Evaluation of spatial heterogeneity of watershed through HRU concept using SWAT. Xuesong Zhang

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1 Evalaton of spatal heterogenety of watershed throgh HRU concept sng SWAT Xesong Zhang Abstract The accrate smlaton of SWAT can assst the government n makng correct decsons abot water management practces, whch are mportant for hman health, agrcltral management, ndstry development, envronmental qalty, flood rsk assessment, and recreaton. In ths project, the PIs were tryng to mprove the smlaton accracy of the SWAT model throgh developng new algorthm to obtan accrate ranfall npt. The PIs developed a GIS based atomatc ranfall nterpolaton program whch ncorporates sx nterpolaton methods to estmate ranfall feld for the dstrbted hydrologc model SWAT (Sol and Water Assessment Tool). The smlated reslts show that the areal mean ranfall depths estmated by dfferent methods are smlar to each other, whle the spatal dstrbton of a ranfall feld cold exhbt great dfferences. The stream flow smlated by the SWAT model s senstve to ranfall felds estmated by dfferent methods, especally for the daly temporal scale, both hydrograph shape and flow volme cold show bg dfferences. The accracy of ranfall feld nformaton s essental for dstrbted hydrologc modelng, and the tool developed n ths stdy wll be sefl for accrately estmatng ranfall felds. The tools developed n these stdes are expected to be sed n the HAWQS (Hydrologc and Water Qalty System) project spported by the USEPA. Developng a GIS tool for Accrately Estmatng ranfall feld for the SWAT model. Introdcton Nmeros feld experments have revealed that hydrologcal processes and parameters can show consderable spatal varablty (Merz and Bárdossy, 998). Dstrbted hydrologc models offer the ablty to smlate hydrologc processes sng spatally dstrbted npt data, whch makes them preferable tools to predct water avalablty, sedments delvery and ntrents transport at a regonal scale for sstanable water resorce management, food secrty, hman health and natral ecosystems (Chaplot et al. 2005). As ranfall s one of the prmary hydrologc model npts, t s essental to accrately represent ranfall n tme and space for dstrbted ranfallrnoff modelng. Prevos stdes have shown that the spatal varablty of ranfall felds can have a large nflence on smlated rnoff volme, tme shft of hydrographs, sedment delvery and ntrent yeld (Dawdy and Bergman, 969; Wlson et al., 979; Trotman, 983; Dncan et al., 993; Farès et al., 995; Shah et al., 996a; Shah et al., 996b; Lopes, 996; Koren et al., 999; Chabey et al., 999; Arnad et al., 2002; Merz and Bárdossy, 998; Smth et al., 2004; Chaplot et al. 2005). Generally, the methods sed to stdy the senstvty of hydrologc models to spatal ranfall varablty are ran gage network densty and ranstorm dsplacement (Arnad, et al., 2002). What needs to be noted s that n order to npt spatally dstrbted ranfall nto a dstrbted hydrologc model, the ranfall vales at ran gage ponts need to be nterpolated to estmate ranfall vale at the pont wthot observed data. There are many nterpolaton methods that cold be sed for ranfall feld estmaton, and these methods wll provde dfferent ranfall felds. The tasks are to fnd how dfferent the estmated ranfall felds wll be and how mch mpact these dfferences cold exert on dstrbted hydrologc modelng.

