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WATER QUALITY AND LAND USE CHANGES IN THE ALAFIA ANDHILLSBOROUGH RIVER WATERSHEDS, FLORIDA, USA1

Yogesh P. Khare, Christopher J. Martinez, and Gurpal S. Toor2

ABSTRACT: Spatial distribution of land use can have a substantial effect on surface and groundwater quality.Our objective was to test for trends in flow components and water quality related to changes in land use in theAlafia and Hillsborough River watersheds in Florida, USA, over the period 1974-2007. In addition, water qualitystatistics were evaluated in the perspective of numeric water quality criteria and proposed reclassification ofsegments of the Alafia River. Trends in 10 water quality parameters and three discharge variables were evalu-ated using a nonparametric trend detection test. Results of land use analysis indicated substantial urbanizationand loss of agricultural land in the study area. Discharge variables did not exhibit significant trends, whereastrends in the majority of water quality concentrations were negative or nonsignificant with total nitrogen andtotal Kjeldahl nitrogen as exceptions showing positive trends. Changes in nutrient pathways could not be clearlyidentified. Considering recently promulgated numeric nutrient criteria and standards for dissolved fluoride,much of the Alafia River was found to be out of compliance. While there were land use changes and changes inwater quality over the study period, it was difficult to identify a direct cause-effect relationship. Responses toregulatory efforts, such as the Clean Water Act and improvements in phosphate mining practices, may havehad greater impacts on water quality than changes in land use.

(KEY TERMS: watershed; water quality; trends; land use; urban.)

Khare, Yogesh P., Christopher J. Martinez, and Gurpal S. Toor, 2012. Water Quality and Land Use Changes inthe Alafia and Hillsborough River Watersheds, Florida, USA. Journal of the American Water Resources Associa-tion (JAWRA) 48(6): 1276-1293. DOI: 10.1111 ⁄ j.1752-1688.2012.00686.x

INTRODUCTION

Human development of any kind (urban, indus-trial, agricultural, etc.) consumes large quantities offreshwater. Increasing water demands in developedand developing communities and uncertainties inwater availability due to global climate change con-cerns will further stress this limited resource (Zhu

et al., 2008). The primary effect of urban and indus-trial development in watersheds is an increase inimpervious surfaces, which alter the natural hydro-logical condition by increasing the volume and rate ofsurface runoff and decreasing groundwater rechargeand base flow (Moscrip and Montgomery, 1997; Bedi-ent et al., 2008). Schueler (1994) concluded thatstreams with >10% watershed impervious cover couldbe classified as impaired and those with >25% could

1Paper No. JAWRA-11-0082-P of the Journal of the American Water Resources Association (JAWRA). Received June 28, 2011; acceptedJuly 25, 2012. ª 2012 American Water Resources Association. Discussions are open until six months from print publication.

2Respectively, Graduate Research Assistant (Khare) and Assistant Professor (Martinez), Department of Agricultural and Biological Engi-neering, Institute of Food and Agricultural Sciences, University of Florida, P.O. Box 110570, Gainesville, Florida 32611; and Assistant Pro-fessor (Toor), Soil and Water Science Department, Institute of Food and Agricultural Sciences, University of Florida, Gulf Coast Researchand Education Center, Wimauma, Florida 33598 (E-Mail ⁄ Khare: [email protected]).

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be classified as nonsupportive to their designateduses; for example hydrology, channel stability, biolog-ical diversity, etc. up to a point that they cannot berestored to predevelopment status. Results of 70% of65 peer-reviewed research studies confirmed or rein-forced this impervious cover model (Schueler et al.,2009).

Water quality status can be qualitatively deter-mined by assessing the extent and distribution ofland uses within a watershed. Tong and Chen (2002),showed positive correlation between agricultural andurban land uses with nitrogen (N) and phosphorus(P) concentrations at regional and watershed scalesin the state of Ohio. Several studies on water qualityin urbanizing watersheds, typically characterized byhigh impervious cover, showed degradation of waterquality due to runoff from nonpoint sources (Goveet al., 2001; Atasoy et al., 2006; Dougherty et al.,2006; Conway, 2007; Tu et al., 2007). Similar observa-tions were made by Xian et al. (2007) for the TampaBay watershed. Schilling and Lutz (2004) foundexcessive nitrate-nitrogen exports (1972-2000 mean:6.7 mg ⁄ l) to downstream regions in an agriculturalwatershed in Iowa. Understanding the pathways ofpollutants (e.g., via storm flow or base flow) can helpin the determination of probable pollutant sources,such as nitrate-N export in base flow due to agricul-tural drainage (Schilling and Zhang, 2004) and Pexport in storm flow due to higher sediment concen-trations during storm events (Pionke et al., 1999).Hence, it is necessary to evaluate historical andfuture land use changes in a watershed when formu-lating strategies to maintain and improve water qual-ity. The detection of changes in export pathways canserve as a proxy of change in pollutant sourceimpacted by land cover change.

The Tampa Bay region has been one of the fastestgrowing areas in the state of Florida. It has experi-enced a shift of land cover to urban land use duringthe 20th Century (Xian and Crane, 2005). Xian andCrane (2005) found a threefold increase in imperviouscover from 1991 to 2002 in the Tampa Bay watershedand predicted an increase to 38% of the totalwatershed by 2025. The region has also faced watersupply issues over the last few decades. There is aneed to reduce dependence on groundwater to reduceenvironmental impacts of over-pumping while fulfill-ing current and future demands of water supply(Tampa Bay Water, 2007). As of 2008, 28% of thewater supplied by Tampa Bay Water, the largestwater wholesaler in the region, was from surfacewater sources, with the remainder supplied fromdesalinated water from Tampa Bay (11%) andgroundwater (61%) (http://www.tampabaywater.org/supplies/). Of the surface water sources, the Hillsbor-ough River ⁄ Tampa Bypass Canal is the main source

of surface water (75%) with remaining contribution(25%) from the Alafia River (Tampa Bay Water,2007).

The objective of this study was to assess the rela-tionship between water resource conditions and landuse changes by detecting trends in hydrologicalvariables and water quality parameters in theHillsborough and the Alafia River watersheds duringa 34-year period (1974-2007). Ten water quality con-stituents and three discharge variables (streamflow,base flow, and percent base flow) were evaluated fortrends. On the basis of discharge characteristics,water quality constituents were stratified to identifyconstituent pathways. Land use changes over thestudy period were evaluated to study the relationwith water quality trends. Trends in water are alsodiscussed in light of recently promulgated numericnutrient criteria for Florida waters (USEPA, 2010)and proposed reclassification of portions of the AlafiaRiver for potable water supply (Tampa Bay Water,2007).

STUDY AREA

The study area is located in west-central Florida(Figure 1). The Hillsborough River watershed (HWS)and the Alafia River watershed (AWS) together forma major part, approximately 45% by area (27.7 and17.3%, respectively), of the greater Tampa Baywatershed. The elevation in the HWS increases from0 at the river mouth to 82 m in the north and east,whereas in the AWS, the elevation increases to 79 mfrom west to east. The Floridan aquifer system, avery productive and permeable system, encompassesthe study area (USGS, 1990). The annual mean pre-cipitation for the region is approximately 135 cm. Themajor portion of precipitation (55-60%) occurs duringfour months from June to September (SWFWMD,2006, 2007).

