patterns of watershed urbanization and impacts on water quality

ABSTRACT: Urban runoff contributes to nonpoint source pollution,but there is little understanding of the way that pattern and extentof urbanization contributes to this problem. Indicators of type anddensity of urbanization and access to municipal services wereexamined in six urban watersheds in Durham, North Carolina.Principal components analysis (PCA) was used to identify patternsin the distribution of these variables across the urban landscape.While spatial variation in urban environments is not perfectly cap-tured by any one variable, the results suggest that most of the vari-ation can be explained using several variables related to the extentand distribution of urban development. Multiple linear regressionmodels were fit to relate these urbanization indicators to totalphosphorus, total kjeldahl nitrogen, total suspended solids, andfecal coliforms. Development density was correlated to decreasedwater quality in each of the models. Indicators of urbanization typesuch as the house age, amount of contiguous impervious surface,and stormwater connectivity explained additional variation. In thenutrient models, access to city services was also an important fac-tor. The results indicate that while urbanization density is impor-tant in predicting water quality, indicators of urbanization type andaccess to city services help explain additional variation in the mod-els.(KEY TERMS: urban water management; watershed management;water quality; land use planning; nonpoint source pollution; nutri-ents; runoff.)

Carle, Melissa Vernon, Patrick N. Halpin, and Craig A. Stow, 2005. Patterns ofWatershed Urbanization and Impacts on Water Quality. Journal of the AmericanWater Resources Association (JAWRA) 41(3):693-708.

INTRODUCTION

Since the U.S. Congress passed the Clean WaterAct in 1972, environmental managers and policy

makers have made substantial headway in identifyingand reducing point source discharge into the nation’swaterways (USEPA, 2000). Yet many of these waterbodies remain impaired, often because of nonpointsource pollution (Carpenter et al., 1998). According tothe U.S. Environmental Protection Agency (USEPA),nonpoint source pollution is the reason that as manyas 40 percent of the nation’s surveyed water bodiesare not meeting their designated uses (USEPA, 1996).Nonpoint source pollution from urban areas poses aparticular problem for water quality managementbecause responsibility is spread among entire popula-tions, complicating source identification and reduc-tion.

The impacts of urbanization on water quality havebeen well documented. Regional case studies demon-strate that urban streams exhibit increased levels ofphosphorus (Novotny, 1991; Soranno et al., 1996; Mayet al., 1997), nitrogen (Novotny, 1991; Lenat andCrawford, 1994; McMahon and Harned, 1998; Basny-at et al., 2000), total suspended solids (Novotny, 1991;May et al., 1997; McMahon and Harned, 1998), bio-chemical oxygen demand (BOD; Fitzpatrick, 1995),metals (Lenat and Crawford, 1994; Mumley, 1995;Fitzpatrick, 1995; May et al., 1997; Bhaduri et al.,2000), oil and grease (Fitzpatrick, 1995), and fecal col-iform bacteria (Schueler, 1994; Duda et al., 1998) rela-tive to reference streams. While specific sources aredifficult to identify, potential pollutants in urban envi-ronments include street litter, end products from fos-sil fuel combustion, rubber and metal eroded from

1Paper No. 04044 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2005). Discussions are open untilDecember 1, 2005.

2Respectively, Wetlands Specialist, North Carolina Division of Coastal Management, Mail Service Center 1638, Raleigh, North Carolina27669 (formerly Research Associate, Duke University); Assistant Professor of the Practice of Landscape Ecology, Nicholas School of the Envi-ronment and Earth Sciences, Duke University, Box 90328, Durham, North Carolina 27708; and Associate Professor, Department of Environ-mental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina 29208 (Email/Carle:[email protected]).

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 693 JAWRA

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONJUNE AMERICAN WATER RESOURCES ASSOCIATION 2005

PATTERNS OF WATERSHED URBANIZATIONAND IMPACTS ON WATER QUALITY1

Melissa Vernon Carle, Patrick N. Halpin, and Craig A. Stow2

vehicles, corrosion of galvanized roofing materials, petwastes, and fertilizers and pesticides applied to lawns(Loehr, 1974; Cole et al., 1984; Bannerman et al.,1993).

In urban watersheds, water chemistry depends notonly on the extent of urbanization but also on the typeof urbanization (Paul and Meyer, 2001; Strayer et al.,2003), its arrangement relative to other land uses,and differences in management practices (Jones etal., 2001). Percent impervious surface cover can beused to represent a continuum of urbanization nottaken into account by traditional land use classifica-tions. For example, a study conducted in the city ofOlympia, Washington, found that urban areas rangedfrom 10 to 90 percent impervious depending on thespecific land use. Many studies suggest that impervi-ous surfaces exhibit a threshold effect, with majorstream degradation occurring where as little as 10percent of the surface area is impervious (Brun andBand, 2000; Wang et al., 2001; Jennings and Jarna-gin, 2002). Impervious surfaces can be further classi-fied based on the type of surface, its function, and itsconnectivity to the stream. Runoff from streets andparking lots will have different properties, from awater quality standpoint, than runoff from residentialrooftops (Arnold and Gibbons, 1996). Additionally,impervious surface that is directly connected to thestream via a stormwater drain will have a greaterimpact than impervious surfaces that drain to anadjacent lawn (COPWD, 1995; Booth and Jackson,1997; Wang et al., 2001).

