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Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration

Abstract

Transpiration, the dominant component of terrestrial evapotranspiration (ET), directly connects the water, energy and carbon cycles and is typically restricted by soil and atmospheric (for example, the vapour pressure deficit (VPD)) moisture stresses through plant hydraulic processes. These sources of stress are likely to diverge under climate change, with a globally enhanced VPD but more variable and uncertain changes in soil moisture. Here, using a model–data fusion approach, we demonstrate that the common empirical approach used in most Earth system models to evaluate the ET response to soil moisture and VPD, which neglects plant hydraulics, underestimates ET sensitivity to VPD and compensates by overestimating the sensitivity to soil moisture stress. A hydraulic model that describes water transport through the plant better captures ET under high VPD conditions for wide-ranging soil moisture states. These findings highlight the central role of plant hydraulics in regulating the increasing importance of atmospheric moisture stress on biosphere–atmosphere interactions under elevated temperatures.

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Fig. 1: Comparison between measured and inferred ψ50.
Fig. 2: Model performance in estimating observed ET.
Fig. 3: Restriction effect of hydroclimatic stresses on ET through stomatal conductance across the sites estimated using the empirical and hydraulic models.
Fig. 4: Reference stomatal conductance and VPD sensitivity estimated using the empirical and hydraulic models.

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Data availability

All datasets used in this study are publicly available from the referenced sources.

Code availability

The source code of the soil-plant model and the used MCMC algorithm is available at https://github.com/YanlanLiu/model-data-fusion.

References

  1. Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).

    CAS  Google Scholar 

  2. Seager, R. et al. Projections of declining surface-water availability for the southwestern United States. Nat. Clim. Change 3, 482–486 (2013).

    Google Scholar 

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

    CAS  Google Scholar 

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

    Google Scholar 

  5. Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    CAS  Google Scholar 

  6. Konings, A., Williams, A. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–289 (2017).

    CAS  Google Scholar 

  7. Rigden, A. J. & Salvucci, G. D. Stomatal response to humidity and CO2 implicated in recent decline in US evaporation. Global Change Biol. 23, 1140–1151 (2017).

    Google Scholar 

  8. Mirfenderesgi, G. et al. Tree level hydrodynamic approach for resolving aboveground water storage and stomatal conductance and modeling the effects of tree hydraulic strategy. J. Geophys. Res. Biogeosci. 121, 1792–1813 (2016).

    Google Scholar 

  9. Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).

    CAS  Google Scholar 

  10. Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

    CAS  Google Scholar 

  11. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  12. Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Ann. Rev. Plant Biol. 40, 19–36 (1989).

    Google Scholar 

  13. Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).

    CAS  Google Scholar 

  14. Feng, X., Dawson, T. E., Ackerly, D. D., Santiago, L. S. & Thompson, S. E. Reconciling seasonal hydraulic risk and plant water use through probabilistic soil–plant dynamics. Global Change Biol. 23, 3758–3769 (2017).

    Google Scholar 

  15. Oleson, K. W. et al. Technical Description of Version 4.5 of the Community Land Model (CLM) NCAR Technical Note NCAR/TN-503+STR (National Center for Atmospheric Research, 2013).

  16. Milly, P. C. et al. An enhanced model of land water and energy for global hydrologic and earth-system studies. J. Hydrometeorol. 15, 1739–1761 (2014).

    Google Scholar 

  17. Bonan, G., Williams, M., Fisher, R. & Oleson, K. Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum. Geosci. Model Dev. 7, 2193–2222 (2014).

    Google Scholar 

  18. Anderegg, W. R. et al. Plant water potential improves prediction of empirical stomatal models. PloS ONE 12, e0185481 (2017).

    Google Scholar 

  19. Anderegg, W. R. Spatial and temporal variation in plant hydraulic traits and their relevance for climate change impacts on vegetation. New Phytol. 205, 1008–1014 (2015).

    Google Scholar 

  20. Meinzer, F. C., McCulloh, K. A., Lachenbruch, B., Woodruff, D. R. & Johnson, D. M. The blind men and the elephant: the impact of context and scale in evaluating conflicts between plant hydraulic safety and efficiency. Oecologia 164, 287–296 (2010).

    Google Scholar 

  21. Katul, G. G., Palmroth, S. & Oren, R. Leaf stomatal responses to vapour pressure deficit under current and CO2-enriched atmosphere explained by the economics of gas exchange. Plant Cell Environ. 32, 968–979 (2009).

    CAS  Google Scholar 

  22. Manzoni, S. et al. Optimizing stomatal conductance for maximum carbon gain under water stress: a meta-analysis across plant functional types and climates. Funct. Ecol. 25, 456–467 (2011).

    Google Scholar 

  23. Mrad, A. et al. A dynamic optimality principle for water use strategies explains isohydric to anisohydric plant responses to drought. Front. For. Global Change 2, 49 (2019).