2 The general methods sed by dstrbted hydrologcal models to estmate ranfall felds nclde Thessen polygon and IDW (Inverse Dstance Weghted). For example, SWAT and MIKE SHE se Thessen Polygon, and VIC ses IDW. The theory of Thessen polygon and IDW are smple and easy to be realzed programmatcally, bt Goovaerts (2000) work showed that the accracy of these two methods s not as good as several other methods, ncldng Lnear Regresson, Ordnary Krgng, and Smple Krgng wth varyng local means. Lloyd (2004) also showed that dfferent nterpolaton methods cold vary n accracy of estmated ranfall dstrbton. In ths work, sx dfferent nterpolaton methods were sed: Thessen polygon and IDW, whch are wdely sed n dstrbted hydrologc modelng; Splne and Ordnary Krgng, whch are wdely sed to nterpolate spatally dstrbted envronmental varables; and Lnear Regresson and Smple Krgng wth varyng local means, whch ncorporate elevaton nto spatal nterpolaton. Areal mean, coeffcent of varablty, and accracy of ranfall felds predcted by dfferent methods wll be calclated and compared. The dstrbted ranfall felds nterpreted by the varos methods wll be npt nto a physcally based dstrbted hydrologc model SWAT (Sol and Water Assessment Tool), and the smlaton reslts wll be compared and dscssed at the annal, monthly, and daly temporal scales. 2. Materals and Methods 2. Stdy Area Descrpton The stdy ste was selected at the downstream area of the Lohe Rver, whch s the largest trbtary of the downstream Yellow Rver (YR), whose area s abot 5239 km 2. The stdy area covers the land of 2 ctes and contes: Yyang, Shanxan, Lonng, Ryang, Ynchan, Manch, Yma, Xnan, Lanchan, Mengjn, Yansh and Loyang, whch are characterzed by flat allval and foothll plans. The average elevaton of the Lohe basn s abot 520 m. The Lohe Rver Basn belongs to the warm temperate clmate zone wth average annal ranfall depth 600 mm. Forty-one ran gages, located wthn or arond the stdy area, wth daly ranfall records wll be sed n ths stdy. As shown n Fgre, the 3 sold crcles denote the ran gages wll be sed to nterpolate ranfall spatal dstrbton, and the 0 sold trangles on the left denote the ran gages sed to test the accracy of estmated ranfall feld.

3 Fgre. The ran gages sed for ranfall feld nterpolaton and valdaton. 2.2 Interpolaton Methods 2.2. Thessen Polygon Thessen polygons, also referred to as Vorono Dagrams, are polygons whose bondares defne the area that s closer to that polygon s centrod pont than all other ponts. Then the pont wthot observed ranfall vale wll be assgned the closest ran gage s record (Thessen, 9). Z Let (, =,, n) be the set of ranfall data measred at n ran gages, and here n = 3. The ranfall depth Z at an nsampled locaton s estmated sng fncton below: Z = Z h < hj j. () Z where s the nterpolated vale, Z s the data vale of th sampled locaton, h denotes the dstance between nsampled locaton and the sampled locaton, h j denotes the dstance between nsampled locaton and the sampled locaton j Splne The Splne method ses a basc mnmm-crvatre technqe to nterpolate a spatal srface, whch ) passes exactly throgh the data ponts, 2) has mnmal crvatre (ESRI, 2005). The Splne fncton ses the followng formla for the srface nterpolaton: Z n = T + λ R( h ) (2) = Where, Z s the nterpolated vale, n s the nmber sampled locaton ponts, λ are coeffcents fond by the solton of a system of lnear eqatons, h s the dstance between the nsampled pont to the sampled locaton. Trend fncton T and generatng fncton R h ) are (

4 determned by the REGULARIZED or TENSION opton of Splne. In ths work, we se REGULARIZED Splne, whch ncorporate thrd dervatves nto the smooth semnorm (Mtas and Mtasova, 988). For detaled ntrodcton to Splne method, please reference to Franke (982), and Mtas and Mtasova (988) IDW The IDW nterpolaton method explctly mplements the assmpton that thngs that are close to one another are more alke than those that are farther apart. It weghts the ponts closer to the predcton locaton greater than those farther away. Wth nverse dstance weghtng, data ponts are weghted drng nterpolaton so that the nflence of one data pont relatve to another declnes wth dstance from the nterpolaton pont: n Z = n λz λ = (3) h λ = = Z where s the nterpolated vale, n represents the total nmber of sample data vales arond the nsampled locaton that wll be sed n nterpolaton, Z s the th ran gage vale, h denotes the separaton dstance between nsampled locaton and the measred data vale locaton, and λ denotes the weght of the th measred data vale Krgng Krgng s an advanced geostatstcal procedre that provdes a best lnear nbased estmaton model. The nknown ranfall depth Z at the nsampled locaton s estmated by a lnear combnaton of observed neghborng ran gage vales: Z = n = λ Z (4) where Z and Z have the same meanng as descrbed above. Instead of weghtng nearby data ponts by some power of ther nverted dstance, the Ordnary Krgng reles on the spatal correlaton strctre of the data to determne the weghtng vales. Ordnary Krgng determnes the weghts λ nder two assmptons: ) ensrng the nbased natre of the estmator, E { Z } * Z = 0 ; 2) mnmzng the estmaton varance, { } * * Var Z Z, where Z denotes the measrement vale. Krgng ses sem-varogram to dentfy the weghts of the ponts that srrond the predcted ponts throgh solvng a seres of lnear fncton known as the Ordnary Krgng system (Goovaerts, 2000): n j= n j= λ γ ( h ) μ( ) = γ ( h j j ) =,...,n (5) λ = (6) j where μ () s the Lagrange parameter accontng for the constrant on the weghts. h denotes the separaton dstance between nsampled locaton and sampled locaton, h j denotes the

