Bardossy, A.

Devaraju, B.

Fersch, B.

Kindt, H.

Kunstmann, H.

Riegger, J.

Sneeuw, N.

English

KITopen

DirectWBInstitutfürWasserbauGeodätischesInstitutInstitutfürMeteorologieundKlimaforschungThe global continental water balance using GRACEspaceborne gravimetry and high-resolution consistentgeodetic-hydrometeorological data analysis – Phase IN. Sneeuw1, A. Bárdossy2, J. Riegger2, H. Kunstmann3,B. Devaraju1, H. Kindt2, and B. Fersch3.1. Geodätisches Institut, Universität Stuttgart; 2. Institut für Wasserbau, Universität Stuttgart;3. Institut für Meteorologie und Klimaforschung (IMK-IFU), Karlsruhe Institute of Technology (KIT), Forschungszentrum Karlsruhe.GRACE and continental hydrologyGRACE gravity measurements provide a direct measure of water stor-age changes over continents. As such, it enables—for the first time—to close the continental water balance on large scales, and a direct deter-mination of actual evapotranspiration ETa—the unknown component of wa-ter balance—from terrestrial precipitation P and runoff measurements R onlarge scales. Atmospheric moisture flux divergence (!Q) offers another in-dependent way of determining water storage changes, where there is noneed for ETa information. This allows for a mutual intercomparison of datafrom three independent disciplines and an evaluation of hydrological and at-mospheric models. Thus the overall objectives of the DWB project are• the direct analysis oflarge-scale water bal-ances, and• the quantification of therelated uncertainties forlarge-scale catchment ar-eas in different climaticzones.Figure 1: Project overviewIn order to achieve consistent water balances, the mass change rates fromGRACE, hydrology, and hydrometeorology have to be evaluated with re-spect to natural fluctuations and intrinsic errors. Statistical investigationsare needed to characterize the respective contributions. This is the mainobjective in Phase 1 (2007–2008). An open loop evaluation scheme forselected catchments for the different data sources is shown in figure 2.HydrometeorologyGeodesyHydrologyDWB Project, Phase 1gauged basinsopen loop evaluationregional modelingconstrainedevapo-transpiration(ETa)filter bankSH synthesisPprecipitationVIMFD∇ QdischargeRevaluation∇ Q –R=∂SA∂t∂M∂t∂SA∂tevaluation basins∂SH∂tP–R–(ETa)=∂M∂t∂SH∂tdirect water balancepilot basins∂M∂tETa=P–R–GRACEKlm , σlm∂M∂tstoragechangeevaluation∂SH∂t∂SA∂tFigure 2: Project integration in Phase 1 – Open loop evaluationGeodesyTasks• Design, implementation, and testing of spherical harmonic filters.• Computation and provision of mass change rates to the hydrology andhydrometeorology groups.• Development of FFT-based tools for error propagation of full errorvariance-covariance matrices.• Creation of an initial framework for constraining GRACE mass changerates using reliable hydrological mass change rates.Filtering GRACE dataA set of isotropic, anisotropic, and stochastic filters were implemented anddesigned. Mass change rates after applying a few of the filters are shownin figures 3(a)–3(f). Similarly, time-series of mass change rates of one small(Aravalli, western India) and one large catchment (Ob, Siberia) are shown infigures 4(a) and 4(b), respectively.