5046352937 e bfd 7573

A comparison of downscaled and raw GCM output: implicationsfor climate change scenarios in the San Juan River basin, Colorado

R.L. Wilbya,b,* , L.E. Hayc, G.H. Leavesleyc

aNational Center for Atmospheric Research, Boulder, CO 80307, USAbDivision of Geography, University of Derby, Kedleston Road, Derby DE22 1GB, UK

cWater Resources Division, US Geological Survey, Denver Federal Center, Denver, CO 80225, USA

Received 2 November 1998; received in revised form 23 April 1999; accepted 1 September 1999

Abstract

The fundamental rationale for statistical downscaling is that the raw outputs of climate change experiments from GeneralCirculation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes atregional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (mostnotably those pertaining to the hydrological cycle) and because GCM output is often unreliable at individual and sub-grid boxscales. By establishing empirical relationships between grid-box scale circulation indices (such as atmospheric vorticity anddivergence) and sub-grid scale surface predictands (such as precipitation), statistical downscaling has been proposed as apractical means of bridging this spatial difference. This study compared three sets of current and future rainfall-runoff scenarios.The scenarios were constructed using: (1) statistically downscaled GCM output; (2) raw GCM output; and (3) raw GCM outputcorrected for elevational biases. Atmospheric circulation indices and humidity variables were extracted from the output of theUK Meteorological Office coupled ocean-atmosphere GCM (HadCM2) in order to downscale daily precipitation and tempera-ture series for the Animas River in the San Juan River basin, Colorado. Significant differences arose between the modelledsnowpack and flow regimes of the three future climate scenarios. Overall, the raw GCM output suggests larger reductions inwinter/spring snowpack and summer runoff than the downscaling, relative to current conditions. Further research is required todetermine the generality of the water resource implications for other regions, GCM outputs and downscaled scenarios.q 1999Elsevier Science B.V. All rights reserved.

Keywords: Climate change; Downscaling; Runoff; Snowpack; General circulation model; Colorado

1. Introduction

An often stated justification for statistical down-scaling is that the raw output of climate changeexperiments from General Circulation Models(GCMs) are an inadequate basis for assessing land-surface impacts at regional scales (DOE, 1996). Thisis because the spatial resolution of GCM grids is too

coarse to resolve many important sub-grid scaleprocesses (most notably those pertaining to the hydro-logical cycle) and because GCM output is often unre-liable at individual grid and sub-grid box scales(IPCC, 1996). This mismatch, between what theclimate impacts community requires and what theGCMs are able to supply, has been a confoundingissue affecting the confidence placed in impactscenarios at the basin scale (Hostetler, 1994).

A wide variety of techniques exist for assessing theeffects of climate change on water resources (see the

Journal of Hydrology 225 (1999) 67–91

0022-1694/99/$ - see front matterq 1999 Elsevier Science B.V. All rights reserved.PII: S0022-1694(99)00136-5

www.elsevier.com/locate/jhydrol

* Corresponding author.E-mail address:[email protected] (R.L. Wilby)

overview by Leavesley (1994)). The most conven-tional solution to the problem is to perturb historicaltime series of high resolution meteorological variablesby the difference (or ratio) of the means of GCMoutput between the altered and control climate runs(e.g. Arnell, 1996). An alternative methodology,statistical downscaling, involves bridging the twodiscordant scales by establishing empirical relationsbetween features reliably simulated by the GCM atgrid-box scales (such as geopotential height fields)and surface predictands at sub-grid scales (such asprecipitation occurrence or amounts). These proceduresare analagous to the so-called “Model Output Statis-tics” (MOS) and “Perfect Prog” (PP) techniques usedsince the 1970s for short range numerical weatherprediction (Klein and Glahn, 1974).

Although many studies have discussed the theoryand practice of statistical downscaling (e.g. Kim et al.,1984; Karl et al., 1990; Wigley et al., 1990; Bardossyand Plate, 1992; Hay et al., 1992; von Storch et al.,1993), relatively few have explicitly considered thelimitations of such techniques (Giorgi and Mearns,1991; Wilby and Wigley, 1997). However, sensitivityanalyses have demonstrated the susceptibility ofdownscaled scenarios to season definitions, the choiceof data standardisation technique, length of calibrationperiod, function form and predictor variable(s) (e.g.Winkler et al., 1997). It has also been shown that differ-ent circulation schemes (Buishand and Brandsma,1997) and downscaling methodologies (Wilby et al.,1998b)yield markedlydifferent regional climate changescenarios, even when common sets of GCM predic-tors are used. Finally, there is skepticism regarding theassumed stationarity of predictor–predictand relations(Pielke, personal communication; Wilby, 1997) andthe reproduction of low-frequency surface climatevariability in downscaling schemes continues to beproblematic (Katz and Parlange, 1996). Although itis not our intention to investigate such assumptions, itis appropriate at the outset to flag sources of uncer-tainty that are inherent to most statistical downscalingapproaches.

Given the aforementioned limitations, it is impor-tant that the relative merits of downscaled and rawGCM output should be properly compared. Althoughthere have been numerous validations of the synopticcirculation patterns and climate variables produced byGCMs (e.g. Santer and Wigley, 1990; McCabe and

Legates, 1992; Hulme et al., 1993; Airey and Hulme,1995; McKendry et al., 1995; Osborn and Hulme,1998), comparatively little is known about the“value-added” (or indeed “value-subtracted”) ofdownscaled versus raw GCM output, especiallywhen applied to (non-linear) impact models. Inother words, to what extent do downscaled scenarios(resulting from a range of subjective methodologicaldecisions and driven by imperfect GCM predictors)actually yield regional climate change impacts thatare significantly different to those derived from rawGCM output?

