Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewed

Intensification of Global Hydrological Droughts Under Anthropogenic Climate Warming

Lei Gu, Jiabo Yin, Louise Slater, Jie Chen, Hong Xuan, Huimin Wang, Chen Lu, Zhiqiang Jiang, Tongtiegang Zhao

Water Resources Research · 2022

Read source ↗ All evidence

Summary

This global modelling study integrated bias-corrected climate experiments, multiple hydrological models, and machine learning to assess how anthropogenic climate warming will affect hydrological drought frequency and characteristics across 6,688 catchments in five Köppen–Geiger climate zones. The authors found that whilst overall drought frequency may remain stable, extreme hydrological droughts are projected to intensify significantly, with 30-year return period events potentially doubling in frequency across 60% of global catchments by 2071–2100. Temperature is identified as an emerging primary driver of droughts in high-latitude regions, displacing precipitation as the dominant factor.

Regional applicability

This global-scale analysis provides projections relevant to United Kingdom catchments, particularly regarding temperature-driven drought intensification in temperate zones. However, site-specific vulnerability will depend on local hydrological characteristics and adaptation measures; the findings suggest that UK temperate catchments may experience greater climate model uncertainty than some other regions.

Key measures

Frequency of hydrological droughts; joint return period (JRP) of extreme events; precipitation and temperature stress as drought drivers; uncertainty quantification from climate models and hydrological models

Outcomes reported

The study examined the frequency and characteristics of hydrological droughts across 6,688 global catchments under climate warming scenarios. It assessed how extreme droughts (30-year joint return period events) are projected to change and identified the primary drivers of drought development historically and under future warming.

Theme
Climate & resilience
Subject
Climate & greenhouse gas mitigation
Study type
Research
Study design
Modelling study with machine learning framework
Source type
Peer-reviewed study
Status
Published
Geography
Global
System type
Other
DOI
10.1029/2022wr032997
Catalogue ID
SNmqopeucv-bss1xc

Topic tags

Pulse AI · ask about this record

Dig deeper with Pulse AI.

Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.