Assessment of the WATCLASS hydrological model result of the Mackenzie River basin using the GRACE...

HYDROLOGICAL PROCESSESHydrol. Process. 23, 3391–3400 (2009)Published online 26 August 2009 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/hyp.7450

Assessment of the WATCLASS hydrological model resultof the Mackenzie River basin using the GRACE

satellite total water storage measurement

Sitotaw Z. Yirdaw,1* Kenneth R. Snelgrove,2 Frank R. Seglenieks,3 Clement O. Agboma2

and Eric D. Soulis3

1 Department of Civil Engineering, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V62 Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St John’s, Newfoundland, Canada AlB 3X5

3 Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1

Abstract:

Earlier efforts have been geared towards modelling the hydrological water balance of the Mackenzie River basin and itssub-basins using a coupled land surface–hydrological model for the Canadian cold region known as WATCLASS. The goal ofthis current study is to effectively inter-compare the resulting total water storage anomalies estimated from the gravity recoveryand climate experiment (GRACE) satellite analysis with those estimated from the atmospheric-based water balance approachas well as the model output from WATCLASS over the 1 Ð 8 ð 106 km2 Mackenzie River basin in Canada. Since the successof the parameter estimation stage of the coupled land surface–hydrological model, WATCLASS over this large catchment, wasentirely based on a goodness of fit between the simulated and observed flows, it is often desirable to assess the reliability of thegenerated state variables prior to concluding on the overall efficiency of this model in reproducing the relevant hydrologicalprocesses over this region. A major challenge here lies in finding suitable dataset with which this comparison can be madeto further assess the ability of the model in accurately reproducing other mass fluxes. The outcome of this inter-comparisonreveals the potential application of the GRACE-based approach as a veritable tool required for the closure of the hydrologicalwater balance of the Mackenzie River basin as well as serving as a dependable source of data for the calibration of traditionalhydrological models. Copyright 2009 John Wiley & Sons, Ltd.

KEY WORDS GRACE; WATCLASS; Mackenzie River basin; water balance; cold region; watershed

Received 6 October 2008; Accepted 17 July 2009

INTRODUCTION

Successful modelling of the Mackenzie River basinhydrologic processes is by no means an easy task asa result of the complexity of the terrain and the factthat data availability is sparse which makes hydrologicalmodel calibration and validation difficult. Prior to theMackenzie Global Energy and Water Cycle ExperimentStudies (MAGS), a project designed to increase theunderstanding of the interaction between the moisture andenergy fluxes over this basin, a myriad of earlier attemptshad been made in studying this watershed with interestfocusing on issues to support navigation, hydropower andpipeline construction (Solomon et al., 1977; Soulis andVincent, 1977; Fassnacht, 1997). Efforts have recentlybeen made to characterize the Mackenzie sub-basin’shydrological processes in detail, drawing upon the skillsand lessons learnt from modelling the whole region asone large watershed and to arrive in the development ofa hydrological model that will facilitate future predictionof streamflow and state variables at any designated outletwithin this basin.

* Correspondence to: Sitotaw Z. Yirdaw, Department of Civil Engineer-ing, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6.E-mail: [email protected]

The goal of this new effort is to adequately capturethe physics of the watershed, especially with respect tothe cold processes, in order to close, with total feedback,the water and energy budgets for all such applicationsthroughout the Mackenzie basin in conjunction with pro-viding cold soil algorithms suitable for use in atmosphericmodels. Calibration of this model was achieved throughthe comparison of model’s simulated streamflow with theavailable measured streamflow record. However, assess-ing the performance and reliability of any hydrologicalmodel solely on a visual match of the observed and simu-lated streamflow and employing a number of performancetest statistics alone might lead to flawed conclusions.Unarguably, streamflow comparisons are very importantand often desirable as part of a hydrological model per-formance assessment. However, it has to be noted thatstreamflow is the aggregation of many different hydrolog-ical processes. In certain instances, it is possible to incor-rectly simulate internal state variables that could cancelout and yet result in correct streamflow simulations. Forexample, an incorrect hydrological model simulation thatled to an overestimation of snowmelt runoff could becompensated for by an overestimation of evapotranspi-ration resulting in accurate or near-accurate streamflowsimulations, but for the incorrect reasons.

Copyright 2009 John Wiley & Sons, Ltd.

