WATERSHED-SCALE FATE AND TRANSPORT OF BACTERIA

Transcription

1 WATERSHED-SCALE FATE AD TRASPORT OF BACTERIA D. A. Chn, D. Sakura Lemessy, D. D. Bosch, P. A. Gay ABSTRACT. The added dmensonalty provded by usng multple models to predct the fate and transport of bactera at the watershed scale were nvestgated. Both HSPF and SWAT were appled to the 5.6 km catchment K of the Lttle Rver Expermental Watershed (LREW) n Georga. Over the seven year perod from 996 to 00, SWAT provded a more accurate descrpton of fecal colform concentratons, wth an SE of 0.73 compared to 0.33 for HSPF. For ths partcular watershed, the SWAT process equatons are more representatve of the watershed scale fate and transport of bactera than the HSPF process equatons. Based on ths comparatve analyss, t can be nferred that elevated levels of fecal colform n the recevng stream are prmarly due to n stream sources. Ths source characterzaton could not be acheved by usng only the HSPF model, whch ndcates a much greater contrbuton from groundwater and terrestral nonpont sources. A model averagng approach n whch a weghted average of the HSPF and SWAT predctons are used to predct bactera concentratons n the recevng stream demonstrates that model weghts can be determned such that the SE of the combned models wll be greater than ether of the models taken ndvdually. However, n the present case, the margnal mprovements n SE obtaned through ths ntegraton were small. Keywords. Bactera, Fecal colform, HSPF, Hydrology, Modelng, SWAT, Watershed. The leadng cause of mparment of rvers and streams n the U.S. s excessve levels of pathogen ndcator bactera (USEPA, 008), and the most common ndcator bactera n freshwater bodes are fecal colform and Eschercha col (E. col). Federal regulatons requre that all states montor and assess publc water bodes wthn ther borders, dentfy those that are mpared, and determne the lmtng contamnant loadngs that would be requred for the mpared waters to meet ther applcable water qualty standards; these lmtng loadngs are called total maxmum daly loads (TMDLs). Implementaton of loadng reductons to comply wth adopted TMDLs generally requres dentfcaton of the lkely sources of contamnaton and estmaton of the quanttatve relatonshp between contamnant source loadngs and contamnant concentratons n recevng waters. Snce contamnant sources causng mparments are generally a combnaton of nonpont and pont sources, watershed scale fate and transport models are partcularly useful n dentfyng lkely contamnant sources and quantfyng the relatonshp between source loadngs and contamnant concentratons n recevng waters. The most commonly used watershed scale fate and transport models for TMDL applcatons are HSPF (Bcknell et al., 00) and Submtted for revew n October 008 as manuscrpt number SW 7749; approved for publcaton by the Sol & Water Dvson of ASABE n January 009. The authors are Davd A. Chn, Professor, Department of Cvl Engneerng, Unversty of Mam, Coral Gables, Florda; Donna Sakura Lemessy, Assstant Professor, Department of atural Scences, Albany State Unversty, Albany, Georga; Davd D. Bosch, ASABE Member, Research Hydraulc Engneer, USDA ARS Southeast Watershed Research Laboratory, Tfton, Georga; and Page A. Gay, Assstant Research Scentst, Department of Bologcal and Agrcultural Engneerng, Unversty of Georga, Tfton, Georga. Correspondng author: Davd A. Chn, Department of Cvl Engneerng, 5 Memoral Dr., Unversty of Mam, Coral Gables, FL 334; phone: ; fax: ; E Mal: dchn@mam.edu. SWAT (etsch et al., 005), wth HSPF more commonly used n urbanzed watersheds and SWAT more commonly used n agrcultural watersheds, although both models can be used n most watersheds. An essental component n utlzng watershed scale fate and transport models s accountng for predctve uncertanty, whch can be attrbuted to a combnaton of parameter uncertanty, structural uncertanty, and data uncertanty. In most models, structural uncertanty s domnant and lmts the mnmum predctve uncertanty that can be acheved by any gven model. Snce the geness of structural uncertanty n any model s the nadequacy of the process equatons ncorporated n the model, the only way to reduce structural uncertanty and lower the lmt of predctve uncertanty s to use a dfferent model wth more accurate process equatons. A model averagng approach (Claeskens and Hjort, 008; Ajam et al., 006) can be partcularly useful n reducng structural uncertanty relatve to the sngle model approach, where the weghts allocated to dfferent models are adjusted dependng on the predctve uncertanty of the ndvdual models. The weghted average model wll lkely have less predctve uncertanty than f only one model was unversally used. A second beneft of the mult model approach s that the process equatons ncorporated n the better performng model are presumably a more accurate representaton of the fate and transport processes n the watershed, and provde valuable nsght nto and quantfcaton of the sources and movement of contamnants wthn the watershed. Ths artcle demonstrates the use of the mult model approach n whch both HSPF and SWAT are appled to predct the fate and transport of ndcator bactera n a partcular watershed, and the predcton results are used to dentfy the domnant fate and transport processes wthn the watershed. The reducton n predctve uncertanty usng the two model approach s also quantfed, and conclusons are drawn regardng the effcacy of usng the two model approach to den Transactons of the ASABE Vol. 5(): Amercan Socety of Agrcultural and Bologcal Engneers ISS

