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Ecological and hydroclimatic determinants of vegetation water-use strategies

Nature Ecology & Evolution volume 9, pages 1791–1799 (2025)Cite this article

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Abstract

Vegetation responses to soil moisture limitation play a key role in land–atmosphere interactions and are a major source of uncertainty in future projections of the global water and carbon cycles. Vegetation water-use strategies—that is, how plants regulate transpiration rates as the soil dries—are highly dynamic across space and time, presenting a major challenge to inferring ecosystem responses to water limitation. Here we show that, when aggregated globally, water-use strategies derived from point-based soil moisture observations exhibit emergent patterns across and within climates and vegetation types along a spectrum of aggressive to conservative responses to water limitation. Water use becomes more conservative, declining more rapidly as the soil dries, as mean annual precipitation increases and as woody cover increases from grasslands to savannahs to forests. We embed this empirical synthesis within an ecohydrological framework to show that key ecological (leaf area) and hydroclimatic (aridity) factors driving demand for water explain up to 77% of the variance in water-use strategies within ecosystem types. All biomes respond to ecological and hydroclimatic demand by shifting towards more aggressive water-use strategies. However, woodlands reach a threshold beyond which water use becomes increasingly conservative, probably reflecting the greater hydraulic risk and cost of tissue damage associated with sustaining high transpiration rates under water limitation for trees than grasses. These findings highlight the importance of characterizing the dynamic nature of vegetation water-use strategies to improve predictions of ecosystem responses to climate change.

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Fig. 1: Emergent patterns of vegetation water use from soil moisture dynamics.
Fig. 2: Biogeographic and geographic distributions of ISMN stations.
Fig. 3: Median soil moisture loss functions by vegetation type and rainfall.
Fig. 4: Median q values as a function of antecedent aridity and LAI by vegetation type.
Fig. 5: Patterns of q by vegetation type.

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Data availability

All soil moisture data used in this study are available from the International Soil Moisture Network58,59 (https://ismn.earth/en/). The PET data are available from ref. 67, accessible at https://data.bris.ac.uk/data/dataset/qb8ujazzda0s2aykkv0oq0ctp. CHIRPS rainfall data are available from the Climate Hazards Center (https://www.chc.ucsb.edu/data/chirps). MODIS Landcover and LAI data were retrieved from NASA’s Application for Extracting and Exploring Analysis Ready Samples (AppEEARS; https://appeears.earthdatacloud.nasa.gov). GLDAS data are available at https://ldas.gsfc.nasa.gov/gldas/soils.

Code availability

All data processing and analyses were performed in Python 3.12. A custom Python package was developed to process the soil moisture drydowns and fit the nonlinear model. This package is available via GitHub at https://github.com/ecohydro/drydowns. The code for performing the analyses and creating the figures in this study is available via GitHub at https://github.com/ecohydro/ismn-drydowns.

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Acknowledgements

B.E.M. and K.K.C. acknowledge support from the Zegar Family Foundation (grant number SB220237). B.E.M. acknowledges support from the Horton Research Grant from the American Geophysical Union. A.T.T. acknowledges funding from the National Science Foundation (grant number 2003205), the Gordon and Betty Moore Foundation (grant number GBMF11974), the USDI National Park Service (award numbers P24AC00910 and P24AC01425) and the CALFIRE Forest Health Research Program (grant number 60164685).

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Author notes

  1. Bryn E. Morgan

    Present address: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Authors and Affiliations

  1. Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, USA

    Bryn E. Morgan, Ryoko Araki, Anna T. Trugman & Kelly K. Caylor

  2. Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA

    Bryn E. Morgan, Anna T. Trugman & Kelly K. Caylor

  3. Department of Geography, San Diego State University, San Diego, CA, USA

    Ryoko Araki

  4. Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA, USA

    Kelly K. Caylor

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Contributions

B.E.M. and K.K.C. designed the study. B.E.M. and R.A. wrote the code, and B.E.M. performed the analysis. B.E.M. wrote the paper with input from all co-authors. B.E.M., R.A., A.T.T. and K.K.C. provided input on the methodology and contributed to interpretation of the results.

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Correspondence to Bryn E. Morgan.

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Nature Ecology & Evolution thanks Maoya Bassiouni, Andrew F. Feldman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Observed values of the nonlinear parameter, q, by (a) vegetation type and (b) mean annual precipitation.

Violin plots show the distribution of q values in each category. Box plots show the medians and 25th to 75th percentile of q values; whiskers extend 1.5 times the inter-quartile range from each box. Outliers beyond this range are denoted by points. The dashed line shows where q = 1. Grasslands (n = 11, 252), savannas (n = 5, 909), woodlands (n = 9, 586), and bare soil (n = 3, 507) were significantly different from each other at p < 0.001 (two-sided Dunn’s test with Bonferroni correction for multiple comparisons). Letters in (b) denote statistically significant differences between median values (p < 0.05) where groups that do not contain the same letter are different. Exact p-values for (b) are given in Table S1. Regions with mean annual rainfall of 0-300 mm (n = 362) and 300-600 mm (n = 10, 333) had the lowest q values, followed by regions with 600-900 mm (n = 6, 070), 900-1200 mm (n = 2, 917), and 1200-1500 mm (n = 5, 091) of annual rainfall. Median q values were highest in regions with the highest annual rainfall (>1500 mm, n = 1, 395). For both plots, only events where LAI ≥0.5 are included, except for the “bare soil” category, which includes all events where LAI < 0.5 from any IGBP class except for urban and open water cover types.

Extended Data Fig. 2 Distributions of the median values of the nonlinear parameter, q, by (a) vegetation type and (b) mean annual precipitation.

Curves show the kernel density estimates of the distribution of median q values for each class, obtained by resampling from the respective distributions of q. Dashed lines show the median values of the original distributions.

Extended Data Fig. 3 Observed values of (a) the nonlinear parameter, q, and (b) critical soil moisture threshold, θ*, by soil sand fraction.

Violin plots show the distribution of q values in each category. Box plots show the medians (center line) and 25th to 75th percentile of q values; whiskers extend 1.5 times the inter-quartile range from each box. Outliers beyond this range are denoted by points. The dashed line in (a) shows where q = 1. Letters denote statistically significant differences between median values (two-sided Dunn’s test with Bonferroni correction, p < 0.05) where groups that do not contain the same letter are different. Exact p-values for (a) are given in Table S2. All groups in (b) were significantly different from each other at p < 0.0001. Median q values did not show a coherent trend with sand fraction; locations with 0-20% sand (n = 1, 222) had the highest q values, followed by those with the highest sand fraction (> 80%, n = 1, 577). Soils comprised of 40 − 60% sand (n = 11, 338) had q values very close to 1, while those with 20 − 40% sand (n = 11, 604) and 60 − 80% sand (n = 733) had lower q values. On the other hand, θ* showed a clear trend, decreasing with increasing sand fraction. For both plots, only events where LAI ≥ 0.5 are included.

Extended Data Table 1 Summary statistics and uncertainty estimates for q by vegetation type and rainfall
Full size table
Extended Data Table 2 Coefficients of the surfaces in Fig. 4
Full size table

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Morgan, B.E., Araki, R., Trugman, A.T. et al. Ecological and hydroclimatic determinants of vegetation water-use strategies. Nat Ecol Evol 9, 1791–1799 (2025). https://doi.org/10.1038/s41559-025-02810-8

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