5 separaton dstance between sampled locaton and j. The sem-varogram γ (h) s compted sng the eqaton below: N ( h) 2 γ ( h) = ( z z + h ) (7) 2N( h) where h s the dfference between two pont locaton, N (h) s the nmber of pars of ponts separated by h, z z + h s the vale dfference between pont and another pont separated by dstance h. For more nformaton on Krgng, please refer to Edward and Srvastava (989) Lnear Regresson Wth the orographc effect, ranfall tends to ncrease wth ncreasng elevaton. Heves et al. (992) and Goovaerts (2000) both reported a sgnfcant correlaton between average annal precptaton and elevaton n Nevada and sotheastern Calforna, and Aogarve (the most sothern regon of Portgal) respectvely. Goovaerts (2000) also reported average monthly ranfall has close correlaton wth elevaton for most months except for Jly and Agst. A smple method to ncorporate elevaton nto ranfall dstrbton estmaton s to develope the lnear regresson fncton: Z = f ( y ) = a0 + a y (8) where y s the elevaton at predcton pont, the a 0 and a are regresson coeffcents estmated wth a set of collocated ranfall and elevaton data {( Z, y ), =, L, n) } Smple Krgng wth varyng local means Goovaerts (2000) presented the basc form of Smple Krgng wth varyng local means (SKlm), whch replaces the known statonary mean n the smple Krgng estmate by known varyng means m derved from the secondary nformaton: Z n m = λ ( R ) (9) = where R = Z m. In ths work the local means are derved sng lnear regresson fncton (8). Then the estmated ranfall at nsampled pont can be expressed as: Z n = f ( y ) + λ ( R ) (0) = where the weghts λ are obtaned by solvng the smple Krgng system: n j= λ C ( h ) = C ( h ) =, L, n () j R j R where C R (h ) s the covarance fncton of the resdal R, not that of Z tself (Goovaerts, 2000). And other varables denote the same meanng as stated above. For more detaled nformaton on SKlm, please refer to Goovaerts (997). 2.3 Dstrbted hydrologc model SWAT s a contnos-tme, long-term, dstrbted-parameter model (Arnold et al., 998). SWAT sbdvdes a watershed nto sb-basns, and frther delneates Hyrologc Response Unt

