Paddy rice methane emissions across Monsoon Asia

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of obs...

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Autores principales: Ouyang, Zutao, Jackson, Robert B., McNicol, Gavin, Fluet-Chouinard, Etienne, Runkle, Benjamin R.K., Papale, Dario, Knox, Sara H., Cooley, Sarah, Delwiche, Kyle B., Feron, Sarah, Irvin, Jeremy Andrew, Malhotra, Avni, Muddasir, Muhammad, Sabbatini, Simone, Alberto, Ma. Carmelita R., Cescatti, Alessandro, Chen, Chi-Ling, Dong, Jinwei, Fong, Bryant N., Guo, Haiqiang, Hao, Lu, Iwata, Hiroki, Jia, Qingyu, Ju, Weimin, Kang, Minseok, Li, Hong, Kim, Joon, Reba, Michele L., Nayak, Amaresh Kumar, Roberti, Debora Regina, Ryu, Youngryel, Swain, Chinmaya Kumar, Tsuang, Benjei, Xiao, Xiangming, Yuan, Wenping, Zhang, Geli, Zhang, Yongguang
Formato: Journal Article
Lenguaje:Inglés
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://hdl.handle.net/10568/164003
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author Ouyang, Zutao
Jackson, Robert B.
McNicol, Gavin
Fluet-Chouinard, Etienne
Runkle, Benjamin R.K.
Papale, Dario
Knox, Sara H.
Cooley, Sarah
Delwiche, Kyle B.
Feron, Sarah
Irvin, Jeremy Andrew
Malhotra, Avni
Muddasir, Muhammad
Sabbatini, Simone
Alberto, Ma. Carmelita R.
Cescatti, Alessandro
Chen, Chi-Ling
Dong, Jinwei
Fong, Bryant N.
Guo, Haiqiang
Hao, Lu
Iwata, Hiroki
Jia, Qingyu
Ju, Weimin
Kang, Minseok
Li, Hong
Kim, Joon
Reba, Michele L.
Nayak, Amaresh Kumar
Roberti, Debora Regina
Ryu, Youngryel
Swain, Chinmaya Kumar
Tsuang, Benjei
Xiao, Xiangming
Yuan, Wenping
Zhang, Geli
Zhang, Yongguang
author_browse Alberto, Ma. Carmelita R.
Cescatti, Alessandro
Chen, Chi-Ling
Cooley, Sarah
Delwiche, Kyle B.
Dong, Jinwei
Feron, Sarah
Fluet-Chouinard, Etienne
Fong, Bryant N.
Guo, Haiqiang
Hao, Lu
Irvin, Jeremy Andrew
Iwata, Hiroki
Jackson, Robert B.
Jia, Qingyu
Ju, Weimin
Kang, Minseok
Kim, Joon
Knox, Sara H.
Li, Hong
Malhotra, Avni
McNicol, Gavin
Muddasir, Muhammad
Nayak, Amaresh Kumar
Ouyang, Zutao
Papale, Dario
Reba, Michele L.
Roberti, Debora Regina
Runkle, Benjamin R.K.
Ryu, Youngryel
Sabbatini, Simone
Swain, Chinmaya Kumar
Tsuang, Benjei
Xiao, Xiangming
Yuan, Wenping
Zhang, Geli
Zhang, Yongguang
author_facet Ouyang, Zutao
Jackson, Robert B.
McNicol, Gavin
Fluet-Chouinard, Etienne
Runkle, Benjamin R.K.
Papale, Dario
Knox, Sara H.
Cooley, Sarah
Delwiche, Kyle B.
Feron, Sarah
Irvin, Jeremy Andrew
Malhotra, Avni
Muddasir, Muhammad
Sabbatini, Simone
Alberto, Ma. Carmelita R.
Cescatti, Alessandro
Chen, Chi-Ling
Dong, Jinwei
Fong, Bryant N.
Guo, Haiqiang
Hao, Lu
Iwata, Hiroki
Jia, Qingyu
Ju, Weimin
Kang, Minseok
Li, Hong
Kim, Joon
Reba, Michele L.
Nayak, Amaresh Kumar
Roberti, Debora Regina
Ryu, Youngryel
Swain, Chinmaya Kumar
Tsuang, Benjei
Xiao, Xiangming
Yuan, Wenping
Zhang, Geli
Zhang, Yongguang
author_sort Ouyang, Zutao
collection Repository of Agricultural Research Outputs (CGSpace)
description Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.
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spelling CGSpace1640032024-12-19T14:12:52Z Paddy rice methane emissions across Monsoon Asia Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang climate change greenhouse gas emissions machine learning paddy rice remote sensing Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years. 2023-01 2024-12-19T12:53:19Z 2024-12-19T12:53:19Z Journal Article https://hdl.handle.net/10568/164003 en Open Access Elsevier Ouyang, Zutao; Jackson, Robert B.; McNicol, Gavin; Fluet-Chouinard, Etienne; Runkle, Benjamin R.K.; Papale, Dario; Knox, Sara H.; Cooley, Sarah; Delwiche, Kyle B.; Feron, Sarah; Irvin, Jeremy Andrew; Malhotra, Avni; Muddasir, Muhammad; Sabbatini, Simone; Alberto, Ma. Carmelita R.; Cescatti, Alessandro; Chen, Chi-Ling; Dong, Jinwei; Fong, Bryant N.; Guo, Haiqiang; Hao, Lu; Iwata, Hiroki; Jia, Qingyu; Ju, Weimin; Kang, Minseok; Li, Hong; Kim, Joon; Reba, Michele L.; Nayak, Amaresh Kumar; Roberti, Debora Regina; Ryu, Youngryel; Swain, Chinmaya Kumar; Tsuang, Benjei; Xiao, Xiangming; Yuan, Wenping; Zhang, Geli and Zhang, Yongguang. 2023. Paddy rice methane emissions across Monsoon Asia. Remote Sensing of Environment, Volume 284 p. 113335
spellingShingle climate change
greenhouse gas emissions
machine learning
paddy rice
remote sensing
Ouyang, Zutao
Jackson, Robert B.
McNicol, Gavin
Fluet-Chouinard, Etienne
Runkle, Benjamin R.K.
Papale, Dario
Knox, Sara H.
Cooley, Sarah
Delwiche, Kyle B.
Feron, Sarah
Irvin, Jeremy Andrew
Malhotra, Avni
Muddasir, Muhammad
Sabbatini, Simone
Alberto, Ma. Carmelita R.
Cescatti, Alessandro
Chen, Chi-Ling
Dong, Jinwei
Fong, Bryant N.
Guo, Haiqiang
Hao, Lu
Iwata, Hiroki
Jia, Qingyu
Ju, Weimin
Kang, Minseok
Li, Hong
Kim, Joon
Reba, Michele L.
Nayak, Amaresh Kumar
Roberti, Debora Regina
Ryu, Youngryel
Swain, Chinmaya Kumar
Tsuang, Benjei
Xiao, Xiangming
Yuan, Wenping
Zhang, Geli
Zhang, Yongguang
Paddy rice methane emissions across Monsoon Asia
title Paddy rice methane emissions across Monsoon Asia
title_full Paddy rice methane emissions across Monsoon Asia
title_fullStr Paddy rice methane emissions across Monsoon Asia
title_full_unstemmed Paddy rice methane emissions across Monsoon Asia
title_short Paddy rice methane emissions across Monsoon Asia
title_sort paddy rice methane emissions across monsoon asia
topic climate change
greenhouse gas emissions
machine learning
paddy rice
remote sensing
url https://hdl.handle.net/10568/164003
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