With this question in mind–as well as the addi-tional effort required to generate downscaled climatescenarios–the present study compares three sets ofcurrent and future daily rainfall-runoff scenarios.These scenarios were constructed using: (1) statisti-cally downscaled GCM output; (2) raw GCM output;and (3) raw GCM output corrected for elevationalbiases. Atmospheric circulation indices and humidityvariables derived from the UK Meteorological Officecoupled ocean–atmosphere GCM (Johns et al., 1997;Mitchell and Johns, 1997) were used to downscaledaily precipitation and temperature series for theAnimas River, a sub-basin of the San Juan River,Colorado (see Fig. 1). The Animas River basin has adrainage area of 1820 km2 and elevation that rangesfrom approximately 2000 to 4000 m. The climatescenarios generated were then used to drive a distrib-uted hydrological model. Changes in the modelleddaily flow regime and snowpack behaviour betweencurrent and future climate scenarios were compared.Finally and in the light of these results, we discuss therelative merits of the three techniques for regionalscenario development and climate change impactassessment.

2. Data

Three data sets were compiled for the purposes ofclimate scenario generation and hydrological modelapplication. Data sets were compiled on a water-year(WY) basis with October 1980 to September 1981being WY 1981.

2.1. Station data

Daily maximum and minimum temperatures

R.L. Wilby et al. / Journal of Hydrology 225 (1999) 67–9168

(TMAX and TMIN) and precipitation amounts(PRCP) from 37 stations in and around the San JuanRiver basin were compiled (see Fig. 1 and Table 1).Twenty-two of the precipitation stations are relativelyhigh elevation (2500–3500 m) Snow Telemetry(SNOTEL) stations. The remainder of the stationsare National Weather Service (NWS) stations.

2.2. Re-analysis data

The ultimate choice of predictors for downscalingis constrained by three main factors. The predictor

variables should be (1) reliably simulated by theGCM under consideration, (2) readily available from(in this case, daily) archives of GCM output and (3)strongly correlated with the surface variable(s) ofinterest. Using these criteria, daily grid point datafor mean sea level pressure (mslp), 500 hPa geopoten-tial heights (H), 2 m (near surface) temperatures(T2m) and 0.995 sigma level (near surface) relativehumidities (RH) were obtained from the NationalCenter for Environmental Prediction (NCEP) re-analysis (Kalnay et al., 1996) for the period WY1981–1995. All data were re-gridded from the NCEP

R.L. Wilby et al. / Journal of Hydrology 225 (1999) 67–91 69

Fig. 1. Location map showing the meteorological station network of the San Juan River basin and Animas River sub-basin, Colorado.

grid (1.8758 latitude by 1.8758 longitude) to the GCMgrid (2.58 latitude by 3.758 longitude).

The mslp andH data were used to calculate fourdaily airflow indices for the surface and upper atmo-sphere, respectively, according to the methodologydescribed by Jones et al. (1993). At each atmosphericlevel the derived circulation indices were: zonal andmeridional components of the geostrophic airflow,UandV; total shear vorticity,Z; and divergence,D. Apositive value ofU is indicative of an airflow from

west to east and positiveV corresponds to an airflowfrom south to north. The vorticity (Z) is a measure ofatmospheric rotation with negative values indicativeof anticyclonic (high pressure weather) and positivevalues corresponding to cyclonic (low pressureweather) conditions. The divergence (D) is a measureof atmospheric motion in the vertical plane, beingpositive when air flows diverge and negative whenconvergent.

Daily mean temperatures and relative humidities at


Table 1Station details for the San Juan River basin (the stations marked with ‘p’ were selected for the Animas River sub-basin downscaling; all stationswere used to calculate area average PRCP, TMAX and TMIN for the San Juan River basin–a domain equating to a typical HadCM2 grid-boxarea)

Station type Station number Station name Elevation (m) Start of record

National WeatherService Station

1 Farmington Experimental Station 1716 19482 Azetc Ruins National Monument 1719 19483 Bloomfield 1771 19484 Durango 2012 19485 Dulce 2070 19486 Otis 2097 19577 Cuba 2149 19488 Pagosa Springs 2167 19489 Lybrook 2179 1951

10 Johnson Ranch 2195 194811 Fort Lewis 2316 194812p Vallecito Dam 2332 194813 Lemon Dam 2466 198214 Rico 2676 194815 Telluride 2682 1948

Snow TelemetryStation SNOTEL

16 Chamita 2560 197917 Senorita Divide 2621 198018 Cascade 2707 197819 Cascade 2 2719 199020 Scotch Creek 2774 198521 Upper Rio Grande 2865 198622 Mancos 3048 199423 Cumbers Trestle 3054 197924p Mineral Creek 3060 197825 Upper San Juan 3088 197826 El Diente Peak 3109 198527 Lizard Head Pass 3109 197928 Molas Lake 3200 198529 Spud Mountain 3249 198630 Columbus Basin 3287 199431 Vallecito 3316 198632 Lily Pond 3353 197933p Wolf Creek Summit 3353 198634 Red Mountain Pass 3399 198035 Stump Lakes 3414 198636 Middle Creek 3429 197937 Beartown 3536 1982

the surface were used to estimate daily mean specifichumidities via the non-linear approximation ofRichards (1971). Finally, daily values of all 15 candi-date variables (Table 2) were extracted from theglobal fields for the GCM grid-box (2084) nearest tothe San Juan River basin.

2.3. General circulation model output

The GCM used was the UK Meteorological Office,Hadley Centre’s coupled ocean/atmosphere model(HadCM2) forced by combined CO2 and albedo (asa proxy for sulphate aerosol) changes (Johns et al.,1997; Mitchell and Johns, 1997). In this ‘SUL’(sulphate-plus-greenhouse gas) experiment, the modelrun begins in 1861 and is forced with an estimate ofhistorical forcing to 1990 and a projected futureforcing scenario over 1990–2100. The historicalforcing is only an approximation of the “true” forcingso the GCM results for WY 1981–1995 would not beexpected to exactly represent present-day conditions(for more details see Wilby et al., 1998b, AppendixA), nor are the GCM years directly equivalent toactual years due to the difference in observed climateand GCM forcing (see Wilby et al., 1998b). Withthese caveats in mind, HadCM2 output for the periodWY 1981–1995 was used as the best available proxyfor the present climate as in previous downscalingstudies (e.g. Conway et al., 1996; Pilling et al.,

1998; Wilby et al., 1998a,b). With the exception ofdaily mslp, the normalised predictor variablesproduced by HadCM2 for the years 1981–1995were statistically indistinguishable (P , 0:05) fromthose of NCEP for the same period. The discrepencyfor mslp was attributed, in part, to missing data in theHadCM2 archive for all Januarys between 1981 and1990 (totalling 300 days).

