河流湖泊碳循环研究进展

围绕河流湖泊的碳循环,明晰了不同水生生态系统中的颗粒有机碳(POC)、颗粒无机碳(PIC)、溶解有机碳(DOC)等各类碳组分的迁移转化过程,进而梳理了影响河流湖泊碳通量的关键因素,归纳了目前可用于水体碳组分测定的仪器和实验室技术.重点探讨了遥感技术在河湖碳循环组分反演和估算方面的应用,系统归纳了近年来利用遥感数据估算P...

Research progress on the carbon cycle of rivers and lakes

This study focuses on the carbon cycle of rivers and lakes. It first illustrates the migration and transformation processes of various carbon components, such as particulate organic carbon (POC), particulate inorganic carbon (PIC), and dissolved organic carbon (DOC), in different aquatic ecosystems, and then elucidates the key factors affecting the carbon flux of rivers and lakes. After that, it summarizes the instruments and laboratory techniques that can be used to determine the carbon content in water. In particular, this study discusses the application of remote sensing technology in carbon component inversion and estimation. At last, it systematically summarizes the research advances in recent years using remote sensing data to estimate the carbon components of rivers and lakes, such as POC, DOC, colored dissolved organic matter (CDOM) and chlorophyll a (Chla). A comprehensive comparison of the carbon cycle models of inland waters that had been established at home and abroad is conducted. Based on the literature review, it is found that different types of rivers and lakes at different latitudes have different influencing factors on carbon flux and carbon cycle, which is determined by local water cycle and water-atmosphere exchange. Traditional laboratory measurements can measure the carbon components of water bodies accurately, whereas satellite remote sensing is able to monitor carbon components in rivers and lakes, such as CDOM, DOC, POC and phytoplankton, at large scales. It provides an effective means to achieve regional water quality carbon cycle processes monitoring. Constructing carbon cycle model is helpful for analyzing the sources and characteristics of carbon components, and revealing the biogeochemical cycle processes. But further improvement on the applicability and robustness of existing models is required.