Abstract
The effect of biomass dynamics on the estimation of watercolumn primary production is analysed, by coupling a primary production model to a simple growth equation for phytoplankton. The production model is formulated with depth- and time-resolved biomass, and placed in the context of earlier models, with emphasis on the canonical solution for watercolumn production. A relation between the canonical solution and the general solution for the case of an arbitrary depth-dependent biomass profile was derived, together with an analytical solution for watercolumn production in case of a depth dependent biomass profile described with the shifted Gaussian function. The analysis was further extended to the case of a time-dependent, mixed-layer biomass and two additional analytical solutions to this problem were derived, the first in case of increasing mixed-layer biomass and the second in case of declining biomass. The solutions were tested with Hawaii Ocean Time-series data. The canonical solution for mixed-layer production has proven to be a good model for this data set. The shifted Gaussian function was demonstrated to be an accurate model for the measured biomass profiles and the shifted Gaussian parameters extracted from the measured profiles were further used in the analytical solution for watercolumn production and results compared with data. The influence of time-dependent biomass on mixed-layer production was studied through analytical solutions. Re-examining the Critical Depth Hypothesis we derived an expression for the daily increase in mixed-layer biomass. Finally, the work was placed in a remote sensing context and the time-dependent model for biomass related to the remotely sensed-biomass.
Highlights
In the ocean, phytoplankton form the foundation of the pelagic ecosystem and by virtue of their phototsynthesis act as a source of organic carbon for the remainder of the ecosystem (Chavez et al, 2011)
We explore the general case of depth dependent biomass and give an exact solution for watercolumn production with the shifted Gaussian biomass
After that we explore the case of time dependent mixed layer biomass and provide analytical solutions for the case of growing and declining biomass
Summary
Phytoplankton form the foundation of the pelagic ecosystem and by virtue of their phototsynthesis (primary production) act as a source of organic carbon for the remainder of the ecosystem (Chavez et al, 2011). Through the action of the so-called biological pump, a complex ecosystem process which starts with primary production (Volt and Hoffert, 1985), phytoplankton contribute to the transfer of carbon into the deep ocean (Longhurst and Harrison, 1989) and subsequently affect atmospheric carbon concentration on longer time scales (from decadal to millennial) (Honjo et al, 2008). With this in mind, prediction of primary production is important, not just for the open ocean, and for coastal seas, and is relevant to fisheries and climate change research. Given the vastness of the ocean, the basic means by which such predictions are made is through the combined use of primary production models and ocean-color data, acquired by satellites (Platt and Sathyendranath, 1991; Siegel et al, 2014)
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