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Global dependency of canopy height on vapour pressure deficit and its projections under climate change

Nature Ecology & Evolution volume 10, pages 59–69 (2026)Cite this article

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Abstract

Canopy height is an important aspect of forest structure and functioning. Although water availability is important for canopy height growth, the climatic niche for tall trees remains poorly understood. Here we use global spaceborne lidar-derived canopy height to study its dependence on climate variables. We find that vapour pressure deficit (VPD) strongly controls geographical patterns of canopy height, observing a negative association also in tropical regions where water limitations are modest. Taller trees are prevalent in humid tropical regions, but canopy height decreases sharply as mean annual VPD surpasses 0.68 kPa. By 2100, projected increases in VPD under a warming climate could enhance limitations to canopy height growth, resulting in height losses in 87% of the humid tropical regions. Conversely, we project a widespread increase in canopy height across drylands, linked primarily to changing precipitation regimes. These results suggest that limitations on height growth driven by shifts in atmospheric dryness could lead to reduced future forest carbon sequestration.

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Fig. 1: Spatial patterns of canopy height/tall trees and their distributions across different ecoregions and hygrothermal space.
Fig. 2: Climatic dependency of canopy height and tall trees.
Fig. 3: Environmental determinants of canopy height.
Fig. 4: Potential for future canopy height changes.

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Data availability

GEDI L2A products are available at https://lpdaac.usgs.gov/products/gedi02_av002/. ERA5 climate data are available at https://cds.climate.copernicus.eu/datasets. Soil-property data were obtained from the Regridded Harmonized World Soil Database v.1.2 (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). Terrestrial Ecoregions data are available at https://databasin.org/datasets/68635d7c77f1475f9b6c1d1dbe0a4c4c/. Forest area data are available at https://data.worldbank.org/indicator/AG.LND.FRST.ZS. Forest age map is available at https://www.bgc-jena.mpg.de/geodb/projects/FileDetails.php. Burned areas products are available at https://climate.esa.int/en/projects/fire/data/. ISIMIP3b outputs are available at https://data.isimip.org/10.48364/ISIMIP.842396.1. Copernicus global land cover is provided at https://land.copernicus.eu/en/products/global-dynamic-land-cover.

Code availability

All analyses were conducted in either R or Python computing environment. The relevant code is available via Zenodo at https://zenodo.org/uploads/14031401 (ref. 81).

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Acknowledgements

We thank the many experts for generating the relevant datasets (for example, GEDI and ERA5 climate) used in this study. W.Z. acknowledges support from the Zijiang Excellent Young Scholars Program of East China Normal University and funding from the Independent Research Fund Denmark, Unravelling climate change impacts on savanna vegetation ecosystems (CLISA), grant ID 10.46540/2032-00026B. R.F. acknowledges funding from the Independent Research Fund Denmark, Unravelling climate change impacts on savanna vegetation ecosystems (CLISA), grant ID 10.46540/2032-00026B and the Danish National Research Foundation, Center for Remote Sensing and Deep Learning of Global Tree Resources (TreeSense), DNRF192. X.T. was supported by the National Key R&D Program for Young Scientists (2023YFF1305700), National Natural Science Foundation of China (42371129). Y.X. acknowledges support from the National Natural Science Foundation of China (42501130).

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Authors and Affiliations

  1. Key Laboratory of Geographic Information Science, Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, China

    Wenmin Zhang

  2. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Martin Brandt & Rasmus Fensholt

  3. School of Life Sciences, Nanjing University, Nanjing, China

    Chi Xu

  4. Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China

    Xiaowei Tong

  5. Huanjiang Agriculture Ecosystem Observation and Research Station of Guangxi, Guangxi Key Laboratory of Karst Ecological Processes and Services, Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang, China

    Xiaowei Tong

  6. State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China

    Yanbiao Xi

  7. Institute of Geography, Augsburg University, Augsburg, Germany

    Zhongxiang Fang

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  1. Wenmin Zhang
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Contributions

W.Z. conceived the research and performed the analyses. R.F., M.B. and C.X. provided comments to improve the paper, with X.T., Y.X. and Z.F. contributing to the discussion and interpretation of the results. W.Z. wrote the original paper, and all the co-authors contributed to improving its texts.

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Correspondence to Wenmin Zhang.

