Quantification of a Primary Production Model Using Two Versions of the Spectral Distribution of the Phytoplankton Absorption Coefficient

Abstract

In a recent publication, we discussed and presented a semi-empirical phytoplankton primary production model. In the present paper, our main purpose is to determine how the algorithms of a primary production model change when different values of specific absorption coefficient by phytoplankton are used in the model. A new version of our earlier model was quantified for this purpose. Differences between the previous and the new models are as follows: (a) the spectra of the specific absorption coefficient of light by phytoplankton differ in the new model from those used in the previous model, and (b) the quantification of the new model brings about a change in the parameters of the algorithm for the quantum yield of carbon fixation. We compared the results of primary production profiles obtained by the new model with those measured in situ and also with the values obtained by the previous model. Due to an adequate choice of quantification parameters, both the old and new models give rather close values of phytoplankton primary production. In the present study, the computational algorithms of both models have been automated. The resulting programs calculate the temporal-spatial variability of phytoplankton primary production, providing hourly values from morning to evening, daily sums, and monthly sums. The input of a table of initial parameters and selected depths produces rapid calculations of the model's results, which are given as vertical profiles of primary production and areal values.

Spatial and temporal changes of primary production in a deep peri-alpine lake

“…where Fmax = 0.08 mol C (mol photons) −1 , and M is the parameter that can depend on incident irradiance as well as on the bio-optical characteristics of the waterbody. In this study we used algorithms by Arst et al (2012) to calculate M according to the Cchl values: The details of the model are explained in previous studies by Arst et al (2008Arst et al ( , 2012 that describe the development of this model.…”

Development and application of a phytoplankton primary production model for well-mixed lakes

“…The main sources of uncertainties are (a) the semi-empirical formulation of the model itself; (b) errors in the measurements of PAR ( 0), q z = chl , C and d,PAR ; K (c) frequent interpolation of the values of chl C and d,PAR . K Some irregular profiles of ( , meas) P z suggest uncertainties in the measurement results as well (Arst et al, 2008a(Arst et al, , 2012. In shallow, well-mixed lakes in which chl C and d,PAR K do not change vertically one cannot expect an irregular depth profile of ( , meas).…”

Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data

“…The PP model, originally developed by Arst et al [20,52] and further adapted for remote sensing data by Kauer et al [41] and Soomets et al [42] was used in this study. The main principle of this model is that the PP is a function of photosynthetically absorbed radiation and the quantum yield of carbon fixation [63]:…”

“…For meaningful PP results over a longer time period (days, months, and years), a large number of direct consecutive contact measurements of photosynthesis rate (e.g., using the 14 C [16], 13 C [17], or dissolved oxygen method [18,19]) are needed. However, these methods are costly, time-consuming, in some cases need the manipulation of radioactive material, and are difficult to apply for large-scale routine monitoring [20]. Therefore, a complementary modelling approach is necessary.…”