Articles | Volume 14, issue 1
https://doi.org/10.5194/bg-14-163-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-14-163-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Jan 2017
Research article |
| 12 Jan 2017
Uncertainties in the national inventory of methane emissions from rice cultivation: field measurements and modeling approaches
Wen Zhang
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
Wenjuan Sun
CORRESPONDING AUTHOR
LVEC, Institute of Botany, Chinese Academy of Sciences, Beijing, China
Tingting Li
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, China
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Cited
17 citations as recorded by crossref.
- Estimation of methane emissions based on crop yield and remote sensing data in a paddy field Y. Shi et al. 10.1002/ghg.1946
- Methane budget of East Asia, 1990–2015: A bottom-up evaluation A. Ito et al. 10.1016/j.scitotenv.2019.04.263
- Paddy rice methane emissions across Monsoon Asia Z. Ouyang et al. 10.1016/j.rse.2022.113335
- The relationship between phosphine, methane, and ozone over paddy field in Guangzhou, China J. Ma et al. 10.1016/j.gecco.2019.e00581
- Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields Z. Jiang et al. 10.1016/j.fcr.2023.108821
- Exploring the effects of extreme weather events on methane emissions from croplands: A study combining site and global modeling Y. Xia et al. 10.1016/j.agrformet.2023.109454
- A comparative study of anthropogenic CH<sub>4</sub> emissions over China based on the ensembles of bottom-up inventories X. Lin et al. 10.5194/essd-13-1073-2021
- Satellite-Based Monitoring of Methane Emissions from China’s Rice Hub R. Liang et al. 10.1021/acs.est.4c09822
- Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators T. Zhang et al. 10.2134/jeq2017.10.0398
- Impacts of Rice Cropping System Changes on Paddy Methane Emissions in Southern China M. Jiang et al. 10.3390/land12020270
- A Multi-Method Approach to Flood Mapping: Reconstructing Inundation Changes in the Cambodian Upper Mekong Delta C. Orieschnig et al. 10.1016/j.jhydrol.2022.127902
- Decrease in the annual emissions of CH4 and N2O following the initial land management change from rice to vegetable production L. Wu et al. 10.1007/s11356-018-1559-4
- Model-based evaluation of methane emissions from paddy fields in East Asia A. ITO et al. 10.2480/agrmet.D-21-00037
- Quantitative analysis of the methane gas emissions from municipal solid waste in India C. Singh et al. 10.1038/s41598-018-21326-9
- Estimating Methane Emissions in Rice Paddies at the Parcel Level Using Drone-Based Time Series Vegetation Indices Y. Song et al. 10.3390/drones8090459
- Effects of irrigation regime and rice variety on greenhouse gas emissions and grain yields from paddy fields in central China Z. Feng et al. 10.1016/j.agwat.2021.106830
- Accuracy and uncertainty analysis of staple food crop modelling by the process-based Agro-C model Q. Zhang et al. 10.1007/s00484-020-02053-1
17 citations as recorded by crossref.
- Estimation of methane emissions based on crop yield and remote sensing data in a paddy field Y. Shi et al. 10.1002/ghg.1946
- Methane budget of East Asia, 1990–2015: A bottom-up evaluation A. Ito et al. 10.1016/j.scitotenv.2019.04.263
- Paddy rice methane emissions across Monsoon Asia Z. Ouyang et al. 10.1016/j.rse.2022.113335
- The relationship between phosphine, methane, and ozone over paddy field in Guangzhou, China J. Ma et al. 10.1016/j.gecco.2019.e00581
- Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields Z. Jiang et al. 10.1016/j.fcr.2023.108821
- Exploring the effects of extreme weather events on methane emissions from croplands: A study combining site and global modeling Y. Xia et al. 10.1016/j.agrformet.2023.109454
- A comparative study of anthropogenic CH<sub>4</sub> emissions over China based on the ensembles of bottom-up inventories X. Lin et al. 10.5194/essd-13-1073-2021
- Satellite-Based Monitoring of Methane Emissions from China’s Rice Hub R. Liang et al. 10.1021/acs.est.4c09822
- Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators T. Zhang et al. 10.2134/jeq2017.10.0398
- Impacts of Rice Cropping System Changes on Paddy Methane Emissions in Southern China M. Jiang et al. 10.3390/land12020270
- A Multi-Method Approach to Flood Mapping: Reconstructing Inundation Changes in the Cambodian Upper Mekong Delta C. Orieschnig et al. 10.1016/j.jhydrol.2022.127902
- Decrease in the annual emissions of CH4 and N2O following the initial land management change from rice to vegetable production L. Wu et al. 10.1007/s11356-018-1559-4
- Model-based evaluation of methane emissions from paddy fields in East Asia A. ITO et al. 10.2480/agrmet.D-21-00037
- Quantitative analysis of the methane gas emissions from municipal solid waste in India C. Singh et al. 10.1038/s41598-018-21326-9
- Estimating Methane Emissions in Rice Paddies at the Parcel Level Using Drone-Based Time Series Vegetation Indices Y. Song et al. 10.3390/drones8090459
- Effects of irrigation regime and rice variety on greenhouse gas emissions and grain yields from paddy fields in central China Z. Feng et al. 10.1016/j.agwat.2021.106830
- Accuracy and uncertainty analysis of staple food crop modelling by the process-based Agro-C model Q. Zhang et al. 10.1007/s00484-020-02053-1
Latest update: 14 Dec 2024
Short summary
Regional estimated uncertainties originate from methodological failures, errors, and supporting data insufficiency. A case study showed that the fallacy of the CH4MOD contributed 56.6 % to the uncertainty of a national inventory, with the remaining 43.4 % attributed to the scarcity of model inputs. We also revealed a dilemma between model performance and data availability: a model with better performance may reduce uncertainty from model fallacy but increases the uncertainty from data scarcity.
Regional estimated uncertainties originate from methodological failures, errors, and supporting...
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