Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3103-2022
https://doi.org/10.5194/hess-26-3103-2022
Research article
 | 
20 Jun 2022
Research article |  | 20 Jun 2022

Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network

Leah A. Jackson-Blake, François Clayer, Sigrid Haande, James E. Sample, and S. Jannicke Moe

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Cited articles

Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., and Salmerón, A.: Bayesian networks in environmental modelling, Environ. Model. Softw., 26, 1376–1388, https://doi.org/10.1016/j.envsoft.2011.06.004, 2011. 
Barton, D. N., Saloranta, T., Moe, S. J., Eggestad, H. O., and Kuikka, S.: Bayesian belief networks as a meta-modelling tool in integrated river basin management – Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin, Ecol. Econ., 66, 91–104, https://doi.org/10.1016/j.ecolecon.2008.02.012, 2008. 
Bergström, A.-K. and Karlsson, J.: Light and nutrient control phytoplankton biomass responses to global change in northern lakes, Global Change Biol., 25, 2021–2029, https://doi.org/10.1111/gcb.14623, 2019. 
Bertani, I., Steger, C. E., Obenour, D. R., Fahnenstiel, G. L., Bridgeman, T. B., Johengen, T. H., Sayers, M. J., Shuchman, R. A., and Scavia, D.: Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?, Sci. Total Environ., 575, 294–308, 2017. 
Boukabour, S. and Masmoudi, A.: Semiparametric Bayesian networks for continuous data, in: Communications in Statistics – Theory and Methods, Taylor & Francis, 1–23, https://doi.org/10.1080/03610926.2020.1738486, 2020. 
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Short summary
We develop a Gaussian Bayesian network (GBN) for seasonal forecasting of lake water quality and algal bloom risk in a nutrient-impacted lake in southern Norway. Bayesian networks are powerful tools for environmental modelling but are almost exclusively discrete. We demonstrate that a continuous GBN is a promising alternative approach. Predictive performance of the GBN was similar or improved compared to a discrete network, and it was substantially less time-consuming and subjective to develop.