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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.

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.

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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

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).

Supplementary information

Supplementary Information

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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