Tim GriffisDepartment of Soil, Water, and Climate

University of Minnesota-Twin CitiesEmail: timgriffis@umn.edu

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

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

BACKGROUND AND MOTIVATION

International Plant Nutrition Institute, 2012

Source: Smith et al., 2012, Phil. Trans. R. Soc. B. 367, 1169-1174

Global Top-Down Constraint on N2O Emissions

Ozone Depletion and Radiative Forcing

IPCC AR5Ravishankara et al. (2009) Science

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

• 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

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

Objective 1: Indirect Emissions from Streams

Turner et al., 2015PNAS, 112, 9839-9843

• 9 Stream orders• 19 stream systems• Flux chambers• >200 observations

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

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

Scaling Up N2O Emissions

Turner et al., 2015PNAS, 112, 9839-9843

Regional Scale Controls on dissolved N2O in the Upper Mississippi River

Turner et al., 2016Geophysical Research Letters, 43, 4400-4407

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

Chen et al., 2016Global Biogeochemical Cycles, 43, 4400-4407

Objective 2: Tall Tower Observations and Atmospheric Inverse Modeling

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.

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.

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

Fu et al., 2016Atmospheric Chemistry and Physics, in review

Spatial Characteristics of the Mean Modeled N2O Mixing Ratio Enhancement

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

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?

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)

•