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Quantifying methane emissions from rice paddies in Northeast China by integrating remote sensing mapping with a biogeochemical model

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Author(s): Y. Zhang | Y. Y. Wang | S. L. Su | C. S. Li

Journal: Biogeosciences Discussions
ISSN 1810-6277

Volume: 8;
Issue: 1;
Start page: 385;
Date: 2011;
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ABSTRACT
The Sanjiang Plain located in Northeastern China is one of the major rice producing regions in the country. However, differing from the majority rice regions in Southern China, the Sanjinag Plain possesses a much cooler weather. Could the rice paddies in this domain be an important source of global methane? To answer this question, we calculated methane (CH4) emissions from the region by integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition or DNDC. To quantify regional CH4 emissions from the plain, we first tested the model against a two-year dataset of CH4 fluxes measured at a typical rice field within the domian. A sensitivity test was conducted to find out the most sensitive factors affecting CH4 emissions in the region. Based on the understanding gained from the validation and sensitivity tests, a geographic information system (GIS) database was constructed to hold the spatially differentiated input information to drive DNDC for its regional simulations. The GIS database included a rice map derived from the Landsat TM images, which provided crucial information about the spatial distribution of the rice fields within the domain of 10.93 million hectares. The modeled results showed that the total 1.44 million ha of rice paddies in the plain emitted 0.43–0.58 Tg CH4-C per year with spatially differentiated annual emission rates ranging between 100–800 kg CH4-C/ha, which are comparable with that observed in Southern China. The modeled data indicated that the high SOC contents, long crop season and high rice biomass enhanced CH4 production in the cool paddies. The modeled results proved that the northern wetland agroecosystems could make important contributions to global greenhouse gas inventory.

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