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. 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, T.; Sakabe, Ayaka; Schuur, Edward A; Shortt, Robert; Sullivan, Ryan C; Szutu, Daphne J; Tuittila, Eeva-Stiina; Varlagin, Andrej; Verfaillie, Joeseph G; Wille, C.; Windham-Myers, Lisamarie; Poulter, Benjamin; Jackson, Robert B

doi:10.1016/j.agrformet.2021.108528

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

Authors

Irvin, Jeremy
External Organizations;

Zhou, Sharon
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McNicol, Gavin
External Organizations;

Lu, Fred
External Organizations;

Liu, Vincent
External Organizations;

Fluet-Chouinard, Etienne
External Organizations;

Ouyang, Zutao
External Organizations;

Knox, Sara Helen
External Organizations;

Lucas-Moffat, Antje
External Organizations;

Trotta, Carlo
External Organizations;

Papale, Dario
External Organizations;

Vitale, Domenico
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Mammarella, Ivan
External Organizations;

Alekseychik, Pavel
External Organizations;

Aurela, Mika
External Organizations;

Avati, Anand
External Organizations;

Baldocchi, Dennis
External Organizations;

Bansal, Sheel
External Organizations;

Bohrer, Gil
External Organizations;

Campbell, David I
External Organizations;

Chen, Jiquan
External Organizations;

Chu, Housen
External Organizations;

Dalmagro, Higo J
External Organizations;

Delwiche, Kyle B
External Organizations;

Desai, Ankur R
External Organizations;

Euskirchen, Eugenie
External Organizations;

Feron, Sarah
External Organizations;

Goeckede, Mathias
External Organizations;

Heimann, Martin
External Organizations;

Helbig, Manuel
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Helfter, Carole
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Hemes, Kyle S
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Hirano, Takashi
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Iwata, Hiroki
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Jurasinski, Gerald
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/persons/resource/akalhori

Kalhori, Aram
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Kondrich, Andrew
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Lai, Derrick YF
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Lohila, Annalea
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Malhotra, Avni
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Merbold, Lutz
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Mitra, Bhaskar
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Ng, Andrew
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Nilsson, Mats B
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Noormets, Asko
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Peichl, Matthias
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Rey-Sanchez, A. Camilo
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Richardson, Andrew D
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Runkle, Benjamin RK
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Schäfer, Karina VR
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Sonnentag, Oliver
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Stuart-Haëntjens, Ellen
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Sturtevant, Cove
External Organizations;

Ueyama, Masahito
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Valach, Alex C
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Vargas, Rodrigo
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Vourlitis, George L
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Ward, Eric J
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Wong, Guan Xhuan
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Zona, Donatella
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Alberto, Ma. Carmelita R
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Billesbach, David P
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Celis, Gerardo
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Dolman, Han
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Friborg, Thomas
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Fuchs, Kathrin
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Gogo, Sébastien
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Gondwe, Mangaliso J
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Goodrich, Jordan P
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Gottschalk, Pia
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Hörtnagl, Lukas
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Jacotot, Adrien
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Koebsch, Franziska
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Kasak, Kuno
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Maier, Regine
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Morin, Timothy H
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Nemitz, Eiko
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Oechel, Walter C
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Oikawa, Patricia Y
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Ono, Keisuke
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Sachs, T.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Sakabe, Ayaka
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Schuur, Edward A
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Shortt, Robert
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Sullivan, Ryan C
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Szutu, Daphne J
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Tuittila, Eeva-Stiina
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Varlagin, Andrej
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Verfaillie, Joeseph G
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/persons/resource/cwille

Wille, C.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Windham-Myers, Lisamarie
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Poulter, Benjamin
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Jackson, Robert B
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Citation

Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., Ouyang, Z., Knox, S. H., Lucas-Moffat, A., Trotta, C., Papale, D., Vitale, D., Mammarella, I., Alekseychik, P., Aurela, M., Avati, A., Baldocchi, D., Bansal, S., Bohrer, G., Campbell, D. I., Chen, J., Chu, H., Dalmagro, H. J., Delwiche, K. B., Desai, A. R., Euskirchen, E., Feron, S., Goeckede, M., Heimann, M., Helbig, M., Helfter, C., Hemes, K. S., Hirano, T., Iwata, H., Jurasinski, G., Kalhori, A., Kondrich, A., Lai, D. Y., Lohila, A., Malhotra, A., Merbold, L., Mitra, B., Ng, A., Nilsson, M. B., Noormets, A., Peichl, M., Rey-Sanchez, A. C., Richardson, A. D., Runkle, B. R., Schäfer, K. V., Sonnentag, O., Stuart-Haëntjens, E., Sturtevant, C., Ueyama, M., Valach, A. C., Vargas, R., Vourlitis, G. L., Ward, E. J., Wong, G. X., Zona, D., Alberto, M. C. R., Billesbach, D. P., Celis, G., Dolman, H., Friborg, T., Fuchs, K., Gogo, S., Gondwe, M. J., Goodrich, J. P., Gottschalk, P., Hörtnagl, L., Jacotot, A., Koebsch, F., Kasak, K., Maier, R., Morin, T. H., Nemitz, E., Oechel, W. C., Oikawa, P. Y., Ono, K., Sachs, T., Sakabe, A., Schuur, E. A., Shortt, R., Sullivan, R. C., Szutu, D. J., Tuittila, E.-S., Varlagin, A., Verfaillie, J. G., Wille, C., Windham-Myers, L., Poulter, B., Jackson, R. B. (2021): Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. - Agricultural and Forest Meteorology, 308-309, 108528.
https://doi.org/10.1016/j.agrformet.2021.108528

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007227

Abstract

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).