May 29, 2004 - [1] The uncertainty in carbon emissions from fire was estimated for the boreal region of. Alaska over the 50 years of recorded wildfire. Building ...
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, D14S08, doi:10.1029/2003JD003635, 2004
Uncertainty in estimating carbon emissions from boreal forest fires Nancy H. F. French Altarum Institute, Ann Arbor, Michigan, USA
Pierre Goovaerts Biomedware, Inc. & PGeostat, LLC, Ann Arbor, Michigan, USA
Eric S. Kasischke Department of Geography, University of Maryland, College Park, Maryland, USA Received 26 March 2003; revised 9 July 2003; accepted 20 August 2003; published 29 May 2004.
[1] The uncertainty in carbon emissions from fire was estimated for the boreal region of
Alaska over the 50 years of recorded wildfire. Building on previous work where carbon emissions were estimated using a geographic information systems-based model, the uncertainty attached to the different parameters of the basic equation was assessed and propagated through the equation using Monte Carlo simulation. The result is a distribution of possible values for total carbon and three carbon-based gases (CO2, CO, and CH4) that provides a measure of the uncertainty in the output estimates. Additionally, the relative impact of each input parameter on the output uncertainty has been quantified (sensitivity analysis). Assumptions were made in building the uncertainty model regarding the shape of the distribution of each model parameter since this information is unavailable. Because of the lack of information on the precision of input parameter estimates, a range of possible spread values for the probability distributions, as defined by the coefficient of variation (CV; standard deviation/mean), was considered. Using the ‘‘best guess’’ values for input CVs, the resulting estimate of total annual carbon emission can be as high as 10.6 TgC or as low as 1.1 TgC, a CV of 24%. Lowering the input CVs to 5% results in an output CV of 4.2% for total carbon emissions. For the three carbon-based gases the CV of simulated carbon distributions for the ‘‘best guess’’ scenario ranges from 23 to 27%. The sensitivity analysis reveals that ground-layer fraction consumed, bg, is the most important parameter in terms of output uncertainty. The results of this work emphasize that current estimates of carbon emission from biomass burning are not well constrained because input data sets are incomplete and lack adequate error information. Furthermore, we conclude that although burn area estimates are improving, more effort is needed in quantifying fuel and consumption variables at fire sites if accurate estimates of carbon INDEX TERMS: 0315 Atmospheric Composition and Structure: emissions from fire are to be made. Biosphere/atmosphere interactions; 1615 Global Change: Biogeochemical processes (4805); 1694 Global Change: Instruments and techniques; KEYWORDS: biomass burning, carbon cycling, Monte Carlo simulations Citation: French, N. H. F., P. Goovaerts, and E. S. Kasischke (2004), Uncertainty in estimating carbon emissions from boreal forest fires, J. Geophys. Res., 109, D14S08, doi:10.1029/2003JD003635.
1. Introduction [2] In recent years, attention has been paid to understanding the impact of wildland fire on atmospheric carbon [Conard et al., 2002; Dixon et al., 1994; FIRESCAN Science Team, 1996; Harden et al., 2000; Hinzman et al., 2003; Kajii et al., 2002; Kasischke and Bruhwiler, 2002; Kasischke et al., 1995a; Kasischke and Stocks, 2000; Stocks et al., 2002]. Both the immediate and long-term effects of fire are seen as important factors in carbon cycling, by directly influencing levels of carbon-based gas in the Copyright 2004 by the American Geophysical Union. 0148-0227/04/2003JD003635$09.00
atmosphere. Although the problem of quantifying direct emissions from wildland fire has received attention, very little has been done to assess the uncertainty in the resulting estimates. In studying the impact of fire on atmospheric carbon, variations in vegetation structure, vegetation type, soil carbon, weather, fuel moisture, and fire behavior need to be considered. Models used in estimating carbon and carbon-based gases released during biomass burning need to account for these variables, either by directly including the variable in the model or by acknowledging the inherent variation within each model parameter. [3] Much of the recent effort to improve estimates of fire’s impact on the atmosphere has focused on improving the estimates of how much land is subjected to fire (area
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burned) [Eva and Lambin, 1998; Fraser et al., 2000; A.I. Sukhinin et al., unpublished manuscript, 2004]. In some biomes, such as tropical savannahs, the amount of area that is subjected to fire during a given time period is the greatest unknown; vegetation and fire conditions in these regions are such that variations are small in fuel and fire type. Boreal forest ecosystems, however, support a broad range of fuel types, with various densities and that burn in a variety of severities depending on time of season, climate, etc. [Amiro et al., 2001; Shvidenko and Nilsson, 2000; Stocks and Kauffman, 1997]. Boreal fires are generally large and relatively easy to map, and the larger fires represent 98% of the total area burned within a year [Kasischke et al., 2002; Murphy et al., 2000; Stocks et al., 2002]. In the boreal regions, therefore, the largest uncertainties are likely the fuel and fire conditions, which are more difficult to measure and quantitatively characterize than area burned. [4] In the study presented in this paper, we have endeavored to quantify the uncertainty in estimates of emissions from wildfire in Alaska based on the uncertainty present in the model inputs. Such a propagation of uncertainty extends the work presented in the work of French et al. [2002] where GIS-based estimates of carbon and carbon-based gases released from fire in Alaska were described. The basic model used for this study is the same as presented in the work of French et al. [2002] (see below) and is based on the standard equation presented by Seiler and Crutzen [1980]. The objectives of the research were twofold: [5] 1. Assess the uncertainty attached to the different parameters of the basic equation, and propagate this uncertainty using Monte Carlo simulation, yielding a distribution of possible values for total carbon and three carbon-based gases released during burning in Alaska. [6] 2. Quantify the relative impact of each input parameter on the output uncertainty (sensitivity analysis) to identify priorities in terms of data collection and information accuracy. [7] The purpose of this paper is to present the approach and results of our propagation of uncertainty model that illustrates how the various factors contribute to uncertainty. We review the sources of uncertainty in estimating carbon emissions, including the uncertainty of each model input. We also discuss how the community can begin to improve our understanding of emissions from wildfire by working toward a common terminology and quantifying error in measured or modeled data.