3392 S. Z. YIRDAW ET AL.

If internal variables simulated from this hydrologicalmodel can be shown to compare well with measuredvalues, then it will increase the confidence in the gen-erated streamflow data. In addition, it is imperative tocompare the model results with independent data sourcesbecause model-to-model comparisons can lead to incor-rect conclusions. In light of this, it is proposed in thiscurrent study to undertake an assessment of the perfor-mance of the coupled land surface–hydrological modelWATCLASS (Snelgrove, 2002) that has been deployedon the Mackenzie River basin; by comparing its esti-mated total water storage anomalies with the total waterstorage anomalies estimated from gravity recovery andclimate experiment (GRACE) remote sensing satellitesystem (GRACE mission: Tapley et al., 2004) and thatestimated from the atmospheric-based water balance.

Different studies currently exist which detail the originand numerous applications in which the GRACE satel-lite remote sensing mission had been employed (Rodellet al., 2002; Swenson et al., 2003; Rodell et al., 2004;Tapley et al., 2004; Yeh et al., 2006; Syed et al., 2005,2007; Yirdaw et al., 2008). This remote sensing satellitehas provided accurate estimates of the temporary changesin the terrestrial water storage in relatively large water-sheds since its first launch less than a decade ago (March,2002). A demonstration of the potential utility of thegravity data obtained from GRACE satellite for hydro-logical applications within the domain of large watershed(>150 000 km2) was extensively covered in the work ofRodell and Famiglietti (1999). This study further exem-plified the robustness of the data retrieved from GRACEin constraining the estimated water storage in numeri-cal land surface models as well as detecting variations ingroundwater levels and in the intermediate zone storages.The usefulness of the data simulated from the GRACE-based technique in catchments larger than 400 000 km2

for the estimation of the monthly changes in total waterstorage was the focus of the study by Swenson et al.(2003); accuracies of about 1 cm of water thickness wasobserved in this study. In light of these earlier success-ful applications, there is a reliable basis upon whichthis study would benefit from the use of the GRACEtotal water storage estimation as a data source for use inthe assessment of the performance of the coupled landsurface–hydrological model (WATCLASS) which is theobjective of this study.

The coupled hydrologic–land surface model, WAT-CLASS, employs the vertical process mechanisms inthe Canadian land surface model (CLASS) (Verseghy,1991) which is suited for studying cold region hydro-logic processes while making use of the routing algorithmin the hydrological model, WATFLOOD (Kouwen et al.,1993). In order to assess the reliability of the GRACE-retrieved data for use in assessing the performance of thecoupled land surface–hydrological model (WATCLASS)employed in this study, the atmospheric-based water bal-ance estimation was employed in the validation of theGRACE-based computed terrestrial water storage anoma-lies. As noted in Oki et al. (1995), estimation of water

balance components from atmospheric data has becomeeasier than ever owing to the increasing availabilityof high-resolution atmospheric data in various temporalscales. The underlying approach in this study involves thedetermination of the precipitation less evaporation fromthe atmospheric-based water balance method and thenfactoring in the measured streamflow from the designatedoutlets in the individual sub-basins to estimate the corre-sponding individual basin total water storage anomalies.Thereafter, comprehensive comparison of these estimatedtotal water storage anomalies for these basins result-ing from the prior technique are compared with thatobtained from GRACE. These are then inter-comparedwith the total water storage simulated from the hydrolog-ical model, which will enable us to make an evaluationon the performance of WATCLASS in the geologicallycomplex Mackenzie basin.

METHODOLOGY

Three methods were adopted in the estimation of the totalwater storage anomalies over the Mackenzie River basinand these are the focus of discussion in this section. Thetotal water storage anomalies retrieved from the GRACE-based technique were validated with those estimatedfrom the atmospheric-based water balance approach.Subsequently, an inter-comparison was done with thewater storage anomalies simulated from the coupled landsurface–hydrologic model suited for the cold climatehydrologic processes referred to as WATCLASS. Theunderlying mathematical and physical principles involvedin each of these methods are summarily laid out in thesubsequent paragraphs.

Site descriptions

The Mackenzie River originated from the Great SlaveLake in the Northwest Territories, which is one ofthe largest reservoirs of water on the continent thatcontributes runoff to the Mackenzie River flowing intothe Arctic Ocean. This river is about the longest inCanada with an approximate length of 4240 km. Itsbasin gross drainage area with its outlet at the ArcticRed River spans an area of approximately 1 Ð 8 ð 106

km2, constituting approximately 20% of the landmass ofCanada. Geographically, it ranges from about 52 °N to70 °N latitudinally and 102 °W to 140 °W longitudinally(from the Rocky Mountains on the western flank to theCanadian Shield on the east). The Mackenzie River basinis further divided into six sub-basins: Athabasca, Bear,Liard, Peace, Peel and Slave with the gross drainage areaand the location of each sub-basins as shown in Table Iand Figure 1, respectively.

