Effects of urbanization on stream hydrology and water ...kalinla/papers/HP2012.pdf · Effects of...

HYDROLOGICAL PROCESSESHydrol. Process. 26, 2019–2030 (2012)Published online 9 November 2011 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.8336

Effects of urbanization on stream hydrology and water quality:the Florida Gulf Coast

R. Chelsea Nagy,1* B. Graeme Lockaby,2 Latif Kalin2 and Chris Anderson21 Brown University, Department of Ecology and Evolutionary Biology, Providence, RI 02912, USA

2 Auburn University, School of Forestry and Wildlife Sciences, Auburn, AL 36849, USA

*CEcoE-m

Co

Abstract:

At the global scale, the population density of coastal areas is nearly three times that of inland areas, and consequently, landdevelopment represents a threat to coastal ecosystems. It is critical to understand how increasing urbanization affects coastalwatersheds. To that end, the objective of this study was to examine the influence of urban development on stream water qualityand hydrology in a coastal setting, a scenario that has received less attention than other physiographic regions. Streamhydrologic, physicochemical, and microbial data were collected in watersheds near Apalachicola, Florida with a range ofimpervious surfaces from 0 to 15%. Watersheds with greater impervious cover exhibited higher pH, specific conductance, andtemperature, elevated nutrient concentrations and loads (Cl�, NO3

�, SO42�, Na+, K+, Mg2+, Ca2+, and total phosphorus), higher

bacterial concentrations (fecal coliform and Escherichia coli), and increased maximum flow and hydrograph flashiness. Differentresponses to development here compared to other physiographic regions included lower total suspended solid concentrations,higher total dissolved solid concentrations, and a lack of response of base flow to increased urbanization. Additionally, Na+ andCl� concentrations were elevated to a greater extent than is often the case in non-coastal areas. In the coming years, urbandevelopment is projected to increase substantially in coastal zones and thus there is risk of further stream degradation in coastalwatersheds. Copyright © 2011 John Wiley & Sons, Ltd.

KEY WORDS urbanization; nutrients; sediment; bacteria; surface water hydrology

Received 11 January 2011; Accepted 9 September 2011

INTRODUCTION

Humans influence the quality and quantity of waterresources in a variety of ways. A growing populationinduces conversion of forests and wetlands to agriculturaland urban land uses. Globally, more than half of the humanpopulation now lives in cities, and this is expected to reach60% by 2030 (Paul and Meyer, 2001; Faulkner, 2004).Urban lands in the southeast U.S. will occupy 55 millionacres (~22 million hectares) by 2020 and 81 million acres(~33 million hectares) by 2040 at the expense ofagricultural and forested land (Exum et al., 2005).The societal value or level of ecosystem services provided

by coastal systems is extremely high in comparison to that ofmany other types of ecosystems worldwide (Agardy andAlder, 2005). However, for centuries, populations have beendrawn to water bodies because of access to resources andservices, thus stimulating the growth of population centersalong coastlines. Approximately, 40% of the world’spopulation lives within 100 km of a coast, and coastalpopulations and utilization pressures are increasing rapidly.Consequently, the greatest threat to coastal systems is fromland use conversion and development (Agardy and Alder,2005). In theUnited States,more than 50%of the populationlives in coastal counties (DiDonato et al., 2009), and the rate

orrespondence to: R. Chelsea Nagy, Brown University, Department oflogy and Evolutionary Biology, 80 Waterman St., Providence, RI, 02912.ail: [email protected]

pyright © 2011 John Wiley & Sons, Ltd.

of development in coastal areas may be greater than the rateof population increase. For example, the amount ofdeveloped land near Charleston, South Carolina increasedby 250% from 1974 to 1994, while the population increased40% during the same period (Allen and Lu, 2003). Beach(2002) estimated that by 2015, the population density ofcoastal regions will be 125 people km�2 compared to thenon-coastal U.S. average of 23.Despite the immense societal value as well as rising threats

to the integrity of coastal systems, there is less informationavailable on the effects of development on water resourcesalong coastlines than for inland physiographic regions. This iscertainly the case within the southeastern United States and,in particular, for smallwatersheds along coastlines as opposedto large river basins. As a result, a better understanding ofwatershed responses to urbanization in these systems isgreatly needed.Coastal development can drastically alter hydrology

(Crawford, 2007), particularly when it involves conver-sion of forest to urban land cover. Higher and morefrequent flood pulses and lower base flows may resultfrom reduced infiltration and increased runoff velocity inurban areas (Rose and Peters, 2001; Calhoun et al., 2003;Schoonover et al., 2006; de la Crétaz and Barten, 2007).Hydrographs reflect these alterations and thus aretypically more flashy (less stable) in urban than forestedstreams (Beighley et al., 2003; Calhoun et al., 2003;Schoonover et al., 2006; Crim, 2007; de la Crétaz andBarten, 2007).

2020 R. C. NAGY ET AL.

Contamination of water resources including increasednutrients, sediment, bacteria, and metals may result fromgrowing populations, land conversion and managementpractices, and impervious surfaces (IS). Forestedwatershedstend to have lower stream concentrations of sediment andnutrients than either urban or agricultural watersheds(Lowrance et al., 1985; Wahl et al., 1997; West, 2002;Schoonover et al., 2005; Lehrter, 2006; Schoonover andLockaby, 2006; Crim, 2007; de la Crétaz and Barten, 2007).An urbanized coastal stream in South Carolina had a totalsuspended solids (TSS) load of 2.7 kgm�2 yr�1 comparedto 1.6 kgm�2 yr�1 in a nearby forested stream (Wahl et al.,1997). Concentrations of nitrate and total phosphorus (TP)were significantly greater in urban streams than forestedstreams in South Carolina, while ammonium and totalnitrogen (TN) were greater (although not significantly) inforested than urban streams (Tufford et al., 2003). Bacteriafrom human and animal waste may also be higher in non-forested watersheds than forested watersheds (Mallin et al.,2000; Holland et al., 2004; Schoonover and Lockaby, 2006;Crim, 2007; de la Crétaz and Barten, 2007; DiDonato et al.,2009; Mallin et al., 2009). In the Coastal Plain of SouthCarolina, the mean fecal coliform (FC) concentration at lowtide was 2448CFU 100ml�1 for forested watershedscompared to 134,333CFU 100ml�1 for urban watersheds(Holland et al., 2004).TheApalachicolaBay typifies a coastal area under pressure

