Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Partitioning global land evapotranspiration using CMIP5 models constrained by observations

Nature Climate Change volume 8, pages 640–646 (2018)Cite this article

Subjects

Abstract

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface–atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 ± 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 ± 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Simulated and observation-based estimates of T/ET at the global and stand levels.
Fig. 2: Emergent constraint on the model-simulated global T/ET.
Fig. 3: Contribution of environmental drivers to spatial variations of T/ET in ESMs.
Fig. 4: Implications for the model-derived T/ET of replacing dominant drivers with observations.
Fig. 5: Impact of modelled T/ET on the runoff responses to rising atmospheric CO2.

Similar content being viewed by others

References

  1. Jasechko, S. et al. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–350 (2013).

    Article  CAS  Google Scholar 

  2. Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).

    Article  CAS  Google Scholar 

  3. Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Rev. Geophys. 50, RG2005 (2012).

    Article  Google Scholar 

  4. Zhang, Y. et al. Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. 6, 19124 (2016).

    Article  CAS  Google Scholar 

  5. Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).

    Article  CAS  Google Scholar 

  6. Zeng, Z. et al. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat. Clim. Change 7, 432–436 (2017).

    Article  Google Scholar 

  7. Schlesinger, W. H. & Jasechko, S. Transpiration in the global water cycle. Agric. For. Meteorol. 189–190, 115–117 (2014).

    Article  Google Scholar 

  8. Good, S. P., Noone, D. & Bowen, G. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177 (2015).

    Article  CAS  Google Scholar 

  9. Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. Discuss. 10, 1903–1925 (2017).

    Article  Google Scholar 

  10. Wei, Z. et al. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophys. Res. Lett. 44, 2792–2801 (2017).

    Article  Google Scholar 

  11. Sutanto, S. et al. HESS opinions: a perspective on different approaches to determine the contribution of transpiration to the surface moisture fluxes. Hydrol. Earth Syst. Sci. 11, 2583–2612 (2014).

    Article  Google Scholar 

  12. Kool, D. et al. A review of approaches for evapotranspiration partitioning. Agric. For. Meteorol. 184, 56–70 (2014).

    Article  Google Scholar 

  13. Coenders-Gerrits, A. M. et al. Uncertainties in transpiration estimates. Nature 506, E1–2 (2014).

    Article  CAS  Google Scholar 

  14. Schlaepfer, D. R. et al. Terrestrial water fluxes dominated by transpiration: comment. Ecosphere 5, 1–9 (2014).

    Article  Google Scholar 

  15. Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).

    Article  CAS  Google Scholar 

  16. Evaristo, J., Jasechko, S. & McDonnell, J. J. Global separation of plant transpiration from groundwater and streamflow. Nature 525, 91–94 (2015).

    Article  CAS  Google Scholar 

  17. Milly, P. C., Dunne, K. A. & Vecchia, A. V. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438, 347–350 (2005).

    Article  CAS  Google Scholar 

  18. Seager, R. et al. Projections of declining surface-water availability for the southwestern United States. Nat. Clim. Change 3, 482–486 (2012).

    Article  Google Scholar 

  19. Taylor, R. G. et al. Ground water and climate change. Nat. Clim. Change 3, 322–329 (2012).

    Article  Google Scholar 

  20. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  21. Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).

    Article  CAS  Google Scholar 

  22. Mystakidis, S., Davin, E. L., Gruber, N. & Seneviratne, S. I. Constraining future terrestrial carbon cycle projections using observation-based water and carbon flux estimates. Glob. Change Biol. 22, 2198–2215 (2016).

    Article  Google Scholar 

  23. Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).

    Article  CAS  Google Scholar 

  24. Villegas, J. C. et al. Sensitivity of regional evapotranspiration partitioning to variation in woody plant cover: insights from experimental dryland tree mosaics. Glob. Ecol. Biogeogr. 24, 1040–1048 (2015).

    Article  Google Scholar 

  25. Lloyd, J. et al. Optimisation of photosynthetic carbon gain and within-canopy gradients of associated foliar traits for Amazon forest trees. Biogeosciences 7, 1833–1859 (2010).

    Article  CAS  Google Scholar 

  26. Clark, D. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).

    Article  Google Scholar 

  27. Gu, L. et al. Response of a deciduous forest to the Mount Pinatubo eruption: enhanced photosynthesis. Science 299, 2035–2038 (2003).

    Article  CAS  Google Scholar 

  28. Mercado, L. M. et al. Impacts of changes in diffuse radiation on the global land carbon sink. Nature 458, 1014–1017 (2009).

    Article  CAS  Google Scholar 

  29. Knohl, A. & Baldocchi, D. D. Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem. J. Geophys. Res. Biogeosci. 113, G02023 (2008).

    Article  CAS  Google Scholar 

  30. Oliveira, P. J. C., Davin, E. L., Levis, S. & Seneviratne, S. I. Vegetation-mediated impacts of trends in global radiation on land hydrology: a global sensitivity study. Glob. Change Biol. 17, 3453–3467 (2011).

    Article  Google Scholar 

  31. Berry, J. A., Beerling, D. J. & Franks, P. J. Stomata: key players in the earth system, past and present. Curr. Opin. Plant Boil. 13, 232–239 (2010).