Owing principally to its varying climatic conditions,the basin has received considerable attention as a result ofefforts from the global energy and water balance experi-ment (GEWEX) activities developed by the world climateresearch program (WCRP). The Mackenzie GEWEXStudy (MAGS) had as one of its objectives the need

Copyright 2009 John Wiley & Sons, Ltd. Hydrol. Process. 23, 3391–3400 (2009)DOI: 10.1002/hyp

ASSESSMENT OF THE WATCLASS HYDROLOGICAL MODEL RESULT 3393

Table I. Gross drainage area of the sub-basins in the MackenzieRiver basin as reported by Mackenzie River basin board (MRBB)

Sub-basins Area (km2)

Athabasca 268 624Bear 475 146Liard 274 794Peace 323 017Peel 74 271Slave 379 159

Figure 1. Location of the study area (Mackenzie River basin and itssub-basins)

to understand and model the high altitude water andenergy cycles and to improve the ability to assess thechanges to Canada’s water resources (Ronald, 2002). Theriver system within this basin receives an average annualprecipitation of 426 mm with an average annual evapo-transpiration and runoff estimated as 185 and 241 mm,respectively (Snelgrove et al., 2005).

GRACE-based moisture storage

The geopotential spherical harmonic coefficients(SHCs) (level 2 dataset) transformed from the raw datasetretrieved from the GRACE satellite remote sensing arethe tasks of the Centre of Space Research at the Univer-sity of Texas, the National Aeronautics and Space Admin-istration (NASA) as well as the Deutsches Zentrum furLuft und Raumfahrt (DLR) in Germany. This dataset issubsequently distributed freely up to 120° and orders tomultiple GRACE data users worldwide, for use in vari-ous hydrological applications and related research areas.In accordance with Chen et al. (2005a), the atmosphericand oceanic contributions with the solid earth tides (non-hydrological gravitational components) are removed fromthe level 2 products. This is essentially achieved by uti-lizing atmospheric mass variation reanalysis and oceanictide model to correct for the atmospheric and oceanic con-tributions to the terrestrial water storage estimate from the

GRACE remote sensing satellite. Consequently, as wouldbe expected, the uncertainties in these corrective modelscould have significant effects on the level of uncertaintyin the GRACE estimated total water storage anomalies.In essence, the preliminary step involved in the study ofthe monthly changes in temporary variations in the waterstorage usually entails the extraction of these monthlyvariations in these storages from hydrological reservoirs.

Gravity field estimates comprising sequences of 46monthly variations were extracted from the GRACE level2 data (CSR RL01) retrieved from April 2002 to May2006 with the exclusion of the May, June, July 2002and June 2003. Owing to the high-nonphysical variabil-ity in GRACE data, an exclusion of C20 geopotential SHCfrom these computations was necessary. Since harmonicdegrees greater than 15 are dominated by errors in accor-dance with Wahr et al. (2004), only the first 15 SHCswere utilized for this study. Successful retrieval of aver-age terrestrial water storages from gravity measurementsentails an initial basic step of transforming the GRACEgeopotential SHCs into spatially time-varying series ofgeopotential height data. These were then converted intowater equivalent amounts (storages). The average ter-restrial moisture storage estimated from GRACE wasdetermined in accordance with the approach of Heiska-nen and Moritz (1967), Wahr et al. (1998) and Ramillienet al. (2004). Subsequently, an 800 km Gaussian smooth-ing radius was employed to retrieve GRACE-based ter-restrial water storage yielding a minimum root meansquare (RMS) residual over the land surface (Chen et al.,2005b). These storages were then validated using stor-ages computed from the atmospheric-based water balanceapproach in conjunction with the measured streamflowfor the Mackenzie River basin and its sub-basins adopt-ing a technique similar to that of Rodell et al. (2004),Hirsch et al. (2006) and Yirdaw et al. (2008).

Atmospheric-based water balance

Description. Atmospheric-based water budget studiesby Strong et al. (2002); Walsh et al. (1994) attemptedto calculate evaporation (E) (m/s) less precipitation(P) (m/s) based on the net advection of atmosphericmoisture through a closed atmospheric volume. From anatmospheric perspective, this is expressed as:

∂W

∂tC r Ð Q D E � P �1�

where W (m) is the water content in an atmospheric col-umn, r Ð Q the divergence or net outflow of water vapouracross the side of the atmospheric column and Q (m/s)the vertically integrated moisture flux vector derived fromwind and relative humidity measurements. The diver-gence measures the difference between the inflow andoutflow to a specific region, in other words, it measuresthe rate at which fluid is being piped into or out of theregion at any given point. A positive divergence meansthat outflow is greater than inflow, conversely, a neg-ative divergence (or convergence) indicates that inflow



is greater than outflow. The land surface water balancecan be compared with the atmospheric water balance byformulating a similar equation. From the land surfaceperspective, the water balance can be expressed as:

