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Comparison of fuel consumption (FC) estimates derived by combining FRP and burned-area data. (a) Fuel consumption derived using SEVIRI FRP data. (b) Correlation between fuel consumption estimates based on the SEVIRI and MODIS FRP data. (c) Fuel consumption derived using MODIS FRP data. (d) MODIS-based fuel consumption estimates as a percentage of the SEVIRI-based estimates. For comparison both SEVIRI-and MODIS-based estimates are shown for the same period (2010–2014) and the MODIS FRE data are uncorrected (see Sect. 4.2). Note that on average MODIS-derived FC is about twice as large as SEVIRI-derived FC. Grid cells with dominant land cover " forest " or " bare or sparsely vegetated " were excluded from our analysis and are masked grey, while water and grid cells with less than 50 MODIS FRP detections are shown in white in all figures.
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Landscape fires occur on a large scale in (sub)tropical savannas and
grasslands, affecting ecosystem dynamics, regional air quality and
concentrations of atmospheric trace gasses. Fuel consumption per unit of area
burned is an important but poorly constrained parameter in fire emission
modelling. We combined satellite-derived burned area with fire...
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... provide new insights into the specific qualities and lim- itations of polar-orbiting and geostationary-based FRP data, we compared the mean fuel consumption (kg m −2 ) estimates based on our approach using SEVIRI FRP data ( Fig. 2a) with our approach using MODIS FRP data (Fig. 2c). Al- though later on the MODIS-based FRE estimates are cali- brated against field measurements, here we use the uncor- rected FRE estimates to provide insights into the effect of sensor characteristics and our methods on absolute FRE es- timates. We used linear regression fitted through ...
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... provide new insights into the specific qualities and lim- itations of polar-orbiting and geostationary-based FRP data, we compared the mean fuel consumption (kg m −2 ) estimates based on our approach using SEVIRI FRP data ( Fig. 2a) with our approach using MODIS FRP data (Fig. 2c). Al- though later on the MODIS-based FRE estimates are cali- brated against field measurements, here we use the uncor- rected FRE estimates to provide insights into the effect of sensor characteristics and our methods on absolute FRE es- timates. We used linear regression fitted through the origin (Fig. 2b) in order to compare the ...
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... with our approach using MODIS FRP data (Fig. 2c). Al- though later on the MODIS-based FRE estimates are cali- brated against field measurements, here we use the uncor- rected FRE estimates to provide insights into the effect of sensor characteristics and our methods on absolute FRE es- timates. We used linear regression fitted through the origin (Fig. 2b) in order to compare the results. Total estimated FRE, and thus fuel consumption, based on the MODIS in- struments was roughly two times larger than SEVIRI-derived fuel consumption. On top of these absolute differences, the spatial patterns were not uniform ( Fig. 2b and d), for which we identified two main causes: first, the ...
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... absolute FRE es- timates. We used linear regression fitted through the origin (Fig. 2b) in order to compare the results. Total estimated FRE, and thus fuel consumption, based on the MODIS in- struments was roughly two times larger than SEVIRI-derived fuel consumption. On top of these absolute differences, the spatial patterns were not uniform ( Fig. 2b and d), for which we identified two main causes: first, the MODIS-based fuel consumption was consistently higher in south-eastern Africa (e.g. Mozambique and Madagascar), likely because of the decreasing sensitivity of the SEVIRI instrument at the greater off-nadir angle over this region (e.g. Freeborn et al., 2014b), and second, the relative ...
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... the relative fraction of FRE emitted during peri- ods that FRP values were below the SEVIRI detection thresh- old, a function of the absolute FRP values and the shape of the fire diurnal cycle. Fires with high FRP (related to high fire spread rates and/or fuel consumption) are often equally well observed by both instruments (i.e. red colouring in Fig. 2d), while areas with low fuel consumption are often character- ized by a larger differences between the MODIS and SE- VIRI estimates (i.e. green colouring in Fig. 2d). To prevent these differences from affecting our estimated correlation too much, we only included frequently burning grid cells (burned area ≥ 15 % yr −1 ) and those that ...
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... of the fire diurnal cycle. Fires with high FRP (related to high fire spread rates and/or fuel consumption) are often equally well observed by both instruments (i.e. red colouring in Fig. 2d), while areas with low fuel consumption are often character- ized by a larger differences between the MODIS and SE- VIRI estimates (i.e. green colouring in Fig. 2d). To prevent these differences from affecting our estimated correlation too much, we only included frequently burning grid cells (burned area ≥ 15 % yr −1 ) and those that have a surface area of the SEVIRI FRP-PIXEL product grid cells below 12 km 2 (min- imum value is 9 km 2 at nadir) during the linear regression shown in Fig. 2b. This ...
