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| Titel |
Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach |
| VerfasserIn |
W. Zhang, Q. Zhang, Y. Huang, T. T. Li, J. Y. Bian, P. F. Han |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 3 ; Nr. 7, no. 3 (2014-06-27), S.1211-1224 |
| Datensatznummer |
250115638
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| Publikation (Nr.) |
 copernicus.org/gmd-7-1211-2014.pdf |
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| Zusammenfassung |
| Rice paddies are a major anthropogenic source of the atmospheric methane.
However, because of the high spatial heterogeneity, making accurate estimations of the
methane emission from rice paddies is still a big challenge, even with
complicated models. Data scarcity is one of the substantial causes of the
uncertainties in estimating the methane emissions on regional scales. In the
present study, we discussed how data scarcity affected the uncertainties in
model estimations of rice paddy methane emissions, from county/provincial
scale up to national scale. The uncertainties in methane emissions from the rice
paddies of China was calculated with a local-scale model and the Monte Carlo
simulation. The data scarcities in five of the most sensitive model
variables, field irrigation, organic matter application, soil properties,
rice variety and production were included in the analysis. The result showed
that in each individual county, the within-cell standard deviation of
methane flux, as calculated via Monte Carlo methods, was 13.5–89.3%
of the statistical mean. After spatial aggregation, the national total
methane emissions were estimated at 6.44–7.32 Tg, depending on the base scale
of the modeling and the reliability of the input data. And with the given
data availability, the overall aggregated standard deviation was 16.3% of
the total emissions, ranging from 18.3–28.0% for early, late and
middle rice ecosystems. The 95% confidence interval of the estimation was
4.5–8.7 Tg by assuming a gamma distribution. Improving the data
availability of the model input variables is expected to reduce the
uncertainties significantly, especially of those factors with high model
sensitivities. |
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