6 (HRU) consstng of nqe combnatons of land cover and sols n each sb-basn. HRU delneaton can mnmze comptatonal costs of smlatons by lmpng smlar sol and land se areas nto a sngle nt (Netsch et al., 2000). The hydrologc rotnes wthn SWAT accont for snow fall and snow melt, vadose zone processes (.e., nfltraton, evaporaton, plant ptake, lateral flows and percolaton), and grond water flows. Srface rnoff volme s estmated sng a modfed verson of the SCS CN method (USDA-SCS, 972). A knematc storage model (Sloan et al., 983) s sed to predct lateral flow. And retrn flow s smlated by creatng a shallow aqfer (Arnold et al. 993; Arnold et al., 998). Channel flood rotng ses the Mskngm method. Otflow from a channel s also adjsted for transmsson losses, evaporaton, dversons, and retrn flow. As a physcally based dstrbted model, SWAT needs many npt data:. Topography: the :250,000 DEM obtaned from Natonal Geomatcs Center of Chna wll be sed to provde terran characterstcs of Lohe basn. 2. Sol: the sol map at :4,000,000 scale obtaned from Insttte of Sol Scence, Chnese Academy of Scences (CAS), provdes the sol spatal dstrbton and physcal propertes lke blk densty, textre, satrated condctvty, etc. 3. Land se: Land-se classfcatons sch as cropland, pastre, forest, etc were obtaned from Insttte of Geographcal Scences and Natral Resorces Research, CAS at :,000,000 scale. 4. Weather: Water Resorces Conservancy Commttee of the YR basn provded precptaton, ar temperatre, relatve hmdty, solar radaton and wnd speed, etc. 2.4 GIS based ranfall feld nterpolaton program GIS s a very powerfl tool to facltate geospatal related research, ncldng spatally nterpolated clmate data and analyss of storm knematcs (Jeffrey et al., 200; Tsans and Gad, 200). In ths work, we wanted to nterpolate the daly ranfall n 99 and otpt the spatally dstrbted ranfall nto the dstrbted hydrologc model, whch reqred mch manal work. An atomatc nterpolaton program developed as an extenson of ArcGIS 9.0 was sed to facltate ranfall feld estmaton and otpt job. The fncton of ths GIS based program ncldes: atomatcaly and contnosly estmatng ranfall dstrbton sng the sx nterpolaton methods descrbed above; calclatng each day s areal mean ranfall depth and coeffcent of varablty (CV) of estmated ranfall felds; valdatng the accracy of estmated ranfall felds sng Mean Absolte Error (MAE); calclatng each hydrologc nt s mean ranfall, whch wll be npt nto dstrbted hydrologc model. Snce ArcObject doesn t provde the Smple Krgng estmator, n order to se the SKlm method, the ser stll has to manally perform the Smple Krgng nterpolaton procedre sng the Geostatstcal Analyst extenson of ArcGIS. Ths shortcomng of the program s expected to be overcome when ArcObject provdes the Smple Krgng estmator. The general work flow chart of ths program s shown n Fgre 2. The eqatons for calclatng CV and MAE are: n o p MAE = Z Z () n l CV = (2) z ave

7 where o Z s the tre ranfall depth vale, p Z s estmated vale, n s the nmber of ran gages sed to valdate the accracy of estmated ranfall felds, s standard devaton of ranfall feld and z ave s areal mean ranfall. Rangage pont shape fle Each ran gage s ranfall records n text format Inpt every tme step s ranfall to the correspondng rangage pont Do nterpolaton for every tme step sng ser selected methods Calclate otpt varable for basn and hydrologc nt Hydrologc nt polygon shape Average ranfall for each Hydrologc Unt Areal mean ranfall CV MAE DEM Inpt Man Procedre Otpt Fgre 2. Work flowchart of GIS based nterpolaton program. 3. Reslts and Dscsson 3. Dfference between ranfall felds estmated by dfferent methods The daly ranfall data from the 3 ran gages n 99 were sed to test the dfferences between ranfall felds estmated by dfferent methods dscssed n secton 2.. The parameters of dfferent methods are lsted n Table. There were 52 total storms whose ranfall depth was larger than 2 mm, and the accracy, areal mean and spatal varablty of these 52 storms wll be compared and dscssed.

8 Table. Parameters sed by dfferent methods for ranfall feld estmaton. Parameters Search rads Ot pt cell Methods settng (Nmber sze Others of ponts) (m) Thessen polygon IDW 2 00 Dstance Porwer: 2 Splne 2 00 Splne type: Reglarzed Weght: 0. Lnear regresson Krgng 2 00 Sem-varogram model: Sphercal SKlm 2 00 Covarance model: Sphercal 3.. Accracy comparson of dfferent methods Fgre 3 shows the accracy for ranfall felds estmated by sx dfferent nterpolaton methods. There s no one method that can always predct better reslts than the other methods. For example, for storm No. 4 Splne gves the worst reslt, whle for storm No.35 t s the best predctor. It s hard to say whch method s the best, and dfferent methods may be applcable to predct dfferent types of storms. In order to gve a general dea abot whch method s more relable, the average MAE for the 52 storms was sed to assess performance of dfferent methods. SKlm predcted the smallest average MAE (2.65). Krgng and IDW gve smlar reslts (2.77 and 2.80 respectvely). Thessen polygon gves the largest average MAE (3.80). Lnear regresson and Splne predct 3.23 and 3.75 respectvely. Ths reslt s smlar to Goovaerts (2000), bt the complex geostatstcal method SKlm doesn t show mch advantage over other methods. The reason maybe that the topography of the stdy area s not complex, elevaton does not change mch and has no great mpact on ranfall dstrbton. Snce we can t get the actal ranfall feld, n the followng analyss, we wll choose the ranfall feld estmated by SKlm as a standard for comparson prposes.

