Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances

Christof Lorenz Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

Search for other papers by Christof Lorenz in
Current site
Google Scholar
PubMed
Close
,
Harald Kunstmann Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

Search for other papers by Harald Kunstmann in
Current site
Google Scholar
PubMed
Close
,
Balaji Devaraju Institute of Geodesy, University of Stuttgart, Stuttgart, Germany

Search for other papers by Balaji Devaraju in
Current site
Google Scholar
PubMed
Close
,
Mohammad J. Tourian Institute of Geodesy, University of Stuttgart, Stuttgart, Germany

Search for other papers by Mohammad J. Tourian in
Current site
Google Scholar
PubMed
Close
,
Nico Sneeuw Institute of Geodesy, University of Stuttgart, Stuttgart, Germany

Search for other papers by Nico Sneeuw in
Current site
Google Scholar
PubMed
Close
, and
Johannes Riegger Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, Stuttgart, Germany

Search for other papers by Johannes Riegger in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and Gravity Recovery and Climate Experiment (GRACE) satellite-derived water storage changes are employed. The derived ensemble of hydrological and hydrometeorological budget–based runoff estimates, together with results from different land surface hydrological models [Global Land Data Assimilation System (GLDAS) and the land-only version of MERRA (MERRA-Land)] and a simple predictor based on the precipitation–runoff ratio, is compared with observed monthly in situ runoff for 96 catchments of different sizes and climatic conditions worldwide. Despite significant shortcomings of the budget-based methods over many catchments, the evaluation allows for the demarcation of areas with consistently reasonable runoff estimates. Good agreement was particularly observed when runoff followed a dominant annual cycle like the Amazon. This holds true also for catchments with an area far below the spatial resolution of GRACE, like the Rhine. Over catchments with low or nearly constant runoff, the budget-based approaches do not provide realistic runoff estimates because of significant biases in the input datasets. In general, no specific data combination could be identified that consistently performed over all catchments. Thus, the performance over a specific single catchment cannot be extrapolated to other regions. Only in few cases do specific dataset combinations provide reasonable water budget closure; in most cases, significant imbalances remain for all the applied datasets.

Denotes Open Access content.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-13-0157.s1.

Corresponding author address: Christof Lorenz, Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany. E-mail: christof.lorenz@kit.edu

Abstract

The performance of hydrological and hydrometeorological water-balance-based methods to estimate monthly runoff is analyzed. Such an analysis also allows for the examination of the closure of water budgets at different spatial (continental and catchment) and temporal (monthly, seasonal, and annual) scales. For this analysis, different combinations of gridded observations [Global Precipitation Climatology Centre (GPCC), Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC), Climatic Research Unit (CRU), and University of Delaware (DEL)], atmospheric reanalysis models [Interim ECMWF Re-Analysis (ERA-Interim), Climate Forecast System Reanalysis (CFSR), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)], partially model-based datasets [Global Land Surface Evaporation: The Amsterdam Methodology (GLEAM), Moderate Resolution Imaging Spectroradiometer (MODIS) Global Evapotranspiration Project (MOD16), and FLUXNET Multi-Tree Ensemble (FLUXNET MTE)], and Gravity Recovery and Climate Experiment (GRACE) satellite-derived water storage changes are employed. The derived ensemble of hydrological and hydrometeorological budget–based runoff estimates, together with results from different land surface hydrological models [Global Land Data Assimilation System (GLDAS) and the land-only version of MERRA (MERRA-Land)] and a simple predictor based on the precipitation–runoff ratio, is compared with observed monthly in situ runoff for 96 catchments of different sizes and climatic conditions worldwide. Despite significant shortcomings of the budget-based methods over many catchments, the evaluation allows for the demarcation of areas with consistently reasonable runoff estimates. Good agreement was particularly observed when runoff followed a dominant annual cycle like the Amazon. This holds true also for catchments with an area far below the spatial resolution of GRACE, like the Rhine. Over catchments with low or nearly constant runoff, the budget-based approaches do not provide realistic runoff estimates because of significant biases in the input datasets. In general, no specific data combination could be identified that consistently performed over all catchments. Thus, the performance over a specific single catchment cannot be extrapolated to other regions. Only in few cases do specific dataset combinations provide reasonable water budget closure; in most cases, significant imbalances remain for all the applied datasets.