Two time series of daily mean sea level pressure,500 hPa geopotential heights, surface relative humid-ity, maximum and minimum temperatures wereobtained from the HadCM2 ‘SUL’ experiment. Thefirst set of data, representative of the current (WY1981–1995) climate, parallels the NCEP re-analysisdata; the second, represents future (WY 2081–2095)climate conditions due to anthropogenic forcing. Both15 year data sets were used to derive the chosenpredictor variables for the statistical downscaling(Table 2). Finally, daily PRCP, TMAX and TMINfor the HadCM2 grid-box (2084) were retained forboth time periods in order to simulate changes in thedaily flows of the Animas River basin using the rawGCM scenarios.

3. Methodology

The compiled data sets were used to developsix current and three future climate scenarios to


Table 2Candidate predictor variables (p denotes variables used in downscaling)

Predictor variable Abbrevation Source

Surface variablespMean sea level pressure mslp NCEPZonal velocity component Us Derived from mslpMeridional velocity component Vs Derived from mslpStrength of the resultant flow (hPa) Fs Derived from mslpVorticity (hPa) Zs Derived from mslpDivergence (hPa) Ds Derived from mslp2 m temperatures (8C) T2m NCEPRelative humidities (%) RH NCEPpSpecific humidity (gm/kg) SH Derived from RH and T2mUpper-atmosphere variables(500 hPa)p500 hPa geopotential heights (m) H NCEPZonal velocity component Uu Derived from HMeridional velocity component Vu Derived from HStrength of the resultant flow (hPa) Fu Derived from HVorticity (hPa) Zu Derived from HDivergence (hPa) Du Derived from H

investigate the raw GCM and downscaled GCMmethods of scenario generation in the AnimasRiver basin. The description of the methodologiesinvolved will follow the routes shown in Fig. 2.Daily precipitation and temperature time seriesproduced from observed, downscaled and rawGCM output were distributed spatially over theAnimas River basin using monthly lapse ratescalculated from observed data. These spatiallydistributed hydrometeorological variables werethen used as input to the watershed model PRMS(Precipitation-Runoff Modelling System) (Leavesleyet al., 1983; Leavesley and Stannard, 1995). Finally,modelled daily flows, snow-covered area and snow-pack water equivalents were used as measures toevaluate the scenarios and future climate changeimpact. The following sections provide details ofthe methodologies involved at each stage of theanalysis.

3.1. Climate scenario definitions

The S_3c and S_37c series correspond to observedstation data for current (c) conditions in the AnimasRiver basin (three stations) and entire San Juan Riverbasin (37 stations), respectively. S_3c data were usedto calibrate the downscaling model and S_37c wereused to compare observed climate conditions withGCM output for the current climate (i.e. the entireSan Juan River basin area of 37,500 km2 equates toa single HadCM2 grid-box). N_dsc (Fig. 2) representsthe downscaling model estimate of current observedconditions in the Animas River basin given only grid-box values of the chosen NCEP predictor variables(see later). G_dsc and G_dsf are the downscaledtime-series produced for current and future (f) climateperiods using GCM (HadCM2) output as the predictorvariable source. G_raw and G_elv denote the raw andelevation bias corrected GCM output, respectively


Fig. 2. Methods for generation of current (c) and future (f) rainfall-runoff scenarios for the Animas River basin. (S_3c uses three stations andS_37c uses 37 stations).

(see Section 3.2.2). The G_rawc, G_rawf, G_elvc andG_elvf scenarios all use daily values of PRCP, TMAXand TMIN obtained from HadCM2 at the grid-pointnearest to the San Juan River basin for the current andfuture climate.

3.2. Statistical downscaling model

The statistical downscaling model was calibratedusing S_3c data, originating from the three stations(denoted by ‘p’ in Table 1) which produced the bestsimulations of observed daily flows in the AnimasRiver basin (see Section 3.2.2). The downscalingmodel parameters were estimated by linear least-squares regression using daily data for the nine WYs1987–1995 and evaluated using the six WYs 1981–1986. Although data were available for previous yearsthe station network was not as dense, contributing tolikely underestimates of true area-average wet-dayfrequencies and precipitation totals (see Osborn andHulme, 1997).

The downscaling model was calibrated using dailyarea-averaged series (S_3c) of wet-day occurrence(O), wet-day amounts (R), maximum (TMAX) andminimum (TMIN) temperatures for WYs 1987–1995, with separate regressions for each climatologi-cal season, i.e. individual winter (DJF), spring(MAM), summer (JJA) and autumn (SON) sub-models. All daily predictor variables were firstnormalised using the corresponding period meansand standard deviations (as advocated by Karl et al.(1990)). Three predictor variables were selectedfollowing a stepwise multiple linear regression analy-sis of the 15 candidate variables listed in Table 2. Thechosen predictors were grid-box (HadCM2 number2084) values of daily specific humidity (SH), meansea level pressure (mslp) and 500 hPa geopotentialheights (H). These three predictors (SH, mslp andH) were selected by the stepwise regression on12/16, 11/16 and 15/16 occassions, respectively (i.e.4 predictands× 4 seasons� 16 sub-models). Thus, itwas possible to downscale all four surface predictandswith a parsimonious, yet physically plausible, set ofpredictors.