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... colouring in Fig. 2d). To prevent these differences from affecting our estimated correlation too much, we only included frequently burning grid cells (burned area ≥ 15 % yr −1 ) and those that have a surface area of the SEVIRI FRP-PIXEL product grid cells below 12 km 2 (min- imum value is 9 km 2 at nadir) during the linear regression shown in Fig. 2b. This resulted in reasonable correlation (r 2 = 0.42; n = 6569). Partial cloud cover and missing data were also affecting the analysis, and we found that 54 % of the annual burned area occurred during periods of reduced data availability (below 80 % during the 15-day time win- dow). When excluding these events, the absolute difference ...
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... and missing data were also affecting the analysis, and we found that 54 % of the annual burned area occurred during periods of reduced data availability (below 80 % during the 15-day time win- dow). When excluding these events, the absolute difference between MODIS-and SEVIRI-based fuel consumption be- came somewhat smaller (i.e. the slope in Fig. 2b became 0.59), demonstrating that periods of reduced observations were partly responsible for the underestimation in SEVIRI- derived fuel consumption. However, by excluding this 54 % of the data, the correlation between MODIS-and SEVIRI- based fuel consumption was reduced (r 2 = 0.28) due to the heterogeneous nature of fuel ...
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... understanding of the im- plications of these differences, we compared the fuel con- sumption estimates based on both platforms using the FRE- to-DM-burned conversion factor found during laboratory ex- periments ( Wooster et al., 2005). At first sight, very similar spatial patterns were found using polar-orbiting or geosta-tionary data (compare Fig. 2a and c), providing confidence in the spatial distribution of the fuel consumption estimates. However, many differences were also present (Fig. 2d). We found that a large part of the differences could be attributed to the different sensors characteristics and methods used here. The shape of the fire diurnal cycle, for example, affects both ...
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... to-DM-burned conversion factor found during laboratory ex- periments ( Wooster et al., 2005). At first sight, very similar spatial patterns were found using polar-orbiting or geosta-tionary data (compare Fig. 2a and c), providing confidence in the spatial distribution of the fuel consumption estimates. However, many differences were also present (Fig. 2d). We found that a large part of the differences could be attributed to the different sensors characteristics and methods used here. The shape of the fire diurnal cycle, for example, affects both MODIS-based fuel consumption estimates due to the lim- ited number of daily overpasses but also the SEVIRI-derived fuel consumption estimates ...
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... et al., 2009, 2014b, Roberts and Wooster, 2008). In our analysis a small part of the structural difference could also be explained by the fact that we did not correct for cloud cover and/or missing data in the SEVIRI-based FC estimates. Not surprisingly, the best com- parison between both methods was found in areas of high fuel consumption rates (Fig. 2d), for example areas where fires can spread over large areas to form large fire fronts ( Archibald et al., 2013), and areas of high fuel consumption; these fires with high FRP are likely to be well observed by both ...
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... With the growing use of the FRP, it is possible to retrieve FC by reverse calculation of the amount of combusted fuel required to produce the measured energy. Thus, this methodology has been applied at continental [67,68] and global [69] scales despite the fact that the FRP-derived FC has been found to be typically lower than those used in "bottom-up" methodologies such as GFED [67][68][69]. Moreover, differences have been reported in the agreement between FRP-derived FC and GFED estimates across the southern and northern hemispheres of Africa [70]. ...
... With the growing use of the FRP, it is possible to retrieve FC by reverse calculation of the amount of combusted fuel required to produce the measured energy. Thus, this methodology has been applied at continental [67,68] and global [69] scales despite the fact that the FRP-derived FC has been found to be typically lower than those used in "bottom-up" methodologies such as GFED [67][68][69]. Moreover, differences have been reported in the agreement between FRP-derived FC and GFED estimates across the southern and northern hemispheres of Africa [70]. ...