9 Fgre 3. Accracy of ranfall estmated sng dfferent methods for 52 storm events Areal mean ranfall Fgre 4 reveals that the areal mean ranfall depths nterpreted by dfferent methods are closely related, except for storm No. 3, 32 and 34. Usng the areal mean estmated by SKlm as a standard, the relatve error of areal mean ranfall for the other fve methods was calclated. The absolte relatve error for IDW and Krgng s no more than 20%, and for most cases was wthn 0%. For the Thessen polygon, Splne and Lnear methods, the absolte relatve error was always wth 25%, and for most cases was wthn 5%. Generally, there was lttle varaton among areal mean ranfall depths for the dfferent nterpolaton methods.

10 Fgre 4. Areal mean ranfall estmated sng dfferent methods for 52 storm events Spatal dstrbton of ranfall The coeffcent of varablty of the ranfall felds estmated by dfferent methods n Fgre 5 shows that dfferent methods can predct dfferent ranfall dstrbton. Splne predcted the largest ranfall dstrbton varablty for almost all the 52 storms (average CV for 52 storms s 0.93), whle Lnear regresson predcted the lowest average CV vale Thessen polygon, IDW, Krgng and SKlm predcted smlar CV, 0.7, 0.63, 0.55 and 0.59 respectvely. The No. storm event was sed as an example to vsally show the dfference among sx nterpolated ranfall felds by dfferent methods n Fgre 6, and the statstcal characterstcs of dfferent ranfall felds are lsted n Table 2. It s obvos that there s bg vsal and statstcal dfferences among the ranfall felds estmated by the varos methods. Splne even predcted negatve vale npt nto hydrologc model, whch s set to zero to avod negatve ranfall when npt nto the SWAT model. And as ranfall has no sgnfcant relatonshp wth elevaton (R 2 = 0.06) for ths storm, the spatal pattern predcted by SKlm s dfferent than that of Lnear regresson. The bg dfference n spatal patterns of ranfall felds wll exert mpact on the processes that rote a ran drop throgh the basn.

11 Fgre 6. Ranfall felds estmated sng dfferent methods.

12 Fgre 5. Spatal varablty of ranfall fled estmated sng dfferent methods for 52 storm events. Table 2. Statstcal characterstc of ranfall feld estmated by dfferent methods. Varable Areal Hghest Lowest Methods CV mean vale vale Thessen polygon IDW Splne Lnear regresson Krgng SKlm Impact of estmated ranfall felds on dstrbted hydrologc modelng The otpts from the GIS based nterpolaton program were npt nto the SWAT model. Hydrologc modelng of flow at the otlet of the Lohe Rver was condcted at a daly tme scale sng daly ranfall data n 99. In order to reflect dfferences of estmated ranfall felds on the water yeld n the stdy area, the pstream nflow was not npt nto model. The parameters sed here were the defalt vales n SWAT. The dfferences of smlated flow were dscssed at the annal, monthly and daly temporal scale, and we took the smlated flow wth ranfall npt nterpreted by SKlm as the standard for comparson prpose.

13 Fgre 7 shows the annal flow smlated accordng to dfferent nterpolaton methods. The Splne method predcted hghest flow volme wth relatve dfference of 25% compared to SKlm. The Thesen polygon predcted a relatve dfference of 6%. Lnear regresson, IDW and Krgng predcted a relatve dfference wthn 0%. At the annal temporal scale, the smlated flow volme dfference s small. Fgre 7. Smlated annal flow volme n 99 sng dfferent nterpolaton methods. The smlate monthly flows are shown n Fgre 8. The general hydrograph shape smlated accordng to dfferent ranfall feld estmaton methods s smlar, and the peaks appear at the same month (September). Bt for each ndvdal month, the smlated flow volme shows obvos dfferences. For example, n Aprl, the relatve dfference s 6% for Thessen polygon, 65% for IDW, 85% for Splne, 33% for Lnear regresson and -3% for Krgng. The hghest flow nterpreted by Thessen polygon reached 4.28m 3 /s, whle the lowest flow nterpreted by Krgng s only.9m 3 /s n Aprl. And n Jly, the relatve dfference s 3% for Thessen polygon, 7.6% for IDW, 46% for Splne, -7.7% for Lnear regresson and -5% for Krgng. The dfference between Splne and Krgng s 4 m 3 /s, whch acconts for 50% of the flow volme smlated by Krgng. At the monthly temporal scale, the dfference between smlated flows nterpreted by dfferent methods s mch more apprecable than at annal scale.

