湖泊科学2026，Vol.38Issue(1)：170-183,中插18-中插19,16.DOI:10.18307/2026.0123
汉江流域河流pCO2时空格局及其控制因子
Spatiotemporal pattern of riverine pCO2 and its controlling factors in the Hanjiang River Basin
摘要
Abstract
Rivers are links connecting the biogeochemical processes among terrestrial,atmospheric,and oceanic carbon pools,and are important participants in the global water and carbon cycles.Riverine partial pressure of carbon dioxide(pCO2)is a key indi-cator reflecting the CO2 exchange process at the riverine water-air interface,which exhibits complex spatiotemporal variations due to the co-impacts of various natural and anthropogenic factors.However,the current understanding of the main controlling factors and their effects on riverine pCO2 is still limited.In this study,the spatiotemporal distribution characteristics of riverine pCO2 were i-dentified,and the relative contributions and controlling effects of potential controlling factors were quantified and revealed using an interpretable machine learning method(boost regression tree(BRT)and accumulated local effects(ALE)),based on monthly datasets with high spatial resolution in the Hanjiang River Basin(HRB).Results indicated that multi-year average riverine pCO2 in the HRB showed an increasing trend from upstream to downstream,and was higher than the atmospheric average.The fluctua-tion type of multi-year monthly average riverine pCO2 in the HRB could be classified into three types based on the k-Shape cluste-ring algorithm,with stationary(T1),unimodal(T2),and bimodal(T3)structures,respectively.The BRT model effectively simulated the multi-year average and multi-year monthly average values of riverine pCO2 in the HRB,showing high performance(r＞0.86,NSE＞0.75)and acceptable errors(MAE＜212.18 μatm,RMSE＜274.16 μatm)in replicate experiments.Multi-year average riverine pCO2 was primarily influenced by temperature factors,accounting for approximately 66.1％of the total relative contribution rate.The relative contributions of the controlling factors for multi-year monthly average riverine pCO2 exhibited signifi-cant variation among each fluctuation type,while temperature continued to play a critical role(approximately 26.6％-46.9％).The findings of the study demonstrated that vegetation and water quantity factors exerted a significant influence on types T2 and T3,respectively.Conversely,the importance of water quality factors was found to be comparatively limited,with their contribution ran-ging below 20.1％.The non-linear and non-monotonic relationships between riverine pCO2 and its potential controlling factors were revealed based on ALE analysis,which showed significant differences between multi-year average and multi-year monthly average scales,as well as between different fluctuation types.The present study revealed the complex spatiotemporal variations of the main controlling factors and their effects on riverine pCO2 in the HRB,thus improving the understanding of riverine carbon cycle processes.关键词
二氧化碳分压/时空格局/控制因子/可解释性机器学习/汉江流域Key words
Partial pressure of carbon dioxide/spatiotemporal pattern/controlling factor/interpretable machine learning/Han-jiang River Basin引用本文复制引用
Chen Menghan,Wu Yue,Lu Mingshen,Wu Shiqiang,Liu Pan,Xia Jun,Cheng Lei..汉江流域河流pCO2时空格局及其控制因子[J].湖泊科学,2026,38(1):170-183,中插18-中插19,16.基金项目
国家自然科学基金项目(U2340207,52394233)和湖北省自然科学基金项目(2022CFA094)联合资助. （U2340207,52394233）