The HWS drains 1,750 km2 of land from Hillsbor-ough County, southwestern Pasco County, and north-western Polk County (Figure 1). Its headwatersoriginate in the southwestern portion of the GreenSwamp north of the city of Lakeland. From there, itflows southwesterly 87 km to Upper HillsboroughBay. The Hillsborough River Dam is constructedapproximately 16 km upstream of the bay, up towhich the river is tidally influenced (Chen et al.,2000). Cypress Creek, Blackwater Creek, and TroutCreek are main tributaries. Flows in the Hillsbor-ough River are partially derived from discharges fromCrystal Spring and Sulphur Spring (SWFWMD,1999). The Hillsborough River Dam reservoir is

WATER QUALITY AND LAND USE CHANGES IN THE ALAFIA AND HILLSBOROUGH RIVER WATERSHEDS, FLORIDA, USA

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 1277 JAWRA

connected to the Tampa Bypass Canal by HarneyCanal, to divert floodwaters to McKay Bay, bypassingthe cities of Tampa and Temple Terrace (SWFWMD,2006). Urban and built-up areas dominate the land-scape in the southern quarter of the watershed,which includes the urban and suburban areas ofTampa, Plant City, and Lakeland. The HillsboroughRiver below the dam is a highly modified system. Thealterations to the Lower Hillsborough River havedeteriorated hydrologic functions associated with thefloodplain and estuarine wetlands have essentiallybeen lost (SWFWMD, 1999, 2006).

The AWS is located south of the HWS and drainsapproximately 1,093 km2 to Tampa Bay (Figure 1).The major portion of the watershed is located inHillsborough County and the remaining portion is inPolk County. The North Prong and South Prong arethe main tributaries to the Alafia River, contributing80% of the Alafia River discharge measured at LithiaSpring. Bell Creek, Turkey Creek, and FishhawkCreek are the minor tributaries that discharge to thelower reaches of the Alafia River. The river is tidallyinfluenced for 18 km from its mouth (Chen, 2004). Li-thia Spring, a second magnitude spring (10-100 ft3 ⁄ s)(Spechler and Schiffer, 1995) and Buckhorn Springcontribute a major portion of the river’s dischargeduring the dry season (SWFWMD, 2007). A large por-tion of the AWS is located in the Bone Valley forma-tion, a phosphate-rich geological stratum that hasbeen extensively mined since the early 20th Century(Long and Orne, 1990).

DATA

Daily streamflow data were obtained from the U.S.Geological Survey (USGS) National Water Informa-tion System program (NWIS) (http://waterdata.usgs.gov/nwis/) (USGS, 2011). Streamflow gages wereselected based on their record length and proximityto water quality sampling stations (Table 1,Figure 1).

Water quality data were obtained from the Envi-ronmental Protection Commission for HillsboroughCounty (EPCHC) (http://www.epchc.org/surface_water_info.htm) (EPCHC, 2011). For each watershed,six stations were selected (Table 2, Figure 1). For thisstudy, monthly data, obtained from grab sample for10 water quality parameters were evaluated: total P(TP), total N (TN), total Kjeldahl N (TKN), nitrate ⁄nitrite-N (NOx), ammonium-N (NH4

+), biochemicaloxygen demand (BOD), fecal coliform (FC), turbidity,

FIGURE 1. Locations of the Hillsborough and Alafia RiverWatersheds Along with Gages Used for Analysis. EPCHC

stations 115 and 116 are on the North Prong and South Prongof the Alafia River, respectively.

TABLE 1. U.S. Geological Survey (USGS) Discharge Gages, Their Positions, Available Period of Record and Corresponding Drainage Areas.

USGS Gage Number Latitude Longitude Data Available1 Location Drainage Area (km2)

Alafia River watershed23010002 27.88 )82.10 1950-2010 North Prong Alafia 3502301300 27.80 )82.12 1962-2010 South Prong Alafia 2772301500 27.87 )82.21 1932-2010 Alafia River 867

Hillsborough River watershed2303000 28.15 )82.23 1939-2010 Hillsborough River 5702303800 28.09 )82.41 1964-2010 Cypress Creek 4142304500 28.02 )82.43 1938-2010 Hillsborough River 1,616

1The length of record used is this analysis is 34 years from 1974 to 2007.2USGS discharge gage on North Prong had missing data for a period of three years (October 1992 to June 1995).

KHARE, MARTINEZ, AND TOOR


dissolved fluoride (DF), and chlorophyll-a (Chl-a). Allwater quality parameters were in units of mg ⁄ l withthe exception of turbidity (nephelometric turbidityunits, NTU), Chl-a (lg ⁄ l), and FC (colonies per100 ml). Further details on the original sources of thewater quality data and the analytical methods usedare available from the EPCHC (2011).

Land use GIS (metadata) data for 1974, 1990, 1995,2000, and 2007 (vector format) were obtainedfrom Southwest Florida Water Management District(SWFWMD) (1999-2007), whereas watershed and riverdata were obtained from the Florida Department ofEnvironmental Protection (1989). Land use GIS data-sets are based on photo interpretations of USGS colorinfrared digital orthophoto quarter quadrangles(DOQQs) (1990-2007) and USGS Water ManagementDistrict tiles for land use (1974), prepared from theNational Aeronautics and Space Administration’s aer-ial photos. The scale of these datasets differs from yearto year, with 1974 data providing the coarsest estimateof land use and 2007 providing the finest.

METHODS

The AWS and the HWS were delineated into sub-watersheds based on water quality gage locationsusing ArcHydro v2.0 tools (ESRI, 2011). Theupstream contributing area of each station is asreported in Table 2. Land uses for the years 1974,1990, 1995, 2000, and 2007 were reclassified into ninegroups as shown in Figure 2. This grouping was basedon the Florida Land Use and Cover Classification Sys-tem, FLUCCS (Florida Department of Transportation,1999). Major land uses were identified from land usemaps and were tracked for changes over the study

period by plotting percentage land uses against time(Figures 3 and 4) to find possible relationships withwater quality trends. Due to resolution differences inthe original land use datasets and impreciseness ofrasterization and reclassification conducted herein(based on maximum coverage within each 30 m gridcell), the percentage land use and land use changesreported herein should be viewed as approximate val-ues. It is important to note that these delineatedcontributing areas may not accurately describe thecontributing groundwater-shed, and thus the evalua-tion of base flow water quality in relation withchanges in land use should be viewed with caution.

