Mid-latitude freshwater availability reduced by projected vegetation responses to climate change


Plants are expected to generate more global-scale runoff under increasing atmospheric carbon dioxide concentrations through their influence on surface resistance to evapotranspiration. Recent studies using Earth System Models from phase 5 of the Coupled Model Intercomparison Project ostensibly reaffirm this result, further suggesting that plants will ameliorate the dire reductions in water availability projected by other studies that use aridity metrics. Here we complicate this narrative by analysing the change in precipitation partitioning to plants, runoff and storage in multiple Earth system models under both high carbon dioxide concentrations and warming. We show that projected plant responses directly reduce future runoff across vast swaths of North America, Europe and Asia because bulk canopy water demands increase with additional vegetation growth and longer and warmer growing seasons. These runoff declines occur despite increased surface resistance to evapotranspiration and vegetation total water use efficiency, even in regions with increasing or unchanging precipitation. We demonstrate that constraining the large uncertainty in the multimodel ensemble with regional-scale observations of evapotranspiration partitioning strengthens these results. We conclude that terrestrial vegetation plays a large and unresolved role in shaping future regional freshwater availability, one that will not ubiquitously ameliorate future warming-driven surface drying.

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Fig. 1: Vegetation and water availability projections.
Fig. 2: Relative changes in the hydrological budget at the land surface.
Fig. 3: Absolute changes in the hydrological budget at the land surface.
Fig. 4: The relationship between relative and absolute hydrological changes.

Data availability

All data supporting the findings of this study are freely available from the following locations: CMIP5 model data: https://pcmdi.llnl.gov.

Code availability

All code to reproduce the results of this study are available on reasonable request from the corresponding author.


  1. 1.

    Kallis, G., Kiparsky, M., Milman, A. & Ray, I. Glossing over the complexity of water. Science 314, 1387 (2006).

    Article  Google Scholar 

  2. 2.

    Taylor, K. E. & Penner, J. E. Response of the climate system to atmospheric aerosols and greenhouse gases. Nature 369, 734–737 (1994).

    Article  Google Scholar 

  3. 3.

    Seager, R., Naik, N. & Vecchi, G. A. Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Clim. 23, 4651–4668 (2010).

    Article  Google Scholar 

  4. 4.

    Ciais, P. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 6 (IPCC, Cambridge Univ. Press, 2013).

  5. 5.

    Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant Cell Environ. 18, 1214–1225 (1995).

    Article  Google Scholar 

  6. 6.

    Idso, S. B. & Brazel, A. J. Rising atmospheric carbon dioxide concentrations may increase streamflow. Nature 312, 51–53 (1984).

    Article  Google Scholar 

  7. 7.

    Betts, R. A. et al. Projected increase in continental runoff due to plant responses to increasing carbon dioxide. Nature 448, 1037–1041 (2007).

    Article  Google Scholar 

  8. 8.

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

    Article  Google Scholar 

  9. 9.

    Swann, A. L. S., 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  Google Scholar 

  10. 10.

    Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).

    Article  Google Scholar 

  11. 11.

    Jasechko, S. Plants turn on the tap. Nat. Clim. Change 8, 562–563 (2018).

    Article  Google Scholar 

  12. 12.

    Lian, X. et al. Partitioning global land evapotranspiration using CMIP5 models constrained by observations. Nat. Clim. Change 8, 640–646 (2018).

    Article  Google Scholar 

  13. 13.

    Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–49 (2019).

    Article  Google Scholar 

  14. 14.

    Cook, B. I., Smerdon, J. E., Seager, R. & Coats, S. Global warming and 21st century drying. Clim. Dyn. 43, 2607–2627 (2014).

    Article  Google Scholar 

  15. 15.

    Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).

    Article  Google Scholar 

  16. 16.

    Huang, J., Yu, H., Dai, A., Wei, Y. & Kang, L. Drylands face potential threat under 2 °C global warming target. Nat. Clim. Change 7, 417–422 (2017).

