a model to predict impervious surface for regional and municipal land use planning purposes

www.elsevier.com/locate/eiar

Environmental Impact Assessment Review

24 (2004) 363–382

A model to predict impervious surface

for regional and municipal land use

planning purposes

James Reillya,*, Patricia Maggiob, Steven Karpc

aMaryland Department of Planning, 301 W. Preston Street, Suite 1101,

Baltimore, MD 21201-2305, USAbGSIS Remote Sensing, City of Trenton, 319 East State Street, Trenton, NJ 08608, USA

cOffice of Smart Growth, Department of Community Affairs, 101 South Broad Street, PO Box 204,

Trenton, NJ 08625-0204, USA

Received 1 May 2003; received in revised form 1 October 2003; accepted 1 October 2003

Abstract

The area of impervious surface in a watershed is a forcing variable in many hydrologic

models and has been proposed as a policy variable surrogate for water quality. We report a

new statistical model which will allow land use planners to estimate impervious surface

given minimal, readily available information about future growth. Our model is suitable

for master planning purposes. In more urbanized areas, it tends to produce quite accurate

forecasts. However, in less developed, more rural places, forecast error will increase.

D 2003 Elsevier Inc. All rights reserved.

Keywords: Ecological planning; Water quality; Regional planning; Watershed management

1. Introduction

About 90% of the rain that falls on natural vegetated landscapes infiltrates the

soil while the remaining rainfall runs off into streams. Where man-made surfaces

(such as roofs, walkways or roadways) have been created, less rain is able to

infiltrate the soil and runoff increases (Mockus, 1949—as reported by Miller and

0195-9255/$ – see front matter D 2003 Elsevier Inc. All rights reserved.

doi:10.1016/j.eiar.2003.10.022

* Corresponding author. Tel.: +1-410-767-1409; fax: +1-410-767-4480.

E-mail address: [email protected] (J. Reilly).

J. Reilly et al. / Environmental Impact Assessment Review 24 (2004) 363–382364

Cronshey, 1989; Sherman, 1949; Andrews, 1954; Ogrosky, 1956). Increased

runoff has been associated with more frequent flooding (Leopold, 1968; Espey

and Winslow, 1974; Harley, 1978; Sauer et al., 1983); changes to the morphology

of streams and their channels (Hammer, 1972; Shaver et al., 1994; Galli, 1993;

and, Poff et al., 1997); changes in both water quality and temperature (Kline,

1972; Booth and Jackson, 1997); changes in stream biodiversity (Schueler, 1994,

Booth and Jackson, 1997; Ayers et al., 2000); and reductions in groundwater

recharge (Evett, 1994).

These urbanization-associated water quality impacts make it clear that land

use planning (and the integration of Best Management Practices into zoning

codes) could be a powerful tool to mitigate water pollution and preserve raw

water quality.

To face this challenge, land use planners (both professionals and citizen

planners) need sophisticated, easy-to-use tools to evaluate the impact of land use

master plans on water bodies and streams within their municipalities and regions.

This paper provides a simple, quick method to estimate impervious surface using

the types of data which would be readily available to planners.

2. Problem statement

We found that impervious surface data is not easily measured. Many

watershed runoff studies (NEMO, 2000; Martin, 2000; Spencer, 2000; RESAC,

2000) empirically measure impervious surface using manual interpretation of

maps, interpretation of aerial or satellite photos, or analysis of multi-spectral and

hyper-spectral imagery. These methods are time consuming and highly technical

(often requiring a specialist to conduct the analysis). Most master planning efforts

do not have the talent, time or budget to produce empirical measurements of

impervious surface. Most importantly, these methods do not yield predictions of

future conditions.

Very few predictive methodologies are available. Stankowski (1972) and

Harley (1978) relate impervious area to population density or urban land area at

a county scale. However, the accuracy of such a relationship at the municipal scale

is unreported. One might also use factor models, which assign a fixed impervious

percentage to specific land uses. Such methodologies have been incorporated into

larger hydrology models, such as TR 55,1 the New Jersey Department of

Environmental Protection ground-water recharge method and the University of

Connecticut NEMO Project (NEMO, 2000). We test both methods and report their

reliability and ease of use.

1 These models appear to have a common ancestor in various studies, including Horner and Flynt

(1936), Harris and Rantz (1964) and work done in 1973 by the Maryland Department of Planning

(Klein, 1979).

