A Water Balance–Based, Spatiotemporal Evaluation of Terrestrial Evapotranspiration Products across the Contiguous United States

Elizabeth Carter Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York

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Christopher Hain NASA Short-Term Prediction Research and Transition Center, Huntsville, Alabama

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Martha Anderson Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

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Scott Steinschneider Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York

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Abstract

Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy balance–based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long-term climatology and interannual variability in ET derived from a terrestrial water budget (ETWB) for 671 gauged basins across the contiguous United States. All three ETEB products have low spatial bias and accurately capture interannual variability of ETWB in the central United States, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on interannual time scales, ΔTWS and error in precipitation estimates are nonnegligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0186.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Elizabeth Carter, ekc76@cornell.edu

Abstract

Accurate gridded estimates of evapotranspiration (ET) are essential to the analysis of terrestrial water budgets. In this study, ET estimates from three gridded energy balance–based products (ETEB) with independent model formations and data forcings are evaluated for their ability to capture long-term climatology and interannual variability in ET derived from a terrestrial water budget (ETWB) for 671 gauged basins across the contiguous United States. All three ETEB products have low spatial bias and accurately capture interannual variability of ETWB in the central United States, where ETEB and ancillary estimates of change in total surface water storage (ΔTWS) from the GRACE satellite project appear to close terrestrial water budgets. In humid regions, ETEB products exhibit higher long-term bias, and the covariability of ETEB and ETWB decreases significantly. Several factors related to either failure of ETWB, such as errors in ΔTWS and precipitation, or failure of ETEB, such as treatment of snowfall and horizontal heat advection, explain some of these discrepancies. These results mirror and build on conclusions from other studies: on interannual time scales, ΔTWS and error in precipitation estimates are nonnegligible uncertainties in ET estimates based on a terrestrial water budget, and this confounds their comparison to energy balance ET models. However, there is also evidence that in at least some regions, climate and landscape features may also influence the accuracy and long-term bias of ET estimates from energy balance models, and these potential errors should be considered when using these gridded products in hydrologic applications.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0186.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Elizabeth Carter, ekc76@cornell.edu

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  • Adam, J. C., and D. P. Lettenmaier, 2003: Adjustment of global gridded precipitation for systematic bias. J. Geophys. Res., 108, 4257, https://doi.org/10.1029/2002JD002499.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., J. M. Norman, G. R. Diak, W. P. Kustas, and J. R. Mecikalski, 1997: A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ., 60, 195216, https://doi.org/10.1016/S0034-4257(96)00215-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., and Coauthors, 2011: Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sci., 15, 223239, https://doi.org/10.5194/hess-15-223-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., and Coauthors, 2012: Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign. Adv. Water Resour., 50, 162177, https://doi.org/10.1016/j.advwatres.2012.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, M. C., C. Hain, J. Otkin, X. Zhan, K. Mo, M. Svoboda, B. Wardlow, and A. Pimstein, 2013: An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. Drought Monitor classifications. J. Hydrometeor., 14, 10351056, https://doi.org/10.1175/JHM-D-12-0140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, R. G., M.-H. Lo, S. Swenson, J. S. Famiglietti, Q. Tang, T. H. Skaggs, Y. H. Lin, and R. J. Wu, 2015: Using satellite-based estimates of evapotranspiration and groundwater changes to determine anthropogenic water fluxes in land surface models. Geosci. Model Dev., 8, 30213031, https://doi.org/10.5194/gmd-8-3021-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bivand, R., and D. Yu, 2017: Spgwr: Geographically weighted regression, version 0.6-31/r1743. R package, https://cran.r-project.org/web/packages/spgwr/index.html.

  • Brunsdon, C., S. Fotheringham, and M. Charlton, 1998: Geographically weighted regression. Statistician, 47 (3), 431443.