from developmentwith an estimated 20.2% rise in populationfrom 2004–2010 and a concentration of residential expansionalong the coast (Clouser and Cothran, 2005). Historically, thetown of Apalachicola, Florida has relied on fishing, andspecifically, the oyster industry. Around 90% of Florida’soysters are harvested in the Apalachicola Bay (ApalachicolaBay Chamber of Commerce, 2010). However, urbandevelopment along the Bay may reduce water quality andoyster habitat. An initial investigation of urban runoff alongApalachicola Bay showed that FC contamination (fromhuman and non-human origins) is a serious threat to theseafood industry and local economy. High coliformconcentrations can result in temporary closure of oysterharvesting and loss of local income (Marchman, 2000).Currently, predictions of FC concentrations in the Bay areused to determineBay openings and closures regarding oysterharvests, and, consequently, there is a strong need tounderstand the relationship between terrestrial sources ofFC (e.g. some land use activities) and inputs to the Bay.The goal of this study was to determine the effects of

development in small coastal watersheds on water qualityand hydrology of streams and to ascertainwhether responsesalong this flat, sandy coastal region differed from thosereported in other physiographic and urbanization scenarios.Although Beach (2002) identified 10% IS as the thresholdthat should be used in planning for protection of marineecosystems, we hypothesized that the influence ofurbanization would be less pronounced (i.e. higher ISthresholds) here than in other physiographic regions. Thishypothesis was based on the predominance of level terrainand the larger size fractions of surface soils, i.e. sands, alongthis section of the Gulf Coast compared to other regions

Copyright © 2011 John Wiley & Sons, Ltd.

where runoff may gain more velocity due to steeper slopesand the ease with which fine particles, i.e. clays, may beentrained (Nagy et al., 2011).

METHODS

Study area

The climate of Apalachicola is humid subtropical withan average annual rainfall of 1425mm (National ClimaticData Center, 2010). The geology of the study areaconsists of Undifferentiated, Beach and Ridge, andAnastasia formations of the Pleistocene (Scott, 2001).Soils on upland sites are commonly sandy with adequatedrainage and range from well drained Quartzipsammentsto Alorthods and Alaquods (e.g. the Mandarin, Leon,Rutlege, and Kureb series). Forested wetlands also liewithin the study area and have poorly drained soilsincluding Fluvaquents and Endoaquepts (Chowan andBrickyard series respectively). Common tree speciesinclude slash pine (Pinus elliottii), sand pine (Pinusclausa), live oak (Quercus virginiana), water tupelo(Nyssa aquatica), and titi (Cyrilla racemiflora).Watersheds near Apalachicola were chosen to reflect a

range of IS in order to evaluate the influences of IS onhydrology and water quality. In total, 13 watershedsassociated with low order streams were selected (Figure 1)ranging in size from 90 to 480ha. Aerial photographs (2004)with 1m spatial resolutionweremanually digitized to classifythe land cover of each watershed. Categories included IS,evergreen forest, mixed forest, emergent wetlands, forestedwetlands, shrub, lawn, open space (grass), bare ground, andwater. Ground truthing was performed in the watersheds toensure accuracy. The accuracy for each land cover categoryranged from 77.1 to 100%with an overall accuracy of 96.2%for all land covers combined. Impervious cover of thewatersheds ranged from 0 to 15%. This range is appropriatebecause most studies of relationships between land use/coverand surface water have indicated that thresholds ofdegradation occur between 5 and 15% IS (Schueler, 1995;Bolstad and Swank, 1997; Schoonover et al., 2005; Clintonand Vose, 2006; Crim, 2007; Cunningham et al., 2009). Allhydrologic measurements and water sampling took place inperennial streams at the outlet of each watershed.

Hydrological analysis

Stream discharge was determined at each sampling (on abimonthly schedule from October to May and monthly in theremainder of the year) by measuring the cross-sectional areaand velocity of the stream. Stream velocity was measuredwith aMarsh-McBirney Flo-Mate 2000. Cross-sectional areawas determined by measuring stream depth at 0.1, 0.2, or0.5m intervals depending on the width of the stream. Stagewas continuously recorded on ten watersheds at 15-minintervals using InSitu pressure transducers (InSitu, Laramie,WY), and dischargemeasurements were used to create stage–discharge curves. Flow was monitored over a 13monthperiod of Oct 1st 2007 to Oct 31st 2008, and the stage levelanalysis was based on these data.

Hydrol. Process. 26, 2019–2030 (2012)

Figure 1. Map of selected watersheds along the Florida Gulf Coast

2021URBANIZATION EFFECTS ON WATER RESOURCES

We explored the effects of land use/cover on various flowcharacteristics including average flow, median flow,minimum flow, 0.1% flow (i.e. flow exceeded 99.9% ofthe time), 1% flow, 99% flow, 99.9% flow, maximum flow,base flow, and flashiness. Discharge values were divided bytheir respective watershed sizes before analysis to minimizethe effect of watershed size. All the flow statistics exceptbase flowwere computed from 15-min flow data. Percentileflows were obtained from flow duration curves for the givenduration. Base flow was obtained from daily average flowsusing the WHAT base flow separation program (Lim et al.,2005, available at: http://cobweb.ecn.purdue.edu/~what).WHAT offers two options for separating base flow. One isbased on the local minimum method, and the other is basedon digital filtering. The latter was used in this study. Baseflow index is defined as the ratio of base flow volume to totalstreamflow volume. The Richards-Baker (RB) flashinessindex (Baker et al., 2004) was used as the metric tocharacterize flashiness of the watersheds. This indexintegrates several flow regime characteristics associatedwith stream flashiness and is calculated as:

RB ¼Pn

i¼1 Qi � Qi�1j jPn

i¼1Qi(1)

where Qi and Qi�1 are average daily flows of the ith and(i�1)th (i.e. the preceding) day of the flow time series,respectively; and n is the total number of days contained inthe flow time series.