J. Reilly et al. / Environmental Impact Assessment Review 24 (2004) 363–382 365

3. Data set used in this study

The data used in this study consists of mapped polygons of land use and land

cover (LU/LC), and impervious surface (IS). The data was produced, under

contract to the New Jersey Department of Environmental Protection (NJDEP), by

Aerial Information Systems of Redland, CA. The LU/LC data set was initially

developed from an interpretation of a statewide coverage of LU/LC developed

from 1986 aerial photographs (1:24,000 scale, reference). An update of this

information was derived by comparing the 1986 photos to ortho-rectified digital

images (1:12,000 scale, reference) of aerial photography flown in March 1995

and March 1997. Changed areas were re-classified by interpretation by the

contractor and field checked by NJDEP. The impervious surface information was

estimated by the contractor for all polygons based on the 1995–1997 imagery.

The project metadata provides the following additional information:

The imagery. . .was converted to standard USGS DOQCIR 3 band image files

at 1:12000 scale with 1 meter pixel resolution. The ortho-rectified digital

images were projected to New Jersey State Plane Feet, NAD83, compressed to

Map 1. Municipalities used in this study.

2

comm3

used


JPEG files. . .and meet the National Map Accuracy Standards (NMAS). The

minimum mapping unit for both the LU/LC and IS polygons = 1.00 acre. . .

The baseline LU/LC layer was originally complied using a modified

Anderson classification at level II with some level III detail. For the 1995/97

LU/LC project, III and IV level detail was added to the 1100 and 4000 land

use categories. . .

The impervious Surface (IS) codes represent the percentage of impervious

surface within each LU/LC polygon. The IS values were assigned based

upon a visual interpreted estimate of the impervious cover. . . Due to the

scale of the mapping project and the method of assigning the IS values, the

IS percentages were broken down into 5% increments, ranging from 0% to

100%. . ..

We intended to use the entire statewide coverage (an area of 7505 square miles

excluding water surfaces), however, quality checking of the entire impervious

surface (IS) coverage was not completed during our study period. As a result,

only portions of the statewide coverage were available for use. We used a 172

municipality, six-county, data set to develop our model (the darkest areas in Map

1) and a 194 municipality, seven-county, data set to validate our model (the light

gray areas in Map 1). This data set covered the northern half of New Jersey

(USA), an area of about 3500 square miles.

4. Evaluation of existing predictive methods to estimate impervious surface

We first examined, at the municipal scale, the county-scale linear relationship

between the log10 of population density and the log10 of IS reported by

Stankowski (1972) and later by Harley (1978). We found a relationship between

these variables at the more discrete municipal scale, but note that the form of the

relationship is best expressed as an exponential2 function relating total impervi-

ous surface to the natural log of population density. As shown in (Chart 1), this

slightly revised model produced an R2 of 0.767.

We also examined the land use-impervious surface factors found in TR 55’s

Table 2-2a, ‘‘Runoff curve numbers for urban areas’’ (United State Department of

Agriculture, 1986, pp. 2–5).3 We note two problems with these factors, both of

which are displayed in Tables 1 and 2. First we note that comparison is very

difficult because the land use categories used in TR 55 do not match those used in

We would expect that this relationship would be more logistic in shape had sufficiently dense

unities been included in our data.

TR 55 was chosen for evaluation because the model is well documented and program is widely

in the United States.

Chart 1. Municipal population density and municipal impervious surface.


the USGS (Anderson et al.) LU/LC system. When we did attempt to relate TR 55

land uses to those found in the USGS categorization system, we found some

interesting differences. It appears that the job-related impervious surface percen-

tages in TR 55 are consistently higher than those found in the New Jersey sample.

We also found that impervious surfaces for New Jersey houses on 1 acre or larger

lots are higher than the impervious surface reported in TR 55, while high-density

residential development in New Jersey has a much lower impervious surface than

that reported in TR 55.

