Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CHjats:sub4</jats:sub>) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CHjats:sub4</jats:sub> ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CHjats:sub4</jats:sub> ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CHjats:sub4</jats:sub> ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CHjats:sub4</jats:sub> ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CHjats:sub4</jats:sub> forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistence null model. Our forecasts with iterative model refitting estimated CHjats:sub4</jats:sub> ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 logjats:sube</jats:sub>(mg CHjats:sub4</jats:sub> mjats:sup−2</jats:sup> djats:sup−1</jats:sup>) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CHjats:sub4</jats:sub> models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CHjats:sub4</jats:sub> ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CHjats:sub4</jats:sub> ebullition predictions.