Water sample collection and analysis

Water samples were collected for physicochemicalanalyses between January 2007 and July 2009 on abimonthly schedule from October to May and monthly inthe remainder of the year. Water samples for biologicalparameters were collected between September 2008 andJuly 2009 on the same schedule. The periodic grab samplingapproach was selected based on reports by Swank andWaide (1988) which demonstrated that grab samplingestimates were within 1–5% of estimates derived fromproportional sampling at the Coweeta Hydrologic Labora-tory, Coweeta, NC. Stream sampling locationswere selectedfar enough upstream to minimize tidal influences. This was


determined based on the absence of halophytic vegetationalong the stream and in-situ measurements of water salinity.A YSI Model 63–10 (YSI Incorporated, Yellow Springs,

OH) was used to measure stream temperature, pH, andconductivity. Grab samples were collected in polypropylenebottles that had previously been thoroughly washed, rinsedseveral times with deionized water, and capped until use.Bottles were rinsed again with stream water onsite prior tosample collection. Additional samples were collected intissue culture flasks to ensure detection of low-level cationand anion concentrations. All samples were kept on ice andthen stored at 4 �C until analyzed.Samples were analyzed within two days after collection.

Anions and cations (Cl�, NO3�, NO2

�, PO43�, SO4

2�, Na+,NH4

+, K+, Mg2+, Ca2+) were measured using the DionexICS-1500 ion chromatograph (Dionex Corporation,Sunnyvale, CA). TP was determined by digestion withHNO3 and HCl followed by analysis of the extract using themolybdate-bluemethod (Murphy andRiley, 1962;Watanabeand Olsen, 1965). Dissolved organic carbon (DOC) and totaldissolved nitrogen (TDN) were determined with a ShimadzuTOC-VCPN analyzer with a TNM1 (Shimadzu, Columbia,MD). TSSweremeasured by the filtrationmethods describedby the American Public Health Association (APHA) (1995).The following equation was used to determine total dissolvedsolids (TDS): TDS= conductance *0.6791. The conversionfactor of 0.6791 was determined by evaporating samples andweighing remaining solids in the glass beaker afterevaporation and falls within the range of conversion factorscited in Brooks et al. (2003). Based on stage–dischargecurves, the average discharge per day per hectare for eachwatershed was estimated. Weighted (based on discharge atthe time of sample collection) average concentrations ofnutrients and sediment for eachwatershedweremultiplied byaverage discharge to calculate loads. Finally, the filtermembrane method 10029 outlined in APHA (1998) wasused in the analysis of total coliform and E. coli.

Statistical analyses

All statistical analyseswere conducted in SAS version 9.1(SAS Institute, 2002–2003). Relationships were consideredsignificant at p< 0.05 although results at p< 0.10 are alsopresented for informational purposes. The land cover

Hydrol. Process. 26, 2019–2030 (2012)

http://cobweb.ecn.purdue.edu/~what


classification was used to group watersheds into categories.IS are linked with hydrological and chemical changes inwater resources so watersheds were grouped into low(<5%), medium (5–10%), and high (>10%) imperviouscover. Kruskal–Wallis non-parametric tests were used tocompare water quality parameters among impervious covergroups. Spearman correlations (non-parametric) were usedto test relationships between water quality parameters andland cover percentages (impervious cover, evergreen forest,mixed forest, forested wetland, and total forest) anddischarge. Physicochemical (median concentrations andloads) and hydrologic parameters for each watershed wereevaluated along an IS gradient using simple linearregression. Additionally, stepwise multiple regression wasemployed to examine interactions among land covervariables (IS, evergreen forest, mixed forest, forestedwetland, and total forest).

Figure 2. Plots of significant linear regression relationships of median pHand conductance with watershed impervious surfaces (IS)

RESULTS

Effects of urbanization on stream variables were evidentwhen comparing among IS groups. Watersheds with >10%IS had significantly higher median pH and specificconductance than the less urbanized watersheds (Table I,Figure 2). Watersheds in the intermediate range of urbandevelopment (5–10% IS) had the highest median streamtemperature which was significantly higher than in streams inthe low development range (0–5% IS) (Table I). Meandischarge throughout the study period is displayed in Figure 3for each of the three IS groups (low, medium, and high).

Concentrations and loads

Watersheds in the high IS cover class (>10% IS) hadsignificantly higher median concentrations of Cl�, NO3

�,SO4

2�, Na+, K+, Mg2+, Ca2+, and TDS than the low IScover (0–5%) or intermediate IS cover (5–10%) water-sheds (Table II). Median TP concentrations weresignificantly higher in watersheds with high and inter-mediate IS cover compared to watersheds with low IScover. Median concentrations of DOC and TDN werehighest in the low IS cover group (0–5% IS) and lowest inwatersheds with intermediate IS cover (5–10%). MedianTSS concentrations were highest in the intermediate IScover group (5–10% IS) and were significantly higherthan median concentrations in watersheds with low urbancover (Table II). Median FC and E. coli concentrations

Table I. Mean, standard error (SE), and median of physical waterclasses (

0–5% IS

Variable Mean SE MedianpH 5.0 0.1 4.8 (c)Temperature (�C) 18.8 0.3 18.4 (b)Specific Conductance (mS cm�1) 73 4 79 (b)Salinity (%) 0.0 0.0 0.0 (a)

Significant difference among IS classes indicated by different letters.


were highest in the watersheds with highest IS (>10%)and were significantly higher than in watersheds with lowIS cover (Table II). Concentrations of PO4

