Selection of hydrologic modeling approaches for climate change assessment: A comparison of model scale and structures

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1 Selection of hydrologic modeling pproches for climte chnge ssessment: A comprison of model scle nd structures Christopher G. Surfleet, Desirèe Tullos, Heejun Chng Il-Won Jung summry A wide vriety of pproches to hydrologic (rinfll runoff) modeling of river bsins confounds our bility to select, develop, nd interpret models, prticulrly in the evlution of prediction uncertinty ssocited with climte chnge ssessment. To inform the model selection process, we chrcterized nd compred three structurlly-distinct pproches nd sptil scles of prmeteriztion to modeling ctchment hydrology: lrge-scle pproch (using the VIC model; 671, km 2 re), bsin-scle pproch (using the PRMS model; 29,7 km 2 re), nd site-specific pproch (the GSFLOW model; 47 km 2 re) forced by the sme future climte estimtes. For ech pproch, we present mesures of fit to historic observtions nd predictions of future response, s well s estimtes of model prmeter uncertinty, when vilble. While the site-specific pproch generlly hd the best fit to historic mesurements, the performnce of the model pproches vried. The site-specific pproch generted the best fit t unregulted sites, the lrge scle pproch performed best just downstrem of flood control projects, nd model performnce vried t the frthest downstrem sites where stremflow regultion is mitigted to some extent by unregulted tributries nd wter diversions. These results illustrte how selection of modeling pproch nd interprettion of climte chnge projections require () pproprite prmeteriztion of the models for climte nd hydrologic processes governing runoff genertion in the re under study, (b) understnding nd justifying the ssumptions nd limittions of the model, nd (c) estimtes of uncertinty ssocited with the modeling pproch. 1. Introduction The prediction nd interprettion of uncertin hydrologic responses to climte chnge is mjor chllenge for wter resource mngers (Brekke et l., 29). An importnt effect of climte chnge is modifiction of locl nd regionl wter vilbility due to the climte system s interction with the hydrologic cycle (e.g., Btes et l., 28). Studies of climte chnge impcts on wter resources in the Pcific Northwest (PNW) suggest chnges will occur in the mgnitude nd timing of runoff (e.g., Chng nd Jung, 21; Elsner et l., 21; Hmlet et l., 21), the frequency nd intensity of floods nd droughts (e.g., Mote et l., 23; Jung nd Chng, 211b), wter temperture (Mntu et l., 21; Chng nd Lwler, 211), nutrient nd sediment loding (Prskievicz nd Chng, 211), nd quntity of wter vilble for humn use (e.g., IPCC, 27; Mote et l., 23). These hydrologic chnges, in turn, influence vrious spects of wter resource mngement, including municipl, irrigtion, nd industril supply, hydropower genertion, flood mngement, chnnel morphology, nd qutic hbitt conservtion. Some of these effects my not necessrily be negtive, but need to be evluted becuse of the socio-economic importnce of wter (Jing et l., 27). Downscled Generl Circultion Model (GCM) simultions re frequently used within hydrologic model to predict how the chnges to climte ffect the wter blnce nd wter-relted sectors using vriety of pproches nd scles of nlysis (e.g., Wilby et l., 29). Lrge uncertinties re inherent in the predictions, depending on GCM structure nd prmeteriztion, downscling procedure, greenhouse gs (GHG) emission scenrio, hydrologic model used, nd hydrologic model prmeters (e.g., Murer, 27; Surfleet nd Tullos, 212; Xu et l., 25; Im et l., 21). The effect on hydrologic predictions using different GCMs, downscling techniques, nd GHG emission scenrios hve received considerble ttention (e.g., Murer, 27; Wood et l., 24; Murer nd Duffy, 25). However, fewer studies (e.g., Jing et l., 27; Njfi et l., 211) hve focused on differences in uncertinties of

2 predictions ssocited with the vrious hydrologic modeling pproches, though uncertinty should be considered in the selection of hydrologic models. The choice of the hydrologic model my depend on number of selection criteri, including the chrcter (e.g., relevnt sptil nd temporl scle, cceptble level of error nd uncertinty for lterntive screening vs. detiled design) (e.g., Clrk et l., 28) of the wter resource mngement issue. In ddition, the scle of vribility in physicl chrcteristics (e.g., lnd use, elevtion, geology) tht influences importnt hydrologicl processes (e.g., evpotrnspirtion, snow ccumultion nd melt, or groundwter rechrge nd dischrge) cn be principle fctor in selecting hydrologic models. Finlly, spects of the individul models my influence its ppropriteness for n ppliction, including ese of use tht includes pre- nd post-processing, hrdwre requirements, rigor nd comprehensiveness of modeled processes, vilbility nd qulity of required dt, dptbility of source code, model vilbility, nd cost (Singh, 1995). In the PNW, severl different hydrologic modeling pproches hve been conducted for climte impct ssessment. When continentl scle informtion for vriety of climte predictions were needed, the VIC mcroscle (5 6 km grid cells) hydrologic model ws pplied (Nijssen et l., 1997; Hmlet nd Lettenmier, 1999; Elsner et l., 21). If there is complexity nd differences in hydrologic processes cross the study re, but representtion of smllscle sptil differences is not needed, then use of bsin scle or regionl prmeters my be dequte (e.g., Chng nd Jung, 21; Jung nd Chng, 211). If sptil heterogeneity in hydrogeology or subtle differences in hydrologicl processes over time hve n importnt influence on runoff genertion, then site-specific modeling pproch my be needed. For exmple, Tgue et l. (28) investigted the sensitivity of two Oregon Cscdes bsins, chrcterized by different geologic chrcteristics, under synthetic temperture wrming scenrios using the Regionl Hydro-Ecologic Simultion System (RHESSys). In urbnizing wtersheds with multiple lnd use nd wter qulity issues, Frnczyk nd Chng (29) nd Prskievicz nd Chng (211) used US EPA s physiclly-bsed model, BASINS-SWAT nd BASINS-HSPF, respectively, in site-specific pproch. With the gol of fcilitting discussion on hydrologic model selection nd development for use in wter resources plnning nd design, we undertook the comprison of three modeling pproches using identicl climte forcing dt. We differentite the modeling pproches by the sptil scle of the model ppliction (Lrge Scle, Bsin Scle, or