Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

Zhu, Qing; Jacob, Daniel J.; Yuan, Kunxiaojia; Li, Fa; Runkle, Benjamin K.; Chen, Min; Bloom, A. Anthony; Poulter, Benjamin; East, James D.; Riley, William J.; McNicol, Gavin; Worden, John; Frankenberg, Christian; Halabisky, Meghan

doi:10.1088/1748-9326/adad02

Title: Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

Journal Article · 04 February 2025 · Environmental Research Letters

DOI: https://doi.org/10.1088/1748-9326/adad02 · OSTI ID:2510902

^[1]; Jacob, Daniel J. ^[2];

^[1]; Li, Fa ^[3];

^[4]; Chen, Min ^[5]; Bloom, A. Anthony ^[6];

^[7];

^[2];

^[1]; McNicol, Gavin ^[8]; Worden, John ^[7]; Frankenberg, Christian ^[9]; Halabisky, Meghan ^[10]

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Harvard Univ., Cambridge, MA (United States)
Stanford Univ., CA (United States); Univ. of Wisconsin, Madison, WI (United States)
Univ. of Arkansas, Fayetteville, AR (United States)
Univ. of Wisconsin, Madison, WI (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL)
NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
Univ. of Illinois, Chicago, IL (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States)
Univ. of Washington, Seattle, WA (United States)

Wetlands are the single largest natural source of atmospheric methane (CH₄), contributing approximately 30% of total surface CH₄ emissions, and they have been identified as the largest source of uncertainty in the global CH₄ budget based on the most recent Global Carbon Project CH₄ report. High uncertainties in the bottom–up estimates of wetland CH₄ emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH₄ emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH₄ concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH₄ emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH₄ yr^-1); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH₄ yr^-1); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH₄ yr^-1). Bottom–up estimates are subject to significant uncertainties (~80 Tg CH₄ yr^-1), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH₄ biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH₄ emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top–down inversions to improve bottom–up estimates. Recent advances in surface measurements of CH₄ fluxes (e.g. FLUXNET-CH₄) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom–up estimates of wetland CH₄ estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH₄ emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH₄ observations directed at wetlands.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Aeronautics and Space Administration (NASA); USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2510902

Journal Information:: Environmental Research Letters, Journal Name: Environmental Research Letters Journal Issue: 2 Vol. 20; ISSN 1748-9326

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

References (89)

Variation of energy and carbon fluxes from a restored temperate freshwater wetland and implications for carbon market verification protocols: Variability in Wetland Fluxes Anderson, Frank E.; Bergamaschi, Brian; Sturtevant, Cove Journal of Geophysical Research: Biogeosciences, Vol. 121, Issue 3 https://doi.org/10.1002/2015JG003083	journal	March 2016
Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing: Greenhouse Gas Radiative Forcing Etminan, M.; Myhre, G.; Highwood, E. J. Geophysical Research Letters, Vol. 43, Issue 24 https://doi.org/10.1002/2016GL071930	journal	December 2016
Mathematical Modelling of Arctic Polygonal Tundra with Ecosys: 2. Microtopography Determines How CO ₂ and CH ₄ Exchange Responds to Changes in Temperature and Precipitation : GHG Exchange in Arctic Polygonal Tundra Grant, R. F.; Mekonnen, Z. A.; Riley, W. J. Journal of Geophysical Research: Biogeosciences, Vol. 122, Issue 12 https://doi.org/10.1002/2017JG004037	journal	December 2017
Estimating wetland methane emissions from the northern high latitudes from 1990 to 2009 using artificial neural networks: METHANE EMISSIONS FROM NORTHERN WETLANDS Zhu, Xudong; Zhuang, Qianlai; Qin, Zhangcai Global Biogeochemical Cycles, Vol. 27, Issue 2 https://doi.org/10.1002/gbc.20052	journal	June 2013
Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4): ANALYSIS OF BURNED AREA Giglio, Louis; Randerson, James T.; van der Werf, Guido R. Journal of Geophysical Research: Biogeosciences, Vol. 118, Issue 1 https://doi.org/10.1002/jgrg.20042	journal	March 2013
Global Warming Potential Is Not an Ecosystem Property Neubauer, Scott C. Ecosystems, Vol. 24, Issue 8 https://doi.org/10.1007/s10021-021-00631-x	journal	April 2021
Temperature sensitivity of greenhouse gas production in wetland soils of different vegetation Inglett, K. S.; Inglett, P. W.; Reddy, K. R. Biogeochemistry, Vol. 108, Issue 1-3 https://doi.org/10.1007/s10533-011-9573-3	journal	January 2011
Production, oxidation, emission and consumption of methane by soils: A review Le Mer, Jean; Roger, Pierre European Journal of Soil Biology, Vol. 37, Issue 1 https://doi.org/10.1016/S1164-5563(01)01067-6	journal	January 2001
Perspectives on removal of atmospheric methane Ming, Tingzhen; Li, Wei; Yuan, Qingchun Advances in Applied Energy, Vol. 5 https://doi.org/10.1016/j.adapen.2022.100085	journal	February 2022
Hydrometeorological sensitivities of net ecosystem carbon dioxide and methane exchange of an Amazonian palm swamp peatland Griffis, T. J.; Roman, D. T.; Wood, J. D. Agricultural and Forest Meteorology, Vol. 295 https://doi.org/10.1016/j.agrformet.2020.108167	journal	December 2020
Causality guided machine learning model on wetland CH4 emissions across global wetlands Yuan, Kunxiaojia; Zhu, Qing; Li, Fa Agricultural and Forest Meteorology, Vol. 324 https://doi.org/10.1016/j.agrformet.2022.109115	journal	September 2022
Development and validation of a global database of lakes, reservoirs and wetlands Lehner, Bernhard; Döll, Petra Journal of Hydrology, Vol. 296, Issue 1-4 https://doi.org/10.1016/j.jhydrol.2004.03.028	journal	August 2004
A global review of methane policies reveals that only 13% of emissions are covered with unclear effectiveness Olczak, Maria; Piebalgs, Andris; Balcombe, Paul One Earth, Vol. 6, Issue 5 https://doi.org/10.1016/j.oneear.2023.04.009	journal	May 2023
Wetland Heterogeneity Determines Methane Emissions: A Pan-Arctic Synthesis Bao, Tao; Jia, Gensuo; Xu, Xiyan Environmental Science & Technology, Vol. 55, Issue 14 https://doi.org/10.1021/acs.est.1c01616	journal	July 2021
Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation Bey, Isabelle; Jacob, Daniel J.; Yantosca, Robert M. Journal of Geophysical Research: Atmospheres, Vol. 106, Issue D19 https://doi.org/10.1029/2001JD000807	journal	October 2001
Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations Bergamaschi, P.; Frankenberg, C.; Meirink, J. F. Journal of Geophysical Research, Vol. 112, Issue D2 https://doi.org/10.1029/2006JD007268	journal	January 2007
Global inundation dynamics inferred from multiple satellite observations, 1993–2000 Prigent, C.; Papa, F.; Aires, F. Journal of Geophysical Research, Vol. 112, Issue D12 https://doi.org/10.1029/2006JD007847	journal	January 2007