We speculate that change in the physical pattern and nature of development

has occurred since the TR 55 information was collected. For example, there has

been a major economic shift from highly impervious industrial employment to

service related business, many of which prefer to locate in less densely developed

campus-like settings. Similarly, housing today tends to be both larger and located

on larger lots than those commonly found in the 1960s, 1950s and 1940s (when

most sprawl occurred in New Jersey). Today’s urbanization also appears to have

larger lot sizes that those found in the urban and trolley car suburban develop-

Table 1

Comparison of TR 55 and New Jersey IS averages for job-related development

Job-related

land use

TR 55

(%)

LU 1200

(%)

LU 1300

(%)

LU 1400

(%)

LU 1500

(%)

All job

related (%)

Commercial and business 85 77.81 86 72.8

Industrial 72 76.43 54.44 86

Table 2

Comparison of TR 55 and New Jersey IS averages for residential development

Residential

land uses

TR 55 LU 1110 LU 1120 LU 1130 LU 1140 LU 1150 All

At least 8 DUSa

per acre

65% 59% 43% to

45%

30% to

34%

2 to 4 DUS

per acre

25% to 38% 33% to

35%

1 to 2 DUS

per acre

20% to 25% 22%

1 to .5 DUS

per acre

12% to 20% 14%

a DUS—dwelling units.


ment which preceded World War II—development which covers many areas in

our GIS data sample. These hypotheses were tested by other researchers working

at the New Jersey Long Island Office of USGS. USGS researchers superimposed

a 1973 aerial and a 1986 aerial over the 1995–1997 LU/LC and Impervious

Surface coverage. They assumed that development reported in the 1995–1997

LU/LC was the same development reported in earlier aerials. Based on these

assumptions, three data sets were developed: a LU/LC and Impervious Surface

set of polygons for development built by 1973; another data sets for development

built between 1973 and 1986; and, a third data set for development built after

1986. The resulting impervious surface averages for the three development

periods is displayed in Table 3. As expected, both residential and job-related

development (for a more complete listing of the land uses included in the term

job-related, please see Table 5) built after 1973 has less impervious surface than

found in development built before 1974.

Despite these findings, we found TR 55 to be a highly predictive method to

estimate impervious surface. We used the 170 municipal data’s developed area

information and applied the impervious cover fractions found in TR 55. We

converted USGS land uses to TR 55 land uses consistent with the associations

shown in Tables 1 and 2. For example, the municipal total (acres) of land use

1120 found in Allendale, Bergen County, was multiplied by 0.33 (the mid-point

in the range 25–38% found in TR 55) to estimate the impervious surface

associated with all Allendale houses with densities ranging from 2 to 4 dwelling

units per acre. Similarly, the sum of all residential-, commercial-, and industrial-

related impervious surface was calculated and the resulting TR 55 impervious

Table 3

Average percent of developed area estimated as impervious

Period IS—residential (%) IS—commercial/industrial (%)

Pre-1974 (1973 data) 32.7 80.4

1973 to 1986 22.3 72.0

1986 to 1995/1997 23.2 72.0

Chart 2. Comparison of TR 55 forecasts to New Jersey GIS data.


surface estimate compared to acres of impervious surface reported in the GIS

data. Chart 2 shows this comparison. A test of the symmetry of TR 55 predictions

and the actual data produced an Adjusted R2 of 0.987.

From the preceding analysis, we concluded that the Stankowski model, as

rephrased by our analysis, is predictive at the municipal scale but is not highly

accurate. TR55 projections very closely match the estimates of impervious surface

found in our GIS data set. Despite this agreement between TR 55 and our GIS

data, our analysis suggests that TR 55’s Table 2-2a (which converts land use to an

estimate of impervious cover) over-estimates the amount of impervious surface

being created by post-1973 development in New Jersey.

But in the final analysis, and despite its very good predictive ability, TR 55’s

methodology is not well suited for use in regional or municipal-wide planning. It

requires as input the developed area for each land use category. We are not aware

of any method to estimate developed area, other than to map it. For studies of

existing growth, this mapping is expensive and time consuming. Alternatively,

one needs to know the specific future program by type of housing unit and type of

employment. This requirement is practical if the exact nature of the development

program is known, for example if one were to estimate runoff for a designed, but

not built, subdivision. However, this detailed information generally is not

available for long-range master planning purposes. Therefore, we felt that TR-

55 is not well suited for master planning studies of future growth.

