Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Creators: name: Irvin, Jeremy
name: Zhou, Sharon
name: McNicol, Gavin
name: Lu, Fred
name: Liu, Vincent
name: Fluet-Chouinard, Etienne
name: Ouyang, Zutao
name: Knox, Sara Helen
name: Lucas-Moffat, Antje
name: Trotta, Carlo
name: Papale, Dario
name: Vitale, Domenico
name: Mammarella, Ivan
name: Alekseychik, Pavel
name: Aurela, Mika
name: Avati, Anand
name: Baldocchi, Dennis
name: Bansal, Sheel
name: Bohrer, Gil
name: Campbell, David I.
name: Chen, Jiquan
name: Chu, Housen
name: Dalmagro, Higo J.
name: Delwiche, Kyle B.
name: Desai, Ankur R.
name: Euskirchen, Eugenie
name: Feron, Sarah
name: Goeckede, Mathias
name: Heimann, Martin
name: Helbig, Manuel
name: Helfter, Carole
name: Hemes, Kyle S.
name: Hirano, Takashi
name: Iwata, Hiroki
name: Jurasinski, Gerald
name: Kalhori, Aram
name: Kondrich, Andrew
name: Lai, Derrick Y. F.
name: Lohila, Annalea
name: Malhotra, Avni
name: Merbold, Lutz
name: Mitra, Bhaskar
name: Ng, Andrew
name: Nilsson, Mats B.
name: Noormets, Asko
name: Peichl, Matthias
name: Rey-Sanchez, A. Camilo
name: Richardson, Andrew D.
name: Runkle, Benjamin R. K.
name: Schäfer, Karina V. R.
name: Sonnentag, Oliver
name: Stuart-Haëntjens, Ellen
name: Sturtevant, Cove
name: Ueyama, Masahito
name: Valach, Alex Constantin
name: Vargas, Rodrigo
name: Vourlitis, George L.
name: Ward, Eric J.
name: Wong, Guan Xhuan
name: Zona, Donatella
name: Alberto, Ma. Carmelita R.
name: Billesbach, David P.
name: Celis, Gerardo
name: Dolman, Han
name: Friborg, Thomas
name: Fuchs, Kathrin
name: Gogo, Sébastien
name: Gondwe, Mangaliso J.
name: Goodrich, Jordan P.
name: Gottschalk, Pia
name: Hörtnagl, Lukas
name: Jacotot, Adrien
name: Koebsch, Franziska
name: Kasak, Kuno
name: Maier, Regine
name: Morin, Timothy H.
name: Nemitz, Eiko
name: Oechel, Walter C.
name: Oikawa, Patricia Y.
name: Ono, Keisuke
name: Sachs, Torsten
name: Sakabe, Ayaka
name: Schuur, Edward A.
name: Shortt, Robert
name: Sullivan, Ryan C.
name: Szutu, Daphne J.
name: Tuittila, Eeva-Stiina
name: Varlagin, Andrej
name: Verfaillie, Joeseph G.
name: Wille, Christian
name: Windham-Myers, Lisamarie
name: Poulter, Benjamin
name: Jackson, Robert B.
Publisher: name: Elsevier
Year: 2021

description: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).