Work

Identifying the determinants of the spatial patterns and temporal fluctuation characteristics of riverine pCO2 of the largest subtropical river using machine learning methods

Year: 2025

Type: article

Abstract: Study region the Yangtze River Basin (YRB), China Study focus Carbon dioxide (CO 2 ) exchange at the riverine water–air interface is critical for global carbon cycle and is controlled by the partial pressure of CO 2 ( p CO 2 ). However, the spatiotemporal characteristics of riverine p CO 2 and quantitative contributions of its determinants at large scales are largely unexplored. This study identified the spatial and temporal characteristics of riverine p CO 2 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 p CO 2 , which are important for understanding the riverine carbon cycle . New hydrological insights for the region Riverine p CO 2 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 p CO 2 . Climate and vegetation factors dominated the spatial distribution of yearly riverine p CO 2 , 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 %.

Source: Journal of Hydrology Regional Studies

DOI: 10.1016/j.ejrh.2025.102284