TY - JOUR

T1 - Gap-filling eddy covariance methane fluxes

T2 - Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

AU - Irvin, Jeremy

AU - Zhou, Sharon

AU - Mcnicol, Gavin

AU - Lu, Fred

AU - Liu, Vincent

AU - Fluet-chouinard, Etienne

AU - Ouyang, Zutao

AU - Knox, Sara Helen

AU - Lucas-moffat, Antje

AU - Trotta, Carlo

AU - Papale, Dario

AU - Vitale, Domenico

AU - Mammarella, Ivan

AU - Alekseychik, Pavel

AU - Aurela, Mika

AU - Avati, Anand

AU - Baldocchi, Dennis

AU - Bansal, Sheel

AU - Bohrer, Gil

AU - Campbell, David I

AU - Chen, Jiquan

AU - Chu, Housen

AU - Dalmagro, Higo J

AU - Delwiche, Kyle B

AU - Desai, Ankur R

AU - Euskirchen, Eugenie

AU - Feron, Sarah

AU - Goeckede, Mathias

AU - Heimann, Martin

AU - Helbig, Manuel

AU - Helfter, Carole

AU - Hemes, Kyle S

AU - Hirano, Takashi

AU - Iwata, Hiroki

AU - Jurasinski, Gerald

AU - Kalhori, Aram

AU - Kondrich, Andrew

AU - Lai, Derrick Yf

AU - Lohila, Annalea

AU - Malhotra, Avni

AU - Merbold, Lutz

AU - Mitra, Bhaskar

AU - Ng, Andrew

AU - Nilsson, Mats B

AU - Noormets, Asko

AU - Peichl, Matthias

AU - Rey-sanchez, A. Camilo

AU - Richardson, Andrew D

AU - Runkle, Benjamin Rk

AU - Schäfer, Karina Vr

AU - Sonnentag, Oliver

AU - Stuart-haëntjens, Ellen

AU - Sturtevant, Cove

AU - Ueyama, Masahito

AU - Valach, Alex C

AU - Vargas, Rodrigo

AU - Vourlitis, George L

AU - Ward, Eric J

AU - Wong, Guan Xhuan

AU - Zona, Donatella

AU - Alberto, Ma. Carmelita R

AU - Billesbach, David P

AU - Celis, Gerardo

AU - Dolman, Han

AU - Friborg, Thomas

AU - Fuchs, Kathrin

AU - Gogo, Sébastien

AU - Gondwe, Mangaliso J

AU - Goodrich, Jordan P

AU - Gottschalk, Pia

AU - Hörtnagl, Lukas

AU - Jacotot, Adrien

AU - Koebsch, Franziska

AU - Kasak, Kuno

AU - Maier, Regine

AU - Morin, Timothy H

AU - Nemitz, Eiko

AU - Oechel, Walter C

AU - Oikawa, Patricia Y

AU - Ono, Keisuke

AU - Sachs, Torsten

AU - Sakabe, Ayaka

AU - Schuur, Edward A

AU - Shortt, Robert

AU - Sullivan, Ryan C

AU - Szutu, Daphne J

AU - Tuittila, Eeva-stiina

AU - Varlagin, Andrej

AU - Verfaillie, Joeseph G

AU - Wille, Christian

AU - Windham-myers, Lisamarie

AU - Poulter, Benjamin

AU - Jackson, Robert B

PY - 2021/10/1

Y1 - 2021/10/1

N2 - 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).

AB - 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).

U2 - 10.1016/j.agrformet.2021.108528

DO - 10.1016/j.agrformet.2021.108528

M3 - Journal article

VL - 308-309

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

M1 - 108528

ER -