Nitrous oxide (N2O) emissions within the US Corn Belt have been previously estimated to be 200–900% larger than predictions from emission inventories, implying that one or more source categories in bottom-up approaches are underestimated. Here we interpret hourly N2O concentrations measured during 2010 and 2011 at a tall tower using a time-inverted transport model and a scale factor Bayesian inverse method to simultaneously constrain direct and indirect agricultural emissions. The optimization revealed that both agricultural source categories were underestimated by the Intergovernmental Panel on Climate Change (IPCC) inventory approach. However, the magnitude of the discrepancies differed substantially, ranging from 42 to 58% and from 200 to 525% for direct and indirect components, respectively. Optimized agricultural N2O budgets for the Corn Belt were 319 � 184 (total), 188 � 66 (direct), and 131 � 118 Gg N yr−1 (indirect) in 2010, versus 471 � 326, 198 � 80, and 273 � 246 Gg N yr−1 in 2011. We attribute the interannual differences to varying moisture conditions, with increased precipitation in 2011 amplifying emissions. We found that indirect emissions represented 41–58% of the total agricultural budget, a considerably larger portion than the 25–30% predicted in bottom-up inventories, further highlighting the need for improved constraints on this source category. These findings further support the hypothesis that indirect emissions are presently underestimated in bottom-up inventories. Based on our results, we suggest an indirect emission factor for runoff and leaching ranging from 0.014 to 0.035 for the Corn Belt, which represents an upward adjustment of 1.9–4.6 times relative to the IPCC and is in agreement with recent bottom-up field studies. �2016. American Geophysical Union. All Rights Reserved.
Department of Soil, Water, and Climate, University of Minnesota, Twin Cities, Saint Paul, MN, United States; School of Forestry and Environmental Studies, Yale University, New Haven, CT, United States; United States Department of Agriculture, Agricultural Research Service, Saint Paul, MN, United States; Department of Mathematics, Augsburg College, Minneapolis, MN, United States
Recommended Citation:
Chen Z,, Griffis T,J,et al. Partitioning N2O emissions within the U.S. Corn Belt using an inverse modeling approach[J]. Global Biogeochemical Cycles,2016-01-01,30(8)