3�, NO2�, and

NH4+ were consistently below detection and thus were

removed from further statistical analyses.Median loads of Cl�, NO3

�, SO42�, Na+, K+, Mg2+, Ca2+,

and TP were all significantly higher in the 5–10% IS and>10% IS watersheds compared to the 0–5% IS watersheds(Table III). Median TSS loads were also significantly higherin watersheds with intermediate and high urban land usethan watersheds with low IS cover, but the highest medianTSS load was found in watersheds with intermediate IScover (Table III). Although not significant (p= 0.07),median TDS loads were numerically higher in watershedswith high IS cover compared to watersheds with eitherintermediate or low IS cover (Table III).

quality variables compared among watershed impervious surface% IS)

5–10% IS >10% IS

Mean SE Median Mean SE Median5.9 0.0 6.0 (b) 6.1 0.1 6.2 (a)20.3 0.4 20.2 (a) 19.7 0.5 19.6 (ab)

110 10 89 (b) 140 15 110 (a)0.0 0.0 0.0 (a) 0.1 0.0 0.0 (a)

Hydrol. Process. 26, 2019–2030 (2012)


Correlations with land cover variables

Correlations among water parameters (concentrationsand loads), land use/cover, and discharge were examined.

Figure 3. Mean discharge of watersheds with low, medium, and highimpervious surfaces (IS) throughout the study period

Table II. Mean, standard error (SE), and median physicochemical cocompared among watershed imp

0–5 % IS

Variable Mean SE Median MeanCl- 23 1 20 (c) 43NO3

� 0.0 0.0 0.0 (c) 0.1SO4

2� 6 0 4 (c) 21Na+ 11 0 10 (c) 22K+ 0 0 0 (c) 2Mg2+ 1 0 1 (c) 3Ca2+ 3 0 2 (c) 11TP 0.3 0.0 0.3 (b) 0.4DOC 38 1 37 (a) 30TDN 0.9 0.0 0.8 (a) 0.7TSS 5 1 2 (b) 7TDS 49 3 54 (b) 73fecal coliform 6400 800 3900 (b) 6800E. coli 800 400 100 (b) 400

Significant difference among IS classes indicated by different letters.TP = total phosphorus, DOC= dissolved organic carbon, TDN= total dissolve

Table III. Mean, standard error (SE), and median loads (g d�1 ha�1

0–5 % IS

Variable Mean SE Median MeanCl- 150 15 71 (b) 460NO3

� 0.2 0.0 0.0 (c) 1.8SO4

2� 45 6 14 (b) 260Na+ 76 8 36 (b) 220K+ 3 0 0 (b) 17Mg2+ 7 1 2 (b) 26Ca2+ 25 4 8 (b) 99TP 2 0 1 (b) 4DOC 270 28 140 (a) 290TDN 7 1 3 (a) 8TSS 19 4 4 (c) 53TDS † 440 58 92 (a) 770

Significant difference among IS classes indicated by different letters.† p= 0.07TP= total phosphorus, DOC= dissolved organic carbon, TDN= total dissolve


Most nutrient concentrations were negatively correlatedwith watershed forest cover, including evergreen forestsand forested wetlands, but were positively correlated withmixed forests (Table IV). The % total forest cover wasnegatively correlated with all nutrient concentrationsexcept DOC and TDN (Table IV). TSS concentrationswere also significantly, negatively correlated with ever-green forest and total forest cover (Table IV). Therelationships between impervious cover and streamnutrients and sediment varied by season (Table V).Generally, the relationships with % IS were strongest inwinter, except for Cl�, Mg2+, and TDS which had strongerrelationships in the spring and DOC and TDN which hadstronger relationships (both negative) in the summer(Table V). All nutrient and sediment loads had a significantnegative correlation with total forest cover, and many werenegatively correlated with evergreen forests and forestedwetlands (Table VI). Concentrations of many of the soluteswere also significantly correlated with discharge (NO3

�,r = 0.25, p< 0.001; SO4

2�, r = 0.18, p< 0.001; K+,

ncentrations (mgL�1) and microbial populations (CFU 100mL�1)ervious surface classes (% IS)

5–10 % IS >10 % IS

SE Median Mean SE Median3 22 (b) 46 2 54 (a)0.0 0.1 (b) 0.4 0.0 0.4 (a)2 12 (b) 28 1 31 (a)1 12 (b) 19 1 23 (a)0 1 (b) 2 0 2 (a)0 2 (b) 4 0 4 (a)0 9 (b) 17 1 21 (a)0.0 0.4 (a) 0.4 0.0 0.4 (a)1 25 (c) 31 1 31 (b)0.0 0.7 (b) 0.8 0.0 0.7 (a)1 3 (a) 4 1 2 (a)7 60 (b) 94 10 74 (a)

1100 4500 (b) 11,000 1600 8600 (a)100 200 (ab) 400 100 300 (a)

d nitrogen, TSS= total suspended solids, and TDS= total dissolved solids.

) compared among watershed impervious surface classes (% IS)

5–10 % IS >10 % IS

SE Median Mean SE Median60 150 (a) 290 39 170 (a)0.2 0.5 (b) 2.4 0.3 1.4 (a)32 76 (a) 200 32 110 (a)26 85 (a) 130 19 68 (a)2 8 (a) 13 2 7 (a)3 7 (a) 22 5 11 (a)9 47 (a) 100 12 60 (a)1 2 (a) 4 1 2 (a)

31 160 (a) 230 43 120 (a)1 4 (a) 6 1 3 (a)9 17 (a) 33 11 8 (b)

120 110 (a) 610 120 310 (a)

d nitrogen, TSS= total suspended solids, and TDS= total dissolved solids.

