Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3103-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3103-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2022
Research article |
| 20 Jun 2022
| 20 Jun 2022
Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network
Leah A. Jackson-Blake
CORRESPONDING AUTHOR
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
François Clayer
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
Sigrid Haande
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
James E. Sample
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
S. Jannicke Moe
Norwegian Institute for Water Research (NIVA), 0599 Oslo, Norway
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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.
We develop a Gaussian Bayesian network (GBN) for seasonal forecasting of lake water quality and...