    Google Scholar 

  24. Oren, R. et al. Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 22, 1515–1526 (1999).

    Google Scholar 

  25. Mrad, A., Domec, J.-C., Huang, C.-W., Lens, F. & Katul, G. A network model links wood anatomy to xylem tissue hydraulic behaviour and vulnerability to cavitation. Plant Cell Environ. 41, 2718–2730 (2018).

    CAS  Google Scholar 

  26. Venturas, M. D., Sperry, J. S. & Hacke, U. G. Plant xylem hydraulics: what we understand, current research, and future challenges. J. Integr. Plant Biol. 59, 356–389 (2017).

    Google Scholar 

  27. Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).

    CAS  Google Scholar 

  28. Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Global Change Biol. 24, 35–54 (2018).

    Google Scholar 

  29. Eller, C. B. et al. Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics. Phil. Trans. R. Soc. B 373, 20170315 (2018).

    Google Scholar 

  30. Kennedy, D. et al. Implementing plant hydraulics in the community land model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).

    Google Scholar 

  31. Liu, Y. et al. Increasing atmospheric humidity and CO2 concentration alleviate forest mortality risk. Proc. Natl Acad. Sci. USA 114, 9918–9923 (2017).

    CAS  Google Scholar 

  32. Katul, G., Manzoni, S., Palmroth, S. & Oren, R. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration. Ann. Bot. 105, 431–442 (2009).

    Google Scholar 

  33. Farquhar, G. D., Caemmerer, S. V. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    CAS  Google Scholar 

  34. Huang, C.-W. et al. The effect of plant water storage on water fluxes within the coupled soil–plant system. New Phytol. 213, 1093–1106 (2017).

    CAS  Google Scholar 

  35. Cowan, I. & Farquhar, G. Stomatal function in relation to leaf metabolism and environment. Symp. Soc. Exp. Biol. 31, 471–505 (1977).

    CAS  Google Scholar 

  36. Hari, P., Mäkelä, A., Korpilahti, E. & Holmberg, M. Optimal control of gas exchange. Tree Physiol. 2, 169–175 (1986).

    Google Scholar 

  37. Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biol. 17, 2134–2144 (2011).

    Google Scholar 

  38. Sperry, J. S. et al. Predicting stomatal responses to the environment from the optimization of photosynthetic gain and hydraulic cost. Plant Cell Environ. 40, 816–830 (2017).

    CAS  Google Scholar 

  39. Manzoni, S., Vico, G., Porporato, A. & Katul, G. Biological constraints on water transport in the soil–plant–atmosphere system. Adv. Water Resourc. 51, 292–304 (2013).

    Google Scholar 

  40. Clapp, R. B. & Hornberger, G. M. Empirical equations for some soil hydraulic properties. Water Resourc. Res. 14, 601–604 (1978).

    Google Scholar 

  41. Katul, G., Leuning, R. & Oren, R. Relationship between plant hydraulic and biochemical properties derived from a steady–state coupled water and carbon transport model. Plant Cell Environ. 26, 339–350 (2003).

    CAS  Google Scholar 

  42. FLUXNET 2015 Tier 1 Dataset (FLUXNET, accessed 25 July 2018); http://fluxnet.fluxdata.org/data/fluxnet2015-dataset

  43. Myneni, R., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500 m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, accessed 21 January 2019); https://doi.org/10.5067/MODIS/MCD15A3H.006

  44. Ukkola, A. M., Haughton, N., Kauwe, M. G. D., Abramowitz, G. & Pitman, A. J. FluxnetLSM R package (v1. 0): a community tool for processing FLUXNET data for use in land surface modelling. Geosci. Model Develop. 10, 3379–3390 (2017).

    CAS  Google Scholar 

  45. Healey, S. et al. CMS: GLAS LiDAR-derived Global Estimates of Forest Canopy Height, 2004–2008 (ORNL DAAC, accessed 21 January 2019); https://doi.org/10.3334/ORNLDAAC/1271

  46. 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).

    CAS  Google Scholar 

  47. Jackson, R. et al. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411 (1996).

    CAS  Google Scholar 

  48. Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).

    Google Scholar 

  49. Harmonized World Soil Database Version 1.2 (FAO, accessed 22 June 2016); http://www.fao.org/soils-portal

  50. Thompson, S. E. et al. Comparative hydrology across AmeriFlux sites: the variable roles of climate, vegetation, and groundwater. Water Resourc. Res. 47, W00J07 (2011).

    Google Scholar 

  51. Kattge, J. et al. TRY—a global database of plant traits. Global Change Biol. 17, 2905–2935 (2011).

    Google Scholar 

  52. Martin-StPaul, N., Delzon, S. & Cochard, H. Plant resistance to drought depends on timely stomatal closure. Ecol. Lett. 20, 1437–1447 (2017).

    Google Scholar 

  53. Ji, C. & Schmidler, S. C. Adaptive Markov Chain Monte Carlo for Bayesian variable selection. J. Comput. Graph. Stat. 22, 708–728 (2013).