Hydrol. Process. 26, 2019–2030 (2012)

Table IV. Spearman correlation coefficients of stream concentrations with forest cover

Forest Cover

Variable % Evergreen % Mixed % Forested Wetland % Total Forest †

Cl- �0.23 0.22 NS �0.10NO3

� �0.25 0.16 �0.45 �0.53SO4

2� �0.37 0.30 �0.42 �0.57Na+ �0.29 0.27 NS �0.15K+ �0.62 0.43 �0.36 �0.64Mg2+ �0.37 0.33 �0.12 �0.31Ca2+ �0.58 0.50 �0.34 �0.55TP �0.14 0.10 NS �0.11DOC 0.19 �0.18 0.37 0.46TDN 0.17 �0.24 0.26 0.29TSS �0.13 NS NS �0.14TDS NS NS NS NS

† Total forest includes evergreen, mixed, and forested wetlands.NS indicates the relationship was not significant at the 0.05 probability level.TP = total phosphorus, DOC=dissolved organic carbon, TDN= total dissolved nitrogen, TSS= total suspended solids, and TDS= total dissolved solids.

Table V. Spearman correlation coefficients of stream concentrations with watershed impervious cover (% IS) by season

Season

Variable Summer Fall Winter Spring--------------Correlation with % IS-------------

Cl- NS 0.34 0.41 0.42NO3

� 0.40 0.54 0.67 0.58SO4

2� 0.63 0.64 0.75 0.72Na+ NS 0.36 0.50 0.37K+ 0.67 0.79 0.85 0.70Mg2+ 0.40 0.37 0.47 0.51Ca2+ 0.67 0.74 0.85 0.73TP NS NS 0.18 0.18DOC �0.53 �0.41 �0.29 �0.41TDN �0.45 �0.25 NS �0.29TSS NS NS 0.25 NSTDS NS NS 0.26 0.32

NS indicates the relationship was not significant at the 0.05 probability level.TP = total phosphorus, DOC=dissolved organic carbon, TDN= total dissolved nitrogen, TSS= total suspended solids, and TDS= total dissolved solids.

Table VI. Spearman correlation coefficients of stream loads with forest cover

Forest Cover

Variable % Evergreen % Mixed % Forested Wetland % Total Forest †

Cl- �0.15 NS �0.15 �0.32NO3

� �0.23 NS �0.42 �0.55SO4

2� �0.25 0.11 �0.35 �0.53Na+ �0.15 NS �0.14 �0.33K+ �0.48 0.27 �0.34 �0.62Mg2+ �0.23 0.14 �0.14 �0.30Ca2+ �0.40 0.24 �0.31 �0.55TP NS NS �0.13 �0.28DOC NS �0.19 NS �0.13TDN NS �0.20 NS �0.19TSS �0.17 NS �0.13 �0.30TDS NS NS NS �0.13

† Total forest includes evergreen, mixed, and forested wetlands.NS indicates the relationship was not significant at the 0.05 probability level.TP = total phosphorus, DOC=dissolved organic carbon, TDN= total dissolved nitrogen, TSS= total suspended solids, and TDS= total dissolved solids.


Copyright © 2011 John Wiley & Sons, Ltd. Hydrol. Process. 26, 2019–2030 (2012)


r = 0.24, p< 0.001; TDN, r = 0.18, p< 0.001; TSS,r=�0.10, p< 0.05, and TDS, r= 0.11, p< 0.05).FC concentrations were significantly (positively) corre-

lated with % IS (r=0.20). E. coli concentrations had asignificant positive correlation with % IS and %mixed forest(r=0.20 and 0.17, respectively) and a significant negativecorrelation with % evergreen and % total forest (r=�0.19and �0.18, respectively).

Regression relationships with land cover variables

Regression was used to examine the relationshipsbetween % IS and water quality parameters. Significantpositive linear relationships were found between % IS and

Figure 4. Plots of significant linear regression relationships of median nutri


concentrations of NO3�, SO4

2�, K+, Mg2+, Ca2+, TP, andTDS, pH, conductance, and E. coli (Figure 2, Figure 4,Figure 5). Stepwise multiple regression of nutrient andsediment concentrations and loads identified only onerelationship that included multiple significant independentvariables: NO3

� concentration = 0.0084 + 0.0305 (% IS) �0.0047 (% Mixed forest); R2 =0.81, p <0.001.Linear regression of hydrologic variables with land cover

data revealed interesting relationships that varied by landuse/cover type. IS had significant positive relationships withQmax and RB (Table VII). A significant positive relationshipwas also found between lawn and RB (Table VII). Mixedand evergreen forests together were significantly related toQmin, Q0.1%, Q1%, and BF (all negative relationships).

ent and sediment concentrations with watershed impervious surfaces (IS)

Hydrol. Process. 26, 2019–2030 (2012)

Figure 5. Plot of significant linear regression relationship between medianE. coli concentration and watershed impervious surfaces (IS)


Wetlands (emergent +woody) had a significant negativerelationship with Q50%, but relationships with Qavg, Q99.9%,

Qmax, and BF were also marginally significant (Qavg:R2 = 0.43, p = 0.06; Q99.9%: R2 = 0.41, p = 0.07; Qmax:R2 = 0.41, p= 0.06; and BF: R2 = 0.34, p= 0.10). Theamount of open space and grass within watersheds wassignificantly related to Qmin, Q0.1%, Q1%, Q50%, and BF (allpositive relationships). Bare ground had a significantpositive relationship with BF (Table VII). Stepwisemultipleregression of hydrologic variables and land cover identifiedonly one relationship with multiple significant independentvariables: RB=0.42 � 0.03(% open space) + 0.17(% IS);R2 = 0.91, p< 0.001.

DISCUSSION

For many parameters, our hypothesis that the influence ofIS would be less pronounced here than in otherphysiographic regions was not supported. Near Apalachi-cola, the median stream temperature was highest in thewatersheds with intermediate IS cover (Table I), which

Table VII. Significant linear regression equations of flow statistics (d(independent variables,

Flow statistic Land cover variable R

Qavg % open space/grass yQmin % forest y

% open space/grass yQ0.1% % forest y

% open space/grass yQ1% % forest y

% open space/grass yQ50% % wetland y

% open space/grass yQmax % impervious yRB % impervious y

% lawn yBF % forest y

% open space/grass y% bare ground y

Qavg= average flow, Qmin=minimum flow, Q0.1% =flow exceeded 99.9% oQmax=maximum flow, RB= flashiness index, and BF= base flow.


indicates that alterations in stream conditions can beobserved at low levels of development. Runoff flowingover IS may be heated and contribute to higher streamtemperatures in urban watersheds (Paul and Meyer,2001). However, Holland et al. (2004) did not find asignificant relationship between temperature and % IS intidal creeks of South Carolina.The Holland et al. (2004) study also differed from the

present results in that there were no differences amongland uses in terms of pH, whereas watersheds nearApalachicola with the highest levels of IS cover had thehighest median pH (Table I and Figure 2). The differencesbetween the two studies may reflect the tidal hydrology ofthe South Carolina streams versus the lack of tidalinfluence in this study. In cases where water samples arecollected in tidal zones, physicochemistry may be lessreflective of landscape influences due to the influx ofmarine waters.