Site-Specific) (Fig. 1) the model used, nd the quntifiction of uncertinty within the modeling pproch. () Lrge scle () deterministic pproch by the Vrible Infiltrtion Cpcity (VIC) model (Ling et l., 1994) for the Columbi River bsin considering GCM uncertinty. (b) Bsin scle prmeters nd uncertinty (BSPU) effort using surfce runoff model, Precipittion-Runoff Modeling System (PRMS) (Levesley et l., 1983), with GCM uncertinty cscded through prmeter uncertinty ssessment using existing prmeter set rnges. (c) Site-specific modeling with uncertinty (SSMU) effort with coupled groundwter nd surfce-wter flow model (GSFLOW) (Mrkstrom et l., 28; Hrbugh, 25) with GCM uncertinty cscded through prmeter uncertinty ssessm. The objectives of this nlysis re: () to compre fit to historic hydrologic observtions cross three hydrologic modeling pproches with vrying model structures nd sptil scles of prmeteriztion; (b) exmine differences in predictions of future hydrology from the three modeling pproches, nd; (c) investigte the physicl processes responsible for differences in predictions to fcilitte discussion on hydrologic model selection nd prmeteriztion. Model simultion results re summrized into four clsses of hydrologic responses (extreme pek flows events, extreme low flow events, verge monthly flow, nd snowmelt) tht re generlly relevnt to wter resources mngement. 2. Methods 2.1. Study res, model comprison loctions, nd timefrmes The Sntim River Bsin (SRB, 47 km 2 ) is tributry to the Willmette River Bsin (WRB, 29,7 km 2 ), which is itself tributry to the Columbi River Bsin (CRB, 67 km 2 ). Locted on the western slopes of the Cscde Rnge in Oregon, USA (Fig. 1), the SRB is vluble cse study for model comprison becuse it is chrcterized by sptilly heterogeneous hydrogeology, creting sptil vribility in hydrologic response to chnges in climte. The SRB vries from mountin terrin in high elevtion lpine res (3199 m) to low relief foothills to lluvil res (5 m) tht re hydrologiclly connected to the Willmette Vlley. The lnd use clssifiction within the bsin is 8% forest, 15% griculture, 2% urbn, nd 3% rnge (USGS, 29). The soils in the SRB re clssified (NRCS, 27) s 8% in Hydrologic Group B, with moderte rtes of wter trnsmission (infiltrtion nd dringe) nd 2% in Hydrologic Group A, with slow rtes of wter trnsmission. Precipittion vries from rin t the bsin outlet to primrily snow t higher elevtions, with mix of rin nd snow between the two (Fig. 1). Furthermore, two hydrologiclly-distinct sesons exist in the bsin, wet seson (ember through il) during which pproximtely 85% of precipittion occurs, nd dry seson (My through ober) during which 15% of precipittion occurs (NRCS, 211). The runoff from the SRB is regulted by four flood control projects, Detroit nd Big Cliff dms on the North Fork Sntim River nd Foster nd Green Peter Dms on the South Fork Sntim River. The high elevtion res of the Sntim River re composed of High Cscdes geology where runoff is influenced by dischrge from substntil, deep groundwter quifer nd springs (Tgue et l., 28; Chng nd Jung, 21; Surfleet nd Tullos, 212). The lower lluvil section of the bsin include res of considerble rechrge for groundwter ssocited with the Willmette Vlley quifer, where low flow stremflow is strongly ffected by quifer conditions (Lee nd Risley, 22). The reminder of the bsin hs Western Cscde geology, chrcterized by moderte to low hydrulic conductivities coupled with shllow soils tht result in rpid runoff response with little groundwter storge (Tgue et l., 28). Our hydrologic model predictions were compred t four loctions within the SRB (Fig. 1) with one dditionl loction for historicl stremflow only; South Sntim t Cscdi. The four loctions were selected due to the vilbility of output from the model, proximity to river guging sttion, nd sptil differences in bsin chrcteristics ffecting hydrologic response (Tble 1). We summrized results of the model simultions for three time periods: historic (196 26), 24s (23 259), nd 28s (27 299). These time periods, representtive of the middle nd the end of the21st century, were used to llow comprison to lredy completed VIC modeling (Hmlet et l., 21). The VIC modeling used 3 yer time period tht brcketed 24 nd 28 to represent these respective time periods. The historicl vlues for the BSPU nd SSMU pproches were clculted from USGS stremflow dt. We used the published vlues from the VIC modeling

3 Fig. 1. Model pproches nd Sntim River bsin study re, Oregon, USA. of the CRB (Hmlet et l., 21) for the historicl vlues in fitness comprisons mde with the pproch Hydrologic models nd pproches We evluted three hydrologic (rinfll runoff) modeling pproches for their bility to predict stremflow t four loctions (Tble 1) within the SRB with importnt distinctions in model structure nd ppliction (Tble 2). Ech of the models solve full wter nd energy blnces tht consider the effect of meteorologicl observtions on potentil evpotrnspirtion (from vegettion nd lnd cover), wter storge nd routing (soil moisture, ground- wter, snow, nd strem chnnel), nd the subsequent runoff (stremflow). The primry differences mong the models re in the representtion of hydrologic processes, s defined by the prmeteriztion, clibrtion, vlidtion, nd sptil scle of modeling. The pproch to the modeling differed s well with two of the pproches (BSPU nd SSMU) considering prmeter uncertinty nd one pproch tht did not (). In ll three pproches, the sme 1/16 resolution meteorologicl forcing dt ws used for historicl nd downscled future predictions for the SRB (Tble 3). We used eight GCM simultions with two emission scenrios (B1nd A1B), which were sttisticlly downscled using the bis correction nd sptil downscling method (Wood et l., 24). Tble 1 Chrcteristics ffecting hydrologic response for the four loctions of hydrologic model comprison in the Sntim River bsin, Oregon. Loction Are Nturl or Mjor Men Geology Groundwter influence on runoff (km 2 ) regulted runoff precipittion elev. (m) (high, moderte, low) type North Fork Sntim River 555 Nturl Snow % High Cscde, 5% Western Cscde, High below Boulder Creek 5% Alluvium North Fork Sntim River t 17 Regulted Rin nd snow 116 3% High Cscde, 6% Western Moderte Mehm Cscde, 1% Alluvium South Fork Sntim River t 166 Regulted Rin nd snow 765 9% Western Cscde, 5% Alluvium, 3% Low Wterloo Bslt, 2% High, Cscde Sntim River t Jefferson 47 6% of Bsin Rin nd snow 74 15% High Cscde, 6% Western Moderte regulted Cscde, 1% Alluvium, 5% Bslt Regulted by flood control dms.

