Regional-Scale Assessment of N2O Emissions within the US Corn Belt: The Impact of Precipitation and...
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Tim GriffisDepartment of Soil, Water, and Climate
University of Minnesota-Twin CitiesEmail: [email protected]
Regional-scale assessment of N2O emissions within the US Corn Belt: Impact of precipitation and agricultural drainage on indirect emissions
USDA-NIFA Project Directors MeetingWashington, DC, October 13, 2016
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Acknowledgements
Funding: USDA-AFRI Grant# 2013-67019-21364
Co-PIs: Xuhui Lee and John Baker
Students: Peter Turner and Zichong Chen
Collaborators and Technical Support: Rod Venterea; Dylan Millet; Jeff Wood; Congsheng Fu; John Crawford; Luke Loken; Bill Breiter; Mike Dolan; Ke Xiao; Joel Fassbinder; Kendall King; Natalie Schultz; Cody Winker; and Ming Chen
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BACKGROUND AND MOTIVATION
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International Plant Nutrition Institute, 2012
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Source: Smith et al., 2012, Phil. Trans. R. Soc. B. 367, 1169-1174
Global Top-Down Constraint on N2O Emissions
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Ozone Depletion and Radiative Forcing
IPCC AR5Ravishankara et al. (2009) Science
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US Corn Belt: Top-down vs Bottom-up EstimatesKort et al. (2008) Geophys. Res. Lett.
Griffis et al., 2013Global Biogeochemical Cycles, 27, 746-754
IPCC
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• Large differences between top-down and bottom-up estimates imply the existence of N2O emission hot spots or missing sources not fully accounted for in bottom-up inventories
• Reconciling these differences is a crucial step towards developing and assessing strategies to mitigate N2O emissions and understanding how emissions will change in the future (i.e. climate vs management)
BACKGROUND AND MOTIVATION
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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 inverse modeling to partition emissions;
3. Forecast how changing climate in the Upper Midwest might impact regional N2O emissions
OBJECTIVES
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Objective 1: Indirect Emissions from Streams
Turner et al., 2015PNAS, 112, 9839-9843
• 9 Stream orders• 19 stream systems• Flux chambers• >200 observations
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Relation between N2O flux and stream order
• Exponential decline in the flux density
• Decreases with stream order
• High confidence in streams > 5
• Further work needed to constrain emissions from low-order streams
Turner et al., 2015PNAS, 112, 9839-9843
Mis
siss
ippi
Hea
dwat
er
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Site Description• South of St. Paul, Minnesota• 350,000 ha 70% agriculture• Majority of flux observations• Footprint of UMN TGO
Required Data• Stream order – equation• Average width by stream order • Annual fertilizer and manure inputs • Area receiving fertilizer
Assumptions1. Seasonality is captured
in scaling function2. Emissions are constant
during the ice free season (no dry streams)
3. When land-use characteristics are similar, scaling function is appropriate
Turner et al., 2015PNAS, 112, 9839-9843
Scaling Up N2O Emissions
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Scaling Up N2O Emissions
Turner et al., 2015PNAS, 112, 9839-9843
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Regional Scale Controls on dissolved N2O in the Upper Mississippi River
Turner et al., 2016Geophysical Research Letters, 43, 4400-4407
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Mapping Dissolved N2O Concentrations
N2Osat NO3-N2Osat
• Mean N2Osat = 2.5x equilibrium• Important N2O source• Potential for long-term monitoring at key locations
Turner et al., 2016Geophysical Research Letters, 43, 4400-4407
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Chen et al., 2016Global Biogeochemical Cycles, 43, 4400-4407
Objective 2: Tall Tower Observations and Atmospheric Inverse Modeling
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Chen et al., 2016Global Biogeochemical Cycles, 43, 4400-4407
Tall Tower N2O Observations and Source Footprints based on WRF and STILT Modeling
Figure 3. Average seasonal source footprints for measurements at the KCMP tall tower
(indicated by symbol ‘×’), 2010 [units: log10(ppm µmol-1 m2 s)] for a). Spring: Mar., April, May;
b). Summer: June, July, Aug.; c). Fall: Sep., Oct., Nov.; d). Winter: Dec., Jan., Feb.
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Chen et al., 2016Global Biogeochemical Cycles, 43, 4400-4407
Estimating Direct and Indirect Emissions with Bayesian Inverse Analyses
Figure 5. Comparison of N2O budgets for the US Corn Belt in year 2010 estimated using
different methods [tall Tower: boundary layer method at the KCMP tall tower [Griffis et al.,
2013], IPCC EFs: estimate from IPCC EF method]. Error bars indicate the uncertainties of
regional budget estimate from direct and indirect emissions, respectively.
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Locations of the N2O monitoring sites, scope of the Corn Belt, modeling domains, and the default N2O emission flux in nmol m-2 s-1. KCMP – Minnesota; NWR –Niwot Ridge, Colorado; AMT – Argyle, Maine; BAO – Boulder Atmospheric Observatory, Colorado; LEF – Park Falls, Wisconsin; SCT – Beech Island, South Carolina; WBI – West Branch, Iowa; WKT – Moody, Texas.
Eulerian Modeling using WRF-CHEM
Fu et al., 2016Atmospheric Chemistry and Physics, in review
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Fu et al., 2016Atmospheric Chemistry and Physics, in review
Spatial Characteristics of the Mean Modeled N2O Mixing Ratio Enhancement
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Griffis et al., in prep
Objective 3: Inter-annual Variability and Sensitivity of N2O Emissions to Climate
2010 2011 2012 2013 2014 20150
100
200
300
400
500
600
Year
Tota
l em
issi
ons
(Gg
N 2O-N
)
directindirect
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Simulation of N2O emissions using CLM45-BGC-CROP for the US Corn Belt from 2011 to 2050.
Griffis et al., in prep
2010 2020 2030 2040 2050
350
400
450
500
550
Year
N2O
em
issi
on (G
g N
2O
-N y
-1)
2010 2020 2030 2040 20506
7
8
9
10
11
12
Year
Air
Tem
p (
oC
)
2010 2020 2030 2040 2050600
700
800
900
1000
1100
1200
Year
Pre
cipi
tatio
n (m
m)
Simulations were performed using the CMIP5 (scenario RCP8.5) forcing data
Implications for mitigation strategies and assessment?
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Summary• Stream order measurements and top-down inversions indicate that
IPCC N2O emissions associated with leaching and runoff are likely biased low. We suggest an upward adjustment of 1.9 to 4.6 times.
• Equilibration-based measurements could be used for routine/long-term monitoring to improve emissions estimates associated with runoff
• Lagrangian and Eulerian models support that emissions from the US Corn Belt have been underestimated based on inventories
• Six years of tall tower observations indicate that N2O emissions are highly sensitive to climate with a regional emission factor ranging from 4 to 7%
• Land surface modeling indicates that emissions are likely to increase due to warmer and wetter conditions for the region (2011-2050)
•