∂S

∂tC r Ð F D P � E �2�

where S (m) is the total water storage in the basin,r Ð F (m/s) represents the lateral transport of waterwith E and P representing evaporation and precipitation,respectively. If a watershed is assumed to be a closedsystem, which means that there is no watershed boundaryleakage, then the only lateral flow across the boundary ofthe watershed is the runoff, R (m/s) at the outlet of thebasin. Therefore, this provides a means of estimating thevalue of P–E for a specific basin. The usual approachadopted by most hydrologists is to deal with thesecomponents of the water budget in terms of their timedependencies, which is given mathematically as:

S

tC R D P � E �3�

Combining Equations (1) and (3) yields the expressionshown in Equation (4).

�(

∂W

∂tC r Ð Q

)D

(S

tC R

)D �P � E�

S

tD �

(∂W

∂tC r Ð Q

)� R �4�

Therefore, the negative divergence combined with theprecipitable atmospheric water and the runoff at the outletof the basin yields an estimate of the change in the waterstorage in the basin. The perceptible atmospheric watercontent is computed using Equation (5).

W D∫ pt

ps

q

gdp �5�

where q is the specific humidity (kg/kg), p the pressure(kg/m/s2) and g the gravitational constant (9 Ð 81 m/s2).The limits of integration are the surface pressure (ps) andthe pressure at the top of the atmosphere (pt).

The divergence in spherical coordinates is computedusing Equation (6):

r Ð Q D 1

Re cos �

(∂Q�

∂�C ∂�Q� cos ��

∂�

)�6�

The vertically integrated vapour fluxes Q� and Q� areestimated according to Equations (7) and (8).

Q� D � 1

g�

∫ pt

ps

qudp �7�

Q� D � 1

g�

∫ pt

ps

qvdp �8�

Q� and Q� are the east–west and north–south com-ponents of vapour flux in (m2/s), respectively, while �is the density of water (1000 kg/m3). Re is the meanEarth radius taken as 6 Ð 3712 ð 106 m, � and � are,

respectively, the longitude and latitude in radians. Thenegative sign arises due to the fact that the hydrostaticassumption was used when converting from elevation topressure. The limits of integration are the surface pressure(ps) and the pressure at the top of the atmosphere (pt).

Finally, the total water storage anomaly in a hydrologicreservoir can be computed according to Equation (9).

Sn D Sn�1 C(

S

t

)n

�9�

where Sn is the storage anomaly for the current month,Sn�1 the storage anomaly for the previous month andS/t the average change in monthly storage forthe current month obtained from Equation (4). Theseanomalies are equivalent to storage anomalies fromGRACE which are averaged approximately over 1-monthperiod and used for comparison with storages estimatedfrom the two methods (GRACE- and atmospheric-basedcomputed computations). In determining the initial valueof total water storage anomaly, i.e. when n D 1 inEquation (9) above, the estimated terrestrial moisturestorage anomaly from GRACE for the first month (April,2002) was utilized.

Dataset for atmospheric water balance computation.The water content in the atmospheric column and theaverage divergence or net outflow of water vapour acrossthe side of the atmospheric column in Equation (8) werecomputed using the data retrieved from the CanadianMeteorological Centre (CMC). The analysis variablesemployed for this study are the dew point depression,surface pressure, temperature, east–west and north–southwind speeds for the specified 16 pressure levels. Theprovided dataset spans the North American continentat a spatial resolution of 1° by 1° and a temporalresolution of 6 h commencing April 2002 to May 2006.The vapour pressure and saturated vapour pressure werecalculated from the dew point temperature ( °C), Td

and the actual temperature, respectively. The specifichumidity, q, which is the concentration of water vapourexpressed as the mass of water vapour per unit of air, iscalculated using Equation (10).

q D �v

�aD 0Ð622 ð e

P�10�

where P is the pressure at different pressure level inmillibar. Moreover, the average monthly streamflow dataare continuously being recorded for the Mackenzie Riverbasin by the Water Survey of Canada (WSC) at the ArcticRed River outlet and for other sub-basins as depicted inFigure 1. The gross drainage areas bordering these sub-basins are given in Table I.

