The impact of extreme climate on soil organic carbon in China

Abstract

Quantitative studies on the national-scale effects of extreme climatic events on soil organic carbon (SOC) remain scarce, thus limiting our understanding of SOC dynamics. This study utilized 4515 publicly available soil samples to quantify the impacts of 19 extreme climatic indices (ECIs) on ΔSOC reservoirs in China through a hybrid space-for-time and meta-analysis approach. Overall, 16/19 ECIs were negatively correlated with ΔSOC, with the minimum temperature of the coldest night (TNn) showing the strongest negative correlation. Notably, topographic factors played a pivotal role in the modeling process, contributing an average of 25 %, followed by ECIs. Under the influence of the ECIs, SOC exhibited spatial variation. Extreme heat resulted in the greatest SOC losses in cold regions, such as North China, with average reductions of > 5 %, whereas its impact was weaker in South China, with SOC losses of ∼3 %. Extreme cold and wet indices promoted SOC accumulation in the Northeast China, with increases of ∼3 %, but showed a weaker response in the humid region, where the SOC increased by only 1 %. At the national scale, the impacts of extreme climatic events on SOC in the 0–20 cm ranged from −2.36 Pg to 2.34 Pg. Different ecosystems responded variably, with forest and grassland ecosystems being more sensitive to ECIs, potentially due to higher organic matter inputs and greater ecosystem complexity. In contrast, bare land exhibited weaker responses due to limited vegetation cover and organic inputs. These findings provide valuable insights into SOC dynamics at national scale during extreme climatic events.

Keywords

Soil organic carbon / Space for time substitution / Meta analysis / Digital soil mapping / Extreme climate

Data availability

The data supporting the findings of this study will be made available upon reasonable request. Researchers interested in accessing the data may contact the corresponding author for further details.

CRediT authorship contribution statement

Zipeng Zhang: Software, Visualization, Conceptualization, Writing – original draft, Formal analysis, Validation. Jianli Ding: Conceptualization, Funding acquisition. Liangyi Li: Supervision, Visualization, Resources, Writing – original draft, Software, Validation. Jinhua Cao: Validation, Formal analysis, Writing – original draft, Software, Supervision, Visualization, Resources. Keqiang Wang: Investigation, Formal analysis, Methodology. Chuanmei Zhu: Methodology, Visualization. Xiangyu Ge: Resources, Validation. Jinjie Wang: Writing – original draft. Chaolei Yang: Supervision, Conceptualization. Fujie Li: Resources. Jingzhe Wang: Visualization, Writing – original draft, Funding acquisition.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the Open Project of Technology Innovation Center for Natural Ecosystem Carbon Sink (Grant No. CS2023D02), the Open Research Fund of Key Laboratory of Digital Earth Science, Aerospace Information Research Institute Chinese Academy of Sciences, Chinese Academy of Sciences (Grant No. 2022LDE007), the Talent Program “Tianchi Talent (Young Doctor)” in Xinjiang Uygur Autonomous Region, National Natural Science Foundation of China (Grant No. 42401065), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515011273), and Shenzhen Polytechnic University Research Fund (Grant No. 6025310064K), the Innovation Training Program for Undergraduates at the Autonomous Region Level in 2024 (Grant No. S202410755009), the Innovation Training Program for Undergraduates at the University Level in 2024 (Grant No. XJU-SRT-24008), and the National Innovation Training Program for College Students in 2024 (Grant No. 202410755009).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100356.

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