4 Tble 2 Comprison of modeling pproches nd input dt used in this study. Approch Hydrologic Prmeteriztion Clibrtion Clcultion Clcultion Lnd use lnd Soil type Aspect Computing nd scle model of models time scle time scle sptil scle cover type resources per lnd b re Lrge Scle (): VIC One prmeter set optimized for Monthly Wter blnce = 24 h, 1/16, 32 km 2 grid 47% Evergreen, Prmeters Aspect ssumed Moderte entire CRB using 11 sub-bsins energy blnce = sub- size 39% woodlnd, 8% empiriclly uniform cross dily, snow closed, 4% derived from soil grids processes = 1 h grsslnd, 2% open texture from LDAS c Bsin Scle Prmeters PRMS Initil prmeter rnges clibrted from WRB; Dily Wter blnce = 24 h, Hydrologic Response 8% Forest, 15% Bsed on NRCS 3 HRUs re Moderte nd Uncertinty posterior prmeter distribution from DREAM energy Units (bsin griculture, 2% soil types: 8% ssigned spect (BSPU): uncertinty ssessment for three sub-bsins of blnce = dylight verge = 17 km 2, urbn, 3% rnge Type B, 2% Type direction for Sntim River fit to dily time series length, snow rnge.8 A energy processes = 12 h 264 km 2 ) clcultions Site Specific Modeling GSFLOW Posterior prmeter rnges developed for three sub- Seprte dily Wter blnce = 24 h, Hydrologic Response 8% Forest, 15% Bsed on NRCS 4 HRUs re High nd Uncertinty bsins of Sntim River wtershed using DREAM time series for energy Units (verge griculture, 2% soil types: 8% ssigned spect (SSMU): uncertinty ssessment fit to summer nd winter winter nd blnce = dylight <3 km 2 ) urbn, 3% rnge Type B, 2% Type direction for dily time series summer length, snow A energy processes = 1 h clcultions Initil prmeters rnges nd vlidtion for PRMS from Lenen nd Risley (1997) nd Chng nd Jung (21). Lnd Dt Assimiltion System Ntionl Aeronuticl Spce Administrtion (s cited in Hmlet et l., 21). Nturl Resources Conservtion Service (27). b c The verge chnge of men nnul precipittion, men dily mximum ir temperture, nd men dily minimum ir tempertures for the wet seson (ember through il) nd dry seson (My through ober) from the downscled GCM dt used s input to the SRB modeling is presented (Tble 4). The modeling pproch is represented by VIC modeling t grids of the sme scle s the downscled 1/16 GCM dt (Fig. 1). This equtes to pproximtely 15 grid cells per 5 km 2 (Hmlet et l., 21) ( 33 km 2 per grid cell). The VIC model ws clibrted for eleven lrge bsins locted est of the Cscde mountin divide within the CRB. One prmeter set ws developed from the VIC model clibrtion nd used over the entire CRB. The prmeter selections were deterministic; No nlysis of equifinlity nd prmeter uncertinty ws undertken. Clibrtion of the VIC model ws bsed on djusting infiltrtion, Ds, Ws, Dsmx, nd soil depth using the MOCOM-UA method to fit monthly dt; for greter detil on VIC model clibrtion nd vlidtion, plese see Hmlet et l. (21). Considertion of GCM uncertinty ws ddressed using different GCMs nd severl different sttisticl downscling techniques (Hmlet et l., 21). Vegettion nd soil prmeters used by VIC for the pproch cme from the LDAS (Lnd Dt Assimiltion System) (see Hmlet et l., 21) ssimilted from scle of 1 km 2. Lef re index is the primry prmeter used within VIC to model effects of vegettion on potentil evpo-trnspirtion (PET). Soil prmeters re used for clcultion of vrible infiltrtion cpcity, which influence bseflow bsed on differences in soil moisture through time (Ling et l., 1994). Subgrid elevtion bnds re used to compenste for bove-ground energy differences due to elevtion. To clculte stremflow in lrger bsins, dily runoff nd bseflow re used s input to routing model (bsed on Lohmnn et l. (1996)). For the BSPU modeling pproch, we used nd renlyzed results from PRMS model tht discretizes the lndscpe into Hydrologic Response Units (HRUs) t finer scle (on verge <17 km 2 ) thn ws used for the VIC model. The delinetion of HRUs defined res of similr vegettion type, lnd use, soil, spect, nd geology (Fig. 1) to serve s the sptil scle for model clcultions. Prmeters used to represent effects of vegettion types nd lnd use were developed from GIS lyers t 3 m resolution obtined from the United Sttes Geologicl Survey (USGS, 29). A 3 m digitl elevtion model ws used to represent topogrphic chnges of elevtion nd spect (USGS, 29). Soil ttributes for model prmeters were developed from soil dt for the stte of Oregon (NRCS, 1986). For fitting the PRMS nd GSFLOW models to historicl stremflow nd snow dt, we djusted thirteen sensitive model prmeters, s identified in previous PRMS models for the re (Chng nd Jung, 21; Lenen nd Risley, 1997; Jung nd Chng, 211,b) (Tble 5). The published rnges of model prmeters previously pplied in the region were used s the priori prmeter distributions for n uncertinty ssessment (see Section 2.3). The SSMU modeling pproch used the GSFLOW model with clcultions t the lnd surfce performed t the sme HRUs Tble 3 The eight Globl Climte Models (GCM) used in the three modeling pproches. GCM References CCSM3 Collins et l. (26) CNRM-CM3 Terry et l. (1998) ECHAM5/MPI-OM Jungclus et l. (26) ECHO-5 Min et l. (25) IPSL-CM4 Mrti et l. (25) MIROC3.2 K-1 Developers (24) PCM Wshington et l. (2) UKMO-HdCM3 Gordon et l. (2)

5 Tble 4 Chnge to men nnul precipittion nd men dily ir temperture from eight GCMs for A1B nd B1 emission scenrios for nnul, wet seson (ember il), nd dry seson (My ober) time periods for Sntim River Bsin, Oregon. Climte Men historic Men chnge in nnul Men chnge in wet seson Men chnge in dry seson 24 B1 24 A1B 28 B1 28 A1B 24 B1 24 A1B 28 B1 28 A1B 24 B1 24 A1B 28 B1 28 A1B Precipittion (mm) Dily ir temp. ( C) defined for the BPSU pproch. The SSMU pproch with GSFLOW dds the MODFLOW groundwter model to simulte sub-surfce wter. In GSFLOW infiltrted wter psses from the smller HRU scles, modeled by PRMS, into the deeper groundwter MODFLOW grids ( 4 km finite difference grid) with two to three- sub-surfce lyers for modeling of sub-surfce wter (Fig. 1) (for more detils see Surfleet nd Tullos, 212). The groundwter model component of GSFLOW ws clibrted by fitting model predictions to groundwter elevtions from wells in the Willmette Vlley nd summer low flow s no groundwter elevtion mesurements were vilble for the mountinous portion of the SRB. A DREAM uncertinty ssessment (see Section 2.3) ws used for three sub-bsins of the SRB for the SSMU pproch to develop posterior distributions of prmeter rnges for up to 13 model prmeters (Tble 5) in the surfce wter component of GSFLOW. Both the BSPU nd SSMU were prmeterized by the sme thirteen PRMS prmeters (Tble 5) (Chng nd Jung, 21; Lenen nd Risley, 1997). To clculte precipittion differences for elevtions nd HRUs, observed