Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and automated a near-term, iterative water quality forecasting system (FLARE – Forecasting Lake And Reservoir Ecosystems) that is generalizable for lakes and reservoirs. FLARE is composed of: water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from the General Lake Model and calibrate model parameters, and an ensemble-based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (parameters, driver data, initial conditions, and process) to each daily forecast of water temperature at multiple depths. Overall, FLARE provides an open-source and easily-generalizable system for water quality forecasting for lakes and reservoirs to improve management
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(Image credit: Cayelan Carey & Kate Hamre)