Denotes Open Access content.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-13-0157.s1.

Corresponding author address: Christof Lorenz, Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany. E-mail: christof.lorenz@kit.edu

Supplementary Materials

    • Supplemental Materials (PDF 806.54 KB)
Save
  • Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Alsdorf, D. E., Rodríguez E. , and Lettenmaier D. P. , 2007: Measuring surface water from space. Rev. Geophys., 45, RG2002, doi:10.1029/2006RG000197.

    • Search Google Scholar
    • Export Citation
  • Berrisford, P., Dee D. , Fielding K. , Fuentes M. , Kallberg P. , Kobayashi S. , and Uppala S. , 2009: The ERA-Interim archive version 1.0. ERA Rep. Series, Rep. 1, ECMWF, 16 pp. [Available online at http://old.ecmwf.int/publications/library/ecpublications/_pdf/era/era_report_series/RS_1.pdf.]

  • Bettadpur, S., 2012: GRACE UTCSR level-2 processing standards document (for level-2 product release 0005). Rev. 4.0, Doc. GRACE 327-742 (CSR-GR-12-xx), Center for Space Research, The University of Texas at Austin, 17 pp. [Available online at ftp://podaac.jpl.nasa.gov/allData/grace/docs/L2-CSR0005_ProcStd_v4.0.pdf.]

  • Bonan, G. B., 1998: The land surface climatology of the NCAR Land Surface Model coupled to the NCEP Community Climate Model. J. Climate, 11, 13071326, doi:10.1175/1520-0442(1998)011<1307:TLSCOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and Coauthors, 1996: Modeling of land-surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 72517268, doi:10.1029/95JD02165.

    • Search Google Scholar
    • Export Citation
  • Chen, J. L., Rodell M. , Wilson C. R. , and Famiglietti J. S. , 2005: Low degree spherical harmonic influences on Gravity Recovery and Climate Experiment (GRACE) water storage estimates. Geophys. Res. Lett.,32, L14405, doi:10.1029/2005GL022964.

  • Chen, M., Shi W. , Xie P. , Silva V. B. S. , Kousky V. E. , Higgins R. W. , and Janowiak J. E. , 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res.,113, D04110, doi:10.1029/2007JD009132.

  • Cheng, M., and Ries J. , 2014: Monthly estimates of C20 from 5 SLR satellites based on GRACE RL05 models. GRACE Tech. Note 07, Center for Space Research, The University of Texas at Austin, 1 pp. [Available online at ftp://podaac.jpl.nasa.gov/allData/grace/docs/TN-07_C20_SLR.txt.]

  • Crowley, J. W., Mitrovica J. X. , Bailey R. C. , Tamisiea M. E. , and Davis J. L. , 2006: Land water storage within the Congo basin inferred from GRACE satellite gravity data. Geophys. Res. Lett.,33, L19402, doi:10.1029/2006GL027070.

  • Dahle, C., Flechtner F. , Gruber C. , König D. , König R. , Michalak G. , and Neumayer K.-H. , 2013: GFZ GRACE level-2 processing standards document for level-2 product release 0005. Scientific Tech. Rep. STR12/02, GFZ German Research Centre for Geosciences, 26 pp., doi:10.2312/GFZ.b103-1202-25.

  • Dai, Y., and Coauthors, 2003: The Common Land Model. Bull. Amer. Meteor. Soc., 84, 10131023, doi:10.1175/BAMS-84-8-1013.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Derber, J. C., Parrish D. F. , and Lord S. J. , 1991: The new global operational analysis system at the national meteorological center. Wea. Forecasting, 6, 538547, doi:10.1175/1520-0434(1991)006<0538:TNGOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ek, M. B., Mitchell K. E. , Lin Y. , Rogers E. , Grunmann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of NOAH land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res.,108, 8851, doi:10.1029/2002JD003296.

