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Macroscale ecohydrodynamic modeling on the Northwest European Continental Shelf
In: Journal of Marine Systems. Elsevier: Tokyo; Oxford; New York; Amsterdam. ISSN 0924-7963; e-ISSN 1879-1573
Ook verschenen in:Delhez, E.J.M. (Ed.) (1998). Modelling hydrodynamically dominated manne ecosystems. Journal of Marine Systems, 16(1-2). Elsevier: Amsterdam. 190 pp., meer
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| Abstract |
A 3D coupled hydrodynamic-biological model is applied to the simulation of the biological processes on the Northwest European Continental Shelf. The model operates in the macroscale spectral window (time scales of a month or a season) without explicit description of the higher frequency processes but with an adequate modeling of their influence on larger time scales. The hydrodynamic sub-model is 3D, baroclinic and includes a refined turbulence closure. The non-linear interactions of mesoscale fluctuations are described by means of the generalized mesoscale Reynolds stresses and the Stokes drift transport velocity field. The biological sub-model describes the nitrogen and carbon cycles through the food web with 17 state variables representing 9 compartments: inorganic nutrients, small phytoplankton, large phytoplankton, dissolved organic matter, pelagic bacteria, heterotrophic flagellates, pelagic detritus, zooplankton and benthic organic detritus. The simulation emphasizes the strong influence of the local depth and of the stability of the water column on the whole annual cycle of phytoplankton. In well-mixed shallow areas, the chlorophyll concentration increases in early spring and the primary production occurs steadily until October. In deeper areas, the spring bloom is much sharper and appears later, after the set up of the seasonal stratification. The main part of the primary production happens during the bloom period. The macroscale approach provides results that are comparable with observations and results of other more classical models describing explicitly the higher frequency processes. The current method allows, however, a more direct insight into the dynamics of the system and into the interactions between the hydrodynamics and the biology. Also, it greatly reduces the CPU requirements and is therefore particularly suited for repeated or long term simulations. |
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