Numerical simulation from WATCLASS

WATCLASS. WATCLASS, the model used for the esti-mation of the water and energy budget components forthe Mackenzie River basin is an offspring arising from



the coupling of the CLASS (Verseghy, 1991) and a phys-ically based semi-distributed hydrological model devel-oped originally at the University of Waterloo in Canadacalled WATFLOOD (Kouwen et al., 1993). These mod-els employ the distribution of land covers for sub-gridprocess modelling although CLASS approach is tai-lored towards utilizing a properties-summed-by-area sin-gle landscape unit technique. Conversely, WATFLOODapproach is based on the use of a fluxes-summed-by-area multiple hydrologic units generally referred to as thegroup response unit (GRU) (Kouwen et al., 1993). Thestrength of the CLASS model lies in its detailed represen-tation of the lower atmosphere and land surface processesrequired for the computation of the energy fluxes (radi-ation and convective) with all state variables intrinsicincluding soil moisture. The emphasis of WATFLOODrests in its representation of the horizontal processes inthe shallow and deep unsaturated layer within a hydro-logic system and hence, on the grid cell runoff routing aswell as on the streamflow generation. However, it doespossess a rudimentary yet robust treatment of the energybalance and all other state variables.

WATCLASS’ vertical hydrologic process representa-tion remains the same as that of the parent CLASS modelbut with an enhancement made to the treatment of soilcolumn, which in this case is represented as a slopinglayer. In addition, it employs a set of generic groupedresponse units that are more detailed than those earlierimplemented in the WATFLOOD model. These groupedresponse units are capable of representing any land sur-face cover, for instance, it is possible to generate thehydrological response of a peat plateau or glaciers, how-ever, the most common representation remains that ofthe classical hillslope terrain. The approach adopted inthis representation entails an assignment of local slopesto various landscape units irrespective of their types tomake provision for a gradient necessary for the lateralflow transport. In essence, this could be viewed as mod-elling using ‘Aggregated Sloped Tiles’ which possessbetter parameterizations as opposed to that employed inthe parent model, WATFLOOD. The works of Snelgrove(2002) as well as that of Soulis and Seglenieks (2007)contain comprehensive discussion of the processes imple-mented in the coupled model, WATCLASS.

Dataset for numerical simulation. The slopes of thestream network in the basin, which serve as one of theinputs into the model, are extracted from the GTOPO-30digital elevation dataset for the Mackenzie River basin.This is followed by an aggregation of the initial 31 landcover classes represented in the original Canadian CCRS-II classification (Cihlar and Beaubien, 1998) to sevendistinct classes, namely, water, wetland, agricultural,tundra, coniferous forest, mixed forest and glacier whichare depicted in Figure 2. Incidentally, the forcing datasetrequired to drive WATCLASS were made availablefrom the earlier MAGS project, which were derivedfrom the regional finite element (RFE) environmentCanada operational weather forecasting model and later

substituted with the output from the global environmentalmodel (GEM) (Cote et al., 1998).

The precipitation data retrieved from the RFE andGEMs were fraught with problems, which is not uncom-mon with these kinds of GCMs and numerical weatherprediction’s (NWP) precipitation retrievals. In order tocompensate for the shortcoming inherent in this dataset,there was a need to make adjustments to the RFE/GEMprecipitation data utilizing a set of measured monthly pre-cipitation data (Louie et al., 2002) that was available andspans a period of 1994–2000. Extensive discussion of thecalibration and validation of the WATCLASS model arecovered in the work of Souils and Seglenieks (2007), andinterested readers are referred to this paper for exhaustivecoverage of the procedures involved.

In order to estimate the total water storage anomaliesfrom WATCLASS, the simulated runoff (R), simulatedevaporation (E) and measured precipitation (P) were usedas inputs into Equations (3) and (9) for each GRU. Thistotal water storage anomaly was then averaged over eachof the sub-basins of the Mackenzie River basin. Similarto the total water storage anomalies computed from theatmospheric-based water balance technique as discussedearlier, these obtained results were compared with thetotal water storage anomalies from GRACE.

RESULTS AND DISCUSSIONS

Computation and analyses of the GRACE-derived grav-ity measurements over the continental scale reveal spa-tial variability in moisture storage distribution globally.These were transformed into terrestrial water storageanomalies in unit of centimetres of water equivalentthickness in relation to the mean computed storage asdepicted in Figure 3. The upper and lower plots are themean monthly terrestrial moisture storage anomalies forthe months of March and October 2004 obtained at aspatial resolution of 1° by 1° and are deviations from theestimated time series of the average value retrieved fromthe GRACE-based total water storage.

