Seasonal dynamics of dissolved organic matter in the Mackenzie Delta, Canadian Arctic waters

• Main Authors: Juhls, Bennet, Matsuoka, A., Lizotte, M., Bécu, G., Overduin, P. P., El Kassar, J., Devred, E., Doxaran, D., Ferland, J., Forget, M. H.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Satellite ocean colour remote sensing; Dissolved organic matter; Mackenzie River; Beaufort Sea; Sentinel-3 OLCI; Land-ocean fluxes; ddc:550; Arctic; Arctic Ocean; Mackenzie Delta; The Sentinel; Mackenzie river; permafrost
• Online Access: https://refubium.fu-berlin.de/handle/fub188/37769; https://doi.org/10.17169/refubium-37482; https://doi.org/10.1016/j.rse.2022.113327

Increasing air temperatures and associated permafrost thaw in Arctic river watersheds, such as the Mackenzie River catchment, are directly affecting the aquatic environment. As a consequence, the quantity and the quality of dissolved organic carbon (DOC) that is transported via the Mackenzie River into the Arctic Ocean is expected to change. Particularly in these remote permafrost regions of the Arctic, monitoring of terrigenous organic carbon fluxes is insufficient and knowledge of distribution and fate of organic carbon when released to the coastal waters is remarkably lacking. Despite its poorly evaluated performance in Arctic coastal waters, Satellite Ocean Colour Remote Sensing (SOCRS) remains a powerful tool to complement monitoring of land-ocean DOC fluxes, detect their trends, and help in understanding their propagation in the Arctic Ocean. In this study, we use in situ and SOCRS data to show the strong seasonal dynamics of the Mackenzie River plume and the spatial distribution of associated terrigenous DOC on the Beaufort Sea Shelf for the first time. Using a dataset collected during an extensive field campaign in 2019, the performance of three commonly-used atmospheric correction (AC) algorithms and two available colored dissolved organic matter (CDOM) retrieval algorithms were evaluated using the Ocean and Land Colour Instrument (OLCI). Our results showed that in optically-complex Arctic coastal waters the Polymer AC algorithm performed the best. For the retrieval of CDOM, the gsmA algorithm (Mean Percentage Error (MPE) = 35.7%) showed slightly more consistent results compared to the ONNS algorithm (MPE = 37.9%). By merging our measurements with published datasets, the newly-established DOC-CDOM relationship for the Mackenzie-Beaufort Sea region allowed estimations of DOC concentrations from SOCRS across the entire fluvial-marine transition zone with an MPE of 20.5%. Finally, we applied SOCRS with data from the Sentinel-3 OLCI sensor to illustrate the seasonal variation of DOC concentrations in the ...