Landscape metrics quantifying the distribution ofland use classes have been used to examine the rela-tionship between landscape pattern and acceptedmeasures of stream health (Hunsaker and Levine,1995; Richards et al., 1996; Jones et al., 2000, 2001;He et al., 2000; Gergel et al., 2002). Preliminaryresearch suggests that landscape metrics quantifyingthe arrangement of human altered land in a catch-ment can improve water chemistry predictions offeredby proportion based land use models. However, littleis known about the underlying mechanisms of suchrelationships (Gergel et al., 2002), and further studywill be necessary before refined spatial models can bedeveloped to effectively predict water chemistry basedon measures of land use arrangement (Jones et al.,2000). Most studies have focused on the arrangementof broad land use classes (Hunsaker and Levine, 1995;Richards et al., 1996; Jones et al., 2000, 2001; He etal., 2000; Gergel et al., 2002). The potential to applysuch metrics to the analysis of impervious surfacesand other indicators of urbanization has not been suf-ficiently explored.

The objective of this study was to quantify the rela-tionship between stream water quality and the typeand pattern of watershed urbanization. Urbanization

was defined using metrics for the type and distribu-tion of impervious surfaces, household census data,city infrastructure, and natural features. Principalcomponents analysis was used to determine the spa-tial distribution of urbanization metrics. In addition,multiple regression analysis was used to relate theurbanization metrics to different measures of waterquality.

METHODS

Study Site

This study examined six urban and urbanizingwatersheds in Durham, North Carolina. Durham is amedium-sized city of 223,314 inhabitants (TJCOG,2000), which, along with the cities of Raleigh andChapel Hill, makes up the rapidly growing ResearchTriangle area in the central North Carolina Pied-mont. The Piedmont ecoregion of central North Car-olina is characterized by gently sloping, well roundedhills with long valleys and ridges (Daniel and Dahlen,2002). Due to the high clay and low permeability ofsoils in this region, smaller streams are susceptible toboth high runoff during storm events and low flowduring dry periods (NCDENR, 2001).

The ridgeline between the Neuse and Cape FearRiver basins divides the City of Durham, with 40 per-cent of the city’s area contained in the Neuse Riverbasin and the remaining 60 percent contained in theCape Fear River basin. Six streams were consideredin this analysis: Ellerbe Creek, Little Lick Creek, andStirrup Iron Creek from the Neuse River basin andNew Hope Creek, Third Fork Creek, and NortheastCreek from the Cape Fear River basin (Figure 1). Ofthese six watersheds, Ellerbe Creek and Third ForkCreek contain the highest density of urban develop-ment (Table 1), as they each drain substantial por-tions of the downtown area. While Third Fork Creekwatershed contains slightly more impervious surfaceper unit area than Ellerbe Creek watershed, the lat-ter contains the highest population density and onaverage the oldest development. Little Lick Creek andStirrup Iron Creek flow mostly outside of the city, andeach watershed contains less than 10 percent imper-vious surface cover. New Hope Creek and NortheastCreek watersheds fall between these extremes, with10 to 20 percent impervious surface area.

Ellerbe Creek, Third Fork Creek, New Hope Creek,and Little Lick Creek are all currently listed on NorthCarolina’s 303(d) list of impaired water bodies notmeeting water quality standards as defined by theFederal Clean Water Act. These streams do not fully

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CARLE, HALPIN, AND STOW


PATTERNS OF WATERSHED URBANIZATION AND IMPACTS ON WATER QUALITY

Figure 1. Location of the Six Watersheds Draining Durham, North Carolina.

support aquatic life, as indicated by poor benthicmacroinvertebrate samples and fish communityassessments. The North Carolina Department of Nat-ural Resources has identified urban nonpoint sourcepollution as a probable source of water quality impair-ment (NCDENR, 2001).

Data Sources

The City of Durham Public Works Department pro-vided planimetrics files containing impervious surfacedata digitized from 1994 black and white digitalorthophotography at 0.46 m ground resolution (City ofDurham, 1994). Additionally, the city provided a 0.61m vertical resolution point elevation coverage (Mar-cus Bryant, City of Durham, GIS Department, July30, 2001, unpublished data), stormwater inlet andoutlet locations (John Cox, City of Durham, Stormwa-ter Services, June 18, 2002, unpublished data), andsewer lines (City of Durham, 1994). Other geographicinformation system (GIS) datasets included 1:24,000streams (USGS, 2001), 2000 digital census data (U.S.Census Bureau, 2001, 2002), and county level soils(USDA-NRCS, 1998).

City of Durham Stormwater Services supplied totalphosphorus (TP), total kjeldahl nitrogen (TKN), totalsuspended solids (TSS), and fecal coliform data col-lected at 22 monitoring points throughout the cityfrom February 2000 through June 2002 (John Cox,City of Durham, Stormwater Services, June 18, 2002,unpublished data). The data are from seasonal grabsamples performed as part of Durham’s stormwaterNational Pollutant Discharge Elimination System(NPDES) compliance program. During 2000, the siteswere sampled monthly from January through Novem-ber. In 2001, sampling was restricted to three timesper monitoring station from February to June. In2002, the city settled on the current seasonal sam-pling regime of once every three months. Due to

changes in monitoring frequency, as well as the addi-tion and removal of sampling stations during thisperiod, the quantity of data is not consistent acrosssites. The number of samples at each site ranges from4 to 12.

