1) Researchers measured nitrous oxide (N2O) emissions from agricultural land and drainage networks in the US Corn Belt to better understand indirect N2O emissions. 2) Chamber measurements showed that N2O emissions decrease with increasing stream order, indicating lower order streams are hotspots for indirect emissions. 3) Inverse modeling using tall tower measurements estimated seasonal indirect N2O emissions from the Corn Belt, finding current estimates underestimate indirect emissions significantly.

1. Regional-Scale Assessment of Nitrous Oxide Emissions within the US Corn Belt: The Impact of
Precipitation and Agricultural Drainage on Indirect Emissions
Tim Griffis1, Xuhui Lee2, John Baker3, Peter Turner1, Dylan Millet1, Zichong Chen1, Jeff Wood1, and Rod Venterea3
1. Department of Soil, Water, and Climate, University of Minnesota; 2. School of Forestry and Environmental Studies, Yale University; 3. USDA-ARS & Department of Soil, Water, and Climate, University of Minnesota
RESEARCH APPROACH
FIELD OBSERVATIONS
Method
tall tower upscaled IPCC+ EDGAR GEIA
Regionalflux(nmolm
-2
s
-1
)
0.0
0.1
0.2
0.3
0.4
0.5
Surface type
corn soybean natural water urban
Fluxdensity(nmolm
-2
s
-1
)
0.0
0.1
0.2
0.3
0.4
0.5
a
b
IPCC Agric
Non Agric
Agric BNF
manure
residue
volatization
runoff
fertilizer
others
MOTIVATION AND BACKGROUND
Nitrous oxide (N2O) is a greenhouse gas with a large global warming potential and is a
major cause of stratospheric ozone depletion. Croplands are the dominant source of
N2O, but mitigation strategies have been limited by the large uncertainties in both
direct and indirect emission factors (EFs) implemented in “bottom-up” emission
inventories. The Inter-governmental Panel on Climate Change (IPCC) recommends
EFs ranging from 0.75 to 2% for the various N2O pathways in croplands. Consideration
of the global Nitrogen (N) budget yields a much higher EF ranging between 3.8 and
5.1%. We have used two-years of hourly high-precision N2O concentration
measurements on a very tall tower to evaluate the IPCC bottom-up and global “top-
down” EFs for the United States Corn Belt, a vast region spanning the US Midwest
that is dominated by intensive N inputs to support corn cultivation. The results showed
(Figure 1) that agricultural sources in the Corn Belt released 420 ± 50 Gg N (mean ± 1
standard deviation; 1 Gg = 109 g) in 2010, in closer agreement with the top-down
estimate of 350 ± 50 Gg N and 80% larger than the bottom-up estimate based on the
IPCC EFs (230 ± 180 Gg N). The large difference between the tall-tower measurement
and the bottom-up estimate implies the existence of N2O emission hot spots or missing
sources within the landscape that are not fully accounted for in the IPCC and other
bottom-up emission inventories. Reconciling these differences is a crucial step towards
developing practical strategies to mitigate N2O emissions.
Figure 1: Comparison of N2O flux densities. a) Annual mean flux densities for the
surface types in the tall tower footprint. b) Comparison of regional fluxes using
different methods. The figure insets show each IPCC component (Griffis et al., 2013)
The goals of our current research are therefore to:
1. Quantify the distribution and importance of drainage networks on indirect N2O
emissions;
2. Evaluate the magnitude of indirect emissions on the regional N2O budget using tall
tower observations and a novel inverse modeling approach;
3. Forecast how changing precipitation patterns in the Upper Midwest might impact
regional N2O emissions
The overall research approach involves:
1. High precision and continuous measurements of N2O from the University of
Minnesota Tall Tower Trace Gas Observatory (TGO);
2. Chamber flux measurements and geospatial sampling to assess N2O emissions
along a hydrological gradient defined by time and distance of water transport;
3. Inverse and land surface modeling to estimate regional N2O emissions and to
partition total emissions into direct and indirect contributions. Finally, modeling will
be used to assess the potential impacts of changing precipitation on N2O
emissions.
RESEARCH APPROACH
Figure 2: Overview of research approach.
Measurements, modeling, geospatial, and
geostatistical techniques at multiple spatial scales
are used to constrain the direct versus indirect
N2O emissions at local and regional scales.
Figure 3. Tall tower observations. Hourly N2O
mixing ratios have been measured using a tunable
diode laser at 100 m and 185 m above the ground
from 2010 to 2015. The wavelet analyses above
(annual ensemble) show that the N2O signal is
dominated by short-term variations, has weak
seasonality, and is linked to snow melt. Cross-
wavelet analyses are being used to determine the
sensitivity of N2O emissions to weather. Regional
N2O emissions are obtained using inverse
methods based on the Weather Research and
Forecasting (WRF) Model, the Community Land
Model (CLM), and the Stochastic Time-Inverted
Lagrangian Transport (STILT) model.
Figure 4: Quantifying indirect N2O emissions.
Chamber measurements from agricultural
drainage networks including tile drains from farm
fields, and first order to higher order rivers are
used to assess IPCC emission factors.
Figure 5: Emissions scale with stream order.
N2O emissions were measured using a flow-
through non-steady-state chamber system from
Strahler Stream Order 1 (i.e. drainage ditches) to
stream order 9 (i.e. Mississippi River). Two years
of flux measurements (n > 200) indicate that the
emissions decrease exponentially as stream order
increases. The error bars also scale with stream
order indicating that emission hot spots are
associated with lower stream order and that the
uncertainty in emissions can be reduced by
increasing our monitoring efforts at lower order
streams (Turner et al., 2015).
MODELING RESULTS
Figure 8: Inverse modeling approach. Tall tower
N2O concentration observations, atmospheric
transport modeling, and prior estimates of indirect
and direct N2O emissions from CLM and EDGAR,
respectively are used with a Bayesian optimization
method to constrain the direct and indirect
emissions independently.
Figure 9: Seasonal variation of indirect N2O
emissions within the US Corn Belt derived
from Bayesian inverse modeling.
Figure 6: Results from upscaling N2O
emissions. A. Comparison of local indirect
sources from default IPCC emission factors vs our
stream scaling method. B. Total US corn belt
emissions from three methods. C. Flux densities
related to each emission estimate.
Figure 7: First-order default EF5r underestimation
from the Corn Belt region. The bias is defined as
the difference between the IPCC emission factor
(EF5r) and the results from stream order scaling.
Figure 10: Regional N2O emissions. The
Bayesian inverse model estimate of N2O
emissions is in excellent agreement with tall tower
boundary layer budget estimates (Griffis et al.,
2013) and bottom-up scaling (Turner et al., 2015).
The inverse modeling allows for independent
constraint of the direct and indirect emissions.
These results confirm that indirect emissions are
severely underestimated by bottom-up inventories.
Support for this research has been provided by the United States Department of Agriculture, Grant number: USDA-NIFA
2013-67019-21364 and the Minnesota Supercomputing Institute for Advanced Computational Research
(https://www.msi.umn.edu/)
Publications
Turner, P.A., T.J. Griffis, X. Lee, J.M. Baker, R.T. Venterea, and J.D. Wood,
Indirect nitrous oxide emissions from streams within the US Corn Belt scale
with stream order, Proceedings of the National Academy of Sciences of the
United States of America, 2015, doi/10.1073/pnas.150359812