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Predicting the standing stock of organic carbon in surface sediments of the North–West European continental shelf
Diesing, M.; Kröger, S.; Parker, R.; Jenkins, C.; Mason, C.; Weston, K. (2017). Predicting the standing stock of organic carbon in surface sediments of the North–West European continental shelf. Biogeochemistry 135(1-2): 183-200.
In: Biogeochemistry. Springer: Dordrecht; Lancaster; Boston. ISSN 0168-2563; e-ISSN 1573-515X, more
Peer reviewed article  

Available in  Authors 

Author keywords
    Organic carbon, Continental shelf, Sediment, Spatial prediction, Europe

Authors  Top 
  • Diesing, M.
  • Kröger, S.
  • Parker, R., more
  • Jenkins, C.
  • Mason, C.
  • Weston, K.

    Shelf seas and their associated benthic habitats represent key systems in the global carbon cycle. However, the quantification of the related stocks and flows of carbon are often poorly constrained. To address benthic carbon storage in the North–West European continental shelf, we have spatially predicted the mass of particulate organic carbon (POC) stored in the top 10 cm of shelf sediments in parts of the North Sea, English Channel and Celtic Sea using a Random Forest model, POC measurements on surface sediments from those seas and relevant predictor variables. The presented model explains 78% of the variance in the data and we estimate that approximately 250 Mt of POC are stored in surficial sediments of the study area (633,000 km2). Upscaling to the North–West European continental shelf area (1,111,812 km2) yielded a range of 230–882 Mt of POC with the most likely estimate being on the order of 476 Mt. We demonstrate that the largest POC stocks are associated with coarse-grained sediments due to their wide-spread occurrence and high dry bulk densities. Our results also highlight the importance of coastal sediments for carbon storage and sequestration. Important predictors for POC include mud content in surficial sediments, annual average bottom temperature and distance to shoreline, with the latter possibly a proxy for terrestrial inputs. Now that key variables in determining the spatial distribution of POC have been identified, it is possible to predict future changes to the POC stock, with the presented maps providing an accurate baseline against which to assess predicted changes.

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