Grab samples were collected during low flow condi-tions, which is classified as receiving less than 0.1inch (2.54 mm) of rainfall during the 24-hour periodprior to sampling (NCDENR, 2003). Total P was mea-sured according to the colorimetric, two-reagentmethod (USEPA, 1983). Total kjeldahl nitrogen wasmeasured with the potentiometric method by ionselective electrode (USEPA, 1983). Total suspendedsolids were measured using the non-filterable residue,gravimetric method by drying oven at 103 to 105ºC(USEPA, 1983). Fecal coliforms were counted usingthe membrane filtration procedure (Clesceri et al.,1998). These four parameters were chosen from a fullsuite of water chemistry parameters measured foreach sample and were selected because nutrients, sed-iments, and fecal coliforms are of particular concernin the study region.

Spatial Analysis

The point elevation data were used to construct a1.524 m ground resolution digital elevation model(DEM) using inverse distance weighted interpolation.A flow accumulation grid derived from the digital ele-vation model was used to determine if the actual sam-pling locations could be used to delineate watershedsbased on the DEM. Because the actual stream chan-nel did not perfectly match the stream channel as pre-dicted by the DEM, the monitoring point locationswere shifted so that they fell inside of the theoreticalstream channel (average shift of 5.52 m). The correct-ed monitoring points were used to delineate a seriesof 22 nested watersheds. While this nested designmay impose a slight spatial dependence among the



TABLE 1. Summary Characteristics for the Six Watersheds Draining the City of Durham, North Carolina.

Area Percent Population Imperviousness House AgeWatershed River Basin (acres) in City (estimated)* (percent) (years)* Impairment**

Ellerbe Neuse 63.3 0.73 41,671 20.2 45 NS (2002)

Little Lick Neuse 25.7 0.45 8,354 9.6 27 NS (2002)

Stirrup Iron Neuse 4.2 0.28 111 4.0 23 NR (2002)

New Hope Cape Fear 45.3 0.72 29,214 16.3 29 PS (2000)

Third Fork Cape Fear 42.3 0.93 36,034 20.3 34 NR (2000)

Northeast Cape Fear 17.9 0.34 1,886 10.0 31 PS (2000)

**Population and median house age estimates calculated based on data from the U.S. Census Bureau (2002).**Impairment ratings are defined by the North Carolina Division of Water Quality (S = fully supporting, PS = partially supporting, NS = not **supporting, NR = not rated) and are published in the Neuse and Cape Fear Basinwide Water Quality Plans (NCDENR 2000, 2002).

observations within a watershed, the nested structurewas necessary to fully capture the data available foreach watershed.

A series of 14 GIS derived variables was calculatedfor each of the nested watersheds (Figure 2). Indica-tors of urbanization included two variables relating tourbanization density (percent impervious surface areaand household density), four variables related to typeof urbanization (percent connected impervious surfacearea, mean impervious surface patch size, medianimpervious surface patch size, and median houseage), and four variables related to access to city ser-vices (density of sewer system connections, septictank density, stormwater outfall density, and percentof the watershed inside city limits). To account fornatural differences between watersheds, an addition-al five variables were calculated related to naturalwatershed features (hydric soil density, mean saturat-ed hydraulic conductivity, mean soil erosivity, andwetland density).

Impervious surface percentages were derived fromthe planimetrics dataset, which was clipped to eachindividual watershed. Connected impervious surfacearea was determined by intersecting the planimetricsdata with stormwater inlet locations provided byDurham Stormwater Services (John Cox, City ofDurham, Stormwater Services, June 18, 2002, unpub-lished data). Mean and median impervious surfacepatch sizes were calculated by grouping neighboringimpervious surface cells using an eight-directionneighborhood analysis to form patches of contiguousimpervious surface. To prevent the formation of onecontiguous patch for each watershed, linear featuressuch as roads and sidewalks were excluded from theanalysis.

Weighted averages were estimated for all censusdata variables (population density, household density,median house age, septic tank density, sewer systemdensity) by weighting the value of each census blockpartially or completely contained within the water-shed by the percentage of the watershed area that itrepresents. When appropriate, this value was dividedby watershed area to yield a density estimate. Thesame approach was used to calculate variables associ-ated with soil properties for each watershed. All otherspatial variables were calculated by clipping thedataset to the individual watersheds and dividing thequantity of each variable (km2 of wetland, km ofsewer lines) by watershed area to yield a density mea-surement.

Principal Components Analysis

Principal components analysis (PCA) was used toexplore variation of the explanatory variables across

space in the urban environment. Principal compo-nents analysis is a statistical data reduction tech-nique that forms a new set of orthogonal variablesthat are linear composites of the original variables(Sabins, 1987; Sharma, 1996). The new variables,called principal components, are derived by rotatingthe axes in the multidimensional space created by theoriginal set of variables. The first principal compo-nent is chosen such that it accounts for the maximumvariance in the original dataset. The second principalcomponent accounts for the maximum variation notaccounted for by the first principal component and isorthogonal to the first principal component. Addition-al principal components can be derived following thissame method until most of the variation in the origi-nal dataset has been captured (Sharma, 1996; John-son and Wichern, 1998). The result is a smallernumber of uncorrelated variables that captures thedistribution of the initial dataset (Sabins, 1987).Loadings values are generated that represent correla-tions between the principal components and the origi-nal variables (Sharma, 1996).

