Long-term variations and driving factors of dissolved organic carbon transport in the Yangtze River using machine learning

doi:10.3969/j.issn.1000-5641.2026.03.002

Abstract

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 0.0025 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.

Key words: Yangtze River, dissolved organic carbon, machine learning, driving factors

CLC Number:

P734

Xingyu LU, Ying WU, Junning FAN, Yuping YE, Jia WANG. Long-term variations and driving factors of dissolved organic carbon transport in the Yangtze River using machine learning[J]. J* E* C* N* U* N* S*, 2026, 2026(3): 17-29, 43.

Figures/Tables 9

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月度变量				年度变量
核主成分	特征值	解释率/%	累计解释率/%	核主成分	特征值	解释率/%	累计解释率/%
1	31.56	50.50	50.50	1	35.65	46.40	46.40
2	14.53	23.30	73.80	2	22.80	29.60	76.00
3	4.63	7.42	81.22	3	10.38	13.50	89.50
4	4.05	6.48	87.70	4	5.16	6.70	96.20
5	3.25	5.21	92.91	5	2.92	3.80	100
6	2.58	4.13	97.00
7	1.86	2.98	100

变量		不同核主成分下的载荷
变量		1	2	3	4	5	6	7
月度变量	Temp	0.940	0.035	0.125	−0.187	−0.202	−0.052	0.057
	NDVI	0.907	0.053	0.293	0.101	−0.129	−0.066	0.050
	SM	0.901	0.075	0.326*	0.155	0.122	−0.033	−0.029
	PET	0.931	−0.045	0.079	−0.305	−0.111	−0.046	0.033
	GPP	0.958*	0.036	0.153	−0.150	−0.088	−0.041	−0.020
	Precip	0.888	−0.142	0.086	−0.258	0.293	0.028	0.040
	Q	0.847	−0.188*	0.316	−0.043	0.001	0.287	−0.067
年度变量	Grassland	−0.894	−0.037	−0.289	0.079	0.024
	Forest	0.806	0.291*	0.456	−0.136	0.023
	Cropland	−0.807	0.010	−0.563*	0.032	0.158
	PD	0.941*	0.033	0.306	0.045	−0.110
	Shrub	−0.922	−0.068	−0.130	−0.032	0.195

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