Nutrients

Nutrients may be introduced to urban streams through avariety of pathways. For example, water treatment facilitiesconvert ammonia to nitrate, thus increasing nitrate concentra-tions downstream (de la Crétaz and Barten, 2007), andchlorination of water may increase concentrations of Cl�

downstream (Crim, 2007; Peters, 2009). Septic systems,fertilizer use, and atmospheric deposition contribute additionalnitrogen to urban streams (de la Crétaz and Barten, 2007). Inthe present study, median concentrations of NO3

�, SO42�, K+,

Mg2+, and Ca2+ increased along an increasing gradient of IS(Figure 4). Additionally, median concentrations and loads ofCl� and Na+ were significantly higher in more urbanizedwatersheds compared to watersheds with low IS cover (0–5%)(Tables II and III). The positive correlation between % IS andall nutrients except DOC (negative correlation) and TDN(negative correlation) is indicative of the influence ofurbanization on biogeochemical inputs to streams (Table V).

ependent variable, shown as y in table) and land cover percentagesshown as x in table)

egression equation R2 p-value

= 0.077 + 0.003x 0.48 0.04= 0.0503 � 0.0008x 0.50 0.03=�0.0005 + 0.0013x 0.92 <0.01= 0.0532 � 0.0008x 0.53 0.03=�0.0004 + 0.0013x 0.93 <0.01= 0.0587 � 0.0009x 0.54 0.02=�0.0003 + 0.0015x 0.94 <0.01= 0.124 � 0.003x 0.54 0.03= 0.040 + 0.003x 0.75 <0.01= 0.78 + 0.32x 0.52 0.03= 0.30 + 0.13x 0.69 0.01= 0.59 + 0.10x 0.52 0.03= 0.804 � 0.003x 0.53 0.03= 0.591 + 0.004x 0.55 0.02= 0.57 + 0.04x 0.66 0.01

f the time, Q1% =flow exceeded 99% of the time, Q50% =median flow,

Hydrol. Process. 26, 2019–2030 (2012)


Similarly, Schoonover et al. (2005) reported higher concentra-tions and loads of Cl�, NO3

�, SO42�, Na+, and K+ in west

Georgia watersheds with >5% IS compared to watershedswith<5% IS. The relationship between% IS andwater qualityparameters is generally strongest in winter or spring months(Table V) which coincide with the greatest hydrologicconnectivity between terrestrial and aquatic systems in thesoutheast U.S. (Lockaby et al., 1993).Median concentrations of some nutrients (Na+, Cl�, SO4

2�,Ca2+, Mg2+) were higher than those reported in several studiesconducted in other physiographic regions (Schoonover et al.,2005; Clinton and Vose, 2006; Crim, 2007). Factorscontributing to higher concentrations may be enhancedleaching in the sandy soils of the coastal zone (especiallyCa2+ and Mg2+) coupled with atmospheric deposition ofmarine aerosols in coastalwatersheds (especiallyNa+,Cl�, andSO4

2�) (Mast and Turk, 1999; Vitousek, 2004; Lewis et al.,2007). Higher concentrations of Ca2+ andMg2+may also stemfrom the use of seashells as fill and surfacing of manyunimproved dirt roads in the area.The concentrations of NO3

� in the watersheds with thehighest IS levels (0.4mgL�1) were noticeably higher thanthose of the Marchman (2000) study done in and nearApalachicola (0.12mgL�1 in storm flow) although the total Nconcentrations were similar. Concentrations of TDN werebelow the suggested mesotrophic–eutrophic boundary forstreams, but in many cases were above the oligotrophic–mesotrophic boundary (1.5mgL�1 and 0.7mgL�1, respect-ively for total N) (Dodds et al., 1998). Additionally, themagnitude of increase inNO3

� concentrations between lowandhigh ISwatershedswasmuch greater here than in some studiesin the Piedmont (Schoonover et al., 2005; Crim, 2007) andmay reflect the influence of sandy soils which would facilitateNO3

� passage from wastewater effluent, or leaky septicsystems (de la Crétaz and Barten, 2007). However, there aresome cases inwhichNO3

� concentrations have not increased tothe magnitude reported here following development in sandy,coastal regions as found by Tufford et al. (2003) and Wahlet al. (1997) in the South Carolina Coastal Plain. A similarmagnitude of NO3

� concentration increase was observed in theSouth Carolina Piedmont (Muthukrishnan et al., 2007) butover a wider range of watershed impervious cover.Increased TP in urban streams is commonly associated

with wastewater and fertilizer use in urban areas (Garabedianet al., 1998; Paul and Meyer, 2001). Higher TP concentra-tions and loads were observed in watersheds with higher IScover (Tables II and III and Figure 4) and corroborate thefindings of Tufford et al. (2003) in South Carolina coastalstreams. These results are higher than the TP concentrationsreported in the same area by Marchman (2000) and differfrom those of Schoonover et al. (2005) that did not indicatesignificant relationship between TP concentration or load andIS cover inwatersheds of thewest Georgia Piedmont.Medianconcentrations of TP in all IS groups were higher (3–4�)compared to those found in some studies (Tufford et al.,2003; Schoonover et al., 2005; Crim, 2007) and exceeded theEPA ambient water quality criteria for rivers in the South-eastern Coastal Plain (0.04mgL�1) by an order of magnitudein some watersheds (U.S. EPA, 2000). However, TP