Principal components analysis can be performed onraster data by treating each individual grid cell as amember of the population sample. This method iscommonly used in remote sensing to determine theoptimal combinations of light spectrum channels toseparate land cover classes based on reflectance val-ues (Sabins, 1987). The resulting principal compo-nents represent land features such as water, naturalvegetation, and urban areas that cannot be mappedusing reflectance values from only one portion of thelight spectrum. Applying this same method to a rangeof variables representing urbanization density, extent,and natural features may reveal patterns in theurban landscape that are not visible using any oneindicator variable alone.

Prior to performing the PCA, each explanatoryvariable was converted to a raster grid clipped to theentire study area. Neighborhood analysis was used tocalculate local densities for density dependent vari-ables (e.g., impervious surface cover, sewer line densi-ty). A neighborhood sum function was run using a4.57 m2 neighborhood window to yield a grid repre-senting neighborhood sums across the study area.This grid was divided by the neighborhood windowsize to generate a grid representing neighborhooddensities across the study area. Principal componentsanalysis was performed using the PCA function in theERDAS Imagine 8.6 software package with grids rep-resenting each of the 14 explanatory variables asinput data. Eigenvalues and loadings were calculatedfor each of the principal components, and output gridswere generated to map the distribution of the firstthree principal components across the study area.





Figure 2. Examples of Urbanization Indicators Used in the Subwatershed Analysis: (a) Household Density; (b) House Age; (c) Impervious Surface; (d) Connected Impervious Surface;

(e) Impervious Surface Patches; and (f) Stormwater Outfall Density.

Statistical Analysis

Multiple linear regression models were fit to relate16 candidate predictor variables to TP, total nitrogen(TN), TSS, and fecal coliforms from the 22 ambientmonitoring locations. The candidate predictor vari-ables included the 14 spatially derived variables plusvariables for recent rainfall and watershed area(Table 2). Recent rainfall data were calculated as theamount of rainfall, in inches, during the 72 hours

prior to each sampling event. Mean recent rainfallwas used as an explanatory variable to account forslight variations in dry-weather sampling conditionamong sites. Watershed area was included to accountfor the influence of drainage area on stream waterquality.

To remain consistent with the analytical tech-niques used by the City of Durham Stormwater Ser-vices (CDSS, 1999), the three-year geometric meanTP, TN, TSS, and fecal coliform count was calculated



TABLE 2. Legend for Explanatory Variables Considered in the Multiple Regression Analysis, Arranged by Thematic Group.

Variable Code Data Source Data Units

Urbanization Density

Impervious Surfaces IS Planimetrics (City of Durham, 1994) Percent of total watershed area

Connected Impervious Surfaces CI Planimetrics (City of Durham, 1994) Percent of total watershed areaand Stormwater Inlets (John Cox, City ofDurham, Stormwater Services, June 18, 2002,unpublished data)

Household Density HD Census Survey (U.S. Census Bureau 2001, 2002) Households/km2

Urbanization Type

Mean Patch Size MN Planimetrics (City of Durham, 1994) ft2

Median Patch Size MD Planimetrics (City of Durham, 1994) ft2

Median house age HA Census Survey (U.S. Census Bureau 2001, 2002) Years

City Services

Sewer System Density SS Census Survey (U.S. Census Bureau 2001, 2002) Sewer Connections/km2

Septic Tank Density ST Census Survey (U.S. Census Bureau 2001, 2002) Septic Tanks/km2

Stormwater Outfall Density SO Stormwater Inventory (John Cox, City of Durham, Outfalls/km2

Stormwater Services, June 18, 2002, unpublisheddata)

Percent of Watershed in City Limits CL City Boundaries ( City of Durham, 2000) Percent of watershed area

Natural Features

Hydric Soil Density HS Soil Survey (USDA-NRCS, 1998) Percent of watershed with hydric soils

Mean Saturated Hydraulic Conductivity K Soil Survey (USDA-NRCS, 1998) Inches/hour

Mean Erosivity E Soil Survey (USDA-NRCS, 1998) Tons/acre/year

Wetland Density W National Wetlands Inventory (USFWS, 1992, Percent of watershed occupied by1994, 1995a,b) wetlands

Watershed Area WA Digital Elevation Model (Marcus Bryant, City km2

of Durham, GIS Department, July 30, 2001,unpublished data)

Rainfall R Stormwater Services (John Cox, City of Durham, InchesStormwater Services, June 18, 2002, unpublisheddata)

at each site for all samples collected from 2000 to2002. While averaging across the study time periodignored seasonal effects at each individual samplinglocation, all watersheds were treated equally withregard to data averaging. This provides a sufficientbasis for comparing water quality among watershedsduring the study period.

Initially, full models were considered that relatedall 16 candidate explanatory variables to the waterquality response variables. Mallows’ Cp (Mallows,1973, 1995), Bayesian Information Criterion (BIC;Schwartz, 1978), and adjusted r2 (Ezekiel, 1930) werecalculated for all subset models. Subset models select-ed for further investigation were those with the bestMallows’ Cp (closest to the number of model parame-ters), lowest BIC, highest adjusted r2, and in whichall of the explanatory variables were statistically sig-nificant. The final models were selected by examiningresidual plots to determine which models best met theassumptions of the standard linear regression model.

In theory, Mallows’ Cp balances bias due to exclud-ing important explanatory variables and the extravariance created by including too many (Ramsey andSchafer, 1997). Models with redundant variablesshould not receive high Cp ratings because the addi-tional variables create variance in the model withoutexplaining additional variation in the response vari-able. Despite receiving high Cp values, the final mod-els contained several variables in the same thematicgroup, indicating a potential for collinearity.Collinearity between two or more variables can cause

instability in the estimates of the coefficients for theexplanatory variables (Montgomery and Peck, 1982).This complicates the interpretation of the relationshipbetween the collinear variables and the response vari-able.

