Routing of terrestrial organic matter from the Congo River to the ultimate sink in the abyss: a mass balance approach (André Dumont medallist lecture 2017)
|Author(s)||Baudin François1, Rabouille Christophe2, Dennielou Bernard3|
|Affiliation(s)||1 : ISTeP, SU & CNRS, Paris, France
2 : LSCE, CEA-CNRS-UVSQ & IPSL, Gif-sur-Yvette, France.
3 : IFREMER, Unité de Recherche Géosciences marines, Plouzané, France.
|Source||Geologica Belgica (1374-8505) (Geologica Belgica), 2020 , Vol. 23 , N. 1-2 , P. 41-52|
|WOS© Times Cited||11|
|Keyword(s)||recent sediments, Congo turbidite system, organic carbon, burial efficiency, source-to-sink|
We address the role of the Congo River sediment dispersal in exporting and trapping organic carbon into deep offshore sediments. Of particular interest is the Congo submarine canyon, which constitutes a permanent link between the terrestrial sediment sources and the marine sink. The Congo River delivers an annual sediment load of ~40 Tg (including 2 Tg of C) that feed a mud-rich turbidite system. Previous estimates of carbon storage capacity in the Congo turbidite system suggest that the terminal lobe complex accounts for ~12% of the surface area of the active turbidite system and accumulates ~18% of the annual input of terrestrial particulate organic carbon exiting the Congo River. In this paper, we extend the approach to the whole active turbidite depositional system by calculating an average burial of terrestrial organic matter in the different environments: canyon, channel, and levees. We estimate that between 33 and 69% of terrestrial carbon exported by the Congo River is ultimately trapped in the different parts of turbidite system and we evaluate their relative efficiency using a source to sink approach. Our carbon budget approach, which consider annual river discharge versus offshore centennial accumulation rates, indicates that about half of the total particulate organic matter delivered yearly by the Congo River watershed escapes the study area or is not correctly estimated by our deep offshore dataset and calculations.