Phytoplankton blooms in low-nutrient lakes are unpredictable and short-lived. While factors influencing long-term changes in bloom frequency are known, the specific triggers for individual events remain unclear. Climate change is considered a major driver, but correlations between air temperature or precipitation and blooms are not always compelling. This highlights the complex dynamics and the need for a multi-faceted approach to predict short-lived blooms. In September 2021, Lake Geneva experienced a massive *Uroglena* sp. bloom—an unprecedented event since phosphorus levels drastically decreased in the late 1990s. This event demanded a rapid causal explanation, challenging traditional scientific timescales. A comprehensive understanding requires a multidisciplinary approach combining bio-geophysical, ecological, and atmospheric data, leveraging recent advances in remote sensing and hydrodynamic models. This study integrates in-situ measurements, satellite remote sensing, 3D hydrodynamic modeling, and Lagrangian particle tracking to investigate the formation and spatial dynamics of this exceptional bloom.

Previous research has highlighted the widespread increase in intense lake phytoplankton blooms globally since the 1980s, linking them to climate change and eutrophication. Studies have explored the phenology of blooms using long-term satellite data and the impact of temperature and precipitation on harmful algal blooms (HABs). However, predicting individual bloom events remains challenging due to the intricate interplay of factors, including nutrient availability, light conditions, and hydrodynamic processes. While the role of meteorological conditions in triggering blooms is acknowledged, the specific sequence of events remains poorly understood. Existing models often simplify these complex interactions, emphasizing the need for more comprehensive approaches that integrate multiple data sources and account for the spatial heterogeneity of lake systems.

This study utilized a multidisciplinary approach combining various data sources and analytical techniques. Satellite remote sensing data from Sentinel-2 MSI and Sentinel-3 OLCI were used to characterize the spatial extent and temporal evolution of the algal bloom, estimating chlorophyll-a concentrations and water transparency. In-situ measurements from the LéXPLORE research platform and other monitoring sites provided high-frequency profiles of chlorophyll-a, backscattering, temperature, and other parameters. Water samples were collected for taxonomic identification and biogeochemical analyses, including radiocarbon (¹⁴C) dating to identify carbon sources. A 3D hydrodynamic model (MITgcm) was employed to simulate lake circulation, including wind-induced coastal upwelling and gyre formation. Lagrangian particle tracking was used to analyze transport pathways and the origin and fate of the bloom. Empirical Orthogonal Function (EOF) analysis was performed to identify dominant circulation patterns. Finally, long-term meteorological data (1998–2022) were analyzed to assess the frequency of the specific sequence of meteorological conditions that triggered the bloom.

Satellite imagery and in-situ measurements confirmed the exceptional intensity and spatial heterogeneity of the *Uroglena* bloom. The bloom peaked on September 6th, 2021, with chlorophyll-a concentrations exceeding 50 mg m⁻³ in large areas. Taxonomic identification showed a monospecific bloom dominated by *Uroglena* cells. Hydrodynamic modeling and Lagrangian particle tracking showed that the bloom originated from the southern shore, where wind-induced coastal upwelling brought nutrient-rich waters into the photic zone a few days prior to the bloom's onset. The bloom's spatial distribution was subsequently shaped by basin-scale advection patterns, including gyres and eddies. ¹⁴C analysis revealed that terrestrial organic matter initially fueled the bloom in the littoral zone, while lake bicarbonate sustained its growth in the pelagic zone. Meteorological data showed a sequence of events: (i) extreme rainfall (6-8 weeks prior) leading to elevated nutrient and organic matter loading, (ii) wind-induced upwelling (a few days prior), and (iii) warm, calm conditions (during the bloom) promoting growth and preventing mixing. Statistical analysis of long-term data (1998-2022) indicated that this specific sequence of meteorological events is rare, occurring only three times during this period. The two reported *Uroglena* blooms in Lake Geneva coincided with two of these occasions. Zooplankton grazing was considered, but found to not be the primary control mechanism due to the limited strength of top-down control in Lake Geneva.

This study demonstrates the crucial role of a specific sequence of meteorological events in triggering a massive *Uroglena* bloom in Lake Geneva. The results challenge the traditional high-nutrient paradigm for algal blooms, highlighting the importance of external carbon sources from terrestrial runoff, especially in low-nutrient lakes. The study emphasizes the significance of littoral-pelagic connectivity and the complex interplay of hydrodynamic processes in shaping the spatial distribution of the bloom. The rarity of the triggering sequence explains the infrequent occurrence of such blooms in Lake Geneva. The findings highlight the limitations of using single meteorological variables to predict blooms and underscore the need for a more holistic, process-based approach that accounts for the timing and sequence of meteorological events. The increased frequency and intensity of extreme weather events projected under climate change suggest that the risk of similar blooms may increase in the future.

This research revealed the importance of a specific sequence of meteorological conditions in triggering a rare and large *Uroglena* bloom in Lake Geneva. Extreme rainfall, wind-induced upwelling, and subsequent calm, warm weather created favorable conditions for bloom development. The findings highlight the need to consider the dynamic interplay of meteorological processes and their timing, along with the role of littoral-pelagic connectivity, in predicting algal blooms in low-nutrient lakes. Future research should focus on incorporating growth and mortality rates into models, developing coupled hydrodynamic and biogeochemical models, and establishing open operational systems integrating remote sensing, in-situ data, and models for improved bloom prediction and management.

While this study presents a comprehensive analysis of the 2021 *Uroglena* bloom, several limitations should be acknowledged. The hydrodynamic model resolution (50m) might not fully capture small-scale processes influencing bloom dynamics. The analysis of zooplankton grazing was based on limited data, and further investigation is needed to fully quantify its role. The generalization of these findings to other lakes requires caution as the specific conditions required for *Uroglena* blooms may vary depending on species-specific traits and ecological niches. While the study indicates a temporal sequence of events causing the bloom, a full predictive model for future blooms remains a subject for further research.