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.

A 10 per cent increase in global land evapotranspiration from 2003 to 2019

Abstract

Accurate quantification of global land evapotranspiration is necessary for understanding variability in the global water cycle, which is expected to intensify under climate change1,2,3. Current global evapotranspiration products are derived from a variety of sources, including models4,5, remote sensing6,7 and in situ observations8,9,10. However, existing approaches contain extensive uncertainties; for example, relating to model structure or the upscaling of observations to a global level11. As a result, variability and trends in global evapotranspiration remain unclear12. Here we show that global land evapotranspiration increased by 10 ± 2 per cent between 2003 and 2019, and that land precipitation is increasingly partitioned into evapotranspiration rather than runoff. Our results are based on an independent water-balance ensemble time series of global land evapotranspiration and the corresponding uncertainty distribution, using data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) satellites13. Variability in global land evapotranspiration is positively correlated with El Niño–Southern Oscillation. The main driver of the trend, however, is increasing land temperature. Our findings provide an observational constraint on global land evapotranspiration, and are consistent with the hypothesis that global evapotranspiration should increase in a warming climate.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Water-balance seasonal cycles.
Fig. 2: Comparison of ET with other products.
Fig. 3: Trends in the water balance.

Data availability

The data that support the findings of this study have been added to the Zenodo repository and can be accessed at https://doi.org/10.5281/zenodo.4601596Source data are provided with this paper.

Code availability

The code that produced the findings of this study is available from the corresponding author upon reasonable request.

References

  1. 1.

    Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).

    ADS  Google Scholar 

  2. 2.

    Greve, P. et al. Global assessment of trends in wetting and drying over land. Nat. Geosci. 7, 716–721 (2014); corrigendum 7, 848 (2014).

    ADS  CAS  Google Scholar 

  3. 3.

    Huntington, T. G. Evidence for intensification of the global water cycle: review and synthesis. J. Hydrol. 319, 83–95 (2006).

    ADS  Google Scholar 

  4. 4.

    Mueller, B. et al. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett. 38, L06402 (2011).

    ADS  Google Scholar 

  5. 5.

    Haddeland, I. et al. Multi-model estimate of the global terrestrial water balance: setup and first results. J. Hydrometeorol. 12, 869–884 (2011).

    ADS  Google Scholar 

  6. 6.

    Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536 (2007).

    ADS  Google Scholar 

  7. 7.

    Rodell, M. et al. The observed state of the water cycle in the early twenty-first century. J. Clim. 28, 8289–8318 (2015).

    ADS  Google Scholar 

  8. 8.

    Fisher, J. B., Tu, K. P. & Baldocchi, D. D. 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, 901–919 (2008).

    ADS  Google Scholar 

  9. 9.

    Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 74 (2019).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Hobeichi, S., Abramowitz, G., Evans, J. & Ukkola, A. Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate. Hydrol. Earth Syst. Sci. 22, 1317–1336 (2018).

    ADS  Google Scholar 

  11. 11.

    Baldocchi, D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob. Change Biol. 9, 479–492 (2003).

    ADS  Google Scholar 

  12. 12.

    Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).

    ADS  Google Scholar 

  13. 13.

    Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F. & Watkins, M. M. GRACE measurements of mass variability in the Earth system. Science 305, 503–505 (2004).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Trenberth, K., Smith, L., Qian, T., Dai, A. & Fasullo, J. Estimates of the global water budget and its annual cycle using observational and model data. J. Hydromeorol. 8, 758–769 (2007).

    ADS  Google Scholar 

  15. 15.

    Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).

    ADS  Google Scholar 

  16. 16.

    Anderson, M. C., Allen, R. G., Morse, A. & Kustas, W. P. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ. 122, 50–65 (2012).

    ADS  Google Scholar 

  17. 17.

    Allan, R. et al. Advances in understanding large-scale responses of the water cycle to climate change. Ann. NY Acad. Sci. 1472, 49–75 (2020).

    ADS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).

    ADS  Google Scholar 

  19. 19.

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Miralles, D. G. et al. El Niño–La Niña cycle and recent trends in continental evaporation. Nat. Clim. Change 4, 122–126 (2014).

    ADS  Google Scholar 

  21. 21.