2 tfy source loadng reductons that would be effectve n mplementng TMDLs. PROCESS EQUATIOS Both HSPF and SWAT codes contan hydrology and water qualty components. In developng water qualty models usng these codes, t s standard practce to frst calbrate the hydrology component of the model by adjustng the hydrologc process parameters untl model streamflow predctons agree wth observed streamflows to an acceptable degree. The second step s to calbrate the water qualty component by adjustng the water qualty process parameters of the model untl the model tracer concentraton predctons at specfed locatons n streams agree wth observed concentratons to an acceptable degree. The hydrologc process equatons n HSPF and SWAT are fundamentally dfferent, wth the most notable external dfference beng that HSPF smulates the ranfall/runoff process at hourly tme steps, whle SWAT smulates the ranfall/runoff process at daly tme steps. Many prevous studes have documented the comparatve hydrologc process equatons and performances of HSPF and SWAT (e.g., Van Lew et al., 003). Bacteral fate and transport processes ncorporated n HSPF and SWAT codes are also fundamentally dfferent, thereby ncreasng the lkelhood that reducton n structural uncertanty can be acheved usng a mult model approach. A comparatve analyss of the HSPF and SWAT process equatons for modelng the fate and transport of bactera can be found n Benham et al. (006), and a bref revew of these processes wth some supplementary materal s provded here. In both HSPF and SWAT codes, bactera are added to hydrologcally homogeneous land segments wthn the watershed at a specfed rate that can vary wth tme. In HSPF, the bactera loadng rate s specfed drectly (cfu ha - h - ) and can be ether constant or vary monthly, whle n SWAT the loadng s specfed as the product of the bactera content of manure (cfu g - ) and the manure loadng rate (kg ha - d - ), whch can be ether constant or vary daly. In HSPF, deposted bactera can be transported by ether drect entranment n overland flow or by assocaton wth sedment contaned n overland flow, n whch case a potency factor (Chn, 006) must be specfed. In SWAT, bactera are parttoned nto both soluble and sorbed phases usng a lnear sotherm of the form: S = k p c () where S s the sorbed bactera densty (cfu g - ), k p s the parttonng coeffcent (ml g - ), and c s the bactera concentraton n soluton (cfu ml - ) wthn the top 0 mm of sol. In realty, bacteral transport s assocated wth both dssoluton n overland flow and sorpton onto sedments. However, sgnfcant uncertanty n modelng both bacteral parttonng and sedment transport has caused most models to treat bactera transport as beng entrely assocated wth dssoluton n surface runoff (e.g., Paul et al., 004; Jameson et al., 004). Ths approach s adopted n ths artcle by specfyng the potency factor (n HSPF) and partton coeffcent (n SWAT) both equal to zero. Ths formulaton does not neglect bactera transport by sedment attachment, but smply assumes that such transport can be descrbed by an effectve dssoluton rate. HSPF ndrectly smulates the deoff of bactera on the land surface by lmtng the amount of accumulated bactera, whle SWAT explctly smulates the deoff of sorbed and soluton bactera on the land surface usng Chck's law, whch can be expressed n the form: t = 0 exp(-k d t ) () where t s the number of bactera at tme t (cfu), 0 s the ntal number of bactera (cfu), and k d s a net decay constant that vares wth temperature. HSPF tracks a sngle type of bactera deposted on the ground, whle SWAT dvdes bactera nto persstent and lesspersstent categores, and can also smulate the deposton and washoff of bactera on folage. Durng each tme step ( t), the amount of soluton bactera released ( M r ) from the sol soluton nto the surface runoff s gven by: M s[ exp( k Q ] M r = M sk Q (HSPF) (SWAT) where M s s the amount of bactera n soluton n the solstorage layer at the begnnng of the tme nterval, Q s the runoff wthn the tme nterval (cm), and k and k are releaserate constants (cm - ). Ranfall that nfltrates nto the sol can contrbute to streamflow va nterflow and outflow from the shallow groundwater. However, a major dfference between codes s that HSPF allows specfcaton of constant or monthly varatons n bactera concentraton n the nterflow and shallow groundwater nflow to streams, whle SWAT requres that subsurface bactera concentratons be zero. Experments on bactera transport n sols and groundwater usually conclude that bactera move n the subsurface, and that temporal or permanent saturaton allows ndcator bactera to move qute far along preferental pathways (Benham et al., 006). In addton to bactera transported to streams by surface runoff, both HSPF and SWAT allow the drect nput of bactera nto stream segments as ether pont sources or drect nonpont sources, wth HSPF allowng hourly varaton of loadng (cfu h - ) and SWAT determnng loadng as the product of a specfed daly source flow (m 3 d - ) and source concentraton (cfu m -3 ). Loadngs from terrestral and drect sources are added together to gve the total flux of bactera nto the recevng stream segment, and each segment s smulated as a completely mxed reactor wth frst order decay. The relatve valdty of the above watershed scale fate and transport process equatons can only be nferred by comparng the performance of models based on these fundamentally dfferent process equatons, and assocatng the more accurate process equatons (for a partcular watershed) wth the better performng models. Such an ntercomparson wthn the Lttle Rver Expermental Watershed s descrbed n ths artcle, and the mplcatons of these results on reducng predctve uncertanty and n developng mplementaton plans for TMDLs are subsequently dscussed. LITTLE RIVER EXPERIMETAL WATERSHED Hydrologc montorng n the Lttle Rver Expermental Watershed (LREW) n south central Georga s conducted by the USDA ARS Southeast Watershed Research Laboratory (SEWRL) n Tfton, Georga, and the locaton of the LREW wthn the state of Georga s shown n fgure. The LREW 46 TRASACTIOS OF THE ASABE

