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.; 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 YF; 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 RK; Schäfer, Karina VR; Sonnentag, Oliver; Stuart-Haëntjens, Ellen; Sturtevant, Cove; Ueyama, Masahito; Valach, Alex C.; Vargas, Rodrigo; Vourlitis, George L.; Ward, Eric J.; Wong, Guan Xhuan; Zona, Donatella; Alberto, Ma. 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; Jackson, Robert B.

doi:10.1016/j.agrformet.2021.108528

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

Journal Article · Fri Oct 01 00:00:00 EDT 2021 · Agricultural and Forest Meteorology

DOI:https://doi.org/10.1016/j.agrformet.2021.108528· OSTI ID:1840759

Irvin, Jeremy; Zhou, Sharon;

; Lu, Fred;

; Fluet-Chouinard, Etienne;

; Knox, Sara Helen; Lucas-Moffat, Antje; Trotta, Carlo; Papale, Dario; Vitale, Domenico;

; Alekseychik, Pavel; Aurela, Mika; Avati, Anand; Baldocchi, Dennis;

;

; Campbell, David I. more »

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).

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); Gordon and Betty Moore Foundation; National Science Foundation (NSF); Natural Sciences and Engineering Research Council of Canada (NSERC); Helmholtz Association of German Research Centres; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Contributing Organization:: R Core Team

Grant/Contract Number:: AC02-06CH11357; 1752083; VH-NG-821; N18B 315-11; 146373; 407340_172433; 2011-67003-30371; 7079856; AC02-05CH11231

OSTI ID:: 1840759

Alternate ID(s):: OSTI ID: 1902660

Journal Information:: Agricultural and Forest Meteorology, Vol. 308-309; ISSN 0168-1923

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Journal Article · Thu Apr 15 00:00:00 EDT 2021 · Nature Communications · OSTI ID:1840759

Chang, Kuang-Yu; Riley, William J.; Knox, Sara H.; +50 more

Related Subjects

54 ENVIRONMENTAL SCIENCES
42 ENGINEERING
machine learning
flux
gap-filling
imeseries
imputation
methane
wetlands

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

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