5. Development of a new impervious surface model

We attempted to develop a deterministic statistical model to predict impervi-

ous surface using the type of information generally available during a master

planning exercise. Our review of the literature suggests that impervious surface

area is related to some measure of development density and some mix of land

Table 4

USGS (Anderson) land use categories and densities

USGS category USGS (Anderson) description of urbanization Density (DUs=dwelling units)

1110 Residential—high density or multiple dwelling unit 5 to 8 DUs per acre

1120 Residential—single unit, medium density 8 to 2 DUs per acre

1130 Residential—single unit, low density 2 to 1 DUs per acre

1140 Residential—rural, single unit 1 to 2 acres per DU

1150 Mixed residential None specified

1200 Commercial and services None specified

1300 Industrial None specified

1400 Transportation, communication, utilities None specified

1500 Industrial and commercial complexes None specified

1600 Mixed urban or built up None specified

1700 Other urban or built up None specified

1800 Recreation None specified


uses. Therefore, we decided to use some readily constructed measure of density

and various forms of growth forecasts as predictor variables.

Before we could define a new model, several data issues needed to be

resolved. The 1995–1997 NJDEP coverage reports impervious surface as

categorical data (impervious surface is reported in 5% intervals) for each

polygon. The modified USGS LU/LC classification system groups residential

data into categories which contain density ranges but does not provide densities

for job-related land uses, as displayed in Table 4. Furthermore, some of the USGS

LU/LC categories include four-digit elements, which for the purpose of this

Table 5

Consolidation of land use land cover categories

USGS Description of urbanization New land use development categories

categoryResidential Job related Other

1110 Residential—high density or multiple DU X

1120 Residential—single unit, medium density X

1130 Residential—single unit, low density X

1140 Residential—rural, single unit X

1150 Mixed residential X

1200 Commercial and services X

1211 Military installations X

1300 Industrial X

1400 TCU X

1461 Wetland ROW X

1500 Industrial commercial Complexes X

1600 Mixed urban or built up X

1700 Other urban or built up X

1750 Managed wetlands X

1800 Recreation land X

1804 Community recreation areas X

1850 Not defined in metadata X

Table 6

Characteristics of the 170 municipalities in the data set

Mean Minimum Maximum

Area in square miles 6.8 0.12 54.93

Population in 1990 19,450 270 228,517

Dwelling units in 1995 7577 68 90,723

Jobs in 1995 9492 53 83,116

Population density 1990 (90 pop/area sq. miles) 5360 135 51,870


study, needed to be re-aggregated. For example, the general category Transpor-

tation, Communications and Utilities, which might be considered a job-related

land use, includes wetland rights-of-way (1461).

To resolve the problem with categorical data, we unioned the available 1995–

1997 coverages4 with county and municipal boundary files to produce LU/LC

and IS polygons within municipal boundaries. Then, the LU/LC and impervious

surface polygons within each municipality were summed to produce continuous

municipal scale data suitable for statistical analysis.5 We also aggregated selected

LU/LC category to produce municipal totals which then could be related to

readily available census and employment data. For example, LU/LC categories

1110, 1120, 1130, 1140 and 1150 polygons in a municipality were summed to

produce municipal totals for residential-developed area and residential-related

impervious surface. Table 5 displays the list of USGS LU categories found in the

municipal data set and shows how this information was combined to create the

more general land use categories used in this analysis.

Two municipalities were excluded from the 172 municipal set of cases. Newark

is atypically large and dense (for New Jersey) and acts with undue influence on

analyses based on means. Teterboro is a small town with a very large, mostly im-

pervious, airport. Table 6 displays characteristics of the 170 municipality data set.

We assembled 26 variables using forecast data which would be readily

available during master planning exercises. Several of these variables exhibited

long-tailed distributions, suggesting that the use of OLS (as opposed to GLM)

regression method might produce the most stable model (Fernandez and Steel,

2000). To ensure that all variables, especially the dependent ones, had normal

distributions, we used c2 tests and the Kolmogorov–Smirnov and Anderson–

Darling nonparametric tests to identify the variables’ distributions.6 Table 7

displays the variables we examined, provides their definitions, characterizes their

distributions,7 and identifies normally distributed transformed variables. It should

5 IS for each LU/LC polygon was reported both as categorical data (increments of 5%) and as

square feet of impervious surface.

4 Much of the State was still being proof checked when this analysis was conducted.

6 We used Crystal Ball 2000, produced by Decisioneering to assist with this analysis. The

program examines the data and produces ‘best fit’ cases for 17 distributions. It then reports several

goodness of fit test results comparing the ‘best fit’ hypothetical distribution to the actual data.7 It should be noted that we were unable to produce satisfactory transformation for extreme value,

Gamma, Gompertz, beta, and Weibull distributions.

