Spatial distribution, drivers, and future variation of soil organic carbon in China’s ecosystems: A meta-analysis and machine-learning assessment

• A framework combining meta-analysis and machine learning was proposed to assess spatiotemporal dynamics of soil organic carbon density (SOCD). • Grassland SOCD was higher in the 0–20 cm layer, while forest SOCD exceeded grasslands in the 0–100 cm layer. • Climate was the primary driver of SOCD in both surface and deeper layers across all four ecosystems. • Human activities had a negative impact on the surface SOCD of both forests and grasslands. • All three future SSP emission scenarios are projected to reduce SOCD, with wetlands facing the most severe losses, especially under SSP5-8.5. The characteristics of soil carbon pools across various ecosystems remain uncertain under different Shared Socioeconomic Pathways (SSPs) in China. Here, we conducted a meta -analysis of existing data and integrated machine-learning models to project spatiotemporal changes in soil organic carbon density (SOCD) by 2050 and 2100 under three Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios. For China’s terrestrial ecosystems, national average SOCD was 4.08 kg C m 2 in the 0–20 cm soil layer and 9.42 kg C m 2 in the 0–100 cm layer, with corresponding carbon stocks of 39.18 Pg C and 90.46 Pg C, respectively. Wetlands exhibited the highest SOCD but contributed minimally to total carbon stock due to their limited area, while forests and grasslands served as the dominant carbon reservoirs, particularly in deeper soils. Spatially, SOCD was highest in northeastern China and the eastern Qinghai–Tibet Plateau, and lowest in northwestern arid regions. Climate was the most critical determinant of SOCD in both soil depth, although its explanatory power was relatively weaker for deep SOCD, where soil factors gained prominence. Human activities significantly reduced surface SOCD in forests and grasslands. Future climate change would exacerbate the decline of surface SOCD, particularly in wetlands, posing substantial challenges to achieving the 4 per mil initiative goals.

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Publication Details

Journal
Ecological Indicators
Published
2025-10-01
DOI
https://doi.org/10.1016/j.ecolind.2025.114255
Citations
2
Primary Topic
Soil Carbon and Nitrogen Dynamics
Type
article
Field-Weighted Citation Impact
2.37
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article

Spatial distribution, drivers, and future variation of soil organic carbon in China’s ecosystems: A meta-analysis and machine-learning assessment

Boyi Song, Xiaobing Zhou, Shihang Zhang, Mingming Wang et al.
2 citations
Ecological Indicators
Soil Carbon and Nitrogen Dynamics
2.37
article

Spatial distribution, drivers, and future variation of soil organic carbon in China’s ecosystems: A meta-analysis and machine-learning assessment

Boyi Song, Xiaobing Zhou, Shihang Zhang, Mingming Wang, Lei Zhang, Yuanming Zhang, Hao Guo, Xing Guo, Yongxing Lu
article en
2 citations

Abstract

• A framework combining meta-analysis and machine learning was proposed to assess spatiotemporal dynamics of soil organic carbon density (SOCD). • Grassland SOCD was higher in the 0–20 cm layer, while forest SOCD exceeded grasslands in the 0–100 cm layer. • Climate was the primary driver of SOCD in both surface and deeper layers across all four ecosystems. • Human activities had a negative impact on the surface SOCD of both forests and grasslands. • All three future SSP emission scenarios are projected to reduce SOCD, with wetlands facing the most severe losses, especially under SSP5-8.5. The characteristics of soil carbon pools across various ecosystems remain uncertain under different Shared Socioeconomic Pathways (SSPs) in China. Here, we conducted a meta -analysis of existing data and integrated machine-learning models to project spatiotemporal changes in soil organic carbon density (SOCD) by 2050 and 2100 under three Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios. For China’s terrestrial ecosystems, national average SOCD was 4.08 kg C m 2 in the 0–20 cm soil layer and 9.42 kg C m 2 in the 0–100 cm layer, with corresponding carbon stocks of 39.18 Pg C and 90.46 Pg C, respectively. Wetlands exhibited the highest SOCD but contributed minimally to total carbon stock due to their limited area, while forests and grasslands served as the dominant carbon reservoirs, particularly in deeper soils. Spatially, SOCD was highest in northeastern China and the eastern Qinghai–Tibet Plateau, and lowest in northwestern arid regions. Climate was the most critical determinant of SOCD in both soil depth, although its explanatory power was relatively weaker for deep SOCD, where soil factors gained prominence. Human activities significantly reduced surface SOCD in forests and grasslands. Future climate change would exacerbate the decline of surface SOCD, particularly in wetlands, posing substantial challenges to achieving the 4 per mil initiative goals.

Ecological IndicatorsVol. 179
Chinese Academy of Sciences (CN), Xinjiang Institute of Ecology and Geography (CN), Institute of Mountain Hazards and Environment (CN), University of Chinese Academy of Sciences (CN), Xinjiang University (CN)
Openalex Percentile: Top 10%
Soil Carbon and Nitrogen Dynamics
2.37
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