Effects of spatial and temporal weather data resolutions on streamflow modeling of a semi-arid basin, Northeast Brazil

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1 Capturg Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No Effects of spatal ad temporal weather data resolutos o streamflow modelg of a sem-ard bas, Northeast Brazl Daelle de Almeda Bressa 1,2, Raghava Srvasa 2, Charles Alla Joes 2, Eduardo Maro Medodo 1,3 (1. Uversty of São Paulo USP, Egeerg School of São Carlos-EESC, SHS, , São Carlos/SP, Brazl; 2. Departmet of Ecosystem Scece ad Maagemet, SSL, Texas A&M Uversty (TAMU), College Stato, TX 77843, USA; 3. Brazla Ceter of Motorg ad Early Warg of Natural Dsasters, CEMADEN/MCTI, São José dos Campos/SP, , Brazl) Abstract: Oe major dffculty the applcato of dstrbuted hydrologcal models s the avalablty of data wth suffcet quatty ad qualty to perform a adequate evaluato of a watershed ad to capture ts dyamcs. The Sol & Water Assessmet Tool (SWAT) was used ths study to aalyze the hydrologc resposes to dfferet sources, spatal scales, ad temporal resolutos of weather puts for the sem-ard Jaguarbe watershed ( km 2 ) ortheaster Brazl. Four dfferet smulatos were coducted, based o four groups of weather ad precptato puts: Group 1- SWAT Weather Geerator based o mothly data from four arport weather statos ad daly data based o 124 local ra gauges; Group 2- daly local data from 14 weather statos ad 124 precptato gauges; Group 3- Daly values from a global coupled forecast model (NOAA s Clmate Forecast System Reaalyss - CFSR); ad Group 4- CFSR data wth 124 local precptato gauges. The four smulatos were evaluated usg multple statstcal effcecy metrcs for four streamflow gauges, usg: Nash-Sutclffe coeffcet (NSE), determato coeffcet (R 2 ), the rato of the root mea square to the stadard devato of the observed data (RSR), ad the percet bas (PBIAS). The Group 4 smulato performed best overall (provded the best statstcal values) wth results raked as good or very good o all four effcecy metrcs suggestg that usg CFSR data for weather parameters other tha precptato, coupled wth precptato data from local ra gauges, ca provde reasoable hydrologc resposes. The secod best results were obtaed wth Group 1, whch provded good results three of four effcecy metrcs. Group 2 performed worse overall tha Groups 1 ad 4, probably due to ucertaty related to daly measures ad a large percetage of mssg data. Groups 2 ad 3 were usatsfactory accordg to three or four of the effcecy metrcs, dcatg that the choce of weather data s very mportat. Keywords: clmate data resoluto, hydrology, SWAT model, sem-ard bas, Brazl DOI: /j.jabe Ole frst o [ ] Ctato: Bressa D A, Srvasa R, Joes C A, Medodo E M. Effects of spatal ad temporal weather data resolutos o streamflow modelg of a sem-ard bas, Northeast Brazl. It J Agrc & Bol Eg, 2015; 8(3): Itroducto Clmate varablty has substatal mpact o hydrologc systems; cludg the avalablty ad qualty Receved date: Accepted date: Bographes: Raghava Srvasa, PhD, Professor. Research terests: watershed maagemet, hydrology, modelg. Emal: r-srvasa@tamu.edu; Charles Alla Joes, PhD, Seor Research Scetst. Research terests: agrculture, ladscape ecology, botay-physology. Emal: cajoes@tamu.edu; Eduardo Maro Medodo, PhD, Professor, Drector. terest: hydrology, extreme evets. Emal: emm@sc.usp.br. Research of water, as well as the frequecy ad severty of floods ad droughts. clmate varablty ad ts hydrologc mpacts s a major challege the developmet of a hydrologcal model. Weather data are the most fudametal drvg Correspodg author: Daelle de Almeda Bressa, Evrometal Egeer; Doctoral Studet, Prevous Vstg Researcher. Research terests: Watershed & Water Resources Maagemet, Hydrology. Departmet of Hydraulcs ad Satato (SHS) (EESC), Uversty of São Paulo (USP), Av. Trabalhador Sãocarlese, 400 CP 359, , São Carlos-SP, Brazl. Emal: daelle.bressa@usp.br; daebressa@gmal.com.