3 s centered at approxmately 3.6 and W and covers an area of approxmately 334 km. The LREW s nstrumented to measure ranfall and streamflow wthn the prmary watershed and the seven nested subwatersheds, as shown n fgure, where the subwatersheds are dentfed by ther pour ponts (M, K, J, I, F,, O, B) and the subwatersheds range n sze from 3 to 5 km. Overall, the LREW les wthn an area of broad floodplans, rver terraces, and gently slopng uplands. Most land surface slopes are less than 5%, although some valley slopes range from 5% to 5%. Streams n the LREW have channel slopes rangng from 0.% to 0.4% (Bosch and Sherdan, 007). The LREW contans sandy sols underlan by lmestone, and a seasonally dependent shallow phreatc aqufer exsts throughout the watershed. Physographcally, the LREW s located n the Tfton Upland, and stratgraphcally the subsurface conssts of Quartenary sand overlayng the parent Mocene Hawthorne Formaton (Asmussen, 97). The depth of the surfcal alluvum ranges from m n the headwater streams to about 6 m at the lower end of the watershed, and the hydrology s typcal of that found n the southern Coastal Plan, where surface materals have hgh nfltraton rates, low surface runoff, and hgh groundwater nflow to streams. There are 46 contnuously recordng ran gauges coverng the LREW and the surroundng area, and these gauges are spaced approxmately.4 km apart n the upper watershed and 4.8 km apart n the lower watershed (Bosch and Sherdan, 007). Most ran gauges consst of TE55 tppng bucket gauges (Texas Electroncs, Inc., Dallas, Texas) wth mnmum measurement precson of 0.54 mm, accuracy of ±0.5 mm h -, and a recordng nterval of 5 mn. The present study focuses on catchment K, whch s shown n fgure and covers an area of approxmately 6.7 km. Hourly ranfall n catchment K was calculated from 5 mn measurements at ran gauge RG43 (UTM m, 468 m E) located approxmately n the center of catchment K. The correlaton Fgure. Lttle Rver Expermental Watershed, Georga. of the upscaled daly ranfall at RG43 wth the catchmentaveraged daly ranfall provded by SEWRL was 0.98, whch provded confdence that RG43 can be used to characterze the hourly catchment averaged ranfall. Other meteorologcal data avalable for catchment K nclude hourly solar radaton, cloud cover, ar temperature, dew pont, and potental evapotranspraton. Data collected at Met staton located n Valdosta, Georga, approxmately 70 km from the LREW were used to descrbe these clmatc varables. Although ths dstance s not deal, t was deemed tolerable snce these latter clmatc varables are not expected to vary sgnfcantly over ths length scale and are of secondary mportance to ranfall n smulatng the ranfall/ runoff process n the catchment. Flow exts catchment K va a thrd order stream, and flowrates from the catchment are measured by a compound rectangular wer wth a centered V notch (Bosch and Sherdan, 007). The rectangular wer has a crest length of 7.8 m, a V notch depth of 44. cm wth 0: sde slopes, and s desgned for a 5 year maxmum flowrate of 6.5 m 3 s -. The catchment K outlet concdes wth a four barrel box culvert that provdes roadway cross dranage, and the flowmeasurement wer s located between the upstream wng walls, approxmately 3 m upstream from the culvert. Watersurface elevatons upstream and downstream of the flowmeasurement wer were measured to the nearest mm every 5 mn wth a pressure transducer and recorded by a dgtal data logger. The ratng curve for the flow measurement structure was developed based on laboratory model testng and feld measurements (Bosch and Sherdan, 007). HSPF MODEL The USEPA BASIS system was used to delneate catchment K based on the atonal Elevaton Dataset (ED) and the specfed catchment outlet. The catchment area delneated usng ths dgtal elevaton model was 5.6 km, compared to 6.7 km commonly assocated wth catchment K (Bosch and Sherdan, 007). For consstency n usng the ED for stream delneaton, a catchment area of 5.6 km was used n the model smulatons. The regonal land use GI RAS layer ndcates that the catchment conssts of 7. km of agrcultural land (45%) and 8.5 km of forest land (55%). Ths proporton of agrcultural land (45%) n the GIRAS database s hgher than has been reported by Bosch et al. (006) at 35% to 40% and Feyeresen et al. (007a) at 37%. The relatonshp between stage and runoff n the stream segment (FTABLE) was generated usng the mplct channel geometry n BASIS (Techncal ote, 007). The seven year calbraton perod for the model was from January 996 to 8 December 00, durng whch tme hourly flow measurements and a total of 53 nstantaneous measurements of fecal colform (FC) concentraton at the catchment outlet were avalable. Analyss for fecal colform was conducted usng the EC Medum test, whch utlzes a membrane fltraton technque and mfc meda to dentfy fecal colform colones (APHA, 995). Precson and accuracy of the FC analyses were documented usng sample duplcates, laboratory blanks, and atonal Insttute of Standards and Technology (IST) traceable reference standards. All of the duplcated samples devated by less than 5% from the calculated mean, wth over 75% devatng by 0% or less from the mean. Vol. 5():