The final models were screened for collinearity byexamining a correlation matrix of the explanatoryvariables (Table 3). Problems associated withcollinearity increase as the correlation coefficientnears one, but there is no widely accepted cutoff fordetecting collinearity based on pairwise correlation(Belsley, 1991). Conventionally, correlation coeffi-cients ranging from 0.7 to 0.9 have been used toscreen variables for potential collinearity (Gunst andMason, 1980). For the purposes of this study, vari-ables with pairwise correlations greater than 0.7 werechosen for further investigation. These variables weresequentially dropped from each of the water qualitymodels to test their effect on the coefficient estimatesfor the other variables. Generally, if the removal ofone variable causes a sign change or large shifts inthe coefficients of one or more of the other variables,it is an indication that the model is affected bycollinearity (Gunst and Mason, 1980).

The correlation analysis revealed strong bivariatecorrelations between household density, median houseage, sewer line density, percent impervious surfacearea, and percent connected impervious surface area.Strong correlations also existed between hydric soildensity and both watershed area and wetland density.Two or more correlated variables were contained in



TABLE 3. Correlation Matrix for Explanatory Variables [high correlations are shown in bold (> 0.70)].

Area HD HA ST SS K E HS W SO IS CI MN MD CL

HD -0.25

HA -0.18 0.75

ST 0.23 -0.58 -0.46

SS -0.30 0.90 0.72 -0.61

K 0.027 0.22 0.10 0.25 0.26

E 0.31 -0.55 -0.47 0.43 -0.43 0.33

HS 0.86 -0.16 -0.18 0.011 -0.19 0.22 0.35

W 0.77 -0.31 -0.46 0.16 -0.34 0.017 0.33 0.73

SO 0.089 0.030 0.0075 -0.17 0.18 0.042 0.11 0.14 0.057

IS -0.28 0.89 0.88 -0.65 0.88 0.080 -0.62 -0.18 -0.39 0.080

CI -0.11 0.81 0.77 -0.61 0.77 0.13 -0.47 0.033 -0.21 0.23 0.90

MN 0.054 0.33 -0.34 -0.21 -0.33 -0.48 -0.085 0.15 0.16 0.00032 -0.21 -0.046

MD 0.21 0.12 -.013 -0.23 0.12 -0.14 0.20 0.26 0.47 0.20 0.089 0.30 0.20

CL -0.21 0.64 0.57 -0.60 0.64 -0.029 -0.25 -0.15 -0.22 0.46 0.68 0.65 -0.36 0.37

G 0.091 0.34 -0.29 0.023 -.037 -0.60 0.29 -0.085 0.18 0.13 -0.29 -0.20 0.27 0.46 0.15

Note: High correlations are shown in bold (> 0.70).

the fitted models for TKN (household density andmedian house age), TSS (household density and per-cent connected impervious surface area; hydric soildensity, wetland density, and watershed area), andfecal coliforms (household density, house age, and per-cent impervious surface area; hydric soil density andwatershed area). In many cases, sequentially drop-ping these potentially collinear variables from themodels resulted in changes in the sign or value of thecoefficients estimates for the other variables. Thissuggests that these three models are influenced bycollinearity. Reliable estimates cannot be generatedfor the coefficients of the correlated variables.Collinearity does not, however, influence the predic-tive power of linear models (Montgomery and Peck,1982). Nor does it indicate that the correlated vari-ables contribute no useful information to the predic-tive model (Bowerman and O’Connell, 1990). Sincethe correlated variables represent aspects of water-shed urbanization that may influence water quality,removing them from the models would exclude poten-tially important predictive information. To avoid this,the correlated variables were kept in the model withthe understanding that caution should be taken in theinterpretation of coefficient estimates for those vari-ables.

RESULTS

Principal Components Analysis

The results of the principal components analysisshow that most of the spatial variation in theexplanatory variables can be reduced to the first threeprincipal components (Figure 3). Eighty-five percentof the variation in the explanatory variables isresolved by the first principal component, which hasnearly equal positive loadings from indicators ofurbanization density (household density), indicatorsof urbanization type (mean and median impervioussurface patch size), access to city services (sewer den-sity, septic system density, and stormwater outfalldensity), and soil properties (overall soil erosivity, toplayer erosivity, and mean saturated hydraulic conduc-tivity). An additional 7 percent of the variation isresolved by the second principal component, which isdominated by equal negative loadings from two indi-cators of urbanization type – mean and medianimpervious surface patch size. The third principalcomponent contributes an additional 7 percent of thevariation in the explanatory variables and consists ofnegative loadings from indicators of urbanization den-sity (household density), access to city services (sewer

density, septic system density), and soil properties(overall soil erosivity, top layer soil erosivity, and sat-urated hydraulic conductivity) and positive loadingsassociated with indicators of urbanization type (meanand median impervious surface patch size). Together,the first three principal components account for 99percent of the overall spatial variation in the explana-tory variables (Table 4).

