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Correlations between several environmental factors affecting the bloom events of cyanobacteria in Liptovska Mara reservoir (Slovakia)—A simple regression model

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  • Onderka, Milan

Abstract

A 6-year study conducted from 1997 to 2002 in a eutrophic temperate water reservoir (Liptovska Mara, Slovakia) revealed the importance of some environmental factors in controlling the growth dynamics of water-bloom forming cyanobacteria. A correlation between several water chemistry data, water temperature and the actual cyanobacteria cell count in the reservoir is subject of this paper. In order to find the limiting factor in the cyanobacteria growth dynamics, chemical composition of the water and water temperature were used in a simple regression predictive model. Predictions regarding bloom risk were based on total phosphorus:total nitrogen ratio (TP:TN ratio), nitrogen and phosphorus limitation (N and P limitation), including the influence of water temperature. The study revealed that none of the variables used in the regression models seems to be dominating with respect to controlling cyanobacterial blooms in Liptovska Mara reservoir. Results of this study suggest that a simple model based on regression analysis, such as the one developed in this study, can serve as a useful tool for environmental health officers in deciding to impose regulatory measures in a timely matter and deciding when sampling of microbiological water quality should be performed.

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  • Onderka, Milan, 2007. "Correlations between several environmental factors affecting the bloom events of cyanobacteria in Liptovska Mara reservoir (Slovakia)—A simple regression model," Ecological Modelling, Elsevier, vol. 209(2), pages 412-416.
    • Handle: RePEc:eee:ecomod:v:209:y:2007:i:2:p:412-416
    DOI: 10.1016/j.ecolmodel.2007.07.028
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    References listed on IDEAS

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    1. Maier, H.R. & Dandy, G.C., 1997. "Modelling cyanobacteria (blue-green algae) in the River Murray using artificial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 43(3), pages 377-386.
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    Cited by:

    1. Yu Liu & Du-Gang Xi & Zhao-Liang Li, 2015. "Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.
    2. Ratté-Fortin, Claudie & Chokmani, Karem & El Alem, Anas & Laurion, Isabelle, 2022. "A regional model to predict the occurrence of natural events: Application to phytoplankton blooms in continental waterbodies," Ecological Modelling, Elsevier, vol. 473(C).

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