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

    • Crossref
    • 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, https://doi.org/10.1029/95JD02165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, M., W. P. Kustas, M. C. Anderson, R. G. Allen, F. Li, and J. H. Kjaersgaard, 2009: An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, US) during SMACEX. Agric. For. Meteor., 149, 20822097, https://doi.org/10.1016/j.agrformet.2009.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cleugh, H. A., R. Leuning, Q. Mu, and S. W. Running, 2007: Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sens. Environ., 106, 285304, https://doi.org/10.1016/j.rse.2006.07.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Döll, P., H. Hoffmann-Dobrev, F. T. Portmann, S. Siebert, A. Eicker, M. Rodell, G. Strassberg, and B. R. Scanlon, 2012: Impact of water withdrawals from groundwater and surface water on continental water storage variations. J. Geodyn., 59–60, 143156, https://doi.org/10.1016/j.jog.2011.05.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Falcone, J. A., D. M. Carlisle, D. M. Wolock, and M. R. Meador, 2010: GAGES: A stream gage database for evaluating natural and altered flow conditions in the conterminous United States. Ecology, 91, 621–621, https://doi.org/10.1890/09-0889.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferguson, C. R., J. Sheffield, E. F. Wood, and H. Gao, 2010: Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA. Int. J. Remote Sens., 31, 38213865, https://doi.org/10.1080/01431161.2010.483490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • French, A. N., and Coauthors, 2005: Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sens. Environ., 99, 5565, https://doi.org/10.1016/j.rse.2005.05.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., Q. Tang, C. R. Ferguson, E. F. Wood, and D. P. Lettenmaier, 2010: Estimating the water budget of major US river basins via remote sensing. Int. J. Remote Sens., 31, 39553978, https://doi.org/10.1080/01431161.2010.483488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glenn, E. P., and Coauthors, 2011: Actual evapotranspiration estimation by ground and remote sensing methods: The Australian experience. Hydrol. Processes, 25, 41034116, https://doi.org/10.1002/hyp.8391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haddeland, I., and Coauthors, 2011: Multimodel estimate of the global terrestrial water balance: Setup and first results. J. Hydrometeor., 12, 869884, https://doi.org/10.1175/2011JHM1324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hain, C. R., W. T. Crow, M. C. Anderson, and M. T. Yilmaz, 2015: Diagnosing neglected soil moisture source–sink processes via a thermal infrared–based two-source energy balance model. J. Hydrometeor., 16, 10701086, https://doi.org/10.1175/JHM-D-14-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, E., W. T. Crow, C. R. Hain, and M. C. Anderson, 2015: On the use of a water balance to evaluate interannual terrestrial ET variability. J. Hydrometeor., 16, 11021108, https://doi.org/10.1175/JHM-D-14-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hijmans, R. J., and J. van Etten, 2014: Raster: Geographic data analysis and modeling, version 2. R package, https://cran.r-project.org/web/packages/raster/index.html.

  • Jiménez, C., and Coauthors, 2011: Global intercomparison of 12 land surface heat flux estimates. J. Geophys. Res., 116, D02102, https://doi.org/10.1029/2010JD014545.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalma, J. D., T. R. McVicar, and M. F. McCabe, 2008: Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys., 29, 421469, https://doi.org/10.1007/s10712-008-9037-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kustas, W. P., and Coauthors, 2012: Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area. Adv. Water Resour., 50, 120133, https://doi.org/10.1016/j.advwatres.2012.07.005.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z. L., R. Tang, Z. Wan, Y. Bi, C. Zhou, B. Tang, G. Yan, and X. Zhang, 2009: A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9, 38013853, https://doi.org/10.3390/s90503801.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, W., L. Wang, J. Zhou, Y. Li, F. Sun, G. Fu, X. Li, and Y.-F. Sang, 2016: A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method. J. Hydrol., 538, 8295, https://doi.org/10.1016/j.jhydrol.2016.04.006.

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

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, A. J., and Coauthors, 2015: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209223, https://doi.org/10.5194/hess-19-209-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, M., and Coauthors, 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res., 108, 8850, https://doi.org/10.1029/2003JD003994.

    • Search Google Scholar
    • Export Citation
  • Pervez, M. S., and J. F. Brown, 2010: Mapping irrigated lands at 250-m scale by merging MODIS data and national agricultural statistics. Remote Sens., 2, 23882412, https://doi.org/10.3390/rs2102388.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., S. V. Kumar, D. M. Mocko, and Y. Tian, 2011: Estimating evapotranspiration with land data assimilation systems. Hydrol. Processes, 25, 39793992, https://doi.org/10.1002/hyp.8387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinker, R. T., and Coauthors, 2003: Surface radiation budgets in support of the GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8844, https://doi.org/10.1029/2002JD003301.