    Article  Google Scholar 

  17. 17.

    Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).

    Article  Google Scholar 

  18. 18.

    Ault, T. R., Mankin, J. S., Cook, B. I. & Smerdon, J. E. Relative impacts of mitigation, temperature, and precipitation on 21st-century megadrought risk in the American Southwest. Sci. Adv. 2, e1600873 (2016).

    Article  Google Scholar 

  19. 19.

    Ward, E. J. et al. Evapotranspiration and water yield of a pine-broadleaf forest are not altered by long-term atmospheric [CO2] enrichment under native or enhanced soil fertility. Glob. Change Biol. 24, 4841–4856 (2018).

    Article  Google Scholar 

  20. 20.

    Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).

    Article  Google Scholar 

  21. 21.

    Van Der Sleen, P. et al. No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased. Nat. Geosci. 8, 24–28 (2015).

    Article  Google Scholar 

  22. 22.

    Nowak, R. S. et al. Elevated atmospheric CO2 does not conserve soil water in the Mojave Desert. Ecology 85, 93–99 (2004).

    Article  Google Scholar 

  23. 23.

    Evans, R. D. et al. Greater ecosystem carbon in the Mojave Desert after ten years exposure to elevated CO2. Nat. Clim. Change 4, 394–397 (2014).

    Article  Google Scholar 

  24. 24.

    Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change 6, 75–78 (2016).

    Article  Google Scholar 

  25. 25.

    Frank, D. C. et al. Water-use efficiency and transpiration across European forests during the Anthropocene. Nat. Clim. Change 5, 579–583 (2015).

    Article  Google Scholar 

  26. 26.

    Keenan, T. F. & Riley, W. J. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Change 8, 825–829 (2018).

    Article  Google Scholar 

  27. 27.

    Jiang, L. et al. Scale-dependent performance of CMIP5 earth system models in simulating terrestrial vegetation carbon. J. Clim. 28, 5217–5232 (2015).

    Article  Google Scholar 

  28. 28.

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

    Article  Google Scholar 

  29. 29.

    Trancoso, R., Larsen, J. R., McVicar, T. R., Phinn, S. R. & McAlpine, C. A. CO2–vegetation feedbacks and other climate changes implicated in reducing base flow. Geophys. Res. Lett. 44, 2310–2318 (2017).

    Article  Google Scholar 

  30. 30.

    Mankin, J. S., Smerdon, J. E., Cook, B. I., Williams, A. P. & Seager, R. The curious case of projected twenty-first-century drying but greening in the American West. J. Clim. 30, 8689–8710 (2017).

    Article  Google Scholar 

  31. 31.

    Mankin, J. S. et al. Blue water trade-offs with ecosystems in a CO2-enriched climate. Geophys. Res. Lett. 45, 3115–3125 (2018).

    Article  Google Scholar 

  32. 32.

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

    Article  Google Scholar 

  33. 33.

    O’Gorman, P. A. Precipitation extremes under climate change. Curr. Clim. Change Rep. 1, 49–59 (2015).

    Article  Google Scholar 

  34. 34.

    Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).

    Article  Google Scholar 

  35. 35.

    Norby, R. J. et al. Model–data synthesis for the next generation of forest free-air CO2 enrichment (FACE) experiments. New Phytol. 209, 17–28 (2016).

    Article  Google Scholar 

  36. 36.

    Medlyn, B. E. et al. Using ecosystem experiments to improve vegetation models. Nat. Clim. Change 5, 528–534 (2015).

    Article  Google Scholar 

  37. 37.

    Walker, A. P. et al. Comprehensive ecosystem model–data synthesis using multiple data sets at two temperate forest free-air CO2 enrichment experiments: model performance at ambient CO2 concentration. J. Geophys. Res. Biogeosci. 119, 937–964 (2014).

    Article  Google Scholar 

  38. 38.

