Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins

Oldrich Rakovec Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Oldrich Rakovec in
Current site
Google Scholar
PubMed
Close
,
Rohini Kumar Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Rohini Kumar in
Current site
Google Scholar
PubMed
Close
,
Juliane Mai Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Juliane Mai in
Current site
Google Scholar
PubMed
Close
,
Matthias Cuntz Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Matthias Cuntz in
Current site
Google Scholar
PubMed
Close
,
Stephan Thober Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Stephan Thober in
Current site
Google Scholar
PubMed
Close
,
Matthias Zink Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Matthias Zink in
Current site
Google Scholar
PubMed
Close
,
Sabine Attinger Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Sabine Attinger in
Current site
Google Scholar
PubMed
Close
,
David Schäfer Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by David Schäfer in
Current site
Google Scholar
PubMed
Close
,
Martin Schrön Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Martin Schrön in
Current site
Google Scholar
PubMed
Close
, and
Luis Samaniego Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Search for other papers by Luis Samaniego in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Accurately predicting regional-scale water fluxes and states remains a challenging task in contemporary hydrology. Coping with this grand challenge requires, among other things, a model that makes reliable predictions across scales, locations, and variables other than those used for parameter estimation. In this study, the mesoscale hydrologic model (mHM) parameterized with the multiscale regionalization technique is comprehensively tested across 400 European river basins. The model fluxes and states, constrained using the observed streamflow, are evaluated against gridded evapotranspiration, soil moisture, and total water storage anomalies, as well as local-scale eddy covariance observations. This multiscale verification is carried out in a seamless manner at the native resolutions of available datasets, varying from 0.5 to 100 km. Results of cross-validation tests show that mHM is able to capture the streamflow dynamics adequately well across a wide range of climate and physiographical characteristics. The model yields generally better results (with lower spread of model statistics) in basins with higher rain gauge density. Model performance for other fluxes and states is strongly driven by the degree of seasonality that each variable exhibits, with the best match being observed for evapotranspiration, followed by total water storage anomaly, and the least for soil moisture. Results show that constraining the model against streamflow only may be necessary but not sufficient to warrant the model fidelity for other complementary variables. The study emphasizes the need to account for other complementary datasets besides streamflow during parameter estimation to improve model skill with respect to “hidden” variables.

Corresponding author address: Luis Samaniego, Helmholtz Centre for Environmental Research–UFZ, Permoserstrasse 15, 04318 Leipzig, Germany. E-mail: luis.samaniego@ufz.de

Abstract

Accurately predicting regional-scale water fluxes and states remains a challenging task in contemporary hydrology. Coping with this grand challenge requires, among other things, a model that makes reliable predictions across scales, locations, and variables other than those used for parameter estimation. In this study, the mesoscale hydrologic model (mHM) parameterized with the multiscale regionalization technique is comprehensively tested across 400 European river basins. The model fluxes and states, constrained using the observed streamflow, are evaluated against gridded evapotranspiration, soil moisture, and total water storage anomalies, as well as local-scale eddy covariance observations. This multiscale verification is carried out in a seamless manner at the native resolutions of available datasets, varying from 0.5 to 100 km. Results of cross-validation tests show that mHM is able to capture the streamflow dynamics adequately well across a wide range of climate and physiographical characteristics. The model yields generally better results (with lower spread of model statistics) in basins with higher rain gauge density. Model performance for other fluxes and states is strongly driven by the degree of seasonality that each variable exhibits, with the best match being observed for evapotranspiration, followed by total water storage anomaly, and the least for soil moisture. Results show that constraining the model against streamflow only may be necessary but not sufficient to warrant the model fidelity for other complementary variables. The study emphasizes the need to account for other complementary datasets besides streamflow during parameter estimation to improve model skill with respect to “hidden” variables.

Corresponding author address: Luis Samaniego, Helmholtz Centre for Environmental Research–UFZ, Permoserstrasse 15, 04318 Leipzig, Germany. E-mail: luis.samaniego@ufz.de
Save
  • Alfieri, L., Pappenberger F. , Wetterhall F. , Haiden T. , Richardson D. , and Salamon P. , 2014: Evaluation of ensemble streamflow predictions in Europe. J. Hydrol., 517, 913922, doi:10.1016/j.jhydrol.2014.06.035.