The selection of these months for discussion, whichare subset of the total analysis period (46 months in all),is of great relevance in illustrating how the initial dataset

Figure 2. Land cover from the CCRS-II classification (after Soulis andSeglenieks, 2007)



Figure 3. Terrestrial water storage anomalies in centimetre of water equivalent thickness relative to the mean storage for March 2004 (top) andOctober 2004 (bottom)

retrieved from the GRACE satellite remote sensing whichin this case are the gravity harmonic coefficients weretransformed into spatially varying terrestrial moisturestorages. An additional task borders on the procedureinvolved in averaging the derived monthly total waterstorage over the Mackenzie River and its sub-basins withsubsequent comparison of these with the terrestrial mois-ture storage simulated from the coupled hydrologic–landsurface model, WATCLASS, and that computed from theatmospheric-based water balance.

In assessing the reliability of the estimated GRACE-based terrestrial moisture storage for subsequent inter-comparison with the soil moisture feedback from thecoupled hydrologic–land surface model, WATCLASS, itwas necessary to undertake a prior validation of thesecomputed total storages with those obtained from theatmospheric-based water balance. A good qualitative

visual match or high-statistical correlation coefficient inthese differently computed total storage values means thatthe GRACE-derived total water storage anomalies is suf-ficiently reliable for use in comparison with the totalwater storage anomalies simulated from WATCLASSover the Mackenzie River basin and its constituent sub-catchments. Time series plots of the spatially varyingfeedbacks from the three approaches employed in com-puting the terrestrial water storage anomalies are asdepicted in Figures 4 and 5.

The approach employed in the estimation of theGRACE-based terrestrial water storages is tailored afterthe relevant expressions developed in Section onAtmospheric-based Water balance with Equation (9)being the mathematical expression for the computation ofthe total storage anomalies at discrete time steps whichin this case is monthly. Successful closure of the water



Figure 4. Total water storage anomalies derived from GRACE, atmospheric-based water balance technique and WATCLASS for the Mackenzie Riverbasin

balance for the Mackenzie River basin and its corre-sponding sub-basins entails the utilization of the sim-ulated streamflow in Equation (3) for the coupled landsurface–hydrologic model, WATCLASS, while the mea-sured streamflow at the designated outlets over the basinswere used in closing the water budget for the atmospher-ically derived water balance over the basin. As statedearlier, the estimation of the total water storage anoma-lies over the basin in WATCLASS relies on the use ofthe simulated runoff and evaporation from this model inconjunction with the precipitation used in forcing it.

As illustrated in Figure 4, the patterns in the time seriesplots of the GRACE-based and atmospherically derivedtotal water storage anomalies over the Mackenzie Riverbasin appeared to agree qualitatively with each other,suggesting that the two techniques do seem to capturethe dynamics of the relevant moisture storage processesover this basin. As regards the seasonal amplitudes inthe time series of the estimated total water storageanomalies from these techniques, these appeared topeak at a water equivalent thickness amount of about10 cm. Corresponding minimum values in the estimatedterrestrial water storage anomalies fluctuate betweenvalues of �8 and �10 cm water equivalent thicknesswhich occurred in the months of July, August andSeptember for the 4-year period of analyses (i.e. April2002 to May 2006). Conversely, the maximum computedterrestrial moisture storage anomalies are observable inthe months of January, February and March with valuesranging from about 8 to 10 cm of water equivalentthickness. Incidentally, these peaks in total water storageanomalies correspond to the period of maximum snowaccumulation at the end of the winter season over thisbasin. The spring season is characterized by the meltingof the snow that had been initially accumulated andredistributed over the basin, hence, this melting processcontributes to the obvious loss of moisture which as seenin Figure 4 is well captured by the two techniques.

Furthermore, an assessment of Figure 4 does revealthe pattern of the temporal variability in the computedtotal water storage anomalies over the Mackenzie Riverbasin from the coupled land surface–hydrologic model,

WATCLASS. As depicted in these time series plots, thereis a good visual agreement in the seasonal cycles ofthe computed terrestrial moisture anomalies from thecoupled model with those derived from the GRACE-and the atmospherically based techniques. Interestingly,WATCLASS performed acceptably in its estimation ofthe maximum terrestrial moisture storage anomalies inabout all the seasonal cycles but unlike with the GRACE-and atmospheric-based techniques, it did not quite capturethe representation of the hydrologic processes responsiblefor the minimum values of the seasonal cycles in theterrestrial moisture storage anomalies over the basin.