  • Entekhabi, D., and Coauthors, 2010: The soil moisture active passive (SMAP) mission. Proc. IEEE, 98, 704716, doi:10.1109/JPROC.2010.2043918.

    • Search Google Scholar
    • Export Citation
  • Falloon, P., Betts R. , Wiltshire A. , Dankers R. , Mathison C. , McNeall D. , Bates P. , and Trigg M. , 2011: Validation of river flows in HadGEM1 and HadCM3 with the TRIP river flow model. J. Hydrometeor., 12, 11571180, doi:10.1175/2011JHM1388.1.

    • Search Google Scholar
    • Export Citation
  • Fekete, B. M., and Vörösmarty C. J. , 2007: The current status of global river discharge monitoring and potential new technologies complementing traditional discharge measurements. IAHS Publ.,309, 129–136. [Available online at http://iahs.info/uploads/dms/309015.pdf.]

  • Fekete, B. M., Vörösmarty C. J. , and Grabs W. , 2002: High-resolution fields of global runoff combining observed river discharge and simulated water balances. Global Biogeochem. Cycles, 16, 1042, doi:10.1029/1999GB001254.

    • Search Google Scholar
    • Export Citation
  • Fekete, B. M., Looser U. , Pietroniro A. , and Robarts R. D. , 2012: Rationale for monitoring discharge on the ground. J. Hydrometeor., 13, 19771986, doi:10.1175/JHM-D-11-0126.1.

    • Search Google Scholar
    • Export Citation
  • Fersch, B., Kunstmann H. , Bárdossy A. , Devaraju B. , and Sneeuw N. , 2012: Continental-scale basin water storage variation from global and dynamically downscaled atmospheric water budgets in comparison with GRACE-derived observations. J. Hydrometeor., 13, 15891603, doi:10.1175/JHM-D-11-0143.1.

    • Search Google Scholar
    • Export Citation
  • GRDC, 2013: Tenth meeting of the GRDC Steering Committee, 15–17 June 2011, Koblenz, Germany. GRDC Rep. Series, Rep. 42, Global Runoff Data Centre, 31 pp., doi:10.5675/GRDC_Report_42.

  • Gupta, H., Shrooshian S. , and Yapo P. O. , 1999: Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J. Hydrol. Eng., 4, 135143, doi:10.1061/(ASCE)1084-0699(1999)4:2(135).

    • Search Google Scholar
    • Export Citation
  • Harris, I., Jones P. D. , Osborn T. J. , and Lister D. H. , 2013: Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 dataset. Int. J. Climatol., 34, 623–642, doi:10.1002/joc.3711.

    • Search Google Scholar
    • Export Citation
  • Hrachowitz, M., and Coauthors, 2013: A decade of Predictions in Ungauged Basins (PUB)—A review. Hydrol. Sci. J., 58, 11981255, doi:10.1080/02626667.2013.803183.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Bolvin D. T. , and Gu G. , 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett.,36, L17808, doi:10.1029/2009GL040000.

  • Jones, P., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 22042210, doi:10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jung, M., Reichstein M. , and Bondeau A. , 2009: Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 20012013, doi:10.5194/bg-6-2001-2009.

    • Search Google Scholar
    • Export Citation
  • Jung, M., and Coauthors, 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951954, doi:10.1038/nature09396.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kerr, Y. H., and Coauthors, 2010: The SMOS Mission: New tool for monitoring key elements of the global water cycle. Proc. IEEE,98, 666–687, doi:10.1109/JPROC.2010.2043032.

  • Kistler, R., and Coauthors, 2001: The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247268, doi:10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kopp, T. J., and Kiess R. B. , 1996: The air force global weather central cloud analysis model. Preprints, 15th Conf. on Weather Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc., 220222.

  • Koren, V., Schaake J. , Mitchell K. , Duan Q. Y. , Chen F. , and Baker J. M. , 1999: A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J. Geophys. Res., 104, 19 569–19 585, doi:10.1029/1999JD900232.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Suarez M. J. , 1996: Energy and water balance calculations in the Mosaic LSM. NASA Tech. Memo. 104606, Tech. Rep. Series on Global Modeling and Data Assimilation, Vol. 9, 60 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Koster130.pdf.]