    • Search Google Scholar
    • Export Citation
  • Andersen, O. B., Seneviratne S. I. , Hinderer J. , and Viterbo P. , 2005: GRACE-derived terrestrial water storage depletion associated with the 2003 European heat wave. Geophys. Res. Lett., 32, L18405, doi:10.1029/2005GL023574.

    • Search Google Scholar
    • Export Citation
  • Batalla, R. J., Gómez C. M. , and Kondolf G. , 2004: Reservoir-induced hydrological changes in the Ebro River basin (NE Spain). J. Hydrol., 290, 117136, doi:10.1016/j.jhydrol.2003.12.002.

    • Search Google Scholar
    • Export Citation
  • Berger, K. P., and Entekhabi D. , 2001: Basin hydrologic response relations to distributed physiographic descriptors and climate. J. Hydrol., 247, 169182, doi:10.1016/S0022-1694(01)00383-3.

    • Search Google Scholar
    • Export Citation
  • Bergström, S., 1995: The HBV model. Computer Models of Watershed Hydrology, V. P. Singh, Ed., Water Resources Publications, 443–476.

  • Blöschl, G., 1999: Scaling issues in snow hydrology. Hydrol. Processes, 13, 21492175, doi:10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8.

    • Search Google Scholar
    • Export Citation
  • Blöschl, G., 2001: Scaling in hydrology. Hydrol. Processes, 15, 709711, doi:10.1002/hyp.432.

  • Blöschl, G., Grayson R. B. , and Sivapalan M. , 1995: On the representative elementary area (REA) concept and its utility for distributed rainfall–runoff modelling. Hydrol. Processes, 9, 313330, doi:10.1002/hyp.3360090307.

    • Search Google Scholar
    • Export Citation
  • Boone, A., and Coauthors, 2004: The Rhône-Aggregation land surface scheme intercomparison project: An overview. J. Climate, 17, 187208, doi:10.1175/1520-0442(2004)017<0187:TRLSSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Budyko, M., 1974: Climate and Life. D. H. Miller, Ed., International Geophysics Series, Vol. 18, Academic Press, 508 pp.

  • Cai, X., Yang Z.-L. , David C. , Niu G.-Y. , and Rodell M. , 2014: Hydrological evaluation of the Noah-MP land surface model for the Mississippi River basin. J. Geophys. Res. Atmos., 119, 2338, doi:10.1002/2013JD020792.

    • Search Google Scholar
    • Export Citation
  • Clark, M. P., Kavetski D. , and Fenicia F. , 2011: Pursuing the method of multiple working hypotheses for hydrological modeling. Water Resour. Res., 47, W09301, doi:10.1029/2010WR009827.

    • Search Google Scholar
    • Export Citation
  • Crawford, N. H., and Linsley R. K. , 1966: Digital simulation in hydrology: Stanford Watershed Model IV. Tech. Rep. 39, Dept. of Civil Engineering, Stanford University, 210 pp.

  • Cristea, N. C., Kampf S. K. , and Burges S. J. , 2012: Revised coefficients for Priestley–Taylor and Makkink–Hansen equations for estimating daily reference evapotranspiration. J. Hydraul. Eng., 18, 12891300, doi:10.1061/(ASCE)HE.1943-5584.0000679.

    • Search Google Scholar
    • Export Citation
  • Cuntz, M., and Coauthors, 2015: Computationally inexpensive identification of noninformative model parameters by sequential screening. Water Resour. Res., 51, 64176441, doi:10.1002/2015WR016907.

    • Search Google Scholar
    • Export Citation
  • Dawdy, D. R., and Lichty R. W. , 1968: Methodology of hydrologic model building. IAHS Publ., 81, 347355. [Available online at http://iahs.info/uploads/dms/iahs_081_0347.pdf.]

    • Search Google Scholar
    • Export Citation
  • Dingman, S., 2004: Physical Hydrology. Prentice-Hall, 646 pp.

