Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

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

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 whilstmore » 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).« less

Authors:
; ; ORCiD logo; ; ORCiD logo; ; ORCiD logo; ; ; ; ; ; ORCiD logo; ; ; ; ; ORCiD logo; ORCiD logo; more »; ; ORCiD logo; ORCiD logo; ; ORCiD logo; ; ; ORCiD logo; ; ; ; ORCiD logo; ORCiD logo; ORCiD logo; ; ORCiD logo; ; ORCiD logo; ; ; ORCiD logo; ; ; ORCiD logo; ; ; ORCiD logo; ORCiD logo; ; ; ; ORCiD logo; ; ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo; ; ; ; ; ; ; ; ; ; ; ORCiD logo; ORCiD logo; ; ; ORCiD logo; ; ORCiD logo; ; ; ; ; ; ; ORCiD logo; ; ; ; ORCiD logo; ORCiD logo; ; ; ORCiD logo; ORCiD logo; ; ; « less
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
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 Org.:
R Core Team
OSTI Identifier:
1840759
Alternate Identifier(s):
OSTI ID: 1902660
Grant/Contract Number:  
AC02-06CH11357; 1752083; VH-NG-821; N18B 315-11; 146373; 407340_172433; 2011-67003-30371; 7079856; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Agricultural and Forest Meteorology
Additional Journal Information:
Journal Volume: 308-309; Journal ID: ISSN 0168-1923
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 42 ENGINEERING; machine learning; flux; gap-filling; imeseries; imputation; methane; wetlands

Citation Formats

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, and Jackson, Robert B. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. United States: N. p., 2021. Web. doi:10.1016/j.agrformet.2021.108528.
Copy to clipboard
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. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. United States. https://doi.org/10.1016/j.agrformet.2021.108528
Copy to clipboard
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, and Jackson, Robert B. Fri . "Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands". United States. https://doi.org/10.1016/j.agrformet.2021.108528. https://www.osti.gov/servlets/purl/1840759.
Copy to clipboard
@article{osti_1840759,
title = {Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands},
author = {Irvin, Jeremy and Zhou, Sharon and McNicol, Gavin and Lu, Fred and Liu, Vincent and Fluet-Chouinard, Etienne and Ouyang, Zutao and Knox, Sara Helen and Lucas-Moffat, Antje and Trotta, Carlo and Papale, Dario and Vitale, Domenico and Mammarella, Ivan and Alekseychik, Pavel and Aurela, Mika and Avati, Anand and Baldocchi, Dennis and Bansal, Sheel and Bohrer, Gil and Campbell, David I. and Chen, Jiquan and Chu, Housen and Dalmagro, Higo J. and Delwiche, Kyle B. and Desai, Ankur R. and Euskirchen, Eugenie and Feron, Sarah and Goeckede, Mathias and Heimann, Martin and Helbig, Manuel and Helfter, Carole and Hemes, Kyle S. and Hirano, Takashi and Iwata, Hiroki and Jurasinski, Gerald and Kalhori, Aram and Kondrich, Andrew and Lai, Derrick YF and Lohila, Annalea and Malhotra, Avni and Merbold, Lutz and Mitra, Bhaskar and Ng, Andrew and Nilsson, Mats B. and Noormets, Asko and Peichl, Matthias and Rey-Sanchez, A. Camilo and Richardson, Andrew D. and Runkle, Benjamin RK and Schäfer, Karina VR and Sonnentag, Oliver and Stuart-Haëntjens, Ellen and Sturtevant, Cove and Ueyama, Masahito and Valach, Alex C. and Vargas, Rodrigo and Vourlitis, George L. and Ward, Eric J. and Wong, Guan Xhuan and Zona, Donatella and Alberto, Ma. R. and Billesbach, David P. and Celis, Gerardo and Dolman, Han and Friborg, Thomas and Fuchs, Kathrin and Gogo, Sébastien and Gondwe, Mangaliso J. and Goodrich, Jordan P. and Gottschalk, Pia and Hörtnagl, Lukas and Jacotot, Adrien and Koebsch, Franziska and Kasak, Kuno and Maier, Regine and Morin, Timothy H. and Nemitz, Eiko and Oechel, Walter C. and Oikawa, Patricia Y. and Ono, Keisuke and Sachs, Torsten and Sakabe, Ayaka and Schuur, Edward A. and Shortt, Robert and Sullivan, Ryan C. and Szutu, Daphne J. and Tuittila, Eeva-Stiina and Varlagin, Andrej and Verfaillie, Joeseph G. and Wille, Christian and Windham-Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B.},
abstractNote = {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).},
doi = {10.1016/j.agrformet.2021.108528},
journal = {Agricultural and Forest Meteorology},
number = ,
volume = 308-309,
place = {United States},
year = {Fri Oct 01 00:00:00 EDT 2021},
month = {Fri Oct 01 00:00:00 EDT 2021}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.agrformet.2021.108528
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Methane Emission Reductions from the Alternate Wetting and Drying of Rice Fields Detected Using the Eddy Covariance Method
journal, December 2018

