2022

Hänninen, Jari; Mäkinen, Katja; Nordhausen, Klaus; Laaksonlaita, Jussi; Loisa, Olli; Virta, Joni

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.