The legitimacy of the regression analysis of dailyln(R), TMAX, TMIN, mslp, H and SH was verified bytesting for normality. All variables were found to beapproximately normal within the following percentile

ranges: ln(R) all values; TMAX 5–95%; TMIN,95%; mslp between 5 and 95%; SH 25–75%; andH , 90%: Overall, the assumption of normality heldfor all but the most extreme values of the predictorsand predictands, with the most serious violation beingfor values of SH outside the inter-quartile range.Detailed descriptions of each of the downscalingmodel components are now provided.

3.2.1. Daily precipitation occurrence�Oi�Daily probabilities of non-zero precipitation (a wet-

day) Oi for a given dayi were downscaled using thethree grid-box predictor variables SH, mslp andH anda lag21 autocorrelation parameter. The random vari-ableOi was modelled using the following regressionequation:

Oi � a0 1 aOi21Oi21 1 aSHSHi 1 amslpmslpi 1 aHHi

�1�The a parameters were estimated using linear leastsquares regression. A uniformly distributed randomnumberr �0 # r # 1� was used to determine whetherprecipitation occurs. For a given site and day, a wet-day was returned ifr # Oi .

3.2.2. Daily precipitation amounts�Ri�If determined that precipitation has occurred, the

daily precipitation amount was also downscaledusing the three grid-box predictor variables SH,mslp andH. Since the wet-day precipitation amounts(Ri) for a given dayi are always non-zero, it is appro-priate to formulate the following regression model(following Kilsby et al., 1998):

Ri � exp�b0 1 bSHSHi 1 bmslpmslp1 bHHi 1 ei��2�

where theb ‘s were parameters estimated using linearleast squares regression andei was random or model-ling error. The expected value was given by

E�Ri� � fcR exp�b0 1 bSHSHi 1 bmslpmslpi 1 bHHi��3�

wherecR was an empirically derived correction ratiothat allows for the bias resulting from the re-transfor-mation of ln(R) to R and the fact thatei came from askewed distribution. The value ofcR was constrainedsuch that observed and downscaled precipitation


totals were equal for the simulation period. A randomscaling factorf (with a mean of 1) was used toincrease the variance ofR to agree better with obser-vations (as in Hay et al., 1991). Note that a lag21

autoregressive component was not used to modelRi

because its inclusion did not significantly improve theexplained variance in wet-day amounts for theAnimas River basin. However, it is acknowledged


Fig. 3. Observed lapse rates by month for (a) precipitation, (b) maximum and (c) minimum temperatures in the San Juan River basin, 1988–1997.

that the inclusion of this parameter may be appropri-ate at other locations.

3.2.3. Daily temperatures (TMAXi and TMINi)Daily maximum (TMAXi) and minimum (TMINi)

temperatures for a given dayi were downscaled usingthe three grid-box predictor variables SH, mslp andHand the preceding days’ maximum (TMAXi21) andminimum (TMINi21) temperatures, respectively. Thedaily temperature series were modelled using thefollowing regression equations:

TMAX i � d0 1 dTMAX i21TMAX i21 1 dSHSHi

1 dmslpmslpi 1 dHHi 1 zi �4�

TMIN i � g0 1 gTMIN i21TMIN i21 1 gSHSHi

1 gmslpmslpi 1 gHHi 1 zi �5�whered andg were parameters estimated by linearleast squares regression andzi andji were random ormodelling errors. Bothzi andji were assumed to benormally distributed with mean zero and standarddeviation s equal to the standard error of theregression equation. Both sets of residuals were

modelled stochastically using conventional MonteCarlo methods.

3.3. Spatial distribution and elevation correctionschemes

Daily values of PRCP, TMAX and TMIN weredistributed across the Animas River basin using amonthly lapse rate for each variable. Lapse rateswere calculated using observed data from NWS andSNOTEL stations (see Table 1) for the period 1988–1997. A mean value of PRCP, TMAX and TMIN forthese data stations and their corresponding meanelevations were computed daily. These daily meanvalues were then used with the monthly lapse ratesto distribute PRCP, TMAX and TMIN according tothe mean elevations of individual basin sub-areas deli-neated for hydrological modelling purposes. Thesehydrological response units (HRUs) are discussed inSection 3.4. Monthly lapse rates and correspondingadjustedR-squared values are shown in Fig. 3a–c.

A Monte Carlo analysis was used to determine theoptimal number of stations for use in the distributionmethodology. To initiate the Monte Carlo (MC)analysis, each station was tested individually for


Fig. 3. (continued)

distributing PRCP, TMAX and TMIN in the AnimasRiver basin. Thirty-seven time-series of PRCP,TMAX and TMIN were calculated and used as inputinto the hydrological model (see later). In the preli-minary screening, meteorological stations yielding thehighest absolute errors between observed and simu-lated runoff for the period 1988–1997 (30% of thestations) were rejected.

Further MC testing using all possible combinationsof two, three and four of the remaining stations wasnext conducted. For each set, the best station combi-nation was determined. The MC analysis ended whenthe absolute error associated with the best stationcombination showed no significant improvementfrom one set to the next. In this exercise no improve-ment was seen by increasing from three to fourstations. The optimal station set for the AnimasRiver basin was determined to be the three stations:Vallecito Dam, Mineral Creek and Wolf CreekSummit (see Table 1). The selection of Wolf CreekSummit by the MC analysis highlights the problem ofgauge under catch, estimated to be in the region of20–50% for snowfall in mountainous terrain(Severuk, 1989). Note that no correction was madefor gauge under catch in this study. The choice ofWolf Creek Pass compensates for gauge under catchsince this station generally has precipitation amountsin the winter time that are 20% higher than thosemeasured at stations with similar elevations in theSan Juan River basin.

The three stations were used to construct the S_3c

scenario against which the downscaling model wascalibrated and then used to generate the N_dsc,G_dsc and G_dsf scenarios. Downscaled PRCP,TMAX and TMIN time-series data for present(N_dsc, G_dsc) and future (G_dsf) conditions werethen spatially distributed across the Animas Riverbasin prior to input into the hydrological model. Alldownscaled time-series were treated as one station inthe distribution methodology with a mean elevation of2915 m (the mean elevation of the three stationschosen in the MC analysis). This information wasused along with the monthly lapse rates to distributePRCP, TMAX and TMIN to each HRU.

