Uncertainty

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.

In this module, students will generate an ecological forecast for a NEON site and quantify the different sources of uncertainty within their forecast. This module will introduce students to the conceptof uncertainty within an ecological forecast; where uncertainty in a forecast comes from; how uncertainty can be quantified within a forecast; and how uncertainty can be managed.

Create open-source software for flexible, scalable, robust, and near-real time iterative ecological forecasts in lakes and reservoirs.