Biomass burning (BB) results from complex interactions between ecosystems, humans, and climate, releasing large amounts of gases and particles. Accurate BB emission estimates are essential for air quality, climate studies, and impact assessments. Various existing bottom-up BB emission inventories show significant discrepancies, varying by factors of 2 to 4 due to uncertainties in burned areas (BAs), emission factors (EFs), and vegetation parameters such as biomass density (BD) and burning efficiency (BE). Here, we investigate the role of vegetation parameters in these discrepancies in Africa. Two BB emission inventories, AMMABB-like (African Monsoon Multidisciplinary Analysis Biomass Burning) and GFED-like (Global Fire Emission Database) were developed for Organic Carbon (OC) and Black Carbon (BC). Both inventories used identical fire products, vegetation maps, and EF but different BD and BE values. Results highlight substantial differences in BD and BE, with relative gaps ranging from 44% to 85.12%, explaining the observed differences between BB emission inventories. Key vegetation classes responsible for BB emissions were identified. Discrepancies of 2.4 to 3.9 times were observed between AMMABB-like and GFED4-like, with higher values in the Southern Hemisphere. Better BD and BE estimates with regional distinctions for both hemispheres would improve BB emission accuracy in Africa.
... For example, biomass burning aerosols from the Amazon have been observed to cross the Andes and disperse over the Pacific Ocean, highlighting the long-range transport of these particles [23]. Considering this, and the dynamicity of fires, orbital remote sensing combined with modelling remains the most viable approach to estimate BB emissions, and their impacts, on regional to global scales [18,[24][25][26][27][28][29][30]. ...
Fires drive global ecosystem change, impacting carbon dynamics, atmospheric composition, biodiversity, and human well-being. Biomass burning, a major outcome of fires, significantly contributes to greenhouse gas and aerosol emissions. Among these, fine particulate matter (PM2.5) is particularly concerning due to its adverse effects on air quality and health, and its substantial yet uncertain role in Earth’s energy balance. Variability in emission factors (EFs) remains a key source of uncertainty in emission estimates. This study evaluates PM2.5 emission sensitivity to EFs variability in Brazil’s Amazon and Cerrado biomes over 2002–2023 using the 3BEM_FRP model implemented in the PREP-CHEM-SRC tool. We updated the EFs with values and uncertainty ranges from Andreae (2019), which reflect a more comprehensive literature review than earlier datasets. The results reveal that the annual average PM2.5 emissions varied by up to 162% in the Amazon (1213 Gg yr⁻¹ to 3172 Gg yr⁻¹) and 184% in the Cerrado (601 Gg yr⁻¹ to 1709 Gg yr⁻¹). The Average peak emissions at the grid-cell level reached 5688 Mg yr⁻¹ in the “Arc of Deforestation” region under the High-end EF scenario. Notably, the PM2.5 emissions from Amazon forest areas increased over time despite shrinking forest cover, indicating that Amazonian forests are becoming more vulnerable to fire. In the Cerrado, savannas are the primary land cover contributing to the total PM2.5 emissions, accounting for 64% to 80%. These findings underscore the importance of accurate, region-specific EFs for improving emission models and reducing uncertainties.
... It has been shown (e.g., Andela et al. (2016)) that fire return periods are influenced by fuel (vegetation) build up over several years; surface conditions are therefore expected to be different the year before or the year after a fire Total burned area during the fire season by year, corresponding to the total area of pixels identified as burned in the MCD64A1 data set. In northern hemisphere Africa (NHA, blue), a decline of 2.1 × 10 4 km 2 per year is observed p = 1.2 × 10 6 ) . ...
We present an analysis of 20 years of fire and albedo data in Africa. We show that, in the mean, the sub‐Saharan Africa post‐fire surface albedo anomaly can be parameterized using an exponential recovery function, recovering from a decrease of 0.019±0.001 immediately after a fire with a time constant of 34.0±0.4 days. Although the magnitude of albedo changes shows large spatial and temporal variations and a strong land cover type (LCT) dependency, exponential recovery is observed in the majority of LCTs. We show that fires cause long‐term surface brightening, with an Africa‐wide albedo increase of (9.5±0.2)×10−4 10 months after a fire, but we find this is driven almost exclusively by slow vegetation recovery in the Kalahari region, confirming previous findings. Using downward surface shortwave flux (DSSF) estimates, we calculate the fire‐induced surface radiative forcing (RF), peaking at 5±2 Wm⁻² in the burn areas, albeit with a significantly smaller effect when averaged temporally and spatially. We find that the long‐term RF in months 5–10 after a burn averaged over the continent is negative because of the brightening observed. Despite a well‐documented reduction in burning in Africa in the recent decades, our temporal analysis does not indicate a decrease in the overall fire‐induced RF likely due to large interannual variability in albedo anomaly and DSSF data. However, we observe a decline in the short‐term RF in southern hemisphere Africa, driven by both a reduction in fires and changes in LCT distributions.
