Paddy rice methane emissions across Monsoon Asia

Authors: OUYANG, ZUTAO - Stanford University; JACKSON, ROBERT - Stanford University; MCNICOL, GAVIN - Stanford University; FLUET-CHOUINARD, ETIENNE - Stanford University; RUNKLE, BENJAMIN - University Of Arkansas; PAPALE, DARIO - University Of Tuscia; KNOX, SARAH - University Of British Columbia; COOLEY, SARAH - Stanford University; DELWICHE, KYLE - Stanford University; SABBATINI, SIMONE - University Of Tuscia; Fong, Bryant; Reba, Michele

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/27/2022
Publication Date: 11/14/2022
Citation: Ouyang, Z., Jackson, R.B., Mcnicol, G., Fluet-Chouinard, E., Runkle, B.R., Papale, D., Knox, S.H., Cooley, S., Delwiche, K.B., Sabbatini, S., Reba, M.L. 2022. Paddy rice methane emissions across Monsoon Asia. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113335.
DOI: https://doi.org/10.1016/j.rse.2022.113335

Interpretive Summary: Rice is an important agricultural source of methane emissions globally. However, it is challenging to measure and contribute to variability of global estimates. In an effort to improve estimates, field data from 23 global paddy rice locations collected using eddy covariance sensors and remote sensing techniques were combined to generate gridded estimates of methane emission at 5000-m resolution across Monsoon Asia. Model results were good for multi-day estimates of methane flux and temperature, biomass, and water availability were the most important predictors. Generally, a decline in methane emission from paddy rice in Monsoon Asia declined from 2007 through 2015 to a reduction rice growing area and emission rates per area. These findings are important for rice production world-wide and help inform and improve global estimates of emissions.

Technical Abstract: 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 emis- sions 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 in- ventories 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 pro- duce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 pre- dictors. 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 reso- lution 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.