Global column-averaged methane mixing ratios from 2003 to 2009 as derived from SCIAMACHY: Trends and variability Frankenberg, C.; Aben, I.; Bergamaschi, P. Journal of Geophysical Research, Vol. 116, Issue D4 https://doi.org/10.1029/2010JD014849	journal	January 2011
The El Niño-Southern Oscillation and wetland methane interannual variability: WETLAND METHANE EMISSIONS AND ENSO Hodson, E. L.; Poulter, B.; Zimmermann, N. E. Geophysical Research Letters, Vol. 38, Issue 8 https://doi.org/10.1029/2011GL046861	journal	April 2011
The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation Collier, Nathan; Hoffman, Forrest M.; Lawrence, David M. Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 11 https://doi.org/10.1029/2018MS001354	journal	November 2018
A Data‐Driven Global Soil Heterotrophic Respiration Dataset and the Drivers of Its Inter‐Annual Variability Yao, Yitong; Ciais, Philippe; Viovy, Nicolas Global Biogeochemical Cycles, Vol. 35, Issue 8 https://doi.org/10.1029/2020GB006918	journal	August 2021
Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions Ma, Shuang; Worden, John R.; Bloom, A. Anthony AGU Advances, Vol. 2, Issue 3 https://doi.org/10.1029/2021AV000408	journal	September 2021
Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison McNicol, Gavin; Fluet‐Chouinard, Etienne; Ouyang, Zutao AGU Advances, Vol. 4, Issue 5 https://doi.org/10.1029/2023AV000956	journal	September 2023
Cold‐Season Methane Fluxes Simulated by GCP‐CH₄ Models Ito, A.; Li, T.; Qin, Z. Geophysical Research Letters, Vol. 50, Issue 14 https://doi.org/10.1029/2023GL103037	journal	July 2023
Three-dimensional model synthesis of the global methane cycle Fung, I.; John, J.; Lerner, J. Journal of Geophysical Research, Vol. 96, Issue D7 https://doi.org/10.1029/91JD01247	journal	January 1991
Global carbon exchange and methane emissions from natural wetlands: Application of a process-based model Cao, Mingkui; Marshall, Stewart; Gregson, Keith Journal of Geophysical Research: Atmospheres, Vol. 101, Issue D9 https://doi.org/10.1029/96JD00219	journal	June 1996
Large emissions from floodplain trees close the Amazon methane budget Pangala, Sunitha R.; Enrich-Prast, Alex; Basso, Luana S. Nature, Vol. 552, Issue 7684 https://doi.org/10.1038/nature24639	journal	December 2017
Three decades of global methane sources and sinks Kirschke, Stefanie; Bousquet, Philippe; Ciais, Philippe Nature Geoscience, Vol. 6, Issue 10 https://doi.org/10.1038/ngeo1955	journal	September 2013
Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions Chang, Kuang-Yu; Riley, William J.; Knox, Sara H. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-22452-1	journal	April 2021
Phenology is the dominant control of methane emissions in a tropical non-forested wetland Helfter, Carole; Gondwe, Mangaliso; Murray-Hudson, Michael Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-021-27786-4	journal	January 2022
Machine learning-based global maps of ecological variables and the challenge of assessing them Meyer, Hanna; Pebesma, Edzer Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29838-9	journal	April 2022
Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems Liu, Licheng; Zhou, Wang; Guan, Kaiyu Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-023-43860-5	journal	January 2024
Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100 Leifeld, Jens; Wüst-Galley, Chloé; Page, Susan Nature Climate Change, Vol. 9, Issue 12 https://doi.org/10.1038/s41558-019-0615-5	journal	November 2019
Seasonal increase of methane emissions linked to warming in Siberian tundra Rößger, Norman; Sachs, Torsten; Wille, Christian Nature Climate Change, Vol. 12, Issue 11 https://doi.org/10.1038/s41558-022-01512-4	journal	October 2022
Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity Yuan, Kunxiaojia; Li, Fa; McNicol, Gavin Nature Climate Change, Vol. 14, Issue 3 https://doi.org/10.1038/s41558-024-01933-3	journal	February 2024
Rewetting global wetlands effectively reduces major greenhouse gas emissions Zou, Junyu; Ziegler, Alan D.; Chen, Deliang Nature Geoscience, Vol. 15, Issue 8 https://doi.org/10.1038/s41561-022-00989-0	journal	July 2022
Extensive global wetland loss over the past three centuries Fluet-Chouinard, Etienne; Stocker, Benjamin D.; Zhang, Zhen Nature, Vol. 614, Issue 7947 https://doi.org/10.1038/s41586-022-05572-6	journal	February 2023
Towards data-driven discovery of governing equations in geosciences Song, Wenxiang; Jiang, Shijie; Camps-Valls, Gustau Communications Earth & Environment, Vol. 5, Issue 1 https://doi.org/10.1038/s43247-024-01760-6	journal	October 2024
The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times Bloom, A. Anthony; Exbrayat, Jean-François; van der Velde, Ivar R. Proceedings of the National Academy of Sciences, Vol. 113, Issue 5 https://doi.org/10.1073/pnas.1515160113	journal	January 2016
Emerging role of wetland methane emissions in driving 21st century climate change Zhang, Zhen; Zimmermann, Niklaus E.; Stenke, Andrea Proceedings of the National Academy of Sciences, Vol. 114, Issue 36 https://doi.org/10.1073/pnas.1618765114	journal	August 2017
Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics Poulter, Benjamin; Bousquet, Philippe; Canadell, Josep G. Environmental Research Letters, Vol. 12, Issue 9 https://doi.org/10.1088/1748-9326/aa8391	journal	September 2017
Machine learning models inaccurately predict current and future high-latitude C balances Shirley, Ian A.; Mekonnen, Zelalem A.; Grant, Robert F. Environmental Research Letters, Vol. 18, Issue 1 https://doi.org/10.1088/1748-9326/acacb2	journal	January 2023
Critical needs to close monitoring gaps in pan-tropical wetland CH₄ emissions Zhu, Qing; Yuan, Kunxiaojia; Li, Fa Environmental Research Letters, Vol. 19, Issue 11 https://doi.org/10.1088/1748-9326/ad8019	journal	October 2024
A roadmap to achieve the global methane pledge Malley, Christopher S.; Borgford-Parnell, Nathan; Haeussling, Seraphine Environmental Research: Climate, Vol. 2, Issue 1 https://doi.org/10.1088/2752-5295/acb4b4	journal	February 2023
The added value of satellite observations of methane forunderstanding the contemporary methane budget Palmer, Paul I.; Feng, Liang; Lunt, Mark F. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 379, Issue 2210 https://doi.org/10.1098/rsta.2021.0106	journal	September 2021
Environmental and physical controls on northern terrestrial methane emissions across permafrost zones Olefeldt, David; Turetsky, Merritt R.; Crill, Patrick M. Global Change Biology, Vol. 19, Issue 2 https://doi.org/10.1111/gcb.12071	journal	November 2012
Regional trends and drivers of the global methane budget Stavert, Ann R.; Saunois, Marielle; Canadell, Josep G. Global Change Biology, Vol. 28, Issue 1 https://doi.org/10.1111/gcb.15901	journal	October 2021
Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates Chang, Kuang‐Yu; Riley, William J.; Collier, Nathan Global Change Biology https://doi.org/10.1111/gcb.16755	journal	May 2023
Large increases in methane emissions expected from North America’s largest wetland complex Bansal, Sheel; Post van der Burg, Max; Fern, Rachel R. Science Advances, Vol. 9, Issue 9 https://doi.org/10.1126/sciadv.ade1112	journal	March 2023
Large-Scale Controls of Methanogenesis Inferred from Methane and Gravity Spaceborne Data Bloom, A. A.; Palmer, P. I.; Fraser, A. Science, Vol. 327, Issue 5963 https://doi.org/10.1126/science.1175176	journal	January 2010
FLUXNET-CH ₄ Synthesis Activity: Objectives, Observations, and Future Directions Knox, Sara H.; Jackson, Robert B.; Poulter, Benjamin Bulletin of the American Meteorological Society https://doi.org/10.1175/BAMS-D-18-0268.1	journal	July 2019
New version of the TOMCAT/SLIMCAT off-line chemical transport model: Intercomparison of stratospheric tracer experiments Chipperfield, M. P. Quarterly Journal of the Royal Meteorological Society, Vol. 132, Issue 617 https://doi.org/10.1256/qj.05.51	journal	April 2006
The Potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to Provide Multi-scale Constraints on the Carbon Cycle in the Americas Moore III, Berrien; Crowell, Sean M. R.; Rayner, Peter J. Frontiers in Environmental Science, Vol. 6 https://doi.org/10.3389/fenvs.2018.00109	journal	October 2018
High Methane Emission From Palm Stems and Nitrous Oxide Emission From the Soil in a Peruvian Amazon Peat Swamp Forest Soosaar, Kaido; Schindler, Thomas; Machacova, Katerina Frontiers in Forests and Global Change, Vol. 5 https://doi.org/10.3389/ffgc.2022.849186	journal	March 2022
Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data Schroeder, Ronny; McDonald, Kyle; Chapman, Bruce Remote Sensing, Vol. 7, Issue 12 https://doi.org/10.3390/rs71215843	journal	December 2015
MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane Ehret, Gerhard; Bousquet, Philippe; Pierangelo, Clémence Remote Sensing, Vol. 9, Issue 10 https://doi.org/10.3390/rs9101052	journal	October 2017
What are the greenhouse gas observing system requirements for reducing fundamental biogeochemical process uncertainty? Amazon wetland CH₄ emissions as a case study Bloom, A. Anthony; Lauvaux, Thomas; Worden, John Atmospheric Chemistry and Physics, Vol. 16, Issue 23 https://doi.org/10.5194/acp-16-15199-2016	journal	December 2016
Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015 Maasakkers, Joannes D.; Jacob, Daniel J.; Sulprizio, Melissa P. Atmospheric Chemistry and Physics, Vol. 19, Issue 11 https://doi.org/10.5194/acp-19-7859-2019	journal	January 2019
Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments Qu, Zhen; Jacob, Daniel J.; Shen, Lu Atmospheric Chemistry and Physics, Vol. 21, Issue 18 https://doi.org/10.5194/acp-21-14159-2021	journal	September 2021
Quantifying methane emissions from the global scale down to point sources using satellite observations of atmospheric methane Jacob, Daniel J.; Varon, Daniel J.; Cusworth, Daniel H. Atmospheric Chemistry and Physics, Vol. 22, Issue 14 https://doi.org/10.5194/acp-22-9617-2022	journal	July 2022
A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases Balasus, Nicholas; Jacob, Daniel J.; Lorente, Alba Atmospheric Measurement Techniques, Vol. 16, Issue 16 https://doi.org/10.5194/amt-16-3787-2023	journal	August 2023
Improvement of the retrieval algorithm for GOSAT SWIR XCO ₂ and XCH ₄ and their validation using TCCON data Yoshida, Y.; Kikuchi, N.; Morino, I. Atmospheric Measurement Techniques, Vol. 6, Issue 6 https://doi.org/10.5194/amt-6-1533-2013	journal	January 2013
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5 Dagon, Katherine; Sanderson, Benjamin M.; Fisher, Rosie A. Advances in Statistical Climatology, Meteorology and Oceanography, Vol. 6, Issue 2 https://doi.org/10.5194/ascmo-6-223-2020	journal	January 2020
Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP) Melton, J. R.; Wania, R.; Hodson, E. L. Biogeosciences, Vol. 10, Issue 2 https://doi.org/10.5194/bg-10-753-2013	journal	January 2013
Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties Zhang, Zhen; Zimmermann, Niklaus E.; Kaplan, Jed O. Biogeosciences, Vol. 13, Issue 5 https://doi.org/10.5194/bg-13-1387-2016	journal	January 2016
Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach Jung, Martin; Schwalm, Christopher; Migliavacca, Mirco Biogeosciences, Vol. 17, Issue 5 https://doi.org/10.5194/bg-17-1343-2020	journal	January 2020
Exploring constraints on a wetland methane emission ensemble (WetCHARTs) using GOSAT observations Parker, Robert J.; Wilson, Chris; Bloom, A. Anthony Biogeosciences, Vol. 17, Issue 22 https://doi.org/10.5194/bg-17-5669-2020	journal	November 2020
Spatial and temporal patterns of CH ₄ and N ₂ O fluxes in terrestrial ecosystems of North America during 1979–2008: application of a global biogeochemistry model Tian, H.; Xu, X.; Liu, M. Biogeosciences, Vol. 7, Issue 9 https://doi.org/10.5194/bg-7-2673-2010	journal	January 2010
Constraining global methane emissions and uptake by ecosystems Spahni, R.; Wania, R.; Neef, L. Biogeosciences, Vol. 8, Issue 6 https://doi.org/10.5194/bg-8-1643-2011	journal	January 2011
Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM Riley, W. J.; Subin, Z. M.; Lawrence, D. M. Biogeosciences, Vol. 8, Issue 7 https://doi.org/10.5194/bg-8-1925-2011	journal	January 2011
Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty Ito, A.; Inatomi, M. Biogeosciences, Vol. 9, Issue 2 https://doi.org/10.5194/bg-9-759-2012	journal	January 2012
Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations Peltola, Olli; Vesala, Timo; Gao, Yao Earth System Science Data, Vol. 11, Issue 3 https://doi.org/10.5194/essd-11-1263-2019	journal	January 2019
EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012 Janssens-Maenhout, Greet; Crippa, Monica; Guizzardi, Diego Earth System Science Data, Vol. 11, Issue 3 https://doi.org/10.5194/essd-11-959-2019	journal	January 2019
The Global Methane Budget 2000–2017 Saunois, Marielle; Stavert, Ann R.; Poulter, Ben Earth System Science Data, Vol. 12, Issue 3 https://doi.org/10.5194/essd-12-1561-2020	journal	January 2020
Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M) Zhang, Zhen; Fluet-Chouinard, Etienne; Jensen, Katherine Earth System Science Data, Vol. 13, Issue 5 https://doi.org/10.5194/essd-13-2001-2021	journal	January 2021
FLUXNET-CH₄: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands Delwiche, Kyle B.; Knox, Sara Helen; Malhotra, Avni Earth System Science Data, Vol. 13, Issue 7 https://doi.org/10.5194/essd-13-3607-2021	journal	January 2021
The Boreal–Arctic Wetland and Lake Dataset (BAWLD) Olefeldt, David; Hovemyr, Mikael; Kuhn, McKenzie A. Earth System Science Data, Vol. 13, Issue 11 https://doi.org/10.5194/essd-13-5127-2021	journal	January 2021
GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020 Zhang, Xiao; Liu, Liangyun; Zhao, Tingting Earth System Science Data, Vol. 15, Issue 1 https://doi.org/10.5194/essd-15-265-2023	journal	January 2023
Mapping global non-floodplain wetlands Lane, Charles R.; D'Amico, Ellen; Christensen, Jay R. Earth System Science Data, Vol. 15, Issue 7 https://doi.org/10.5194/essd-15-2927-2023	journal	July 2023
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method Zhang, Xiao; Zhao, Tingting; Xu, Hong Earth System Science Data, Vol. 16, Issue 3 https://doi.org/10.5194/essd-16-1353-2024	journal	March 2024
The global methane budget 2000–2012 Saunois, Marielle; Bousquet, Philippe; Poulter, Ben Earth System Science Data, Vol. 8, Issue 2 https://doi.org/10.5194/essd-8-697-2016	journal	January 2016
A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0) Bloom, A. Anthony; Bowman, Kevin W.; Lee, Meemong Geoscientific Model Development, Vol. 10, Issue 6 https://doi.org/10.5194/gmd-10-2141-2017	journal	January 2017
Building a machine learning surrogate model for wildfire activities within a global Earth system model Zhu, Qing; Li, Fa; Riley, William J. Geoscientific Model Development, Vol. 15, Issue 5 https://doi.org/10.5194/gmd-15-1899-2022	journal	March 2022
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) Li, Lingcheng; Fang, Yilin; Zheng, Zhonghua Geoscientific Model Development, Vol. 16, Issue 14 https://doi.org/10.5194/gmd-16-4017-2023	journal	July 2023
AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics Li, Fa; Zhu, Qing; Riley, William J. Geoscientific Model Development, Vol. 16, Issue 3 https://doi.org/10.5194/gmd-16-869-2023	journal	February 2023
Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHyMe v1.3.1 Wania, R.; Ross, I.; Prentice, I. C. Geoscientific Model Development, Vol. 3, Issue 2 https://doi.org/10.5194/gmd-3-565-2010	journal	January 2010
Modelling sub-grid wetland in the ORCHIDEE global land surface model: evaluation against river discharges and remotely sensed data Ringeval, B.; Decharme, B.; Piao, S. L. Geoscientific Model Development, Vol. 5, Issue 4 https://doi.org/10.5194/gmd-5-941-2012	journal	January 2012
The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design Huntzinger, D. N.; Schwalm, C.; Michalak, A. M. Geoscientific Model Development, Vol. 6, Issue 6 https://doi.org/10.5194/gmd-6-2121-2013	journal	January 2013
The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators Halabisky, Meghan; Miller, Dan; Stewart, Anthony J. Hydrology and Earth System Sciences, Vol. 27, Issue 20 https://doi.org/10.5194/hess-27-3687-2023	journal	October 2023

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54 ENVIRONMENTAL SCIENCES
bottom–up inventories
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global wetlands
methane emission
uncertainty

Title: Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

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