    Article  Google Scholar 

  32. Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).

    Article  CAS  Google Scholar 

  33. Wei, S., Yi, C., Fang, W. & Hendrey, G. A global study of GPP focusing on light-use efficiency in a random forest regression model.Ecosphere 8, e01724. (2017).

    Article  Google Scholar 

  34. Lawrence, D. M., Thornton, P. E., Oleson, K. W. & Bonan, G. B. The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: impacts on land–atmosphere interaction. J. Hydrometeorol. 8, 862–880 (2007).

    Article  Google Scholar 

  35. Llorens, P. & Domingo, F. Rainfall partitioning by vegetation under Mediterranean conditions. A review of studies in Europe. J. Hydrol. 335, 37–54 (2007).

    Article  Google Scholar 

  36. Heitman, J., Horton, R., Sauer, T. & DeSutter, T. Sensible heat observations reveal soil-water evaporation dynamics. J. Hydrometeorol. 9, 165–171 (2008).

    Article  Google Scholar 

  37. Yang, Y., Donohue, R. J. & McVicar, T. R. Global estimation of effective plant rooting depth: implications for hydrological modeling. Wat. Resour. Res. 52, 8260–8276 (2016).

    Article  Google Scholar 

  38. Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

    Article  CAS  Google Scholar 

  39. Parolari, A. J., Goulden, M. L. & Bras, R. L. Ecohydrological controls on grass and shrub above-ground net primary productivity in a seasonally dry climate. Ecohydrology 8, 1572–1583 (2015).

    Article  Google Scholar 

  40. Oleson, K. W. et al. Technical description of version 4.0 of the Community Land Model (CLM). Geophys. Res. Lett. 37, 256–265 (2010).

    Article  CAS  Google Scholar 

  41. Miralles, D. G., Gash, J. H., Holmes, T. R. H., de Jeu, R. A. M. & Dolman, A. J. Global canopy interception from satellite observations. J. Geophys. Res. 115, D16122 (2010).

    Article  Google Scholar 

  42. Wang, D., Wang, G. & Anagnostou, E. N. Impact of sub-grid variability of precipitation and canopy water storage on hydrological processes in a coupled land–atmosphere model. Clim. Dynam. 32, 649–662 (2009).

    Article  Google Scholar 

  43. Field, C., Jackson, R. & Mooney, H. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant Cell Environ. 18, 1214–1255 (1995).

    Article  Google Scholar 

  44. Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).

    Article  Google Scholar 

  45. Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).

    Article  CAS  Google Scholar 

  46. Solomon, S. et al. Contributions of stratospheric water vapor to decadal changes in the rate of global warming. Science 327, 1219–1223 (2010).

    Article  CAS  Google Scholar 

  47. Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).

    Article  CAS  Google Scholar 

  48. Archer, C. L. & Jacobson, M. Z. Spatial and temporal distributions of U.S. winds and wind power at 80 m derived from measurements. J. Geophys. Res. Atmos. 108, 469–474 (2003).

    Article  Google Scholar 

  49. Devis, A., Demuzere, M. & van Lipzig, N. P. M. A height dependent evaluation of wind and temperature over Europe in the CMIP5 Earth System Models. Clim. Res. 61, 41–56 (2014).

    Article  Google Scholar 

  50. Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).

    Article  Google Scholar 

  51. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  52. De’ath, G. Boosted trees for ecological modeling and prediction. Ecology 88, 243–251 (2007).

    Article  Google Scholar 

  53. Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (41561134016 and 41530528), the 111 Project (B14001) and the National Youth Top-notch Talent Support Program in China. C.H. is grateful for funding from the Centre for Ecology and Hydrology National Capability fund in the UK.

Author information

Authors and Affiliations

  1. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Xu Lian, Shilong Piao, Yue Li, Zhenzhong Zeng, Xuhui Wang, Shushi Peng & Hui Yang

  2. Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Shilong Piao & Tao Wang

  3. Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China

    Shilong Piao & Tao Wang

  4. Centre for Ecology and Hydrology, Wallingford, UK

    Chris Huntingford

  5. Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS UVSQ, Gif Sur Yvette, France

    Philippe Ciais & Catherine Ottlé

  6. CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

    Tim R. McVicar & Yongqiang Zhang

  7. Australian Research Council Centre of Excellence for Climate System Science, Sydney, New South Wales, Australia

    Tim R. McVicar

  8. State Key Laboratory of Hydro-Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

    Yuting Yang

Authors
  1. Xu Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shilong Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chris Huntingford
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhenzhong Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xuhui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Philippe Ciais
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tim R. McVicar
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shushi Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Catherine Ottlé
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yuting Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yongqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.P. designed the research; X.L. performed the analysis; X.L., S.P. and C.H. drafted the paper and all the authors contributed to the interpretation of the results and to the text.

Corresponding author

Correspondence to Shilong Piao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Text 1–4, Supplementary Tables 1–4, Supplementary Figures 1–7

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lian, X., Piao, S., Huntingford, C. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nature Clim Change 8, 640–646 (2018). https://doi.org/10.1038/s41558-018-0207-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-018-0207-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing