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.

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.