Reference : Numerical modeling of the deep Black Sea ecosystem functioning during the late 80’s (...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
Numerical modeling of the deep Black Sea ecosystem functioning during the late 80’s (eutrophication phase)
Grégoire, Marilaure mailto [Université de Liège - ULiège > Département des sciences et gestion de l'environnement > Océanologie >]
Raick, Caroline [Université de Liège - ULiège > Département des sciences et gestion de l'environnement > Océanologie >]
Soetaert, Karline [Netherlands Institute of Ecology > Center for Estuarine and Marine Ecology]
Progress in Oceanography
Pergamon Press - An Imprint of Elsevier Science
Yes (verified by ORBi)
United Kingdom
[en] Ecosystem-hydrodynamic interactions ; Parameter estimation ; Mathematical Modeling
[fr] Black Sea
[en] A one-dimensional coupled physical–biogeochemical model has been developed to simulate the ecosystem of the central Black Sea at the end of the 1980s when eutrophication and invasion by gelatinous organisms seriously affected the stability and dynamics of the system. The physical model is the General Ocean Turbulence Model (GOTM) and the biogeochemical model describes the foodweb from bacteria to gelatinous carnivores through 24 state variables including three groups of phytoplankton: diatoms, small phototrophic flagellates and dinoflagellates, two zooplankton groups: micro- and mesozooplankton, two groups of gelatinous zooplankton: the omnivorous and carnivorous forms, an explicit representation of the bacterial loop: bacteria, labile and semi-labile dissolved organic matter, particulate organic matter. The model simulates oxygen, nitrogen, silicate and carbon cycling. In addition, an innovation of this model is that it explicitly represents processes in the anoxic layer. Biogeochemical processes in anaerobic conditions have been represented using an approach similar to that used in the modeling of diagenetic processes in the sediments lumping together all the reduced substances in one state variable [Soetaert, K., Herman, P., 1996. A model of early diagenetic processes from the shelf to abyssal depths. Geochimica et Cosmochimica Acta 60 (6) 1019–1040]. In this way, processes in the upper oxygenated layer are fully coupled with anaerobic processes in the deep waters, allowing to perform longterm simulations.
The mathematical modeling of phytoplankton and zooplankton dynamics, detritus and the microbial loop is based on the model developed by Van den Meersche et al. [Van den Meersche, K., Middelburg, J., Soetaert, K., van Rijswijk P.H.B., Heip, C., 2004. Carbon–nitrogen coupling and algal–bacterial interactions during an experimental bloom: Modeling a 13c tracer experiment. Limnology and Oceanography 49 (3), 862–878] and tested in the modeling of mesocosm experiments and of the Ligurian sea ecosystem [Raick, C., Delhez, E., Soetaert, K., Gregoire, M., 2005. Study of the seasonal cycle of the biogeochemical processes in the Ligurian sea using an 1D interdisciplinary model. Journal of Marine Systems 55 (3–4) 177–203]. This model has been extended to simulate the development of top predators, the aggregation of detritus as well as the degradation and chemical processes in suboxic/anoxic conditions (e.g. denitrification, anoxic remineralization, redox reactions).
The coupled model extends down to the sediments (’2000 m depth) and is forced at the air–sea interface by the 6 hourly ERA-40 reanalysis of ECMWF data. The model has been calibrated and validated using a large set of data available in the Black Sea TU Ocean Base. The biogeochemical model involves some hundred parameters which are first calibrated by hand using published values. Then, an identifiability analysis has been performed in order to determine a subset of identifiable parameters (i.e. ensemble of parameters that can be together estimated from the amount of data we have at our disposal, see later in the text). Also a subset of 10 identifiable parameters was isolated and an automatic calibration subroutine (Levenberg Marquart) has been used to fine tune these parameters. Additionally, in order to assess the sensitivity of model results to the parameterization of the two gelatinous groups, Monte Carlo simulations were performed perturbing all the parameters governing their dynamics.
In order to calibrate the particle dynamics and export, the chemical model was run off-line with the particle and microbial loop model in order to check its capacity of simulating anoxic waters. After a 104 year run, the model simulated NH4 and H2S profiles similar to observations but steady state was not reached suggesting that the Black Sea deep waters are not at steady state. The fully coupled model was then used to simulate the period 1988–1992 of the Black Sea ecosystem.
The model solution exhibits a complex dynamics with several years of transient adjustment. This complexity is
imparted by the explicit modeling of top predators. The integrated chlorophyll and phytoplankton biomasses, the maximum concentration and depth of maximum, mesozooplankton biomass, depth of oxycline, primary production, bacterial production, surface concentrations of nutrients and plankton simulated by the model and obtained from available data analysis were compared and showed a satisfactory agreement. Also, as in the data, the model shows a continuous development of phytoplankton throughout the year, with an intense spring bloom dominated by diatoms and a fall bloom dominated by dinoflagellates. Dinoflagellates dominate from summer until late fall while small phototrophic flagellates are never dominant in terms of biomass, but are present almost throughout the year except in winter. The model simulates an intense silicate removal associated to increased diatoms blooms which were promoted by increased nutrient conditions, and by the presence of gelatinous zooplankton. This silicate pumping leads to silicate limitation of diatoms development in summer allowing the development of dinoflagellates.
Centre Interfacultaire de Recherches en Océanologie - MARE

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