GCM grid-box PRCP, TMAX and TMIN time-series data were spatially distributed in the AnimasRiver basin using two methods. The first methodused the mean elevation of the GCM grid node

(1900 m) as the mean station elevation in the distribu-tion methodology and was used to produce scenarioG_rawc and G_rawf. The second method adjusts theGCM elevation value and was used to produce scenar-ios G_elvc and G_elvf. This was necessary becausethe GCM grid-box PRCP, TMAX and TMIN valueswere not representative of current climate conditionsat 1900 m, but had representative elevations thatvaried by month and were all higher than 1900 m.In order to produce realistic estimates of currentPRCP, TMAX and TMIN, the second method usedthe elevations shown in Fig. 4 to distribute these vari-ables. These elevations were determined by calculat-ing the elevation value needed to make monthlyestimates of PRCP, TMAX and TMIN used in theG_rawc scenario consistent with observed data.Compared to the first method, the use of higher repre-sentative elevations increased temperature (due tonegative lapse rates) and decreased precipitation(due to positive lapse rates) over the basin.

3.4. Hydrological modelling

The hydrological model used was the US Geologi-cal Survey’s (USGS) Precipitation-Runoff ModellingSystem (PRMS) (Leavesley et al., 1983; Leavesleyand Stannard, 1995). PRMS is a distributed-parameter,physical process watershed model. Distributed-para-meter capabilities are provided by partitioning awatershed into units, using characteristics such asslope, aspect, elevation, vegetation type, soil typeand precipitation distribution. Each unit is assumedto be homogeneous with respect to its hydrologicresponse and to the characteristics listed above.Each unit is termed a hydrologic response unit(HRU). A water balance and an energy balance arecomputed daily for each HRU. The sum of theresponses of all HRUs, weighted on a unit-areabasis, produces the daily watershed response.

PRMS was applied using the USGS ModularModeling System (MMS) (Leavesley et al., 1996).A major component of MMS is the GIS Weasel, aGeographic Information System (GIS) interfacedeveloped to provide a variety of GIS tools to delin-eate, characterise and parameterise the topographicand hydrologic features of a watershed. Using theGIS Weasel, the Animas River basin was delineatedinto 34 HRUs. Parameters for each HRU related to


topographic, vegetation and soils characteristics werecomputed using three digital databases: (1) USGS3-arc second digital elevation models; (2) State SoilsGeographic (STATSGO) 1 km gridded soils data (USDepartment of Agriculture, 1994); (3) Forest Service1 km gridded vegetation type and density data (USDepartment of Agriculture, 1992). Other model para-meters were estimated using model applications incomparable basins of this region (Leavesley et al.,1992). To prevent biasing parameter estimates toany particular meteorological data set, no parameteroptimisation was performed.

Snow is the major form of precipitation input to theAnimas River basin and the major source of stream-flow. The snow components of PRMS simulate theaccumulation and depletion of a snowpack on eachHRU. A snowpack is maintained and modified bothas a water reservoir and as a dynamic heat reservoir. Awater balance is computed daily and an energybalance is computed twice each day. The energy-balance computations include net shortwave andlongwave radiation, approximations of convectionand condensation terms and the heat content ofprecipitation.

In summary, PRMS uses daily inputs of solar radia-tion and the variables PRCP, TMAX and TMIN. Solarradiation was distributed to each HRU as a function ofHRU slope and aspect. Solar radiation data were not

available on a daily basis for either the measurementstations or the archived GCM output and so werecomputed using existing algorithms in PRMS.Estimates of daily shortwave radiation received on ahorizontal surface were computed using air tempera-ture, precipitation and potential solar radiation.

4. Results

The stochasticity of the downscaling model allowsan infinite number of simulations to be produced–theresults presented below were constructed using anensemble of 20 runs for N_dsc, G_dsc and G_dsf.The other scenarios have only one realisation.

4.1. Evaluation of downscaling model

When calibrated using the S_3c data set for theWYs 1987–1995 the downscaling model generallyexplained more than 80% of the variance (E%) indaily temperatures (TMAXi and TMINi) and 30–45% in daily wet-day amounts (Ri). As Table 3 indi-cates the model performance was better during spring(MAM) and autumn (SON) and worse during winter(DJF) and summer (JJA). The relatively lowexplained variance for the wet-day amounts in allseasons is consistent with previous studies (e.g.Burger, 1996; Wilby et al., 1998a) and underlines


Fig. 4. Corrected elevations by month for HadCM2 daily precipitation, maximum and minimum temperatures.

the difficulty of downscaling local precipitation seriesfrom regional (grid-box) scale predictors. At presentthe unexplained component in daily precipitationamounts must be treated stochastically by the down-scaling model.

The E% statistic is not an appropriate measure ofthe model fit for precipitation occurrence�Oi� becausethis predictand is a discrete variable. A more usefulmetric is the percentage of correct wet and dry dayclassifications (Wilks, 1995). From Table 3 it isevident that on an average the downscaling modelreproduces wet days better than dry days: the successrate for the former was 62% and for the latter 52%.These precipitation statistics were determined froma single realisation of the stochastic downscaling–asevere test of the occurrence model performance.According to the results in Table 3, wet-dayoccurrence is modelled best in spring and dry-dayoccurrence in autumn.

Validating the downscaling model using lengthyseries of independent data was problematic since theWolf Creek Pass SNOTEL station has no data prior to1986. Table 4 compares the downscaling model(N_dsc) estimates of daily PRCP, TMAX and TMIN

for the WYs 1981–1986 with the observed series S_3c

for the same period. From Table 4 it is evident that thedownscaling model produces significantly higherannual precipitation totals, wet-day frequencies andmean wet-day amounts than was recorded at VallecitoDam and Mineral Creek. Conversely, the downscalingmodel yielded significantly lower estimates of dailymean TMAX and TMIN, as well as the respective90th-percentile values than observed. These differ-ences are entirely consistent with what would beexpected since the mean elevation of the data shownin Table 4 for S_3c is 2696 m compared with 2915 mfor the N_dsc.

