Soil organic carbon (SOC) in the active layer (0–2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors—including parent material, climate, vegetation, topography, soil, and human activities—inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0–1 m and 1–2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0–1 m was significantly higher (p < 0.05) than at 1–2 m in alpine meadows, alpine wet meadows, and swamp meadows. Conversely, SOC stock in alpine deserts, steppe meadows, and barren land did not differ significantly between the two depths (p > 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m⁻² year⁻¹), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73–0.84, LAI range of 11.06–15.94, and MI range of 0.46–0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.

Permafrost carbon controlled by vegetation and climate in the Tibetan Plateau

期刊论文
发表日期：

2025年5月26日

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Xin Zhang,
Shihang Zhang,
Hongjin Chen,
Jian-rong FAN

摘要

Soil organic carbon (SOC) in the active layer (0–2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors—including parent material, climate, vegetation, topography, soil, and human activities—inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0–1 m and 1–2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0–1 m was significantly higher (p < 0.05) than at 1–2 m in alpine meadows, alpine wet meadows, and swamp meadows. Conversely, SOC stock in alpine deserts, steppe meadows, and barren land did not differ significantly between the two depths (p > 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m⁻² year⁻¹), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73–0.84, LAI range of 11.06–15.94, and MI range of 0.46–0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.