks, should provide useful data for building 

reliable empirical response models. Such 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, et al. 2001, and Wickham, et al. 2002), bacterial

contamination (Mallin, et al. 2000) and for pesticide/herbicide contamination. 

103 
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112 
Appendix 

The following procedures were used to calculate the current and projected %TIA for the 

eight Southeastern United States: Alabama, Florida, Georgia, Kentucky, Mississippi, North 

Carolina, South Carolina and Tennessee. All the GIS work was done in Environmental Systems 

Research Institute, Inc.(ESRI)’s ArcView 3.2 and ArcInfo Workstion 9. The scripts in Section 

XIII are in the ArcView scripting language Avenue. Source data used in these processing steps 

include the following and are referred to by data type number (1-6) in the stepwise procedures. 

1. NLCD 92 – 21 category land cover classification with 30 m spatial resolution raster 

(gridded) coverage (http://edc.usgs.gov/products/landcover/nlcd.html). Data was provided on CD 

by USEPA Region 4 for the eight Southeastern states from regional data archives. 

2. National Transportation Atlas (NTA) available on CD from U.S. Department of 

Transportation (USDOT (2001) as a shapefile. Class 1 (interstates and major highways) roads for 

the eight Southeastern states were saved as a shapefile for use in analysis. 

3. U.S. Bureau of Census 1990 and 2000 block level population and population density 

data. Block level data as a shapefile was provided by USEPA Region 4 from regional data 

archives for the eight Southeastern states. 

4. U.S. Bureau of Census 2000 block level vacant housing data. Data was downloaded 

from U.S. Bureau of Census website (www.census.gov) and processed as described in Section V

below. 

5. 12 or 14 digit HUC shapes files obtained from individual Southeastern states 

6. County level population projections obtained from individual Southeastern states. 

A state scale version for each of these data sets was developed and stored in state specific 

directories. The following procedures were used to calculate values for each of the eight 

individual states. 

I. To calculate road area:

Step 1: remove road arcs that run through commercial industrial and mining cells to prevent

double-accounting:

A. 	To obtain only the high intensity commercial grid cells (value=23) from data set 1, 

from the grid prompt in:

GRID: outgrid = select(mrlc grid, ‘value = 23') 

B.	 Convert the value23 grid to a polygon coverage by:


ARC: gridpoly value23 value23_poly


C. Convert road shapefile (data set 2) to a coverage using the shapearc command in ARC. 

D. After setting up the ArcEdit: environment, ensure that the following two commands 

execute: 

AE: nodesnap off

AE: intersectarcs all

 then “get” the poly arcs into the road coverage to make intersections at all polygons.

 Save and then select all arcs with lanes = 0 and delete. 

E. Convert the roads coverage back to a shapefile (newroadshape). 

F. 	In ArcView, use the select by theme option and select the road arcs in newroadshape 

that are completely within the value = 23 polygons. Delete the selected arcs. 

G. Repeat steps A, B, C, and F to remove roads in grid cells classified as mining value = 

32. Replace the number 23 with 32 in each of these steps. 

Step 2: calculate road area: 

A. 	Select the 2-lane arcs (Lanes = 2) from newroadshape and create a new shapefile from 

the selected two lane arcs. 

113 
B. 	Select-by-theme the huc polygons that are intersected by the 2-lane road arcs. Reverse 

the selection to select huc polygons that do not contain road arcs and calculate the 

2-lane area to -9999. Then run the rd_clip.ave script.

C. Repeat steps A & B using 4-lane roads. 

D. 	The rd_clip.ave script will clip the road arcs with the huc polygon and calculate the 

new road arc lengths in meters. The road lengths are then converted to road area 

in square miles using the following formula:

road length x 3.2808 x 24 (or 48) x 0.00000003587006

Where 3.2808 is the conversion constant for meters to feet and 24 is the 

assumed width, in feet, of a two-lane road (48 is the width, in feet, for a 

four-lane road) and 0.00000003587006 is the conversion constant for 

square feet to square miles. 

