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DRAFT Inventory of U.S Greenhouse Gas Emissions and Sinks

2017_complete_report

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 (2) The IPCC Tier 1 and 2 methods have a simplified spatial resolution in which data are aggregated to soil types in climate regions, of which there about 30 of combinations in the United States. In contrast, the Tier 3 model simulates soil C dynamics at more than 300,000 individual NRI survey locations in individual fields. (3) The IPCC Tier 1 and 2 methods use simplified equilibrium step changes for changes in C emissions. In contrast, the Tier 3 approach simulates a continuous time period. More specifically, the DAYCENT model (i.e., daily time-step version of the Century model) simulates soil C dynamics (and CO 2 emissions and uptake) on a daily time step based on C emissions and removals from plant production and decomposition processes. These changes in soil C stocks are influenced by multiple sources that affect primary production and decomposition, including changes in land use and management, weather variability and secondary feedbacks between management activities, climate, and soils. Historical land-use patterns and irrigation histories are simulated with DAYCENT based on the 2012 USDA NRI survey (USDA-NRCS 2015). Additional sources of activity data are used to supplement the land-use information from the NRI. The Conservation Technology Information Center (CTIC 2004) provided annual data on tillage activity at the county level for the conterminous United States between 1989 and 2004, and these data are adjusted for long-term adoption of no-till agriculture (Towery 2001). No-till adoption is assumed to remain constant from 2005 through 2015 due to lack of data, but there is a planned improvement to update the tillage histories with a dataset that was recently released by the USDA (Conservation Effects Assessment Program Data, See Planned Improvements section). Information on fertilizer use and rates by crop type for different regions of the United States are obtained primarily from the USDA Economic Research Service. The data collection program was known as the Cropping Practices Surveys through 1995 (USDA-ERS 1997), and then became the Agricultural Resource Management Surveys (ARMS) (USDA-ERS 2015). Additional data are compiled through other sources particularly the National Agricultural Statistics Service (NASS 1992, 1999, 2004). Frequency and rates of manure application to cropland for 1997 are estimated from data compiled by the USDA Natural Resources Conservation Service (Edmonds et al. 2003), and then adjusted using county-level estimates of manure available for application in other years. Specifically, county-scale ratios of manure available for application to soils in other years relative to 1997 are used to adjust the area amended with manure (see Annex 3.12 for further details). Greater availability of managed manure N relative to 1997 is assumed to increase the area amended with manure, while reduced availability of manure N relative to 1997 is assumed to reduce the amended area. Data on the county-level N available for application are estimated for managed systems based on the total amount of N excreted in manure minus N losses during storage and transport, and include the addition of N from bedding materials. Nitrogen losses include direct N 2O emissions, volatilization of ammonia and NO x, N runoff and leaching, and the N in poultry manure used as a feed supplement. More information on livestock manure production is available in Section 5.2 - Manure Management and Annex 3.11. Daily weather data are another input to the model simulations. These data are based on a 4 kilometer gridded product from the PRISM Climate Group (2015). Soil attributes are obtained from the Soil Survey Geographic Database (SSURGO) (Soil Survey Staff 2016). The C dynamics at each NRI point are simulated 100 times as part of the uncertainty analysis, yielding a total of over 18 million simulation runs for the analysis. Uncertainty in the C stock estimates from DAYCENT associated with parameterization and model algorithms are adjusted using a structural uncertainty estimator accounting for uncertainty in model algorithms and parameter values (Ogle et al. 2007, 2010). Carbon stocks and 95 percent confidence intervals are estimated for each year between 1990 and 2012 using the NRI survey data, which is available through 2012. C stock change rates from 2013 to 2015 are assumed to be similar to 2012 for this Inventory. 39 However, the areas may have changed through the process in which the NRI survey data are reconciled with the Forest Inventory and Analysis (FIA) survey data and the National Land Cover Dataset (Homer et al. 2007; Fry et al. 2011; Homer et al. 2015). This process ensures that the areas of Forest Land Remaining Forest Land and Land Converted to Forest Land are consistent in all three datasets, and leads to some modification of other lands use areas to ensure the total land area of the United States does not change. For example, if the FIA estimate less Cropland Converted to Forest Land than the NRI, then the amount of area for this land use conversion is reduced in the NRI dataset and re-classified as Cropland Remaining Cropland (See Section 0, 39 Note: CRP enrollment is modified from 2013 to 2015 as described in the section on the Tier 2 Approach. 6-52 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2015

