HR: 10:20h
AN: B52C-01    [Abstracts]
TI: Comparison and Synthesis of Gridded Inventories of Methane Emissions From Rice Agriculture
AU: * Butenhoff, C L
EM: cbuten@pdx.edu
AF: Dept. of Physics Portland State University, PO Box 751, Portland, OR 97207, United States
AU: Khalil, M K
EM: aslamk@pdx.edu
AF: Dept. of Physics Portland State University, PO Box 751, Portland, OR 97207, United States
AU: Biberic, A
EM: aidab@pdx.edu
AF: Dept. of Physics Portland State University, PO Box 751, Portland, OR 97207, United States
AU: Shearer, M J
AF: Dept. of Physics Portland State University, PO Box 751, Portland, OR 97207, United States
AU: Xiong, Z Q
EM: zhengqin@pdx.edu
AF: Dept. of Physics Portland State University, PO Box 751, Portland, OR 97207, United States
AB: Rice agriculture contributes some 30 to 100 Tg of methane per year to the global atmosphere. It may be the single largest anthropogenic source of atmospheric methane. The production of rice is in transition as the availability of inexpensive inorganic fertilizers increases and their use rapidly replaces traditional organic types. Water management of paddy soils is also changing as irrigation water becomes less available. Production of rice is forecast to increase in the foreseeable future to meet growing world demand, as strategies continue to evolve to help mitigate the release of methane from rice crops. These factors make the study of rice emissions especially important today. Emission factors for rice are notoriously heterogeneous in space and time due to numerous factors such as soil types, climate, water management, organic input, among others. Proper accounting requires a spatially high resolution inventory to fully capture these complexities. Several inventories currently exist and are available to the modeling community. These inventories use a variety of techniques such as process models, satellite imagery, country-reported data, to determine and distribute emissions over the rice-producing areas. Not unexpectedly, these inventories differ significantly not only in their spatial distributions, but in their integrated emissions as well. To best utilize these datasets, these differences must be known. In this study we first identify the similarities and discrepancies between the existing datasets. We then rank and assess the respective techniques used in each inventory, and create a synthesized weighted distribution of emissions on a 1x1 degree grid.
DE: 0300 ATMOSPHERIC COMPOSITION AND STRUCTURE
DE: 0315 Biosphere/atmosphere interactions (0426, 1610)
DE: 0322 Constituent sources and sinks
DE: 0325 Evolution of the atmosphere (1610, 8125)
DE: 0365 Troposphere: composition and chemistry
SC: Biogeosciences [B]
MN: 2007 Fall Meeting