Our analytical scope aligns with the IPCC (2006, 2019) definition of LULUCF fluxes, encompassing all GHG sources and sinks from US managed lands. We also broaden LULUCF to include N2O and CH4 emissions from agricultural soil management and rice methane for two reasons: (1) the USA uses a single model, DayCent, to jointly calculate carbon stock change and non-CO2 fluxes on agricultural soils, and (2) previous studies identified agricultural soil N2O emissions as the largest source of economy-wide NGHGI uncertainty (Ramírez et al. 2008; Winiwarter and Muik 2010; Petrescu et al. 2020), so including these inventory categories would likely impact our analysis.
We describe here the two components of our analysis:
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Uncertainty attribution: We quantify the contribution of each uncertainty element to the 95% CIs of all relevant LULUCF inventory categories.
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Omitted flux estimation: We provide initial estimates of known omitted fluxes, using literature review, expert input, and Tier 1 and 2 methods.
Uncertainty attribution
To identify sources of NGHGI uncertainty, we must first justify an uncertainty taxonomy tailored to the LULUCF NGHGI context. Based on the literature review described in SM Sect. 2, chapter 1.2, we define an uncertainty element as an individual input, parameter, model or submodel, and any instance of design-based sampling error. We refer to input, parameter, and model structure uncertainty collectively as model uncertainty, as distinct from sampling error. In some cases, we aggregate uncertainty elements into a group of inputs or parameters for ease of analysis and interpretation.
Given this taxonomy, we review methods for each LULUCF inventory category and identify all uncertainty elements. For inventory categories where it was possible to recalculate the central flux estimate given available data, we attribute uncertainty to each element using the contribution index method (Eq. 1).
Equation 1: Contribution index
$$Index\left(i,k\right)= \frac{Range\left(full,k\right)-Range(i,k)}{\sum_{j=1}^{J}Range\left(full,k\right)-Range(j,k)} \times 100$$
(1)
where
i = 1,…,J: refers to uncertainty element i
Range(full,k): is inventory category k 95% CI magnitude (97.5th quantile minus 2.5th quantile)
Range(i,k): is inventory category k 95% CI magnitude holding element i at its mean or point estimate
Index(i,k): is percentage contribution of element i to Range(full,k)
Other methods for uncertainty attribution have been utilized in the literature, including sensitivity analysis (McRoberts et al. 2016; Rypdal and Flugsrud 2001), uncertainty importance elasticities (Smith and Heath 2001; Winiwarter and Muik 2010), regression correlation coefficients (Peltoniemi et al. 2006; Winiwarter and Muik 2010), and Gaussian error propagation (Harmon et al. 2007; Phillips et al. 2000). We chose the contribution index method for its ability to account for full probability distributions, to allow for non-linear relationships between elements and model outputs and dependencies among uncertainty elements, and because we would be able to use previously published analyses for some inventory categories (Smith and Heath 2001; Ogle et al. 2003; Skog et al. 2004).
Where flux estimate recalculation was not possible, due to lack of access to data or methods, we use published uncertainty attribution results or, in the case of Tier 3 cropland and grassland fluxes, expert elicitation. US EPA recognizes expert elicitation as one method for NGHGI quality assurance and uncertainty analysis (US EPA 2002). We tailored US EPA (2002) NGHGI expert elicitation guidance to the objectives of our study (methods described in more detail below).
Uncertainty elements that we identified but were not able to quantify are listed in SM Table 2-1. Table 2 summarizes the uncertainty attribution methods used for each LULUCF inventory category.
Table 2 Uncertainty attribution methods for each GHG flux category Omitted GHG flux estimation
Most of the omitted fluxes identified in this paper are already recognized in the US LULUCF GHG inventory as planned improvements. We identified additional omitted fluxes by reviewing IPCC (2006, 2019) guidance, by including prompts to identify omitted GHG fluxes in the cropland and grassland expert elicitation survey, and by prompting US LULUCF NGHGI inventory compilers to identify omitted GHG fluxes through direct communication.
For each identified omitted flux, we reviewed the literature to identify activity data and emission factors. The resulting omitted GHG flux estimates are meant to be useful only for purposes of prioritizing future work.
Methods by land use and inventory categories
We briefly summarize the methods used for each LULUCF inventory category here, with further details provided in the SM. Our analysis is based on the 2018 US NGHGI report, which covers inventory years 1990 to 2016 and which was the most complete inventory report available while the majority of our analytical work was completed. In the SM, we note any significant methodological updates in more recent US NGHGI reports, none of which meaningfully influences our findings.
Forests
Above- and belowground biomass in living and standing dead trees (SM Sect. 2, chapter 2.1): We recalculate the carbon stock change flux and 95% CI for above- and belowground tree biomass and standing dead trees (hereafter, tree biomass), accounting for uncertainty in nine groups of allometric model parameters (Table 2-2) as well as sampling error. We use Forest Inventory and Analysis (FIA) data and allometric models specific to eastern Texas as the basis for analysis to reduce Monte Carlo computational burden. Eastern Texas was chosen as a representative region for national forest carbon fluxes, encompassing both shrub species common in the western USA and hardwood and softwood species present in higher precipitation regions. We find that eastern Texas tree biomass exhibits similar proportional uncertainty to national uncertainty reported in the US NGHGI (see SM Sect. 2, chapter 2.1 for more detail).
