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Space and seasonal distributions of nitrates in the Mediterranean Sea derived from a variational inverse model
Denis-Karafistan, A.; Martin, J.-M.; Minas, H.; Brasseur, P.; Nihoul, J.C.J.; Denis, C. (1998). Space and seasonal distributions of nitrates in the Mediterranean Sea derived from a variational inverse model. Deep-Sea Res., Part 1, Oceanogr. Res. Pap. 45(2-3): 387-408
In: Deep-Sea Research, Part I. Oceanographic Research Papers. Elsevier: Oxford. ISSN 0967-0637; e-ISSN 1879-0119, meer
Peer reviewed article  
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Auteurs  Top 
  • Denis-Karafistan, A.
  • Martin, J.-M.
  • Minas, H.
  • Brasseur, P.
  • Nihoul, J.C.J., meer
  • Denis, C.
Abstract
    A variational inverse model was applied to the processing of biogeochemical data in order to generate conceptual models useful for understanding the role of anthropogenic and physical factors in eutrophication and other environmental characteristics of the Mediterranean Sea. For this purpose, nitrate concentrations available in our data base were grouped into synoptic or quasi-synoptic subsets representative of averaged values of the measured vertical profiles in typical water layers. These data sets were then used as input for our two-dimensional variational inverse model based on a minimum principle to reconstruct mean horizontal distributions of the nitrate data fields. The latter are obtained as gridded solutions to the measured concentrations. The resulting seasonal distributions of nitrates provide an interesting general view, and indicate gaps where new data must be supplemented for further investigations. In some coastal regions where data are available, known phenomena such as nutrient loadings from rivers, upwelling regions and gyres were reproduced by this model in both the western and eastern Mediterranean basins. Seasonal graphical visualisations of the variational inverse model results are presented for nitrate concentrations. Besides providing a new insight into the distribution of nitrates in the Mediterranean Sea, our results confirm the need for more up-to-date and good quality data before any definite conclusion concerning long-term seasonal modelling and the fate of nutrients can be drawn.
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