Shift in controlling factors of carbon stocks across biomes on the Qinghai-Tibetan Plateau

Abstract

The Qinghai-Tibetan Plateau (TP) accumulated a large amount of organic carbon, while its size and response to environmental factors for the whole area remain uncertain. Here, we synthesized a dataset to date with the largest data volume and broadest geographic coverage over the TP, composing of 7196 observations from multiple field campaigns since the 1980s, and provided a comprehensive assessment of the size and spatial distribution of carbon pools for both plant and soils on the TP using machine learning algorithms. The estimated soil organic carbon (SOC) storage to 1 m depth was ${\text{32}}{\text{.01}}_{{\text{19}}{\text{.69}}}^{{\text{47}}{\text{.9}}}$ Pg ( ${\text{11}}{\text{.72}}_{{\text{7}}{\text{.2}}}^{{\text{17}}{\text{.53}}}$ kg m ^−2 on average), accounting for approximately ${\text{37}}{\text{.2}}_{{\text{22}}{\text{.9}}}^{{\text{55}}{\text{.6}}}$ % of China’s SOC stock on its <30% land area. There was ${\text{15}}{\text{.52}}_{{\text{9}}{\text{.91}}}^{{\text{23}}{\text{.52}}}{ }$ Pg C stored in grassland soils (1 m), which played as the largest C pool on the TP, followed by shrubland ( ${\text{7}}{\text{.52}}_{{\text{4}}{\text{.8}}}^{11.6}$ Pg) and forest ( ${\text{3}}{\text{.72}}_{{\text{2}}{\text{.5}}}^{{\text{5}}{\text{.36}}}$ Pg). The estimated plant C pool was ${\text{2}}{\text{.4}}_{{\text{0}}{\text{.95}}}^{{\text{5}}{\text{.16}}}$ Pg ( ${\text{1}}{\text{.03}}_{{\text{0}}{\text{.2}}}^{{\text{2}}{\text{.7}}}$ Pg in aboveground biomass (AGB) and ${\text{1}}{\text{.37}}_{{\text{0}}{\text{.75}}}^{{\text{2}}{\text{.45}}}$ Pg in belowground biomass). Soil and biomass C density presented a similar spatial pattern, which generally decreased from the east and southeast parts to the central and western parts. We found both vegetation and soil C (1 m depth) were primarily regulated by climatic variables and C input across the entire TP. However, main driving factors of the C stocks varied among vegetation types and depth intervals. Though AGB played as an important role in SOC variation for both topsoil (0–30 cm) and subsoil (30–100 cm), the strength of the correlation weakened with depth and was gradually attenuated from grassland to shrubland, and forest. The outcomes of this study provided an updated geospatial estimate of SOC stocks for the entire TP and their relationships with environmental factors, which are essential to carbon model benchmarking and better understanding the feedbacks of C stocks to global change.

Keywords

• Qinghai-Tibetan Plateau
• SOC mapping
• machine learning algorithms
• driving factors
• uncertainties