  • Dorigo, W., and Coauthors, 2014: Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens. Environ., 162, 380395, doi:10.1016/j.rse.2014.07.023.

    • Search Google Scholar
    • Export Citation
  • Duan, Q., Sorooshian S. , and Gupta V. , 1992: Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resour. Res., 28, 10151031, doi:10.1029/91WR02985.

    • Search Google Scholar
    • Export Citation
  • Duan, Q., Gupta V. , and Sorooshian S. , 1993: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl., 76, 501521, doi:10.1007/BF00939380.

    • Search Google Scholar
    • Export Citation
  • ESA, 2015: Soil moisture CCI. European Space Agency, accessed 1 July 2015. [Available online at http://www.esa-soilmoisture-cci.org/node/136.]

  • Euser, T., Winsemius H. C. , Hrachowitz M. , Fenicia F. , Uhlenbrook S. , and Savenije H. H. G. , 2013: A framework to assess the realism of model structures using hydrological signatures. Hydrol. Earth Syst. Sci., 17, 18931912, doi:10.5194/hess-17-1893-2013.

    • Search Google Scholar
    • Export Citation
  • FAO/IIASA/ISRIC/ISSCAS/JRC, 2012: Harmonized World Soil Database (version 1.2). FAO and IIASA, accessed 9 October 2013. [Available online at http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/index.html?sb=1.]

  • Forman, B. A., Reichle R. H. , and Rodell M. , 2012: Assimilation of terrestrial water storage from GRACE in a snow-dominated basin. Water Resour. Res., 48, W01507, doi:10.1029/2011WR011239.

    • Search Google Scholar
    • Export Citation
  • Gentine, P., Troy T. J. , Lintner B. R. , and Findell K. L. , 2012: Scaling in surface hydrology: Progress and challenges. J. Contemp. Water Res. Educ., 147, 2840, doi:10.1111/j.1936-704X.2012.03105.x.

    • Search Google Scholar
    • Export Citation
  • Giuntoli, I., Renard B. , Vidal J.-P. , and Bard A. , 2013: Low flows in France and their relationship to large-scale climate indices. J. Hydrol., 482, 105118, doi:10.1016/j.jhydrol.2012.12.038.

    • Search Google Scholar
    • Export Citation
  • Göckede, M., and Coauthors, 2008: Quality control of CarboEurope flux data—Part 1: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences, 5, 433450, doi:10.5194/bg-5-433-2008.

    • Search Google Scholar
    • Export Citation
  • Gupta, H. V., Kling H. , Yilmaz K. K. , and Martinez G. F. , 2009: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol., 377, 8091, doi:10.1016/j.jhydrol.2009.08.003.

    • Search Google Scholar
    • Export Citation
  • Gupta, H. V., Clark M. P. , Vrugt J. A. , Abramowitz G. , and Ye M. , 2012: Towards a comprehensive assessment of model structural adequacy. Water Resour. Res., 48, W08301, doi:10.1029/2011WR011044.

    • Search Google Scholar
    • Export Citation
  • Gupta, H. V., Perrin C. , Blöschl G. , Montanari A. , Kumar R. , Clark M. , and Andréassian V. , 2014: Large-sample hydrology: A need to balance depth with breadth. Hydrol. Earth Syst. Sci., 18, 463477, doi:10.5194/hess-18-463-2014.

    • Search Google Scholar
    • Export Citation
  • Haddeland, I., Matheussen B. V. , and Lettenmaier D. P. , 2002: Influence of spatial resolution on simulated streamflow in a macroscale hydrologic model. Water Resour. Res., 38, 1124, doi:10.1029/2001WR000854.

    • Search Google Scholar
    • Export Citation
  • Hargreaves, G., and Samani Z. , 1982: Estimating potential evapotranspiration. J. Irrig. Drain. Div., 108, 225230.

  • Haylock, M. R., Hofstra N. , Klein Tank A. M. G. , Klok E. J. , Jones P. D. , and New M. , 2008: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

    • Search Google Scholar
    • Export Citation
  • Hofstra, N., Haylock M. , New M. , and Jones P. D. , 2009: Testing E-OBS European high-resolution gridded data set of daily precipitation and surface temperature. J. Geophys. Res., 114, D21101, doi:10.1029/2009JD011799.