precipittion is djusted by monthly correction fctors (rin_dj, snow_dj). Dily mximum infiltrtion of snowmelt into the soil is defined for PRMS nd GSFLOW (snowinfill_mx). The surfce runoff is computed using nonliner eqution tht tkes into ccount ntecedent soil moisture nd rinfll (smidx_coef, smidx_exp). When the soil wter reches mximum soil wter holding cpcity, dditionl infiltrtion is routed to the subsurfce nd ground wter reservoirs (soil2gw_mx). Subsurfce runoff is simulted s nonliner coefficient to route subsurfce reservoir to stremflow (ssrcoef_sq). Within PRMS, the groundwter reservoir is conceptulized s liner reservoir recession coefficient (gwflow_coef). PRMS lso simultes the movement of wter from subsurfce reservoir to groundwter reservoir, computed s routing function (ssr2gw_rte, ss2gw_exp). For clculting potentil evpotrnspirtion, the Hmon method (Hmon, 1961) ws used (hmon_coef). In the SSMU pproch we included monthly corrections of mximum nd minimum dily ir tempertures for differentition of energy blnce clcultions within HRUs. An importnt distinction between the SSMU nd BSPU pproches is the use of different rnges of prmeters to predict the different hydrologic regimes in wet nd dry sesons (e.g., Gn et l., 1997) of the SRB for the SSMU pproch. The BSPU pproch pplied existing prmeter sets developed for lrger bsin to simulte the SRB stremflow, therefore the sme prmeter sets were used between wet nd dry sesons. However, we found better fit of the SSMU model (GSFLOW) when different vlues were used for the evpo-trnspirtion prmeter (hmon_coef), surfce runoff exponent (smidx_exp), nd groundwter routing coefficients (ssr2gw_rte, ssr2gw_exp, gwflow_coef) between the hydrologiclly ctive wet seson compred to the bseflow driven dry seson. The prmeters for monthly corrections of precipittion nd temperture did not improve model performnce for the dry seson nd were not djusted from priori vlues for the SSMU pproch Uncertinty ssessment For the ssessment of uncertinty in posterior prmeter rnges for the SSMU nd BSPU pproches, we pplied the Differentil Evolution Adptive Metropolis (DREAM) ssessment (Vrugt et l., 29). DREAM is forml Byesin pproch tht uses Mrkov Chin Monte Crlo smpling lgorithm to estimte the posterior probbility density function of prmeters, utomticlly tuning the scle nd orienttion of the priori distribution during evolution of the posterior prmeter distributions. Posterior distributions of prmeter vlues were developed from DREAM for three sub-bsins representing the rnge of topogrphic nd geologic conditions within the SRB. The posterior distributions from the

6 Tble 5 Rnge of prmeter distributions for SSMU nd BSPU modeling pproches produced with the DREAM uncertinty ssessment. Model Prmeter description A priori SSMU wet seson posterior SSMU dry seson posterior BSPU posterior prmeter prmeter prmeter rnge prmeter rnge prmeter rnge rnge Rin_dj Monthly rin djustments by HRU Snow_dj Monthly snow djustments by HRU Tmx_lpse Monthly mximum temperture lpse rtes Tmin_lpse Monthly minimum temperture lpse rtes Hmon_coef Hmon evpotrnspirtion coefficient Smidx_coef Coefficient in surfce runoff contributing.1.1 re computtions Smidx_exp Exponent coefficient in surfce runoff contributing re computtions Ssr2gw_rte Coefficient to route wter from subsurfce to groundwter Ssr2gw_exp Exponent coefficient to route wter from subsurfce to groundwter Ssrmx_coef Mximum grvity dringe to groundwter.1 1. Soil2gw_mx Mximum soil wter to groundwter per dy Gwflow_coef Liner coefficient to route groundwter to strems Snowinfil_mx Mximum snow infiltrtion per dy Posterior prmeter rnge ws no different thn the priori prmeter rnge. DREAM ssessment were extrpolted to the reminder of the SRB bsed on similr physicl chrcteristics to the three sub-bsins. GCM nd prmeter uncertinties were ddressed by cscding the rnge of model output from the posterior distribution of prmeter sets through the eight GCMs. For further detils on the DREAM uncertinty ssessment nd GSFLOW model vlidtion, plese see Surfleet nd Tullos (212) Evlution of historicl model fitness The fit of modeled stremflow for the three hydrologic modeling pproches ws compred to mesured dily nd monthly stremflow (Tble 6) for five USGS strem guging sttions for the historic period of (Fig. 1). The sttions on the South Sntim River t Wterloo, North Sntim River t Mehm, nd Sntim River t Jefferson re below reservoirs. For consistency with previous modeling efforts (Hmlet et l., 21; Chng nd Jung, 21), we mde no correction to the mesured stremflow to reflect reservoir modifictions of the flow regime. We evluted fit of historicl stremflow bove reservoirs t the North Sntim River below Boulder Creek nd Sntim River t Cscdi, though no VIC output ws directly vilble for the Sntim River t Cscdi loction. We thus djusted the VIC output for the South Sntim River t Wterloo by the unit re of South Sntim River t Cscdi. We lso compred pek nd low flow predictions to historic observtions (Tble 7). The Generlized Extreme Vlue distribution ws used to estimte the 2, 5, nd yer return pek dily stremflow nd the 1-yer 7-dy low flow for the three model pproches nd mesured stremflow. We lso evluted the fitness of the models to predict the Snow Wter Equivlent (SWE) during the historic period. We were only ble to perform this evlution for the North Fork Sntim below Boulder Creek sub-bsin becuse it ws the only sub-bsin entirely within the snow-dominted climte of the SRB (Fig. 1), nd longterm snow mesurements were not vilble for low elevtion res of the SRB. Sttisticl fit of the monthly nd dily time series to mesured stremflow ws evluted by the Nsh Sutcliffe efficiency (NS), Reltive Efficiency (E rel), nd percent bis (Pbis). The NS efficiency is common mesure of goodness-of-fit for hydrologic models tht uses squred vlues (see the nnottion of Tble 6 for fitness mesure equtions), mking them sensitive to high stremflow events. The E rel vlue modifies the NS s reltive devitions, djusting model fit bsed on size of event, thus better reflecting fit of the entire series nd reducing the influence of the bsolute differences during high flows. As result, E rel vlues re more sensitive to systemtic over- or under-prediction, in prticulr during low flow conditions (Kruse et l., 25), with higher vlues indicting higher model fit. Pbis describes the over- or under-estimtion of simulted dt reltive to observed dt, nd tends to vry more during periods of low stremflow thn high stremflow (Gupt et l., 1999). For Pbis, higher vlues indicte higher error or bis to observed dt. Sttisticl fit to SWE in the North Sntim below Boulder Creek sub-bsin ws evluted using the NS sttistic Comprison of projected chnge in future runoff For the pproch, the rnge of estimtes of pek flows nd low flows from ech of eight GCMs represent GCM uncertinty. No prmeter uncertinty ws vilble from the VIC modeling. For the BSPU nd SSMU pproches, hydrologic response mesures were clculted from 2.5, 5, nd 97.5 percentile vlues of model output cscded through eight GCMs to represent the uncertinty ttributed to hydrologic model prmeters. For the BSPU nd SSMU pproches, we compred the GCM ensemble men of the 2.5, 5, nd 97.5 percentile vlues to the sme percentiles from the rnge of historic predictions from the GCMs. The pproch used bis corrected dt nd compred future predictions to single historicl vlue (Hmlet et l., 21). 