Forecasting Lake And Reservoir Ecosystems (FLARE)

To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. We developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.

This study examines how the frequency of data assimilation affects the accuracy of near-term ecological forecasts, specifically for water temperature in a eutrophic reservoir. Using the FLARE forecasting system, researchers tested daily, weekly, fortnightly, and monthly data assimilation to predict water temperature 1 to 35 days ahead. They found that daily assimilation produced the most accurate short-term forecasts (1–7 days), while weekly assimilation performed best for longer-term predictions (8–35 days). Seasonal and depth variations influenced forecast accuracy, with higher-frequency assimilation being especially important during summer stratification. The findings suggest that even lower-frequency data (e.g., weekly) can yield skillful forecasts, broadening forecasting applications beyond ecosystems with high-frequency sensors.

We scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. Forecasts were more accurate than a null model. Lake characteristics interact with weather to control the predictability of thermal structure.

We scaled a near-term, iterative water temperature forecasting system to six conterminous NEON lakes. We generated forecasts using a hydrodynamic model. Forecasts were more accurate than a null model. Lake characteristics interact with weather to control the predictability of thermal structure.