环境科学
水文学(农业)
溶解有机碳
水槽(地理)
碳汇
分水岭
流域
二氧化碳
总有机碳
生态系统
生态学
环境化学
地质学
化学
地理
岩土工程
地图学
生物
机器学习
计算机科学
作者
Michaela Ladeira de Melo,Cristian R. Teodoru,Paul A. del Giorgio
标识
DOI:10.1016/j.scitotenv.2023.162308
摘要
Boreal rivers transport and process large amounts of organic and inorganic materials derived from their catchments, yet quantitative estimates and patterns of carbon (C) transport and emissions in these large rivers are scarce relative to those of high-latitude lakes and headwater streams. Here, we present the results of a large-scale survey of 23 major rivers in northern Québec sampled during the summer period of 2010, which aimed to determine the magnitude and spatial variability of different C species (carbon dioxide - CO2, methane - CH4, total carbon - TC, dissolved organic carbon - DOC and inorganic carbon - DIC), as well as to identify their main drivers. In addition, we constructed a first order mass balance of total riverine C emissions to the atmosphere (outgassing from the main river channel) and export to the ocean over summer. All rivers were supersaturated in pCO2 and pCH4 (partial pressure of CO2 and CH4), and the resulting fluxes varied widely among rivers, especially the CH4. There was a positive relationship between DOC and gas concentrations, suggesting a common watershed source of these C species. DOC concentrations declined as a function of % land surface covered by water (lentic + lotic systems) in the watershed, suggesting that lentic systems may act as a net sink of organic matter in the landscape. The C balance suggests that the export component is higher than atmospheric C emissions in the river channel. However, for heavily dammed rivers, C emissions to the atmosphere approaches the C export component. Such studies are highly important for the overall efforts to effectively quantify and incorporate major boreal rivers into whole-landscape C budgets, to determine the net role of these ecosystems as C sinks or sources, and to predict how these might shift under anthropogenic pressures and dynamic climate conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI