The “Seili-index” for the Prediction of Chlorophyll-α Levels in the Archipelago Sea of the northern Baltic Sea, southwest Finland

Abstract To build a forecasting tool for the state of eutrophication in Archipelago Sea, we fitted Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over years 2011–2019 by an automated profiling buoy at Seili ODAS-station. The resulting “Seili-index” can be used predict chlorophyll- α (chl-a) concentration seawater number days ahead using temperature forecast as covariate. An array test predictions with two separate models on 2019 data set showed that index is adept predicting amount chl-a especially upper water layer. visualization 10 level presented online https://saaristomeri.utu.fi/seili-index/ . We also applied GAMMs abrupt blooms cyanobacteria basis and wind conditions found model feasible short-term predictions. use GAMM assessing effects natural resource management pollution risks discussed.