Streamflow was separated into base flow and stormflow components to evaluate trends in flow and constit-uent pathways. The Web-based Hydrograph AnalysisTool (WHAT) (Lim et al., 2005) was used to performthe base flow separation using the digital filter pro-posed by Eckhardt (2005) with recommended valuesfor perennial rivers with porous aquifers (filter param-eter = 0.98 and the ratio of long-term base flow tostreamflow, BFImax = 0.8) (Lim et al., 2005; Eckhardt,2005, 2008). Following Schilling and Zhang (2004), athreshold value of 90% base flow was used to stratifywater quality samples as representative of base flowconditions. For clarity, we will refer to streamflow sam-ples as total streamflow samples, whereas base flow sam-ples as base flow only samples. Note that base flow onlysamples are a subgroup of total streamflow samples.

The nonparametric Seasonal Kendall trend detec-tion test was employed in this study. Nonparametrictechniques have advantages over parametric trenddetection techniques, which suffer in many casesbecause of the underlying assumptions, such as datanormality and homoscedasticity (Hirsch et al., 1991;Helsel and Hirsch, 2002). The Seasonal Kendalltest (Hirsch and Slack, 1984) is a modification of thenonparametric test proposed by Hirsch et al. (1982)

TABLE 2. Environmental Protection Commission for Hillsborough County (EPCHC) Water Quality Stations,Their Position, Available Period of Record, and Location.

EPCHC StationNumber Latitude Longitude Period Location

DrainageArea (km2) Position Relative to USGS Gage

Alafia River watershed74 27.86 )82.38 1974-2007 Alafia River 1,088

111 27.94 )82.19 1974-2007 Turkey Creek 44114 27.86 )82.27 1974-2007 Alafia River 975 Downstream of 2301500 (distance �5.7 km)115 27.86 )82.14 1974-2007 North Prong Alafia 362 Downstream of 2301000 (distance �4.3 km)116 27.86 )82.14 1974-2007 South Prong Alafia 367 Downstream of 2301300 (distance �7.5 km)139 27.72 )82.06 1981-2007 South Prong Alafia 134

Hillsborough River watershed2 27.94 )82.46 1974-2007 Hillsborough River 1,750

105 28.02 )82.44 1974-2007 Hillsborough River 1,694 Downstream of 2304500 (distance �0.73 km)106 28.05 )82.36 1974-2007 Hillsborough River 1,614108 28.15 )82.22 1974-2007 Hillsborough River 614 Upstream of 2303000 (distance �1 km)120 28.09 )82.41 1976-2007 Cypress Creek 414 Approximately same location as 2303800137 27.97 )82.48 1979-2007 Hillsborough River 1,740



and accounts for data ties, censored (nondetectableconcentrations) and missing data. The Seasonal Ken-dall test addresses serial correlation by dividing datainto a number of seasons to then calculate the Mann-Kendall test statistic for each. The results from theindividual seasons are then added to calculate theSeasonal Kendall test statistic. Hirsch and Slack(1984) recommended using a number of seasons cor-responding to sampling frequency. The trends wereevaluated at three different significance levels (a), 1,5, and 10% considering two-tailed tests. The resultsof the discharge and water quality trend analysis

were reported by the sign of the trend (+1, )1, 0), andthe number of data points used in the analysis (n).

RESULTS AND DISCUSSION

Land Use Changes

The major land use categories in the AWS wereurban, mining, and agriculture, whereas dominant

FIGURE 2. Land Use in the Hillsborough and Alafia Watersheds.



land uses in the HWS were urban, agricultural, andwetlands (Figures 2-4). Note that all percentage landuses and percent land use changes are absolute val-ues, i.e. percentages with respect to entire watershedor subwatershed area. From 1974 to 2007, urban andresidential land in the AWS increased from 10 to21%, whereas mining land increased from 11 to 37%.These increases were at the expense of agriculturalland, which was reduced from 36 to 19%. Other landuses, which underwent substantial change wererangeland (reduction from 17 to 1%) and forest(reduction from 13 to 8%). These changes correspondto the entire watershed (area drained by EPCHC 74)and do not represent the specific changes in land use

of areas contributing to upstream stations (Figure 2).Details of land use changes over the study period insubwatersheds of the AWS, based on water qualitygage locations, are presented in Figure 3. Miningactivities, predominantly phosphate mines, whichwere fairly uniform throughout the AWS (10%) at thebeginning of the study period are now concentratedin the central and eastern parts of the AWS, drainedby EPCHC 139, 114, 115, and 116 (35-90% by drain-age area) (Figure 3). The urban and residential areaswere sparse at the beginning of the study period, butbecame concentrated in the northeast (drained byEPCHC 111 and 115) and west (downstream ofEPCHC 114) over the 34 years. While agricultural

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FIGURE 3. Change in Land Uses over the 34 Years (1974-2007) in the Alafia River Watershed.



activities, which were present predominantly in cen-tral and northeastern parts of the AWS, later becamerestricted to the southwest and north (Figure 2).

The lands classified as phosphate mines includeactive as well as closed (reclaimed and released)mines along with detention ponds and lands underreclamation. The reclamation process involves recon-touring, revegetation, and restoration of water bodiesas well as wetlands (FAC, 2006). Approximately, 15-20% of the southern phosphate mining district inFlorida was originally overlain by wetlands (FloridaInstitute of Phosphate Research, 1985). The Florida

Department of Natural Resources (1988, 1990) andFlorida Department of Environmental Protection(2000, 2005, 2009) report that the amount ofreclaimed mining lands increased from 40 to 70% (oftotal land mined since 1975) in the southern miningdistrict over 21 years. On the basis of this, it is esti-mated that in the AWS approximately 70% of thearea classified as phosphate mining is currently inthe reclaimed state. However, the exact distributionof land covers, e.g., forested, pasture, wetlands, etc.,within these rehabilitated areas remains unavail-able ⁄ unknown.

Urban Mining Agriculture

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FIGURE 4. Change in Land Uses over the 34 Years (1974-2007) in the Hillsborough River Watershed.



Unlike the AWS, the distribution of land use andthe change in land use over the study period in theHWS were found to be fairly uniform in all subwater-sheds. The land uses, which underwent considerablechanges include urban and residential and agricul-tural (Figure 4). These two classes were the dominantland use classes along with wetlands, whichremained fairly stable (20-30%) over the study period.Among the HWS subwatersheds, urban and residen-tial uses comprised from 24 to 34% of the land coverin 2007, compared with between 5 and 20% in 1974(Figure 4). The typical increase in urban and residen-tial areas was 18%. This was accompanied by 19%(typical value) decrease in agricultural land use,which constituted 42-47% of the land at the start ofthe study period. Rangelands decreased from 17 to5% with corresponding increase in upland forests by9% over the same period. The southwestern portionof the HWS is a part of metropolitan Tampa (Fig-ure 2), where urbanization was concentrated at thebeginning of the study period. However, it laterspread north and to the eastern part of thewatershed. Agriculture was spread throughout thewatershed (except in the southwest) at the beginningof the study period. As time progressed, agriculturewas mainly present in the northern and eastern partsof the watershed (Figure 2).