4.2. Comparison of current rainfall-runoff scenarios

Accordingly, Fig. 5a compares simulated meandaily streamflow by month from the N_dsc and S_3c

scenarios with observed streamflow values. N_dsc

results are presented as box plots to show the rangeof simulated mean daily streamflow by month for theensemble of 20 runs. Fig. 5b shows the percent modelerror by year for N_dsc and S_3c. The modelledhydrographs using the S_3c consistently show a


Table 3Downscaling model calibration “fit” expressed in terms of the percentage of explained variance (E%) in daily Ri ; TMAX i and TMINi for theAnimas River basin, WYs 1987–1995 (the values forOi are the percentage of dry and wet days that were correctly assigned by the downscalingmodel in each season)

Season Precipitationoccurrence�Oi �

Wet-dayprecipitationamounts�Ri �

Maximumtemperature(TMAX i)

Minimumtemperature(TMIN i)

Dry Wet

DJF 46 60 45 85 88MAM 47 67 39 91 92JJA 54 61 29 79 85SON 60 59 43 95 96

Table 4Comparison of downscaling model output and observed area average PRCP, TMAX and TMIN for the Animas River basin, WYs 1981–1986(note that the observed statistics do not include data for the SNOTEL site at Wolf Creek Summit which was used in model calibration)

PRCP (mm) TMAX (8C) TMIN (8C)

Mean SD 90% %Wet Total Mean SD 90% Mean SD 90%

Model 5.0 4.6 10.4 62.0 1300 9.3 8.6 20.7 24.8 7.9 214.9Obs 4.6 5.0 10.8 51.4 869 13.9 9.0 26.1 22.4 8.1 213.3

significant underestimation (34–44%) of the gaugedrunoff for WYs 1981–1986. However, for WYs1987–1995, errors in S_3c simulated runoff volumesrange from 2 to 12% for all years except 1994 whichhad an error of 18%.

Fig. 5a and b also provide an independent check ofthe validity of the downscaling since the statisticalmodels were calibrated using data for WYs 1987–1995. The downscaling model was able to reconstruct

mean daily values for PRCP, TMAX and TMIN priorto 1986 that significantly improved the modelledrunoff (relative to the simulation produced by S_3c)for this period and thereafter the monthly mean of theN_dsc runs is comparable to the results of the S_3c

scenario. These results are taken as a strong endorse-ment of the downscaling model’s capability to recon-struct sub-grid meteorology and hence observed flows(even for an independent period), using just three


Fig. 5. Comparison of: (a) observed and simulated monthly mean runoff produced by the N_dsc and S_3c scenarios; (b) percent model error byyear for the N_dsc and S_3c scenarios.

atmospheric predictor variables. They also indicatethe potential of the downscaling technique for datareconstruction. Given the problems described forscenario S_3c and the improved performance of theN_dsc scenarios for WYs 1981–1986, the N_dsc

scenarios were assumed to be most representative ofthe current climatological and hydrological regimesfor the purpose of comparisons with all current andfuture scenarios.

The simulated runoff produced by the two GCMscenarios for the current climate (G_rawc andG_elvc) was evaluated by comparing them with therunoff simulated by S_37c and against observed flows(Fig. 6). As noted previously, scenario S_37c usedstation data covering the entire San Juan Riverbasin, an area similar in size to a single GCM grid-box. Comparing monthly mean streamflow values inFig. 6 shows that after redistribution, G_rawc signifi-cantly overestimates streamflow from May to Septem-ber. The G_elvc scenario yielded improved estimatesof peak and minimum flows compared with S_37c.The consequences of ‘correcting’ the GCM elevationby month can be seen when comparing the G_rawc

and G_elvc scenarios in Fig. 6. The use of higherrepresentative elevations in G_elvc (Fig. 4) results ina drier and warmer regime.

Differences among the current climate scenarios arehighlighted by the monthly mean values of PRCP,TMAX and TMIN (Fig. 7a–c, respectively). Eachfigure shows the range of the monthly mean valuesfor the ensemble runs of N_dsc and G_dsc plus thesingle realisation of the monthly mean values forG_elvc. Similar trends in PRCP are shown for all


Fig. 5. (continued)

Fig. 6. Simulated monthly mean runoff for observed, S_37c, G_elvc and G_rawc scenarios.


Fig. 7. Monthly mean (a) daily precipitation amounts, (b) maximum and (c) minimum temperatures of the current climate scenarios N_dsc, S_3c,G_dsc and G_elvc.

scenarios but the magnitudes of the scenarios showlarge seasonal differences. G_dsc has lower monthlymean PRCP than N_dsc for most of the winter,summer and autumn months while G_elvc has valuescomparable to N_dsc for the winter to early summerperiod but less than N_dsc for the remainder of theyear. The reasonable agreement between G_elvc andobserved streamflow (Fig. 6) reflects the generalagreement in winter and spring PRCP between theG_elvc and N_dsc scenarios and the minimal contri-bution of summer precipitation to streamflow in thisregion of the United States. Monthly mean TMAXand TMIN for G_dsc and G_elvc are generally warmerthan those for N_dsc for all months except those inspring when G_dsc was cooler.

4.3. Comparison of future rainfall-runoff scenarios

Evaluation of the magnitude of the changesbetween the current and future climates on a seasonalbasis after spatial redistribution (Table 5) reveals thatthe downscaling produced larger increases in summerand winter precipitation than the elevation correctedGCM scenario. Changes in TMAX were greater in thedownscaled scenario for spring and the GCM scenariofor autumn. Changes in downscaled TMIN were lessthan those in the GCM for autumn and winter. Suchvariations, between the downscaled and raw GCMscenarios, are noteworthy given that all scenariosoriginate from the same GCM.

