Multi-model approach to predict phytoplankton biomass and composition dynamics in a eutrophic shallow lake governed by extreme meteorological events

The authors are very grateful to the Uruguayan water supply company (OSE-UGD), the Uruguayan Institute of Meteorology (INUMET) and the Uruguayan National Water Directorate (DINAGUA) for providing long-term data on physicochemical, biological, hydrological and meteorological variables. We also thank to Dra. Carla Kruk for her comments on phytoplankton morphology-based functional groups dynamics, to MSc. Magdalena Crisci for her assistance in the digitalization of bathymetric data, to Dra.Valeria Durañona for her advice on the analysis of extreme wind events in Uruguay, and to Paula Santos for language revision.International audienceA 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 (