E. 	The field total_rd_area is calculate by adding the 2-lane and 4-lane area fields. The 

null values (-9999) will be re-calculated to -1111 by the script and then 

selected and recalculated after script completion to 0 since they contain no 

highway segment. 

II. To calculate industrial and mining area:

1. 	Obtain the high intensity commercial grid cells from data type 1 in ArcInfo using 

GRID; 

GRID: outgrid = select(<grid name>, ‘value = 23') 

2. 	Then convert the outgrid into a polygon coverage:


ARC: gridpoly outgrid value23_poly


3. Convert value23_poly to a shapefile. 

4. 	In ArcView select the huc polygons (Data type 5) that completely contain the features 

of value23_poly.shp

5. Reverse the selection and calculate the selected records’ commercial area field to - 9999 and run the fixed_loop.ave. The cell count will be calculated to the area 

field. Converted to square meters, then convert to sq. miles. The other records 

(-9999) will be re-calculateded to -1111 and should be re-calculated to 0 since they 

contain no commercial property. 

6. For mining, repeat above steps using the value 32 instead of 23. 

7. 	The areas in square miles are calculated to the fields indus_area (or comm_area) and 

mining_area respectively. 

III. Populations for the years 2000 and 1990 and state population projections: 

1. 	The FIPs code was calculated for the block shapefile (data set 2) by adding a field 

([FIPS]) as a string with a width of five and calculating it equal to the [Areakey] 

field. 

2. 	A new field was added to the block shapefile called [orig_area] and the value was 

calculated (Shape.ReturnArea).

3. 	The block shapefile was exported as a geodatabase and then converted back to a 

shapefile.

4. 	The avenue script clip_blocks.ave was run, which clips the blocks by the HUCs (data 

type 2).

5. 	The clipped blocks are then merged back together using the GeoProcessing Wizard in 

ArcView. 

6 	A new field, [new_area] is added and calculated. A [factor] field is also added and is 

calculated by [new_area] / [orig_area]. 

7. 	A selection is run on records with a factor > 1.01 and those are deleted. These values 

are believe to exist because of problems within the original block shapefile due to 

manipulation by the original creators to account for changes between 1990 and 

114 
2000. Records that have a zero population for both 1990 and 2000 are also deleted 

as they will not play a factor in any population estimates. 

8. 	New population fields are added for 1990 and 2000 to the block shapefile and are 

calculated by multiplying the original populations by the factor value to get the

corrected population for the split blocks (new_pop2000 and new_pop1990). 

9. 	An excel table containing the Census 1990 & 2000 figures per county along with the 

state’s population estimates for any or all of the following years: 2010, 2015, 

2020, 2025, and 2030 was joined by FIPS code field to the block shapefile. 

10. Block population estimates are calculated in the block shapefile by: 

block_popX = new_pop2000 / County_pop2000 * County_popX, 

where X = year estimated. 

11. 	The block population estimates are summed and calculated to the HUCs by running 

the calc_pop.ave script. Minor edits are made to the script for each year’s 

estimate. 

IV. Population Density:

1. All population values divided by square miles. 

V. Vacant Housing Data:

Step 1: import data from Census website (data type 4):

A. 	Download each state’s files (for example, Alabama’s data would be named 

al00037ufl.zip (data) and algeoufl.zip (geography)) from the Census Bureau’s FTP 

site and unzip each file.

B. 	Dowloaded the Summary File template file for MSAccess97 from the Census 

Bureau’s website. This file enables the import of the data and geographic files into 

Access because it contains the field names and structure for the data. This site also 

includes instructions for importing the files into MSAccess. 

C. 	Change the *00037.ufl and *geo.ufl file extensions to .txt and follow the import

procedures into MSAccess (instructions located at Census website.)