2 126 Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No.3 varables for hydrologc models; however, t s ofte dffcult to acqure good-qualty weather data, especally developg coutres. Weather statos are ofte adequate umber, spatal dstrbuto ad perods of operato. I addto, data are ofte mssg ad strumets are sometmes poorly calbrated [1,2]. There s a serous lmt o the applcato of hydrologc models whe good qualty measured weather data are ot avalable, especally for large-scale watersheds [2,3]. May models, such as the Sol ad Water Assessmet Tool (SWAT) water quatty ad qualty model [4,5],use the earest weather stato for each subbas. However, f the stato s far away or f t has poor qualty data, the smulato qualty wll be adversely affected [1]. Daly data from a adequately sted ad mataed weather stato etwork may represet well the dyamcs eeded for a good hydrologc smulato [3,6]. Numercal weather predcto models wth greater resoluto may provde a alteratve data source for developg ad testg large-scale hydrologcal models [2]. Iapproprate choces of data sources ca have sgfcat mpacts o model estmates, troducg ucertates. Quatfcato of errors ad estmato of the ucertaty of meteorologcal put data ca help to terpret the processes smulated [3]. It s eve more mportat to evaluate the qualty of put data ad ts effects o the relablty of model estmates whe the results are used for decso support [3,7]. May watersheds are poorly gauged or ugauged, ad streamflow smulato such bass s a ogog problem [8]. A umber of studes have vestgated the mpacts of dfferet sources of clmatc data o watershed modelg [8-18], cludg usg ra gauge ad Tropcal Rafall Measurg Msso (TRMM) data for SWAT modelg [8] ; combg ra gauges, TRMM ad Specal Sesor Mcrowave Imager (SSM/I) datasets [9], ad usg both ra gauge wth radar-based precptato data [10]. These three studes [8-10] demostrated that usg more tha oe source of precptato data ca mprove the effcecy of streamflow smulato. Further testg has bee coducted o how use of groud-based precptato (Multsesor Precptato Estmator - MPE) ad space-based products (TRMM) affected hydrologc modelg results for sx bass across the Uted States [11]. The MPE approach produced superor hydrologc smulatos, although both versos of TRMM products resulted acceptable hydrologc results. MPE (or Stage IV Next-Geerato Radar) data were vestgated regardg potetal mproved accuracy of stream flow smulatos usg SWAT [12,13]. It was suggested that modellg efforts watersheds wth poor ra gauge coverage ca be mproved wth MPE radar data, especally at short tme steps [12]. O the other had, MPE Stage IV data was ot adequate for smulato of a moutaous bas [13]. Clmate Forecast System Reaalyss (CFSR) precptato ad temperature data have also bee used to force SWAT for several dfferet watersheds, resultg stream flow smulatos as good as or better tha correspodg smulatos based o tradtoal weather gaugg statos [14]. Esemble precptato modelg has also bee foud to cosderably crease the level of cofdece smulato results, partcularly data-poor regos [15]. Obtag represetatve meteorologcal data for hydrologcal modelg ca be dffcult ad tme cosumg [14], especally regos that lack adequate weather stato coverage. Ths pots to a eed to vestgate dfferet sources of clmate data to dscer whch optos ca support hydrologc ad water qualty modelg studes. Thus the am of ths study s to aalyze how hydrologc predctos respod to dfferet weather puts wth dfferet resolutos for the sem-ard Jaguarbe Rver watershed ortheaster Brazl. Specfcally, the objectves of ths research are to: (1) assess the sestvty of the warm-up perod duratos ad dfferet evapotrasprato methods wth the basele SWAT streamflow calbrato ad valdato process for the Jaguarbe Rver watershed, ad (2) aalyze the mpacts of four dfferet combatos of clmate data sources o SWAT streamflow estmates for the Jaguarbe Rver watershed. 2 Materals ad methods 2.1 Study area The Jaguarbe Watershed s stuated the state of

3 Jue, 2015 Bressa D A, et al. Effects of weather data resolutos o streamflow modelg of a sem-ard bas Vol. 8 No Ceará ortheast Brazl (Fgure 1), betwee lattude 4 30 ad 7º45 south ad logtude ad west. The total legth of the Jaguarbe Rver s about 610 km, whch dras a area of approxmately km 2. The rego s prevalg bome s the Brazla Caatga (Steppe Savaa) ad the watershed s located a zoe wth a predomatly sem-ard clmate [20,21], whch s characterzed by strog seasoal precptato ad ter-aual varablty, related to El Nño, that results recurrg droughts. Fgure 1 The locato of the Jaguarbe watershed study area ortheast Brazl Most of the rvers the rego are termttet, so water maagemet ad the use of reservors are vtal for both rrgated agrculture ad mucpal water supply, sce the watershed also exports water to the metropolta rego of Fortaleza, wth a populato of approxmately 8.5 mllo people [22-24]. 2.2 SWAT model SWAT has bee appled may studes aroud the world, especally research related to water balace, lad maagemet, sedmet, utret ad pestcde trasportato, water qualty, ad clmate ad lad use chages [25-29]. It s a mathematcally complex sem-dstrbuted model, developed by the US Departmet of Agrculture, Agrcultural Research Servce (USDA-ARS). It s usually operated o a cotuous daly tme-step, ad smulates water, sedmet, utret ad pestcde trasportato at a watershed scale [30,31]. It s a process-based model that takes to accout hydrologc, physcal ad chemcal processes [32]. SWAT smulatos are costructed by deleatg a watershed to multple subbass, ad the further subdvdg each subbas to hydrologc respose uts (HRUs) that cosst of homogeeous laduse, sol, ad ladscape characterstcs whch are ot spatally detfed wth the gve subbas;.e., HRUs represet percetages of lad areas wth a subbas. Flow ad pollutat losses are tally estmated at the HRU level, the aggregated to the subbas level ad fally routed through the smulated stream system to the watershed outlet [25-29]. SWAT requres the followg daly weather data: precptato, maxmum ad mmum temperature, solar radato, wd speed ad relatve humdty. These weather data ca be etered ether from measured sources ad/or geerated terally the model usg SWAT s weather geerator [19]. Log-term statstcs are put to the weather geerator to geerate daly weather puts. The weather geerator s used to smulate data f the user specfes ths opto, or whe measured data s mssg. For example, for precptato, the umber of wet days s determed the weather geerator based o a frst order Markov Cha model; skewed or expoetal dstrbutos are the used to estmate the rafall amouts [1,19]. 2.3 Model set up ad data sets The Jaguarbe SWAT model was costructed usg freely avalable formato. Most of the data were obtaed va data collected through a World Bak program partershps wth local govermet ageces [33]. The Dgtal Elevato Map (DEM) was bult from the U.S. Geologcal Survey s (USGS) publc doma Shuttle Radar Topography Msso (SRTM) DEM data [34], whch cossts of 3 arc-secod, approxmately 90 m resoluto. The 1: sols map was vectorzed by the Ceará State Water Resources ad Meteorologcal Foudato (FUNCEME) [35]. The lad use map used was also obtaed from FUNCEME [33]. The Jaguarbe Watershed model setup was costructed usg the ArcSWAT terface wth the ArcGIS 10.0 platform [36]. The frst step costructg the SWAT smulatos was to deleate the subbass. Ths was performed ArcSWAT by deleatg the stream etwork, based o the SRTM DEM ad settg the mmum draage area for each subbas to 250 km 2.

