|Seasonal variation of average phytoplankton concentration in the Kattegat: a periodical point model|Toompuu, A.; Carstensen, J.; Müller-Karulis, B. (2003). Seasonal variation of average phytoplankton concentration in the Kattegat: a periodical point model. J. Sea Res. 49(4): 323-335. dx.doi.org/10.1016/s1385-1101(03)00036-4
In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101, more
|Also published as |
- Toompuu, A.; Carstensen, J.; Müller-Karulis, B. (2003). Seasonal variation of average phytoplankton concentration in the Kattegat: a periodical point model, in: Ohlson, M. et al. (Ed.) Proceedings of the 22nd Conference of the Baltic Oceanographers (CBO), Stockholm, Sweden, 25-29 November 2001. Journal of Sea Research, 49(4): pp. 323-335, more
Atmospheric forcing; Biomass; Chlorophylls; Dissolved inorganic matter; Mathematical models; Nitrogen; Phosphorus; Phytoplankton; Primary production; Runoff; Seasonal variations; Solar radiation; ANE, Kattegat [Marine Regions]; Marine
|Authors|| || Top |
- Toompuu, A.
- Carstensen, J.
- Müller-Karulis, B.
Seasonal variations in primary production, phytoplankton biomass, chlorophyll-a, dissolved inorganic phosphorus and nitrogen concentrations in the upper 10 m of the Kattegat were analysed by means of monitoring data from 1993-1997. Spatial optimal analysis, based on a stochastic model, was used to reconstruct weekly constituent fields onto a spatial grid. The reconstructed fields were spatially integrated, resulting in a relatively smooth seasonal variability of the average variables. A simple dynamical model, set up as a periodical boundary problem, is suggested for the average phytoplankton concentration, dissolved inorganic nitrogen and entrainment depth as state variables. The model is forced by the solar radiation, nitrogen load from the land sources and atmosphere as well as by nitrogen supply from the lower nutrient-rich layer. The latter process is modelled proportional to the water entrainment into the upper euphotic layer and is driven by atmospheric forcing, river runoff and the Baltic water inflow. Four model coefficient values were fitted by minimising the root mean square difference between the integrated monitoring data and the model output. The suggested diagnostic model reflects the main features in seasonal variability of phytoplankton and nitrogen concentrations by average values, including the magnitude and timing of such dynamic events as the spring and late summer phytoplankton blooms. The importance of different forcing factors is quantified and estimates of unobserved components such as new primary production can be computed.