A1 Journal article (refereed)
The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland (2022)


Hänninen, J., Mäkinen, K., Nordhausen, K., Laaksonlaita, J., Loisa, O., & Virta, J. (2022). The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland. Environmental Modeling and Assessment, 27(4), 571-584. https://doi.org/10.1007/s10666-022-09822-9


JYU authors or editors


Publication details

All authors or editorsHänninen, Jari; Mäkinen, Katja; Nordhausen, Klaus; Laaksonlaita, Jussi; Loisa, Olli; Virta, Joni

Journal or seriesEnvironmental Modeling and Assessment

ISSN1420-2026

eISSN1573-2967

Publication year2022

Publication date02/03/2022

Volume27

Issue number4

Pages range571-584

PublisherSpringer Science and Business Media LLC

Publication countryNetherlands

Publication languageEnglish

DOIhttps://doi.org/10.1007/s10666-022-09822-9

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/80053


Abstract

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.


Keywordsseasseawaterwater systemseutrophicationforecastspredictabilitytemperaturecyanobacteriachlorophyllenvironmenteffects (results)environmental effectsmodels (objects)modelling (representation)

Free keywordschlorophyll; cyanobacteria; temperature; wind; profling buoy; Generalized Additive Mixed Model (GAMM)


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-30-04 at 17:07