4 128 Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No.3 As a result, a total of 232 sub-bass were deleated SWAT, wth a average area of 315 km 2. Also 1,145 HRUs were geerated, accordg to the watershed s lad use, sol types, ad slope characterstcs. The sol layer data requred to defe sol characterstcs for the sols map were obtaed from the ISRIC - World Sol Iformato world data base [37]. Prevously developed Pedo Trasfer Fuctos (PTF) [38] were used wth ISRIC sol texture, orgac matter ad sol depth data to estmate the other sol parameters requred for SWAT. The tal lad use map cossted of a lmted set of broad categores. Data from the Mucpal Agrculture Producto for Ceará State from the Brazla Isttute of Geography ad Statstcs (IBGE) [39] were used to determe the plated area of dfferet crops the rego. Major crops produced the rego clude maze, dry beas, dry rce, cassava ad cashew [23], ad Ceará also produces 20 percet of the cowpeas Brazl [40]. The tradtoal agrculture the rego [23] cossts of (1) drylad systems domated by short-cycle cor ad bea producto, ad (2) sugarcae ad cashew producto [20,23] whch are sometmes rrgated. Cosderg these dfferet agrcultural producto characterstcs of the rego, the drylad crops smulated wth SWAT smulatos were cor ad cowpea, potato (whch was substtuted for cassava because the SWAT crop parameter database does ot have cassava parameters) whle sugarcae ad baaa (substtuted for cashew due to a lack of cashew crop parameters the SWAT crop parameter database) were smulated as rrgated crops. There are three large reservors the watershed that were ot cluded o the Jaguarbe SWAT model, because at the tme o suffcet data was made avalable to perform ad capture reservor water balace dyamcs through smulato. Regardg maagemet operatos, smplfed approaches were used whch cluded: (1) a sgle model-determed applcato of troge fertlzer per year for each crop system, whch was trggered whe the stress factor of the plat decled below 0.75, ad (2) a sgle auto-rrgato of sugarcae ad baaa, trggered whe the plat water stress factor reached 0.75, wth the maxmum of 50 mm per rrgato. I addto, sugar-cae was smulated as a three year rotato cosstg of a plat crop ad two ratoos Clmate data puts ad clmate scearos Dfferet clmate data sestvty smulatos were coducted wth four groups of precptato ad other weather data puts (Fgure 2 ad Table 1), holdg all the other model puts ad cofguratos costat. These groups of data were chose to test dfferet spatal ad temporal puts ad are descrbed detal as follows: Fgure 2 Locato of weather ad ra gauges that were used for the four groups of Precptato ad weather data Table 1 Overvew of the four weather ad precptato gauge groups Group Weather data scearo Precptato data source Other daly weather data sources a Weather geerator data sources b 1 Arports ad local ra gauges Local Ra gauges (ANA+FUNCEME) Geerated terally SWAT Arports 2 Weather statos ad local ra gauges Local Ra gauges (ANA+FUNCEME) Local Statos (INMET) INMET 3 Global Database-CFSR CFSR CFSR Arports 4 CFSR ad local ra gauges Local Ra gauges (ANA+FUNCEME) CFSR Arports Note: a Icludes maxmum ad mmum temperature, solar radato, wd speed, ad relatve humdty clmatc puts. b The SWAT weather geerator was used to geerate o-precptato data for Group 1; the weather geerator was also used to geerate mssg precptato data for all four groups ad ay other mssg data for Groups 2, 3 ad 4.