4 A three step approach was used to calbrate the hydrology and water qualty components of the model. The steps followed were: () dentfy the model parameters that potentally nfluence the output varable of nterest; () sequentally maxmze the condtonal lkelhood of each parameter; and (3) repeat step untl there are no changes to maxmumlkelhood parameter values. Parameters havng a sgnfcant nfluence on surface runoff n HSPF models are commonly taken as (Van Lew et al., 003): nfltraton rate (IFILT), lower zone nomnal storage (LZS), rato of maxmum to mean sol nfltraton capactes (IFEXP), exponent that determnes how much a devaton from nomnal lower zone storage affects the nfltraton rate (IFILD), fracton of groundwater that s lost to the deep aqufer (DEEPFR), actve groundwater recesson coeffcent (AGWRC), and the nterflow recesson coeffcent (IRC). Other potentally nfluental varables nclude the upper zone nomnal storage (UZS), groundwater recesson flow parameter (KVARY), nterflow nflow parameter (ITFW), and the weghtng factor for hydraulc routng (KS). Followng the calbraton procedure descrbed n detal by Chn (009), all of the aforementoned parameters were vared sequentally to determne the maxmum condtonal lkelhood, where the condtonal lkelhood (L) for parameter s gven by: L( y θ, ^j ) = exp ( y m ) S = (4) S where y s the set of measured data (.e., daly flows), are values of the parameter beng vared, ^j s the set of values of the other parameters beng held constant, and S s the standard devaton of the measurements (y ) relatve to the correspondng model predctons (m ) and s gven by: S = = ( y m ) (5) At each step n the calbraton sequence, all parameters values except one ( ) were fxed, and the maxmum condtonal lkelhood value of that varable ( ) was found teratvely. Ths parameter value was then fxed at ts maxmum condtonal lkelhood value, and the maxmum condtonal lkelhood value of the next parameter was found. Ths process was repeated for subsequent parameters, cyclng through all parameters untl the parameter set converged to the maxmum lkelhood parameter set shown n table. Ths teratve approach converged to the maxmumlkelhood parameter set wthn three cycles and was automated by developng control software to run the teratons wthout stoppng. It s recognzed that the maxmumlkelhood parameter set mght not be unque due to correlatons between model parameters. In such cases where sgnfcant parameter correlatons exst, alternatve maxmum lkelhood parameter sets could be dentfed by usng dfferent ntal parameter estmates (Chn, 009). Usng the maxmum lkelhood parameter set, the predcted and measured flows are compared n fgure a for a three year tme wndow wthn the seven year calbraton perod (996 00). The three year tme wndow n fgure and subsequent fgures was selected prmarly to facltate llustraton, snce t s long enough to show a sgnfcant segment of modeled results versus measured data, and yet stll dscrmnate between daly measurements. Quanttatve comparson between the measured and predcted flows s most easly done usng the ash Sutclffe effcency (SE), defned as: ( y = m ) SE = (6) ( y y ) = where y s the mean of the measurements. For the seven year perod of calbraton, the SE s 0.87 for daly averaged flows and 0.89 for monthly averaged flows, and the dscrepancy n the cumulatve outflow volume s 7%. Collectvely, these metrcs ndcate that the model provdes excellent agreement between predctons and measurements at both daly and monthly tme scales. Table. HSPF maxmum lkelhood parameters. Parameter Value Unts Descrpton Hydrology AGWRC 0.95 d Basc groundwater recesson rate DEEPFR 0.05 Fracton of groundwater lost from the system IFILT 0.86 mm h Infltraton capacty ndex IFEXP.0 Exponent n the nfltraton equaton IFILD.0 Rato between maxmum and mean nfltraton ITFW 7 Interflow nflow parameter IRC 0.5 d Interflow recesson parameter KVARY 0. cm Groundwater recesson flow parameter LZS 6. cm Lower zone nomnal storage UZS 64.5 mm Upper zone nomnal storage KS 0.58 Weghtng factor for hydraulc routng Water qualty ACQOP 0.5 [a],.45 [b] 0 8 FC ha d Rate of accumulaton of FC FSTDEC.09 d Frst order decay rate of FC n stream IOQC 43 cfu/00 ml Interflow FC concentraton PSRC FC d Mass flux from drect source SQOLIM FC ha Maxmum surface storage of FC WSQOP 0.94 mm h Rate of surface runoff that removes 90% of FC n h [a] Agrcultural land. Forest land. 48 TRASACTIOS OF THE ASABE

5 Fgure. Comparson of measured and smulated flows. The mechansm of fecal colform (FC) transport from the land surface to streams s assumed to be by both surface runoff and subsurface transport by nterflow, wth bactera concentratons n the groundwater assumed to be zero. In addton, contrbutons by dstrbuted n stream sources are accounted for by drect nonpont source nputs. The parameters that contrbute to FC fate and transport were dentfed as: the rate of terrestral accumulaton of FC (ACQOP), the storage lmt of FC on the land surface (SQOLIM), the rate of surface runoff that removes 90% of stored FC per hour (WSQOP), nterflow FC concentraton (IOQC), FC mass flux from a drect nonpont source (PSRC), and the frst order decay rate of FC n the recevng stream (FSTDEC). These parameters were vared sequentally usng the same approach as for the hydrology calbraton, wth the lkelhood measure gven by equatons 4 and 5, and y s taken as the set of FC measurements. The maxmum lkelhood parameter set s lsted n table. A partcularly appealng feature of the calbraton scheme used n ths study s that the condtonal lkelhood dstrbuton of each parameter can be determned for the maxmumlkelhood parameter set, as shown n fgure 3a. These curves show how the lkelhood of each parameter changes as t vares between one half and twce ts optmal value. The results n fgure 3a provde a drect llustraton of the senstvty of the model output to that parameter, showng that the model predctons are relatvely nsenstve to values of SQOLIM (= maxmum accumulaton of bactera) and most senstve to IOQC (= nterflow bactera concentraton). Vol. 5():