    De Kauwe, M. G. et al. Forest water use and water use efficiency at elevated CO2: a model–data intercomparison at two contrasting temperate forest FACE sites. Glob. Change Biol. 19, 1759–1779 (2013).

    Article  Google Scholar 

  39. 39.

    Calfapietra, C. et al. Challenges in elevated CO2 experiments on forests. Trends Plant Sci. 15, 5–10 (2010).

    Article  Google Scholar 

  40. 40.

    Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).

    Article  Google Scholar 

  41. 41.

    Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).

    Article  Google Scholar 

  42. 42.

    Kolby Smith, W. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).

    Article  Google Scholar 

  43. 43.

    Kovenock, M. & Swann, A. L. S. Leaf trait acclimation amplifies simulated climate warming in response to elevated carbon dioxide. Glob. Biogeochem. Cycles 32, 1437–1448 (2018).

    Article  Google Scholar 

  44. 44.

    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).

    Google Scholar 

  45. 45.

    Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33–57 (2011).

    Article  Google Scholar 

  46. 46.

    Gu, H., Zong, Z. & Hung, K. C. A modified superconvergent patch recovery method and its application to large deformation problems. Finite Elem. Anal. Des. 40, 665–687 (2004).

    Article  Google Scholar 

  47. 47.

    Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 236–244 (2017).

    Article  Google Scholar 

  48. 48.

    Cook, B. I., Mankin, J. S. & Anchukaitis, K. J. Climate change and drought: from past to future. Curr. Clim. Change Rep. 4, 164–179 (2018).

    Article  Google Scholar 

  49. 49.

    Oleson, K. W. et al. Technical Description of Version 4.0 of the Community Land Model (CLM) Technical Note No. NCAR/TN-478+STR NCAR (Univ. Corporation for Atmospheric Research, 2010).

  50. 50.

    The NCAR Command Language v.6.6.2 (NCAR, 2019).

  51. 51.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

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We thank the Earth System Grid Federation and their archiving of the Coupled Model Intercomparison Project (phase 5) data and N. Henderson and H. Liu for the data serving and computing support in the Division of Ocean and Climate Physics at Lamont-Doherty Earth Observatory of Columbia University. We also greatly appreciate Z. Wei for kindly sharing his ET partitioning data29. J.S.M. was funded by the Burke Research Initiation Award and the National Science Foundation award AGS‐1243204, which also supported R.S. and J.E.S. Additional support for R.S. and J.E.S. was provided by National Science Foundation awards GS‐1401400, AGS‐1805490, AGS-1602581 and OISE-1743738. B.I.C. was supported by the NASA Modeling, Analysis, and Prediction. A.P.W. was supported by National Science Foundation award AGS-1703029 and NASA award 16-MAP16-0081. Lamont contribution #8359.

Author information




J.S.M., R.S., J.E.S., B.I.C. and A.P.W. conceived the study. J.S.M. designed and performed the analysis. All authors interpreted the results. J.S.M. wrote the manuscript with all authors providing critical input.

Corresponding author

Correspondence to Justin S. Mankin.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Precipitation partitioning in the present and future.

a., WY partitioning [mm/yr] of precipitation (P) among the canopy (C/P, green), runoff (Q/P, blue), and soil + storage (S/P, red), shown as fluxes in the current climate. All else equal, high [CO2] and warming-induced VPD could close stomata, decreasing transpiration and increasing soil water and runoff, as in b. Soil water and runoff could alternatively decrease as longer and warmer growing seasons and CO2 fertilization increase ecosystem water demands, leading to reductions in soil water and runoff, as in c.

Extended Data Fig. 2 Precipitation and leaf area index (LAI) changes.

End-of-century changes in average a., WY precipitation (mm mo-1) and b., annual-scale leaf area index (LAI, unitless) for the ensemble mean. Red hatching indicates insignificant change based on a K-S test of the historical period (1976–2005) versus the future (2070–2099) and that at least 80% of the models agree on the direction of mean change at that point.

