Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

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dc.contributor.authorMcClure, Ryan P.en
dc.contributor.authorThomas, R. Quinnen
dc.contributor.authorLofton, Mary E.en
dc.contributor.authorWoelmer, Whitney M.en
dc.contributor.authorCarey, Cayelan C.en
dc.date.accessioned2021-12-04T18:48:47Zen
dc.date.available2021-12-04T18:48:47Zen
dc.date.issued2021-12en
dc.date.updated2021-12-04T18:48:44Zen
dc.description.abstractNear-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 (CH<jats:sub>4</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 CH<jats:sub>4</jats:sub> ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH<jats:sub>4</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 CH<jats:sub>4</jats:sub> ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH<jats:sub>4</jats:sub> ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH<jats:sub>4</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 CH<jats:sub>4</jats:sub> ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 log<jats:sub>e</jats:sub>(mg CH<jats:sub>4</jats:sub> m<jats:sup>−2</jats:sup> d<jats: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 CH<jats:sub>4</jats:sub> models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH<jats:sub>4</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 CH<jats:sub>4</jats:sub> ebullition predictions.en
dc.description.versionPublished versionen
dc.format.extentPages 756603-756603en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fenvs.2021.756603en
dc.identifier.eissn2296-665Xen
dc.identifier.orcidThomas, Robert [0000-0003-1282-7825]en
dc.identifier.urihttp://hdl.handle.net/10919/106835en
dc.identifier.volume9en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subject0502 Environmental Science and Managementen
dc.titleIterative Forecasting Improves Near-Term Predictions of Methane Ebullition Ratesen
dc.title.serialFrontiers in Environmental Scienceen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Natural Resources & Environmenten
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/Forest Resources and Environmental Conservationen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/CNRE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten

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