14 Fgre 8. Smlated monthly flow volme n 99 sng dfferent nterpolaton methods. Fnally, we wanted to examne the smlated daly flow dfference. Fgre 9 shows the daly flow dscharge hydrograph smlated by the SWAT model based on ranfall felds estmated by the dfferent methods. Generally, the hydrographs have smlar shape drng low flow perod, whle predcted peak flows show bg dfference drng a flood event. Here we selected daly flows drng Jly as an example to analyze, Fgre0. Ths fgre shows that the hydrograph shape estmated by the dfferent methods doesn t correlate well. The peak flow smlated by Thessen polygon appears on Jly 23, Lnear regresson on Jly 24, Splne on Jly 9, IDW on Jly 24, Krgng on Jly 24, and SKlm on Jly 23. The flow volme smlated by dfferent methods also shows dramatc dfferences. For example, on Jly 9 the Thessen polygon predcted 32.4 m 3 /s, Lnear regresson 0.7 m 3 /s, Splne 46. m 3 /s, IDW 9.9 m 3 /s, Krgng 8.9 m 3 /s, and SKlm 0.8 m 3 /s. Also sgnfcant s that the hydrographs drng the flood recesson perod (Jly to 4) generally have a smlar trend and flow volme, bt for the perod wth a large ranfall storm (Jly 5 to 20 and Jly 23 to 28), the hydrographs shapes and flow volmes have obvos dfferences. To some extent, we can nfer that the hydrograph shape and flow volme dfference s manly cased by srface flow rotng drng a storm event, whle the grond water flow smlated based on dfferent methods doesn t show dramatc dfferences. In addton, althogh the accracy, areal mean ranfall and CV for ranfall feld estmated by IDW, Krgng, SKlm are close, we can stll see dfferences n the hydrographs drng Jly 5 to 20 and Jly 22 to 26.

15 Fgre 9. Smlated daly flow volme n 99 sng dfferent nterpolaton methods. Fgre 0. Smlated daly flow volme n Jly, 99 sng dfferent nterpolaton methods.

16 3.3 Dscsson Inttvely, the reslts descrbed above show that dfferent nterpolaton method wll predct obvosly dfferent ranfall feld. Based on prevos reported reslts that the spatal varablty of ranfall cold exert great mpact on ranfall-rnoff smlaton, and combned wth the obvos dfference of spatal varablty of ranfall felds and lttle dfference of areal mean ranfall depth estmated by dfferent nterpolaton methods (Fgre5), t seems that spatal dstrbton s the major reason explanng the dfference of smlated flow. Bt at same tme we shold note that dstrbted hydrologc model descrbng the basn s dynamc behavor s a nonlnear system, small dfference n npt data may case dramatc otpt change. So accordng to the reslts got n ths work, we can t exclde the possblty that the small dfference of areal mean ranfall may generate greatly dfferent flow. More detaled process based analyss of sol water, evapotranspraton, plant growth, flood rotng (overland and channel), and nteracton between srface and sbsrface water may provde s deeper nsght nto the mechansm how ranfall depth and spatal dstrbton mpact ranfall-rnoff process. Also we shold not extrapolate the reslts got n ths work drectly to other watershed and dstrbted hydrologc model. Dfferent types of ranfall-rnoff converson models, characterstc of basn and storm, densty of ran gage network (Arnad, 2002; Koren, 999; Smth, 2004) are also the factors need to be consdered for explanng hydrologc response. As we can t get the accrate ranfall feld, t s mpossble to assess the exact dfference between nterpolated ranfall feld and actal ranfall feld. Bt the average MAE for 52 selected storms show that the dfference between the estmated ranfall feld and actal one s relatvely large, the reason maybe the sparse ran gage network (3 ran gages thogh a 5239 km 2 watershed) sed to nterpolated ranfall felds. The dfference between the hghest and lowest average MAE of all nterpolaton methods s.5 mm, whch s less than half of the lowest average MAE 2.65 mm. And gven the sgnfcant dfference between flow smlated sng dfferent nterpolaton methods, the dfference between the flow smlated sng nterpolated ranfall feld and the flow smlated sng actal ranfall feld may be more sgnfcant. Snce ranfall s a drvng force behnd many knds of polltant release and sbseqent transport and spread mechansms, gnorng ths property of ranfall n the applcaton of dstrbted hydrologc modelng lmts the accracy of the model reslts (Chabey et al., 999). O Cornell and Todn (996) sggested sng radar and dense network of ran gage data to gan better captrng of ranfall fled, whch seems necessary for model developers and sers to redce ranfall npts error. As radar and dense network of ran gage data are dffclt to collect, choosng the best nterpolaton method accordng to accracy evalaton maybe an acceptable compromse. 4. Conclsons In ths work, the athors reported the dfference of ranfall feld estmated by dfferent nterpolaton methods and to how extent ths dfference cold mpact dstrbted hydrologc smlaton n a meso-scale watershed. The objectve of ths work was realzed by combnng a GIS based atomatc ranfall feld nterpolaton program and dstrbted hydrologc model SWAT. The reslts got n ths paper were generalzed below. The estmated 52 storms ranfall feld by sx dfferent nterpolaton methods reveal: ) The accracy of ranfall felds estmated by dfferent nterpolaton cold show bg dfference. Complex geo-statstcal methods can provde more accrate reslts. 2) Areal mean ranfall depth

