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Indirect nitrous oxide emissions from streams within the US Corn Belt scale with stream order

Peter A. Turner, Timothy J. Griffis, Xuhui Lee, John M. Baker, Rodney T. Venterea, Jeffrey D. Wood
Proceedings of the National Academy of Sciences of the United States of America 2015 v.112 no.32 pp. 9839-9843
climate change, emissions factor, greenhouse gas emissions, greenhouse gases, nitrous oxide, streams, Corn Belt region, Minnesota
N ₂O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N ₂O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N ₂O emissions, implying that EFs may be biased low. Here, we measured N ₂O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N ₂O fluxes from headwater streams often exceeded 29 nmol N ₂O-N m ⁻²⋅s ⁻¹ and decreased exponentially as a function of S . This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF ₅ᵣ) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N ₂O emissions by 40%. We show that accounting for zero-order streams as potential N ₂O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF ₅ᵣ underestimation explains the large differences observed between top-down and bottom-up emission estimates.