6 Fgure 3. Margnal lkelhood functons of model parameters. Usng the maxmum lkelhood parameter set, the predcted and measured FC concentratons n the recevng stream are compared n fgure 4a for the same three year tme wndow used to compare flows n fgure a. For the sevenyear calbraton perod, the ash Sutclffe effcency (SE) s 0.33, whch s comparable to the performance of HSPF models reported n other studes. For example, n usng HSPF to develop a TMDL mplementaton plan n a smlar watershed n Georga (Georga DR, 00), the SE was found to be 0.4. It s debatable whether t s approprate to use the SE wthout normalzng the FC values, snce the FC concentraton can vary by orders of magntude. However, the conventonal SE used n ths study s desrable snce t gves more weght to matchng hgh FC values, whch normally determne mparment desgnatons, and s the benchmark for comparson wth other studes. The relatvely low agreement between measurements and observatons could be attrbuted to a varety of factors. For example, the comparson s between nstantaneous FC measurements and predctons that are both stream averaged and daly averaged, whle, n realty, there are lkely to be sgnfcant wthn stream and ntraday varatons n FC concentratons. Fgure 4. Comparson of measured and smulated bactera concentratons. SWAT MODEL The ArcSWAT system (verson.) was used to delneate catchment K usng the same elevaton, land use, and sol database as used n the HSPF model. A mnor modfcaton was that the arc second atonal Elevaton Dataset (ED) used n SWAT had to be downloaded from the USGS atonal Map Server snce the ED bult nto BASIS was not readly transferable to ArcGIS. For consstency wth HSPF, the SWAT model of the watershed contaned two hydrologc response unts (HRUs): a 7. km agrcultural use HRU and an 8.5 km forest use HRU. The parameter that has the most nfluence on surface runoff n SWAT s the runoff curve number for mosture condton (C) (Van Lew et al., 003). For subsurface response, the most nfluental parameters that are commonly cted (e.g., Van Lew et al., 003; Feyeresen et al., 007b) are the parameter that controls the amount of water that moves from the shallow aqufer to the root zone (GW_REVAP), threshold depth of water n the shallow aqufer for movement to the root zone (REVAPM), threshold depth of water n the shallow aqufer requred for return flow to occur to the stream 50 TRASACTIOS OF THE ASABE

7 Table. SWAT maxmum lkelhood parameters. Parameter Value Unts Descrpton Hydrology ALPHA_BF 0.75 d Base flow recesson constant CH_K 3.5 mm h Effectve hydraulc conductvty CH_ Mannng's n n man channel C 35 [a], 6 [b] Curve number for mosture condton II GW_DELAY 8.5 d Groundwater delay tme GWQM 0. mm Threshold depth n shallow aqufer for return flow GW_REVAP 0.5 Groundwater revap coeffcent RCHRG_DP 0.0 Deep aqufer percolaton fracton REVAMM 0. mm Depth n shallow aqufer for percolaton to deep aqufer Water qualty BACTKDQ 0.53 m 3 Mg Bactera sol parttonng coeffcent BACTMIX m 3 Mg Bactera percolaton coeffcent BCST cfu/00 ml Drect source concentraton, flow = 0.0 m 3 d CFRT_KG.0 [a],. [b] kg ha d Applcaton rate at 0 5 cfu g WDPRCH.33 d Bactera deoff coeffcent n streams WDPQ 0.0 d Bactera deoff coeffcent n sols [a] Agrcultural land. Forest land. (GWQM), and the fracton of percolaton from the root zone that recharges the deep aqufer (RCHRG_DP). Other potentally nfluental varables nclude the base flow recesson constant (ALPHA_BF), groundwater delay tme (GW_DELAY), effectve hydraulc conductvty of man channel (CH_K), and Mannng's n of the man channel (CH_). Followng the same procedure used n calbratng HSPF, the maxmum lkelhood parameter set was determned and s gven n table. The predcted and measured flows are compared n fgure b for a three year tme wndow wthn the seven year calbraton perod (996 00). For the perod of calbraton, the ash Sutclffe effcency s 0.65 for daly averaged flows, 0.88 for monthly averaged flows, and the dscrepancy n the cumulatve outflow volume from the watershed s %. Collectvely, these metrcs ndcate that the model provdes far agreement wth measurements on daly tme scales, wth excellent agreement on monthly tme scales (Moras et al., 007). The parameters that contrbute to FC fate and transport were dentfed as: bactera applcaton rate (CFRT_KG), bactera sol parttonng coeffcent (BACTKDQ), bactera percolaton parttonng coeffcent (BACTMIX), deoff factor for bactera n streams (WDPRCH), deoff factor for bactera n sols (WDPQ), and drect nonpont source flux (BCST). These parameters were vared sequentally usng the same approach as for the hydrology calbraton, wth the lkelhood measure taken as the SE for estmatng the 53 FC measurements durng the seven year calbraton perod. The maxmum lkelhood parameter set was determned and s lsted n table. The condtonal lkelhood dstrbutons of the parameters nfluencng FC fate and transport are shown n fgure 3b for parameters varyng between one half and twce ther optmal value. It s evdent that the predctons are senstve to all parameters, wth the greatest senstvty to the bactera loadng rate (CFRT_KG) and least senstvty (although stll moderately senstve) to the bactera percolaton parttonng coeffcent (BACTMIX). Usng the maxmumlkelhood parameter set, the predcted and FC concentratons n the recevng stream are compared n fgure 4b for the same three year tme wndow used to compare flows n fgure b. For the entre calbraton perod, the SE for predctng fecal colform s COMPARISO OF MODELS The performance of the HSPF and SWAT models as measured by ther SE values are compared n table 3. These results clearly show that both HSPF and SWAT perform well n predctng monthly averaged flows, wth HSPF performng much better n predctng daly averaged flows. Ths s not a surprsng or a new