17 doesn t show bg dfference between dfferent methods. Compared wth the vale predcted by SKlm, the relatve dfferences of areal mean ranfall for the other methods are all wthn 25%. 3) Spatal dstrbton of ranfall feld estmated by dfferent nterpolaton methods show obvos dfference. The hghest average CV for 52 storms s 0.93 for Splne method whle lowest CV s 0.25 for Lnear regresson method. Daly flow smlaton sng dstrbted ranfall npt estmated by the sx nterpolaton methods were condcted, and the reslts were analyzed at annal, monthly and daly temporal scale. Wth the temporal scale decrease from annal to monthly and daly, the varaton between smlated flow volmes became more and more obvos, and hydrograph shape smlated at daly tme step cold show dramatc dfference for dfferent methods. Generally, dfferent nterpolaton methods cold yeld obvosly dfferent ranfall feld, and dstrbted hydrologc model cold be very senstve to the methods sed to nterpolate ranfall feld. Also t shold be noted, the reslts got n ths paper are senstve to many factors, sch as types of dstrbted hydrologc models, state or parameters of hydrologc model, characterstc of basn, storm property and densty of ran gage network and so on. Mch care shold be taken when conclsons generalzed here be sed to dfferent statons. References Arnad P., C. Bover, L. Csneros and R. Domngez, (2002). Inflence of ranfall spatal varablty on flood predcton. Jornal of Hydrology, 260: Arnold, J. G., R. Srnvasan, R.S. Mttah, and J. R. Wllams, (998). Large Area Hydrologc Modellng and Assessment Part I: Model Development. Jornal of Amercan Water Resorces Assocaton, 34(): Arnold, J.G., P.M. Allen, and G. Bernhardt, (993). A Comprehensve Srface-Grondwater Flow Model. Jornal of Hydrology. 42: Chaplot V., A. Saleh, D.B. Jaynes. (2005). Effect of the accracy of spatal ranfall nformaton on the modelng of water, sedment, and NO3 N loads at the watershed level. Jornal of Hydrology 2. Chabey I., C.T. Haan, S. Grnwald and J.M. Salsbry, (999). Uncertanty n the model parameters de to spatal varablty of ranfall. Jornal of Hydrology, 220: Dawdy, D.R., J.M. Bergman, (969). Effect of ranfall varablty on streamflow smlaton. Water resorces research, 5: Drks K.N., J.E. Hay, C.D. Stow, D. Harrs, (998). Hgh-resolton stdes of ranfall on Norfolk Island Part II: Interpolaton of ranfall data. Jornal of Hydrology, 208: Dncan, M.R., B. Astn, F. Fabry, and G.L. Astn, (993). The effect of gage samplng densty on the accracy of streamflow predcton for rral catchments. Jornal of Hydrology, 42: Edward H.I., R.M. Srvastava, (989). Appled geostatstcs. New York: Oxford Unversty Press, pp56. ESRI, (2005). How Splne works. Accessed on Sep 8.