5 Jue, 2015 Bressa D A, et al. Effects of weather data resolutos o streamflow modelg of a sem-ard bas Vol. 8 No Group 1: Ths cossted of a combato of mothly average clmate data from the closest four arport statos (Fgure 2), cludg those located outsde of the study area, ad local precptato gauges. The precptato data were based o 124 local ra gauges mataed by the Ceará State Water Resources ad Meteorologcal Foudato (FUNCEME) ad Brazla Natoal Water Assocato (ANA) [41], whch are referred to as FUNCEME-ANA Fgure 2 ad Table 1. The arport data are teret-accessble as provded by the Natoal Clmatc Data Ceter, Natoal Oceac ad Atmospherc Admstrato (NOAA), USA [42]. Daly clmate values were geerated terally SWAT from the mothly average values provded the arport data, cludg precptato data for mssg days the FUNCEME-ANA data. Group 2: The daly precptato data were from 124 FUNCEME-ANA ra gauges (Fgure 2 ad Table 1). The other daly clmate data were put from the 14 INMET statos [43] (Fgure 2 ad Table 1), whle mssg data was geerated SWAT based o mothly statstcs created from log-term measured data avalable from the 14 INMET statos. Solar radato values avalable the INMET local weather stato etwork were estmated based o solato [44-46]. Group 3: All of the daly clmatc values were put from data obtaed from NOAA s Natoal Ceters for Evrometal Predcto Clmate Forecast System Reaalyss (CFSR) [47], a global coupled atmosphereocea-lad surface-sea ce system ad forecast model. The CFSR data are avalable SWAT put format o the SWAT webste [48]. Mssg data were geerated terally SWAT usg the Arports weather geerator data (Fgure 2 ad Table 1). Group 4: Ths represeted a combato of precptato from the local ra gauges (FUNCEME-ANA) ad CFSR daly clmate values (Fgure 2 ad Table 1). Mssg data were aga geerated terally SWAT usg the Arports weather geerator data (Table 1). 2.4 SWAT calbrato process Approprate SWAT streamflow related parameters were chaged from ther default values order to coduct a basc maual calbrato for basele streamflow codtos. The parameters detfed to be chaged were based o the most problematc aspects of the predcted hydrographs comparso wth the observed streamflow ad evaluatos based o Nash-Sutclffe statstcs [49,50]. Some varatos modfed put parameters were tested for all four dfferet groups of weather put smulatos, based o the physcal characterstcs of the watershed. Ultmately, the same chages were performed for all four groups of smulatos ad for the etre watershed, based o the subset of put parameters that resulted the most accurate streamflow results. default values are preseted Table 2. The altered parameters ad Table 2 Default ad altered parameter values or methods for the SWAT smulatos SWAT Parameters Parameters Descrpto SWAT Default Altered ESCO Sol evaporato compesato coeffcet ICN Curve umber methods 0 (sol mosture) 1(ET) CNCOEF ET curve umber coeffcet SHALLST/mm GWQMIN/mm Ital depth of water the shallow aqufer Depth of water shallow aqufer requred for retur flow GW_REVAP Groudwater revaporato RCHRG_DP Deep water percolato fracto REVAPMN/mm Depth of water shallow aqufer for revaporato to occur ALPHA_BF Baseflow recesso costat A key parameter used may SWAT hydrologc calbratos s the sol evaporato compesato coeffcet (ESCO) [26], whch ca be adjusted betwee 0.1 ad 1.0 to affect the depth dstrbuto that s used to meet sol evaporatve demad; decreasg the ESCO value creases the ablty of the model to extract evaporatve demad from lower sol layers [51]. Mglacco & Chaubey (2008) [52] performed a sestvty aalyss ad cocluded that most of the varace the predcted flow ther study resulted from ucertaty the ESCO parameter. Wu & Johsto (2007) [53] determed a ESCO value of 0.5 for average codtos, based o stream flow patters ad o mmzg stream flow devato betwee measured ad smulated data souther Lousaa. Sath et al. (2001) [54] adopted a ESCO value of 0.6 for a rego Texas. Due to the

6 130 Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No.3 sem-ard ad lattude characterstcs of the Jaguarbe watershed, a rage of ESCO values betwee 0.5 ad 0.75 were tested by comparg the smulated streamflow performace wth observed values based o hydrograph comparsos ad calculato of Nash-Sutclffe modelg effcecy (NSE) coeffcets [49,50] ; the resultg best ESCO parameter foud was 0.6. The three methods (Prestley Taylor, Pema- Moteth ad Hargreaves) [51] avalable SWAT to calculate the potetal evapotrasprato were also tested (specfc results are reported the Results ad Dscusso secto). The Pema-Moteth method was chose because t performed slghtly better accordg to the metrcs establshed ad because t s a more complex method that eeds more meteorologcal data ( ths sese evaluatg the four sets of weather puts). The two curve umber methods avalable SWAT [51], as a fucto of sol mosture (ICN=0) or as a fucto of plat evapotrasprato (ICN=1), were also tested. Overall, the daly curve umber calculated as a fucto of plat evapotrasprato performed best, cojucto wth a value of 0.5 for the evapotrasprato curve umber coeffcet (CNCOEF). Groudwater parameters (Table 2) were also estmated to best ft the study area. The Jaguarbe watershed does ot have large volumes of storage aqufers, wth most of the watershed located over crystalle rocks wth low water storage potetal [19]. 