D. Save each database as a .dbf. 

E. Import the .dbf files into ArcView.

F. Perform a join on the <logrecno> fields contained in both the geo and data files. 

G. Export the joined table as a new .dbf file to permanently preserve join. 

H. 	Create <Areakey> field as a string and calculated to match <areakey> in Region 4 

block data. Populate the records by calculating the new field as 

state+county+tract+block (concatenate the values). Join the two tables (census

data + block data).

Step 2: Calculate number of homes per type of vacancy:

A. 	Calculate the following, per HUC (data type 5): total housing units, occupied units, 

vacant units, rental homes, homes for sale, rented or sold homes that are vacant, 

seasonal housing, migrant housing and homes vacant for other reasons. The 

calculations are completed using the calc_vcn.ave script.

B. Density for vacant housing is based on number of homes per square mile. 

Step 3: Calculate estimated population for vacant housing:

A. Calculate population equivalent using the vacant housing information and average 

household size by state (based on data from US Census Bureau website) for each 

HUC. Calculation was a weighted average based on owner-occupied and renteroccupied---

Average population per household for North Carolina = 2.48798 

Average population per household for Alabama = 2.49575 

Average population per household for Florida = 2.4601 

Average population per household for Georgia = 2.645 

115 
Average population per household for Kentucky = 2.46824

Average population per household for Mississippi = 2.62845

Average population per household for South Carolina = 2.52884

Average population per household for Tennessee = 2.48572 

Average person per household multiplied by number of vacant homes. The total 

vacant and seasonal population fields are based only on vacant and seasonal 

housing data. The total vacant and seasonal population density field are based on 

the vacant and seasonal populations + the 2000 population per square mile. 

VII. %TIA values based on population: 

%TIA based on the Hicks (GVSS&DD) calculations use the following formula:

(94 x (1 - (2.7183 ^ (-0.00010938 x PopulationDensityValue)))) + 1 

VIII. Extrapolated 1993 Population Value:

The population value for 1993 was derived using:

1990 Population + (((2000 Population - 1990 Population) / 10) x 3) 

IX. Projected high intensity commercial industrial (HICI) area: 

1. 	HiciX = commercial area x PopulationX / 1993 Population


Where X = projected year


2. 	Query: HiciX < commercial area; if yes, calc HiciX = commercial area 

3. 	Add a temporary field, calculated as: 


commercial area / 1993 population x 10,000


4. 	Evaluate the temporary field to determine if any values are greater than 3. 

5. 	If there are values greater than 3, add a temporary field, calculated as: 

0.6 x (PopulationX - 1993 Population) / 10,000

6. 	Use the value from Step 5 and add it to the commercial area to determine 

HiciX for that record. 

7. 	Reevaluate HiciX < commercial area and recalculate HiciX = commercial 

 area if necessary. 

X. Commercial Total Impervious Area: 

% Commercial TIA is calculated as: 

(HiciX / total area HUC) x 0.90 x 100 

XI. Percent Total Impervious Area: 

Percent TIA is calculated as: 

% road area + % mining area + % Commercial TIA + Hicks calculation 

The field TIA2000_vct using the above formula with the Hicks value (Step VII) being 

calculated from the total vacant population density figure (from Section V, Step 3.) 

XII. Projected Population with Vacant:

1. 	Projected population for 2010, 2015, etc with projected vacant population added: 

Year X projected HUC population * (1 + total vacant population / 2000 

population)

2. 	The density is then calculated by dividing by area in square miles. 

116 
XIII. Scripts: 

• Script 1: Fixed_loop.ave 

'loop_grd_clp.ave


'Christine Perkins, CSC


'this scrip loops through a polygon coverage, selects each polygon


'using an index number,


'clips the grid and returns the count value to be calculated into the


'polygon attribute table.


theProject = av.GetProject


theView = theProject.FindDoc("View1")


PolyThm = theView.FindTheme("huc12_al.shp")