3. Results 3.1. Fit of hydrologic model predictions to historic mesurements Monthly nd dily stremflow Across ll sites, ll three modeling pproches provided cceptble (Morisi et l., 27) fit to mesured monthly stremflow bsed on NS vlues greter thn.7 nd Pbis vlues <1% with the exception of the two stremflow loctions directly downstrem of regulted stremflow from reservoirs (South Sntim t Wterloo nd North Sntim t Mehm) (Tble 6). Models of dily stremflow generted greter rnge in the metrics of sttisticl fit thn were generted for monthly stremflow estimtes,

7 Tble 6 Modeling pproch fit to historic stremflow s mesured t USGS guging sttions (196 26) nd fit to monthly Snow Wter Equivlent for snow dominted North Sntim below Boulder Creek sub-bsin; Monthly stremflow with dily stremflow sttisticl fit in prenthesis; Sntim River t Jefferson, North Sntim t Mehm, nd South Sntim t Wterloo re below reservoirs with regulted flow. USGS guging sttion NS Pbis (%) c E rel BSPU SSMU BSPU SSMU BSPU SSMU Sntim R. t Jefferson.89 (.63).89 (.74).88 (.73) (.6).81 (.49).84 (.86) N Sntim t Mehm.77 (.38).78 (.43).75 (.56) (.48).59 (.22).65 (.74) S Sntim t Wterloo.85 (.5).82 (.66).55 (.52) (.35).51 (.48).39 (.73) N Sntim below Boulder Crk.61 (.51).8 (.62).7(.71) (.51).56 (.67).72 (.79) South Sntim t Cscdi.56 b (.87 b ).91 (.75).91 (.75) (.24).35 (.67).78 (.67) Snow Wter Equivlent monthly N Sntim below Boulder Crk Nsh Sutcliffe efficiency (NS) = ½RðO i 2 OÞ RðO i 2 S i Þ 2 i=rðo i OÞ. Percent bis (Pbis) = ½RS i RO i i=ro i x %. ( ( 2 ( ( 2 O i S i ðo i OÞ Reltive Efficiency (Erel) = 1 R O i =R. O Here, O is observed flow, S is simulted flow, n is number of dt, nd i indictes time. Stremflow not regulted by flood control dm; predomintely snow dominted precipittion. b VIC South Sntim t Cscdi is estimted by unit re from S. Sntim t Wterloo dischrge. c Percent bis is sme for monthly nd dily vlues. Tble 7 Comprisons of hydrologic model estimtes for historic stremflow 1-yer 7-dy low flow. Stremflow North Sntim below Boulder Creek North Sntim t Mehm South Sntim t Wterloo Sntim River t Jefferson source (m 3 /s) (m 3 /s) (m 3 /s) (m 3 /s) Mesured BSPU SSMU Rnge of medin vlues presented bsed on GCM nd prmeter uncertinty. with NS vlues rnging from.38 to.87 (Tble 6). There is no difference in Pbis vlues for dily or monthly stremflow becuse it is clculted by the proportion of sums of totl stremflow. The Erel sttistic results, representing fit of the entire time series but sensitive to low flow fitness of model output, re generlly highest for SSMU thn BSPU nd for dily nd monthly vlues except t the two loctions directly below reservoirs. For the two unregulted loctions (S. Sntim River t Cscdi nd N. Sntim River below Boulder Creek), the SSMU nd BSPU pproches generlly provide higher NS vlues, indicting better fit for high strem flows, thn the pproch. Compring the pproches bsed on Pbis nd Erel, SSMU hd the highest Erel of the three pproches t North Sntim River below Boulder Creek nd t South Sntim River t Cscdi, but lso produced high Pbis compred to the Approch for the sme site. The pproch generted stremflow vlues tht hd lower underestimtion bis (Pbis) but poorest fit cross the time series (Erel) t N. Sntim below Boulder Creek. In contrst, the pproch generted the highest Pbis but performed better thn SSMU by the Erel fitness mesure t the other unregulted site (S. Sntim River t Cscdi). Fitness mesures for BPSU generlly fell between vlues for nd SSMU. The evlution of modeled stremflow fit for the three USGS guging loctions regulted by flood control dms requires cutious interprettion. The simulted stremflow for these loctions did not consider flood control dms, so it is not relistic tht hydrologic model output t loctions in close proximity to dms would hve close fit to mesured stremflow. The flood control dms influence both high nd low flow mgnitudes, though the extent of these effects vry by seson nd yer. For exmple, stremflow records, over the period of , t Foster dm indicte tht the minimum rtio of outflow: inflow is during the wet month of Jnury, indicting inflow is equl to outflow, but the rtio is 2.1 during the driest month of ust, reflecting outflow tht is twice tht of the inflow (Tom Lowry, unpublished dt). These effects of flood regultion re likely to be less evident t the loctions frthest downstrem of the flood control dms for two primry resons. First, lower bsin sites drin lrge re with un-regulted stremflows. For the SRB, pproximtely 4% of the bsin re is locted downstrem of the reservoirs, representing 27% of the totl precipittion tht flls on the bsin (PRISM Climte Group, 212). Second, the number of diversions for irrigtion, including municipl, irrigtion, nd commercil uses, increses with distnce downstrem in the SRB, mitigting, to some extent, the effect of dms on incresing summer bseflow. For exmple, while only 2 cfs (66 points of diversion) hs been llocted bove the site on the S. Fork of the Sntim t Cscdi, over 99 cfs of wter rights (1951 points of diversion) hve been llocted in the Sntim River bove the Jefferson site. Though these vlues reflect wter rights rther thn ctul nnul diversions nd re likely not ll consumptive uses, they illustrte how the intensity of diversions moving downstrem into the griculturl res of the bsin, in combintion with unregulted tributries, likely mitigte some influences of higher bseflow releses from the reservoirs. Thus, while we cknowledge tht the model results do not directly reflect the impcts of wter mngement (flood control regultion nd diversions), it is still constructive to compre model predictions in regulted nd unregulted reches to investigte systemtic errors in the models. In compring the mesured monthly nd dily stremflow to model predictions t the loction frthest downstrem from flood control dms (Sntim River t Jefferson), we find tht the SSMU pproch generted predictions with the highest Erel vlues, though ll three pproches hd similr NS sttistics of monthly