Table 7

Variables, transformed variables—their definitions and distributions

Variable shape—distribution characteristics Transformed variable as a normal distribution Normally

Best-fit shape c2 P value K.S. A.D. Transformed variable c2 P value K.S. A.D.distributed

Measures of development density

PPHH90 Logistic 12.5089 0.4057 0.0440 0.4155 LGPPHH 16.4142 0.1730 0.0674 1.3593 No

HHI90 Extr. value 25.825 0.0111 0.0806 1.5570 LNHHI90 24.0473 0.0200 0.0898 1.7941 No

DUS95 Lognormal 11.7988 0.4620 0.0604 0.6193 LNDUS95 9.3136 0.6707 0.0301 0.2692 Yes

JOBS95 Lognormal 12.6864 0.3932 0.0715 0.8034 LNJOBS95 11.2663 0.5062 0.0536 0.9192 Yes

RESDENSE Lognormal 6.8284 0.8687 0.0411 0.2808 LNRESDEN 6.1183 0.9100 0.0416 0.2827 Yes

JOBAREA Exponential 9.6686 0.7208 0.0588 0.9496

DUAREA Exponential 13.929 0.3789 0.0831 1.8205

JDENSE95 Extr. value 17.8343 0.1208 0.0539 0.7600

Gamma 26.3550 0.0050 0.0667 1.3789

Logistic 20.4920 0.0582 0.0695 1.1191 LGJOBDEN 2,021.97 0.0000 0.4813 57.711 NO

Lognormal 33.8107 0.0007 0.0808 2.5315

ROADDENS Logistic 14.1065 0.2940 0.0576 0.3740 LGRDENS 33.2781 0.0009 0.1018 2.9039 NO

PDENSE Gamma 2.5680 0.9953 0.0362 0.1902

Lognormal 16.9467 0.1516 0.0804 1.7182 NO

Residential developed area variables

TACRESRES Lognormal 11.0888 0.5213 0.0540 0.6258 LNTARES 11.0888 0.5213 0.0544 0.6342 Yes

PCTRTACR Extr. value 8.4260 0.7510 0.0425 0.4094

Beta 18.7219 0.0663 0.0658 0.8536

Logistic 33.2781 0.0009 0.0780 2.1924 LGPCTRTA 37.1834 0.0002 0.1184 3.0138 NO

RESIS Lognormal 5.9408 0.9190 0.0563 0.7084 LNRESIS 5.5858 0.9355 0.0530 0.8426 Yes

PCTRESIS Extr. value 7.5385 0.8201 0.0480 0.3189

Beta 11.0888 0.4359 0.0516 0.7211

Logistic 17.4793 0.1324 0.0613 1.0092 LGPCTRIS 13.2189 0.3533 0.0922 1.7895 NO

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PCTRISTOT Lognormal 49.0769 0.0000 0.1163 3.6451 NO

Job-related developed area variables

TACREJOB Lognormal 13.3964 0.3409 0.0449 0.4430 LNTAJOBS 14.4615 0.2722 0.0455 0.4501 Yes

PCTJTACR Lognormal 11.7988 0.4620 0.0390 0.3214 LNPCTJTA 12.1538 0.4334 0.0394 0.3190 Yes

JOBIS Lognormal 16.9467 0.1516 0.0687 0.6401 LNJOBIS 16.9467 0.1516 0.0693 0.6493 Yes

PCTJOBIS Beta 20.4970 0.0390 0.0360 0.2659

Weibull 20.4970 0.0390 0.0366 0.2904

Extr. value 16.2367 0.1806 0.0442 0.3860

Gamma 17.1243 0.1043 0.0443 0.3467

Logistic 14.9941 0.2418 0.0501 0.9455 LGJOBPIS 20.3195 0.0613 0.0672 0.9164 Yes

PCTJISTOT Gamma 11.7988 0.3790 0.0448 0.2251

Weibull 13.9290 0.2369 0.0448 0.2560

Beta 20.8521 0.0349 0.0537 0.7273 LNPCTJIS 9.3136 0.6759 0.0668 0.5098 Yes

Other LU-related developed area variables

TACREOTR Lognormal 12.5089 0.4057 0.0434 0.5152 LNTAOTR 11.2857 0.5046 0.0411 0.4306 Yes

PCTOTACR Lognormal 10.2012 0.5983 0.0402 0.4905 LNPCTTOAC 9.3214 0.6753 0.0406 0.3930 Yes