2.5 Statstcal evaluato crtera Streamflow estmates for the four smulatos were evaluated usg multple statstcal crtera: NSE, coeffcet of determato (R 2 ), the rato of the root mea square to the stadard devato of the observed data (RSR) ad the percet bas (PBIAS) [15,18,49,50,55,56], as follows: R NSE ( O P) ( O O) ( O O) ( P P) 2 2 ( O ) ( ) 1 O P 1 P 2 ( O ) 1 P RMSE obs 2 ( ) 1 RSR STDEV O O PBIAS 1 1 ( O P ) 100% O 2 2 (1) (2) (3) (4) where, O correspods to the observed data at the tme step ; P to the modeled streamflow at the tme step ; O ad P are the mea values of observed ad predcted streamflow (respectvely) the smulated tme perod, ad s the umber of observatos, beg =1, 2, 3,,. The smulatos were evaluatated o a aggregated mothly tme step whch the SWAT daly streamflows were summed to mothly totals ad compared wth the correspodg observed mothly values. Gudeles for model evaluato used ths study were based o performace ratgs suggested by Moras et al. (2007) [49] for a mothly tme step. Here a gradg method was establshed to determe f the model performace was Very good, Good, Satsfactory or Usatsfactory, by combg the three rages of values of the statstcal methods (NSE, RSR ad PBIAS) evaluated by Moras et al. (2007) [49]. If oe or more of the rages of RSR, NSE ad PBIAS dcated a usatsfactory result, the the model performace was determed usatsfactory. If o usatsfactory results were dcated by the three statstcal methods, the a gradg system based o a overall summato as show Table 3 was used. Table 3 Model Performace Ratgs [49] ad Classfcato used to evaluate the results of the dfferet weather group (Table 1) smulatos Performace Ratg RSR NSE PBIAS/% Gradg for each Classfcato - Sum Very Good 0.00 RSR <NSE 1.00 PBIAS<±10 3 7<E 9 Good 0.50<RSR <NSE 0.75 ±10 PBIAS<±15 2 5<E 7 Satsfactory 0.60<RSR <NSE 0.65 ±15 PBIAS<±25 1 3<E 4 Usatsfactory RSR>0.70 NSE 0.50 PBIAS ±25 Usatsfactory Usatsfactory The SWAT model was used for the 4 proposed weather put scearos, holdg all the other model puts ad cofguratos costat. The smulatos were evaluated based o the comparso amog the

7 Jue, 2015 Bressa D A, et al. Effects of weather data resolutos o streamflow modelg of a sem-ard bas Vol. 8 No dscharges at four flow gauge statos. These four statos were chose due to avalablty of data, ad the spatal dstrbuto of the flow gauges s show Fgure 3. Fgure 3 Locato of streamflow gauge statos the Jaguarbe watershed Executg a warm-up or equlbrato perod s mportat to esure that the hydrologc balace s accurately smulated SWAT, although the use of such a warm-up perod usually becomes less mportat as the smulato perod creases [57]. I ths study t could be hypotheszed that a short warm-up perod would be suffcet, because the smulato perod of 20 years s relatvely log. However, ths may ot be true due to the fact that, for example, the aqufers start empty SWAT, as well as the sol mosture, etc. Thus both a 1-year ad 5-year warm-up perods were tested. Therefore, the rver dscharge was smulated for the perod of 01/01/1984 to 12/31/1999, wth fve years ( ) as warm-up perod, ad for 01/01/1980 to 12/31/1999, wth oly oe year of warm-up perod (1979). 2 performace. Ths dfferece results shows the mportace of usg adequate warm-up perods to better establsh the watershed tal codtos, ad also mples that the legth of eeded warm-up perod ca vary betwee dfferet codtos. The better performace of Group 2 wth the fve-year warm-up perod tha wth the oe-year warm-up perod s probably due to poorer weather data for the frst few years of the tme-seres ad/or the greater sestvty of ths group of weather puts to the tal codtos of the model. The results of testg the three prevously descrbed ET methods at streamflow gauge stato 3 (Fgure 3) are preseted Table 4 terms of PBIAS, NSE ad RSR statstcs as a further assessmet of SWAT smulato performace. Gauge stato 3 was chose for ths phase of testg because t s located the mddle of the watershed, upstream of a major reservor (Fgure 3) that was ot cluded the SWAT model smulato. No absolutely clear patter was establshed betwee the three ET methods. However, Hargreaves (HG) method resulted the most overall usatsfactory evaluatos, whle Pema-Moteth (PM) method had the strogest overall performace (wth oe satsfactory ad oe very good evaluato). Thus, the PM method was selected for the remag weather group testg based o ts performace ad because t was the most complex method that uses a full sute of weather puts. It should also be oted that whle several of the combatos of ET methods ad gauge stato 3 resulted usatsfactory results, stroger overall results were obtaed for two of the clmate groups as dscussed secto Results ad dscusso 3.1 Ital testg of warm-up perod durato ad ET method The statstcal results (NSE values) of testg the four dfferet groups of weather puts (Table 1) wth ether a oe- or fve-year warm-up perod at the four streamflow gauge statos (Fgure 3) are show Fgure 4. It s clear from the plots Fgure 4 that the use of 5 years as warm-up perod provded better results for all of the gauge statos ad weather groups combatos (except for Group 3 o gauge stato 4), especally for the Group Fgure 4 Comparso of Nash-Sutclffe (NSE) values at gauge statos 1-4 (Fgure 2) for the SWAT smulatos (Groups 1-4; Table 1) wth warm-up perods of 1 or 5 years

