Surface Flux Equilibrium Estimates of Evapotranspiration at Large Spatial Scales

Shiliu Chen Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

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Kaighin A. McColl Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts
School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts

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Alexis Berg Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts

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Yuefei Huang State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Qinghai, China

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Abstract

A recent theory proposes that inland continental regions are in a state of surface flux equilibrium (SFE), in which tight coupling between the land and atmosphere allow estimation of the Bowen ratio at daily to monthly time scales solely from atmospheric measurements, without calibration, even when the land surface strongly constrains the surface energy budget. However, since the theory has only been evaluated at quasi-point spatial scales using eddy covariance measurements with limited global coverage, it is unclear if it is applicable to the larger spatial scales relevant to studies of global climate. In this study, SFE estimates of the Bowen ratio are combined with satellite observations of surface net radiation to obtain large-scale estimates of latent heat flux λE. When evaluated against multiyear mean annual λE obtained from catchment water balance estimates from 221 catchments across the United States, the resulting error statistics are comparable to those in the catchment water balance estimates themselves. The theory is then used to diagnostically estimate λE using historical simulations from 26 CMIP6 models. The resulting SFE estimates are typically at least as accurate as the CMIP6 model’s simulated λE, when compared with catchment water balance estimates. Globally, there is broad spatial and temporal agreement between CMIP6 model SFE estimates and the CMIP6 model’s simulated λE, although SFE likely overestimates λE in some arid regions. We conclude that SFE applies reasonably at large spatial scales relevant to climate studies, and is broadly reproduced in climate models.

© 2021 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: Kaighin A. McColl, kmccoll@seas.harvard.edu

Abstract

A recent theory proposes that inland continental regions are in a state of surface flux equilibrium (SFE), in which tight coupling between the land and atmosphere allow estimation of the Bowen ratio at daily to monthly time scales solely from atmospheric measurements, without calibration, even when the land surface strongly constrains the surface energy budget. However, since the theory has only been evaluated at quasi-point spatial scales using eddy covariance measurements with limited global coverage, it is unclear if it is applicable to the larger spatial scales relevant to studies of global climate. In this study, SFE estimates of the Bowen ratio are combined with satellite observations of surface net radiation to obtain large-scale estimates of latent heat flux λE. When evaluated against multiyear mean annual λE obtained from catchment water balance estimates from 221 catchments across the United States, the resulting error statistics are comparable to those in the catchment water balance estimates themselves. The theory is then used to diagnostically estimate λE using historical simulations from 26 CMIP6 models. The resulting SFE estimates are typically at least as accurate as the CMIP6 model’s simulated λE, when compared with catchment water balance estimates. Globally, there is broad spatial and temporal agreement between CMIP6 model SFE estimates and the CMIP6 model’s simulated λE, although SFE likely overestimates λE in some arid regions. We conclude that SFE applies reasonably at large spatial scales relevant to climate studies, and is broadly reproduced in climate models.

© 2021 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: Kaighin A. McColl, kmccoll@seas.harvard.edu
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  • Aminzadeh, M., M. L. Roderick, and D. Or, 2016: A generalized complementary relationship between actual and potential evaporation defined by a reference surface temperature. Water Resour. Res., 52, 385406, https://doi.org/10.1002/2015WR017969.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldocchi, D. D., R. J. Luxmoore, and J. L. Hatfield, 1991: Discerning the forest from the trees: An essay on scaling canopy stomatal conductance. Agric. For. Meteor., 54, 197226, https://doi.org/10.1016/0168-1923(91)90006-C.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barton, I. J., 1979: A parameterization of the evaporation from nonsaturated surfaces. J. Appl. Meteor., 18, 4347, https://doi.org/10.1175/1520-0450(1979)018<0043:APOTEF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., E. F. Wood, T. R. McVicar, M. Zambrano-Bigiarini, C. Alvarez-Garreton, O. M. Baez-Villanueva, J. Sheffield, and D. N. Karger, 2020: Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments. J. Climate, 33, 12991315, https://doi.org/10.1175/JCLI-D-19-0332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berry, J. A., D. J. Beerling, and P. J. Franks, 2010: Stomata: Key players in the Earth system, past and present. Curr. Opin. Plant Biol., 13, 232239, https://doi.org/10.1016/j.pbi.2010.04.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, A. K., 2000: Idealized model for equilibrium boundary layer over land. J. Hydrometeor., 1, 507523, https://doi.org/10.1175/1525-7541(2000)001<0507:IMFEBL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouchet, R., 1963: Evapotranspiration reelle, evapotranspiration potentielle, et production agricole. Ann. Agron., 14, 743824.

