Abstract
Two atmospheric diffusion models, the box model ad the ATDL (Atmospheric Turbulent and Diffusion Laboratory) model, were used to calculate regional methane (CH4) emissions of rice fields in the Beijing area. Compared with conventional closed chamber measurements, the box model overestimated CH4 emission because of meteorological conditions--the ground inverse layer was not favorable for the application of the model during the rice-growing season. The ATDL model, on the other hand, handled this unfavorable meteorological condition and gave reasonable CH4 emission estimates (about 6.1–8.5 mg m−2 h−1) close to conventional measurements (about 0.3–14.3 mg m−2 h−1) in June, a period generally characterized by significant CH4 emission from rice fields. In September, CH4 emission as measured with closed chambers was negligible (about 0–0.3 mg m−2 h−1), but the ATDL model still calculated it to be about 2.8–5.3 mg m−2 h−1, albeit at a low level and considerably below the June emission level. This discrepancy cannot be explained at present and needs further stuy. Most likely causes are measurement artifacts and/or the presence of minor local CH4 sources (ditches, field depressions) in the study area. The application of atmospheric diffusion models for regional CH4 emission estimation depends greatly on meteorological conditions. Moreover, the models tend to give much more reliable results during periods of rather high CH4 emission. This coincides with the time that such regional CH4 emission estimates are most valuable. The atmospheric diffusion models complement the closed chamber method by providing integrated CH4 emission estimates from 1–100-km2 rice areas. Detailed information about agricultural management of rice fields and other potential CH4 sources within the study region are necessary to better understand the integrated regional emission estimates.
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Liu, J., Zhang, Y., Shao, K. et al. Estimation of Regional Methane Emission from Rice Fields Using Simple Atmospheric Diffusion Models. Nutrient Cycling in Agroecosystems 58, 303–310 (2000). https://doi.org/10.1023/A:1009815223989
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DOI: https://doi.org/10.1023/A:1009815223989