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Representation of the U.S. Land Base for more information). Future Inventories will be updated with new NRI activity data when the data are made available, and the time series from 2013 to 2015 will be recalculated. Tier 2 Approach In the IPCC Tier 2 method, data on climate, soil types, land-use, and land management activity are used to classify land area and apply appropriate soil C stock change factors (Ogle et al. 2003, 2006). Reference C stocks are estimated using the National Soil Survey Characterization Database (NRCS 1997) with cultivated cropland as the reference condition, rather than native vegetation as used in IPCC (2006). Soil measurements under agricultural management are much more common and easily identified in the National Soil Survey Characterization Database (NRCS 1997) than are soils under a native condition, and therefore cultivated cropland provided a more robust sample for estimating the reference condition. U.S.-specific C stock change factors are derived from published literature to determine the impact of management practices on SOC storage (Ogle et al. 2003, Ogle et al. 2006). The factors include changes in tillage, cropping rotations, intensification, and land-use change between cultivated and uncultivated conditions. U.S. factors associated with organic matter amendments are not estimated due to an insufficient number of studies in the United States to analyze the impacts. Instead, factors from IPCC (2006) are used to estimate the effect of those activities. Climate zones in the United States are classified using mean precipitation and temperature (1950 to 2000) variables from the WorldClim data set (Hijmans et al. 2005) and potential evapotranspiration data from the Consortium for Spatial Information (CGIAR-CSI) (Zomer et al. 2008, 2007) (Figure A-15). IPCC climate zones are then assigned to NRI point locations. Activity data are primarily based on the historical land-use/management patterns recorded in the 2012 NRI (USDA- NRCS 2015). Each NRI point is classified by land use, soil type, climate region, and management condition. Survey locations on federal lands are included in the NRI, but land use and cropping history are not compiled at these locations in the survey program (i.e., NRI is restricted to data collection on non-federal lands). Land-use patterns at the NRI survey locations on federal lands are based on the National Land Cover Database (NLCD) (Fry et al. 2011; Homer et al. 2007; Homer et al. 2015). Classification of cropland area by tillage practice is based on data from the Conservation Technology Information Center (CTIC 2004; Towery 2001) as described in the Tier 3 approach above. Activity data on wetland restoration of Conservation Reserve Program land are obtained from Euliss and Gleason (2002). Manure N amendments over the inventory time period are based on application rates and areas amended with manure N from Edmonds et al. (2003), in addition to the managed manure production data discussed in the methodology subsection for the Tier 3 approach. Utilizing information from these data sources, SOC stocks for mineral soils are estimated 50,000 times for each year in the time series, using a Monte Carlo stochastic simulation approach and probability distribution functions for U.S.-specific stock change factors, reference C stocks, and land-use activity data (Ogle et al. 2002; Ogle et al. 2003; Ogle et al. 2006). The annual C stock change rates from 2013 through 2015 for the Tier 2 method are assumed to be similar to 2012, but the areas may be adjusted through the process of reconciling NRI and NLCD with the FIA data (See section on the Tier 3 Approach for more information). As with the Tier 3 method, future Inventories will be updated with new NRI activity data when the data are made available, and the time series will be recalculated (see Planned Improvements section). Additional Mineral Soil C Stock Change Annual C stock change estimates for mineral soils between 2013 and 2015 are adjusted to account for additional C stock changes associated with gains or losses in soil C after 2012 due to changes in CRP enrollment (USDA-FSA 2015). The change in enrollment relative to 2012 is based on data from USDA-FSA (2015) for 2012 through 2015. The differences in mineral soil areas are multiplied by 0.5 metric tons C per hectare per year to estimate the net effect on soil C stocks. The stock change rate is based on country-specific factors and the IPCC default method (see Annex 3.12 for further discussion). Organic Soil Carbon Stock Changes Annual C emissions from drained organic soils in Cropland Remaining Cropland are estimated using the Tier 2 method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) rather than default IPCC rates. The final estimates included a measure of uncertainty as determined from the Monte Carlo Stochastic Simulation Land Use, Land-Use Change, and Forestry 6-53

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    1 2 3 4 5 6 irreversible accumulati

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    a Emission estimates reported in th

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    a Emissions from Wood Biomass and E

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    CH4 0.3 0.1 0.1 0.1 0.1 0.2 0.2 Pet

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    1 Table 2-7: Emissions from Agricul

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    1 2 Table 2-8: U.S. Greenhouse Gas

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    1 2 3 4 Overall, in 2015, waste act

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    Cement Production 33.3 45.9 32.0 35

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    Total 1,862.5 2,441.6 2,197.3 2,059

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    Total Emissions 6,366.7 7,315.6 6,7

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    N2O 1.0 1.2 1.1 1.0 1.1 1.1 1.1 Oth

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    International Bunker Fuels a 0.2 0.

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    1 Table 3-4: CO2, CH4, and N2O Emis

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    U.S. Territories a 28.0 50.1 41.7 4

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    Fuel Oil 27.2 45.6 36.7 37.6 37.1 3

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    1 Figure 3-9: Electricity Generatio

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    Medium- and Heavy-Duty 0.5 0.9 0.7

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    Coal b 1,653.7 1,596.3 1,809.1 -3%

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    1 Table 3-20: Adjusted Consumption

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    Gas/Waste Product 1990 2005 2011 20

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    Activity 1990 2005 2011 2012 2013 2

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    Previous Estimated Emissions from S

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    Emissions (w/o Plunger) (MT) 372,28

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    2012 13.8 13,785 2013 14.0 14,028 2

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    1 Table 4-89: CO2 Emissions from Zi

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    New Mexico 70,608 52,250 12.0 0.263

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    C Storage Factor, Proportion of Ini

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    1 Table 7-2: Emissions from Waste (

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    2013 321 10,536 2014 323 10,613 201

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    1 2 Table 7-16: Approach 2 Quantita

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    1 2 3 4 5 6 7 8 9 10 EF i = emissio

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    a Miscellaneous includes TSDFs (Tre

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    Enteric Fermentation NC NC + NC + (

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