  • Runkle, Benjamin R. K.; Suvočarev, Kosana; Reba, Michele L.
  • Environmental Science & Technology, Vol. 53, Issue 2
  • DOI: 10.1021/acs.est.8b05535

Feature Selection: A literature Review
journal, June 2014

Transforming classifier scores into accurate multiclass probability estimates
conference, January 2002

  • Zadrozny, Bianca; Elkan, Charles
  • Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '02
  • DOI: 10.1145/775047.775151

Identifying scale-emergent, nonlinear, asynchronous processes of wetland methane exchange: IDENTIFYING METHANE EXCHANGE PROCESSES
journal, January 2016

  • Sturtevant, Cove; Ruddell, Benjamin L.; Knox, Sara Helen
  • Journal of Geophysical Research: Biogeosciences, Vol. 121, Issue 1
  • DOI: 10.1002/2015JG003054

Probabilistic forecasts, calibration and sharpness
journal, April 2007

  • Gneiting, Tilmann; Balabdaoui, Fadoua; Raftery, Adrian E.
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 69, Issue 2
  • DOI: 10.1111/j.1467-9868.2007.00587.x

FLUXNET-CH 4 Synthesis Activity: Objectives, Observations, and Future Directions
journal, July 2019

  • Knox, Sara H.; Jackson, Robert B.; Poulter, Benjamin
  • Bulletin of the American Meteorological Society
  • DOI: 10.1175/BAMS-D-18-0268.1

Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product
journal, January 2018

Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes
journal, December 2007

Diurnal Patterns of Methane Flux from a Seasonal Wetland: Mechanisms and Methodology
journal, May 2018

Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness
journal, January 2019

  • Tuovinen, Juha-Pekka; Aurela, Mika; Hatakka, Juha
  • Biogeosciences, Vol. 16, Issue 2
  • DOI: 10.5194/bg-16-255-2019

Assessing the carbon and climate benefit of restoring degraded agricultural peat soils to managed wetlands
journal, April 2019

Testing the applicability of neural networks as a gap-filling method using CH 4 flux data from high latitude wetlands
journal, January 2013

The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens
journal, September 2020

  • Koebsch, Franziska; Gottschalk, Pia; Beyer, Florian
  • Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 375, Issue 1810
  • DOI: 10.1098/rstb.2019.0685

Half of global methane emissions come from highly variable aquatic ecosystem sources
journal, April 2021

  • Rosentreter, Judith A.; Borges, Alberto V.; Deemer, Bridget R.
  • Nature Geoscience, Vol. 14, Issue 4
  • DOI: 10.1038/s41561-021-00715-2

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
journal, January 2016

  • Tramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.
  • Biogeosciences, Vol. 13, Issue 14
  • DOI: 10.5194/bg-13-4291-2016

Random errors in carbon and water vapor fluxes assessed with Gaussian Processes
journal, September 2013

Summarizing multiple aspects of model performance in a single diagram
journal, April 2001

  • Taylor, Karl E.
  • Journal of Geophysical Research: Atmospheres, Vol. 106, Issue D7
  • DOI: 10.1029/2000JD900719

FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
journal, January 2021

  • Delwiche, Kyle B.; Knox, Sara Helen; Malhotra, Avni
  • Earth System Science Data, Vol. 13, Issue 7
  • DOI: 10.5194/essd-13-3607-2021

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
journal, May 2021

  • Knox, Sara H.; Bansal, Sheel; McNicol, Gavin
  • Global Change Biology, Vol. 27, Issue 15
  • DOI: 10.1111/gcb.15661

Nongrowing season methane emissions-a significant component of annual emissions across northern ecosystems
journal, April 2018