GridThm = theView.FindTheme("mining_grid")


GridThm.SetActive(true)


PolyFTab = PolyThm.GetFTab


theField = PolyFTab.FindField("Index")


theField2 = PolyFTab.FindField("mining_area")


PolyFTab.SetEditable(true)


theSel = PolyFTab.GetSelection


theValue = 0


for each rec in PolyFTab


theValue = theValue + 1


 QueryString = "[Index] =" + theValue.AsString


PolyFTab.Query(QueryString,theSel,#VTAB_SELTYPE_NEW)


PolyFTab.UpdateSelection


newVal = PolyFTab.ReturnValue(theField2,rec)


if (newVal < 0) then


PolyFTab.SetValueNumber(theField2,rec,-1111)


else


theGrid=theView.GetActiveThemes.Get(0).GetGrid


'Get bounds of clipping area as a rectangle


thePolyThmExtent = PolyThm.getselectedextent


if (thePolyThmExtent .IsEmpty) then thePolyThmExtent =


PolyThm.ReturnExtent end


'Get parameters for the new grid


theFtab = PolyThm.GetFTab


theProj = theView.GetProjection


theCell = theGrid.GetCellSize


theExtent = theGrid.GetExtent


ae = theView.GetExtension(AnalysisEnvironment)


ae.SetExtent(#ANALYSISENV_VALUE, thePolyThmExtent)


ae.SetCellSize(#ANALYSISENV_VALUE, theCell)


' Activate the settings for the analysis envirnonment as returned


' by the above 3 lines of code.


ae.Activate


'the actual extraction occurs here


tempGrid = Grid.MakeFromFtab(theFtab,theProj,nil,{theCell,theExtent})


newGrid = (tempGrid.IsNull).Con (tempGrid, theGrid)


' rename data set


aFN = av.GetProject.GetWorkDir.MakeTmp("mine", "")


newGrid.Rename(aFN)


117 
' check if output is ok


if (newGrid.HasError) then return NIL end


' create a theme 


gridThm = theme.make(newGrid.GetSrcName)


' set name of theme


gridThm.SetName("Huc" + theValue.AsString)


' add theme to the specifiedView


theView.addTheme(gridThm)


' Resets the analysis environment to the maximum of inputs (i.e. the


default)


aRect = Nil


ae = theView.GetExtension(AnalysisEnvironment)


ae.SetExtent(#ANALYSISENV_MAXOF, aRect)


ae.SetCellSize(#ANALYSISENV_MAXOF, aRect)


gridThm.invalidate(true)


newerGrid = gridThm.GetGrid


newVTab = newerGrid.GetVTab


theFieldest = newVTab.FindField("count")


answer5 = newVTab.ReturnValue(theFieldest,0)


PolyFTab.SetValue(theField2,rec,answer5)


theProject.save


end


end


• Script 2: rd_clip.ave 

'clip_themes.ave


'Christine Perkins, May 2001


'this script loops through a polygon-type shapefile, selects each


'polygon,


'clips and creates a road shapefile based on the poly boundaries,


'calculates


'the new road lengths, and plugs the length into the still-selected


'polygon


'uses the extensions Batch Clip and Clip Theme, available online.


'Basic setup


theProject = av.GetProject


theView = theProject.FindDoc("View1")


PolyThm = theView.FindTheme("Huc12_al.shp")


PolyThm.SetActive(true)


LineThm = theView.FindTheme("Four_lanes.shp")


LineThm.SetActive(true)


PolyFTab = PolyThm.GetFTab


LineFTab = LineThm.GetFTab


'Finds necessary fields and sets up tables


theField = PolyFTab.FindField("Index")


theField2 = PolyFTab.FindField("four_lane")


PolyFTab.SetEditable(true)


theSel = PolyFTab.GetSelection


'Starts the loop


theValue = 0


for each rec in PolyFTab


118 
 theValue = theValue + 1


 QueryString = "[Index] =" + theValue.AsString


PolyFTab.Query(QueryString,theSel,#VTAB_SELTYPE_NEW)