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Extended data

Extended Data Fig. 1 Spatial pattern of canopy height.

Spatial pattern of canopy height for a, the maximum of canopy height at a grid cell of 0.01 degree aggregated from the original spatial resolution of GEDI footprints (~0.00026 degree). b, Canopy height density plot at a spatial resolution of 0.01 degree retrieved from the median (p50) and maximum (p100) of canopy height at original spatial resolution. c, The median and maximum canopy height as a function of latitude by 1 degree. The shaded bands show one standard deviation.

Extended Data Fig. 2 Breakpoints identified in the relationship between canopy height and VPD/precipitation.

a, Two breakpoints (bp1 and bp2) of height against VPD were identified at 0.68 (CI 0.678–0.681) kPa, 1.42 (CI: 1.422–1.429) kPa, respectively. b, One breakpoint (bp) of height against precipitation was identified at 846 (CI: 839–854) mm. The breakpoints were identified from the spline curve quantifying the response of the maximum attainable height to VPD/precipitation (n = 10,000), based on a Spline-based quantile regression, with tau = 0.98 (τ = 0.98 quantile spline regression corresponding to blue lines). CI denotes the 95% confidence interval and is indicated by red dashed lines. This analysis was conducted using the “quantreg” and “Segmented” package in R.

Extended Data Fig. 3 Percentage of tall trees (canopy height ≥ 25 m).

Percentage of tall trees (canopy height ≥ 25 m) quantified by (a, b) the maximum of canopy height as a function of mean annual VPD and precipitation. counts (legend) indicate the number of pixels in each hexagon.

Extended Data Fig. 4 Pairwise correlations between canopy height and VPD/ precipitation.

a(a, b) globally (r = -0.48 and 0.49 for global scale), (b, c) the tropics (subtropical regions including Africa, Americas and Asia) and (e, f) dryland areas.

Extended Data Fig. 5 Potential for future canopy height changes under the future climate scenario of SSP585.

(a, b) same as Fig. 4a, b, but under the future climate scenario of SSP585. (c, d) The predicted canopy height at the lower and upper bounds of the 95% confidence intervals (CI), respectively, under SSP245, and (e, f) under SSP585.

Extended Data Fig. 6 Spatial patterns of canopy height.

Spatial patterns of (a) canopy height at a spatial resolution of 1 degree aggregated from the median of canopy height at 0.01-degree grid cell, (b) predicted canopy height at a spatial resolution of 1 degree, and (c, d) VPD driven canopy height by 2100 under future scenarios of SSP245 and SSP585.

Extended Data Fig. 7 Predicted canopy height considering acclimation and stand age dynamics.

(a, b) VPD- and precipitation-driven canopy height by 2100, accounting for VPD acclimation of 0.05 and 0.1 kPa per + 1 °C under climate scenario SSP245, and (c, d) under climate scenario SSP585; all relative to height predicted without considering acclimation. The acclimatation was only tested in areas that are predicted to experience climate warming quantified as an increase in temperature by 2100 relative to the current mean annual temperature (2010-2023), and was calculated by subtracting increases in VPD by 0.05 or 0.1 kPa per + 1 °C. (e, f) Predicted canopy height accounting for stand age dynamics by 2100 relative to current age, under SSP245 and SSP585.

Extended Data Fig. 8 Mechanistic links between VPD and canopy height.

a, A conceptual diagram showing the mechanistic impacts of VPD on tree canopy height through hydraulic and carbon constrains. A one-way arrow indicates an effect relationship between the two variables and a double-headed arrow denotes an association between the variables with feed-back. The arrow in blue indicates a positive effect and arrows in red indicate a negative effect. b, A flow diagram of the pathways by which increasing VPD shows indirect impacts on tree canopy height. Black and red arrows indicate the positive (+) and negative (-) paths with regard to higher VPD the physiological constraints on height growth imposed by higher VPD, based on the published studies with reference numbers. Each number corresponds to the citation number in Supplementary Table 4.

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Supplementary Figs. 1–13, Tables 1–6, the mechanism of VPD effects on height and references.

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Zhang, W., Brandt, M., Xu, C. et al. Global dependency of canopy height on vapour pressure deficit and its projections under climate change. Nat Ecol Evol 10, 59–69 (2026). https://doi.org/10.1038/s41559-025-02913-2

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