Total Kjeldahl Nitrogen Model

The TKN multiple regression model included oneindicator of urbanization density (household density),one indicator of urbanization type (median houseage), one indicator of access to city services (percentof the watershed within city limits), and a variable forrecent rainfall (Figure 4a). Of these variables, house-hold density and recent rainfall were associated withincreased TKN concentrations in the presence of theother variables, while house age and the percentwatershed within city limits were associated withdecreased TKN (Table 5). The model predicts thatwith all other variables held constant, densely devel-oped watersheds that have received more recent rain-fall will have higher TKN concentrations, whilewatersheds with older development and that arelargely contained within city limits will have lowerTKN. However, the correlation between householddensity and median house age makes the coefficientsfor these two variables unstable (Table 3). It is notpossible to determine the exact relationship betweeneach of these variables and TKN concentrations whenthe other variables are held constant.

Total Phosphorus Model

The total phosphorus model revealed significantcorrelations to one indicator of urbanization density(household density), one indicator of urbanizationtype (median impervious surface patch size), one indi-cator of access to city services (percent of the water-shed inside city limits), and a variable for watershedarea (Figure 4b). In the presence of the other vari-ables, household density and median impervious sur-face patch size were associated with an increase inTP, while watershed area and the amount of thewatershed inside city limits were associated with adecrease in TP (Table 5). With all other variables heldconstant, densely developed watersheds with morelarge impervious surfaces tended to have the highestTP levels, while larger watersheds mostly inside citylimits tended to have lower TP levels. None of the



explanatory variables in this model had bivariate correlations greater than 0.7.

Total Suspended Solids Model

The TSS model (Table 5, Figure 4c) showed signifi-cant correlations with one indicator of urbanizationdensity (household density), one indicator of urban-ization type (percent connected impervious surfacearea), one indicator of access to city services(stormwater outfall density), and three variablesrelated to natural watershed features (watershedarea, hydric soil density, and wetland density). In the

presence of the other variables, an increase in house-hold density and hydric soil density was associatedwith an increase in suspended solids. Concurrently,increases in watershed area, wetland density,stormwater outfall density, and percent connectedimpervious surface area were associated withdecreases in suspended solids. With all other vari-ables held constant, densely developed watershedsand watersheds with more hydric soils had highersuspended solids, while large watersheds, watershedswith more wetlands, greater stormwater outfall densi-ties, and more connected impervious surface area hadlower suspended solids. Strong correlations werefound among watershed area, hydric soil density, and



Figure 3. Mapped Principal Components: (a) First Principal Component;(b) Second Principal Component; and (c) Third Principal Component.

wetland density, and between household density andpercent connected impervious surface area (Table 3).The coefficients for these variables are unstable, mak-ing their individual relationships with TSS difficult tointerpret.

Fecal Coliform Model

The fecal coliform model (Table 5, Figure 4d)included two indicators of urbanization density(household density and percent impervious surfacearea), two indicators of urbanization type (medianhouse age and median impervious surface patch size),and three variables related to natural watershed fea-tures (watershed area, hydric soil density, and recentrainfall). In the presence of the other variables,increases in watershed area, housing density, imper-vious surface area, and rainfall were associated withan increase in fecal coliform count. Increases in medi-an house age, hydric soil density, and median impervi-ous surface patch size were associated with a decreasein fecal coliform count. With all other variables heldconstant, larger watersheds with denser housing,

more impervious surface area, and that had receivedmore recent rainfall tended to have higher fecal col-iform counts. Watersheds with older development,more hydric soils, and larger impervious surfacestended to have lower fecal coliform counts. The corre-lation matrix for the explanatory variables revealedstrong bivariate correlations between watershed areaand hydric soil density and among household density,median house age, and percent impervious surfacearea (Table 3). Due to collinearity, the coefficient esti-mates for these variables are unstable and may notaccurately reflect their relationship to fecal coliformlevels.

DISCUSSION

Many different aspects of urban environmentsinteract to influence the water quality of urbanstreams in ways that are not easily captured by a sin-gle land use indicator. Various pollution sources existin the urban environment. Not all of these sources aredirectly related to urbanization density, the most com-mon indicator of urbanization used in watershed mod-eling. The inclusion of variables describingurbanization type and access to city services such assewer and stormwater systems helps to account for awide range of sources. Taken together, these variablesprovide a more comprehensive picture of a complexurban environment with respect to its impact onwater quality than any single variable taken alone.

The complexity of urban areas is seen in the resultsof the PCA, in which various indicators of urbaniza-tion density and type are needed to separate spatialtrends related to urban sprawl from trends related tourbanization in general. Based on equal, positiveloadings from most urbanization indicators, the firstprincipal component roughly corresponds to degree ofurbanization within the study area. The second prin-cipal component represents rural areas characterizedby only a few small, isolated impervious surfaces. Thethird principal component has negative loadings forvariables related to urbanization density and positiveloadings for variables related to impervious surfacepatch size. This is characteristic of the sprawling,“box-style” development that has grown up aroundthe core urban area in Durham. While residentialdevelopment is more spread out in these areas, com-mercial development is dominated by large shoppingcenters and their associated parking lots.

By identifying spatial trends in the urban land-scape, the first three principal components providevaluable information that is useful in interpreting theresults of the four multiple regression models. The



TABLE 4. Original Variable Loadings and theCumulative Percent of Variation Explainedby the First Three Principal Components.