observaton, snce HSPF uses hourly tme steps compared wth the daly tme steps n SWAT, and hence HSPF s better able to resolve the response of the catchment to storms wth subdaly duratons. These results can be further contrasted wth those reported by Feyeresen et al. (007b), who used SWAT to smulate the hydrology of catchment K for the subperod. Feyeresen et al. (007b) found SE day = 0.56 and SE month = 0.88, whle the present model appled to ths same tme perod gves SE day = 0.65 and SE month = The superor performance of the present SWAT model for daly tme steps s lkely a result of a more comprehensve calbraton process nvolvng the sequental dentfcaton of condtonal maxmum lkelhood values of the parameters. In addton to the consensus result that HSPF produces much better agreement wth daly averaged flows than SWAT, the contrbutons of surface runoff, nterflow, and groundwater nflow to the recevng stream ndcated by HSPF are also markedly dfferent than SWAT, wth drect mplcatons for smulatng the fate and transport of contamnants contaned n these flow components. For the smulaton perod (996 00), HSPF gves the relatve contrbutons of surface runoff, nterflow, and groundwater nflow as %, 60%, and 38%, whle SWAT gves the relatve contrbutons as 3%, 4%, and 83%. Wth respect to predctng bactera concentratons at the watershed outlet, the results shown n table 3 ndcate that SWAT performs better than HSPF, wth an SE of 0.73 versus These results suggest that the bactera fate and transport process equatons ncorporated n SWAT provde a better representaton of the fate and transport of bactera n ths partcular watershed. In the context of the present watershed, a sgnfcant dfference between HSPF and SWAT s that SWAT requres the concentraton of bactera n the nterflow and shallow aqufer nflows to the stream segment be equal Vol. 5():

8 Table 3. Performance comparson of HSPF and SWAT models. ahspf HSPF SWAT + ( - a)swat Hydrology SE month SE day Water qualty SE all to zero, whereas HSPF allows nonzero concentratons n the subsurface nflow to the stream. In the present case, the hydrology models ndcate that nterflow and shallowaqufer nflow contrbute between 87% and 98% of the flow to the recevng stream, so ths dfference n model formulaton has sgnfcant mplcatons. Secondly, n comparng the condtonal lkelhood dstrbutons of the HSPF and SWAT fate and transport parameters n fgure 3, t s clear that the predcted concentratons are much more senstve to the SWAT parameters than the HSPF parameters. Ths gves an ndcaton that the SWAT process equatons (and assocated parameters) mght provde a more precse descrpton of FC fate and transport process n the present case. To further explore ths dfference, t s nstructve to separate the contrbutons of terrestral nonpont sources and drect nonpont sources to the bactera concentratons n the recevng stream. The contrbuton of terrestral sources can be extracted by puttng the drect nonpont source mass flux equal to zero and calculatng the stream concentratons of bactera, and the contrbutons of drect nonpont sources can be determned by subtractng the concentratons derved from only terrestral sources from the concentratons resultng from both terrestral and drect nonpont sources (.e., the calbrated model). The cumulatve probablty dstrbuton of concentratons resultng from only drect nonpont sources s compared wth the cumulatve probablty dstrbuton of concentraton from both drect and terrestral nonpont sources n fgure 5a for HSPF and fgure 5b for SWAT. In contrastng these results, the key consderaton s that the drect nonpont source cumulatve dstrbuton represents the dstrbuton of stream concentratons that could be expected f all terrestral sources were elmnated. Wth ths n mnd, fgure 5a ndcates that sgnfcant reductons n both medan and 90 percentle stream concentratons could be obtaned by reducng the contrbutons of terrestral bactera sources, whle fgure 5b ndcates that only very small reductons n medan and 90 percentle stream concentratons could be acheved by reducng (or even elmnatng) the contrbutons of terrestral sources. Snce the SWAT process equatons gve much better predctons of the observed bactera concentratons n the recevng stream, t s reasonable to conclude that fgure 5b s more representatve of feld condtons, and that development of strateges to reduce bactera concentratons n the recevng stream should focus on n stream sources that cause relatvely contnuous bactera nput to the stream. Ths key result could only have been obtaned by comparng the relatve performance of two fate and transport process models and would have been mssed f only the HSPF model were used n the analyss. A second salent feature of fgures 5a and 5b s the comparson of the cumulatve dstrbuton of the measurements (usng the Webull formula) represented by the flled crcles and the cumulatve dstrbuton of the modeled data on the Fgure 5. Contrbuton of terrestral sources to stream bactera. same days as the measurements, represented by the open crcles. Fgures 5a and 5b show far agreement gven by HSPF and good agreement gven by SWAT. For the SWAT model, t s partcularly nformatve to compare the dstrbuton of modeled concentratons on measurement days (whch s n good agreement wth observatons) wth the dstrbuton of modeled concentratons on all days of the smulaton. Based on ths comparson, t s clear that medan and 90 percentle values of the modeled concentratons for the set of measurement days underestmate the medan and 90 percentle values of the modeled concentratons, respectvely, for the set of all days. A lkely reason for ths s that samplng days tend to be tmes of low ranfall/runoff, thereby mssng the hgh concentratons that result from large surface runoff events and sgnfcantly basng the estmated cumulatve probablty dstrbuton of concentratons. Ths phenomenon has also been prevously observed by Baffaut (006) n a watershed n Mssour. The possble sgnfcant bas ntroduced by usng measured data to estmate the cumulatve probablty dstrbuton of concentratons n the stream s partcularly troublesome because complance wth percentlebased water qualty standards s usually assessed based on 5 TRASACTIOS OF THE ASABE