Fig. 8a and b compare the simulated monthly meanstreamflow produced by scenarios N_dsc, G_dsc,G_dsf, G_elvc and G_elvf with observed monthlymean streamflow. For the N_dsc, G_dsc and G_dsf

scenarios, the maximum and minimum streamflowsarising from the ensemble of 20 runs are given. For

current climate conditions (Fig. 8a), the high level ofagreement between N_dsc and observed flows furthersupports the assumption that scenario N_dsc is repre-sentative of the current hydrological regime. G_dsc

underestimates observed flows from October toMay, overestimates flows in June and providescomparable flows for the remainder of the WY.G_elvc provides reasonable agreement of flows forthe period of March–May but underestimatesobserved flows for all other months. Peak flow occursin June for N_dsc, G_dsc and observed, while G_elvc

peaks one month earlier in May.For future climate conditions (Fig. 8b), scenarios

G_dsf and G_elvf indicate higher winter periodflows but lower spring and summer flows than underpresent climate conditions. No change in peak flowtiming from the current climate is indicated for G_dsf

but G_elvf peaks one month earlier. Fig. 9 shows theseasonal mean streamflow for all scenarios. There isno significant change in mean annual volume fromcurrent to future condition in either G_ds or G_elvscenarios, but there are differences in the seasonalmeans.

The reasons for the differences in the monthly flowregimes (Fig. 8a and b) and the similarities in themean annual volumes (Fig. 9) are to be found in themonthly and seasonal differences in mean PRCP,TMAX and TMIN among the scenarios (Figs. 7a–cand 10a–c). For the future scenarios (G_dsf, G_elvf),warmer autumn and winter TMAX and TMIN valuesincrease the amount of precipitation occurring as rainrather than snow during these months and increasesnowmelt rates at lower elevations. These changesresult in increased streamflow for these periods.Differences in the magnitudes of the monthly flowsreflects the differences in seasonal precipitation type


Table 5Changes in daily PRCP, TMAX and TMIN between WYs 1981–1995 and 2081–2095: a comparison of downscaled (G_dsf 2 G_dsc) andHadCM2 (G_elvf 2 G_elvc) scenarios

Season Change in PRCP (mmd21) Change in TMAX (8C) Change in TMIN (8C)

GCM Downscaled GCM Downscaled GCM Downscaled

DJF 10.73 11.38 13.82 13.35 15.68 14.15MAM 10.28 10.02 10.84 12.63 12.33 12.99JJA 20.20 10.88 13.16 13.93 13.43 13.94SON 10.05 10.16 14.87 13.80 14.89 13.43Annual 10.21 10.58 13.17 13.43 14.08 13.63


Fig. 8. Monthly mean runoff of the (a) current and (b) future climate scenarios.

and amount, as well as the variations in the distribu-tion of TMAX and TMIN.

Changes in the flow regimes (Fig. 8a and b) are alsoa consequence of associated changes in snowpackbehaviour (Figs. 11a and b, and 12a and b). If scenarioN_dsc is taken as the datum, it is evident that scenarioG_dsc yields snow-covered area durations that areconsistent with current conditions (Fig. 11a). Incomparison, scenarios G_dsf and G_elvf exhibitsignificant reductions in the duration of basin snow-covered area (Fig. 11b). All scenarios producequalitatively similar seasonal regimes of basinsnow-covered area: maxima consistently occur inDecember–February and minima in July–September.Scenarios G_elvc and G_dsf result in maximum snow-covered areas approximately 10% lower than currentconditions, compared with as much as 35% lower forscenario G_elvf. (Note that none of the snow-coveredarea duration curves reach 100% because a differentsnow area depletion curve was used in PRMS forareas above and below timberline. For areas abovetimberline, snow covered area is limited to amaximum of 70%. This assumption is made toaccount for the effects of snow redistribution thatnormally occurs above timberline).

Comparing snowpack water equivalent relative tothe current climate (Fig. 12a) shows comparablevalues of snowpack water equivalent during theaccumulation phase for N_dsc and G_dsc but adelay in melt by 1 month for G_dsc. This delay isexpressed in the simulated monthly mean stream-flow shown in Fig. 8a. Snowpack water equivalentfor scenario G_elvc is less than N_dsc for allmonths. For future climate conditions (Fig. 12b)scenario G_elvf is by far the most extreme interms of reduction in snowpack, presumably aconsequence of the increases in winter TMAX andTMIN which produce more rain and less snow. Fig.12b also suggests that there may be subtle changesin the timing of the snowpack accumulation/abla-tion under future climate conditions. In scenariosN_dsc, G_dsc and G_elvc, peak accumulation occursin April–May as opposed to March–April in thecase of G_dsf and G_elvf. Scenario G_elvf resultsin significant reductions in total snowpack volumeeven when compared with the other future climatescenario G_dsf. The later date of peak accumulationand melt for scenario G_dsf, as compared to G_elvf,is consistent with the later streamflow peak shownin Fig. 8b.


Fig. 9. Mean annual runoff volumes for the current and future climate scenarios.


Fig. 10. Monthly mean (a) daily precipitation amounts, (b) maximum and (c) minimum temperatures of N_dsc compared with the future climatescenarios G_dsf and G_elvf.


Fig. 11. Monthly mean snow cover areas of the (a) current and (b) future climate scenarios.


Fig. 12. Monthly mean snowpack water equivalent of the (a) current and (b) future climate scenarios.

5. Discussion

This study compared three methods of generatingcurrent and future rainfall-runoff scenarios: (1) statis-tically downscaled GCM output; (2) raw GCM output;and (3) GCM output corrected for elevational biases.The regression-based statistical downscaling modelemployed three grid-box predictor variables (dailymean sea level pressure, surface specific humidityand 500 hPa geopotential heights) to simulate sub-grid scale daily precipitation and temperature series.All climate scenarios, whether originating from thedownscaling procedure or directly from the GCMoutput, were spatially distributed across the AnimasRiver basin using lapse rates and topographic infor-mation for specified hydrological response units.Finally, a distributed hydrological model (PRMS)was used to simulate daily runoff, snow-coveredarea and snow-pack under competing GCM-derivedand downscaled climate scenarios.