18 Farès J.M., D.C. Goodrch, D.A. Woolhser, S. Sorooshan, (995). Impact of small-scale spatal ranfall varablty on rnoff modelng. Jornal of Hydrology, 73: Franke, R., (982), Smooth Interpolaton of Scattered Data by Local Thn Plate Splne. Comp. & Maths. wth Appls, 8(4): Goovaerts P., (997). Geostatstcs for Natral Resorces Evalaton. Oxford Unversty Press, New York. Goovaerts P., (2000). Geostatstcal approaches for ncorporatng elevaton nto the spatal nterpolaton of ranfall. Jornal of Hydrology, 228: Koren V.I., B.D. Fnnerty, J.C. Schaake, M.B. Smth, D.J. Seo, and Q.Y. Dan, (999). Scale dependences of hydrologc models to spatal varablty of precptaton. Jornal of Hydrology, 27: Lkoyd C.D., (2004). Assessng the effect of ntegratng elevaton data nto the estmaton of monthly precptaton n Great Brtan. Jornal of Hydrology, 308: Lloyd C.D., (2005).Assessng the effect of ntegratng elevaton data nto the estmaton of monthly precptaton n Great Brtan. Jornal of Hydrology 308: Lopes V. L., (996). On the effect of ncertanty n spatal dstrbton of ranfall on catchment modelng. Catena, 28: Merz B., A. Bárdossy, (998). Effects of spatal varablty on the ranfall rnoff process n a small loess catchment. Jornal of Hydrology, 22 23: Mtas, L., H. Mtasova, (988). General varatonal approach to the nterpolaton problem. Comp. & Maths. wth Appls, 6(2): Netsch, S.L., A.G. Arnold, J.R. Knry, R. Srnvasan, and J. R. Wllams, (2002). Sol and Water Assessment Tool User s Manal: Verson GSWRL Report 02-02, BRC Report 02-06, Pblshed by Texas Water Resorces Insttte, TR-92, College Staton, Texas, 438 pp. Shah S.M.S., Connell P.E., and Hoskng J.R.M., (996). Modellng the effects of spatal varablty n ranfall on catchment response. 2. Experments wth dstrbted and lmped models. Jornal of Hydrology, 75: 89-. Shah S.M.S., P.E. Connell, and J.R.M. Hoskng, (996). Modellng the effects of spatal varablty n ranfall on catchment response.. Formlaton and calbraton of a stochastc ranfall feld model. Jornal of Hydrology, 75: Sloan, P.G., I.D. Morre, G.B. Coltharp, and J.D. Egel, (983). Modelng Srface and Sbsrface Stormflow on Steeply-Slopng Forested Watersheds. Water Resorces Insttte Report 42, Unversty of Kentcky, Lexngton, Kentcky. Smth M.B., V.I. Koren, Z. Zhang, S.M. Reed, J. Pan, F. Moreda, (2004). Rnoff response to spatal varablty n precptaton: an analyss of observed data. Jornal of Hydrology, 298: Stephen J. Jeffrey *, John O. Carter, Keth B. Moode, Alan R. Beswck. (200) Usng spatal nterpolaton to constrct a comprehensve archve of Astralan clmate data. Envronmental Modellng & Software, 6: Thessen, A.H., (9). Precptaton averages for large areas. Monthly Weather Revew, 39(7): Tsans I.K., M.A. Gad, (200). A GIS precptaton method for analyss of storm knematcs. Envronmental Modellng & Software, 6: USDA-SCS, (972). Natonal Engneerng Handbook, hydrology Secton 4, chap US Dept. of Agrcltre, Sol Conservaton Servce, Washngton, DC, USA.

19 Wlson C.V., J.B. Valdes, I. Rodrgez-Itrbe, (979). On the nflence of spatal dstrbton ranfall on storm rnoff. Water Resorces Research, 5 (2): Acknowledgements The athors wold lke to thank the USGS for provdng fndng throgh the Texas Water Resorces Insttte nder Agreement No The athors also thank Dr. Allan Jones, Dr. Rcard Jensen, and Clnt Wolfe from TWRI for takng care of the progress of ths project. The athors also thank the Chnese Natonal Natral Scence Fondaton for provdng partal fndng nder Agreement No

20 APPENDIX I GIS INTERFACE FOR AUTOMATIC RAINFALL FIELD ESTIMATION PROGRAM Interface for workspace and npt data settng

21 Interface for parameters settng for dfferent nterpolaton methods