9 measurements, and actual percentle concentratons are lkely to be hgher than those estmated from measurements. Fnally, t s useful to compare the maxmum lkelhood process parameters related to terrestral loadng and drect loadng derved from the HSPF and SWAT models. Maxmum lkelhood HSPF terrestral loadng s n the range of to cfu ha - d - for agrcultural and forest land uses, whle maxmum lkelhood SWAT terrestral loadng s n the range to. 0 8 cfu ha - d -. Although these maxmum lkelhood terrestral loadng estmates dffer by an order of magntude, they provde a reasonable level of confdence n pnnng down the exstng bactera loadng to be n the range of 0 7 to 0 9 cfu ha - d -. The maxmum lkelhood drect nonpont source loadng gven by HSPF s cfu d -, and SWAT gves cfu d -, provdng strong support to the asserton that exstng drect loadng from n stream sources s n the farly narrow range of 0 0 to cfu d -. Ths provdes an nstance where usng a mult model approach gves greater confdence n estmated source loadngs than could be derved from usng a sngle model. Snce the area of the catchment s 5.6 km (= 560 ha), terrestral loadng s estmated to be n the range of 0 0 to 0 3 cfu d -, compared to n stream loadngs on the order of 0 0 cfu d -. The predomnant sources of fecal colform n the watershed are lkely assocated wth wldlfe, snce most of the forested lands n catchment K are heavly populated wth wldlfe, and the catchment s commonly used as a recreatonal huntng reserve. The most common wldlfe speces s deer, but the area also has relatvely hgh populatons of wld turkeys, geese, raccoons, and mgratory ducks. Most the game anmals nhabt the rparan areas along the stream banks and lkely contrbute to n stream fecal colform loadngs. dse = 0 a = da = and t can be further shown that: d SE = da ( M S)( H S) ( S H ) = ( S H ) = < 0 (9) (0) whch guarantees that by takng a as specfed by equaton 9, the value of SE wll be maxmzed and be greater than the value of SE derved from ether model by tself. Applyng equaton 9 to HSPF and SWAT smulatons gve a = 0.83 for daly averaged flows, a = 0.56 for monthly averaged flows, and a = 0.09 for bactera concentratons. These values of a provde a drect measure of the relatve abltes of the competng models to smulate the observed data. The SE obtaned by usng model averaged predctons are shown n table 3, where t s clear that the gan n best model performance s relatvely small, although, strctly speakng, the predctons are all mproved by model averagng. These results ndcate that the real beneft to usng multple models mght not be n the fact that mproved performance s acheved by model averagng, but that usng multple models has the potental to dentfy a preferred model that mght demonstrate much better performance than a sngle model, wth no downsde f the second model proves to be less accurate. For example, f t was decded a pror to use the HSPF model, then n the present case an excellent descrpton of the hydrology and a far descrpton of the water qualty would be realzed; by usng the second model (SWAT), the potental for a much mproved descrpton of the water qualty s realzed. MODEL AVERAGIG Model averagng s an approach n whch a weghted average of model output s used as the actual predctor (Claeskens and Hjort, 008). Although ths approach has been appled and tested n several hydrologc studes (e.g., Ajam et al., 006), t has not to date been appled to predctng contamnant levels usng watershed scale models. Consderng HSPF and SWAT as competng models, a model averaged predcton can be expressed n the form: P = ah + ( a) S (7) where P s the predcton at tme step ; H and S are the correspondng estmates by HSPF and SWAT respectvely; and a s a weghtng factor between zero and one. If the ash Sutclffe effcency (SE) s used to measure the performance of the combned models, then: = [ M ah ( a) S ] SE = (8) = ( M M ) where M s the measurement at tme step, and M s the average of the measurements. The value of a that maxmzes SE can be calculated by takng the dervatve of equaton 8, whch yelds: SUMMARY AD COCLUSIOS The development and mplementaton of TMDLs requre the quanttatve determnaton of both terrestral and drect loadngs on mpared waters, the sources of loadng, and the relatonshps between loadng reductons and contamnant concentratons n the mpared waters. To these ends, the watershed scale models HSPF and SWAT are the most wdely used n the U.S., and conventonal practce s to a pror select one of these models n any partcular applcaton. The results presented n ths artcle have demonstrated the added dmensonalty that can be acheved by usng multple models to predct the fate and transport of bactera at the watershed scale. Both HSPF and SWAT were appled to the 5.6 km catchment K of the Lttle Rver Expermental Watershed (LREW) n Georga. It was demonstrated that, over the seven year perod from 996 to 00, HSPF provded a much more accurate descrpton of daly averaged flows wth a ash Sutclffe effcency (SE) of 0.87, both models performed comparably n descrbng monthly averaged flows wth an SE of around 0.89, and SWAT provded a much more accurate descrpton of fecal colform concentratons wth an SE of 0.73 compared to 0.33 for HSPF. The relatve performance of the models n predctng daly averaged flows s expected, snce the HSPF model uses hourly tme steps and s capable of resolvng the response of the watershed to storms wth subdaly tme scales, whle SWAT uses daly tme steps and s unable to accurately resolve re Vol. 5():