    Dong, B. & Dai, A. The uncertainties and causes of the recent changes in global evapotranspiration from 1982 to 2010. Clim. Dyn. 49, 279–296 (2017); correction 53, 3707–3708 (2019).

    Google Scholar 

  22. 22.

    Zhang, K. et al. Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Sci. Rep. 5, 15956 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Douville, H., Ribes, A., Decharme, B., Alkama, R. & Sheffield, J. Anthropogenic influence on multidecadal changes in reconstructed global evapotranspiration. Nat. Clim. Change 3, 59–62 (2013).

    ADS  Google Scholar 

  24. 24.

    Swann, A. L. & Koven, C. D. A direct estimate of the seasonal cycle of evapotranspiration over the Amazon Basin. J. Hydrometeorol. 18, 2173–2185 (2017).

    ADS  Google Scholar 

  25. 25.

    Pascolini-Campbell, M. A., Reager, J. T. & Fisher, J. B. GRACE-based mass conservation as a validation target for basin-scale evapotranspiration in the contiguous United States. Water Resour. Res. 56, e2019WR026594 (2020).

    ADS  Google Scholar 

  26. 26.

    Li, X. et al. Evapotranspiration estimation for Tibetan Plateau headwaters using conjoint terrestrial and atmospheric water balances and multisource remote sensing. Wat. Resour. Res. 55, 8608–8630 (2019).

    ADS  Google Scholar 

  27. 27.

    Ramillien, G. et al. Time variations of the regional evapotranspiration rate from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry. Wat. Resour. Res. 42, W10403 (2006).

    ADS  Google Scholar 

  28. 28.

    Rodell, M. et al. Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett. 31, L20504 (2004).

    ADS  Google Scholar 

  29. 29.

    Billah, M. M. et al. A methodology for evaluating evapotranspiration estimates at the watershed-scale using GRACE. J. Hydrol. 523, 574–586 (2015).

    ADS  Google Scholar 

  30. 30.

    Rodell, M., McWilliams, E. B., Famiglietti, J. S., Beaudoing, H. K. & Nigro, J. Estimating evapotranspiration using an observation based terrestrial water budget. Hydrol. Process. 25, 4082–4092 (2011).

    ADS  Google Scholar 

  31. 31.

    Liu, W. et al. A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method. J. Hydrol. 538, 82–95 (2016).

    ADS  Google Scholar 

  32. 32.

    Dai, A., Qian, T., Trenberth, K. E. & Milliman, J. D. Changes in continental freshwater discharge from 1948 to 2004. J. Clim. 22, 2773–2792 (2009).

    ADS  Google Scholar 

  33. 33.

    Chandanpurkar, H. A., Reager, J. T., Famiglietti, J. S. & Syed, T. H. Satellite- and reanalysis-based mass balance estimates of global continental discharge (1993–2015). J. Clim. 30, 8481–8495 (2017).

    ADS  Google Scholar 

  34. 34.

    Syed, T. H., Famiglietti, J. S., Chambers, D. P., Willis, J. K. & Hilburn, K. Satellite-based global-ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge. Proc. Natl Acad. Sci. USA 107, 17916–17921 (2010).

    ADS  CAS  Google Scholar 

  35. 35.

    Ropelewski, C. F. & Halpert, M. S. North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Weath. Rev. 114, 2352–2362 (1986).

    ADS  Google Scholar 

  36. 36.

    Ropelewski, C. F. & Halpert, M. S. Precipitation patterns associated with the high index phase of the Southern Oscillation. J. Clim. 2, 268–284 (1989).

    ADS  Google Scholar 

  37. 37.

    Trenberth, K. E., Fasullo, J. T. & Kiehl, J. Earth’s global energy budget. Bull. Am. Meteorol. Soc. 90, 311–324 (2009).

    ADS  Google Scholar 

  38. 38.

    Landerer, F. W. et al. Extending the global mass change data record: GRACE Follow-On instrument and science data performance. Geophys. Res. Lett. 47, e2020GL088306 (2020).

    ADS  Google Scholar 

  39. 39.

    Scanlon, B. R. et al. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proc. Natl Acad. Sci. USA 115, E1080–E1089 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wiese, D. N., Landerer, F. W. & Watkins, M. M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 52, 7490–7502 (2016).

