Predicting imminent cyanobacterial blooms in lakes using incomplete timely data

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dc.contributor.authorHeggerud, Christopher M.
dc.contributor.authorXu, Jingjing
dc.contributor.authorWang, Hao
dc.contributor.authorLewis, Mark A.
dc.contributor.authorZurawell, Ron W.
dc.contributor.authorLoewen, Charlie J.G.
dc.contributor.authorVinebrooke, Rolf D.
dc.contributor.authorRamazi, Pouria
dc.date.accessioned2025-04-15T19:22:06Z
dc.date.available2025-04-15T19:22:06Z
dc.date.issued2024
dc.description.abstractToxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long-range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially-monitored data to make near-future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2-week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high-resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced-cost, less data-intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThis research was primarily supported by a Grant from Alberta Conservation Association. Hao Wang was partially supported by an NSERC Individual Discovery Grant RGPIN-2020-03911 and an NSERC Discovery Accelerator Supplement Award RGPAS-2020-00090 as well as a Canada Research Chair. Mark Lewis was supported by an NSERC Discovery RGPIN-2018-05210 and the Gilbert and Betty Kennedy Chair. Pouria Ramazi was partly supported by an NSERC Discovery Grant RGPIN-2022-05199.
dc.identifier.citationHeggerud, C. M., Xu, J., Wang, H., Lewis, M. A., Zurawell, R. W., Loewen, C. J. G., Vinebrooke, R. D., & Ramazi, P. (2024). Predicting imminent cyanobacterial blooms in lakes using incomplete timely data. Water Resources Research, 60(2). https://doi.org/10.1029/2023wr035540
dc.identifier.urihttps://doi.org/10.1029/2023wr035540
dc.identifier.urihttps://hdl.handle.net/1828/21918
dc.language.isoen
dc.publisherWater Resources Research
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.departmentDepartment of Biology
dc.titlePredicting imminent cyanobacterial blooms in lakes using incomplete timely data
dc.typeArticle

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