ExplanatoryVariable PC1 PC2 PC3

Urbanization DensityIS 3.4E-35 -4.3E-36 -9.4E-36HD 0.35 -0.034 -0.12

Urbanization TypeCI 3.4E-35 -4.1E-36 -9.5E-36MN 0.23 -0.20 0.15MD 0.21 -0.20 0.16HA 3.6E-38 -5.2E-39 -10E-39

City ServicesSS 0.35 -0.034 -0.12ST 0.35 -0.034 -0.12SO 0.29 0.030 -0.0079CL 1.3E-37 -1.3E-38 -4.5E-38

Natural FeaturesHS 1.3E-37 -1.2E-38 -4.7E-38K 0.35 -0.034 -0.12E 0.35 -0.034 -0.12W 1.3E-37 -1.3E-38 -4.5E-38

Percent of Variation 85.46 7.30 6.69

Cumulative Variation (%) 85.46 92.76 99.45

PC = Principal Component.

second and third principal components, while repre-senting only a small portion of the variation in theoriginal dataset, provide information that is not readi-ly captured by traditional urbanization indicators.Overall urbanization is relatively easy to quantify, butdistinguishing between types of urbanization andtheir relative impacts on natural systems is not. TheTriangle region of North Carolina was recently ratedthe third most “sprawling” community in the UnitedStates (Ewing et al., 2002). The relative impact of thisstyle of urban development on water resources hasnot been reliably measured. The urbanization indica-tors examined in this study, when used in combina-tion, can effectively distinguish suburban sprawl fromgeneral landscape urbanization. Identifying andquantifying rural development patterns is also help-ful, because it provides a baseline for monitoringlandscape change over time.

Within the four statistical models, indicators ofurbanization density (household density, impervioussurfaces) had significant, positive correlations withnonpoint source pollution. While the individual coeffi-cients cannot be trusted due to collinearity in three ofthe models, the selection of models with these vari-ables supports the results of previous studies thathave linked urbanization with water quality impair-ment (Lenat and Crawford, 1994; Schueler, 1994;Fitzpatrick, 1995; Mumley, 1995; May et al., 1997;Duda et al., 1998; Bhaduri et al., 2000). In most mod-els, household density was the preferred indicator ofurbanization density. This highlights the importancethat residential development, in particular, has on thegeneration of many urban pollutants (Loehr, 1974;Bannerman et al., 1993). It also demonstrates thatindicators of urbanization density derived from cen-sus data can be effective replacements for impervious



Figure 4. Response Versus Fit Plots for the Multiple Regression Models: (a) TKN Model; (b) TP Model; (c) TSS Model;and (d) Fecal Coliform Model. Solid line represents the correlation between predicted and actual values for the

response variables. Dotted line represents perfect correlation between the predicted and actual values.

surface data, particularly in models where impervioussurfaces are also being used to calculate indicators ofurbanization type.

The four regression models suggest that while thequantity of watershed urbanization impacts waterquality, the type of urban development within awatershed is also important. Three indicators ofurbanization type (median house age, percent con-nected impervious surface area, and median impervi-ous surface patch size) were included in one or moreof the water quality models. Median house age wasincluded in the TKN model and the fecal coliformmodel. In both cases, younger residential developmentwas associated with increased pollutant levels, butthe extent of this association cannot be quantified dueto collinearity with indicators of household density inthese models. Only the TSS model contained a vari-able for connected impervious surface area. In thismodel, connected impervious surface was associated

with decreased suspended solid loads. In Durham,most connected impervious surface area is located inolder, built-out sections of the city where ongoingdevelopment is less common. Strict stormwater regulations for new development limits connectedimpervious area in new neighborhoods, but the highconcentration of new construction in these areas canresult in overall higher TSS loads.

Median impervious surface patch size was includedin the models for total phosphorus and fecal coliforms.In the TP model, areas with more large impervioussurfaces tended to have higher TP loads. Since thePCA results show that impervious surface patch sizeis correlated with areas of urban sprawl in Durham,this indicates that sprawling development is associat-ed with elevated TP loads. The opposite correlationwas observed in the fecal coliform model. Areas domi-nated by smaller patches – rural areas and olderneighborhoods, as indicated by the results of the PCA



TABLE 5. Multiple Regression Models for Total Kjeldahl Nitrogen (TKN), TotalPhosphorus (TP), Total Suspended Solids (TSS), and Fecal Coliforms.

Explanatory Variable Coefficient SE P-Value

TKN Model (mg/L)Intercept 1.17 0.21 0.00Household Density 0.0013 0.00030 0.00Median House Age -0.021 0.0077 0.01Percent of Watershed in City Limits -0.54 0.18 0.01Rainfall in the Past 72 hours 0.89 0.48 0.08

TP Model (mg/L)Intercept 0.10 0.036 0.01Watershed Area -0.0012 0.00050 0.04Household Density 0.00020 0.00 0.00Median Impervious Surface Patch Size 0.00010 0.00 0.00Percent of Watershed in City Limits -0.19 0.048 0.00

TSS Model (mg/L)Intercept 11.41 3.29 0.00Watershed Area -0.27 0.11 0.02Household Density 0.022 0.0067 0.00Hydric Soil Density 2.87 0.62 0.00Wetland Density -9.49 3.78 0.02Stormwater Outfall Density -0.10 0.045 0.03Percent Connected Impervious -2.058 0.39 0.00

Fecal Coliform Model (organisms/500ml)Intercept 1,864.13 344.12 0.00Watershed Area 12.69 4.55 0.01Household Density 1.76 0.38 0.00Median House Age -48.67 10.67 0.00Hydric Soil Density -110.65 25.13 0.00Percent Impervious Surface 22.45 10.24 0.04Median Impervious Surface Patch Size -0.28 0.095 0.01Rainfall Last 72 Hours 1,180.53 512.52 0.03

– contribute to high fecal coliform counts. This is con-sistent with the results of Durham’s fecal coliformsource identification and elimination program, whichhas located leaking sewer lines and illicit sewage connections to the stormwater system in older neigh-borhoods and ineffective septic tanks in rural commu-nities (CDSS, 1999).