Furthermore, a look at Figure 4 would reveal the cap-turing of the temporal variability in the computed totalwater storage over the Mackenzie River basin from thecoupled land surface–hydrologic model, WATCLASS,which shows good visual agreement in seasonal cycleswith those from the GRACE- and atmospheric-basedtotal storage anomalies computation. WATCLASS per-formed well in accurately estimating the maximum ter-restrial water storage anomalies in nearly all the seasonalcycles but unlike the GRACE- and atmospheric-basedtechniques, it did not quite capture the representationof the processes responsible for the minimum seasonalcycles in the terrestrial water storage anomalies over thebasin.

Table II shows the computed correlation coefficientestimated for the three techniques in use for this study.Apparently, the correlation coefficient between the ter-restrial water storage anomalies from WATCLASS andthose from the other two techniques are significant (0 Ð 68for WATCLASS vs GRACE and WATCLASS vs atmo-spheric yielded 0 Ð 72). This is a substantial reductionin these computed correlation coefficient values vis-a-vis a correlation coefficient of 0 Ð 86 that was obtainedduring the validation of the GRACE- and atmospheric-based techniques. In essence, this would lead to a rec-ommendation for the recalibration of WATCLASS withthe feedback from GRACE, via utilizing the total waterstorage anomalies estimate as an additional calibra-tion dataset. This would enhance the performance ofWATCLASS over the basin rather than simply relying



Figure 5. Total water storage anomalies derived from GRACE, atmospheric-based water balance techniques and WATCLASS for the differentMackenzie River sub-basins

Table II. Estimated total water storage correlation coefficient (r) between GRACE (G) and the atmospheric-based water balancecomputation (A), GRACE and WATCLASS (H: hydrologic model) as well as WATCLSS and the atmospheric-based water balance

computation

Mackenzie Peel Bear Slave Liard Peace Athabasca

G versus A 0Ð86 0Ð46 0Ð66 0Ð77 0Ð70 0Ð75 0Ð81G versus H 0Ð68 0Ð46 0Ð65 0Ð64 0Ð66 0Ð36 0Ð27A versus H 0Ð72 0Ð59 0Ð83 0Ð61 0Ð74 0Ð40 0Ð42

on the traditional historical streamflow data as the onlysource of calibration dataset for this coupled model.

Total water storage anomaly estimates from the threetechniques being used for this study as obtained fromthe Mackenzie six sub-catchments are the focus of thissection. A quick look at the time series plots of Figure 5with the computed correlation coefficients (Table II) forthe Peel, Bear, Slave, Liard, Peace and Athabasca indicatepositive relationship in the temporal variability of theestimated terrestrial water storage anomalies resulting

from the GRACE- and atmospheric-based techniquesover these sub-catchments. However, the Peel sub-basinappeared to be an oddity to this general high positivecorrelation coefficient values obtained for the other fivesub-basins. This might be attributable to its relativelysmall drainage area resulting in a low correlation coeffi-cient value of 0 Ð 46 because GRACE spatial resolutionis very coarse and applying it over very small catch-ments poses some issues. The least computed correlationcoefficient value in the GRACE- and atmospheric-based



terrestrial moisture storage anomalies in the remainingbasins is 0 Ð 66 occurring at the Bear sub-catchment whilstthe Athabasca basin yielded the highest correlation coef-ficient value of 0 Ð 81.

Figure 5 equally portrays the time series of the sim-ulated total water storage anomalies from the coupledhydrologic–land surface model, WATCLASS, over theMackenzie River sub-catchments. As seen in these plots,there appears to be an agreement in the pattern of thesignature captured by both the GRACE-based techniqueand the hydrological model, WATCLASS. Although, itis evident that there are significant discrepancies in themagnitudes of the estimated total moisture storages indifferent seasonal cycles which might be explained onthe quality of the available data for the calibration of theWATCLASS model to adequately capture the snowmeltprocesses and the computed soil water equivalent (SWE).In addition, these discrepancies might also stem from theerrors associated with the issue of the uncertainty in theGRACE dataset as detailed in Rodell et al. (2004), Wahret al. (2004) and Ole et al. (2005). Expectedly, the valuesof the computed correlation coefficients between GRACEand WATCLASS were also reduced and the Peel, Peaceand Athabasca exhibiting very low positive correlations.An alternate source of error that may be responsible forthese observed discrepancies between the two techniquesmaybe attributed to the spatial scales associated with theoutput from the hydrological model, WATCLASS, andthe remote sensing system, GRACE. Hydrologic outputfrom the WATCLASS model is retrieved at a spatialresolution of 20 ð 20 km while the total water stor-age estimates from GRACE are from a coarser scale ofapproximately 100 ð 100 km. These scaling effects fromthe two sources could significantly reduce the correla-tion level in the estimated total water storage anomaliesfrom these sources given that there is a significant lossof energy at the edges of the basins as well as possibleenergy leakages from the surrounding sub-basins.

