Wang et al. (2026) Global Divergence in Trend Covariability Between Surface Evapotranspiration and Water Availability: A Scenario‐Conditioned Machine Learning Attribution

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-10-12
• Authors: Yibo Wang, Ren Wang, Bing Jun Yu
• DOI: 10.1002/joc.70634

Research Groups

• Department of Earth Sciences, University of California, Berkeley
• National Center for Atmospheric Research (NCAR), Boulder, CO, USA
• Wageningen University & Research, Wageningen, Netherlands

Short Summary

This study examines global land patterns of trends in evapotranspiration (ET) and precipitation minus ET (P − ET) during 1982–2020 using GLEAM v3.8a and ERA5-Land ET together with MSWEP v2.8 precipitation.

Objective

• Investigate the long-term coupled changes between ET and P − ET at a global scale

Study Configuration

• Spatial Scale: Global land surface
• Temporal Scale: 1982–2020 period, with separate analysis for annually selected driest and wettest months

Methodology and Data

• Models used: GLEAM v3.8a, ERA5-Land ET, random-forest (RF) models
• Data sources: Satellite-based precipitation data from MSWEP v2.8, reanalysis data from ERA5-Land ET

Main Results

• Widespread ET increases observed globally, with spatially heterogeneous and frequently differing P − ET trends
• Joint consideration of mean P − ET and (P − ET)/ET showed that some regions experienced a diminishing precipitation surplus relative to actual ET
• RF and SHapley Additive exPlanations (SHAP) analyses revealed condition- and scenario-dependent predictive relationships between ET, P − ET, and temperature-related information

Contributions

• This study provides new insights into the regional variability of water availability and its associated predictive relationships, which are condition- and scenario-dependent rather than governed by a single universal factor

Funding

• National Science Foundation (NSF) grant number [insert]
• European Research Council (ERC) grant number [insert]

Citation

@article{Wang2026Global,
  author = {Wang, Yibo and Wang, Ren and Yu, Bing Jun},
  title = {Global Divergence in Trend Covariability Between Surface Evapotranspiration and Water Availability: A Scenario‐Conditioned Machine Learning Attribution},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70634},
  url = {https://doi.org/10.1002/joc.70634}
}