−60˚ −60˚−30˚ −30˚0˚ 0˚30˚ 30˚60˚ 60˚( a ) Unfiltered−60˚ −60˚−30˚ −30˚0˚ 0˚30˚ 30˚60˚ 60˚( b ) Box-car−60˚ −60˚−30˚ −30˚0˚ 0˚30˚ 30˚60˚ 60˚( c ) Gauss 400 km( d ) Anisotropic Gauss−60˚ −60˚−30˚ −30˚0˚ 0˚30˚ 30˚60˚ 60˚( e ) Wiener−60˚ −60˚−30˚ −30˚0˚ 0˚30˚ 30˚60˚ 60˚−200−150−100−50050100150200[mm]( f ) Destriped & GaussFigure 3: Mass change rates before and after filtering (GFZ release 4, Febru-ary 2003)2003 2004 2005 2006 2007 2008−200−150−100−50050100150200AravalliTime [year]Equivalent water height [mm]( a ) Aravalli2003 2004 2005 2006 2007 2008−200−150−100−50050100150200Time [Year]Equivalent water height [mm]Ob  Box−carGaussian 400kmGaussian 400km destripedAnisotropic GaussianWiener isotropic( b ) ObFigure 4: Time-series of mass change rates of Aravalli and Ob catchmentsError propagation and covariance matrix handlingFFT-based error propagation tools were developed to propagate thespectral error variance-covariance matrix of GRACE coefficients tothe spatial domain, thereby estimating the errors of the masschange rates from GRACE. The covariance structure of GRACE inthe spatial domain, and its corresponding spectral error variance-covariance matrix are shown in figures 5(a) and 5(b), respectively.345˚0˚15˚−15˚0˚15˚−1e+09 −5e+08 0 5e+08 1e+09[sq.mm/sq.month]( a ) Spatial covariances ( b ) Spectral covariances [log10]Figure 5: Spatial and spectral covariances of GRACE mass change ratesSummary• A set of deterministic (isotropic and anisotropic), and stochastic filterswere implemented and designed.• The mass change rates from deterministic anisotropic filters are better (vi-sually more pleasing) than from the isotropic filters. However, since thedeterministic filters are non-physical, they are prone to leakage.• Stochastic Wiener-type filters are not successful due to the lack of fullvariance-covariance information.• The use of different filters provides different mass change rates for thesame catchment, particularly if the catchment considered is small com-pared to the spatial resolution of GRACE, or if the amplitude of the hydro-logical signal is low.• FFT-based tools for error propagation have been completed.• An initial framework for constraining GRACE mass change rates usingreliable hydrological mass change rates has also been developed.HydrometeorologyTasks• Evaluation of global and regional hydrometeorological modelling for conti-nental and basin-scale water budget estimations.• Investigation of the potential of regional atmospheric models for an im-provement of global atmospheric moisture fields with increased spatialand temporal resolution• Development of tools for hydrometeorological data analysis.• Adaptation of WRF regional atmospheric model for vertically integratedmoisture flux divergence (VIMFD) calculations.• Evaluation of the mass change rates from atmospheric modelling com-pared to those from hydrology and GRACE.Atmospheric modelling• Global 6-hourly fields of VIMFD obtained from ECMWF ERA-40 reanal-ysis (1960–2001), ECMWF Operational Analysis (2001-2007), and fromNCAR NCEP-Reanalysis (2002-2006).• Atmospheric forcing and initial conditions were both gathered from NCEP-Reanalysis I (NNRP) and ECMWF Operational Analysis (ECMWF opAnl)for the WRF-ARW model.• For regional downscaling of global atmospheric fields, the computationof VIMFD was implemented into the WRF-ARW model as a diagnosticfunction. Several tests were carried out to assure a correct execution onparallel architectures.• The WRF-model was applied with 30 " 30 km2 horizontal resolution, 28vertical layers and a simulation timestep of 120 seconds.Figure 6: Comparison of !SA!t from atmospheric models with!M!t from GRACE.EvaluationAtmospheric modelling is per-formed for four climatically se-lected test regions: Amazon,Siberia (Yenisei and Lena), Sa-hara, and the central plane ofAustralia. The modelled masschange rates are evaluated withfiltered GRACE mass changerates (destriped and smoothedwith a Gaussian 500 km filter).Summary• For the first time, regional-ized atmospheric fields areapplied in the scope ofGRACE based large-scalewater budget analysis.