2. Estimating Carbon Emissions From Boreal Fires [8] As reviewed in the work of French et al. [2002], calculating total carbon released during biomass burning (Ct) is generally done by estimating the area affected by fire along with the amount of fuel (carbon) consumed during the fire [Amiro et al., 2001; Cahoon et al., 1994; Conard and Ivanova, 1997; Conard et al., 2002; French et al., 2000, 2002; Kasischke et al., 1995b; Seiler and Crutzen, 1980; Shvidenko et al., 1995; Stocks, 1991]. The emission of a particular gas species (Eg) is calculated from Ct using experimentally derived emission factors (Efg), the ratio of gas released to total carbon released.
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For the boreal region, the analysis is often separated into two fuel components, the aboveground or aerial component, which includes trees and shrub components above the ground surface, and the ground-layer organic material component, which includes ground surface vegetation and fully to partially decomposed organic material in the upper portion of the ground surface (also known as duff). This separation is made because of the large differences in these two pools in carbon content, fuel composition (quality, moisture and other factors), and consumption during burning. In estimating the contributions of each gas species, the proportion of flaming and smoldering burning is defined for each component to account for differences in emission factors for the two combustion types. Typically the amount of carbon dioxide (CO2), carbon monoxide (CO) and methane (CH4) released from fires is estimated. By separating carbon pools and combustion type, these fundamental variables are accounted for within the model parameter set. The following equations have been used in modeling fire emissions in previous analyses [French et al., 2000] and were used as a basis for calculations in the current study: Ct ¼ A Ca ba þ Cg bg
ð1Þ
Eg ¼ A Ca ba 0:8 Efgf þ 0:2 Efgs þ Cg bg 0:2 Efgf þ 0:8 Efgs ; ð2Þ
where A is the area burned (hectares, ha); Ca is the carbon density of the aboveground component, which is assumed to be 0.5 of the biomass (t ha1); Cg is the carbon density of the organic material found in the ground-layer, which is composed of the litter and duff layers (t ha1); ba and bg are the proportions of the aboveground vegetation and groundlayer organic carbon, respectively, consumed in the burn; and Efg is the emission factor for each of three gas species, CO2, CO, and CH4 (in units of gas released per unit of carbon consumed). [9] The analysis using (2) is carried out for each gas independently. The f and s subscripts on the emission factor terms in (2) refer to flaming and smoldering combustion, respectively. For simplicity in understanding fire emission uncertainty we have assumed that 80% of the consumption in the aboveground happens in a flaming combustion and 20% happens in smoldering combustion, while 20% of ground-layer burning is flaming combustion and 80% is smoldering combustion based on the rationale presented in previous studies [Kasischke and Bruhwiler, 2002]. The analysis is performed with geographically defined input data; for example, using spatially explicit data on where the fire occurred and the carbon present at the site, as well as consumption information specific to the region of the burn.
3. An Approach for Assessing Uncertainty in Carbon Emissions 3.1. Uncertainty in Model Inputs [10] In developing a model of uncertainty, the error present in each model term needs to be understood and
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statistically described. This error is then propagated through the base model to come up with the distribution of possible values for carbon and carbon gas emissions. Error can be modeled statistically if enough measurements are made. In the case of Alaska, the data used in estimating fire emissions is, in some cases, limited, and in other cases poorly documented and described, so several assumptions were made in modeling the output uncertainty (see model implementation section). [11] Four types of parameters are used to quantify fire emissions: area burned, carbon density, fraction of carbon consumed, and emission factors. Estimates for the nine parameters in (1) and (2) are described in detail in the work of French et al. [2002]; the uncertainty in these input data is described here. [12] The map of area burned in Alaska originates from fire records held by the Alaska Fire Service. These records contain fire boundaries for 1950 to the present, with varying levels of accuracy over the years [Kasischke et al., 2002; Murphy et al., 2000]. From the records, a database of fires, called the Alaska large fire database (LFDB), has been created. It contains the digitized boundary of Alaskan fires greater than 200 ha recorded since 1950. Errors are present in the LFDB; in particular, unburned regions within a fire are often unmapped, leading to an overestimate of total area burned. Burn maps in some older records are difficult to interpret and properly geographically locate, and the outer boundary of the burn can also be poorly mapped, introducing either overestimations or under-estimations from a disregard of the complexity of the burn edge. Despite these defects, the LFDB contains the most complete record of fire location and timing for Alaska. Kasischke et al. [2002] have determined that the database contains a reasonable sample of fire activity for the last 50 years despite some missing records for the early decades. [13] According to Kasischke et al. [2002], missing maps amount to approximately 15% of the total area burned. For a similar LFDB for Canadian fires, Amiro et al. [2001] assume an upper bound of 9% to 13% more than the input estimate based on the fact that the fire maps used do not include smaller fires (fires
