Jiang et al. (2025) Scale-dependent drivers of water use efficiency across China: integrating stable isotopes, remote sensing, and machine learning

Identification

• Journal: CATENA
• Year: 2025
• Date: 2025-09-09
• Authors: Feng Jiang, Xiaoyi Shi, Fuxi Shi, Zhenyi Jia, Xin Song, Tao Pu, Yanlong Kong, Shijin Wang, Lizong Wu, Jia Jia, Zhenzhen Zhang, Jie Wang, Wenqing Han
• DOI: 10.1016/j.catena.2025.109403

Research Groups

• College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua, China
• Yulong Snow Mountain Glacier and Environment Observation and Research Station/State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
• Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang, China
• College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
• Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
• Polar Research Institute of China, Shanghai, China

Short Summary

This study investigated the scale-dependent spatial patterns and drivers of leaf-level intrinsic water use efficiency (iWUE) and ecosystem-scale water use efficiency (WUEEco) across China, revealing inverse spatial patterns and distinct controlling factors for each scale. It also generated a high-resolution national iWUE dataset using machine learning.

Objective

• To investigate the spatial patterns and scale-dependent drivers of leaf-level intrinsic water use efficiency (iWUE) and ecosystem-scale water use efficiency (WUEEco) across different life forms and climate zones in China.

Study Configuration

• Spatial Scale: National/Continental (across China)
• Temporal Scale: Snapshot based on 1,446 collected leaf isotope records

Methodology and Data

• Models used: Machine learning (Random Forest), Hierarchical partitioning, Structural Equation Modeling (SEM)
• Data sources: Stable isotopes (1,446 leaf δ13Cp records), Remote sensing

Main Results

• iWUE and WUEEco exhibited inverse spatial patterns across China. iWUE peaked in arid northwestern grasslands (60.46 μmol mol−1), while WUEEco maxima were found in humid southeastern forests (1.82 g C/kg H2O).
• Elevation indirectly influenced iWUE (17.72 %) and WUEEco (25.64 %) by modifying climatic conditions.
• Vegetation factors (e.g., leaf area index) and climatic factors (e.g., relative humidity) were identified as key direct drivers for iWUE (24.06 %) and WUEEco (15.31 %), primarily regulating photosynthesis–transpiration coupling.
• The Random Forest model demonstrated the best performance for iWUE prediction (R2 = 0.73, NRMSE = 0.122, MBE = − 0.078), enabling the creation of a high-resolution national iWUE dataset.

Contributions

• Provides a comprehensive understanding of the scale-dependent spatial patterns and drivers of iWUE and WUEEco across China, addressing existing systematic gaps.
• Highlights the critical importance of scale in analyzing terrestrial carbon–water interactions.
• Generates a valuable high-resolution national iWUE dataset.
• Offers a significant reference for water resource management strategies in the context of climate change.

Funding

• Not specified in the provided text.

Citation

@article{Jiang2025Scaledependent,
  author = {Jiang, Feng and Shi, Xiaoyi and Shi, Fuxi and Jia, Zhenyi and Song, Xin and Pu, Tao and Kong, Yanlong and Wang, Shijin and Wu, Lizong and Jia, Jia and Zhang, Zhenzhen and Wang, Jie and Han, Wenqing},
  title = {Scale-dependent drivers of water use efficiency across China: integrating stable isotopes, remote sensing, and machine learning},
  journal = {CATENA},
  year = {2025},
  doi = {10.1016/j.catena.2025.109403},
  url = {https://doi.org/10.1016/j.catena.2025.109403}
}