PolyFTab.UpdateSelection


newVal = PolyFTab.ReturnValue(theField2,rec)


if (newVal < 0) then


PolyFTab.SetValueNumber(theField2,rec,-1111)


else


'not really needed


'theView = av.GetActiveDoc


thePrj = theView.GetProjection


activeThemes = theView.GetActiveThemes


' -- get the FTab for the theme to clip, and if two themes are selected,


' -- the FTab of the theme containing the clipping polygons


if (activeThemes.Count = 1) then


sourceTheme = activeThemes.Get(0)


sourceFTab = sourceTheme.GetFTab


 clipFTab = nil


else


 theme1 = activeThemes.Get(0)


ftab1 = theme1.GetFTab


 theme2 = activeThemes.Get(1)


ftab2 = theme2.GetFTab


 ' -- if only one theme is a polygon theme, then it is the clipping


theme


 sourceFTab = nil


 if (ftab1.GetShapeClass.GetClassName = "Polygon") then


if (ftab2.GetShapeClass.GetClassName <> "Polygon") then


sourceTheme = theme2


 sourceFTab = ftab2


 clipFTab = ftab1


end


 else


 if (ftab2.GetShapeClass.GetClassName = "Polygon") then


sourceTheme = theme1


 sourceFTab = ftab1


 clipFTab = ftab2


end


 end


' -- get the output file name


outFileName = FileName.GetCWD.MakeTmp("roadest", "shp")


'outFileName = FileDialog.Put(outFileName,"*.shp","Specify the output


shapefile")


'if (outFileName = Nil) then


'return nil


 'end


shapeType = sourceFTab.FindField("Shape").GetType


if (shapeType = #FIELD_SHAPELINE) then


outClass = POLYLINE


elseif (shapeType = #FIELD_SHAPEMULTIPOINT) then


outClass = MULTIPOINT


119
elseif (shapeType = #FIELD_SHAPEPOINT) then


outClass = POINT


elseif (shapeType = #FIELD_SHAPEPOLY) then


outClass = POLYGON


else


 'MsgBox.Error("Invalid shape field type.",theTitle)


' return nil


end


' -- check if shapes should be projected


'doProjection = false


'if (thePrj <> nil) then


'doProjection = MsgBox.YesNo("Output shapes in projected


coordinates?",theTitle,true)


'end


' -- create one large polygon from the input polygons


if (clipFTab <> nil) then


clipPoly = av.Run("View.ClipThemeUnionFTab",clipFTab)


else


 clipPoly = av.Run("View.ClipThemeUnionGraphics",theView)


if (thePrj <> nil) then


clipPoly = clipPoly.ReturnUnprojected(thePrj)


end


 end


' -- select the features to be processed


oldSelection = sourceFTab.GetSelection.Clone


if (sourceFTab.GetSelection.Count = 0) then


sourceFTab.GetSelection.SetAll


 end


'if (clipOption = "inside") then


sourceFTab.SelectByPolygon(clipPoly,#VTAB_SELTYPE_AND)


processSelection = sourceFTab.GetSelection.Clone


sourceFTab.SetSelection(oldSelection)


sourceFTab.UpdateSelection


' -- create the new shapefile


outFTab = FTab.MakeNew(outFileName, outClass)


outFTab.SetEditable(true)


outFields = sourceFTab.GetFields.DeepClone


outFields.Remove(0)


outFTab.AddFields(outFields)


outFields = outFTab.GetFields


outShapeField = outFTab.FindField("shape")


sourceFields = sourceFTab.GetFields


sourceShapeField = sourceFTab.FindField("shape")


' -- process the features


selCount = processSelection.Count


c = 0


av.ShowMsg("Clipping ...")


av.SetStatus(0)