concentrations in the most urbanized streams of the presentstudy were considerably lower than those reported inGarabedian et al. (1998) for streams in the northeast U.Swith high wastewater inputs (e.g. 1.4mgL�1). In the presentstudy, the increases in TP loads under higher levels of ISweredriven primarily by increases in discharge since TPconcentrations did not vary significantly with discharge(r=�0.01, p=0.91).Higher TDN and DOC concentrations and loads in

watersheds with lower IS cover (Tables II and III) mayreflect the greater extent of forests in those systems. Thesefindings are consistent with those of Lehrter (2006) withhigher DOC and TN concentrations in forested versusurban watersheds in the Coastal Plain of Alabama. Wahlet al. (1997) similarly found that DOC concentrationswere twice as high in forested streams as in urbanstreams, while loads did not show significant differences.However, Schoonover et al. (2005) reported significantlyhigher mean DOC concentrations and loads in base flowconditions in watersheds with >5% IS compared towatersheds with <5% IS in the Georgia Piedmont, andthis was thought to be the result of high C inputs to urbanstreams from sewer overflow. The seasonal patterns ofTDN and DOC are also different from the other nutrientsexamined as their strongest relationships (negative) with% IS were recorded in the summer (Table V) possiblyreflecting enhanced biological activity (e.g. root turnover,litter fragmentation) during that period.Unlike the patterns with IS cover, nutrient concentrations

and loads tended to be negatively correlated with% evergreen,forested wetland, and total forest (Tables IV and VI). Thisindicates that a greater amount of forested land within thewatershed reduces nutrient source activity compared to urbanareas. Other studies in the Piedmont have found negativerelationships between stream nutrient concentrations(e.g. NO3

�) and forest cover (Crim, 2007; Muthukrishnanet al., 2007).

Total suspended solids and total dissolved solids

Sediment is transported to streams during forest clearingand urban development and may be eroded from streamchannels as IS cover increases in the watershed (Finkenbineet al., 2000; Schoonover et al., 2007). Near Apalachicola,TSS concentrations and loads were significantly higher inwatersheds with intermediate (5–10%) and high (>10%) IScover compared to watersheds with low IS cover (Tables IIand III). These results and the strong negative relationshipwith% total forest (Tables IV andVI) suggest that sediment isbeing mobilized from terrestrial or instream sources. Wahlet al. (1997) reported the highest TSS concentrations (somesamples >200mgL�1) in an urban stream in storm flow,although seasonal patterns varied for urban versus forestedstreams. For instance, the greatest sediment increases in urbanstreams occurred in winter (Wahl et al., 1997) which agreeswith the relationships found in the present study (Table V).This stands to reason since higher flows occur during winter.Mallin et al. (2009) also found significantly higher TSSconcentrations in suburban than rural streams in the North

Hydrol. Process. 26, 2019–2030 (2012)


Carolina Coastal Plain, but concentrations in suburbanstreams were not significantly different from urban streams.Median TSS concentrations and loads were lower than thosereported in the Piedmont (Schoonover et al., 2005; Crim,2007) and Southern Appalachians (Price and Leigh, 2006)and may be related to the greater erosion potential of thePiedmont and Southern Appalachians which are character-ized by steeper slopes and finer soil textures.TDS concentrations were more strongly related to

watershed IS than to forest cover (Tables II and IV). Ourresults were similar to those found in the Piedmont whereTDS concentrations and loads were higher in watershedswith >5% IS than watersheds with <5% IS (Schoonoveret al., 2005), although the difference was not significant forTDS loads here (p=0.07) (Tables II and III). Higher medianTDS concentrations here compared to studies in thePiedmont and Southern Appalachians (Schoonover et al.,2005; Price and Leigh, 2006; Crim, 2007) may be a result offine particulate organic matter moving into the streamsthrough base flow. Small watersheds along coastal areas ofthe Florida Panhandle (and many other sections of theSoutheastern coast) tend to exhibit low relief and accumu-lation of large amounts of organic matter in riparian soils(e.g. histosols). Consequently, higher TDS concentrations incoastal streams as a result of forest conversion to urban landuse are reasonable compared to inland physiographicregions with better drained soils.

Bacteria

Bacterial contamination is often a problem in urban areasand arises from both human and animal sources. IS facilitatethe transport of bacteria to streams in overland flow,especially during storms. Near Apalachicola, Marchman(2000) identified leaking septic systems, aging wastewatertreatment systems, and illicit connections as likely factorscontributing to urban stream coliform concentrations. FCconcentrations from the present study were much higherthan those reported by Marchman (2000) and suggest thatany control measures that have been put in place since 2000in the Apalachicola area have not been as effective asneeded. This study indicated that both FC and E. coliconcentrations were significantly higher in watersheds with>10% IS than in watersheds with 0–5% IS (Table II). Theseresults are in agreement with Schoonover et al. (2005) andClinton and Vose (2006) who reported higher FCconcentrations in urban streams than forested streams inthe Piedmont and Southern Appalachians, respectively, andLewis et al. (2007) who reported higher total coliformconcentrations in urban versus rural watersheds in thePiedmont. In the Coastal Plain, higher concentrations of FCand enterococci were found in watersheds with greaterdevelopment and headwater creeks in coastal SouthCarolina (DiDonato et al., 2009). Holland et al. (2004),also working along the South Carolina coast, reported asignificant positive relationship between % IS and FCbacteria in streams. Our E. coli concentrations werecomparable to those of Mallin et al. (2000) in NorthCarolina coastal watersheds. FC concentrations found here


were within the range of those in Holland et al. (2004), buthigher than those of Mallin et al. (2000) and Mallin et al.(2009). The significant positive correlations between % ISand bacteria and significant linear relationship between E.coli concentrations and% IS (Figure 5) suggest that as urbandevelopment continues near Apalachicola, further increasesin bacteria concentrations are possible. However, therelationships between FC and E. coli concentrations andland use/cover while often significant were relatively weak,indicating that other factors are also contributing to thepatterns in bacteria concentrations.

