The experience of extrapolating productivity estimates for the water area of lake Onego

DOI: 10.35595/2414-9179-2025-2-31-259-273

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About the Authors

Natalia M. Kalinkina

Karelian Research Center of the Russian Academy of Sciences,
50, Aleksandra Nevskogo ave., Petrozavodsk, 185030, Russia,
E-mail: cerioda@mail.ru

Elena V. Tekanova

Karelian Research Center of the Russian Academy of Sciences,
50, Aleksandra Nevskogo ave., Petrozavodsk, 185030, Russia,
E-mail: etekanova@mail.ru

Andrey V. Korosov

Karelian Research Center of the Russian Academy of Sciences,
50, Aleksandra Nevskogo ave., Petrozavodsk, 185030, Russia,

Petrozavodsk State University,
30, Lenina ave., Petrozavodsk, 185030, Russia,

E-mail: korosov@mail.ru

Maria T. Syarki

Karelian Research Center of the Russian Academy of Sciences,
50, Aleksandra Nevskogo ave., Petrozavodsk, 185030, Russia,
E-mail: msyarki@mail.ru

Elena M. Makarova

Karelian Research Center of the Russian Academy of Sciences,
50, Aleksandra Nevskogo ave., Petrozavodsk, 185030, Russia,
E-mail: emm777@bk.ru

Ksenia V. Isakova

Karelian Research Center of the Russian Academy of Sciences,
50, Aleksandra Nevskogo ave., Petrozavodsk, 185030, Russia,
E-mail: ksusha_isakova@mail.ru

Abstract

GIS technologies were used to study the spatial distribution of phytoplankton production in the water area of Lake Onego. Empirical hydrobiological data for 1982–2023 were the basis for the map development. Two periods, before and after 2000, were considered separately to assess the impact of climate change and anthropogenic load on the productivity of the reservoir. Empirical values of the number of saprophytic bacterioplankton, concentration of chlorophyll a, and water transparency were used to predict the level of primary production. To do this, we selected and calculated the optimal neural network model (a single-layer perceptron of 4 neurons), which has low and equal standard errors for training and test samples. A network of 142 points, covering the entire water area of Lake Onego was built to predict the level of primary production. Each cell contains the values of the four studied indicators, obtained by the method of spatial interpolation using a triangular smoothing function. The primary production values were calculated at each of the 142 points. The resulting cartograms clearly reflect the main trends in the territorial and temporal distribution of primary production in the water area of Lake Onego. The central part of the water area of Lake Onego is characterized by low productivity. Increased productivity is observed in bays on the coast of which large industrial centers are located (Petrozavodsk and Kondopoga), which is associated with the active pollution of these waters. In recent decades, the productivity of the Petrozavodskaya Bay ecosystem has decreased, while the productivity of the Kondopozhskaya Bay has increased, apparently as a result of the increase in commercial trout production capacity.

Keywords

Lake Onego, lake productivity, GIS, neural network, extrapolation

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For citation: Kalinkina N.M., Tekanova E.V., Korosov A.V., Syarki M.T., Makarova E.M., Isakova K.V. The experience of extrapolating productivity estimates for the water area of lake Onego. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 2. P. 259–273. DOI: 10.35595/2414-9179-2025-2-31-259-273 (in Russian)