  • Treat, Claire C.; Bloom, A. Anthony; Marushchak, Maija E.
  • Global Change Biology, Vol. 24, Issue 8
  • DOI: 10.1111/gcb.14137

XGBoost: A Scalable Tree Boosting System
conference, January 2016

  • Chen, Tianqi; Guestrin, Carlos
  • Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
  • DOI: 10.1145/2939672.2939785

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
journal, July 2020

A perspective on Gaussian processes for Earth observation
journal, March 2019

  • Camps-Valls, Gustau; Sejdinovic, Dino; Runge, Jakob
  • National Science Review, Vol. 6, Issue 4
  • DOI: 10.1093/nsr/nwz028

WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas: NEW CLIMATE SURFACES FOR GLOBAL LAND AREAS
journal, May 2017

  • Fick, Stephen E.; Hijmans, Robert J.
  • International Journal of Climatology, Vol. 37, Issue 12
  • DOI: 10.1002/joc.5086

Comparing ecosystem and soil respiration: Review and key challenges of tower-based and soil measurements
journal, February 2018

Environmental drivers of methane fluxes from an urban temperate wetland park
journal, November 2014

  • Morin, T. H.; Bohrer, G.; Frasson, R. P. d. M.
  • Journal of Geophysical Research: Biogeosciences, Vol. 119, Issue 11
  • DOI: 10.1002/2014JG002750

Moving Beyond Global Warming Potentials to Quantify the Climatic Role of Ecosystems
journal, May 2015

The Global Methane Budget 2000–2017
journal, January 2020

  • Saunois, Marielle; Stavert, Ann R.; Poulter, Ben
  • Earth System Science Data, Vol. 12, Issue 3
  • DOI: 10.5194/essd-12-1561-2020

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
journal, March 2011

  • Derrac, Joaquín; García, Salvador; Molina, Daniel
  • Swarm and Evolutionary Computation, Vol. 1, Issue 1
  • DOI: 10.1016/j.swevo.2011.02.002

Carbon dioxide and methane fluxes from different surface types in a created urban wetland
journal, January 2020

Basic and extensible post-processing of eddy covariance flux data with REddyProc
journal, January 2018

  • Wutzler, Thomas; Lucas-Moffat, Antje; Migliavacca, Mirco
  • Biogeosciences, Vol. 15, Issue 16
  • DOI: 10.5194/bg-15-5015-2018

Prompt rewetting of drained peatlands reduces climate warming despite methane emissions
journal, April 2020

Gap-filling missing data in eddy covariance measurements using multiple imputation (MI) for annual estimations
journal, January 2004

Determination of rice paddy parameters in the global gross primary production capacity estimation algorithm using 6 years of JP-MSE flux observation data
journal, January 2017

  • Muramatsu, Kanako; Ono, Keisuke; Soyama, Noriko
  • Journal of Agricultural Meteorology, Vol. 73, Issue 3
  • DOI: 10.2480/agrmet.D-16-00017

Determining total emissions and environmental drivers of methane flux in a Lake Erie estuarine marsh
journal, April 2018

A method to estimate the additional uncertainty in gap-filled NEE resulting from long gaps in the CO2 flux record
journal, December 2007

Primary production control of methane emission from wetlands
journal, August 1993

  • Whiting, G. J.; Chanton, J. P.
  • Nature, Vol. 364, Issue 6440
  • DOI: 10.1038/364794a0

Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH4 flux measurements using wavelet techniques
journal, January 2019

  • Göckede, Mathias; Kittler, Fanny; Schaller, Carsten
  • Biogeosciences, Vol. 16, Issue 16
  • DOI: 10.5194/bg-16-3113-2019

SciPy 1.0: fundamental algorithms for scientific computing in Python
journal, February 2020

Environmental Controls of Diffusive and Ebullitive Methane Emissions at a Subdaily Time Scale in the Littoral Zone of a Midlatitude Shallow Lake
journal, September 2020

  • Taoka, T.; Iwata, H.; Hirata, R.
  • Journal of Geophysical Research: Biogeosciences, Vol. 125, Issue 9
  • DOI: 10.1029/2020JG005753

Standardisation of eddy-covariance flux measurements of methane and nitrous oxide
journal, December 2018

  • Nemitz, Eiko; Mammarella, Ivan; Ibrom, Andreas
  • International Agrophysics, Vol. 32, Issue 4
  • DOI: 10.1515/intag-2017-0042