Trends

Trends in discharge variables and water qualityparameter concentrations and nutrient concentra-tions in base flow, at three significance levels (a), arepresented in Tables 3, 5, and 6, respectively. Thechoice of a value controls the risk of type 1 and type2 errors. The use of 5% significance level is a commonpractice in statistical analysis. While analyzing thedata from an observational study one may loosen upthe criteria for significance (using a > 5%), as there isnot enough control over the treatments like designedexperiments. For example, Burn and Elnur (2002)used a = 10% for the detection of hydrologic trendsand variability. However, for simplicity, we havemainly focused on trends based on 5% significancelevel. Only in few cases, significant trends at a = 10%were detected, which were nonsignificant at the 5%significance level.

Trends in Discharge. Trends in all three dis-charge variables (streamflow, base flow, and percent-age base flow) were statistically nonsignificant at allstations of both watersheds except one (2304500) onthe Hillsborough River (Table 3, Figure 1). This gageis located at the Hillsborough River Reservoir andmeasures the discharge from dam. The streamflow

trend at this gage was found to be negative, whereaspercentage base flow showed a positive trend. Also,base flow showed a decreasing trend at 10% signifi-cance level. The general lack of trends in streamflowat the remaining stations is in accordance with Kelly(2004) who found that the changes in streamflow ofrivers within the SWFWMD (which includes theAWS and HWS) were statistically nonsignificant from1970 to 1999. He further found streamflows to bedecreasing if a longer record (1940-1999) is consid-ered. This agrees with the observations made by Linsand Slack (1999) who noted decreasing streamflowsin the Southeastern United States over a similar timeperiod. The presence of stormwater retention ⁄ deten-tion systems, which were required of all newurban ⁄ residential developments since 1982 (FAC,1982) may have played a role in producing negligibletrends in base flow. Although urbanization typicallyreduces infiltration and groundwater recharge (andhence, base flow), the retention and detention ofstormwater may have negated this typical impact ofurbanization by enhancing infiltration and poststormrecession of detention systems.

Trends in Water Quality. Overall, constituentconcentrations were higher in the AWS comparedwith the HWS for 5 of the 10 constituents consideredover the study period (Table 4). Among the remainingconstituents, TKN and NH4

+-N median concentrationswere similar between watersheds, and BOD, FC, andChl-a showed slightly higher median concentrations inthe HWS compared with the AWS. Concentrations ofTP were highest at EPCHC station 115 on the North

TABLE 3. Summary of Trends in Discharge Variables in the AlafiaRiver and Hillsborough River Watersheds (1974-2007).

Alafia River Watershed

2301000 2301300 2301500

Trend n Trend n Trend n

Streamflow 0 375 0 408 0 408Base flow 0 375 0 408 0 408Percentage base flow 0 375 0 408 0 408

Hillsborough River Watershed

2303000 2303800 2304500


Streamflow 0 408 0 408 )1*** 408Base flow 0 408 0 408 )1* 408Percentage base flow 0 408 0 408 +1** 408

Notes: ‘‘0’’ = No or Neutral Trend, ‘‘)1’’ = Decreasing or NegativeTrend, and ‘‘+1’’ = Increasing or Positive Trend.*Significant at a = 10% only, **significant at a = 5 and 10%, ***sig-nificant at a = 1, 5, and 10%.



Prong of the Alafia River (Figure 1, Table 4) where�76% of the land is under agriculture, urban, andmining land uses taken together. At Turkey Creek(EPCHC 111), 7 of the 10 parameters except TP, DF,and Chl-a had the highest median concentrations.The area drained by EPCHC 111 is smallest amongthe AWS water quality gages and consists of > 78% ofagricultural and urban areas.

TP showed significant negative trends at 11 of 12locations (Table 5, Figure 5). Only EPCHC station120 (located on Cypress Creek) showed no significanttrend, however, TP concentrations at this stationwere the lowest of all stations (Table 5, Figure 5).Phosphorus inputs to rivers declined nationally atthe beginning of the 1970s in response to the 1972Clean Water Act, which resulted in the ban on theuse of phosphate detergents and upgrading of waste-water treatment plants to reduce P discharge (Litke,1999). This and the reduction of agriculture andrangelands in the HWS may be the likely reasons forthe declining TP trends in the HWS (Figures 3 and

4). Meals et al. (2010) found that the time betweenregulatory action and improvement in water qualitycan range from years to decades for legacy P. Range-lands, which are often used for livestock grazing,were lost in either watershed. Cattle grazing hasbeen found to negatively impact stream water qualityin the western and midwestern United States(Agouridis et al., 2005). In Florida, such grazingactivities have shown to be associated with highphosphate concentrations in the Lake Okeechobeewatershed located southeast of the area in this study(Capece et al., 2007). In the AWS, rangelands weremainly converted to phosphate mining. Hence,decreasing TP concentration trends in the AWS werelikely due to reduction in agriculture and regulatoryefforts, such as strict rules for surface discharge frommined lands and improvements in phosphate miningpractices, etc. As per State of Florida 1975 legislation,reclamation has been made mandatory for all landmined after July 1975. However, the regulations andfocus (e.g., wetland restoration, upland restoration,

TABLE 4. Interquartile Range (IQR)1 and Median (50QT) Water Quality Parameter Concentrations in theAlafia River and Hillsborough River Watersheds (1974-2007).


74 111 114 115 116 139

50QT IQR 50QT IQR 50QT IQR 50QT IQR 50QT IQR 50QT IQR

TP (mg ⁄ l) 1.06 0.93 1.16 1.30 1.91 1.34 4.59 2.95 1.00 0.88 1.23 0.81TN (mg ⁄ l) 1.19 0.57 3.69 1.69 1.85 0.65 1.57 0.70 1.15 0.41 1.40 0.66TKN (mg ⁄ l) 0.85 0.50 1.11 1.22 0.54 0.46 0.66 0.50 0.63 0.39 0.75 0.42NH4

+ (mg ⁄ l) 0.09 0.11 0.13 0.85 0.04 0.06 0.05 0.11 0.05 0.06 0.06 0.09NOx (mg ⁄ l) 0.18 0.35 2.31 1.44 1.27 0.80 0.99 0.81 0.38 0.35 0.54 0.63BOD (mg ⁄ l) 1.80 1.80 2.10 4.63 1.00 0.70 1.00 0.80 1.00 0.70 1.00 0.80FC (col ⁄ 100 ml) 100 70 1,900 4,000 100 118 100 100 100 120 120 190TUR (NTU) 5.00 3.00 7.50 6.00 3.95 4.00 5.00 5.93 3.00 3.00 4.00 2.00DF (mg ⁄ l) 1.06 0.31 0.49 0.14 1.42 0.70 2.53 1.65 1.47 0.65 2.11 0.90Chl-a (lg ⁄ l) 11.02 15.82 3.84 6.12 2.12 2.55 1.70 1.71 1.57 1.30 3.30 4.80


