Paddy rice methane emissions across Monsoon Asia

被引:23
作者
Ouyang, Zutao [1 ]
Jackson, Robert B. [1 ,2 ,3 ]
McNicol, Gavin [1 ,4 ]
Fluet-Chouinard, Etienne [1 ]
Runkle, Benjamin R. K. [5 ]
Papale, Dario [6 ,7 ]
Knox, Sara H. [8 ]
Cooley, Sarah [1 ,9 ]
Delwiche, Kyle B. [1 ,10 ]
Feron, Sarah [1 ,11 ]
Irvin, Jeremy Andrew [12 ]
Malhotra, Avni [13 ]
Muddasir, Muhammad [6 ]
Sabbatini, Simone [6 ,7 ]
Alberto, Ma Carmelita R. [14 ]
Cescatti, Alessandro [15 ]
Chen, Chi-Ling [16 ]
Dong, Jinwei [17 ]
Fong, Bryant N. [18 ]
Guo, Haiqiang [19 ,20 ]
Hao, Lu [21 ]
Iwata, Hiroki [22 ]
Jia, Qingyu [23 ]
Ju, Weimin [24 ]
Kang, Minseok [25 ]
Li, Hong [26 ]
Kim, Joon [27 ]
Reba, Michele L. [18 ]
Nayak, Amaresh Kumar [28 ]
Roberti, Debora Regina [29 ]
Ryu, Youngryel [30 ]
Swain, Chinmaya Kumar [28 ]
Tsuang, Benjei [31 ]
Xiao, Xiangming [32 ]
Yuan, Wenping [33 ]
Zhang, Geli [34 ]
Zhang, Yongguang [24 ]
机构
[1] Stanford Univ, Dept Earth Syst Sci, Stanford, CA 94305 USA
[2] Stanford Univ, Woods Inst Environm, Stanford, CA 94305 USA
[3] Stanford Univ, Precourt Inst Energy, Stanford, CA 94305 USA
[4] Univ Illinois, Dept Earth & Environm Sci, Chicago, IL USA
[5] Univ Arkansas, Dept Biol & Agr Engn, Fayetteville, AR 72701 USA
[6] Univ Tuscia, Dept Innovat Biol Agrofood & Forest Syst DIBAF, Viterbo, Italy
[7] Euro Mediterranean Ctr Climate Change CMCC, IAFES Div, Viterbo, Italy
[8] Univ British Columbia, Dept Geog, Vancouver, BC, Canada
[9] Univ Oregon, Dept Geog, Eugene, OR 97403 USA
[10] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA USA
[11] Univ Groningen, Campus Fryslan, Groningen, Netherlands
[12] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[13] Univ Zurich, Dept Geog, Zurich, Switzerland
[14] Int Rice Res Inst, Laguna, Philippines
[15] European Commiss, Joint Res Ctr JRC, Ispra, Italy
[16] Agr Res Inst Taiwan, Taichung, Taiwan
[17] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
[18] ARS, USDA, Delta Water Management Res Unit, Jonesboro, AR USA
[19] Fudan Univ, Minist Educ, Key Lab Biodivers Sci & Ecol Engn, Shanghai, Peoples R China
[20] Fudan Univ, Minist Educ, Coastal Ecosyst Res Stn Yangtze River Estuary, Shanghai, Peoples R China
[21] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Jiangsu Key Lab Agr Meteorol, Nanjing, Peoples R China
[22] Shinshu Univ, Dept Environm Sci, Matsumoto, Nagano, Japan
[23] China Meteorol Adm, Inst Atmospher Environm, Shenyang, Peoples R China
[24] Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
[25] Natl Ctr AgroMeteorol, Seoul, South Korea
[26] Chongqing Univ, Fac Architecture & Urban Planning, Chongqing, Peoples R China
[27] Seoul Natl Univ, Landscape Architecture & Rural Syst Engn, Seoul, South Korea
[28] ICAR Natl Rice Res Inst Cuttack, Cuttack, Odisha, India
[29] UFSM Fed Univ Santa Maria, Santa Maria, RS, Brazil
[30] Seoul Natl Univ, Dept Landscape Architecture & Rural Syst Engn, Seoul, South Korea
[31] Natl Chung Hsing Univ, Dept Environm Engn, Taichung, Taiwan
[32] Univ Oklahoma, Dept Microbiol & Plant Biol, Ctr Earth Observat & Modeling, Norman, OK 73019 USA
[33] Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Peoples R China
[34] Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China
基金
新加坡国家研究基金会; 美国国家科学基金会;
关键词
Remote sensing; Climate change; Greenhouse gas emission; Machine learning; Eddy covariance; GREENHOUSE-GAS EMISSIONS; NET ECOSYSTEM EXCHANGE; WATER MANAGEMENT; FLUX TOWERS; CHINA; CARBON; FUTURE; REPRESENTATIVENESS; ASSIMILATION; RESPIRATION;
D O I
10.1016/j.rse.2022.113335
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes similar to 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 degrees) 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 similar to 87% of global rice area is cultivated and similar to 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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页数:19
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