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

Daorui Han, Zhongmin Hu, Xuhui Wang, Tao Wang, Anping Chen, Qihao Weng, Minqi Liang, Xiang Zeng, Ruochen Cao, Kai Di, Dengnan Luo, Guangru Zhang, Yuanhe Yang, Honglin He, Jiangwen Fan, Guirui Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

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 32.0119.6947.9 Pg ( 11.727.217.53 kg m-2 on average), accounting for approximately 37.222.955.6 % of China's SOC stock on its <30% land area. There was 15.529.9123.52 Pg C stored in grassland soils (1 m), which played as the largest C pool on the TP, followed by shrubland ( 7.524.811.6 Pg) and forest ( 3.722.55.36 Pg). The estimated plant C pool was 2.40.955.16 Pg ( 1.030.22.7 Pg in aboveground biomass (AGB) and 1.370.752.45 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.

Original languageEnglish
Article number074016
JournalEnvironmental Research Letters
Volume17
Issue number7
DOIs
Publication statusPublished - 1 Jul 2022

Keywords

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

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Public Health, Environmental and Occupational Health

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