Wang et al. (2026) Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

Identification

• Journal: Nature Communications
• Year: 2026
• Date: 2026-01-07
• Authors: Xiang Wang, Zheng Fu, Philippe Ciais, Lixin Wang, N. Buchmann, Trevor F. Keenan, Martin G. De Kauwe, Josep Penuelas, G Z Chen, Xiaoying Gong, Jingfeng Xiao, Xing Li, Qiaoyun Xie, Paul C. Stoy, David Makowski, William K. Smith, Han Wang, Songhan Wang, Fangyue Zhang, Shuli Niu
• DOI: 10.1038/s41467-025-68252-9

Research Groups

• Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
• Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
• Department of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, IN, USA
• Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
• Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
• Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley, CA, USA
• School of Biological Sciences, University of Bristol, Bristol, UK
• CSIC, Global Ecology Unit, CREAF-CSIC-UAB, Barcelona, Catalonia, Spain
• CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, Spain
• College of Ecology and Environment, Chengdu University of Technology, Chengdu, Sichuan, China
• Key Laboratory for Subtropical Mountain Ecology, College of Geographical Sciences, Fujian Normal University, Fuzhou, China
• Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
• School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
• School of Engineering, The University of Western Australia, Perth, WA, Australia
• Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
• Unit Applied Mathematics and Computer Science (UMR MIA-PS) INRAE AgroParisTech Université Paris-Saclay, Palaiseau, France
• School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA
• Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
• College of Agriculture, Nanjing Agricultural University, Nanjing, China
• College of Water Sciences, Beijing Normal University, Beijing, China

Short Summary

This study elucidated the global spatiotemporal dynamics and water-stress responses of leaf-level intrinsic water use efficiency (iWUE) in C3 plants using machine learning and carbon isotope observations, revealing a global iWUE increase of 0.2 ± 0.02 μmol mol⁻¹ year⁻¹ from 2001-2020, with atmospheric dryness (VPD) being a more dominant driver than soil moisture.

Objective

• To quantify the global spatiotemporal distribution and trends of leaf-level intrinsic water use efficiency (iWUE) in C3 plants, investigate its responses to atmospheric and soil dryness, and evaluate the performance of the P-model in simulating these dynamics.

Study Configuration

• Spatial Scale: Global, with a spatial resolution of 0.05°.
• Temporal Scale: 2001–2020, with annual resolution.

Methodology and Data

• Models used:
  • Random Forest (for constructing global Δ¹³C isoscapes)
  • Ma et al. (2021) and Yu et al. (2024) equations (for translating Δ¹³C to iWUE)
  • P-model (ecological optimality model for iWUE estimation and comparison)
  • Multiple linear regression (for sensitivity of iWUE to atmospheric CO₂ concentration)
  • Theil-Sen Median and Mann-Kendall (for trend analysis)
  • Partial correlation analysis (for attributing drivers of iWUE changes)
• Data sources:
  • Carbon isotope observations from 5964 globally-distributed C3 plant foliage samples.
  • MODIS surface reflectance (MOD09CMG) from Terra satellite.
  • Atmospheric CO₂ concentrations (Cₐ, μmol mol⁻¹) and carbon isotope ratios (δ¹³Cₐ, ‰) from Mauna Loa station (NOAA).
  • Solar-induced chlorophyll fluorescence (SIF) from GOSIF and CSIF datasets.
  • Geographic information (latitude, longitude, elevation (m)) from literature and GloElev_30as.
  • Climate data: Mean annual temperature (°C) and annual precipitation (m) from WorldClim; Water vapor pressure difference (VPD, Pa), soil moisture (m), and downward solar radiation flux (W m⁻²) from TerraClimate; Growing season temperatures and VPD from ERA5 (WFDE5).
  • Land cover: MODIS annual land cover data (MCD12C1), C4 vegetation percentage maps.
  • Aridity index (ratio of mean annual precipitation to mean annual potential evapotranspiration).