  • Kummerow, C., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, doi:10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kusche, J., 2007: Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models. J. Geod., 81, 733749, doi:10.1007/s00190-007-0143-3.

    • Search Google Scholar
    • Export Citation
  • Kusche, J., Schmidt R. , Petrovic S. , and Rietbroek R. , 2009: Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model. J. Geod., 83, 903–913, doi:10.1007/s00190-009-0308-3.

    • Search Google Scholar
    • Export Citation
  • Landerer, F. W., Dickey J. O. , and Güntner A. , 2010: Terrestrial water budget of the Eurasian pan-Arctic from GRACE satellite measurements during 2003–2009. J. Geophys. Res.,115, D23115, doi:10.1029/2010JD014584.

  • Liang, X., Lettenmaier D. P. , Wood E. F. , and Burges S. J. , 1994: A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res., 99, 14 41514 428, doi:10.1029/94JD00483.

    • Search Google Scholar
    • Export Citation
  • Longuevergne, L., Scanlon B. R. , and Wilson C. R. , 2010: GRACE hydrological estimates for small basins: Evaluating processing approaches on the High Plains Aquifer, USA. Water Resour. Res.,46, W11517, doi:10.1029/2009WR008564.

  • Lorenz, C., and Kunstmann H. , 2012: The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis. J. Hydrometeor., 13, 13971420, doi:10.1175/JHM-D-11-088.1.

    • Search Google Scholar
    • Export Citation
  • Matsuura, K., and Willmott C. J. , 2012: Terrestrial precipitation: 1900–2010 gridded monthly time series (version 3.02). Center for Climatic Research, University of Delaware, Newark, DE. [Available online at http://climate.geog.udel.edu/~climate/html_pages/download.html#P2011rev.]

  • Miralles, D. G., de Jeu R. A. M. , Gash J. H. , Holmes T. R. H. , and Dolman A. J. , 2011: An application of GLEAM to estimating global evaporation. Hydrol. Earth Syst. Sci. Discuss., 8, 127, doi:10.5194/hessd-8-1-2011.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., Heinsch F. A. , Zhao M. , and Running S. W. , 2007: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ., 111, 519536, doi:10.1016/j.rse.2007.04.015.

    • Search Google Scholar
    • Export Citation
  • Mu, Q., Zhao M. , and Running S. W. , 2011: Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ., 115, 17811800, doi:10.1016/j.rse.2011.02.019.

    • Search Google Scholar
    • Export Citation
  • Mueller, B., and Coauthors, 2011: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett.,38, L06492, doi:10.1029/2010GL046230.

  • Nash, J. E., and Sutcliffe J. V. , 1970: River flow forecasting through conceptual models: Part 1. A discussion of principles. J. Hydrol., 10, 282290, doi:10.1016/0022-1694(70)90255-6.

    • Search Google Scholar
    • Export Citation
  • Parkinson, C., 2003: Aqua: An earth-observing satellite mission to examine water and other climate variables. IEEE Trans. Geosci. Remote Sens.,41, 173183, doi:10.1109/TGRS.2002.808319.

    • Search Google Scholar
    • Export Citation
  • Peixoto, J. P., and Oort A. H. , 1992: Physics of Climate. American Institute of Physics, 520 pp.

  • Reichle, R. H., Koster R. D. , Lannoy G. J. M. D. , Forman B. A. , Liu Q. , Mahanama S. P. P. , and Tour A. , 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 6322–6338, doi:10.1175/JCLI-D-10-05033.1.

    • Search Google Scholar
    • Export Citation
  • Riegger, J., and Tourian M. J. , 2014: Characterization of runoff–storage relationships by satellite gravimetry and remote sensing. Water Resour. Res.,50, 3444–3466, doi:10.1002/2013WR013847.

