1.长江大学资源与环境学院,武汉 430100
2.长江水利委员会长江科学院水土保持研究所,武汉 430010
吴英涛(2000—),男,硕士研究生,主要从事土壤侵蚀与碳循环研究。E-mail: 2448071702@qq.com
黄金权(1983—),男,正高级工程师,博士生导师,主要从事土壤侵蚀与碳循环研究。E-mail: jinquan_cky@163.com
收稿:2025-09-16,
修回:2025-10-20,
录用:2025-10-28,
网络首发:2025-12-17,
纸质出版:2026-04-01
移动端阅览
吴英涛,黄金权.水力侵蚀驱动下土壤有机碳动态模型研究进展[J].水土保持学报,2026,40(2):1-11.
WU Yingtao, HUANG Jinquan. Research progress on dynamic models of soil organic carbon driven by water erosion[J].Journal of Soil and Water Conservation,2026,40(2):1-11.
吴英涛,黄金权.水力侵蚀驱动下土壤有机碳动态模型研究进展[J].水土保持学报,2026,40(2):1-11. DOI: 10.13870/j.cnki.stbcxb.2026.02.024. CSTR: 32310.14.stbcxb.2026.02.024.
WU Yingtao, HUANG Jinquan. Research progress on dynamic models of soil organic carbon driven by water erosion[J].Journal of Soil and Water Conservation,2026,40(2):1-11. DOI: 10.13870/j.cnki.stbcxb.2026.02.024. CSTR: 32310.14.stbcxb.2026.02.024.
目的
2
土壤有机碳库是陆地生态系统中最大的有机碳库,是影响全球碳循环变化的关键角色。水力侵蚀驱动下土壤有机碳动态对土壤有机碳库的影响不可忽略,文章阐述了此过程中土壤有机碳动态机制,探讨了不同尺度水力侵蚀下土壤有机碳输入与输出。
方法
2
系统综述水力侵蚀作用下SOC动态的研究进展,重点从侵蚀-搬运-沉积过程中的碳源/碳汇效应机理、土壤侵蚀碳动态模型的发展路径及方法学突破展开分析。
结果
2
将水力侵蚀驱动下土壤有机碳动态模型分为机理模型和数据驱动型计算模型进行分析,其中机理模型以理论框架完整、结构分析严谨及过程机理清晰成为碳模型的主流;数据驱动型计算模型则依据融合多源数据与机器学习算法实现水力侵蚀下土壤有机碳高精度模拟与预测。
结论
2
综述两大类型碳模型的主要特征、优缺点及应用现状,探讨两大类土壤有机碳动态模型存在的问题,建议将机理模型与数据驱动型计算模型相结合形成更加系统全面的土壤有机碳动态模型。未来应加强多源数据融合与高分遥感应用,发展机理与人工智能相结合的混合模型,并深化极端气候和人类活动驱动下的SOC动态响应研究,为全球碳收支评估和“双碳”目标实现提供科学支撑。
Objective
2
Soil organic carbon (SOC) is the largest carbon pool and plays a key role in influencing the global carbon cycle. The effect of SOC dynamics driven by soil water erosion on this pool cannot be ignored. This paper expounds the mechanisms of SOC dynamics during water erosion and explores the SOC input and output under different scales of water erosion.
Methods
2
This paper systematically reviews the research progress on SOC dynamics under water erosion, focusing on the mechanisms of carbon source/carbon sink effects during the erosion-transport-deposition process, as well as the development pathways and methodological breakthroughs of the SOC dynamic model.
Results
2
SOC dynamic models driven by water erosion can be divided into the mechanistic models and data-driven computational models. Mechanistic models are the mainstream due to their complete theoretical framework, rigorous structural analysis, and clear process mechanism. Data-driven computational models achieve high-precision simulation and prediction of SOC dynamics under erosion based on the fusion of multi-source data and machine learning algorithms.
Conclusion
2
This paper reviews the main characteristics, advantages, disadvantages, and application status of the two types of carbon models. It also discusses the problems faced by each of them. It is suggested to integrate mechanistic and data-driven computational models to establish a more systematic and comprehensive SOC dynamic model. Future research should enhance the applications of multi-source data fusion and high-resolution remote sensing, develop hybrid models that combine mechanisms with artificial intelligence, and deepen the research on SOC dynamic responses to extreme climate and human activities. These efforts will provide scientific support for global carbon budget assessment and the achievement of the dual carbon goals.
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