... Implementing biomass energy systems in remote communities not only boosts energy security but also fosters local economic development and diminishes reliance on imported fuels [10]. Adopting biomass fuel for both urban and remote energy supply programs is crucial for establishing a more sustainable and robust energy landscape while addressing the challenges posed by climate change [11,12]. Numerous studies have examined the potential of utilizing biomass fuel for the operation of cogeneration and multigeneration systems to meet energy needs across various applications. ...
... In the operation of the CCP process, the organic fluid enters the generator at point 24, undergoes a temperature increase through heat recovery with stream 9, and then passes through throttling valve 1 (TV1) at point 11. Subsequently, stream 12 In the SOEC setup, freshwater at point 63 is pressurized to point 24 before passing through Heat Exchanger 1 (HE1) for heating. Further heat recovery processes occur through an Electric Steam Generator (ESG), HE2, HE3, and an Electrical Heater (EH). ...
... Besides, the exergy efficiency and exergy destruction ratio of the component k are formulated in the following [51]: Table 4 contains the exergy equations for the established components. From the thermodynamic point of view, the capacity of products, including the net output power, (Ẇ net ), cooling output (Q cooling ), and liquefied hydrogen production rate (LHPR) are represented as follows [51]: × 86400 (12) where ρ H2 is the density of the hydrogen. ...
... Some papers have tried to provide a more ecological-sound interpretation of satellite retrieved burn severity, using a Random Forest model to estimate CBI from satellite data (Parks et al., 2019), others have combined passive optical with lidar data to detect the relevance of post-fire legacies in vegetation height and biomass (Hoffman et al., 2018;Sato et al., 2016), or in the distribution of snags of different sizes (Vogeler et al., 2016). Fuel consumption has been estimated from active fire and fire radiative power (Andela et al., 2016) or from vegetation optical depth obtained from microwave data . ...
... In middle-to-high productive areas, fire activity is mostly associated to high temperatures and seasonal droughts (Pausas and Ribeiro, 2013), while in low productivity areas the rainfall of the previous year (and therefore the available fuel to burn) is the main factor of fire activity (Krawchuk and Moritz, 2011). On the other hand, extreme fire seasons are associated with exceptionally dry conditions, such as those linked to El Niño events in South East Asia (Andela et al., 2016;Lohberger et al., 2018), or a combination of heatwaves and long droughts in temperate forest, as it was the case of recent anomalous fire seasons of Australia (Bowman et al., 2020b) and Portugal (Turco et al., 2019). Even though the recent burned area trends show a global decrease in fire-affected areas due to agricultural expansion, increased livestock densities and increase net primary productivity Forkel et al., 2019;Zubkova et al., 2019), fire impacts are expected to increase in the future due to climate change, although their effects will vary regionally depending on changes in precipitation patterns. ...
... Since BB emissions spread over most of the global vegetated areas [11,12], the integration of orbital remote sensing and modelling is the most effective approach to estimate them from regional to global scales [12][13][14][15]. BB emission estimation using orbital remote sensing and modelling follows the relationship between burned biomass and the emission factor (EF-mass emitted of a given species per mass of dry matter burned). ...
Biomass burning (BB) emissions negatively impact the biosphere and human lives. Orbital remote sensing and modelling are used to estimate BB emissions on regional to global scales, but these estimates are subject to errors related to the parameters, data, and methods available. For example, emission factors (mass emitted by species during BB per mass of dry matter burned) are based on land use and land cover (LULC) classifications that vary considerably across products. In this work, we evaluate how BB emissions vary in the PREP-CHEM-SRC emission estimator tool (version 1.8.3) when it is run with original LULC data from MDC12Q1 (collection 5.1) and newer LULC data from MapBiomas (collection 6.0). We compare the results using both datasets in the Brazilian Amazon and Cerrado biomes during the 2002–2020 time series. A major reallocation of emissions occurs within Brazil when using the MapBiomas product, with emissions decreasing by 788 Gg (−1.91% year⁻¹) in the Amazon and emissions increasing by 371 Gg (2.44% year⁻¹) in the Cerrado. The differences identified are mostly associated with the better capture of the deforestation process in the Amazon and forest formations in Northern Cerrado with the MapBiomas product, as emissions in forest-related LULCs decreased by 5260 Gg in the Amazon biome and increased by 1676 Gg in the Cerrado biome. This is an important improvement to PREP-CHEM-SRC, which could be considered the tool to build South America’s official BB emission inventory and to provide a basis for setting emission reduction targets and assessing the effectiveness of mitigation strategies.