10 sponses to ndvdual storms. The ablty of SWAT to perform much better than HSPF n predctng fecal colform concentratons n the recevng stream ndcates that, n ths partcular watershed, the SWAT process equatons are more representatve of the watershed scale fate and transport than the HSPF process equatons. As a consequence, t can reasonably be concluded that elevated levels of fecal colform n the recevng stream are prmarly due to drect n stream sources of bactera, and that terrestral sources dstrbuted throughout the watershed have relatvely lttle effect on bactera levels n the stream, except under hgh ranfall/runoff condtons. Ths source characterzaton could not be acheved by usng only the HSPF model, whch ndcates a much greater contrbuton of terrestral sources, whch s dscounted due to the much poorer performance of HSPF n predctng stream concentratons of bactera. A model averagng approach n whch a weghted average of the HSPF and SWAT predctons was used to predct flows and bactera concentratons n the recevng stream was nvestgated. It was shown analytcally that model weghts can be determned such that the SE of the combned models wll be greater than that of ether of the models taken ndvdually. Ths model averagng approach was mplemented; however, the margnal mprovements n best-model SE were relatvely small. Collectvely, the results presented n ths artcle ndcate that the prmary beneft n usng multple models to smulate watershed scale fate and transport s the potental to dentfy a model that mght provde a sgnfcant mprovement n predcton capablty over a sngle model, wth no downsde f the second model proves to be less accurate. The fate and transport processes ncorporated n the more accurate model can then be dentfed as more representatve of the catchment under nvestgaton, a result that s partcularly useful n developng mplementaton plans for TMDLs. ACKOWLEDGEMETS ancy Sammons of the USDA ARS Grassland Sol and Water Research Laboratory n Temple, Texas, provded support and advce n the development of the SWAT model, and Tom Jobes of the St. Johns Rver Water Management Dstrct n Palatka, Florda, provded support and advce n the development of the HSPF model. Chrs Hanson of the Unversty of Mam provded crtcal support on the GIS components of the models. REFERECES Ajam,., Q. Duan, X. Gao, and S. Sorooshan Mult model combnaton technques for hydrologcal forecastng: Applcaton to dstrbuted model ntercomparson project results. J. Hydrometerology 7(4): APHA Standard Methods for the Examnaton of Water and Wastewater. 9th ed. Washngton, D.C.: Amercan Publc Health Assocaton. Asmussen, L. 97. Hydrologc effects of Quarternary sedments above the marne terraces n the Georga Coastal Plan. Southeastern Geology (3): Baffaut, C Lttle Sac Rver watershed fecal colform total maxmum daly load. FAPRI UMC Report o. 06. Columba, Mo.: Unversty of Mssour, Food and Agrcultural Polcy Research Insttute (FAPRI). Benham, B., C. Baffaut, R. Zeckosk, K. Mankn, Y. Pachepsky, A. Sadegh, K. Brannan, M. Soupr, and M. Habersack Modelng bactera fate and transport n watersheds to support TMDLs. Trans. ASABE 49(4): Bcknell, B., J. Imhoff, J. Kttle Jr., and A. Dongan Jr. 00. Hydrologcal Smulaton Program - Fortran (HSPF): User's manual for release. Athens, Ga.: U.S. Envronmental Protecton Agency, atonal Exposure Research Laboratory. Bosch, D., and J. Sherdan Stream dscharge database, Lttle Rver expermental watershed, Georga, Unted States. Water Resources Res. 43(9): W09473, do:0.09/006wr Bosch, D., D. Sullvan, and J. Sherdan Hydrologc mpacts of land use changes n coastal plan watersheds. Trans. ASABE 49(): Chn, D Water Qualty Engneerng n atural Systems. ew York,.Y.: John Wley and Sons. Chn, D Predctve uncertanty n water qualty modelng. J. Envron. Eng. (n revew). Claeskens, G., and. Hjort Model Selecton and Model Averagng. Cambrdge, U.K.: Cambrdge Unversty Press. Feyeresen, G., R. Lowrance, T. Strckland, J. Sherdan, R. Hubbard, and D. Bosch. 007a. Long term water chemstry database, Lttle Rver expermental watershed, southeast Coastal Plan, Unted States. Water Resources Res. 43(9): W09474, do:0.09/006wr Feyeresen, G., T. Strckland, D. Bosch, and D. Sullvan. 007b. Evaluaton of SWAT manual calbraton and nput parameter senstvty n the Lttle Rver watershed. Trans. ASABE 50(3): Georga DR. 00. Total maxmum daly loads (TMDLs) for fecal colform n 303(d) lsted streams n the Oconee Rver Basn. Atlanta, Ga.: Georga Department of atural Resources, Envronmental Protecton Dvson. Jameson, R., R. Gordon, D. Joy, and H. Lee Assessng mcrobal polluton of rural surface waters: A revew of current watershed scale modelng approaches. Agrc. Water Mgmt. 70(): 7. Moras, D., J. Arnold, M. V. Lew, R. Bngner, R. Harmel, and T. Veth Model evaluaton gudelnes for systematc quantfcaton of accuracy n watershed smulatons. Trans. ASABE 50(3): etsch, S., J. Arnold, J. Knry, and J. Wllams Sol and Water Assessment Tool theoretcal documentaton. Verson 005. Temple, Tex.: USDA ARS Grassland Sol and Water Research Laboratory. Paul, S., P. Haan, M. Matlock, S. Mukhtar, and S. Plla Analyss of the HSPF water qualty parameter uncertanty n predctng peak n stream fecal colform concentratons. Trans. ASAE 47(): USEPA Causes of mparment for 303(d) lsted waters. Washngton, D.C.: U.S. Envronmental Protecton Agency. Van Lew, M., J. Arnold, and J. Garbrecht Hydrologc smulaton on agrcultural watersheds: Choosng between two models. Trans. ASABE 46(6): TRASACTIOS OF THE ASABE