Hydrology

Percent imperviousness was found to be highly andpositively correlated with high flows and flashiness (RB) asexpected. Among all hydrological variables, the greatestimpact of imperviousness was on flashiness (RB) (p= 0.01;Table VII). These results support findings in other studies(Paul and Meyer, 2001; Calhoun et al., 2003; Schoonoveret al., 2006; Crim, 2007; de la Crétaz and Barten, 2007).Another component of urban landscapes, lawns, also had asignificant positive effect on flashiness (p = 0.03;Table VII) which is probably due to the higher surfacesoil bulk densities found in Apalachicola lawns comparedto other land uses in that area (Nagy, 2009).Surprisingly, imperviousness did not seem to affect low

flow, base flow, or average flow (all p> 0.10) in this studyarea. This is interesting because imperviousness often reducesbase flow and low flow due to reduced infiltration andconsequently less groundwater recharge (Calhoun et al.,2003; de la Crétaz and Barten, 2007). In areas of flattopography with high water tables such as the FloridaPanhandle, the unconfined aquifer that provides base flowand sustains low flows may receive recharge from a muchlarger area than that suggested by the boundaries of thesurface watershed. The groundwater divide does notnecessarily track the surface watershed boundary, whichitself is extremely hard to define due to near flat topography.As a result, changes in the surface watershed (such asurbanization) may not have a significant impact ongroundwater, and hence on base flow and low flow. Inclusionof a greater number of watersheds from the region (here 10watersheds were used in the hydrological analysis) may helpto clarify the patterns of IS cover on base flow and low flow.Forest cover had quite different trends with hydrologic

variables than IS. Percent forest (mixed+ evergreen) wassignificantly related to low flow and base flow, but did notcorrelate well with high flow. In fact, forest cover had norelationship with flashiness (p = 0.98), average flow(p=0.84), or maximum flow (p=0.61). Linear regressionshowed that as the percent forest increased, low flow andbase flow were reduced (Table VII) likely due to the highevapotranspiration associated with forests in coastal Florida(Lu et al., 2003). Similarly, minimum discharge was lowerin managed and unmanaged forests than in urban water-sheds in west Georgia (Schoonover et al., 2006).Because soils in the study area are highly permeable and

the water table is close to the surface (U.S.D.A. Soil

Hydrol. Process. 26, 2019–2030 (2012)


Conservation Service, 1994), the dominant flow-generatingmechanism is the saturation excess runoff. Areas nearstreams and creeks are quickly saturated as the water tablerises and intersects the ground surface. Those saturated areasexpand and shrink as the rain event continues and recedes,respectively (variable source areas), and may obscure theinfluence of land use/cover to some degree. Rainfall insaturated areas becomes surface runoff and directlycontributes to streamflow.Areas classified as open space or grass had the largest

impact on low, base, median, and average flows, whilethey did not affect high flows (Table VII). These areas aregrassy urban areas not classified as lawn including grassylands in parks, sports fields, and along highways. Thestrong, positive relationship between these areas and lowflows and base flow may be due to irrigation in the case ofsports fields. These areas also have good drainagesystems that supplement recharge to groundwater.Interestingly, RB was best explained by the amount ofopen space (negative relationship) in conjunction with theamount of IS (positive relationship), as identified bymultiple regression.

CONCLUSIONS

Impervious cover induced stream responses of increasednutrients, sediment, and bacteria, and flashy hydrology inthese watersheds of the Florida Coastal Plain. Contrary toour hypothesis, even small changes in % IS wereassociated with alterations in some stream parameters asindicated by significant differences between watershedswith 0–5% IS and 5–10% IS. This suggests that the 10%threshold for coastal protection planning may be too high.Watersheds with 5–10% IS had the highest streamtemperatures and TSS concentrations and loads whichsignal disturbance at early stages of development. Itshould also be noted that while significant differenceswere found for many water quality parameters among ISclasses, relatively weak correlation and regressionrelationships indicate that other factors besides impervi-ous cover are also important to stream parameters.Several unique results arise from intrinsic physiograph-

ic properties of the Coastal Plain. In comparison with ISresponses in other physiographic regions, these coastalwatersheds tended to exhibit clear differences such aslower TSS and higher TDS concentrations, and higherconcentrations of Na+, Cl�, SO4

2�, and Ca2+ and Mg2+

with increasing IS. The sediment trends can be explainedby lower relief and larger particle sizes in the case of TSS(as hypothesized) and the presence of riparian histosols inthe case of TDS. Also, the range between NO3

�

concentrations in low IS versus high IS watersheds wasgreater than that observed in other studies and may reflectthe high leaching potential of sandy soils along the coast.Finally, the lack of influence of IS on base flow contrastssharply with results from studies in many locations and islikely due to high water tables and presence ofgroundwater recharge zones that extend beyond thesurface boundaries of the watershed.


Development near Apalachicola does not reflect thesame spatial pattern often observed in other areas. Thelongitudinal pattern along the coast generated a lowerlevel of impervious cover than often observed aroundurban centers (selected watersheds had a maximum of15% IS) and yet significant changes in water qualityparameters were observed. It could be useful to moreclosely examine the effects among watersheds with 0–5%IS to understand what changes occur when IS are firstintroduced in the watershed. It is apparent that coastalenvironments with flat topography and sandy soils mayrespond differently to urbanization stressors compared tolocales with greater relief and clayey substrates, and,consequently, a stronger research focus on urbanization ofcoastal systems is justified.

ACKNOWLEDGEMENTS

Funding for this research was provided by AuburnUniversity’s Center for Forest Sustainability. We appreciatethe hydrologic advice provided by the Florida Division ofForestry and, in particular, that of Jeff Vowell. We wouldlike to acknowledge Robin Governo for laboratory analysisand Shufen Pan for watershed land cover classification.Thanks to all who participated in sample collection andanalysis and project development, especially Ana Cerro,Danielle Haak, Jennifer Trusty, and Herbert Kesler.

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