    • Search Google Scholar
    • Export Citation
  • Horton, R., 1935: Surface Runoff Phenomena. Part 1: Analysis of the Hydrograph. Horton Hydrologic Laboratory Publication 101, Edward Bros, 73 pp.

  • Hundecha, Y., and Bárdossy A. , 2004: Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model. J. Hydrol., 292, 281295, doi:10.1016/j.jhydrol.2004.01.002.

    • Search Google Scholar
    • Export Citation
  • Hurst, H. E., 1951: Long-term storage of reservoirs: An experimental study. Trans. Amer. Soc. Civ. Eng., 116, 770799.

  • Jacob, T., Wahr J. , Pfeffer W. T. , and Swenson S. , 2012: Recent contributions of glaciers and ice caps to sea level rise. Nature, 482, 514518, doi:10.1038/nature10847.

    • Search Google Scholar
    • Export Citation
  • Jarvis, A., Reuter H. , Nelson A. , and Guevara E. , 2008: Hole-filled SRTM for the globe version 4. CGIAR-CSI SRTM 90m Database, CGIAR-CSI, accessed 18 March 2014. [Available online at http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1.]

  • Jung, M., and Coauthors, 2011: Global patterns of land–atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res., 116, G00J07, doi:10.1029/2010JG001566.

    • Search Google Scholar
    • Export Citation
  • Kessomkiat, W., Franssen H.-J. H. , Graf A. , and Vereecken H. , 2013: Estimating random errors of eddy covariance data: An extended two-tower approach. Agric. For. Meteor., 171–172, 203219, doi:10.1016/j.agrformet.2012.11.019.

    • Search Google Scholar
    • Export Citation
  • Kirchner, J. W., 2006: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resour. Res., 42, W03S04, doi:10.1029/2005WR004362.

    • Search Google Scholar
    • Export Citation
  • Köhli, M., Schrön M. , Zreda M. , Schmidt U. , Dietrich P. , and Zacharias S. , 2015: Footprint characteristics revised for field-scale soil moisture monitoring with cosmic-ray neutrons. Water Resour. Res., 51, 57725790, doi:10.1002/2015WR017169.

    • Search Google Scholar
    • Export Citation
  • Kuichling, E., 1889: The relation between the rainfall and the discharge of sewers in populous districts. Trans. Amer. Soc. Civ. Eng., 20, 156.

    • Search Google Scholar
    • Export Citation
  • Kumar, R., Samaniego L. , and Attinger S. , 2010: The effects of spatial discretization and model parameterization on the prediction of extreme runoff characteristics. J. Hydrol., 392, 5469, doi:10.1016/j.jhydrol.2010.07.047.

    • Search Google Scholar
    • Export Citation
  • Kumar, R., Livneh B. , and Samaniego L. , 2013a: Toward computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme. Water Resour. Res., 49, 57005714, doi:10.1002/wrcr.20431.

    • Search Google Scholar
    • Export Citation
  • Kumar, R., Samaniego L. , and Attinger S. , 2013b: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resour. Res., 49, 360379, doi:10.1029/2012WR012195.

    • Search Google Scholar
    • Export Citation
  • Landerer, F. W., and Swenson S. C. , 2012: Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res., 48, W04531, doi:10.1029/2011WR011453.

    • Search Google Scholar
    • Export Citation
  • Li, B., Rodell M. , Zaitchik B. F. , Reichle R. H. , Koster R. D. , and van Dam T. M. , 2012: Assimilation of GRACE terrestrial water storage into a land surface model: Evaluation and potential value for drought monitoring in western and central Europe. J. Hydrol., 446–447, 103115, doi:10.1016/j.jhydrol.2012.04.035.

    • Search Google Scholar
    • Export Citation
  • 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
  • Lievens, H., and Coauthors, 2015: Optimization of a radiative transfer forward operator for simulating SMOS brightness temperatures over the upper Mississippi basin. J. Hydrometeor., 16, 11091134, doi:10.1175/JHM-D-14-0052.1.