    ADS  Google Scholar 

  41. 41.

    Landerer, F. W., Dickey, J. O. & Guntner, A. Terrestrial water budget of the Eurasian pan-Arctic from GRACE satellite measurements during 2003–2009. J. Geophys. Res. D 115, D23115 (2010).

    ADS  Google Scholar 

  42. 42.

    Long, D., Longuevergne, L. & Scanlon, B. R. Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resour. Res. 50, 1131–1151 (2014).

    ADS  Google Scholar 

  43. 43.

    Adler, R. F. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).

    ADS  Google Scholar 

  45. 45.

    Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    ADS  Google Scholar 

  47. 47.

    Suzuki, T. et al. A dataset of continental river discharge based on JRA-55 for use in a global ocean circulation model. J. Oceanogr. 74, 421–429 (2018).

    Google Scholar 

  48. 48.

    Huffman, G. J. et al. The global precipitation climatology project (GPCP) combined precipitation dataset. Bull. Am. Meteorol. Soc. 78, 5–20 (1997).

    ADS  Google Scholar 

  49. 49.

    Xie, P. P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558 (1997).

    ADS  Google Scholar 

  50. 50.

    Jin, X. & Weller, R. A. Multidecade Global Flux Datasets from the Objectively Analyzed Air–Sea Fluxes (OAFlux) Project: Latent and Sensible Heat Fluxes, Ocean Evaporation, and Related Surface Meteorological Variables OAFlux Project Technical Report OA-2008-01 (OAFlux Project, 2008).

  51. 51.

    Good, S. A., Martin, M. J. & Rayner, N. A. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans 118, 6704–6716 (2013).

    ADS  Google Scholar 

  52. 52.

    Rodell, M. et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).

    ADS  Google Scholar 

  53. 53.

    Huang, B. et al. Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4). Part I: upgrades and intercomparisons. J. Clim. 28, 911–930 (2015).

    ADS  Google Scholar 

  54. 54.

    Lenssen, N. J. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. D 124, 6307–6326 (2019).

    ADS  Google Scholar 

  55. 55.

    Gehne, M., Hamill, T. M., Kiladis, G. N. & Trenberth, K. E. Comparison of global precipitation estimates across a range of temporal and spatial scales. J. Clim. 29, 7773–7795 (2016).

    ADS  Google Scholar 

  56. 56.

    Jolliffe, I. T. Uncertainty and inference for verification measures. Weather Forecast. 22, 637–650 (2007).

    ADS  Google Scholar 

  57. 57.

    Bamber, J. et al. Land ice freshwater budget of the Arctic and North Atlantic oceans: 1. Data, methods, and results. J. Geophys. Res. Oceans 123, 1827–1837 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Rye, C. D. et al. Rapid sea-level rise along the Antarctic margins in response to increased glacial discharge. Nat. Geosci. 7, 732–735 (2014).

    ADS  CAS  Google Scholar 

  59. 59.

    Depoorter, M. A. et al. Calving fluxes and basal melt rates of Antarctic ice shelves. Nature 502, 89–92 (2013); corrigendum 502, 580 (2013).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship acknowledged. This work was supported by funding from NASA’s GRACE-FO Science Team, Principal Investigator J.T.R. We thank A. Bloom for sharing code used to produce the confidence interval plots.

Author information

Affiliations

Authors

Contributions

M.P.-C. conceived, and carried out the research, led the data analysis and wrote the manuscript. J.T.R. conceived the research, designed the analysis and provided comments on the manuscript. H.A.C. produced the global discharge dataset and also provided input on the analysis. M.R. provided comments on the manuscript.

Corresponding author

Correspondence to Madeleine Pascolini-Campbell.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Di Long, Bart Nijssen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Input water cycle timeseries.

ac, The raw time series for global land precipitation (GPCPV2.3, MERRA-2, NOAA–NCEP and ERA-5) (a), discharge (JRA-55, and ocean mass-balance estimates EN4–OAFlux–GPCP, EN4–OAFlux–CMAP, EN4–ERA5, EN4–MERRA2) (b) and change in total water storage (dS/dt) from GRACE/GRACE-FO using three different methods to compute the derivate (backward difference with three-month smoothing, centred finite difference and backward difference) (c).

