Zhou et al. (2026) Spatially divergent responses of global vegetation productivity to wet events

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Identification

• Journal: Ecological Processes
• Year: 2026
• Date: 2026-09-29
• Authors: Xuewen Zhou, Qinchuan Xin, Yuhang Tian, Hanliang Gui, Zhicheng Zhang, Ying Sun, Yongjiu Dai
• DOI: 10.1186/s13717-026-00751-z

Research Groups

Not specified in the provided text.

Short Summary

This study integrates satellite observations and climate model projections to assess global terrestrial Gross Primary Productivity (GPP) responses to wet events, revealing pronounced spatial and biome-specific divergences in sensitivity, resistance, and resilience that are projected to amplify under future high-emission scenarios, posing a growing threat to the global carbon cycle.

Objective

• To assess the global responses of terrestrial Gross Primary Productivity (GPP) to wet events across biomes, using three complementary metrics: immediate reaction magnitude (sensitivity), functional maintenance during disturbance (resistance), and post-event recovery rate (resilience), and to project these responses under future high-emission scenarios.

Study Configuration

• Spatial Scale: Global, with analysis across various biomes including Northern Hemisphere mid- to high-latitudes, tropical regions, Southern Hemisphere, high-biomass biomes, boreal forests, tundra, semi-arid grasslands, sparse vegetation, the Americas, and temperate Asia.
• Temporal Scale: Current responses to wet events and future projections under high-emission climate scenarios.

Methodology and Data

• Models used: Climate model projections (specific models not named).
• Data sources: Satellite observations, climate model projections.

Main Results

• Responses of terrestrial GPP to wet events exhibit pronounced spatial and biome-specific divergence across sensitivity, resistance, and resilience metrics.
• Northern Hemisphere mid- to high-latitudes show negative sensitivity, low resistance, and weak resilience to wet events.
• Tropical and certain Southern Hemisphere regions display positive sensitivity and stronger functional stability.
• With increasing wet event intensity, both resistance and resilience decline more sharply than sensitivity.
• High-biomass biomes generally maintain high stability; however, boreal forests and tundra exhibit high sensitivity, low resistance, and weak resilience. Semi-arid grasslands and sparse vegetation show positive sensitivity, but their stability declines under intense wet conditions.
• Under future high-emission scenarios, these divergent responses are projected to amplify.
• Sensitivity is projected to increase in northern latitudes but decrease in tropical and Southern Hemisphere countries.
• Resistance generally improves globally, with the notable exception of tropical rainforests, while resilience is projected to decline across the Americas and temperate Asia.

Contributions

• Provides a novel global quantification of the complex disturbances triggered by wet events on terrestrial GPP, addressing a previously poorly quantified area.
• Introduces and applies a comprehensive framework using three complementary metrics (sensitivity, resistance, and resilience) to characterize ecosystem responses to wet events.
• Reveals critical spatial and biome-specific divergences in GPP responses to wet events, including their projected changes under future high-emission scenarios.
• Highlights that wet events can significantly erode ecosystem stability, thereby posing a growing and underappreciated threat to the global carbon cycle.
• Underscores the urgent necessity of integrating wet event risks into climate adaptation and ecosystem management strategies.

Funding

Not specified in the provided text.

Citation

@article{Zhou2026Spatially,
  author = {Zhou, Xuewen and Xin, Qinchuan and Tian, Yuhang and Gui, Hanliang and Zhang, Zhicheng and Sun, Ying and Dai, Yongjiu},
  title = {Spatially divergent responses of global vegetation productivity to wet events},
  journal = {Ecological Processes},
  year = {2026},
  doi = {10.1186/s13717-026-00751-z},
  url = {https://doi.org/10.1186/s13717-026-00751-z}
}