OTHERIS Lognormal 19.2544 0.0826 0.0962 1.4311 NO

PCTOTRIS Lognormal 18.3669 0.1050 0.0876 1.5961 NO

Combined developed area and impervious surface variables

RJAREA Lognormal 10.3787 0.5825 0.0456 0.4885 LNRJAREA 11.2663 0.5062 0.0463 0.4986 Yes

RJOAREA Lognormal 10.2012 0.5983 0.0556 0.5556 LNRJOARE 10.5562 0.5673 0.0556 0.5658 Yes

RJIS Lognormal 10.9112 0.5365 0.0638 0.7323 LNRJIS 10.5562 0.5673 0.0644 0.7480 Yes

RJOIS Lognormal 9.3136 0.6759 0.0597 0.7278 LNRJOIS 8.9586 0.7065 0.0604 0.7436 Yes

PCTOISTOT Lognormal 23.8698 0.0212 0.0921 2.6816 NO

PCTISTOT Lognormal 7.0059 0.8522 0.0562 0.4139 LNPCTIST 7.5385 0.8201 0.0568 0.4267 YES

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Notes to Table 7:

K.S.—Kolmogorov–Smirnov goodness of fit test.

A.D.—Anderson–Darling goodness of fit test.

PPHH90—municipal average number of persons in a household in 1990 census.

HHI90—municipal average household income in 1990 census.

ResDense—the average municipal residential density in 1995. The value is produced by taking the

average number of dwelling units expected in 1995 (1990 census plus residential permits 1990

through 1995 minus any residential demolitions) and dividing by the total municipal area of all

residential polygons (Anderson categories 1110, 1120, 1130, 1140 and 1150).

JDENSE95—the total number of non-agricultural jobs in a municipality in 1995 divided by the total

job related area (Anderson categories 1200, 1214, 1300, 1400, 1500 and 1600).

TACREOTR—the total number of municipal acres which are not residential or job related. Includes

Anderson categories: 1211, 1461, 1700, 1750, 1800, 1804 and 1850.

DUAREA—the total number of municipal 1995 dwelling units divided by the total area of the

municipality in square miles.

JOBAREA—the total number of 1995 at-place municipal jobs divided by the total area of the

municipality in square miles.

PCTOTACR—percentage of the total municipal acres which are category other divided by the total

acres in the municipality.

OTHERIS—the total number of impervious acres in a municipality related to other land uses.

PCTOTRIS—the percentage of total municipal impervious surface is relates to the other land use

category.

PCTOISTOT= % other impervious acres/total acres.

TACRERES—the total number of residential acres in a municipality. Include categories 1110, 1120,

1130, 1140 and 1150.

PCTRTACR—the percentage of total municipal acres which are residential.

RESIS—the total number of residential impervious acres in a municipality.

PCTRESIS—the residential impervious area divided by the total impervious area in a municipality.

PCTRISTOT—the percentage of residential impervious acres in a municipality divided by the total

area of the municipality.

TACREJOB—the total number of acres of job related growth in a municipality.

PCTJTACR—the percentage resulting from dividing the total acres of job-related development in a

municipality by the total number of acres in the municipality.

JOBIS—the total number of job-related impervious acres in a municipality.

PCTJOBIS—total job-related impervious surface in a municipality divided by the total impervious

surface in a municipality.

PCTJISTOT—the percentage of total municipal acres which are job-related impervious surfaces.

PCTISTOT—the total number of municipal acres which are impervious divided by the total area of the

municipality.

RJIS—the total number of residential and job-related impervious acres in a municipality.

RJOIS—the total area (acres) of a municipality which consists of residential, job-related and other

impervious surface.

RJAREA—the total municipal area (acres) occupied by residential and job-related uses.

RJOAREA—the total area (acres) of all residential, job-related and other land uses in a municipality.

RESRAODA—the total number of acres of roadway located in residential polygons. Note the

following assumption: roadway width = 33 ft.

JOBROADA—the total number of acres of roadway located within job-related polygons. Assumed

that roadway was 33 ft wide.

ROADDENS—the linear feet of roadway in each municipality divided by the area of the municipality

(area in square miles*640 acres per square mile*43560 square feet in a square mile).

OTRROADA—the total number of acres of roadway located in land uses classified as other. Assumed

a roadway width of 33 ft.