Of the variables related to access to city services,only the variables for city limits and stormwater out-fall density were included in the regression models.The negative correlation between city limits and bothnutrient variables is indicative of the role city ser-vices and regulations play in determining water quali-ty in urban streams. The models predict that areas inthe study watersheds lying beyond city limits tend tocontribute higher nutrient and fecal bacteria loadsthan their urban counterparts, when developed in thesame manner and density as areas within city limits.This points to the relative effectiveness of municipalwastewater treatment as compared to on-site wastew-ater treatment. Stormwater outfall density wasimportant only in the TSS model, where higherstormwater outfall densities were associated withlower TSS loads.

Watershed area, hydric soil density, wetland densi-ty, and recent rainfall were the most important natu-ral features in the four water quality models.Watershed area was included in all but one model.The relationship between watershed size and non-point source pollutant loads has been well document-ed (Novotny and Olem, 1994). Interestingly, thisassociation is reversed in the two nutrient models,with larger watersheds corresponding to lower nutri-ent loads. Due to the nested watershed design, thelargest watersheds were those with sampling pointsfarthest downstream. These watersheds provide moreopportunity for instream processing than smallerwatersheds draining the center of the city. Recentrainfall was included in the TKN and fecal coliformmodels. Samples taken after even light rainfall hadhigher TKN and fecal coliform levels than samplescollected during the driest periods. This reflects therapid instream processing for these pollutants. Final-ly, hydric soil distribution and wetland density wereboth important variables in the TSS model. Despite astrong correlation between hydric soils and wetlanddensity, these two variables have opposite correlationswith measured TSS loads. Given the instability of thecoefficients for these two variables, it is not feasible toclearly determine the individual effect each variablehas on TSS loads when the other variable is held con-stant.

Some of the collinearity in the water quality models results from common zoning ordinances across most of Durham’s watersheds. This would bealleviated by developing a larger dataset involving

watersheds from a variety of cities with different zon-ing ordinances. A significant challenge with develop-ing a dataset involving more than one municipality isthe availability of consistent GIS layers and sufficientwater quality data. This problem may be alleviated asGIS technologies continue to become more accessibleto local governments and as more cities are requiredto develop stormwater NPDES monitoring programs.However, some degree of correlation will always bepresent among indicators of the type and density ofurban development. Principal components regressionshould also be explored as a method for dealing withinherent collinearity in urban watershed analysis.This method produces a new set of orthogonal vari-ables that can be used in regression analysis and mayresult in more robust water quality models (Mont-gomery and Peck, 1982). Since the effects of the origi-nal variables are difficult to interpret using principalcomponents regression, further studies will also beneeded to clarify the roles of individual variables.

Both older, densely developed neighborhoods andyounger, low density suburban sprawl contribute tothe water quality impairment observed in the streamsin this study – albeit in different ways. This studyshows that while overall urbanization is important,management of urban nonpoint source pollutionshould also focus on low-density newer urban develop-ment and watersheds with a dominance of largeimpervious surfaces. While collinearity complicatedthe understanding of individual coefficients for manyof the variables used in this study, it is clear thatmeasures such as impervious surface patch size, con-nected impervious surfaces, extent and distribution ofsewer and stormwater systems, and census data areuseful tools for predicting water quality in urban andurbanizing watersheds. Urban watersheds are com-plex systems, and there is inherent difficulty in mod-eling how streams respond to different patterns ofurbanization. Gaining a better understanding of howdifferent aspects of urbanization interact will allowfor the development of better predictive tools for mod-eling urban nonpoint source pollution. That knowl-edge can then be incorporated into the decisionmaking processes that are critical to addressingurban runoff issues.

CONCLUSIONS

With principal components analysis, it is possibleto separate urban sprawl from spatial trends relatedto general urbanization and rural development usingvariables related to density and type of urbanizationas well as the extent of city services. In multiple lin-ear regression models, development density was



shown to be correlated to increased nitrogen, phos-phorus, suspended solids, and fecal coliform bacteria.Indicators of urbanization type such as the house age,amount of contiguous impervious surface, andstormwater connectivity explained additional varia-tion for some of these pollutants. In nutrient models,access to city services was also an important factor.The results indicate that while urbanization densityremains important in predicting water quality, urbanwatershed models can be improved by including indi-cators of urbanization type and access to city services.

ACKNOWLEDGMENTS

This research was supported by a conservation fellowship fromthe Doris Duke Charitable Foundation to Melissa Vernon Carle andby a National Science Foundation Urban Research Initiative Pro-gram grant to Patrick N. Halpin. The authors would like to thankthe City of Durham Stormwater Services, Planning, and GISDepartments for providing the data used in this project. Specialthanks go to John Cox, Chris Outlaw, Robert Louque, Jane Korest,and George Norris for their support during various stages of thisresearch. The authors are grateful to the Duke University Land-scape Ecology lab group for their thoughtful critique of an earlydraft and to the editor and three reviewers whose comments sub-stantially improved the final draft of this paper.

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patterns of watershed urbanization and impacts on water quality

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