Abstract Dissolved organic matter (DOM) plays an important role in aquatic carbon cycling and is a valuable metric of ecosystem functioning and water quality in freshwater ecosystems. Despite its importance for biogeochemical cycling and water quality, no near‐term iterative forecasts have previously been developed for freshwater DOM concentrations. To advance both our understanding of freshwater DOM dynamics and management, we developed 1–34 days‐ahead forecasts of fluorescent DOM (fDOM) in three drinking water reservoirs. These temperate reservoirs are co‐located in Virginia, USA and experience variable DOM dynamics (range: 5–27 QSU (quinine sulfate units)). We developed six different forecasting models to predict fDOM in each reservoir. Three models were time series models based on forecasted drivers (water temperature and meteorology) that were updated daily from high‐frequency fDOM sensors. The other forecast models included a neural network machine learning model and two baseline reference models (day‐of‐year mean and persistence). Altogether, our forecasts were able to capture observed dynamics over a year in all three reservoirs, with one time series model outperforming the baseline models across the full 34‐day forecast horizon. Aggregated across reservoirs and models over a year, forecast RMSE increased from 0.7 to 4.1 QSU over the 1–34 days‐ahead forecast horizon. Forecast skill varied substantially across seasons, with greatest accuracy in the spring and winter compared to the summer and fall across reservoirs. These forecasts can help improve our understanding of the predictability of DOM and inform management in freshwater ecosystems as carbon dynamics become more variable due to global change.
Howard et al. (Wed,) studied this question.