    Google Scholar 

  54. Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Graph. Stat. 7, 434–455 (1998).

    Google Scholar 

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Acknowledgements

We acknowledge S. C. Schmidler for providing suggestions on statistical inference. A.G.K. and Y.L. were funded by NASA Terrestrial Ecology (award 80NSSC18K0715) through the New Investigator programme. A.G.K. was also funded by the NOAA under grant NA17OAR4310127. M.K. acknowledges support from the National Science Foundation (NSF, EAR-1856054 and EAR-1920425). G.G.K. acknowledges support from the National Science Foundation (NSF-AGS-1644382 and NSF-IOS-1754893).

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Authors and Affiliations

Authors

Contributions

A.G.K. and Y.L. conceived the study. Y.L. prepared data, set up the model and conducted statistical inference, with all the authors providing input. M.K. and G.K. further improved the analysis design. Y.L., M.K. and A.G.K. led the manuscript writing. All the authors contributed to editing the manuscript.

Corresponding author

Correspondence to Yanlan Liu.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Maurizio Mencuccini 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

Extended Data Fig. 1 Root zone soil moisture, soil water potential, and VPD across studied sites.

Each box represents the 25th and 75th percentiles and the range across the entire record period. Outliers are marked using black dots.

Extended Data Fig. 2 Relation between the 95th percentile of the percentage loss of conductivity (PLC) and the flatness of posterior probability distribution of ψ50 across the studied sites.

The flatness is quantified as (q75 − q25)/(p75 − p25), where q75 and q25 are the 75th and 25th percentiles of the posterior distribution, and p75 and p25 are the 75th and 25th percentiles of the prior distribution. A flatness of 0 indicates concentrated posterior and a flatness of 1 indicates a nearly uniformly distributed posterior. Horizontal bars represent the uncertainty ranges across posterior samples.

Extended Data Fig. 3 Correlation coefficient of ψ50 (MPa) with gp,max, a, and λW across posterior samples at the studied sites.

Site information is listed in Supplementary Table 1.

Extended Data Fig. 4 Posterior distributions of retrieved plant hydraulic traits across studied sites.

Each box denotes the 25th/75th percentiles and the range of posterior samples.

Extended Data Fig. 5 Bayesian Information Criterion (BIC) of the hydraulic and empirical models across the studied sites.

Model likelihood averaged across MCMC ensembles at each site was used to calculate BIC.

Extended Data Fig. 6 Restriction effect of soil moisture and VPD on ET across sites with different dryness index.

A replica of Fig. 3 (main text) but color-coded with dryness index. Dryness index is calculated as the ratio between long-term mean potential evapotranspiration and long-term mean precipitation. Circles and triangles represent soil moisture and VPD restricted ET, respectively.

Extended Data Fig. 7 Restriction effect of soil moisture and VPD on ET across sites during four sub-periods.

The four sub-periods are the same as in Fig. 2 (main text), that is, a, high VPD low soil moisture; b, high VPD high soil moisture; c, low VPD low soil moisture; and d, low VPD high soil moisture. Symbols are the same as in Fig. 3 (main text).

Extended Data Fig. 8 Temporal average of the reference stomatal conductance (\(g_s^ \ast\)) and the VPD-sensitivity (m) at a, AU-Wom, b, BE-Vie, and c, IT-Isp.

Blue and red dots represent the estimates under a light-saturated condition using the empirical and hydraulic models, respectively. The red belts indicate the hydraulic constraint. Grey areas show the contours of stomatal conductance (gs).

Extended Data Fig. 9 Impact of the dynamics of the VPD sensitivity (m), the dynamics of the reference stomatal conductance \(g_{\rm{s}}^ \ast\), and the difference in the mean of m and \(g_{\rm{s}}^ \ast\) on the restriction effect of VPD on ET estimated using the hydraulic model (\(\Delta {\mathrm{ET}}_{Hydr}^{VPD}\)).

The impacts averaged over a, the entire record period, and b, the stressed period, that is, when leaf water potential falls below its 75th percentile at each site, are plotted. Sites are listed from left to right in order of increasing dryness, as measured by the ratio of mean annual potential ET to mean annual precipitation.

Extended Data Fig. 10 Relation between the daily average leaf water potential (\(\overline {\psi _{\rm{l}}}\)) and (a–c) the VPD sensitivity (m) of the hydraulic model and (d–f) the reference stomatal conductance (\(g_{\rm{s}}^ \ast\)) at three example sites.

m was calculated using (1 − gs/\(g_{\rm{s}}^ \ast\))/ln(D) under light saturated conditions, where gs and \(g_{\rm{s}}^ \ast\) were calculated using the full stomatal optimization model (equation (5) in Methods).

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Supplementary Notes 1–4, Supplementary Table 1, Supplementary Figures 1–3

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Liu, Y., Kumar, M., Katul, G.G. et al. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat. Clim. Chang. 10, 691–695 (2020). https://doi.org/10.1038/s41558-020-0781-5

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