Litter and soil (SM Sect. 2, chapters 2.2 and 2.3): Using literature estimates of mean litter carbon stocks by forest type (Domke et al. 2016), and the reported model prediction uncertainty for litter carbon stocks (US NGHGI 2018), we use Monte Carlo simulation to estimate the national 95% CI for litter carbon stock change. Similar methods were used for soils, accounting for model prediction uncertainty from estimating soil carbon stocks to 100 cm depth at a subset of FIA plots as well as the random forest model used to extrapolate soil carbon stock estimates to all FIA plots (Domke et al. 2017). A significant shortcoming of our approach for both litter and soil carbon pools is that it requires assuming covariance of carbon stocks between two time periods, because the US NGHGI does not report 95% CIs by forest carbon pool. For this reason, we provide sensitivity analysis for different levels of intertemporal covariance.
Non-CO2 from forest fires (SM Sect. 2, chapter 2.4): We recalculate the CH4 and N2O emissions from forest fires and their respective 95% CIs, using Monte Carlo simulation to account for uncertainty from four input variables (burned area, fuel availability, combustion factor, and emission factor) using standard deviations reported in the US NGHGI (2018) and IPCC (2006).
Harvested wood products (SM Sect. 2, chapter 2.5): We modify contribution index results from Skog et al. (2004) to focus on inputs and parameters used in Skog (2008), which most closely aligns with US NGHGI (2018) methods.
Cropland and grassland
The US NGHGI uses consistent methods across many cropland and grassland inventory categories, so we collapse analysis across the two land uses. The US NGHGI uses Tier 3 methods on 78% of managed cropland and grassland soils, and Tiers 1 and 2 on organic soils, federal grasslands, shaley and gravelly soils, and minor crop types.
Carbon stock change, N2O, and rice CH4 on Tier 3 soils (SM Sect. 2, chapter 3.1): It was not possible to recalculate Tier 3 fluxes, due to National Resources Inventory (NRI) dataset confidentiality. Therefore, we use expert elicitation to identify the largest sources of uncertainty stemming from inputs and structure of the biogeochemical model DayCent as well as scaling NRI plot estimates to population area. The expert elicitation included prompts to identify primary research, model development and intermodel comparison, and data priorities for reducing cropland and grassland Tier 3 flux uncertainty. Participation in uncertainty attribution sections of the survey required knowledge of Century, DayCent, or similar biogeochemical soil models and IPCC GHG accounting guidance. Respondents were asked to confirm that they possessed this knowledge before completing the survey. Respondent expertise was concentrated in soil science (87%), biogeochemistry (67%), and the carbon cycle (67%); 53% worked in academia, 33% in government, and the remainder in NGO or private sectors. Details on the expert elicitation protocol and results are provided in the SM Sect. 2, chapter 3.1, and the full expert elicitation survey is available in SM Sect. 3.
Carbon stock change and N2O in Tier 1 and 2 soils (SM Sect. 2, chapter 3.2): We apply contribution index results from Ogle et al. (2003) to 95% CIs reported in the US NGHGI (2018).
Non-CO2 from grassland fires (SM Sect. 2, chapter 3.3): We recalculate 2014 CH4 and N2O emissions, the most recent year for which burned grassland area estimates are available, and follow methods similar to the forest fire inventory category.
Omitted cropland and grassland GHG fluxes (SM Sect. 2, chapter 3.4): We use IPCC (2006) default equations and literature emission factors to estimate carbon stock change in woody biomass and litter (USDA 2012; Udawatta and Jose 2011); non-CO2 emissions from woody biomass in grassland fires (US NGHGI 2018; IPCC 2006); soil microbial CH4 sink (Dutaur and Verchot 2007; Del Grosso et al. 2000); and select GHG sinks and sources on federal cropland and grassland (US NGHGI 2018).
Settlements
Carbon stock change in urban trees (SM Sect. 2, chapter 4.1): We recalculate the CO2 flux and 95% CI attributable to carbon stock change in urban trees (Nowak et al. 2008; Nowak et al. 2013). We attribute uncertainty to all inputs (Table 2-30) using error propagation and contribution index methods.
Carbon stock change in yard trimmings and food scraps (SM Sect. 2, chapter 4.2): We recalculate CO2 fluxes and 95% CIs attributable to yard trimmings and food scraps discarded in landfills (US NGHGI 2018; De la Cruz and Barlaz 2010), accounting for uncertainty from all inputs.
Omitted settlement GHG fluxes (SM Sect. 2, chapter 4.5): We estimate CO2 emissions resulting from US settlement mineral soils, which is omitted from the US NGHGI due to lack of data, consistent with IPCC (2006) guidelines. Using Tier 1 methods and IPCC (2006) default values, we provide an initial estimate of this flux.
Wetlands
The US NGHGI (2018) indicates that there are 43 million hectares of wetlands in the USA, yet GHG fluxes are calculated for only 2.9 million hectares of wetlands. The omission is due to lack of data that would allow for designating non-coastal wetlands as managed (that is, wetlands directly created by human activity or areas where the water level has been artificially altered) (US NGHGI 2018). Due to this data gap, we were not able to estimate omitted wetland fluxes (SM Sect. 2, chapter 5).
Alaska, Hawaii, and US territories
Alaska, Hawaii, and US territories comprise nearly 20% of the total US land base (nearly all of this in Alaska), but they are not completely accounted for in the US NGHGI. The 2019 US NGHGI included forest carbon stock changes in interior Alaska for the first time, an area covering 24.5 million acres (9% of US managed forest area). We provide estimates for omitted fluxes in Alaska, Hawaii, and Puerto Rico (the largest US territory), based on IPCC (2006) guidance, emission data derived from the US NGHGI (2018, 2019), and literature review (SM Sect. 2, chapter 6).