华东师范大学学报（自然科学版）Issue(3)：17-29,43,14.DOI:10.3969/j.issn.1000-5641.2026.03.002
基于机器学习的长江溶解态有机碳输送的长期变化及驱动因素分析
Long-term variations and driving factors of dissolved organic carbon transport in the Yangtze River using machine learning
摘要
Abstract
Analyzing variations in riverine dissolved organic carbon(DOC)concentration and flux is essential for understanding global carbon cycle processes and refining carbon budget estimations.Machine learning and big data analysis have become invaluable in this field.However,research on Chinese rivers is limited due to lack of long-term continuous observational data and non-uniform temporal distribution of key influencing factors.Consequently,mechanisms driving seasonal and long-term variations in riverine DOC and their influencing factors remain unclear.This study compared various machine learning methods using long-term,monthly DOC concentration data from the Yangtze River's Xuliujing Station,as well as watershed characteristic data.Using the optimal model,we simulated monthly DOC concentration changes at Xuliujing Station from 2001 to 2020 and employed the SHAP(SHapley Additive exPlanations)method to analyze the impact of watershed characteristics on DOC concentration and flux.The findings demonstrated that the Random Forest algorithm yielded the highest accuracy,achieving an R2 of 0.72 and an RMSE of 0.09 mg·L-1.Over the study period,DOC concentrations ranged from 1.24 to 2.27 mg·L-1,with a mean of 1.67 mg·L-1.Annual DOC flux varied between 0.93 and 2.41 Tg·a-1,averaging 1.46 Tg·a-1.Notably,the seasonal pattern of DOC concentration shifted from low during the flood season and high during the dry season to high levels in both seasons.This shift was primarily due to anthropogenic water regulation activities and changes in watershed ecosystem patterns.Over the long term,both DOC concentration and flux at Xuliujing Station have significantly increased,at rates of 0.026 mg·L-1·a-1 and 0.002 5 Tg·a-1,respectively(both p＜0.05).Human activities were the predominant driving factor,accounting for 54.1％of the changes in DOC concentration.This study provides valuable insights into the evolving patterns of DOC concentration and flux in the Yangtze River over recent decades and the mechanisms by which driving factors influence these changes.It also provides a novel perspective for the big data analysis of riverine carbon cycling.关键词
长江/溶解态有机碳/机器学习/驱动因素Key words
Yangtze River/dissolved organic carbon/machine learning/driving factors分类
海洋科学引用本文复制引用
鲁兴宇,吴莹,樊俊宁,叶雨萍,王佳..基于机器学习的长江溶解态有机碳输送的长期变化及驱动因素分析[J].华东师范大学学报（自然科学版）,2026,(3):17-29,43,14.基金项目
上海市科委科技创新行动计划国际合作项目(23230714200) （23230714200）