  • Bou-Zeid, E., W. Anderson, G. G. Katul, and L. Mahrt, 2020: The persistent challenge of surface heterogeneity in boundary-layer meteorology: A review. Bound.-Layer Meteor., 177, 227245, https://doi.org/10.1007/s10546-020-00551-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., 1998: Land-surface water vapor and sensible heat flux: Spatial variability, homogeneity, and measurement scales. Water Resour. Res., 34, 24332442, https://doi.org/10.1029/98WR01340.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., 2015: A generalized complementary principle with physical constraints for land-surface evaporation. Water Resour. Res., 51, 80878093, https://doi.org/10.1002/2015WR017720.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., and H. Stricker, 1979: An advection-aridity approach to estimate actual regional evapotranspiration. Water Resour. Res., 15, 443450, https://doi.org/10.1029/WR015i002p00443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brutsaert, W., and M. B. Parlange, 1998: Hydrologic cycle explains the evaporation paradox. Nature, 396, 30, https://doi.org/10.1038/23845.

  • Budyko, M. I., 1958: The heat balance of the Earth’s surface. Weather Bureau Doc., 259 pp.

  • Culf, A. D., 1994: Equilibrium evaporation beneath a growing convective boundary layer. Bound.-Layer Meteor., 70, 3749, https://doi.org/10.1007/BF00712522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Bruin, H. R., 1983: A model for the Priestley-Taylor parameter. J. Climate Appl. Meteor., 22, 572578, https://doi.org/10.1175/1520-0450(1983)022<0572:AMFTPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeLucia, E. H., and Coauthors, 2019: Are we approaching a water ceiling to maize yields in the United States? Ecosphere, 10, e02773, https://doi.org/10.1002/ecs2.2773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duan, Q., and Coauthors, 2006: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. J. Hydrol., 320, 317, https://doi.org/10.1016/j.jhydrol.2005.07.031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fisher, J. B., K. P. Tu, and D. D. Baldocchi, 2008: Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ., 112, 901919, https://doi.org/10.1016/j.rse.2007.06.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentine, P., D. Entekhabi, and J. Polcher, 2011: The diurnal behavior of evaporative fraction in the soil–vegetation–atmospheric boundary layer continuum. J. Hydrometeor., 12, 15301546, https://doi.org/10.1175/2011JHM1261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Green, J. K., A. G. Konings, S. H. Alemohammad, J. Berry, D. Entekhabi, J. Kolassa, J.-E. Lee, and P. Gentine, 2017: Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci., 10, 410414, https://doi.org/10.1038/ngeo2957.

    • 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
  • Held, I. M., 2005: The gap between simulation and understanding in climate modeling. Bull. Amer. Meteor. Soc., 86, 16091614, https://doi.org/10.1175/BAMS-86-11-1609.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jarvis, P. G., and K. G. McNaughton, 1986: Stomatal control of transpiration: Scaling up from leaf to region. Adv. Ecol. Res., 15, 149, https://doi.org/10.1016/S0065-2504(08)60119-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeevanjee, N., P. Hassanzadeh, S. Hill, and A. Sheshadri, 2017: A perspective on climate model hierarchies. J. Adv. Model. Earth Syst., 9, 17601771, https://doi.org/10.1002/2017MS001038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, A., S. Liang, B. Jiang X. Zhang, and G. Wang, 2018: Comprehensive assessment of global surface net radiation products and uncertainty analysis. J. Geophys. Res. Atmos., 123, 19701989, https://doi.org/10.1002/2017JD027903.

    • Crossref
    • 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, https://doi.org/10.1038/nature09396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kahler, D. M., and W. Brutsaert, 2006: Complementary relationship between daily evaporation in the environment and pan evaporation. Water Resour. Res., 42, W05413, https://doi.org/10.1029/2005WR004541.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kato, S., and Coauthors, 2018: Surface irradiances of edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product. J. Climate, 31, 45014527, https://doi.org/10.1175/JCLI-D-17-0523.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legates, D. R., and C. J. Willmott, 1990: Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Climatol., 10, 111127, https://doi.org/10.1002/joc.3370100202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, D., and L. Wang, 2019: Sensitivity of surface temperature to land use and land cover change-induced biophysical changes: The scale issue. Geophys. Res. Lett., 46, 96789689, https://doi.org/10.1029/2019GL084861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, H.-Y., and Coauthors, 2018: CAUSES: On the role of surface energy budget errors to the warm surface air temperature error over the central United States. J. Geophys. Res. Atmos., 123, 28882909, https://doi.org/10.1002/2017JD027194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, N., and J. Szilagyi, 2019: The CR of evaporation: A calibration-free diagnostic and benchmarking tool for large-scale terrestrial evapotranspiration modeling. Water Resour. Res., 55, 72467274, https://doi.org/10.1029/2019WR024867.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, P., and Coauthors, 2019: Model hierarchies for understanding atmospheric circulation. Rev. Geophys., 57, 250280, https://doi.org/10.1029/2018RG000607.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., 2000: Surface heterogeneity and vertical structure of the boundary layer. Bound.-Layer Meteor., 96, 3362, https://doi.org/10.1023/A:1002482332477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martens, B., and Coauthors, 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev., 10, 19031925, https://doi.org/10.5194/gmd-10-1903-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McColl, K. A., 2020: Practical and theoretical benefits of an alternative to the Penman-Monteith evapotranspiration equation. Water Resour. Res., 56, e2020WR027106, https://doi.org/10.1029/2020WR027106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McColl, K. A., and A. J. Rigden, 2020: Emergent simplicity of continental evapotranspiration. Geophys. Res. Lett., 47, e2020GL087101, https://doi.org/10.1029/2020GL087101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McColl, K. A., G. D. Salvucci, and P. Gentine, 2019: Surface flux equilibrium theory explains an empirical estimate of water-limited daily evapotranspiration. J. Adv. Model. Earth Syst., 11, 20362049, https://doi.org/10.1029/2019MS001685.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNaughton, K. G., and T. W. Spriggs, 1986: A mixed-layer model for regional evaporation. Bound.-Layer Meteor., 34, 243262, https://doi.org/10.1007/BF00122381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, https://doi.org/10.1175/BAMS-87-3-343.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milly, P. C. D., and K. A. Dunne, 2002: Macroscale water fluxes 1. Quantifying errors in the estimation of basin mean precipitation. Water Resour. Res., 38, 1205, https://doi.org/10.1029/2001WR000759.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miralles, D. G., and Coauthors, 2016: The WACMOS-ET project – Part II: Evaluation of global terrestrial evaporation data sets. Hydrol. Earth Syst. Sci., 20, 823842, https://doi.org/10.5194/hess-20-823-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monteith, J. L., 1965: Evaporation and environment. The State and Movement of Water in Living Organisms, G. Fogg, Ed., Cambridge University Press, 205–234.