  • Riegger, J., Tourian M. J. , Devaraju B. , and Sneeuw N. , 2012: Analysis of GRACE uncertainties by hydrological and hydro-meteorological observations. J. Geodyn., 59–60, 1627, doi:10.1016/j.jog.2012.02.001.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation
  • Roads, J. O., Chen S. C. , Guetter A. K. , and Georgakaos K. P. , 1994: Large-scale aspects of the United States hydrologic cycle. Bull. Amer. Meteor. Soc., 75, 15891610, doi:10.1175/1520-0477(1994)075<1589:LSAOTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., Famiglietti J. S. , Chen J. , Seneviratne S. I. , Viterbo P. , Holl S. , and Wilson C. R. , 2004a: Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett.,31, L20504, doi:10.1029/2004GL020873.

  • Rodell, M., and Coauthors, 2004b: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381394, doi:10.1175/BAMS-85-3-381.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., Velicogna I. , and Famiglietti J. , 2009: Satellite-based estimates of ground-water depletion in India. Nature, 460, 9991002, doi:10.1038/nature08238.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Salvucci, G. D., and Gentine P. , 2013: Emergent relation between surface vapor conductance and relative humidity profiles yields evaporation rates from weather data. Proc. Natl. Acad. Sci. USA, 110, 62876291, doi:10.1073/pnas.1215844110.

    • Search Google Scholar
    • Export Citation
  • Sasgen, I., Martinec Z. , and Fleming K. , 2006: Wiener optimal filtering of GRACE data. Stud. Geophys. Geod., 50, 499508, doi:10.1007/s11200-006-0031-y.

    • Search Google Scholar
    • Export Citation
  • Schlosser, C. A., and Houser P. R. , 2007: Assessing a satellite-era perspective of the global water cycle. J. Climate, 20, 13161338, doi:10.1175/JCLI4057.1.

    • Search Google Scholar
    • Export Citation
  • Schmidt, R., Flechtner F. , Meyer U. , Neumayer K.-H. , Dahle C. , König R. , and Kusche J. , 2008: Hydrological signals observed by the GRACE satellites. Surv. Geophys., 29, 319334, doi:10.1007/s10712-008-9033-3.

    • Search Google Scholar
    • Export Citation
  • Schneider, U., Becker A. , Finger P. , Meyer-Christoffer A. , Ziese M. , and Rudolf B. , 2014: GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol., 115, 1540, doi:10.1007/s00704-013-0860-x.

    • Search Google Scholar
    • Export Citation
  • Schrama, E. J. O., and Wouters B. , 2011: Revisiting Greenland Ice Sheet mass loss observed by GRACE. J. Geophys. Res.,116, B02407, doi:10.1029/2009JB006847.

  • Seitz, F., Schmidt M. , and Shum C. K. , 2008: Signals of extreme weather conditions in central Europe in GRACE 4-D hydrological mass variations. Earth Planet. Sci. Lett., 268, 165170, doi:10.1016/j.epsl.2008.01.001.

    • Search Google Scholar
    • Export Citation
  • Seo, K. W., Wilson C. R. , Han S. C. , and Waliser D. E. , 2008: Gravity Recovery and Climate Experiment (GRACE) alias error from ocean tides. J. Geophys. Res.,113, B03405, doi:10.1029/2006JB004747.

  • Sheffield, J., Goteti G. , and Wood E. F. , 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 30883111, doi:10.1175/JCLI3790.1.

    • Search Google Scholar
    • Export Citation
  • Sheffield, J., Ferguson C. R. , Troy T. J. , Wood E. F. , and McCabe M. F. , 2009: Closing the terrestrial water budget from satellite remote sensing. Geophys. Res. Lett.,36, L07403, doi:10.1029/2009GL037338.

  • Shiklomanov, A. I., Lammers R. B. , and Vörösmarty C. J. , 2002: Widespread decline in hydrological monitoring threatens pan-Arctic research. Eos, Trans. Amer. Geophys. Union, 83, 1317, doi:10.1029/2002EO000007.

    • Search Google Scholar
    • Export Citation
  • Swenson, S., and Wahr J. , 2006: Estimating large-scale precipitation minus evapotranspiration from GRACE satellite gravity measurements. J. Hydrometeor., 7, 252270, doi:10.1175/JHM478.1.

