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The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland
https://orcid.org/http://orcid.org/0000-0001-8746-9570
https://orcid.org/http://orcid.org/0000-0002-3668-1528
https://orcid.org/http://orcid.org/0000-0002-3758-8501
https://orcid.org/http://orcid.org/0000-0002-2150-2769
To build a forecasting tool for the state of eutrophication in the Archipelago Sea, we fitted a Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over the years 2011–2019 by an automated profiling buoy at the Seili ODAS-station. The resulting “Seili-index” can be used to predict the chlorophyll-α (chl-a) concentration in the seawater a number of days ahead by using the temperature forecast as a covariate. An array of test predictions with two separate models on the 2019 data set showed that the index is adept at predicting the amount of chl-a especially in the upper water layer. The visualization with 10 days of chl-a level predictions is presented online at https://saaristomeri.utu.fi/seili-index/. We also applied GAMMs to predict abrupt blooms of cyanobacteria on the basis of temperature and wind conditions and found the model to be feasible for short-term predictions. The use of automated monitoring data and the presented GAMM model in assessing the effects of natural resource management and pollution risks is discussed.
The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland
https://orcid.org/http://orcid.org/0000-0001-8746-9570
https://orcid.org/http://orcid.org/0000-0002-3668-1528
https://orcid.org/http://orcid.org/0000-0002-3758-8501
https://orcid.org/http://orcid.org/0000-0002-2150-2769
To build a forecasting tool for the state of eutrophication in the Archipelago Sea, we fitted a Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over the years 2011–2019 by an automated profiling buoy at the Seili ODAS-station. The resulting “Seili-index” can be used to predict the chlorophyll-α (chl-a) concentration in the seawater a number of days ahead by using the temperature forecast as a covariate. An array of test predictions with two separate models on the 2019 data set showed that the index is adept at predicting the amount of chl-a especially in the upper water layer. The visualization with 10 days of chl-a level predictions is presented online at https://saaristomeri.utu.fi/seili-index/. We also applied GAMMs to predict abrupt blooms of cyanobacteria on the basis of temperature and wind conditions and found the model to be feasible for short-term predictions. The use of automated monitoring data and the presented GAMM model in assessing the effects of natural resource management and pollution risks is discussed.
The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland
https://orcid.org/http://orcid.org/0000-0001-8746-9570
https://orcid.org/http://orcid.org/0000-0002-3668-1528
https://orcid.org/http://orcid.org/0000-0002-3758-8501
https://orcid.org/http://orcid.org/0000-0002-2150-2769
To build a forecasting tool for the state of eutrophication in the Archipelago Sea, we fitted a Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over the years 2011–2019 by an automated profiling buoy at the Seili ODAS-station. The resulting “Seili-index” can be used to predict the chlorophyll-α (chl-a) concentration in the seawater a number of days ahead by using the temperature forecast as a covariate. An array of test predictions with two separate models on the 2019 data set showed that the index is adept at predicting the amount of chl-a especially in the upper water layer. The visualization with 10 days of chl-a level predictions is presented online at https://saaristomeri.utu.fi/seili-index/. We also applied GAMMs to predict abrupt blooms of cyanobacteria on the basis of temperature and wind conditions and found the model to be feasible for short-term predictions. The use of automated monitoring data and the presented GAMM model in assessing the effects of natural resource management and pollution risks is discussed.
The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland
Environ Model Assess
Hänninen, Jari (author) / Mäkinen, Katja (author) / Nordhausen, Klaus (author) / Laaksonlaita, Jussi (author) / Loisa, Olli (author) / Virta, Joni (author)
Environmental Modeling & Assessment ; 27 ; 571-584
2022-08-01
14 pages
Article (Journal)
Electronic Resource
English
Chlorophyll , Cyanobacteria , Temperature , Wind , Profiling buoy , Generalized Additive Mixed Model (GAMM) Environment , Math. Appl. in Environmental Science , Mathematical Modeling and Industrial Mathematics , Operations Research/Decision Theory , Applications of Mathematics , Earth and Environmental Science
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