Table 8

Selected characteristics of the NJ IS model

Model summary

Model R R2 Adjusted S.E. of the Change statistics Durbin–

R2 estimateR2

change

F

change

df 1 df 2 Sig. F

change

Watson

1 0.869 0.756 0.754 0.4606110664 0.756 517.128 1 167 0.000

2 0.902 0.813 0.811 0.4039407729 0.057 51.145 1 166 0.000

3 0.951 0.905 0.903 0.2898028128 0.091 157.506 1 165 0.000 2.134

(a) Predictors: (constant), LNJOBS95.

(b) Predictors: (constant), LNJOBS95, LNJDU95.

(c) Predictors: (constant), LNJOBS95, LNJDU95, DUAREA.

(d) Dependent variable: LNRJOIS.


be noted that the results between the various distribution tests sometimes

disagreed and judgment was required to categorize the distribution as normal

or otherwise.

We used stepwise regression to examine several models. We initially related

total municipal impervious surface to the total developed area in the municipality

and other independent variables. This produced a very good predictive model, but

one which required a second model to forecast the developed area footprint. We

then examined models to forecast developed area footprint, but found that these

models produced poor predictions. We abandoned this two-part approach to

minimize the problem of error propagation. Finally, we related total residential,

job-related and other municipal impervious surface (LNRJOS) to the simply

variables of: total employment (LNJOBS); residential density, which is the total

number of dwelling units divided by the total area of the municipality

(DUAREA); and the job to dwelling unit ratio (LNJDU). In other words, all

that one needs to use this model is the existing or forecasted number of jobs and

Notes to Table 7 (continued):

NETRESA—the total number of municipal residential acres less the total number of acres of roadway

contained in the municipality’s residential polygons.

NETRESIS—the total area, in each municipality, of residential-related impervious cover less the total

area of roadways located in residential polygons.

NETRJA—the sum of the residential- and job-related area less the sum of the roadways located in the

residential- and job-related areas.

NETRJOA—the sum of municipal residential-, job-related and other land area in each municipality

less the sum of municipal roadway area in all residential-, job-related and other land use polygons.

NETRJIS—the total (net of estimated road surfaces) impervious area in a municipality resulting from

residential- and job-related development.

NETRJOIS—the total (net of estimated road surfaces) impervious area in a municipality resulting

from residential-, job-related and other development.

Chart 3. Fit of NJDEP IS and predicted IS—194 municipal validation set.


dwelling units and the area of the place. Table 8 displays characteristics of the

model:

LNRJOIS ¼ 0:862LNJOBS95� 0:508LNJDU95� 0:0001053DUAREA

� 0:628

Results show that the model’s predictions produce normally distributed errors,

despite the tendency for regression model using transformed dependent variables

to produce heteroscedastic results (Sen and Srivastava, 1990).

Chart 3 displays a scatter graph of the predicted municipal supply of

impervious surface value compared to the actual impervious surface found in

the 194 municipalities in our validation data set. The adjusted R2 of the

comparison is 0.816. The line diagram displays the forecast error ((forecast acres

IS� actual acres IS)/actual acres IS) produced by the model.

6. Discussion

We discuss three issues in this section. First, we examine possible reason why

our impervious surface model is not more deterministic. Second, we examine the

use of our model for prediction in small areas. Finally, given the flaws of our

impervious surface model, we offer our opinions regarding the appropriateness of

using our model in other locations.


The new model is not as deterministic as the TR 55 model’s predictions but

the model produces a substantially improved forecasting tool when compared to

the Stankowski model (shown earlier in this paper to produce an R2 of 0.767).

Chart 4 compares the predictions produced by the TR 55 and our model to the

impervious surface reported by NJDEP in the 170 municipal data set. Symmet-

rical forecasts are evident.

While it is possible that the errors might be produced by variability in the

impervious mapping, we feel that errors in our model are more likely produced as

a result of the mapping convention used to categorize the land use land cover

polygons. For example, we found that our dependent variable did not include

impervious surfaces contained in polygons of land cover categories not associ-

ated with impervious surfaces. For example, Photo 1 shows a highlighted

polygon, labeled category 4120 (Deciduous forest with crown closures greater

than 50%). This forest polygon also has been interpreted to have a 20%

impervious surface, due to the roadway running through the polygon. Road

fragments also appear in other LU/LC polygons.