• Uncertainty bounds forterrestrial water storagechanges based on atmo-spheric moisture budgets aredetermined.• Results from regional atmo-spheric modelling show thatdynamical downscaling canimprove estimates of VIMFDconsiderably (compared toGPCC with months whereETa # 0).• The differences in air massvariations (8) that emergefrom regional and globalmodels indicate potentialfor improving the atmo-spheric de-aliasing modelsof GRACE.−10 0 10 20 30 40 50−1001020304050SIBERIA, −VIMFD VS GPCC4, 2002−2007correl. for Months:  Oct  −  Apr with low ETaPrecip. (mm/month)−VIMFD (mm/month)WRF−ECMWF, r²: 0.60RB: −0.12, RMAE: −0.12WRF−NNRP, r²: 0.54RB: −0.20, RMAE: −0.20GLB−ECMWF, r²: 0.89RB: −0.28, RMAE: −0.28GLB−NNRP, r²: 0.09RB: −0.90, RMAE: −0.80Figure 7: Correlation and relative bias ofregional (WRF) and global (GLB) fields ofatmospheric moisture flux divergence withGPCC 4.−30−20−10010203040MonthColumn air mass (kg/m²)air mass ECMWF − regionalair mass ECMWF − global●●●●● ●●●●●●●●●●●●●●●●●●A M A Z O N − 2006Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecFigure 8: Deviations from 2006 mean an-nual column air mass from regional andglobal atmospheric modelsHydrologyTasks• Development of selection schemes for space / time patterns of known wa-ter balance.• GIS based selection of areas of constrainable water balance.• Quantification of hydrological mass constraints and related uncertainties.• Evaluation of mass change rates from GRACE and hydrometeorology ver-sus groundbased measurements from hydrologySelection of suitable basinsContinental water storage changes can be determined if actual evapotranspi-ration ETa can be constrained within adequate limits, and if the uncertainty ofthe measured components (P and R) is quantified. Areas with constrainedETa belong to climatic zones, where ETa is limited either by water or energyavailability i. e. deserts and high altitude/latitude areas. For hot deserts (highETa) mass change rates are assumed to be short-term and random due tolower temporal resolution. For boreal regions ETa is energy limited duringthe winter season. During these selected periods GRACE and hydrometeo-rological data are directly comparable to hydrological data P $ R (cf. figure10).Figure 9: Number of months with limited ETaGRACE vs. hydrometeorology vs. hydrologyInputs for progress posterPlease once again replace diagrams in  fig 10BySignal GRACE dM/dt  vrs P-Ry = 0,4798x - 3,1417R2 = 0,0579-50-40-30-20-1001020304050-10 0 10 20 30 40 50P-R    [mm/mo]dM/dt   [mm/mo] Signal (-divQ-R) vrs P-Ry = 0,6256x + 1,351R2 = 0,8592-50-40-30-20-1001020304050-10 0 10 20 30 40 50P-R mm/mo]-divQ-R [mm/mo]Inputs for progress posterPlease once again replace diagrams in  fig 10BySignal GRACE dM/dt  vrs P-Ry = 0,4798x - 3,1417R2 = 0,0579-50-40-30-20-1001020304050-10 0 10 20 30 40 50P-R    [mm/mo]dM/dt   [mm/mo] Signal (-divQ-R) vrs P-Ry = 0,6256x + 1,351R2 = 0,8592-50-40-30-20-1001020304050-10 0 10 20 30 40 50P-R mm/mo]-divQ-R [mm/mo]Signal GRACE dM/dt  vrs (-divQ-R)y = 0,2773x + 0,6047R2 = 0,0088-50-40-30-20-1001020304050-10 0 10 20 30 40 50-divQ-R    [mm/mo]dM/dt   [mm/mo]And figure caption fig 10!"#$%&'()*'+,-..&%/01.2'3&.4&&5'615.708'2.1%-#&',7-5#&2'9%16':;<=>?'78@%101#8'-5@'78@%16&.&1%101#8'!"