8 132 Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No.3 Table 4 Performace metrcs for smulatos coducted wth dfferet ET methods at gauge stato 3 (Fgure 3) Group NSE PBIAS RSR Evaluato PM * PT * HG * PM PT HG PM PT HG PM PT HG Satsfactory Good Usatsfactory Usatsfactory Usatsfactory Usatsfactory Usatsfactory Usatsfactory Usatsfactory Very Good Satsfactory Satsfactory Note: * PM = Pema-Moteth; PT = Prestley-Taylor; HG = Hargreaves. 3.2 Graphcal comparsos of smulated ad observed streamflow Smulated ad observed mothly flows at gauge stato 1 (Fgure 3) are preseted for 01/1984 to 12/1999 Fgure 5, to vsually evaluate the performace of the proposed smulatos. Overall there s a geeral agreemet amog the hygrographs; the SWAT smulato performed wth Group 3 (Table 1) overestmated the peaks more tha the others. hgher tha streamflows from the other three smulatos ad the observed flow. Ths could be due to problems wth the CFSR world weather data for ths rego due to ts proxmty to the ocea. Fgure 5 Observed ad smulated mothly flows from 01/1984 to 12/1999 at Gauge 1 I Fgure 6 the average mothly flows for the etre perod are preseted for the smulated scearos ad observed data, for all four gauge stato locatos (Fgure 3). The Group 3 SWAT smulato (Table 1) overestmated the streamflows more tha the other sources of weather data for gauge stes 1 ad 4. I comparso, the Group 2 SWAT smulato (Table 1) overestmated streamflows the most at gauge stato 2 ad was smlar to the estmated Group 3 streamflow at gauge stato 3. The greatest dfferece SWAT streamflow results was predcted at gauge stato 4, the furthest dowstream gauge stato (Fgure 3). Ths gauge stato s also dowstream of a large reservor, whch was ot cluded the SWAT smulatos; therefore, dffereces betwee smulated ad measured flows would be expected. However, the predcted Group 3 streamflow s much Fgure 6 Observed ad smulated average mothly flows for at Gauge 1 (Fgure 5a), 2 (b), 3(c), ad 4(d) Streamflow for gauge 2 (Fgure 3) s overestmated by Group 2 (Table 1) but s uderestmated by Group 3 (Table 1). I cotrast, for the other three gauge statos, Group 3 overestmated streamflow. Gauge stato 2 s the farthest upper stream ste (Fgure 3), so ths may be a effect of the smaller draage scale of gauge stato 2 ad/or be a problem related to the data of a local INMET stato close to that area. There s also a small reservor upstream of gauge 2, whch was ot corporated the SWAT smulatos ad may have flueced the results. The SWAT smulatos wth Groups 1 ad 4 (Table 1) performed better tha Groups 2 ad 3 at all four stes. Both Groups 1 ad 4 cosstetly uder estmated observed streamflows, wth Group 1 uder estmatg

9 Jue, 2015 Bressa D A, et al. Effects of weather data resolutos o streamflow modelg of a sem-ard bas Vol. 8 No observed flows more tha Group 4 at all four gauges. 3.3 The fluece of the SWAT weather geerator The smulatos made wth Groups 1, 2 ad 4 have the same precptato put data seres (Table 1), but SWAT s weather geerator had a mportat role because the percetage of mssg data s hgh (about 32% for the ANA+FUNCEME ra gauges, due especally to scarce precptato data after 1992). The Group 2 geerated weather s based o local weather statos (INMET; Table 1) whle the Groups 1 ad 4 geerated weather are based o the arport statos (Table 1). Ths dfferece had a fluece o the smulated precptato data for the mssg values of each group ad also o the other weather compoets, leadg to dfferet results. Group 3 reled o the same weather geerator data as Groups 1 ad 4 (Table 1), but there are very few mssg precptato data for Group 3 (<0.2%) so the weather geerator fluece o the Group 3 results was mor. The resultg dffereces related to groups 1 ad 4 versus group 2, especally precptato, ca be attrbuted to the dfferet weather geerator varables. At the same tme the three groups have dfferet weather puts (other tha precptato) that have drect effect o evapotrasprato, whch ths rego accouts for about 80% of precptato. Therefore, relatvely small dffereces o weather put ca have a large mpact o the dfferet hydrologcal compoets. Weather geerator varables for both weather geerators used (Table 1) ad the relatve dfferece betwee them are preseted o Table 5. The varables are weghted averages by stato coverage area. A clear dfferece betwee the precptato put varables for example ca be see: 3% for the total log term average aual precptato, over 64% for the probabltes of beg a wet day followed by a wet day ad early 24% for a wet day followed by a dry day. Other large dffereces ca also be see betwee some of the other weather put varables (Table 5), cludg the mothly average dew pot ad the mothly average of daly mmum temperature stadard devato. 