2 105 106 108 120 137

50QT IQR 50QT IQR 50QT IQR 50QT IQR 50QT IQR 50QT IQR

TP (mg ⁄ l) 0.37 0.34 0.24 0.19 0.26 0.20 0.29 0.42 0.08 0.08 0.27 0.16TN (mg ⁄ l) 0.79 0.48 0.97 0.56 0.94 0.35 1.73 0.48 1.25 0.64 1.07 0.46TKN (mg ⁄ l) 0.76 0.45 0.83 0.53 0.60 0.47 0.41 0.65 1.22 0.59 0.95 0.41NH4

+ (mg ⁄ l) 0.09 0.12 0.09 0.11 0.05 0.04 0.04 0.07 0.06 0.08 0.10 0.11NOx (mg ⁄ l) 0.03 0.07 0.10 0.12 0.20 0.36 1.30 0.68 0.03 0.04 0.10 0.13BOD (mg ⁄ l) 1.55 1.20 1.40 1.19 1.10 0.70 0.90 0.90 1.30 1.10 1.70 1.40FC (col ⁄ 100 ml) 100 275 200 470 100 50 100 170 100 180 200 590TUR (NTU) 3.00 2.50 2.75 2.00 2.00 1.00 2.00 2.00 2.00 3.00 3.00 2.00DF (mg ⁄ l) 0.89 0.30 0.26 0.19 0.22 0.08 0.22 0.10 0.14 0.06 0.51 0.41Chl-a (lg ⁄ l) 10.41 11.48 6.02 9.85 3.00 4.20 1.56 3.35 1.90 4.15 7.80 13.57

Notes: TP, total P; TN, total N; TKN, total Kjeldahl N; NH4+, ammonium-N; NOx, nitrate ⁄ nitrite-N; BOD, biochemical oxygen demand; FC,

fecal coliform; TUR, turbidity; NTU, nephelometric turbidity units; DF, dissolved fluoride; Chl-a, chlorophyll-a.1IQR is the difference between third and the first quartile, i.e., IQR = Q3-Q1.



etc.) have undergone changes since then (Brown,2005). Also, the impacts of wetland and upland resto-ration on mined lands in Florida may take years todecades to show results (Rushton, 1988; Weber, 1994;Brown, 2005; Brown et al., 2010). In that regard, it isdifficult to attribute the exact cause of declining TPconcentrations from mined lands, i.e., land usechanges and ⁄ or changes in mining practices bothbeing the results of regulatory efforts.

Fluoride is typically found in phosphate ores inFlorida (Specht, 1960; May and Sweeney, 1984; Pitt-man, 1990). Concentrations of DF showed negativetrends at four of six stations in the AWS and three ofsix stations in the HWS (Table 4). One additional sta-tion in the HWS (105) showed negative DF trend at10% significance level. Concentrations of DF for allstations in the HWS were lower than the AWSstations due to the relative absence of mined lands inthe HWS (Table 4). Reductions in DF are likely a

result of reduced discharge of process water fromphosphate mining operations and increased treat-ment requirements of discharged water (Hildebrandt,2006).

Concentrations of TN showed negative trends atthree of the six stations in the AWS, whereas no sig-nificant trend was found at the remaining three sta-tions (Table 5). In the HWS, two stations were foundto have significant positive trends. These two stationswere EPCHC station 120 on Cypress Creek and sta-tion 106, which is immediately downstream of theconfluence of Cypress Creek with the HillsboroughRiver (Figure 1). No significant trend was found atthree of the four remaining stations (EPCHC stations2, 105, and 108), whereas a significant negative trendin TN was found at station 137 (Table 5, Figure 1).

The significance and sign of trends of TKN in theHWS followed those for TN for all stations, whereasin the AWS differences in sign and ⁄ or significance

TABLE 5. Summary of Trends in Water Quality Concentrations in Total Streamflow1 in the Alafia andHillsborough River Watersheds (1974-2007).


742 111 114 115 116 139

Trend n Trend n Trend n Trend n Trend n Trend n

TP )1*** 402 )1*** 385 )1*** 406 )1*** 361 )1*** 406 )1*** 319TN 0 405 )1** 386 0 392 )1*** 356 0 385 )1*** 312TKN 0 395 )1*** 375 +1** 396 0 361 +1*** 394 0 320NH4

+ )1*** 382 )1*** 360 )1*** 376 )1*** 356 )1*** 377 )1*** 316NOx 0 403 0 386 )1*** 406 )1*** 349 0 378 0 319BOD )1*** 401 )1*** 384 )1*** 395 )1*** 361 )1*** 398 )1* 319FC )1*** 398 )1*** 381 )1*** 402 )1*** 361 )1*** 400 )1* 319TUR )1* 403 )1*** 385 0 406 +1* 373 0 403 )1*** 319DF )1*** 343 0 351 )1*** 396 )1*** 349 )1*** 395 0 312Chl-a )1*** 403 0 127 +1*** 143 )1*** 356 )1*** 402 0 317


22 105 106 108 120 1372

Trend n Trend n Trend n Trend n Trend n Trend n

TP )1*** 395 )1*** 398 )1*** 397 )1*** 397 0 328 )1*** 339TN 0 397 0 390 +1** 388 0 389 +1*** 320 )1*** 339TKN 0 392 0 392 +1** 392 0 390 +1*** 327 )1*** 338NH4

+ )1*** 380 )1*** 381 )1*** 383 )1*** 371 )1*** 321 )1*** 334NOx )1* 396 )1* 396 0 396 0 396 )1*** 327 )1** 338BOD )1*** 396 )1*** 394 0 392 )1*** 386 0 327 0 336FC )1*** 392 )1** 392 )1*** 392 )1*** 391 0 326 +1*** 339TUR )1*** 396 )1** 396 0 396 )1*** 396 0 327 0 339DF )1*** 198 )1* 367 )1** 367 )1*** 384 0 321 0 330Chl-a )1*** 387 0 392 0 129 )1*** 136 0 321 0 335

Notes: ‘‘0’’ = No or neutral trend, ‘‘)1’’ = Decreasing or negative trend, and ‘‘+1’’ = Increasing or positive trend.TP, total P; TN, total N; TKN, total Kjeldahl N; NH4

+, ammonium-N; NOx, nitrate ⁄ nitrite-N; BOD, biochemical oxygen demand; FC, fecalcoliform; TUR, turbidity; DF, dissolved fluoride; Chl-a, chlorophyll-a.

1Streamflow is the sum of base flow and storm flow. Daily values of streamflow were obtained from USGS.2WQ parameter concentrations were influenced by tidal effects, which were not removed before trend analysis.*Significant at a = 10% only, **significant at a = 5, and 10%, ***significant at a = 1, 5, and 10%.



were found for four of the six stations (Table 5). Sta-tions 114 and 116 (located on the mainstem of the Ala-fia and on the South Prong near the confluence withthe North Prong, respectively) (Figure 1) showed sta-tistically significant positive trends for TKN, but didnot show significant trends in TN. No trends in TKNwere found for stations 115 (North Prong near conflu-ence with the South Prong) and 139 (upstream portionof the South Prong), however, negative trends in TNwere found at both stations. These results indicatethat the proportion of TN as TKN has increased overthe study period at these four stations in the AWS.Overall, the TKN to TN ratio is higher in the HWScompared with the AWS and may be due to a higherproportion of wetland and ⁄or forest cover.

