Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Irvin, Jeremy; Zhou, Sharon; McNicol, Gavin; Lu, Fred; Liu, Vincent; Fluet-Chouinard, Etienne; Ouyang, Zutao; Knox, Sara Helen; Lucas-Moffat, Antje; Trotta, Carlo; Papale, Dario; Vitale, Domenico; Mammarella, Ivan; Alekseychik, Pavel; Aurela, Mika; Avati, Anand; Baldocchi, Dennis; Bansal, Sheel; Bohrer, Gil; Campbell, David I.; ... (2021). Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands Agricultural and Forest Meteorology, 308-30, p. 108528. Elsevier 10.1016/j.agrformet.2021.108528

[img] Text
Irvin et al., 2021 Gap-filling eddy covariance methane fluxes.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (7MB) | Request a copy

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).

Item Type:

Journal Article (Original Article)

Division/Institute:

School of Agricultural, Forest and Food Sciences HAFL
School of Agricultural, Forest and Food Sciences HAFL > Agriculture

Name:

Irvin, Jeremy;
Zhou, Sharon;
McNicol, Gavin;
Lu, Fred;
Liu, Vincent;
Fluet-Chouinard, Etienne;
Ouyang, Zutao;
Knox, Sara Helen;
Lucas-Moffat, Antje;
Trotta, Carlo;
Papale, Dario;
Vitale, Domenico;
Mammarella, Ivan;
Alekseychik, Pavel;
Aurela, Mika;
Avati, Anand;
Baldocchi, Dennis;
Bansal, Sheel;
Bohrer, Gil;
Campbell, David I.;
Chen, Jiquan;
Chu, Housen;
Dalmagro, Higo J.;
Delwiche, Kyle B.;
Desai, Ankur R.;
Euskirchen, Eugenie;
Feron, Sarah;
Goeckede, Mathias;
Heimann, Martin;
Helbig, Manuel;
Helfter, Carole;
Hemes, Kyle S.;
Hirano, Takashi;
Iwata, Hiroki;
Jurasinski, Gerald;
Kalhori, Aram;
Kondrich, Andrew;
Lai, Derrick Y. F.;
Lohila, Annalea;
Malhotra, Avni;
Merbold, Lutz;
Mitra, Bhaskar;
Ng, Andrew;
Nilsson, Mats B.;
Noormets, Asko;
Peichl, Matthias;
Rey-Sanchez, A. Camilo;
Richardson, Andrew D.;
Runkle, Benjamin R. K.;
Schäfer, Karina V. R.;
Sonnentag, Oliver;
Stuart-Haëntjens, Ellen;
Sturtevant, Cove;
Ueyama, Masahito;
Valach, Alex Constantin0000-0003-4782-5766;
Vargas, Rodrigo;
Vourlitis, George L.;
Ward, Eric J.;
Wong, Guan Xhuan;
Zona, Donatella;
Alberto, Ma. Carmelita R.;
Billesbach, David P.;
Celis, Gerardo;
Dolman, Han;
Friborg, Thomas;
Fuchs, Kathrin;
Gogo, Sébastien;
Gondwe, Mangaliso J.;
Goodrich, Jordan P.;
Gottschalk, Pia;
Hörtnagl, Lukas;
Jacotot, Adrien;
Koebsch, Franziska;
Kasak, Kuno;
Maier, Regine;
Morin, Timothy H.;
Nemitz, Eiko;
Oechel, Walter C.;
Oikawa, Patricia Y.;
Ono, Keisuke;
Sachs, Torsten;
Sakabe, Ayaka;
Schuur, Edward A.;
Shortt, Robert;
Sullivan, Ryan C.;
Szutu, Daphne J.;
Tuittila, Eeva-Stiina;
Varlagin, Andrej;
Verfaillie, Joeseph G.;
Wille, Christian;
Windham-Myers, Lisamarie;
Poulter, Benjamin and
Jackson, Robert B.

ISSN:

01681923

Publisher:

Elsevier

Language:

English

Submitter:

Alex Constantin Valach

Date Deposited:

06 Feb 2024 15:07

Last Modified:

23 Jun 2024 01:38

Publisher DOI:

10.1016/j.agrformet.2021.108528

ARBOR DOI:

10.24451/arbor.21047

URI:

https://arbor.bfh.ch/id/eprint/21047

Actions (login required)

View Item View Item
Provide Feedback