    • Search Google Scholar
    • Export Citation
  • Liu, Y. Y., Parinussa R. M. , Dorigo W. A. , De Jeu R. A. M. , Wagner W. , van Dijk A. I. J. M. , McCabe M. F. , and Evans J. P. , 2011: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci., 15, 425436, doi:10.5194/hess-15-425-2011.

    • Search Google Scholar
    • Export Citation
  • Livneh, B., and Lettenmaier D. , 2012: Multi-criteria parameter estimation for the unified land model. Hydrol. Earth Syst. Sci., 16, 30293048, doi:10.5194/hess-16-3029-2012.

    • Search Google Scholar
    • Export Citation
  • Livneh, B., Kumar R. , and Samaniego L. , 2015: Influence of soil textural properties on hydrologic fluxes in the Mississippi River basin. Hydrol. Processes, 29, 46384655, doi:10.1002/hyp.10601.

    • Search Google Scholar
    • Export Citation
  • Lorenzo-Lacruz, J., Vicente-Serrano S. , López-Moreno J. , Morán-Tejeda E. , and Zabalza J. , 2012: Recent trends in Iberian streamflows (1945–2005). J. Hydrol., 414–415, 463475, doi:10.1016/j.jhydrol.2011.11.023.

    • Search Google Scholar
    • Export Citation
  • Mauder, M., Foken T. , Clement R. , Elbers J. A. , Eugster W. , Grünwald T. , Heusinkveld B. , and Kolle O. , 2008: Quality control of CarboEurope flux—Part 2: Inter-comparison of eddy-covariance software. Biogeosciences, 5, 451462, doi:10.5194/bg-5-451-2008.

    • Search Google Scholar
    • Export Citation
  • Merz, R., Parajka J. , and Blöschl G. , 2009: Scale effects in conceptual hydrological modeling. Water Resour. Res., 45, W09405, doi:10.1029/2009WR007872.

    • Search Google Scholar
    • Export Citation
  • Morris, M. D., 1991: Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 161174, doi:10.1080/00401706.1991.10484804.

    • Search Google Scholar
    • Export Citation
  • NASA, 2015: GRACE monthly mass grids—Land. Jet Propulsion Laboratory, accessed 1 July 2015. [Available online at http://grace.jpl.nasa.gov/data/get-data/monthly-mass-grids-land/.]

  • Nash, J. E., 1958: The form of the instantaneous unit hydrograph. IAHS Publ., 45, 114121. [Available online at http://iahs.info/uploads/dms/045011.pdf.]

    • Search Google Scholar
    • Export Citation
  • Nelsen, R. B., 2006: An Introduction to Copulas. Springer-Verlag, 269 pp., doi:10.1007/0-387-28678-0.

  • Oki, T., and Kanae S. , 2006: Global hydrological cycles and world water resources. Science, 313, 10681072, doi:10.1126/science.1128845.

    • Search Google Scholar
    • Export Citation
  • Orth, R., and Seneviratne S. I. , 2015: Introduction of a simple-model-based land surface dataset for Europe. Environ. Res. Lett., 10, 044012, doi:10.1088/1748-9326/10/4/044012.

    • Search Google Scholar
    • Export Citation
  • Papale, D., and Coauthors, 2006: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: Algorithms and uncertainty estimation. Biogeosciences, 3, 571583, doi:10.5194/bg-3-571-2006.

    • Search Google Scholar
    • Export Citation
  • Pokhrel, P., Yilmaz K. K. , and Gupta H. V. , 2012: Multiple-criteria calibration of a distributed watershed model using spatial regularization and response signatures. J. Hydrol., 418–419, 4960, doi:10.1016/j.jhydrol.2008.12.004.

    • Search Google Scholar
    • Export Citation
  • Reed, S., Koren V. , Smith M. , Zhang Z. , Moreda F. , Seo D.-J. , and DMIP Participants, 2004: Overall distributed model intercomparison project results. J. Hydrol., 298, 2760, doi:10.1016/j.jhydrol.2004.03.031.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., and Koster R. D. , 2004: Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501, doi:10.1029/2004GL020938.