Main Results

• The global mean leaf-level iWUE for C3 plants from 2001 to 2020 was estimated at 55.4 ± 1.6 μmol mol⁻¹.
• Spatially, iWUE was higher in cold, arid regions (mean: 60.4 μmol mol⁻¹) and lower in warm, humid areas (mean: 52.5 μmol mol⁻¹).
• Grasslands exhibited the highest mean iWUE, while evergreen broadleaf forests showed the lowest.
• Global leaf-level iWUE significantly increased from 2001 to 2020 at a rate of 0.20 ± 0.02 μmol mol⁻¹ year⁻¹ (p < 0.001), with 78.9% of C3 vegetation regions showing a significant increase.
• Broadleaf forests (Evergreen: 0.30 ± 0.04 μmol mol⁻¹ year⁻¹, Deciduous: 0.29 ± 0.04 μmol mol⁻¹ year⁻¹) showed the fastest iWUE growth rates, while grasslands exhibited the slowest (0.15 ± 0.02 μmol mol⁻¹ year⁻¹).
• Leaf iWUE generally increased with rising water stress (higher VPD, lower soil moisture), but the rate of iWUE increase and its sensitivity to atmospheric CO₂ concentration (diWUE/dCₐ) were higher in humid regions compared to arid regions.
• Atmospheric dryness, primarily driven by vapor pressure deficit (VPD), played a dominant role in driving spatiotemporal changes in leaf iWUE across 65% of the global C3 vegetation area, compared to 35% for soil moisture (SM).
• The P-model, based on ecological optimality theory, successfully captured the global spatial distribution pattern of iWUE but overestimated global mean iWUE by approximately 23% and its increasing trend (0.49 ± 0.02 μmol mol⁻¹ year⁻¹ vs. observed 0.20 ± 0.02 μmol mol⁻¹ year⁻¹).

Contributions

• Provides a novel, spatially continuous, and observation-constrained framework for estimating global leaf-level iWUE across diverse biomes, overcoming limitations of previous sparse or forest-biased studies.
• Offers the first comprehensive global assessment of spatiotemporal dynamics and water-stress responses of leaf-level iWUE in C3 plants.
• Quantifies the differential influence of atmospheric dryness (VPD) versus soil dryness (SM) on global iWUE, identifying VPD as the dominant driver across most C3 vegetation.
• Establishes a benchmark for evaluating and improving Earth system models, highlighting specific biases in the P-model's estimation of iWUE patterns, trends, and responses to atmospheric dryness.
• Suggests that intensifying water stress under future climate warming may lead to a slower rate of global iWUE increase.

Funding

• National Key R&D Program of China (No. 2024YFF1309000)
• National Natural Science Foundation of China (Nos. 42471122, 32588202, 32371683)
• Chinese Academy of Sciences (No. 2024000275)
• NSFC Excellent Young Scientists Fund (Overseas)
• LEMONTREE (Land Ecosystem Models based On New Theory, Observation and Experiments) project (funded by Eric and Wendy Schmidt by recommendation of the Schmidt Futures program)
• NASA Carbon Cycle Science Award 80NSSC21K1705
• Spanish MCIN, AEI/10.13039/501100011033 European Union Next Generation EU/PRTR (grant TED2021-132627 B–I00)
• European project CONCERTO (HORIZON-CL5-2024-D1-01)
• SNF project CERES (IZCOZ0_220291)

Citation

@article{Wang2026Global,
  author = {Wang, Xiang and Fu, Zheng and Ciais, Philippe and Wang, Lixin and Buchmann, N. and Keenan, Trevor F. and Kauwe, Martin G. De and Penuelas, Josep and Chen, G Z and Gong, Xiaoying and Xiao, Jingfeng and Li, Xing and Xie, Qiaoyun and Stoy, Paul C. and Makowski, David and Smith, William K. and Wang, Han and Wang, Songhan and Zhang, Fangyue and Niu, Shuli},
  title = {Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress},
  journal = {Nature Communications},
  year = {2026},
  doi = {10.1038/s41467-025-68252-9},
  url = {https://doi.org/10.1038/s41467-025-68252-9}
}