3.4 Overall water balace results The average values ad compoets of the water balace over the 16-year smulato perod (1984 to 1999) are preseted for the etre watershed for the four dfferet weather put scearos (Table 6). The average aual precptato for Group 3 s about 6% greater tha the precptato for Groups 1, 2 ad 4, whch was a combato of local ANA+FUNCEME precptato gauges combato wth geerated precptato data. The Group 2 smulato (Table 1) geerated more water yeld tha the other clmate groups ad also resulted the smallest geerato of sedmet. I cotrast, the Group 1 smulato (Table 1) resulted the hghest potetal evapotrasprato, whch s 45% hgher tha the other three smulatos. Ths s probably due to the arport statos havg a tedecy to measure hgh temperatures because of both local effects such as black surface ad radato, ad also due to the fact that two of the weather statos were close to the coast ad thus were mpacted by hgh wd speed ad relatve humdty. Table 5 Varables for the weather geerators used for the dfferet clmate groups descrbed Table 1 Varables the weather geerator INMET Arports Dfferece/% Aual average precptato/mm Average mothly probablty of a wet day to follow a dry day Average mothly probablty of a wet day to follow a wet day Mothly average of umber of days of precptato Mothly average solar radato/(mj m -2 d -1 ) Mothly average dew pot/ C Mothly average wd speed/(m s -1 ) Mothly average of mmum daly temperature/ C Mothly average of maxmum daly temperature/ C Mothly average of daly maxmum temperature stadard devato Mothly average of daly mmum temperature stadard devato Table 6 Average aual water balace compoets for the etre watershed ad 16-year smulato perod for the four clmate put groups descrbed Table 1 Water balace compoet Group 1 Group 2 Group 3 Group 4 Precptato/mm Water Yeld/mm Ruoff/mm Percolato/mm Evapotrasprato/mm Potetal Evapotrasprato/mm Sedmet Loadg/(t hm -2 )

10 134 Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No Statstcal evaluato of the streamflow mpacts of the four clmate groups NSE ad R 2 values are show Fgure 7, whch represet comparsos betwee mothly aggregated observed ad smulated flows for the four smulated weather groups ad four streamflow gauge statos, for the 16-year smulated perod (1984 to 1999). The NSE values for Group 3 are egatve for gauge statos 1 ad 4, ad are less tha 0.5 for the other two gauge statos. For Group 2, there s also oe egatve NSE value at gauge stato 2, whch s the worst NSE value determed for ths gauge stato. The Group 1 ad 4 smulatos resulted satsfactory or better NSE values (>0.55 for Group 4 ad >0.65 for Group 1). The mothly R 2 values for Group 3 are the smallest for all gauge statos; the other three group smulatos have overall good results, wth all R 2 values equal to or greater tha Fgure 7 Nash-Sutclffe (NSE) values (a) ad R 2 values (b) betwee observed ad smulated ad mothly flows for the four clmate groups (Table 1) ad the four gaugestatos (Fgure 3) over the 16-year smulato perod (1984 to 1999) The Group 1 smulato (Table 1) resulted good NSE values for all four gauge statos. The Group 4 smulato (Table 1) also performed well for all four gauge statos whle Group 2 (Table 1) resulted good NSE values for gauges 1, 3 ad 4, but a usatsfactory value for Gauge 2. Ths suggests that daly measured weather data from the fourtee local INMET gauge statos are actually provdg worse estmates tha the mothly mea from the four arports that are outsde the study area (Group 1) ad the global database for weather data wth local ra gauges data (Group 4), for the flow gauges where the flow was compared. Ths s due to the ucertaty related to the daly weather measuremets ad that the statos had a great deal of mssg data durg the smulato perod (a average of 36% for the INMET statos). Accordg to Schuol & Abbaspour (2006) [1] the qualty of daly weather data s ot always very relable some regos ad there are also ofte large amouts of mssg data. The authors compared SWAT dscharge outputs for clmatc puts across the orthwest Afrca subcotet from local weather statos versus the Clmatc Research Ut (CRU) coupled wth a weather geerator (dge-cru), whch was based o a Markov Cha approach (smlar to SWAT s weather geerator). The authors cocluded that weather data from the local statos may ot be the best avalable clmatc put ad that the dge-cru data produced sgfcat better estmatos of flow tha the smulato wth the local weather measured data. Smlar results were foud ths study, where Group 1 ad Group 4 performed better tha daly weather data from Group 2. The absolute bas percetage values (PBIAS) ad the rato of the root mea square to the stadard devato of the observed data (RSR) betwee the smulated scearos SWAT ad the observed flows for mothly tme steps for the four flow gauge statos are preseted Fgure 8. The Group 1 ad 4 smulatos (Table 1) performed better overall. Group 4 was the oly Group to have satsfactory [49] PBIAS values for all four streamflow gauge statos per the prevously descrbed crtera Table 3.