120 
for each sRec in processSelection


s = sourceFTab.ReturnValue(sourceShapeField,sRec)


'if (clipOption = "inside") then


if (s.IsContainedIn(clipPoly).Not) then


s = s.ReturnIntersection(clipPoly)


end


 'if (doProjection) then


' s = s.ReturnProjected(thePrj)


' end


 oRec = outFTab.AddRecord


 outFTab.SetValue(outShapeField,oRec,s)


 for each i in 1..(sourceFields.Count - 1)


v = sourceFTab.ReturnValue(sourceFields.Get(i),sRec)


outFTab.SetValue(outFields.Get(i),oRec,v)


end


 c = c + 1


 av.SetStatus((c / selCount) * 100)


end


outFTab.SetEditable(false)


av.ClearMsg


av.ClearStatus


' -- display in a view if requested


viewList = List.Make


for each d in av.GetProject.GetDocs


if (d.Is(View)) then


viewList.Add(d)


end


 end


''add new theme


 newTheme = FTheme.Make(outFTab)


theView.AddTheme(newTheme )


theView.GetWin.Activate


 end


''calculate length of road segments


newTab = newTheme.GetFTab


 newTab.SetEditable(true)


lengthField = Field.Make("Length3",#FIELD_DECIMAL,16,3)


newTab.AddFields({lengthField})


 'calc each segment's length and attribute the record


for each rec in newTab


 theShape = newTab.ReturnValue(newTab.FindField("shape"),rec)


x = theShape.ReturnLength


newTab.SetValue(lengthField,rec,x)


end


 newTab.SetEditable(false)


'add lengths from new shapefile


total = 0


theLength = newTab.FindField("Length3")


for each rec in newTab


 lengths = newTab.ReturnValue(theLength,rec)


121 
 total = total + lengths


end


'plug in length value into polygon record


PolyFTab.SetValue(theField2,rec,total)


theProject.save


end


end


• Script 3: Clip_blocks.ave 

'TITLE: clip_blocks.ave


'AUTHOR: Christine Perkins, CSC


'PURPOSE: clips a block shapefile with a HUC shapefile and adds the


'clipped blocks to the ArcView project


theProject = av.GetProject


theView = theProject.FindDoc("View1")


SRCTheme = theView.FindTheme("Blocks_geo.shp")


interTheme = theView.FindTheme("Arc_huc12.shp")


interTheme.SetActive(true)


interFTab = interTheme.GetFTab


SRCFTab = SRCTheme.GetFTab


interFTab.SetEditable(true)


theField = interFTab.FindField("Index")


theSel = interFTab.GetSelection


theValue = 0


for each rec in interFTab


 theValue = theValue + 1


 QueryString = "[Index] =" + theValue.AsString


interFTab.Query(QueryString,theSel,#VTAB_SELTYPE_NEW)


interFTab.UpdateSelection


' Specify the output shapefile...


outFName = FileName.GetCWD.MakeTmp("clip", "shp") 


shapeType = SRCTheme.GetFTab.FindField("Shape").GetType


if (shapeType = #FIELD_SHAPELINE) then


outClass = POLYLINE


elseif (shapeType = #FIELD_SHAPEMULTIPOINT) then


outClass = MULTIPOINT


elseif (shapeType = #FIELD_SHAPEPOINT) then


outClass = POINT


elseif (shapeType = #FIELD_SHAPEPOLY) then


outClass = POLYGON


else


 MsgBox.Error("Invalid shape field type.", "Merge Themes")


exit


end


'-------------------------------------------------------


'Set the variables


'-------------------------------------------------------


OutputFTab = FTab.MakeNew( outFName, outClass )


SRCfields = List.Make


InterFields = List.Make


122
for each f in SRCTheme.GetFTab.GetFields


 if (f.GetName = "Shape") then


continue


 else 


fCopy = f.Clone


SRCfields.Add(fCopy)


end


end


'-------------------------------------------------------


'add the fields to the output file


'------------------------------------------------------­


if (SRCFields.Count > 0) then


OutputFTab.AddFields( SRCFields )


end


outshpfld = OutputFtab.FindField("Shape")