Negative trends in NH4+-N concentrations were

found for all stations in both watersheds (Table 5).This result indicates that the positive trends seen inTKN and TN at Cypress Creek (EPCHC station 120)and downstream of the confluence of Cypress Creekwith the Hillsborough River (EPCHC 106) are oforganic origin. In addition, this result indicates thatthe increase of the proportion of TN as TKN foundover the study period for four of the six stations inthe AWS is in organic N forms.

Concentrations of NOx-N showed no significanttrends at four stations and negative trends at two

stations in each watershed (Table 5) at a = 5%. In theHWS, two additional stations (2 and 105) showed sig-nificantly negative trends at a = 10%. Overall, it canbe said that trends in inorganic N (NOx + NH4

+) weregenerally negative, whereas those in organic N werepositive. In other words, the proportion of organic Nas a part of TN increased over the study period. Toillustrate this, the time series of nitrogen concentra-tions (TN, organic N [TKN-NH4

+], and inorganic N[NOx + NH4

+]) were plotted (Figure 6). It can be seenthat the organic N to TN ratio is much higher in theHWS compared with the AWS as noted previously.Also, in general, this ratio increased over the studyperiod in both watersheds. The overall negativetrends in inorganic N (NOx and NH4

+) concentrationsare in agreement with the loss of agricultural lands,and corresponding reduction in application of inor-ganic fertilizers in both watersheds (FDACS, 2012).The possible causes for the overall increase of theproportion of N in organic forms (based on TKN andNH4

+ results) remain unclear, i.e., increased use oforganic fertilizers and ⁄ or increased wetland and ⁄ orforest cover, although, we speculate the latter to betrue.

Chl-a concentrations showed negative trends atthree stations in the AWS and at two stations inthe HWS (Table 5). No trends were identified at twostations in the AWS and four stations in the HWS.A positive trend was seen at station 114 on themainstem of the Alafia River (Figure 1). This stationalso showed a positive trend in TKN. While thetrend in TP decreased at this station over the studyperiod, the Alafia River is typically not P-limiteddue to the underlying geology of the watershed,which results in naturally high TP concentrations(Table 4). It should be noted that there were rela-tively fewer Chl-a samples available at stations 111,114, and 108 (Table 5), which could lead to mislead-ing trend results (Konrad and Booth, 2002; Brandeset al., 2005). According to Wolfe and Drew (1990),large algal blooms and fish kills have been reportedin the AWS. However, Chl-a concentrations in theHWS were relatively higher than those in the AWSover the study period (Table 4). Note that the twostations with the highest Chl-a concentrations, 74and 2, are in urban settings and under tidal influ-ence. However, both show negative Chl-a concentra-tion trends.

Concentrations of BOD showed negative trends atthe majority of stations (five) in the AWS at a = 5%(and all stations at a = 10%) and three stations in theHWS at a = 5%. At the remaining stations, BOD con-centrations did not change significantly. BOD is animportant parameter for water pollution as it isrelated to readily decomposable organic carbon. Theincrease in the proportion of N in organic forms

TABLE 6. Summary of Trends in Nutrient Concentrations inBase Flow (base flow only) at Three Stations in the Alafia River

and Hillsborough River Watersheds (1974-2007).


114 115 116


TP )1*** 183 )1*** 148 )1*** 175TN 0 177 )1*** 143 +1** 168TKN +1*** 180 0 147 +1*** 171NH4

+ )1*** 172 )1*** 144 )1*** 165NOx )1** 183 )1*** 149 0 175


105 108 120


TP )1*** 158 )1*** 144 0 161TN 0 154 +1* 139 +1*** 156TKN 0 157 0 143 +1*** 160NH4

+ )1*** 151 )1*** 135 )1* 159NOx 0 157 +1* 143 )1*** 160

Notes: TP, total P; TN, total N; TKN, total Kjeldahl N; NH4+,

ammonium-N; NOx, nitrate ⁄ nitrite-N.‘‘0’’ = No or neutral trend, ‘‘)1’’ = Decreasing or negative trend, and‘‘+1’’ = Increasing or positive trend.

*Significant at a = 10% only, **significant at a = 5 and 10%, ***sig-nificant at a = 1, 5, and 10%.



previously noted and the negative BOD trends mayindicate that the organic forms of N have increasedin recalcitrance during the study period, and indicatethat they may be of plant origin rather than fromhuman or animal wastes. This may be partially sup-ported by the overall increase in wetlands and forestcover in the HWS during the study period (Figure 3)and ecological restoration efforts (wetlands andupland forests) as a part of mining reclamationefforts as noted previously in the AWS. This increaseof wetlands may have resulted in a greater propor-tion of TN export in organic forms (Stepanauskaset al., 1999; Pellerin et al., 2004; Reddy and DeLaune,2008; Kadlec and Wallace, 2009) and inorganic N out-puts are often small relative to organic outputs in for-ested watersheds (Hedin et al., 1995; Groffman et al.,2004; Stanley and Maxted, 2008).

Fecal coliform trends in the AWS were identical toBOD trends (Table 5), further supporting a decreasein organic material from human ⁄ animal sources. Inthe HWS, FC and BOD trends differed at two sta-tions, 106 and 137. Station 106 located on theHillsborough River, downstream of the confluencewith Cypress Creek, showed a negative trend in FC,whereas at station 137, the trend was positive. Nei-ther location showed any trend in BOD.

Turbidity trends were found to follow BOD trendsin the HWS, but not in the AWS (Table 5). Negativetrends were found at two stations in the AWS andthree stations in the HWS, whereas no significanttrends were found at the remaining stations. When10% significance level is considered, two additionalstations in the AWS show significant trends, one neg-ative (74) and one positive (115).

TP 12 per. Mov. Avg. (TP)

0

2.5

5

7.5

10

Jan-70 Jan-80 Jan-90 Jan-00 Jan-10

TP

(m

g/L

) (a) EPCHC 74

0

2.5

5

7.5

10


TP

(m

g/L

) (b) EPCHC 111

0

2.5

5

7.5

10


TP

(m

g/L

) (c) EPCHC 114

0

2.5

5

7.5

10


TP

(m

g/L

) (d) EPCHC 115

0

2.5

5

7.5

10


TP

(m

g/L

) (e) EPCHC 116

0

2.5

5

7.5

10


TP

(m

g/L

) (f) EPCHC 139

0

0.5

1

1.5

2


TP

(m

g/L

) (g) EPCHC 2

0

0.5

1

1.5

2


TP

(m

g/L

) (h) EPCHC 105

0

0.5

1

1.5

2


TP

(m

g/L

) (i) EPCHC 106

0

0.5

1

1.5

2

Jan-70 Jan-80 Jan-90 Jan-00 Jan-10T

P (

mg/

L) (j) EPCHC 108

0

0.5

1

1.5

2


TP

(m

g/L

) (k) EPCHC 120

0

0.5

1

1.5

2


TP

(m

g/L

) (l) EPCHC 137

FIGURE 5. Time Series Plots of TP 12-Month Moving Average Trend Lines Have Been Added to Plotsfor the Sake of Better Visualization and Shall Not Be Confused with Nonparametric Trend Lines.



