Spatially divergent responses of global vegetation productivity to wet events
Wet events are widely regarded as beneficial water inputs, yet as climate change increase their frequency and severity, the potential complex disturbances they may trigger in terrestrial gross primary productivity (GPP) remain poorly quantified. Here, we integrate satellite observations and climate model projections to assess the global responses of terrestrial GPP to wet events across biomes through three complementary metrics: immediate reaction magnitude (sensitivity), functional maintenance during disturbance (resistance), and post-event recovery rate (resilience). Our results reveal pronounced spatial and biome-specific divergence in these responses. Northern Hemisphere mid- to high- latitudes show negative sensitivity, low resistance and weak resilience, whereas tropical and certain Southern Hemisphere regions display positive sensitivity and stronger functional stability. With increasing wet event intensity, resistance and resilience decline more sharply than sensitivity. High-biomass biomes maintain high stability, boreal forests and tundra exhibit high sensitivity, low resistance and weak resilience, semi-arid grasslands and sparse vegetation display positive sensitivity, yet their stability declines under intense wet conditions. Under future high-emission scenarios, these divergent responses are projected to amplify. Sensitivity increases in northern latitudes but decreases in tropical and Southern Hemisphere countries. Resistance generally improves (except in tropical rainforests), while resilience declines across the Americas and temperate Asia. These findings indicate that wet events can erode ecosystem stability, thereby posing a growing threat to the global carbon cycle. Our results underscore the necessity of incorporating wet event risks into climate adaptation and ecosystem management strategies.
Authors
- Qinchuan Xin (ORCID: https://orcid.org/0000-0003-1146-4874)
- Yuhang Tian (ORCID: https://orcid.org/0000-0003-4900-9965)
- Zhicheng Zhang (ORCID: https://orcid.org/0000-0003-3447-141X)
- Hanliang Gui (ORCID: https://orcid.org/0009-0005-3844-8411)
- Yongjiu Dai
- Ying Sun
- Xuewen Zhou
Institutions
- Sun Yat-sen University (CN)
- Shenzhen Polytechnic University (CN)
- Wuhan University (CN)
Publication Details
- Journal
- Ecological Processes
- Published
- 2026-09-29
- DOI
- https://doi.org/10.1186/s13717-026-00751-z
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00