... Satellite-derived fire emissions estimates based on FRP are often systematically lower than those obtained by fuel consumption modelling based on burned area, and this effect is stronger under canopy cover than in open landscapes (Roberts et al., 2018). To explore the magnitude of this effect, a scaling factor derived from comparison of satellite data to FC data in the GFCD by (Andela et al., 2016) was applied to simulate the FRP detected by the satellite sensor. To constrain FRP estimates, the uncertainty of the field-derived fuel consumption estimates was applied to the modeled FRP (95% confidence interval). ...
... Black dotted lines 95% confidence interval for the estimate of fuel consumption. Blue solid line: FRP estimated applying the correction factor ofAndela et al. (2016), blue dotted lines: bounds of this FRP estimate defined by 95% confidence interval of field-derived FC. ...
Dry broad-leaved seasonal forests are widespread in Southeast Asia. They are characterized by drought deciduous tree species, which are adapted to a severe dry season that lasts several months each year. Forest fires are frequent in this vegetation type. To further understanding of fire behavior and fire impact, a series of fire field experiments implemented in the Huay Kha Khaeng (HKK) Wildlife Sanctuary (Uthai Thani Province, Thailand) between 2008 and 2016 was analyzed. A fire behavior model based on the Canadian Fire Behavior Prediction System (Prometheus) was calibrated using the experimental data for the deciduous dipterocarp forest fuel type. The model was then tested on a remotely observed large wildfire in Thailand. Our results confirm the slow fire spread and low to moderate fire intensities observed for this forest type in earlier studies. The fire spread model performs well compared to satellite observations but tends to overestimate area burned and fuel consumption and, consequently, fire emissions when used in air pollution models. Our results indicate that widely used global databases may substantially overestimate fuel consumption and hence fire emissions for this forest type.
... The approach can lack accurate spatial and temporal allocations of the statistical and survey data. On the other hand, the remotely sensed observations (e.g., MODIS, SEVIRI, GOES) have been used to quantify the biomass burning emission [23,25,27,33,35,38,40,42,[50][51][52]. The top-down emissions of biomass burning are estimated, multiplying several parameters of (i) burned areas, (ii) biomass load (FRP, fire radiative power), (iii) combustion efficiency (or conversion factor), and (iv) emission factors [53,54]. ...
In the study, crop residue burning (CRB) emissions were estimated based on field surveys and combustion experiments to assess the impact of the CRB on particulate matter over South Korea. The estimates of CRB emissions over South Korea are 9514, 8089, 4002, 2010, 172,407, 7675, 33, and 5053 Mg year⁻¹ for PM10, PM2.5, OC, EC, CO, NOx, SO2, and NH3, respectively. Compared with another study, our estimates in the magnitudes of CRB emissions were not significantly different. When the CRB emissions are additionally considered in the simulation, the monthly mean differences in PM2.5 (i.e., △PM2.5) were marginal between 0.07 and 0.55 μg m⁻³ over South Korea. Those corresponded to 0.6–4.3% in relative differences. Additionally, the △PM10 was 0.07–0.60 μg m⁻³ over South Korea. In the spatial and temporal aspects, the increases in PM10 and PM2.5 were high in Gyeongbuk (GB) and Gyeongnam (GN) provinces in June, October, November, and December.
... Several factors could contribute to producing such bias in emission inventories based on either satellite-detected burned areas (e.g., van der Werf et al., 2017) or fire radiative power (FRP, e.g., Ichoku and Ellison, 2014). The burned-area-based emission inventories comprise uncertainties in satellite 85 detection of burned areas and fuel load (Randerson et al., 2012;Andela et al., 2016), while FRP-based emission datasets are largely affected by the translation of FRP into rates of biomass combustion (Kaiser et al., 2012). In addition, both emission datasets rely on uncertain emission factors converting burned biomass to trace gas or aerosol emissions (Stockwell et al., 2015). ...
Global models are widely used to simulate biomass burning aerosols (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observation project (AeroCom) model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase III experiments (i.e., Biomass Burning Emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning on boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances (POLDER-GRASP) observations, we found that the modeled AOD values were biased by -93–152 %, with most models showing significant underestimations even for the state-of-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations of AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and mass extinction coefficient (MEC). We found that in the AEROCOM ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström Exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP observed AE suggested that the models tended to overestimate AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models comprise biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.
... GFED was developed primarily for global studies of climate and fire interactions, reflected by GFED's large spatial resolution [16]. According to regional studies, GFED is reliable for estimating emissions from large fires in specific locations, but has difficulties representing small fires and fires in certain regions [20,21]. Emissions estimates included in GFEDv4s are more reliable than estimates created by previous versions of GFED due to the numerous updates, but the remaining uncertainties are significant and challenging to quantify. ...
Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations.