    • Search Google Scholar
    • Export Citation
  • Syed, T. H., Famiglietti J. S. , Chen J. , Rodell M. , Seneviratne S. I. , Viterbo P. , and Wilson C. R. , 2005: Total basin discharge for the Amazon and Mississippi River basins from GRACE and a land–atmosphere water balance. Geophys. Res. Lett.,32, L24404, doi:10.1029/2005GL024851.

  • Syed, T. H., Famiglietti J. S. , Zlotnicki V. , and Rodell M. , 2007: Contemporary estimates of pan-Arctic freshwater discharge from GRACE and reanalysis. Geophys. Res. Lett.,34, L19404, doi:10.1029/2007GL031254.

  • Syed, T. H., Famiglietti J. S. , and Chambers D. P. , 2009: GRACE-based estimates of terrestrial freshwater discharge from basin to continental scales. J. Hydrometeor., 10, 2240, doi:10.1175/2008JHM993.1.

    • Search Google Scholar
    • Export Citation
  • Tapley, B. D., Bettadpur S. , Ries J. C. , Thompson P. F. , and Watkins M. M. , 2004: GRACE measurements of mass variability in the Earth system. Science, 305, 503505, doi:10.1126/science.1099192.

    • Search Google Scholar
    • Export Citation
  • Tourian, M. J., Riegger J. , Sneeuw N. , and Devaraju B. , 2011: Outlier identification and correction for GRACE aggregated data. Stud. Geophys. Geod., 55, 627640, doi:10.1007/s11200-009-9007-z.

    • Search Google Scholar
    • Export Citation
  • Tourian, M. J., Sneeuw N. , and Bárdossy A. , 2013: A quantile function approach to discharge estimation from satellite altimetry (ENVISAT). Water Resour. Res., 49, 4174–4186, doi:10.1002/wrcr.20348.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and Fasullo J. T. , 2013: Regional energy and water cycles: Transports from ocean to land. J. Climate, 26, 78377851, doi:10.1175/JCLI-D-13-00008.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., Smith L. , Qian T. , Dai A. , and Fasullo J. , 2007: Estimates of the global water budget and its annual cycle using observational and model data. J. Hydrometeor., 8, 758769, doi:10.1175/JHM600.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., Fasullo J. T. , and Mackaro J. , 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, doi:10.1175/2011JCLI4171.1.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., and Coauthors, 2001: Global water data: A newly endangered species. Eos, Trans. Amer. Geophys. Union, 82, 5458, doi:10.1029/01EO00031.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., and Coauthors, 2010: Global threats to human water security and river biodiversity. Nature, 467, 555561, doi:10.1038/nature09440.

    • Search Google Scholar
    • Export Citation
  • Wahr, J., Molenaar M. , and Bryan F. , 1998: The time-variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE. J. Geophys. Res., 103, 30 20530 230, doi:10.1029/98JB02844.

    • Search Google Scholar
    • Export Citation
  • Werth, S., Güntner A. , Schmidt R. , and Kusche J. , 2009: Evaluation of GRACE filter tools from a hydrological perspective. Geophys. J. Int., 179, 14991515, doi:10.1111/j.1365-246X.2009.04355.x.

    • Search Google Scholar
    • Export Citation
  • Wessel, P., and Smith W. H. F. , 1991: Free software helps map and display data. Eos, Trans. Amer. Geophys. Union, 72, 441446, doi:10.1029/90EO00319.

    • Search Google Scholar
    • Export Citation
  • Xie, P., and Arkin P. A. , 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., Rodell M. , and Olivera F. , 2010: Evaluation of the Global Land Data Assimilation System using global river discharge data and a source-to-sink routing scheme. Water Resour. Res.,46, W06507, doi:10.1029/2009WR007811.

  • Zhou, X., Zhang Y. , Wang Y. , Zhang H. , Vaze J. , Zhang L. , Yang Y. , and Zhou Y. , 2012: Benchmarking global land surface models against the observed mean annual runoff from 150 large basins. J. Hydrol.,470–471, 269–279, doi:10.1016/j.jhydrol.2012.09.002.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1457 448 20
PDF Downloads 860 165 12