11 Jue, 2015 Bressa D A, et al. Effects of weather data resolutos o streamflow modelg of a sem-ard bas Vol. 8 No dstrbuted weather data ca mprove the accuracy of streamflow smulato. The Group 1 smulato resulted good metrcs (Table 7) for the streamflow comparsos at gauge statos 1-3 (Fgure 3) but usatsfactory streamflow estmate for gauge stato 4, whch s located below a large reservor. The Group 1 streamflow predcto at gauge stato 4 dd result good metrcs for NSE ad RSR (Fgures 7 ad 8), but the PBIAS value slghtly exceeded the defed threshold classfcato value [49], thus producg aoverall usatsfactory result per the crtera of Table 3. The Group 2 (Table 1) smulato resulted a acceptable performace for gauge stato 1 but a usatsfactory outcome for the other three gauge statos (Table 7), due to the hgh absolute values of PBIAS. The Group 3 (Table 1) smulatos were usatsfactory for all four gauges (Table7), leadg to the worst SWAT predctos. Table 7 Model Performace Evaluato based o Effcecy Metrcs ad the gradg system from Table 3 Fgure 8 Bas Percetages (PBIAS) (a) ad RSR (b) values betwee observed ad smulated mothly flows The Group 2 smulato (Table 1) resulted large absolute PBIAS values, especally for ste 2. The hgh PBIAS values for Group 2 ca be attrbuted to a few rafall evets that the model dd ot capture durg the later years of the smulato perod, due to mssg precptato data (after 1992 the precptato data are scarce). Ths s a problem, especally low rafall, sem-ard regos, where the spatal dstrbuto of rafall may ot be captured by a etwork of ra gauges. The Group 3 smulato also resulted hgh absolute PBIAS values, all of whch are cosdered usatsfactory. The composte smulato results based o the Table 3 gradg system are preseted o Table 7. Overall, Group 4 (Table 1) performed best, showg good results for the two upstream gauges ad very good metrcs for the dowstream gauges. These results show the mportace of good qualty weather data that are well dstrbuted spatally. Our results are cosstet wth those of others [8-10] dcatg that that good spatally Statos groups Streamflow Gauge Good Good Good Usatsfactory 2 Good Usatsfactory Usatsfactory Usatsfactory 3 Usatsfactory Usatsfactory Usatsfactory Usatsfactory 4 Good Good Very good Very good Fuka et al. (2013) [14] demostrated that CFSR data could be relably appled to watershed modellg across a varety of hydroclmate regmes ad watersheds ad that t produced as good as or better streamflow predctos tha local ra gauges. However, for the Jaguarbe watershed the streamflow results smulated ths study wth CFSR data for weather ad precptato data were usatsfactory, performg worse tha the other three groups. Group 4 (CFSR ad local ra gauges, Table 1) smulato provded good results, suggestg that the use of CFSR data for weather parameters other tha precptato (whch are usually less relable quatty, qualty ad spatal dstrbuto), coupled wth precptato data from local ra gauges, ca provde reasoable smulatos of hydrologc respose. Ths ca be of advatage, especally developg coutres lke Brazl, sce t s usually easer to obta

12 136 Jue, 2015 It J Agrc & Bol Eg Ope Access at Vol. 8 No.3 adequate precptato data tha data for other weather parameters. Aother advatage of usg CFSR data s that t ca be obtaed from the SWAT web ste SWAT put format (o text fles ready to be used o the model), reducg the effort eeded to reformat data other tha precptato from may weather statos. 4 Coclusos I ths study we demostrated the mportace of usg adequate warm-up perods to better establsh the watershed model tal codtos ad the hgh sestvty to the dfferece warm-up perods used (1 ad 5 years), whch the loger warm-up perod had a overall better smulato respose, especally for oe group of weather puts (Group 2). No absolutely clear patter was foud to establsh whch ET method worked best for the dfferet weather put groups, based o tests of the three dfferet ET methods avalable SWAT combato wth the dfferet weather groups. However, t was clear that the model was very sestve respose to the dfferet ET methods ad that there s a eed for testg ad comparg the ET methods for specfc study regos ad weather put data. I ths study we demostrate that large ucertates hydrologc smulato result from weather put data, ad that the choce of the weather data s very mportat. The smulato wth the world daly data base from NOAA s CFSR coupled model, but wth precptato from the local precptato gauges (Group 4) performed best overall, provdg good predctos at all four stream gauge statos. The smulato that used daly local precptato wth mothly data from the arport statos ad SWAT s weather geerator (Group 1) provded good results for three of the four gauge statos. Ths suggests that usg spatalzed global qualty CFSR data for weather parameters other tha precptato, coupled wth precptato data from local ra gauges, ca provde reasoable smulatos of hydrologc respose ths sem-ard rego. Ths ca be a advatage, sce t s usually dffcult to have qualty data from a dese weather stato etwork for all the weather data eeded for SWAT, but t s easer to have a deser etwork of precptato statos wth loger perods of data. The daly measured weather data from the 14 local gauge statos of INMET (Group 2) (whch are a more dese etwork) actually provded worse estmates tha those geerated wth SWAT s weather geerator from mothly mea data from the 4 arports that are outsde the study area, for the flow gauges where the flow was compared. Ths s probably due to the ucertaty related to daly measures ad to the fact that over oe-thrd of the data from these statos were mssg. The Group 3 smulato wth the Clmate Forecast System Reaalyss (CFSR) data had hgh PBIAS values ad the smallest values of R 2, ad the smulato was cosdered usatsfactory for all of the streamflow gauges. Although t has performed well prevously [14], t was ot a good precptato source for the Jaguarbe watershed rego. Ths dfferece may have occurred because of the rego s sem-ard clmate wth strog seasoal ad ter-aual varablty precptato, whch could have resulted the CFSR precptato data beg poorly calbrated wth local weather statos. Better calbrato of the CFSR precptato data the future could greatly reduce the problems we ecoutered usg ths data source. Ackowledgemets Ths study was made possble through the World Bak project Adaptg Water Resources Plag ad Operato to Clmate Varablty ad Clmate Chage Selected Rver Bass Northeast Brazl, the authors scerely thak all the collaborators of ths project. Ths study was fuded by FAPESP São Paulo Research Foudato for the doctoral scholarshp gve to the frst author, grat 2011/ ad 2012/ ad by the Thematc FAPESP Project Assessmet of Impacts ad Vulerablty to Clmate Chage Brazl ad Strateges for Adaptato Optos, umber 2008/ Thaks to INCLINE- INterdscplary CLmate INvestgato Ceter (NapMC/IAG, USP-SP), ad VIAGUA,CYTED. The authors scerely thak FUNCEME, ANA, INMET ad EMBRAPA for provdg data ad the two aoymous revewers ad the edtor of IJABE for the helpful dscusso ad gudeles to hghly mprove the qualty of ths paper.

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