Nutrient Pathways

At the 5% significance level, base flow only trends(Table 6) as well as total streamflow trends (Table 5)of N and TP concentrations were found to be thesame at all stations, with the exception of the TNtrend at station 116 in the AWS and the NH4

+ trendat station 120 in the HWS. However, at a = 10%,total streamflow and base flow only concentrationtrends differed in more than two cases, unlike the 5%significance level. NOx-N base flow only trend at 105remained nonsignificant, whereas corresponding totalstreamflow concentration trend was found to be nega-tive (at a = 10%). At station 108, TN and NOx-Ntrends in base flow only, which were nonsignificant

at a = 5%, were found to be positive at the 10% signif-icance level. However, NH4

+-N trends in base flowonly and total streamflow concentrations remain non-significant at a = 10%. The trend of TN in base flowat station 116 was found to be positive, whereas thecorresponding trend in total streamflow concentra-tions was not significant, indicating an increase ofTN from base flow with a corresponding decrease instorm flow over the study period, whereas the overalltrend in total streamflow showed no change. Similarobservations can be made for station 108 at the 10%significance level. At a = 5%, NH4

+-N base flow onlyconcentrations at station 120 showed no significanttrend, whereas total streamflow concentrations wereobserved to decrease significantly, indicating a

00.511.522.5

N (

mg/

L)

EPCHC 137

TN InO-N O-N

12 per. Mov. Avg. (TN) 12 per. Mov. Avg. (InO-N) 12 per. Mov. Avg. (O-N)

012345


N (

mg/

L)

(a) EPCHC 74

0369

1215


N (

mg/

L)

(b) EPCHC 111

012345


N (

mg/

L)

(c) EPCHC 114

012345


N (

mg/

L)

(d) EPCHC 115

012345


N (

mg/

L)

(e) EPCHC 116

012345


N (

mg/

L)

(f) EPCHC 139

00.5

11.5

22.5


N (

mg/

L)

(g) EPCHC 2

00.5

11.5

22.5


N (

mg/

L)

(h) EPCHC 105

00.5

11.5

22.5


N (

mg/

L)

(i) EPCHC 106

012345


N (

mg/

L)

(j) EPCHC 108

00.5

11.5

22.5


N (

mg/

L)

(k) EPCHC 120

00.5

11.5

22.5


N (

mg/

L)

(l) EPCHC 137

FIGURE 6. Time Series Plots of TN, Inorganic N (NOx+NH4+), and Organic N (TKN-NH4

+). For better visualization,different vertical scales have been used for individual plots. Also, 12-month moving average trend lines have been included,

which shall not be confused with nonparametric trend line.



decrease of NH4+ in storm flow during the study per-

iod. However, at a = 10%, both the trends were signif-icantly negative. Based on these results attributingthe differences in trends to changes in nutrient path-ways is tenuous. However, an increase in the propor-tion of plant-based organic nitrogen is generallysupported by trends in N concentrations in base flowonly (five stations at a = 10% and four stations ata = 5%).

Implications for Meeting Numeric Criteria

In December 2010, the U.S. Environmental Protec-tion Agency (USEPA) published a final rule fornumeric nutrient criteria for Florida waters (USEPA,2010). According to these criteria, the annual geomet-ric mean of TN or TP concentrations in the west-cen-tral Florida region shall not exceed 1.65 mg ⁄ l and0.49 mg ⁄ l, respectively, more than once in a three-yearperiod. Comparing these criteria with the annual geo-metric means of TP and TN concentrations, duringthe last three years of our study period (2005-2007)(Table 7), five stations in the AWS failed to satisfy theTP standards, whereas the remaining station (station74) exceeded the limit once in the three years. How-ever, annual geometric mean TP concentrationsdecreased at all stations (except 115) in the AWS overthe three years (Table 7) and trends at all stationswere found to be negative over the study period(Table 5). All stations in the HWS satisfied the TP cri-teria. For TN, three stations (111, 114, and 115) in theAWS consistently exceeded the criteria, whereas inthe HWS, only one station consistently exceeded thecriteria (station 108) and station 120 exceeded thelimit once in the three years. The numeric nutrient

criteria promulgated by the USEPA does allow forsite-specific alternative criteria (alternative, scientifi-cally defensible, TN and TP limits) to be used uponreview and approval. Alternative criteria could beadvised for the North and South Prongs of the AlafiaRiver because both have proposed TMDLs that requirea 90 and 50% reduction of TP loads, respectively(USEPA, 2009a,b). The target TP concentrations inthe TMDLs (0.415 and 0.6 mg ⁄ l for the North andSouth Prongs, respectively) were different than thepromulgated criteria for west-central Florida(0.49 mg ⁄ l). These target concentrations may serve asthe basis of future site-specific alternative criteria.

In 2007, a petition was submitted to the FloridaDepartment of Environmental Protection to reclassifysegments of the Alafia River from Class III (fishableand swimmable) to Class I (potable water supply)(Tampa Bay Water, 2007). The portion of the riverpetitioned for reclassification extends from EPCHCstation 114 (Bell Shoals Road) upstream to the con-fluence of the North and South Prongs (Figure 1) andincludes tributaries with the exception of TurkeyCreek.

The FAC for surface water quality standards con-tains both narrative and numeric criteria for differentconstituents (FAC, 2008). Of the constituents evalu-ated in this study, nitrate-N, dissolved fluoride, andbacteriological quality (fecal coliform) have numericstandards in the FAC. Nitrate-N limits for all classesof waters are set to the federal drinking water stan-dard of 10 mg ⁄ l. Limits for DF for Class I and ClassIII waters are 1.5 and 10 mg ⁄ l, respectively, and 800colonies per 100 ml of sample (for Class I and III) forFC. According to FAC Chapter 32-302.530, these lim-its are the maximum that should not be exceeded atany time (FAC, 2008). Table 8 summarizes the

TABLE 7. Annual Geometric Mean of TP and TN and Annual Arithmetic Mean of DF Concentrations forthe Last Three Years of the Study Period (2005-2007).

