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A multi-model approach to predict phytoplankton biomass and composition was performed in a eutrophic Uruguayan shallow lake which is the second drinking water source of the country. We combined statistical (spectral analysis and Machine learning techniques) and physically based models to generate, for the first time in this system, a predictive tool of phytoplankton biomass (chlorophyll-a) and composition (morphology-based functional groups). The results, based on a 11-year time series, revealed two alternating phases in the temporal dynamics of phytoplankton biomass. One phase is characterized by high inorganic turbidity and low phytoplankton biomass, and the other by low inorganic turbidity and variable (low and high) phytoplankton biomass. A threshold of turbidity (29 TNU), above which phytoplankton remains with low biomass (<15–20 ug/l) was established. The periods of high turbidity, which in …