##$%"##&''91%'.7&'A&5-'B-2"5'-5@'()*+,+#Inputs for proposal posterAchievments  ! rename Lessons learned from Phase I• The identification of areas and times of constrainable water balance (ETa ~ 0) provides a sound basis for the evaluation of GRACE and hydrometeorological mass change rates by hydrological groundbased measurements under different climatic and physiografic conditions. • The use of monthly residuals in data comparisons increases sensitivity due to the elimination of the mean seasonal behaviour and thus allows for a detailed investigation of uncertainties• <.612/7&%",'61"2.$%&'90$C'/%1D"@&2'-'D-0$-30&?'"5@&/&5@&5.'@-.-'21$%,&E';&2/&,."D&'78@%16&.&1%101#",-0'@-.-'$2$-008'9".'6$,7'3&..&%'.1'78@%101#",-0'@-.-'.7-5':;<=>'@-.-'F'&2/&,"-008'91%'7"#7'0-.".$@&2E'G7"2'-00142'.1'2&/-%-.&'contributions from climatic variations and intrinsic errors. • >C/&%"&5,&'"5'.7&',71",&'19'9"0.&%"5#'6&.71@2'-5@'4".7'.7&'%&2/&,."D&'7-5@0"5#'19'7$#&'D-%"-5,&H,1D-%"-5,&'6-.%",&2'91%':;<=>'A&D&0HI'/%1@$,.2'/0-82'-5'&22&5."-0'%10&'91%'-5'-@&J$-.&',16/-."3"0".8'4".7'78@%16&.&1%101#",-0'-5@'78@%101#",-0'@-.-E'• ;&#"15-0'61@&00"5#'19'61"2.$%&'90$C'2&&62'.1'"6/%1D&',152"2.&5,8'4".7'6&.&1%101#",-0'-5@'78@%101#",-0'@-.-Figure 10: Scatterplots between monthly storage changes from GRACE, hy-drology and hydrometeorology (2003–07) for the Lena Basin (ETa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igure 11: Signal RMS of GRACE!M!t vs. hydrology (P $R) forETa # 0.( a ) GRACE( b ) P $RFigure 12: Spatial distribution of tempo-ral correlations between Ob and othercatchments obtained from the residuals ofGRACE !M!t and of hydrology (P $ R) forETa # 0.Summary• Direct water balance calculations for pilot basins with large signals deliverreasonable results for mean monthly data.• Monthly variations of GRACE signals are observed to be in the 20–30 %range of signal amplitudes except for tropical basins. This does not al-low for an straight foreward evaluation of different hydrological model ap-proaches without quantification of contributions from climatic variationsand intrinsic errors.• Comparisons between GRACE and hydrological/hydrometeorologicaldata show a reasonable agreement for large signal amplitudes, however,an uncorrelated behaviour for small signals or residuals (high latitudes ordeserts (ETa # 0)).• Inter-comparisons of GRACE, hydrometeorological, and hydrological dataprovide insights into variability contributions from climatic variations andintrinsic errors. Hydrometeorological and hydrological data fit much bettereven for small signals (figure 10), thus identifying GRACE signal variationson the selected areas as error. These results also indicate the suitabilityof the constrained water balance approach—comparing GRACE with boththe hydrological and hydrometeorological data.• Signal RMS for GRACE on selections (ETa # 0) is considerable higherthan for hydrology especially for high latitudes (figure 11).• Investigations on the spatial structure of temporal correlations betweendifferent basins show a much higher (artificial) correlation betweenmonthly residuals (deviations from monthly means) for GRACE-GRACEthan for hydrology-hydrology (figures 12(a) and 12(b)). This calls for astochastic correction scheme for improving GRACE signals based on thestochastic information and mass constraints from hydrology .DFG Priority programme SPP 1257 Kolloquim. 6–8 October 2008, München.

The global continental water balance using GRACE spaceborne gravimetry and high-resolution consistent geodetic-hydrometeorological data analysis. Phase I

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The global continental water balance using GRACE spaceborne gravimetry and high-resolution consistent geodetic-hydrometeorological data analysis. Phase I

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