Alafia River Watershed Hillsborough River Watershed

74 111 114 115 116 139 2 105 106 108 120 137

TP1

2005 0.57 0.81 1.37 2.48 0.81 1.33 0.16 0.17 0.16 0.14 0.07 0.162006 0.48 0.67 1.10 1.60 0.64 0.78 0.22 0.13 0.14 0.10 0.07 0.192007 0.43 0.63 0.87 1.83 0.60 0.74 0.18 0.13 0.17 0.11 0.07 0.20

TN1

2005 0.90 2.94 1.86 1.97 1.09 1.57 0.61 0.89 0.86 1.76 1.07 0.722006 0.73 3.52 1.97 1.76 1.30 1.26 0.58 0.59 1.01 1.67 1.29 0.612007 0.68 2.78 1.67 1.46 1.11 1.36 0.49 0.46 1.23 1.68 1.87 0.71

DF2

2005 1.00 0.53 1.22 1.79 1.45 2.17 0.67 0.23 0.22 0.21 0.14 0.462006 1.09 0.57 1.20 1.77 1.54 2.26 0.81 0.25 0.24 0.23 0.15 0.642007 1.09 0.52 1.16 1.83 1.64 2.45 0.84 0.25 0.23 0.24 0.18 0.66

1Geometric mean.2Arithmetic mean.



number of violations of these criteria during the lastthree and five years of the study period. Station 114,which is on the segment of the Alafia petitioned forreclassification to Class I exceeded 1.5 mg ⁄ l of DF on6 and 12 occasions between 2005-2007 and 2003-2007, respectively. Stations 115, 116, and 139 (ClassIII waters) satisfied their respective criteria. How-ever, stations 115 and 116, which are immediatelyupstream of the confluence of the North and SouthProngs consistently exceeded the Class I DF limit(Table 8). In the HWS, all stations were found to sat-isfy the Class I standards. Station 111 on TurkeyCreek (AWS) violated FC concentrations on 19 and37 instances during 2005-2007 and 2003-2007, respec-tively. Station 74 in the AWS did not experience anyexceedance of FC criteria during 2005-2007, whereasthe only station with no violation was 106 in theHWS (Table 8). All other stations exceeded FC limitson one or more occasions (up to 11 at station 137)during the three and five years under consideration.

These exceedance events (numeric nutrient criteriaand Class I and III criteria) indicate that water qual-ity issues, although present in either watershed to agreater or lesser extent, are more severe in the AWS.This can also be seen from Table 4, which summa-rizes water quality parameter concentration statis-tics. While these observations may appear to belinked to the relative proportions of wetlands and for-ests in the two watersheds, this does not hold at thesubwatershed scale. For example, stations 120 and108 which exceed values for TN have wetland andupland forest coverage similar to other station drain-age areas in the HWS. In fact, station 120 has thehighest relative abundance (�30%) of wetlands. Inaddition, in the AWS, station 115, which arguablyhas the poorest water quality record based on excee-dance events, drains an area with a larger coverageof wetlands and forest (�17%) compared with othergages. TP and DF exceedances appear well related tothe phosphate mining activities, whereas TN excee-

dances seem to depend on the relative abundance ofagriculture area.

SUMMARY AND CONCLUSIONS

A 34-year record (1974-2007) of discharge andwater quality constituent concentrations was used tostudy the relationship between land use change andhydrology and water quality in the Alafia and Hills-borough River watersheds. In addition, constituentconcentrations were evaluated relative to recentlypromulgated numeric nutrient criteria for Floridawaters and numeric criteria relevant to proposedreclassification of portions of the Alafia River.

During the study period, the watersheds transi-tioned from predominantly agricultural to urban.Analyses of total streamflow concentrations showednegative trends in most water quality constituents,indicating an overall improvement of water quality inboth watersheds over the study period. However, thenotable exception was for TN and TKN, whichshowed increased or sustained concentrations in theHWS despite decreased area in agriculture. This waslikely due to an increase in wetland and ⁄ or forestcoverage.

Trend analyses of discharge variables showed nosignificant change in flow components; streamflow,base flow, and percentage base flow, over the studyperiod, despite considerable increase of urban landand hence impervious cover. Trends in base flow con-stituent concentrations generally followed thosefound for streamflow, with some exceptions. The dif-ferences were prominent mainly at the 10% signifi-cance level. Changes in nutrient pathways could notbe clearly elucidated based on our results.

Stations in both watersheds exceeded one or moreof the numeric criteria limits. Whereas in the HWS,

Table 8. Number of Exceedances of Dissolved Fluoride (DF) and Fecal Coliform (FC) Concentration Criteria and Total Number ofObservations (in parentheses) During the Last Three and Five Years of the Analysis Period.

Alafia River Watershed Hillsborough River Watershed

74 111 114 115 116 139 2 105 106 108 120 137

Number of exceedances of DF concentrations criteria (>1.5 mg ⁄ l) for Class I waters2005-2007 0 (36) 0 (34) 6 (36) 27 (36) 18 (36) 32 (36) 0 (36) 0 (36) 0 (36) 0 (36) 0 (32) 0 (36)2003-2007 2 (59) 0 (57) 12 (59) 45 (59) 29 (59) 55 (59) 0 (58) 0 (60) 0 (60) 0 (60) 0 (56) 0 (60)

Number of exceedances of DF concentrations criteria (>10 mg ⁄ l) for Class III waters2005-2007 0 (36) 0 (34) 0 (36) 0 (36) 0 (36) 0 (36) 0 (36) 0 (36) 0 (36) 0 (36) 0 (32) 0 (36)2003-2007 0 (59) 0 (57) 0 (59) 0 (59) 0 (59) 0 (59) 0 (58) 0 (60) 0 (60) 0 (60) 0 (56) 0 (60)

Number of exceedances of FC concentrations (>800 colonies per 100 ml of sample) for Class I and Class III waters2005-2007 0 (36) 19 (34) 3 (36) 2 (36) 2 (36) 3 (35) 4 (36) 4 (36) 0 (36) 1 (35) 2 (32) 5 (36)2003-2007 2 (60) 37 (58) 8 (60) 6 (60) 4 (60) 4 (59) 8 (60) 9 (60) 0 (60) 5 (59) 4 (56) 11 (60)



there were no TP or Class I DF criteria violations, inthe AWS criteria violations were more severe. This islikely due to the geology and mining history (andexpected lag-time of legacy P) of the area further sug-gesting alternative site-specific numeric criteria forthis watershed, as allowed by the USEPA rule, maybe justified. Stations with TN criteria violations,which are less prominent compared with TP and DFviolations, relate well with the prevalence of the agri-cultural land use.

The relations between land uses and water quality(e.g., phosphate mining and TP concentrations) andland use changes and water quality (e.g., increase inthe proportions of N found as recalcitrant organicform (Figure 6) and decrease in agricultural landaccompanied by increase in wetlands and forests),were evident in this study. However, land use changewas likely not the only driving force behind theresults observed. Rather the impact of regulatoryefforts, such as the Clean Water Act, stormwater pol-lution control, and retrofitting activities, improve-ment in the mining practices, etc., have most likelyimpacted the water quality trends more than theland use changes. To achieve future goals, such as re-classifying segments of the Alafia River for watersupply, continued efforts to improve water quality ofboth these rivers will be required.

ACKNOWLEDGMENTS

This project was funded by University of Florida-IFAS researchinnovation grant. We would like to acknowledge Mr. Jeff Burkey(King County, Department of National Resources and Parks) forthe Seasonal Kendall and Mann-Kendall test Matlab scripts basedon the study of Hirsch and Slack (1984), which accounts for serialcorrelation.

LITERATURE CITED

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