8 nd dily stremflow. Pbis t this fr downstrem site ws high for the SSMU nd BSPU pproches, with results trending towrds overestimtion (negtive Pbis) for BSPU nd SSMU nd underestimtion (positive Pbis) for the pproch. At the sites nerest to regulting project (S. Sntim t Wterloo nd N. Sntim t Mehm), the nd BPSU pproches resulted in the highest NS sttistics for monthly stremflow of the three modeling pproches. Interestingly, bsed on Pbis, the SSMU pproch performed worst of the three models for the groundwter-bsed N. Sntim t Mehm while performing best in the mixed surfce wter-groundwter system drining to the S. Sntim t Wterloo loction. An opposite pttern ws seen with Erel vlues, with SSMU performing best of the three modeling pproches t the N. Sntim t Mehm site for both dily nd monthly stremflow nd worst t the S. Sntim t Wterloo loction for the monthly, but not dily, stremflow. For both sites ll pproches underestimted stremflow (positive Pbis), except the overestimtion of stremflow (negtive Pbis) with the pproch for the N. Sntim t Mehm. In summry, we see some generl trends in model performnce cross the lndscpe nd cross model performnce mesures tht re sensitive to different spects of the hydrogrph. These results suggest tht, when compring regulted stremflow observtions to the unregulted model predictions, the SSMU pproch generlly performed best cross ll mesures (except for Pbis t the N. Sntim below Boulder Creek). At the sites just downstrem of the flood control projects outperformed SSMU for monthly sttistics. At the site furthest downstrem of the dms where the hydrologic impct of regultion is likely medited to some extent, ll pproches performed similrly with respect to the high flow fitness mesures (NS) t the monthly resolution, though SSMU showed some greter fitness for dily resolution nd cross the entire series (E rel). However, SSMU nd BSPU performed worse thn with respect to low flow bises (Pbis) Snow Wter Equivlent (SWE) SWE predictions by ech of the modeling pproch fit historicl monthly SWE closely (Tble 6). The NS were high for ll three of the modeling pproches; NS vlues P.82. The BSPU pproch hd only slightly lower NS Vlues thn the nd SSMU pproches. Differences in posterior prmeter vlues for the SSMU nd BSPU pproches ssocited with precipittion nd ir temperture djustments influenced the SWE predictions. The SWE sttisticl fit ws bsed on only one snow mesurement loction nd one sub-bsin of the SRB, mking it difficult to determine the efficcy of the model pproches t predicting SWE cross the entire SRB. However, the hydrology of the sub-bsin contributing to North Sntim below Boulder Creek stremflow is dominted by snow precipittion nd predicting SWE in this bsin gives us confidence in the energy clcultions for snow processes for ll of the model pproches Extreme pek dily stremflow For the unregulted stremflow loction (North Sntim below Boulder Creek), estimtes of the historicl extreme pek dily stremflow (2, 5, nd yer events) were very similr cross the three modeling pproches (Fig. 2). At sites downstrem of flood control projects (the North Sntim t Mehm, South Sntim t Wterloo, nd Sntim River t Jefferson loctions), the SSMU nd BSPU pek flow estimtes were consistently higher thn pek flows clculted from observed stremflow. This overestimtion of pek flows is expected since the influence of regulted stremflow from the flood control projects ws not considered. However, the pproch consistently underestimted the pek flow reltive to mesured stremflow for the three regulted loctions. At the site nerest the dm (South Sntim t Wterloo), the Dischrge (m 3 /sec) North Sntim blw Boulder Creek Mesured BSPU SSMU 2 5 North Sntim t Mehm* 2 5 South Sntim t Wterloo* 2 5 Sntim River t Jefferson* Pek Dily Flow Return Intervl (2, 5, yr) by River Loction Fig. 2. Comprisons of hydrologic modeling pproch estimtes for historic 2, 5, yer pek dily flow. estimte is men vlue of eight GCM estimtes from VIC. SSMU nd BSPU estimtes re the ensemble men of medin vlues nd rnge of medin estimtes from cscding prmeter uncertinty from GSFLOW nd PRMS, respectively, through 8 GCMs. Thick grey line nd thin blck line error brs represent the 95 percentile uncertinty of medin predictions from SSMU nd BSPU pproches respectively. * stremflow mesured downstrem of flood control dms. model performed the best of ll three pproches, s ws seen with the dily nd monthly model fit prmeters. At the site with the lrgest dringe re (Sntim River t Jefferson), the model lrgely under predicted the pek flows, while the BPSU nd SSMU pproches overestimted pek flows. Results of the DREAM nlysis (shown in Fig. 2 for BSPU nd SSMU), reflects uncertinty in the estimtes of pek flows s function of both GCM nd hydrologic model structure nd prmeteriztion. We note tht the rnge of historicl predictions ws not vilble for comprison for the pproch becuse the uthors of the VIC model (Hmlet et l., 21) identified only one historic vlue rther thn the rnge of historic vlues from GCMs nd becuse no uncertinty nlysis ws performed. For our own clcultions with BSPU nd SSMU, we found tht the BSPU vlues hd wider rnge of the medin pek flow predictions thn the SSMU pproch t ll sites (Fig. 2), demonstrting greter uncertinty for BSPU estimtes thn for SSMU. The BSPU rnge of medin predictions spnned pproximtely 15% bove nd below the verge historic pek flow predictions (pproximtely 3% rnge) while the rnge of medin SSMU predicted pek flows spnned pproximtely 1 15% bove nd below the verge historic pek flow predictions (pproximtely 2 3% rnge). The uncertinty increses moving downstrem below the flood control dms, with the highest uncertinty rnge for both modeling pproches generted t the most downstrem site (South Sntim t Jefferson) Yer 7-dy low flow The model underestimted historic low flow for ll mesured stremflow loctions (Tble 7). Both SSMU nd BSPU estimtes were lso generlly lower thn the observed stremflow estimte for the three loctions downstrem of flood control dms, except for the site on the Sntim River t Jefferson where SSMU nd some BSPU estimtes over-predict the observed 1-yer 7-dy low flow, which is lso reflected in the negtive Pbis vlues (Tble 6). This underestimtion of flow is expected since reservoirs re generlly relesing stored winter runoff for irrigtion nd domestic uses during the dry summer period. As noted previously, it is likely tht the