Widespread inhibition of daytime ecosystem respiration
journal, February 2019

  • Keenan, Trevor F.; Migliavacca, Mirco; Papale, Dario
  • Nature Ecology & Evolution, Vol. 3, Issue 3
  • DOI: 10.1038/s41559-019-0809-2

A synthesis of methane emissions from 71 northern, temperate, and subtropical wetlands
journal, April 2014

  • Turetsky, Merritt R.; Kotowska, Agnieszka; Bubier, Jill
  • Global Change Biology, Vol. 20, Issue 7
  • DOI: 10.1111/gcb.12580

Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park
journal, May 2017

Modelling random uncertainty of eddy covariance flux measurements
journal, March 2019

  • Vitale, Domenico; Bilancia, Massimo; Papale, Dario
  • Stochastic Environmental Research and Risk Assessment, Vol. 33, Issue 3
  • DOI: 10.1007/s00477-019-01664-4

Probabilistic Forecasting
journal, January 2014

Parsing the variability in CH 4 flux at a spatially heterogeneous wetland: Integrating multiple eddy covariance towers with high-resolution flux footprint analysis
journal, July 2014

  • Matthes, Jaclyn Hatala; Sturtevant, Cove; Verfaillie, Joseph
  • Journal of Geophysical Research: Biogeosciences, Vol. 119, Issue 7
  • DOI: 10.1002/2014JG002642

Overriding control of methane flux temporal variability by water table dynamics in a Southern Hemisphere, raised bog: Methane fluxes from a S.H. bog
journal, May 2015

  • Goodrich, J. P.; Campbell, D. I.; Roulet, N. T.
  • Journal of Geophysical Research: Biogeosciences, Vol. 120, Issue 5
  • DOI: 10.1002/2014JG002844

Carbon biogeochemistry of a flooded Pantanal forest over three annual flood cycles
journal, May 2018

  • Dalmagro, Higo J.; Lathuillière, Michael J.; Hawthorne, Iain
  • Biogeochemistry, Vol. 139, Issue 1
  • DOI: 10.1007/s10533-018-0450-1

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis
journal, January 2015

Influences of observation errors in eddy flux data on inverse model parameter estimation
journal, January 2008

Gross ecosystem photosynthesis causes a diurnal pattern in methane emission from rice: GEP CAUSES DIURNAL CH
journal, March 2012

  • Hatala, Jaclyn A.; Detto, Matteo; Baldocchi, Dennis D.
  • Geophysical Research Letters, Vol. 39, Issue 6
  • DOI: 10.1029/2012GL051303

Ideas and perspectives: enhancing the impact of the FLUXNET network of eddy covariance sites
journal, January 2020

Salinity Influence on Methane Emissions from Tidal Marshes
journal, July 2011

  • Poffenbarger, Hanna J.; Needelman, Brian A.; Megonigal, J. Patrick
  • Wetlands, Vol. 31, Issue 5
  • DOI: 10.1007/s13157-011-0197-0

Where old meets new: An ecosystem study of methanogenesis in a reflooded agricultural peatland
journal, December 2019

  • McNicol, Gavin; Knox, Sara H.; Guilderson, Thomas P.
  • Global Change Biology, Vol. 26, Issue 2
  • DOI: 10.1111/gcb.14916

On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm
journal, September 2005

Gap filling strategies for defensible annual sums of net ecosystem exchange
journal, March 2001

Carbon dioxide and methane fluxes from an intermittently flooded paddy field
journal, May 2000

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks
journal, July 2020

  • Tramontana, Gianluca; Migliavacca, Mirco; Jung, Martin
  • Global Change Biology, Vol. 26, Issue 9
  • DOI: 10.1111/gcb.15203

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
journal, April 2021

Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales
journal, February 2013

  • Bridgham, Scott D.; Cadillo-Quiroz, Hinsby; Keller, Jason K.
  • Global Change Biology, Vol. 19, Issue 5
  • DOI: 10.1111/gcb.12131

Uncertainty in eddy covariance measurements and its application to physiological models
journal, July 2005

Environmental controls on methane fluxes in a cool temperate bog
journal, February 2020

Uncertainty in United States coastal wetland greenhouse gas inventorying
journal, November 2018

  • Holmquist, James R.; Windham-Myers, Lisamarie; Bernal, Blanca
  • Environmental Research Letters, Vol. 13, Issue 11
  • DOI: 10.1088/1748-9326/aae157

Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
journal, March 2017

  • Roberts, David R.; Bahn, Volker; Ciuti, Simone
  • Ecography, Vol. 40, Issue 8
  • DOI: 10.1111/ecog.02881

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
journal, January 2019

Effects of seasonality, transport pathway, and spatial structure on greenhouse gas fluxes in a restored wetland
journal, January 2017

  • McNicol, Gavin; Sturtevant, Cove S.; Knox, Sara H.
  • Global Change Biology, Vol. 23, Issue 7
  • DOI: 10.1111/gcb.13580

Hot-Moments of Soil CO2 Efflux in a Water-Limited Grassland
journal, August 2018

  • Vargas, Rodrigo; Sánchez-Cañete P., Enrique; Serrano-Ortiz, Penélope
  • Soil Systems, Vol. 2, Issue 3
  • DOI: 10.3390/soilsystems2030047

Methane fluxes show consistent temperature dependence across microbial to ecosystem scales
journal, March 2014

  • Yvon-Durocher, Gabriel; Allen, Andrew P.; Bastviken, David
  • Nature, Vol. 507, Issue 7493
  • DOI: 10.1038/nature13164

A Multi-Year Record of Methane Flux at the Mer Bleue Bog, Southern Canada
journal, April 2011

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)
journal, January 2013

  • Wania, R.; Melton, J. R.; Hodson, E. L.
  • Geoscientific Model Development, Vol. 6, Issue 3
  • DOI: 10.5194/gmd-6-617-2013

Biophysical controls on interannual variability in ecosystem-scale CO 2 and CH 4 exchange in a California rice paddy : INTERANNUAL VARIABILITY RICE CH
journal, March 2016

  • Knox, Sara Helen; Matthes, Jaclyn Hatala; Sturtevant, Cove
  • Journal of Geophysical Research: Biogeosciences, Vol. 121, Issue 3
  • DOI: 10.1002/2015JG003247

Random Forests
journal, January 2001

Comparisons of gap-filling methods for carbon flux dataset: A combination of a genetic algorithm and an artificial neural network
journal, October 2006

Interpretation of the Correlation Coefficient: A Basic Review
journal, January 1990

Evaluating the Classical Versus an Emerging Conceptual Model of Peatland Methane Dynamics: Peatland Methane Dynamics
journal, September 2017

  • Yang, Wendy H.; McNicol, Gavin; Teh, Yit Arn
  • Global Biogeochemical Cycles, Vol. 31, Issue 9
  • DOI: 10.1002/2017GB005622

Biophysical drivers of net ecosystem and methane exchange across phenological phases in a tidal salt marsh
journal, April 2021

FLUXNET-CH4 FI-Lom Lompolojankka
dataset, January 2020

  • Lohila, Annalea; Aurela, Mika; Tuovinen, Juha-Pekka
  • FluxNet; Finnish Meteorological Institute
  • DOI: 10.18140/flx/1669638

FLUXNET-CH4 FI-Si2 Siikaneva-2 Bog
dataset, January 2020

  • Vesala, Timo; Tuittila, Eeva-Stiina; Mammarella, Ivan
  • FluxNet; University of Eastern Finland; University of Helsinki
  • DOI: 10.18140/flx/1669639

FLUXNET-CH4 FI-Sii Siikaneva
dataset, January 2020

  • Vesala, Timo; Tuittila, Eeva-Stiina; Mammarella, Ivan
  • FluxNet; University of Eastern Finland; University of Helsinki
  • DOI: 10.18140/flx/1669640

FLUXNET-CH4 JP-BBY Bibai bog
dataset, January 2020

  • Ueyama, Masahito; Hirano, Takashi; Kominami, Yasuhiro
  • FluxNet; Osaka Prefecture Univeristy
  • DOI: 10.18140/flx/1669646

FLUXNET-CH4 JP-Mse Mase rice paddy field
dataset, January 2020

  • Iwata, Hiroki
  • FluxNet; National Agriculture and Food Research Organization
  • DOI: 10.18140/flx/1669647

FLUXNET-CH4 NZ-Kop Kopuatai
dataset, January 2020

FLUXNET-CH4 SE-Deg Degero
dataset, January 2020

  • Nilsson, Mats; Peichl, Matthias
  • FluxNet; Department of Forest Ecology and Management; Swedish University of Agricultural Sciences
  • DOI: 10.18140/flx/1669659
    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: