Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions
Wetlands are the single largest natural source of atmospheric methane (CH _4 ), contributing approximately 30% of total surface CH _4 emissions, and they have been identified as the largest source of uncertainty in the global CH _4 budget based on the most recent Global Carbon Project CH _4 report....
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/adad02 |
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| author | Qing Zhu Daniel J Jacob Kunxiaojia Yuan Fa Li Benjamin R K Runkle Min Chen A Anthony Bloom Benjamin Poulter James D East William J Riley Gavin McNicol John Worden Christian Frankenberg Meghan Halabisky |
| author_facet | Qing Zhu Daniel J Jacob Kunxiaojia Yuan Fa Li Benjamin R K Runkle Min Chen A Anthony Bloom Benjamin Poulter James D East William J Riley Gavin McNicol John Worden Christian Frankenberg Meghan Halabisky |
| author_sort | Qing Zhu |
| collection | DOAJ |
| description | Wetlands are the single largest natural source of atmospheric methane (CH _4 ), contributing approximately 30% of total surface CH _4 emissions, and they have been identified as the largest source of uncertainty in the global CH _4 budget based on the most recent Global Carbon Project CH _4 report. High uncertainties in the bottom–up estimates of wetland CH _4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH _4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH _4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH _4 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 TgCH _4 yr ^−1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH _4 yr ^−1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH _4 yr ^−1 ). Bottom–up estimates are subject to significant uncertainties (∼80 Tg CH _4 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 CH _4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH _4 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 CH _4 fluxes (e.g. FLUXNET-CH _4 ) 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 CH _4 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 CH _4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH _4 observations directed at wetlands. |
| format | Article |
| id | doaj-art-06f0e7002f8e44cbbcd544280c1febba |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | Environmental Research Letters |
| spelling | doaj-art-06f0e7002f8e44cbbcd544280c1febba2025-02-04T08:08:04ZengIOP PublishingEnvironmental Research Letters1748-93262025-01-0120202300110.1088/1748-9326/adad02Advancements and opportunities to improve bottom–up estimates of global wetland methane emissionsQing Zhu0https://orcid.org/0000-0003-2441-944XDaniel J Jacob1Kunxiaojia Yuan2https://orcid.org/0000-0002-1336-5768Fa Li3Benjamin R K Runkle4https://orcid.org/0000-0002-2583-1199Min Chen5A Anthony Bloom6Benjamin Poulter7https://orcid.org/0000-0002-9493-8600James D East8https://orcid.org/0000-0001-7199-6229William J Riley9https://orcid.org/0000-0002-4615-2304Gavin McNicol10John Worden11Christian Frankenberg12Meghan Halabisky13Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab , Berkeley, CA 94720, United States of AmericaSchool of Engineering and Applied Sciences, Harvard University , Cambridge, MA 02138, United States of AmericaClimate and Ecosystem Sciences Division, Lawrence Berkeley National Lab , Berkeley, CA 94720, United States of AmericaDoerr School of Sustainability, Stanford University , Stanford, CA 94305, United States of America; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaBiological & Agricultural Engineering, University of Arkansas , Fayetteville, AR 72701, United States of AmericaDepartment of Forest and Wildlife Ecology, University of Wisconsin-Madison , Madison, WI 53706, United States of AmericaJet Propulsion Laboratory, California Institute of Technology , Pasadena, CA 91107, United States of AmericaNASA Goddard Space Flight Center, Biospheric Sciences Laboratory , Greenbelt, MD 20771, United States of AmericaSchool of Engineering and Applied Sciences, Harvard University , Cambridge, MA 02138, United States of AmericaClimate and Ecosystem Sciences Division, Lawrence Berkeley National Lab , Berkeley, CA 94720, United States of AmericaDepartment of Earth and Environmental Sciences, University of Illinois Chicago , Chicago, IL 60607, United States of AmericaNASA Goddard Space Flight Center, Biospheric Sciences Laboratory , Greenbelt, MD 20771, United States of AmericaDivision of Geological and Planetary Sciences, California Institute of Technology , Pasadena, CA, United States of AmericaSchool of Environmental and Forest Sciences, University of Washington , Seattle, Washington, WA, United States of AmericaWetlands are the single largest natural source of atmospheric methane (CH _4 ), contributing approximately 30% of total surface CH _4 emissions, and they have been identified as the largest source of uncertainty in the global CH _4 budget based on the most recent Global Carbon Project CH _4 report. High uncertainties in the bottom–up estimates of wetland CH _4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH _4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH _4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH _4 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 TgCH _4 yr ^−1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH _4 yr ^−1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH _4 yr ^−1 ). Bottom–up estimates are subject to significant uncertainties (∼80 Tg CH _4 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 CH _4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH _4 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 CH _4 fluxes (e.g. FLUXNET-CH _4 ) 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 CH _4 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 CH _4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH _4 observations directed at wetlands.https://doi.org/10.1088/1748-9326/adad02global wetlandsmethane emissionbottom–up inventoriesuncertaintyfuture opportunities |
| spellingShingle | Qing Zhu Daniel J Jacob Kunxiaojia Yuan Fa Li Benjamin R K Runkle Min Chen A Anthony Bloom Benjamin Poulter James D East William J Riley Gavin McNicol John Worden Christian Frankenberg Meghan Halabisky Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Environmental Research Letters global wetlands methane emission bottom–up inventories uncertainty future opportunities |
| title | Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions |
| title_full | Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions |
| title_fullStr | Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions |
| title_full_unstemmed | Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions |
| title_short | Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions |
| title_sort | advancements and opportunities to improve bottom up estimates of global wetland methane emissions |
| topic | global wetlands methane emission bottom–up inventories uncertainty future opportunities |
| url | https://doi.org/10.1088/1748-9326/adad02 |
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