Theme1 = SRCTheme


ftab1=Theme1.GetFTab


shpfld1=ftab1.FindField("Shape")


therecs1 = ftab1.GetSelection


theoldsel = ftab1.GetSelection.Clone


if (therecs1.Count=0) then


therecs1=ftab1


end


Theme2 = InterTheme


ftab2=Theme2.GetFtab


shpfld2=ftab2.FindField("Shape")


therecs2 = ftab2.GetSelection


if (therecs2.Count=0) then


therecs2=ftab2.GetSelection.SetAll


 ftab2.UpdateSelection


therecs2 = ftab2.GetSelection


end


OutputFtab.SetEditable(False)


OutputFtab.SetEditable(True)


totalshape=ftab2.ReturnValue(shpfld2, therecs2.GetNextSet(-1))


for each apshape in therecs2


totalshape = totalshape.ReturnUnion(ftab2.ReturnValue(shpfld2,


apshape))


end


'-------------------------------------------------------


'Start processing each record in the selected overlay polys


'-------------------------------------------------------


'Get the polygon shape and select all records within that shape


theSRCshape = totalshape


if (theView.GetProjection.isNull) then


Theme1.SelectbyShapes({theSRCshape}, #VTAB_SELTYPE_NEW)


else


 pshp=theSRCShape.ReturnProjected(theView.GetProjection)


Theme1.SelectbyShapes({pshp}, #VTAB_SELTYPE_NEW)


end


123 
'For each selected record 


recordCount = 0 


for each Selrec in ftab1.GetSelection


 recordCount = RecordCount +1


 av.ShowMsg("Splitting Shapes...")


av.SetStatus((recordCount / ftab1.GetSelection.Count) * 100)


 'Get the shape of the record


SelectedShape = ftab1.ReturnValue(shpfld1,Selrec)


 'If the output is a line


if (outshpfld.getType = #FIELD_SHAPELINE) then


 'If the line is wholly within the polygon (no intersection) then


if (SelectedShape.IsContainedIn(theSRCShape)) then


aLineshp = SelectedShape


'Else split the line using the polygon


else 


aLineShp = SelectedShape.LineIntersection(theSRCshape)


end


 'Add the new record 


theoutrec=outputftab.AddRecord


'Set the shape value


outputFtab.SetValue(outshpfld, theoutrec, alineshp)


'Set the field values 


for each afield in SRCfields


 oldfield=ftab1.FindField(afield.GetName)


' Due to field name renaming between INFO and dBASE,


' some of the fields may not be found. In those cases,


' leave the value blank.


 if (oldfield<>nil) then


oldvalue=ftab1.ReturnValue(oldfield, selrec)


outputftab.SetValue(afield, theoutrec, oldvalue)


end


 end


 'This enters into the polygon loop


 else


 shpIntersect = SelectedShape.ReturnIntersection(theSRCshape) 


if (shpintersect.IsEmpty) then


continue 


end


 ' Geometric operations (such as ReturnIntersection) return


multipoints


' instead of points, so if we are trying to write out points,


convert


 ' from multipoints


if (outshpfld.getType = #FIELD_SHAPEPOINT) then


shpIntersect = shpIntersect.AsList.Get(0)


end


 theoutrec=outputftab.AddRecord


outputFtab.SetValue(outshpfld,theoutrec,shpIntersect)


 for each afield in SRCfields


 oldfield=ftab1.FindField(afield.GetName)


' Due to field name renaming between INFO and dBASE,


' some of the fields may not be found. In those cases,


124 
 ' leave the value blank.