    • Search Google Scholar
    • Export Citation
  • Reichstein, M., and Coauthors, 2005: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm. Global Change Biol., 11, 14241439, doi:10.1111/j.1365-2486.2005.001002.x.

    • Search Google Scholar
    • Export Citation
  • Rodriguez-Iturbe, I., and Valdes J. B. , 1979: The geomorphologic structure of hydrologic response. Water Resour. Res., 15, 14091420, doi:10.1029/WR015i006p01409.

    • Search Google Scholar
    • Export Citation
  • Rubel, F., and Kottek M. , 2010: Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen–Geiger climate classification. Meteor. Z., 19, 135141, doi:10.1127/0941-2948/2010/0430.

    • Search Google Scholar
    • Export Citation
  • Sakumura, C., Bettadpur S. , and Bruinsma S. , 2014: Ensemble prediction and intercomparison analysis of GRACE time-variable gravity field models. Geophys. Res. Lett., 41, 13891397, doi:10.1002/2013GL058632.

    • Search Google Scholar
    • Export Citation
  • Samaniego, L., and Bárdossy A. , 2007: Relating macroclimatic circulation patterns with characteristics of floods and droughts at the mesoscale. J. Hydrol., 335, 109123, doi:10.1016/j.jhydrol.2006.11.004.

    • Search Google Scholar
    • Export Citation
  • Samaniego, L., Kumar R. , and Attinger S. , 2010: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46, W05523, doi:10.1029/2008WR007327.

    • Search Google Scholar
    • Export Citation
  • Samaniego, L., Kumar R. , and Jackisch C. , 2011: Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data. Hydrol. Res., 42, 338355, doi:10.2166/nh.2011.156.

    • Search Google Scholar
    • Export Citation
  • Samaniego, L., Kumar R. , and Zink M. , 2013: Implications of parameter uncertainty on soil moisture drought analysis in Germany. J. Hydrometeor., 14, 4768, doi:10.1175/JHM-D-12-075.1.

    • Search Google Scholar
    • Export Citation
  • Sankarasubramanian, A., and Vogel R. M. , 2002: Comment on the paper: Basin hydrologic response relations to distributed physiographic descriptors and climate by Karen Plaut Berger, Dara Entekhabi, 2001. Journal of Hydrology 247, 169–182. J. Hydrol., 263, 257261, doi:10.1016/S0022-1694(02)00061-6.

    • Search Google Scholar
    • Export Citation
  • SCS, 1973: A Method for Estimating Volume and Rate of Runoff in Small Watersheds. SCS-TP-149, Soil Conservation Service, U.S. Department of Agriculture, 62 pp. [Available online at ftp://ftp.wcc.nrcs.usda.gov/wntsc/H&H/TRsTPs/TP149.pdf.]

  • Seibert, J., 2000: Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrol. Earth Syst. Sci., 4, 215224, doi:10.5194/hess-4-215-2000.

    • Search Google Scholar
    • Export Citation
  • Sherman, L. K., 1932: Streamflow from rainfall by the unit-graph method. Eng. News Rec., 108, 501505.

  • Shuttleworth, W. J., 2012: Terrestrial Hydrometeorology. Wiley, 448 pp., doi:10.1002/9781119951933.

  • Sorooshian, S., and Dracup J. A. , 1980: Stochastic parameter estimation procedures for hydrologie rainfall–runoff models: Correlated and heteroscedastic error cases. Water Resour. Res., 16, 430442, doi:10.1029/WR016i002p00430.

    • Search Google Scholar
    • Export Citation
  • Stöckli, R., Vidale P. L. , Boone A. , and Schär C. , 2007: Impact of scale and aggregation on the terrestrial water exchange: Integrating land surface models and Rhône catchment observations. J. Hydrometeor., 8, 10021015, doi:10.1175/JHM613.1.

    • Search Google Scholar
    • Export Citation
  • Su, H., Yang Z.-L. , Dickinson R. E. , Wilson C. R. , and Niu G.-Y. , 2010: Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information. J. Geophys. Res., 115, D10104, doi:10.1029/2009JD013035.