Source data

Extended Data Fig. 2 Error budget of water-balance components.

a, b, Monthly error time series for precipitation (a) and discharge (b) calculated as the standard deviation of input data sets. c, Error in dS/dt calculated from the formal GRACE JPL RL06 mascon error product, and propagated into derivative. Monthly time series of errors plotted for 2003 to 2019 in units of mm yr−1.

Source data

Extended Data Fig. 3 Ratios of water-balance components.

ac, Ratios between components of the water balance for ET/Q (a), ET/Pr (b) and Q/Pr (c). In each case, the ratios are calculated using the time series with the seasonal cycle removed and 15-month smoothing applied. The ensemble mean for each variable (ET, Pr and Q) is used.

Source data

Extended Data Fig. 4 Water-balance relationship with ENSO.

Left: correlation with ERSST version 4 SST against ET (a), Pr (c), Q (e) and dS/dt (g). Right: time series of MEI index against ET (b), Pr (d), Q (f) and dS/dt (h). For each panel, the SST and water-balance variable have the seasonal cycle removed and a 15-month moving average filter applied. The r value of the correlation between the MEI and water-cycle variable are shown in top left corner (right panels). Stippling on the maps (left panels) indicates that the value of the Pearson correlation between the SST and the water-balance variable (ET, Pr, Q and dS/dt) at that grid point is significant (α = 0.05 level). Maps created using MATLAB with the M_Map package (online at https://www.eoas.ubc.ca/~rich/map.html).

Source data

Extended Data Fig. 5 Effect of ENSO and temperature.

a, Multiple linear regression of global surface temperature (yellow line) and MEI (red line) onto ET (blue line). bd, Multiple regression of MEI (red line) onto Pr (b), Q (c) and dS/dt (d). In each, the input times series data has been filtered using a 15-month moving average. The amount of variability explained is indicated by R2 (top left corner of panels).

Source data

Extended Data Fig. 6 Effect of removing natural climate variability and temperature on ET.

a, ET anomaly time series (solid blue line) and linear trend (dashed blue line), and ET anomaly time series minus the multiple regression model of MEI onto ET (solid red lines) and trend (dashed red line). b, Same as a, but for multiple regression model of surface temperature. The value of the trends in mm yr−1 are indicated in the top left.

Source data

Extended Data Fig. 7 Influence of ENSO on ET products.

Left: correlation with ERSST version 4 SST against different ET products: ET (a), MOD16A2GF (c), FLUXCOM (e), PT-JPL (g) and GLDAS2.2 (i). Right: time series of MEI index against ET (b), MOD16A2GF (d), FLUXCOM (f), PT-JPL (h) and GLDAS2.2 (j). For each panel, the SST and water-balance variable have the seasonal cycle removed and a 15-month moving average filter applied. The r value of the correlation between MEI and ET shown in top left corner (right panels) (r values surrounded by () are not significant at the α = 0.05 level). Stippling on the maps (left panels) indicates that the value of the correlation at that grid point is significant (α = 0.05 level). Maps created using MATLAB with the M_Map package (online at https://www.eoas.ubc.ca/~rich/map.html).

Source data

Extended Data Fig. 8 Contribution of ice sheets to ET.

ac, Seasonal cycle for ET, Pr, Q and dS/dt calculated without Greenland + Antarctica (a), without Antarctica (b) and with all global land (ET from this study) (c). The shading is the standard deviation among the bias-corrected seasonal cycle of the ET ensemble (red shading), and input datasets used for Pr (four datasets, blue shading), Q (five datasets, black shading) and dS/dt (three methods to calculate derivative from JPL RL06 GRACE TWS, teal shading). df, Ensemble of ET compared with other ET products for ET calculated without Greenland + Antarctica (d), without Antarctica (e) and with all global land (ET from this study) (f). The shading represents the confidence intervals for the ensemble of ET (range shown in the colour bar).

Source data

Extended Data Table 1 ET long-term mean and trends
Extended Data Table 2 ET seasonal cycle and trends

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pascolini-Campbell, M., Reager, J.T., Chandanpurkar, H.A. et al. A 10 per cent increase in global land evapotranspiration from 2003 to 2019. Nature 593, 543–547 (2021). https://doi.org/10.1038/s41586-021-03503-5

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links