In light of this, the usefulness of GRACE-derived ter-restrial moisture storage as a validation data source forconstraining the simulation output over a basin from ahydrological model becomes apparent. Successful mod-elling of streamflow over a number of the catchments,especially in the northern flank of Canada, is an enor-mous challenge. This modelling challenge arises fromthe complex nature of the terrain in this region, whichis heavily dominated by millions of highly disconnectedsloughs and wetlands that serve as isolated storages torunoff emanating from the different parts of the basins.In essence, a difficult question that has to be answeredby the modeller developing a hydrological model for ordeploying one over such basins is to ascertain the spatialextent of the basin actually contributing flow to a spe-cific outlet at a particular point in time. Owing to theinexactness in the determination of the area of a catch-ment contributing to streamflow at a chosen outlet, thisconstitutes an added source of uncertainty in the accu-racy of the model-simulated streamflow. Consequently,

the applicability and relevance of GRACE-derived ter-restrial moisture storage for constraining and assessingthe performance of any hydrological models deployedover regions are of paramount importance.

Furthermore, the GRACE-derived total water stor-age dataset in conjunction with the P–E from theatmospheric-based water balance technique would be ofgreat use in the derivation of streamflow data in basinsglobally where paucity or inexistence of streamflow dataare of concern. This lack of data might result from thedifficulty associated with the inaccessibility of the terrainor from harsh climatic conditions making it impossibleto site streamflow gauges along the desired points on ariver reach.

CONCLUSIONS

The potential use of the GRACE-derived total waterstorage as a validation data source for a land sur-face/hydrological model is explored in this study. Thetraditional approach of assessing the performance of ahydrological model based solely on the goodness of fitbetween the simulated and observed streamflow resultsor other statistical methods may lead to inconclusivehydrologic model performance assessment in the stud-ied area. Hence, as demonstrated in this study, othermodel’s state variables or derivatives of these should beassessed with reliable data sources before any conclusioncan be drawn as to the reliability of the model’s outputsor on its overall performance. The estimation of the ter-restrial water storages from GRACE, atmospheric P–Ewater balance and the coupled land surface–hydrologicmodel, WATCLASS, over the Mackenzie River basin andits six sub-catchments was the focus of this study. Priorto employing the GRACE-derived total water storage inassessing the performance of the hydrologic model, WAT-CLASS, over these basins, it was necessary to ascertainthe reliability of the estimated total water storage fromGRACE through validation by an independently com-puted terrestrial water storage from the atmospheric P–Ewater balance technique over these basins. A high valuein the computed Pearson product moment correlation (r)between the GRACE-derived and the atmosphericallyderived total water storage suggests a good linear agree-ment between these techniques. Given that the GRACEestimate of the terrestrial water storage over the basins iswell validated by the atmospherically derived estimatesof the total water storage, this therefore serve as a premiseupon which the GRACE-derived total water storage canbe employed in constraining and assessing the quality ofthe total water storage estimated from the coupled landsurface–hydrologic model in different sub-basins.

Evaluations of the total water storages from GRACEand WATCLASS yielded acceptably high correlationcoefficient values in some of the sub-catchments, how-ever, extremely low positive correlation values wereobtained for the Peel, Peace and Athabasca sub-basins.The rationale behind these low correlation values esti-



mated for these sub-catchments may be attributed to cer-tain issues related to how well the model’s parameterestimation was done as well as on the quality of theinput data used in forcing the hydrological model. Fur-thermore, the difference in scales between the outputsretrieved from these two approaches could also lend someexplanation to the computed low correlation coefficient.Moreover, significant discrepancies in the magnitudes ofthe estimated total moisture storages for the different sea-sons as depicted in the time series plots with respect tothe above-stated three basins may require that the WAT-CLASS parameters be recalibrated such that all relevantmoisture dynamics within these basin are well capturedboth in time and space. Finally, it maybe worthwhile toassess the issue of scale and the filtering radius dimen-sion involved in the estimation of the terrestrial waterstorage from the GRACE remote sensing system overthese basin for the purpose of improving the outcome ofsuch inter-comparison studies.

ACKNOWLEDGEMENTS

Funding for this research was provided by the Universityof Manitoba Graduate Fellowship (UMGF). We wouldlike to extend our thanks to the GRACE team for theirgenerosity in providing the GRACE data. Finally, thanksmust go to all the anonymous reviewers who have madeworthwhile suggestions to the different aspects of thispaper.

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