Spatiotemporal analysis of AGB and BGB in China: Responses to climate change under SSP scenarios

Abstract

Aboveground biomass (AGB) and belowground biomass (BGB) are key components of carbon storage, yet their responses to future climate changes remain poorly understood, particularly in China. Understanding these dynamics is essential for global carbon cycle modeling and ecosystem management. This study integrates field observations, machine learning, and multi-source remote sensing data to reconstruct the distributions of AGB and BGB in China from 2000 to 2020. Then CMIP6 was used to predict the distribution of China under three SSP scenarios (SSP1-1.9, SSP2-4.5, SSP5-8.5) from 2020 to 2100 to fill the existing knowledge gap. The predictive accuracy for AGB (R² = 0.85) was significantly higher than for BGB (R² = 0.48), likely due to the greater complexity of modeling belowground dynamics. NDVI (Normalized Difference Vegetation Index) and soil organic carbon density (SOC) were identified as the primary drivers of AGB and BGB changes. During 2000-2020, AGB in China remained stable at approximately 10.69 Pg C, while BGB was around 5.06 Pg C. Forest ecosystems contributed 88.52% of AGB and 43.83% of BGB. AGB showed a relatively slow annual increase, while BGB demonstrated a significant annual growth rate of approximately 37 Tg C yr^-1. Under the low-emission scenario, both AGB and BGB show fluctuations and steady growth, particularly in South China and the northwestern part of Northeast China. Under the moderate-emission scenario, AGB and BGB show significant declines and increases, respectively. In the high-emission scenario, both AGB and BGB decline significantly, particularly in the southwestern and central regions. These results provide valuable insights into ecosystem carbon dynamics under climate change, emphasizing the relatively low responsiveness of AGB and BGB to climatic variability, and offering guidance for sustainable land use and management strategies.

Keywords

Aboveground biomass / Belowground biomass / Ecosystem / Shared socioeconomic pathways / Climate change

CRediT authorship contribution statement

Chuanmei Zhu: Writing - original draft, Supervision, Method-ology, Conceptualization. Yupu Li: Visualization, Validation, Soft-ware. Jianli Ding: Funding acquisition. Jiexin Rao: Visualization, Validation, Software. Yihang Xiang: Visualization, Validation, Soft-ware. Xiangyu Ge: Software, Methodology. Jinjie Wang: Conceptu-alization. Jingzhe Wang: Resources, Methodology. Xiangyue Chen: Supervision, Resources. Zipeng Zhang: Supervision, Formal analysis, Conceptualization.

Declaration of competing interest

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

Acknowledgments

This study was supported by the Tianchi Talent-Young Doctor Program of the Xinjiang Uygur Autonomous Region, 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), the National Innovation Training Program for College Students in 2024 (Grant No. 202410755009), the National Natural Science Foundation of China (Grant No. 42401065), the Basic and Applied Basic Research Program of Guangdong Province, China (Grant No. 2023A1515011273), and the Research Projects of the Department of Education of Guangdong Province (Grant No. 2023KTSCX315).

Declaration of Generative AI and AI assisted technologies in the writing process

The authors affirm that no generative artificial intelligence (AI) or AI-assisted technologies were used in the writing process.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102038.

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