Identifying the Determinants of the Spatial Patterns and Temporal Fluctuation Characteristics of Riverine Pco2 of the Largest Subtropical River Using Machine Learning Methods
Journal of Hydrology Regional Studies（2025）
Abstract
Study region the Yangtze River Basin (YRB), China Study focus Carbon dioxide (CO2) exchange at the riverine water–air interface is critical for global carbon cycle and is controlled by the partial pressure of CO2 (pCO2). However, the spatiotemporal characteristics of riverine pCO2 and quantitative contributions of its determinants at large scales are largely unexplored. This study identified the spatial and temporal characteristics of riverine pCO2 in the YRB, and quantified the relative contributions of determinants using a machine learning method (boost regression tree, BRT). This study highlights the differences in determinants of the spatial and temporal characteristics of riverine pCO2, which are important for understanding the riverine carbon cycle. New hydrological insights for the region Riverine pCO2 showed an increasing trend from upstream to downstream and its monthly fluctuations gradually evolved from smooth (upstream) to bimodal mode (downstream), which were identified by the k-Shape clustering algorithm. BRT model with nine determinants of climate, vegetation, and water quality effectively simulated the spatiotemporal characteristics of riverine pCO2. Climate and vegetation factors dominated the spatial distribution of yearly riverine pCO2, with total relative contributions exceeding 92.3 ± 3.3 %. Determinants varied with different fluctuation modes, whereas climate factors still played a major role. As the monthly fluctuations evolved from smooth to bimodal mode, vegetation and water quality factors became more important, with total relative contribution increasing from 32.8 ± 0.7 % to 53.1 ± 4.6 %.
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Key words
Riverine pCO2,Spatial pattern,Monthly fluctuation,Relative contribution,Machine learning method
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