9 effects of reservoir releses is dmpened in the downstrem direction by dditionl runoff from tributries nd diversions for griculturl nd municipl uses, hence the improvement in estimtes of historicl low flow t the frthest downstrem site (Sntim River t Jefferson). All three pproches underestimte the 1-yer 7-dy low flow of mesured stremflow for North Sntim t Mehm nd South Sntim t Wterloo, loctions close to reservoirs. The low flow estimtes from the pproch re very low. The highest estimte of 1-yer 7-dy low flow, on the Sntim River t Jefferson, ws only 7% of the 1-yer 7-dy low flow clculted from mesured stremflow (1.9 m 3 /sec compred to 26.7 m 3 /sec; Tble 7). This systemtic underestimtion cross the modeling pproches is likely due to the lck of representtion of reservoir opertions. For the unregulted stremflow loction (North Sntim below Boulder Creek), the rnge of the medin estimtes of 1-yer 7 dy low flow for SSMU nd BSPU spn the 1-yer 7-dy low flow clculted from observed stremflow, while the 1-yer 7-dy low flow estimte by the pproch gretly underestimted the mesured stremflow (Tble 7). In compring BSPU to SSMU, we find tht the rnge of BSPU 1 yer 7-dy low flow estimtes ws wider thn SSMU t three of the sites thn SSMU, similr to the wider rnge of pek flow estimtes of the BSPU pproch (Fig. 2). Further, the rnge of medin estimtes from BSPU often hd vlues tht were much lower thn the 1-yer 7-dy low flow stremflow clculted from mesured stremflow. We believe these differences in low flow mgnitude nd rnge re explined by the use of different prmeter sets for the wet nd dry sesons nd more sophisticted groundwter modeling by the SSMU pproch. However, we cnnot identify the reltive importnce to sesonl prmeteriztion nd groundwter model on model fit Model prmeters nd structure The DREAM uncertinty ssessment for the BSPU nd SSMU modeling pproches produced different rnges of prmeter vlues from the priori prmeter rnge for severl prmeters s well s importnt differences between the BSPU nd SSMU models. These differences re likely due to the wet/dry seson prmeteriztion nd to the interctions of MODFLOW groundwter model with the SSMU pproch. For exmple, the DREAM nlysis converged on monthly rin nd snow djustments with slightly higher but nrrower rnge of vlues for the SSMU pproch compred to BSPU (Tble 5). Air temperture lpse rtes were within smller rnge of vlues for the SSMU pproch compred to BSPU. The Hmon evpotrnspirtion coefficient (hmon coef) ws higher during the wet seson for SSMU thn the dry seson, reflecting the need to increse evportion rtes in the wet seson clcultions. The exponent coefficient in surfce runoff contributing re clcultions, coefficient of re in the non-liner surfce runoff eqution, vried between SSMU wet nd dry sesons, illustrting prmeter sensitivity to wet nd dry conditions in the clcultions. Differences exist in the prmeters tht control groundwter clcultions (ssr2gw_rte, ssr2gw_exp, soil2gw_mx, gwflow_coef) between the SSMU nd BSPU pproches, emphsizing the importnce of groundwter processes in the SRB. The gretest prmeter differences between SSMU nd BSPU were between the coefficients tht route wter to groundwter (ssr2gw_rte, ssr2gw_exp) nd the coefficient tht routes groundwter to strems (gwflow_coef). The exponent coefficient to route wter from subsurfce to groundwter ws much lower for BSPU thn SSMU (nd the priori prmeter rnge). A lower exponent of groundwter routing indictes less groundwter rechrge being predicted for BSPU compred to the SSMU pproch. We lso note tht SSMU groundwter prmeter rnges, when different from the priori rnges, tended to hve higher vlues for the wet seson thn the dry seson in response to greter routing of groundwter to fit the model during wet seson conditions Differences in hydrologic model projections for climte chnge Monthly stremflow The timing nd mgnitude of future runoff vry cross the three modeling pproches (Fig. 3). Generlly higher winter nd lower summer runoff were predicted with nd BSPU pproches thn were predicted by the SSMU pproch, prticulrly for the North Sntim loctions (Fig. 3A nd B) where groundwter hs stronger influence on the hydrology thn t the South Sntim site (Fig. 3C). In North Sntim below Boulder Creek, historiclly with snow dominted precipittion, the SSMU nd BSPU pproches predict greter spring nd summer runoff in the future thn the pproch (Fig. 3A). The North Sntim below Boulder Creek ws the smllest bsin evluted. The differences between the future runoff predictions for the three pproches re most pronounced t this loction. When modeling hydrology in snow dominted bsin, site-specific informtion on spect nd vegettion interception differences become more sensitive for model predictions s the bsin size decreses. Further, the North Sntim below Boulder Creek historiclly hs higher spring nd summer unit re runoff thn the other study loctions in SRB. The higher spring runoff cn be ttributed to spring snowmelt, however the higher summer runoff is ttributed to long residence times nd sustined groundwter dischrges (Tgue et l., 28). The SSMU nd BSPU pproches resulted in the best sttisticl fit to historicl runoff for this loction (Tble 6), with the SSMU pproch providing better fit to summer low flow (highest E rel vlue) of ll pproches. Although it cnnot be stted tht historicl fitness of model corresponds to correct future predictions, we cn stte tht the processes represented in this sub-bsin were better cptured by the SSMU nd BSPU pproches Extreme vlue pek dily flow The BSPU nd SSMU pproches generlly predicted decrese in the -yer event in ll periods nd scenrios, with smll increses predicted by the SSMU models for the North Sntim t Mehm nd South Sntim t Wterloo. In contrst, the pproch predicts increses in the 2-, 5-, nd -yer pek flows (Fig. 4). Where increses in the 2- nd 5-yer events were predicted by the BSPU nd SSMU pproches, they were no greter thn 1 2% of the historicl pek flow, while the pproch predicted lrger increses (5 4% depending on loction, time period, nd emission scenrio). The uncertinty round predictions of BPSU pek flows (Fig. 4) demonstrtes how the use of regionl prmeter sets led to greter vribility in the predictions of extreme pek flows. The BSPU pproch predicted rnge of pek dily flow of up to 25 35% bove nd below the verge vlue (totl rnge of 5 7%) (Fig. 4). The SSMU pproch predicted devition of pek dily flow vlues of pproximtely 1 25% bove nd below the verge vlue (totl rnge between 2% nd 5%). The pproch hd slightly smller rnge of pek flow predictions thn SSMU, pproximtely 5 15% bove nd below the verge vlue. However, becuse no prmeter uncertinty ssessment ws vilble for the pproch, this rnge reflects only uncertinty due to use of different GCMs. In contrst, the uncertinty rnges for the BSPU nd SSMU pproches chrcterize both prmeter nd GCM uncertinty.

