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Dynamic response of deep-sea sediments to seasonal variations: a model
Soetaert, K.; Herman, P.M.J.; Middelburg, J.J. (1996). Dynamic response of deep-sea sediments to seasonal variations: a model. Limnol. Oceanogr. 41(8): 1651-1668. http://dx.doi.org/10.4319/lo.1996.41.8.1651
In: Limnology and Oceanography. American Society of Limnology and Oceanography: Waco, Tex., etc. ISSN 0024-3590; e-ISSN 1939-5590, meer
Peer reviewed article  

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    VLIZ: Open Repository 273903 [ OMA ]

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  • Soetaert, K., meer
  • Herman, P.M.J., meer
  • Middelburg, J.J., meer

Abstract
    We present a dynamic, numerical model of early diagenetic processes that can be used to examine the response of different organic carbon mineralization pathways, concentration vs. depth profiles, and the resultant fluxes to seasonally varying carbon deposition. We show that there can be substantial temporal variability in sediment-water fluxes as well as in the relative contribution of different organic carbon mineralization pathways and oxygen consumption processes in deep-sea sediments. The timing and amplitude of the sediment response are most sensitive to the reactivity of the organic matter. We show that the fluxes of dissolved substances can be approximated by assuming that these constituents are at steady state with the carbon profiles. The model is used to reconcile a time series of community oxygen consumption rates, dissolved oxygen, nitrate, ammonia, and manganese profiles, and solid-phase organic carbon profiles at a deep-sea station in the Pacific. The organic carbon that is deposited is very reactive (7.2 yr-1), which causes a fast response of the sediment community to seasonal pulses in carbon flux and significant temporal variation in oxygen and nutrient fluxes. The model predicts large seasonal variations in the relative importance of oxic (48-71 %), denitrification (12-17%), and anoxic (16-37%) pathways. There is also substantial variation in the porewater profiles of oxygen, nitrate, ammonia, and other reduced substances over the seasonal cycle, indicating that seasonal variability should be taken into account when extrapolating from a single observation to yearly averaged values.

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