It was demonstrated that downscaled daily precipi-tation and temperature series for the observed climate(scenario N_dsc) can result in improved simulations ofdaily runoff for independent periods of record forwhich the station data are missing or less reliable.The quality of the modelled flows in the AnimasRiver for the period prior to 1986 testifies to the poten-tial of the downscaling as a means of reconstructinglocal hydrometeorological variables given only grid-box scale atmospheric predictors. In comparison, thethree GCM-derived scenarios of the current climate(G_dsc, G_rawc and G_elvc) yielded large seasonaldifferences in precipitation and generally highertemperatures relative to N_dsc. The correction forelevation biases in the raw GCM output (G_elvc)resulted in significantly improved simulations of thecurrent runoff regime when compared with theuncorrected output (G_rawc).

The distributed hydrological model was next usedto compare two future climate scenarios originatingfrom: (1) statistical downscaling (G_dsf); and (2) rawGCM output corrected for elevation biases (G_elvf).Scenario G_dsf yielded modest reductions in summerstreamflow and winter snowpack by 2081–2095 rela-tive to current conditions. In comparison G_elvf

yielded far greater reductions in modelled flow andsnowpack area/water equivalents over the sameperiod. Furthermore, seasonal runoff and snowpack

regimes exhibited marked differences between thetwo future climate scenarios G_dsf and G_elvf. Thetiming of the onset of snowpack melt was 1 monthearlier for scenario G_elvf than for G_dsf, with conco-mitant changes in the months of maximum runoff(May and June, respectively).

From these examples it is evident that the choice oftechnique for scenario generation has major implica-tions for the projected climate change impact (in thiscase, basin hydrology). So which regional climatechange scenario should be used for impact analysis?The statistical downscaling has a number of advantagesover the use of raw GCM output. Firstly, the stochasti-city of the model facilitates the generation of ensemblesof future climate realisations–a pre-requisite to confi-dence estimation. Secondly, the downscaling modelmay be tuned to reproduce the unique meteorologicalcharacteristics of individual stations–a valuable assetin heterogeneous landscapes or mountainous terrain.Thirdly, such techniques are far less data intensiveand computationally demanding than dynamicalmethods such as nested or regional climate modelling.

The capability of downscaling to reproduce hydro-logical processes at scales less than a single GCMgrid-box is not at issue; what remains uncertain isthe extent to which the assumed empirical predic-tor–predictand relations are valid under future climateconditions (Wilby, 1997). As mentioned previously,downscaled scenarios are sensitive to many factors,including the choice of predictor variables and down-scaling domains, season definitions, the chosen math-ematical transfer functions and calibration periods(Winkler et al., 1997). For example, Hewitson andCrane (1999) demonstrated that precipitation anoma-lies over South Africa, downscaled using only atmo-spheric circulation predictors, were greater andsometimes of opposite sign to those produced viacirculation and humidity predictors.

The preceding analyses also indicate that the down-scaling model performed better in some seasons thanin others. This might be expected a priori given therelatively limited set of predictor variables. For exam-ple, the gridded atmospheric predictors were anappropriate means of downscaling sub-grid scaleprecipitation amounts during winter and spring(when large weather systems dominate the region),but the same predictors were less proficientfor summer convective systems (when indices of


atmospheric instability or column integrated moisturecontent would be more helpful). Unfortunately, thechoice of daily predictor variables is ultimatelyconstrained by what has been archived for the GCMexperiment under consideration. Until daily moisturevariables are routinely archived for multiple levels inthe atmosphere it will be necessary to employ otherGCM products such as surface humidity, acknowledg-ing that such predictors are often associated withprecipitation as both a cause and an effect (Hostetler,personal communication).

There is clearly a considerable scope for furthermodel development and scenario comparison. Thestatistical downscaling used herein employed proventechniques but it is conceded that different modelconfigurations and sets of predictor variables canyield markedly different regional climate scenarios(see Wilby et al., 1998b). Given the inherent uncer-tainty associated with, in particular, precipitationdownscaling there may be scope for the developmentof alternative techniques that rely more heavily onhigh resolution topographic information–data thatare currently available in digitised form at higherresolutions than from most remotely sensed or ground-based hydrometeorological observation networks(Beven, 1995). However, even the present down-scaling technique has the potential for highresolution real-time flow forecasting in which, forexample, the gridded 0–14 day 850 hPa temperatureand 0–14 day accumulated precipitation ensembleforecasts of the Environmental Modeling Center’s(NCEP) Medium Range Forecast Model might beused to downscale station-scale PRCP, TMAX andTMIN as input to PRMS.

In the meantime, for the Animas River basin, thereis at least a qualitative consensus amongst the modelsthat the future magnitude of low and intermediateflows will increase by 2081–2095, whereas peakflows, basin snow-covered areas and snowpack waterequivalents will all decline relative to current condi-tions. Research is ongoing to determine the generalityof these water resource impacts for other regions, GCMoutputsand downscaledscenarios (e.g.Hay etal., 1999).

Acknowledgements

This is an ACACIA (A Consortium for the

Application of Climate Impact Assessments) contri-bution. ACACIA is sponsored by CRIEPI, EPRI,KEMA and NCAR. We are grateful to David Vinerof the Climate Impacts LINK Project (UK Depart-ment of the Environment Contract EPG1/1/16) forsupplying the HadCM2 data on behalf of the HadleyCentre and UK Meteorological Office. We thankLinda Mearns for her constructive advice on scenariodevelopment and we gratefully acknowledge the inputof Steve Hostetler, Chris Milly and three anonymousreferees. The National Center for AtmosphericResearch is sponsored by the National ScienceFoundation.

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