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 fl...

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Autores principales:	Ryan P. McClure, R. Quinn Thomas, Mary E. Lofton, Whitney M. Woelmer, Cayelan C. Carey
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	biogeochemical fluxes ecological forecasting freshwater greenhouse gases reservoir Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/69c6ee0ad940495a82158a44da2430f3
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spelling	oai:doaj.org-article:69c6ee0ad940495a82158a44da2430f32021-12-02T00:35:55ZIterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates2296-665X10.3389/fenvs.2021.756603https://doaj.org/article/69c6ee0ad940495a82158a44da2430f32021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenvs.2021.756603/fullhttps://doaj.org/toc/2296-665XNear-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 (CH4) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH4 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 CH4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH4 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 CH4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 loge(mg CH4 m−2 d−1) 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 CH4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH4 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 CH4 ebullition predictions.Ryan P. McClureR. Quinn ThomasR. Quinn ThomasMary E. LoftonWhitney M. WoelmerCayelan C. CareyFrontiers Media S.A.articlebiogeochemical fluxesecological forecastingfreshwatergreenhouse gasesreservoirEnvironmental sciencesGE1-350ENFrontiers in Environmental Science, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	biogeochemical fluxes ecological forecasting freshwater greenhouse gases reservoir Environmental sciences GE1-350
spellingShingle	biogeochemical fluxes ecological forecasting freshwater greenhouse gases reservoir Environmental sciences GE1-350 Ryan P. McClure R. Quinn Thomas R. Quinn Thomas Mary E. Lofton Whitney M. Woelmer Cayelan C. Carey Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
description	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 (CH4) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH4 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 CH4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH4 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 CH4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 loge(mg CH4 m−2 d−1) 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 CH4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH4 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 CH4 ebullition predictions.
format	article
author	Ryan P. McClure R. Quinn Thomas R. Quinn Thomas Mary E. Lofton Whitney M. Woelmer Cayelan C. Carey
author_facet	Ryan P. McClure R. Quinn Thomas R. Quinn Thomas Mary E. Lofton Whitney M. Woelmer Cayelan C. Carey
author_sort	Ryan P. McClure
title	Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
title_short	Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
title_full	Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
title_fullStr	Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
title_full_unstemmed	Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates
title_sort	iterative forecasting improves near-term predictions of methane ebullition rates
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/69c6ee0ad940495a82158a44da2430f3
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Iterative Forecasting Improves Near-Term Predictions of Methane Ebullition Rates

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