10 (A) 2 Runoff (mm) Runoff (mm) 15 (C) 2 Runoff (mm) Runoff (mm) B1 28 (B) B Historic B1 24 B1 28 Historic BSPU 15 BSPU SSMU SSMU A1B 24 A1B 28 B1 28 (D) B Historic B1 24 B1 28 Historic 15 BSPU BSPU SSMU SSMU 2 5 A1B 24 A1B 28 2 A1B 24 A1B 28 2 A1B 24 A1B 28 Jn Jn Mr Mr My My Jun Jun Sep Sep Jn Jn Mr Mr My My Jun Jun Sep Sep Runoff (mm) Runoff (mm) Runoff (mm) Runoff (mm) Jn Mr My Jun Jn Mr My Jun Sep Sep Jn Mr Jn Mr My Jun Sep My Jun Sep Fig. 3. Men monthly runoff (mm) for 24 nd 28 time periods compred to historic for two scenrios B1 nd A1B. (A) North Fork Sntim below Boulder Creek, (B) North Sntim t Mehm, (C) South Sntim t Wterloo, nd (D) Sntim t Jefferson Yer 7-dy low flow The nd BSPU pproches predicted decreses in the 1-yer 7-dy low flow for ll future scenrios nd time periods in the SRB (Fig. 5A D). In contrst, the SSMU predicted no chnge in the 1 yer 7-dy low flow in the future for North Sntim below Boulder Creek (Fig. 5A) nd North Sntim t Mehm (Fig. 5B), both sites hevily influenced by groundwter. Further, the SSMU lso predicted much smller decreses in the 1-yer 7-dy low flow thn or BSPU pproches for the South Sntim t Wterloo (Fig. 5C) nd Sntim t Jefferson (Fig. 5D) loctions. Both the high elevtion nd the lower lluvil res of the SRB hve significnt groundwter interctions with stremflow. The high elevtion res, consisting of High Cscde geology, hve long sub-surfce wter residence times producing continul dischrge from sub-surfce wters (e.g., spring fed strems) (Lee nd Risley, 22). The lower lluvil res re loctions of rechrge to the vlley quifer in the wet seson nd dischrges wter for stremflow during the dry seson. Likely s result of the groundwter simultions, the SSMU pproch predicted less chnge in low flow dischrge nd considerbly lower uncertinty round the results thn the BSPU nd pproches (Fig. 5A D). The BSPU pproch hd high rnge of low flow predictions thn SSMU, in some cses s much s four orders of mgnitude (Fig. 5). The BSPU pproch hd its greter rnge of predictions, or highest uncertinty, in the North Sntim below Boulder Creek loction (Fig. 5A). The North Sntim below Boulder Creek hs substntil summer groundwter dischrge nd is not regulted by n upstrem dm. The BSPU nd prmeter sets did not predict this summer groundwter influence s the SSMU pproch, nd generted greter rnge of low flow results. All of the pproch low flow vlues, including the historic vlue, re well below those clculted from mesured USGS stremflow suggesting high degree of uncertinty in the low flow estimtes from the pproch. However, without prmeter uncertinty ssessment for the pproch we do not know the mount of uncertinty ssocited with the pproch predictions Monthly Snow Wter Equivlent (SWE) There ws little reltive difference in the verge monthly SWE predicted by the three modeling pproches for the North Sntim River below Boulder Creek sub-bsin for both 24 nd 28 time periods for B1 nd A1B scenrios (Fig. 6A). While the pproch tended to underestimte SWE during the lte summer months, reltive to BSPU nd SSMU pproches, historicl fitness with the SWE for the North Sntim River below Boulder Creek ws shown to be similr mong the three pproches. This generl similrity suggests tht the bove ground energy clcultions for snow processes in the high elevtion res of the SRB were comprble mong the model pproches. However, the chnge in SWE predictions for the entire SRB (s evluted t the Sntim River t Jefferson) does vry mong the pproches (Fig. 6B). The lower elevtion

11 (A) 14 Pek Dischrge (m 3 /sec) BSPU SSMU Historic B1 24 men B1 28 men A1B 24 men A1B 28 men (B) (C) (D) Pek Dischrge (m 3 /sec) Pek Dischrge (m 3 /sec) Pek Dischrge (m 3 /sec) BSPU SSMU BSPU SSMU BSPU SSMU Return Intervl (yr) Fig. 4. Comprison of 2, 5, nd yer pek dily flow predicted by three modeling pproches for two climte chnge scenrios, B1 nd A1B for 24 nd 28 time periods nd three modeling pproches. (A) North Fork Sntim below Boulder Creek, (B) North Sntim t Mehm, (C) South Sntim t Wterloo, nd (D) Sntim t Jefferson. The mrkers indicte the percent chnge in medin monthly vlues from historicl observtions while error brs indicte the percent chnge for 2.5 nd 97.5 percentile predictions for SSMU nd BSPU pproches or the rnge of results from only GCMs for the pproch. res of the SRB hve rin-dominted climte, with the middle elevtions being mix of rin nd snow depending on the ir temperture during the precipittion event. The pproch predicted slightly smller decrese in SWE thn SSMU or BSPU during the pek snow months (Jnury through Mrch) for the A1B scenrio for both 24 nd 28 time periods for the SRB. The SSMU nd BSPU both predict less chnge in lte summer SWE compred to the pproch in the A1B scenrio. During the B1 scenrio the SSMU pproch predicted pproximtely 15% less decrese in SWE thn pproch during the lte spring to erly summer of the SRB, period of declining snow wter storge due to snow melt. During summer ll model pproches show lrge decreses in summer SWE, however, there re only smll mounts of snow in summer in the SRB, primrily in the highest elevtions.