    • Search Google Scholar
    • Export Citation
  • Sutanudjaja, E., Van Beek L. , De Jong S. , Van Geer F. , and Bierkens M. , 2014: Calibrating a large-extent high-resolution coupled groundwater–land surface model using soil moisture and discharge data. Water Resour. Res., 50, 687705, doi:10.1002/2013WR013807.

    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., and Wahr J. , 2006: Post-processing removal of correlated errors in GRACE data. Geophys. Res. Lett., 33, L08402, doi:10.1029/2005GL025285.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, doi:10.1029/2000JD900719.

    • Search Google Scholar
    • Export Citation
  • Tetzlaff, D., Carey S. K. , Laudon H. , and McGuire K. , 2010: Catchment processes and heterogeneity at multiple scales—Benchmarking observations, conceptualization and prediction. Hydrol. Processes, 24, 22032208, doi:10.1002/hyp.7784.

    • Search Google Scholar
    • Export Citation
  • Thober, S., Kumar R. , Sheffield J. , Mai J. , Schaefer D. , and Samaniego L. , 2015: Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME). J. Hydrometeor., doi:10.1175/JHM-D-15-0053.1, in press.

    • Search Google Scholar
    • Export Citation
  • Troy, T. J., Wood E. F. , and Sheffield J. , 2008: An efficient calibration method for continental-scale land surface modeling. Water Resour. Res., 44, W09411, doi:10.1029/2007WR006513.

    • Search Google Scholar
    • Export Citation
  • Velpuri, N., Senay G. , Singh R. , Bohms S. , and Verdin J. , 2013: A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET. Remote Sens. Environ., 139, 3549, doi:10.1016/j.rse.2013.07.013.

    • Search Google Scholar
    • Export Citation
  • Wada, Y., van Beek L. P. H. , van Kempen C. M. , Reckman J. W. T. M. , Vasak S. , and Bierkens M. F. P. , 2010: Global depletion of groundwater resources. Geophys. Res. Lett., 37, L20402, doi:10.1029/2010GL044571.

    • Search Google Scholar
    • Export Citation
  • Wanders, N., Karssenberg D. , de Roo A. , de Jong S. M. , and Bierkens M. F. P. , 2014: The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol. Earth Syst. Sci., 18, 23432357, doi:10.5194/hess-18-2343-2014.

    • Search Google Scholar
    • Export Citation
  • Werth, S., and Güntner A. , 2010: Calibration analysis for water storage variability of the global hydrological model WGHM. Hydrol. Earth Syst. Sci., 14, 5978, doi:10.5194/hess-14-59-2010.

    • Search Google Scholar
    • Export Citation
  • Wood, E. F., 1995: Scaling behaviour of hydrological fluxes and variables: Empirical studies using a hydrological model and remote sensing data. Hydrol. Processes, 9, 331346, doi:10.1002/hyp.3360090308.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., Sheffield J. , Ek M. B. , Dong J. , Chaney N. , Wei H. , Meng J. , and Wood E. F. , 2014: Evaluation of multi-model simulated soil moisture in NLDAS-2. J. Hydrol., 512, 107125, doi:10.1016/j.jhydrol.2014.02.027.

    • Search Google Scholar
    • Export Citation
  • Xia, Y., Hobbins M. T. , Mu Q. , and Ek M. B. , 2015: Evaluation of NLDAS-2 evapotranspiration against tower flux site observations. Hydrol. Processes, 29, 17571771, doi:10.1002/hyp.10299.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, K. K., Gupta H. V. , and Wagener T. , 2008: A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model. Water Resour. Res., 44, W09417, doi:10.1029/2007WR006716.

    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., Rodell M. , and Reichle R. H. , 2008: Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River basin. J. Hydrometeor., 9, 535548, doi:10.1175/2007JHM951.1.

    • Search Google Scholar
    • Export Citation
  • Zreda, M., Shuttleworth W. , Zeng X. , Zweck C. , Desilets D. , Franz T. , and Rosolem R. , 2012: COSMOS: The cosmic-ray soil moisture observing system. Hydrol. Earth Syst. Sci., 16, 40794099, doi:10.5194/hess-16-4079-2012.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2834 655 112
PDF Downloads 1349 233 21