Extended Data Fig. 3 Relative change in the Blue Water Tradeoff.

As in Fig. 2a, but expressed as a fraction of historical WY runoff.

Extended Data Fig. 4 Accounting for interannual variation in precipitation partitioning in the present and future.

Performance of a statistical model of model- and region-based interannual WY (a) canopy partitioning, C/P, (b) runoff partitioning (Q/P), (c) and soil + storage partitioning (S/P). In each panel, the partitioning predicted by the statistical model (x-axis) is plotted against its actual value as simulated by the CMIP5 models for each grid point in each region. Historical years (1976–2005) and future years (2070–2099) are shown. The statistical model for each partitioning ratio and period is the same: Y = β0 + β1*(WY P) + β2*(WY T) + β3*( < X > ), where Y is either WY C/P, WY Q/P, WY S/P and < X > is a set of model and region control variables. The 1:1 line is shown in each panel.

Extended Data Fig. 5 Model-by-model correlations among partitioning, temperature, and precipitation.

Model- and region-based Spearman’s rank correlation of historical (1976–2005) partitioning ratios with historical precipitation (a-c) and temperature (d-f).

Extended Data Fig. 6 Regional-scale blue water tradeoff time series.

Ensemble mean time series in the runoff loss to the canopy, the blue water tradeoff (BWT) for the five regions as an anomaly from the 1976–2005 baseline. Positive (brown) values indicate the amount of WY precipitation going to the canopies to the sacrifice of runoff; negative (blue) values show the opposite. Bolded lines in each regional time series panel signify signal emergence ( > 1) of the ensemble mean (μ/σ) in that year from the baseline (1976–2005).

Extended Data Fig. 7 BWT accounts for runoff changes.

Performance of a statistical model of model- and region-based centennial-scale changes in WY runoff (∆Q) without (a) and with (b) the canopy-runoff tradeoff (blue water tradeoff, BWT) metric. The model and region based ∆Q predicted by the statistical model (x-axis) is plotted against its actual value as simulated by the CMIP5 models for each region. The statistical model for (a) includes temperature and precipitation and their interaction: ∆Q = β0 + β1*(∆P) + β2*(∆T) + β3*(∆T*∆P). The statistical model for (b) adds the BWT value for each model and region. The 1:1 line is shown in each panel.

Extended Data Fig. 8 Accounting for regional canopy partitioning changes.

Performance of a statistical model of model- and region-based centennial-scale changes in WY C/P (∆(C/P)). The ∆(C/P) predicted by the statistical model (x-axis) is plotted against its actual value as simulated by the CMIP5 models for each region. The statistical model includes temperature and precipitation and their interaction: ∆Q = β0 + β1*(∆LAI) + β2*(∆C/ET) + β3*(∆P) + β4*(∆T) + β5*(∆P*∆T). The 1:1 line is shown. In the standardized version of this model (that is, (X-μX)/σX), the coefficient of ∆(C/ET) is largest, twice that of ∆P.

Extended Data Fig. 9 Observational constraint for the blue water tradeoff.

Constraining the runoff loss to vegetation (blue water tradeoff, BWT) and changes in runoff, ∆Q. Because ∆C/ET dominates changes in C/P (see Fig. S8), we show the relationship between historical (observed period, 1982–2014) C/ET and future C/P in (a) across models and regions. For each region, we show the observational range ( + /− 1σ) of C/ET, derived from the sum of interception and transpiration from Wei et al. GRL (2017) as colored bars mapping to the x-axis, with colors corresponding to the inset map. (b) Bar plot of unconstrained (raw) versus constrained BWT and ∆Q for each region. Whiskers show 1 standard error of the mean.

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Mankin, J.S., Seager, R., Smerdon, J.E. et al. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019). https://doi.org/10.1038/s41561-019-0480-x

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