Guo et al. (2026) Global hydrological drought diverges from atmospheric drying

Identification

• Journal: Nature Water
• Year: 2026
• Date: 2026-09-28
• Authors: Yuhan Guo, Jinghua Xiong, Yuting Yang, SHANBAI LIANG, Li Guo, Dawen Yang
• DOI: 10.1038/s44221-026-00724-8

Research Groups

• Department of Hydraulic Engineering, Tsinghua University, Beijing, China
• State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China
• State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, China

Short Summary

This study provides a global observational assessment of hydrological drought evolution in approximately 19,000 catchments, comparing it with atmospheric drought trends over the past four decades. It reveals that while atmospheric drying is widespread, hydrological drought trends are highly heterogeneous and do not uniformly or proportionally transmit atmospheric drying signals into river flow.

Objective

• To quantify how strongly long-term atmospheric drying, driven by increasing evaporative demand with climate warming, propagates into realized river-flow deficits globally.

Study Configuration

• Spatial Scale: Approximately 19,000 catchments worldwide.
• Temporal Scale: Four decades, specifically from 1981 to 2019.

Methodology and Data

• Models used: Standardized Streamflow Index (SSI) for hydrological drought, Standardized Precipitation Evapotranspiration Index (SPEI) for atmospheric drought. Computations used Python packages lmoments3 and scipy.stats for distribution fitting, with parallel processing via Dask.
• Data sources:
  • Streamflow observations compiled from 29 global and regional hydrometric networks and databases.
  • Historical gridded climate datasets: CHIRPS v3 (precipitation), MSWEP v2 (precipitation), GLEAM4 (evaporation), and hPET (potential evapotranspiration).
  • ISIMIP3a simulation outputs.
  • Metadata and source data are available via Zenodo.

Main Results

• Atmospheric drought trends showed a broad spatial predominance towards drying globally over the past four decades.
• In contrast, streamflow drought trends were substantially more heterogeneous and exhibited highly spatially variable changes at the global scale.
• This divergence is attributed to nonlinear drought propagation, which can attenuate, delay, reshape, or amplify atmospheric drought signals before they manifest in streamflow.
• Long-term atmospheric drying is not transmitted uniformly or proportionally into river flow; it can be weakened in many catchments and reinforced in others.
• Increases in atmospheric drought frequency and magnitude were dominated by weak or directionally decoupled hydrological responses in 62% and 63% of catchments, respectively.
• Conversely, decreases in atmospheric drought characteristics led to amplified hydrological responses in 55% of catchments.

Contributions

• Provides the first global observational assessment of hydrological drought evolution and its comparison with atmospheric drought using a large dataset of approximately 19,000 catchments.
• Offers global observational evidence demonstrating that long-term atmospheric drying does not uniformly or proportionally transmit into river flow, highlighting the complex and heterogeneous nature of drought propagation.
• Emphasizes the critical need to explicitly account for hydrological propagation mechanisms when assessing water-security risks under ongoing climate warming.

Funding

• Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (grant number JYB2025XDXM910)
• National Natural Science Foundation of China (grant number 42471018)
• Ministry of Science and Technology of China (2023YFC3206603)
• Qinghai Department of Science and Technology (grant number 2024-SF-A6)

Citation

@article{Guo2026Global,
  author = {Guo, Yuhan and Xiong, Jinghua and Yang, Yuting and LIANG, SHANBAI and Guo, Li and Yang, Dawen},
  title = {Global hydrological drought diverges from atmospheric drying},
  journal = {Nature Water},
  year = {2026},
  doi = {10.1038/s44221-026-00724-8},
  url = {https://doi.org/10.1038/s44221-026-00724-8}
}