To gain some understanding of the importance of this problem, we examined

the difference between total impervious cover in a municipality and the total area

represented by our dependent variable. While the mean difference is quite small

(less than 2%), the range in our sample of 170 municipalities was 100%

agreement (no difference) to 78% (22% difference) in one case. Stated another

way, in most municipalities the acres of impervious surface represented by our

dependent variable and the actual of impervious surface were within a few acres,

but in a few places the difference was several hundred acres.

Chart 4. Comparison of TR 55 prediction and our model forecast.

Photo 1. A roadway segment in the deciduous forest polygon (white area).


We attempted to reduce the effect of road fragments by eliminating all

road surfaces from measurement, but found that a great deal of effort to

clean the data produced very little change in the resulting model and its

predictions. Further attempts to adjust the model to account for fragments

were abandoned. We note that because of this problem, it is very likely that

our model will produce greater forecast error in rural areas, where much of

the impervious surface likely is contained in land cover, not land use

classifications.

Our second concern is the appropriateness of using our model for very small

areas. There are 566 municipalities in New Jersey and over 1000 sub-watersheds.

Particularly, it is easy to image a municipal political boundary which lies in more

than one sub-watershed. We examined this issue by noting that municipal areas in

our sample ranged from very small to reasonable large (for New Jersey). Chart 5

displays a histograph of the municipalities and their areas in square miles.

Because the sample group is skewed to very small areas, and the standard

deviation is larger than the mean, we feel the model can be applied to fairly small

areas (less than a square mile or so), provided these areas are not smaller than the

minimum data unit size, which is shown in Table 6.

Finally, we note that our model is based on the analysis of New Jersey

data, which tends to reflect rather dense development. Users should examine

the data presented in Table 6 to ensure that the conditions in their study area

fall within this density range. Users also must be aware that our study reflects

American building patterns, typified by houses with very large footprints and

Chart 5. Area (square miles) distribution for the 170 municipal data set.


typically garages. Adjustments to the coefficients would be needed to prevent

overestimation of impervious surface in countries with differing housing

patterns.

We suggest the following methodology to mitigate our model’s predictive

and (possible) scale shortcomings. If an independent reliable assessment of

existing impervious surface is available, base year model forecasts should be

calibrated to these benchmarks. This adjustment will allow the model’s

impervious surface forecast to account for road fragments and other polygon

fragments, as well as differences in housing preference. Calibration also would

allow the model user to adjust for prevalent local densities, which may not be

well represented in the New Jersey data set. Next, projected municipal jobs

and housing should be re-aggregated into sub-watersheds and these totals used

as input to the model.

We feel that our model should not be used for regulatory purposes, even

with calibration,8 but that it is more than suitable for use in master planning.

For example, one might take a growth forecast, convert it (using our model)

into a forecast of impervious surface in the watershed. The model estimate

expressed as a percentage of total watershed area and then be compared to the

impact classification system proposed by Schueler (1994) to estimate water

quality impact. If such an approach is applied in a watershed it might lead to

the establishment of a regional development capacity. Such a policy might

also lead to the use of Transfer Development Rights (TDR) within the sub-

watershed. For example, the funds to redevelop abandoned industrial land into

parkland in one place might be secured by ‘selling’ the rights to new

development (due to the reduction in impervious surface, resulting form the

demolition) in another part of the sub-watershed. Application of an approach

8 Since regulation frequently is done on a site scale inappropriate to this model.


such as this needs further refinement, since it is known that all locations and

land uses in a sub-watershed are not equal, with regard to water quality. For

example, pollution in headwaters has a more profound effect on stream

ecology and destruction (or the restoration) of forests also can have profound

effects.

7. Conclusion

The model described in this paper can be used to forecast impervious surface

in a watershed as part of the master planning process. This estimate of impervious

surface could be used to predict the effects of non-point source pollution either

through linkage to a hydrologic model of the watershed or the supply of

impervious surface can be used as a more general measure. Where impairment

of water quality is threatened, growth restrictions might be imposed or strict

recharge requirements included in building codes.

8. Uncited references

Arnold and Gibbons, 1996

Bicknell

Harbour, 1994

Reilly

Reilly, 1997a

Reilly, 1997b

Wolock and David, 1993

Acknowledgements

Thanks to all who contributed to this project: New Jersey Long Island District,

United States Geological Survey (USGS), National Water Quality Assessment

(NAWQA) Research Team, and Mark Ayers, Project Manager.

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a model to predict impervious surface for regional and municipal land use planning purposes

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