  • Morton, F. I., 1969: Potential evaporation as a manifestation of regional evaporation. Water Resour. Res., 5, 12441255, https://doi.org/10.1029/WR005i006p01244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, B., and S. I. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophys. Res. Lett., 41, 128134, https://doi.org/10.1002/2013GL058055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, B., and Coauthors, 2013: Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrol. Earth Syst. Sci., 17, 37073720, https://doi.org/10.5194/hess-17-3707-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oki, T., and S. Kanae, 2006: Global hydrological cycles and world water resources. Science, 313, 10681072, https://doi.org/10.1126/science.1128845.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penman, H., 1948: Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. London, 193A, 120145, https://doi.org/10.1098/rspa.1948.0037.

    • Search Google Scholar
    • Export Citation
  • Priestley, C. H. B., and R. J. Taylor, 1972: On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Wea. Rev., 100, 8192, https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., 2000: Equilibrium evaporation and the convective boundary layer. Bound.-Layer Meteor., 96, 107142, https://doi.org/10.1023/A:1002675729075.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., 2001: Combination theory and equilibrium evaporation. Quart. J. Roy. Meteor. Soc., 127, 11491181, https://doi.org/10.1002/qj.49712757402.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raupach, M. R., and J. J. Finnigan, 1995: Scale issues in boundary-layer meteorology: Surface energy balances in heterogeneous terrain. Hydrol. Processes, 9, 589612, https://doi.org/10.1002/hyp.3360090509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rigden, A. J., and G. D. Salvucci, 2015: Evapotranspiration based on equilibrated relative humidity (ETRHEQ): Evaluation over the continental U.S. Water Resour. Res., 51, 29512973, https://doi.org/10.1002/2014WR016072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rigden, A. J., D. Li, and G. D. Salvucci, 2018: Dependence of thermal roughness length on friction velocity across land cover types: A synthesis analysis using AmeriFlux data. Agric. For. Meteor., 249, 512519, https://doi.org/10.1016/j.agrformet.2017.06.003.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shuttleworth, W. J., and I. R. Calder, 1979: Has the Priestley–Taylor equation any relevance to forest evaporation? J. Appl. Meteor., 18, 639646, https://doi.org/10.1175/1520-0450(1979)018<0639:HTPTEA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, C. M., C. E. Birch, D. J. Parker, N. Dixon, F. Guichard, G. Nikulin, and G. M. S. Lister, 2013: Modeling soil moisture-precipitation feedback in the Sahel: Importance of spatial scale versus convective parameterization. Geophys. Res. Lett., 40, 62136218, https://doi.org/10.1002/2013GL058511.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trugman, A. T., D. Medvigy, J. S. Mankin, and W. R. L. Anderegg, 2018: Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett., 45, 64956503, https://doi.org/10.1029/2018GL078131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vinukollu, R. K., E. F. Wood, C. R. Ferguson, and J. B. Fisher, 2011: Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches. Remote Sens. Environ., 115, 801823, https://doi.org/10.1016/j.rse.2010.11.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, H., D. Yang, Z. Lei, and F. Sun, 2008: New analytical derivation of the mean annual water-energy balance equation. Water Resour. Res., 44, W03410, https://doi.org/10.1029/2007WR006135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, J., S. Calabrese, E. Daly, and A. Porporato, 2019: The energy side of Budyko: Surface-energy partitioning from hydrological observations. Geophys. Res. Lett., 46, 74567463, https://doi.org/10.1029/2019GL083373.

    • Crossref
    • Search Google Scholar
    • Export Citation
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