Abstract
Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and operationalized a near-term iterative water temperature 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 a hydrodynamic 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. We applied FLARE to a temperate reservoir during a 100-day period that encompassed stratified and mixed thermal conditions and found that daily forecasted water temperatures were on average within 0.91℃ at all depths of the reservoir over a 16-day forecast horizon. FLARE successfully predicted the onset of fall turnover eight days in advance, and identified meteorology driver data and downscaling as the dominant sources of forecast uncertainty. Overall, FLARE provides an open-source and easily-generalizable system for water quality forecasting for lakes and reservoirs to improve management.
Key Points
We created a near-term iterative lake water temperature forecasting system that uses sensors, data assimilation, and hydrodynamic modeling
FLARE quantifies the uncertainty in each daily forecast and provides an open-source, generalizable system for water quality forecasting
16-day forecasted temperatures were within 0.91°C over 100 days in a reservoir case study