    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2015: Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012). Hydrol. Earth Syst. Sci., 19, 20372056, https://doi.org/10.5194/hess-19-2037-2015.

    • Crossref
    • 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, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and J. S. Famiglietti, 2001: An analysis of terrestrial water storage variations in Illinois with implications for the Gravity Recovery and Climate Experiment (GRACE). Water Resour. Res., 37, 13271339, https://doi.org/10.1029/2000WR900306.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahoo, A. K., M. Pan, T. J. Troy, R. K. Vinukollu, J. Sheffield, and E. F. Wood, 2011: Reconciling the global terrestrial water budget using satellite remote sensing. Remote Sens. Environ., 115, 18501865, https://doi.org/10.1016/j.rse.2011.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Save, H., S. Bettadpur, and B. D. Tapley, 2016: High resolution CSR GRACE RL05 mascons. J. Geophys. Res. Solid Earth, 121, 75477569, https://doi.org/10.1002/2016JB013007.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shiklomanov, I. A., 2000: Appraisal and assessment of world water resources. Water Int., 25, 1132, https://doi.org/10.1080/02508060008686794.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swenson, S. C., 2012: GRACE monthly land water mass grids NETCDF release 5.0. PO.DAAC, accessed 10 May 2017, https://doi.org/10.5067/TELND-NC005.

  • Trenberth, K. E., J. T. Fasullo, and J. Kiehl, 2009: Earth’s global energy budget. Bull. Amer. Meteor. Soc., 90, 311323, https://doi.org/10.1175/2008BAMS2634.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Twine, T. E., C. J. Kucharik, and J. A. Foley, 2004: Effects of land cover change on the energy and water balance of the Mississippi River basin. J. Hydrometeor., 5, 640655, https://doi.org/10.1175/1525-7541(2004)005<0640:EOLCCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velpuri, N. M., G. B. Senay, R. K. Singh, S. Bohms, and J. P. Verdin, 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, https://doi.org/10.1016/j.rse.2013.07.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vinukollu, R. K., R. Meynadier, J. Sheffield, and E. F. Wood, 2011: Multi‐model, multi‐sensor estimates of global evapotranspiration: climatology, uncertainties and trends. Hydrol. Processes, 25, 39934010, https://doi.org/10.1002/hyp.8393.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., and Coauthors, 2015: Comparing evapotranspiration from Eddy covariance measurements, water budgets, remote sensing, and land surface models over Canada. J. Hydrometeor., 16, 15401560, https://doi.org/10.1175/JHM-D-14-0189.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, D., and M. Tiefelsdorf, 2005: Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst., 7, 161187, https://doi.org/10.1007/s10109-005-0155-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiese, D. N., 2015: GRACE monthly global water mass grids NETCDF release 5.0, version 5.0. PO.DAAC, accessed 18 March 2017, https://doi.org/10.5067/TEMSC-OCL05.

    • Crossref
    • Export Citation
  • Xia, Y., and Coauthors, 2012: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, D., D. Kane, Z. Zhang, D. Legates, and B. Goodison, 2005: Bias corrections of long‐term (1973–2004) daily precipitation data over the northern regions. Geophys. Res. Lett., 32, L19501, https://doi.org/10.1029/2005GL024057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeng, Z., S. Piao, X. Lin, G. Yin, S. Peng, P. Ciais, and R. B. Myneni, 2012: Global evapotranspiration over the past three decades: Estimation based on the water balance equation combined with empirical models. Environ. Res. Lett., 7, 014026, https://doi.org/10.1088/1748-9326/7/1/014026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zeng, Z., T. Wang, F. Zhou, P. Ciais, J. Mao, X. Shi, and S. Piao, 2014: A worldwide analysis of spatiotemporal changes in water balance-based evapotranspiration from 1982 to 2009. J. Geophys. Res. Atmos., 119, 11861202, https://doi.org/10.1002/2013JD020941.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and Coauthors, 2012: Decadal trends in evaporation from global energy and water balances. J. Hydrometeor., 13, 379391, https://doi.org/10.1175/JHM-D-11-012.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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