 if (oldfield<>nil) then


oldvalue=ftab1.ReturnValue(oldfield, selrec)


outputftab.SetValue(afield, theoutrec, oldvalue)


end


 end


 end


 av.PurgeObjects


end 


'Set editing OFF


OutputFTab.SetEditable(false)


theView.GetGraphics.EndBatch


ftab1.SetSelection(theoldsel)


ftab1.UpdateSelection


if (OutputFTab.HasError) then


MsgBox.Error("The out FTab has an error","")


exit


 end


mergeTheme = FTheme.Make( OutputFTab )


theView.AddTheme( mergeTheme )


end


• Script 4: Calc_pop.ave 

'Title: calc_pop.ave

'Author: Christine Perkins, CSC

'Purpose: calc values of pop90, pop2000, bpop10, bpop20, bpop30, etc

'per huc; where bpop = block population per HUC

'These have already got the factor (in block shapefile) applied. 

theProject = av.GetProject


theView = theProject.FindDoc("View1")


SRCTheme= theView.FindTheme("Merged_blks1.shp")


interTheme=theView.FindTheme("Set1_harrison.shp")


interTheme.SetActive(true)


interFTab = interTheme.GetFTab


SRCFTab = SRCTheme.GetFTab


newHUCField = interFTab.FindField("Bpop30")


interFTab.SetEditable(true)


SRCFTab.SetEditable(true)


theField = interFTab.FindField("Index")


theSel = interFTab.GetSelection


newPopField = SRCFTab.FindField("Bpop30")


theValue = 0


for each rec in interFTab


 theValue = theValue + 1


 QueryString = "[Index] =" + theValue.AsString


interFTab.Query(QueryString,theSel,#VTAB_SELTYPE_NEW)


interFTab.UpdateSelection


SRCTheme.SelectbyTheme(interTheme,#FTAB_RELTYPE_ISCOMPLETELYWITHIN,0,#VT


AB_SELTYPE_NEW)


SRCFTab.UpdateSelection


125
 theBitMP = SRCFTab.GetSelection


 newPopVal = 0


for each rec in theBitMP


 newpop90Val = SRCFTab.ReturnValue(newPopField,rec)


newPopVal = newPopVal + newPop90Val


end 


interFTab.SetValue(newHUCField,rec,newPopVal)


theProject.Save


end


• Script 5: Calc_vcn.ave 

'Title: calc_vcn.ave


'Author: Christine Perkins, CSC


'Purpose: calc values of vacant properties


theProject = av.GetProject


theView = theProject.FindDoc("View1")


SRCTheme= theView.FindTheme("Test_clip.shp")


interTheme=theView.FindTheme("Problem.shp")


interTheme.SetActive(true)


interFTab = interTheme.GetFTab


SRCFTab = SRCTheme.GetFTab


newHUCField = interFTab.FindField("other_vcnt")


interFTab.SetEditable(true)


SRCFTab.SetEditable(true)


theField = interFTab.FindField("Index")


theSel = interFTab.GetSelection


theFactorField = SRCFTab.FindField("Factor")


newPopField = SRCFTab.FindField("other_vcnt")


theValue = 0


for each rec in interFTab


 theValue = theValue + 1


 QueryString = "[Index] =" + theValue.AsString


interFTab.Query(QueryString,theSel,#VTAB_SELTYPE_NEW)


interFTab.UpdateSelection


SRCTheme.SelectbyTheme(interTheme,#FTAB_RELTYPE_ISCOMPLETELYWITHIN,0,#VT


AB_SELTYPE_NEW)


SRCFTab.UpdateSelection


theBitMP = SRCFTab.GetSelection


 newPopVal = 0


for each rec in theBitMP


 newpop90Val = SRCFTab.ReturnValue(newPopField,rec)


theFactorVal = SRCFTab.ReturnValue(theFactorField,rec)


newVal = newpop90Val * theFactorVal


newPopVal = newVal + newPopVal


end 


interFTab.SetValue(newHUCField,rec,newPopVal)


theProject.Save


end


126