scholarly journals Estimation of Direct Fire Emissions from Forests Burning in Siberia

2020 ◽  
Vol 4 (1) ◽  
pp. 12
Author(s):  
Evgenii I. Ponomarev
Keyword(s):  
Carbon Emissions ◽  
Summer Period ◽  
Central Siberia ◽  
Fire Emissions ◽  
Emission Estimate ◽  
Radiative Power ◽  
Order Of Magnitude ◽  
Siberian Forests ◽  
Fire Radiative Power

Using a database on wildfires recorded by remote sensing for 1996–2020, we assessed the seasonal variation of direct carbon emissions from the burning in Siberian forests. We have implemented an approach that takes into account the combustion parameters and the changing intensity of the fire (in terms of Fire Radiative Power (FRP)), which affects the accuracy of the emission estimate. For the last two decades, the range of direct carbon emissions from wildfires was 20–250 Тg С per year. Sporadic maxima were fixed in 2003 (>150 Тg С/year), in 2012 (>220 Тg С/year), and in 2019 (>190 Тg С/year). Preliminary estimation of emissions for 2020 (on 30th of September) was ~180 Tg С/year. Fires in the larch forests of the flat-mountainous taiga region (Central Siberia) made the greatest contribution (>50%) to the budget of direct fire emission, affecting the quality of the atmosphere in vast territories during the summer period. According to the temperature rising and forest burning trend in Siberia, the fire emissions of carbon may double (220 Тg С/year) or even increase by an order of magnitude (>2000 Тg С/year) at the end of the 21st century, which was evaluated depending on IPCC scenario.

scholarly journals Current Trend of Carbon Emissions from Wildfires in Siberia

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 559
Author(s):  
Evgenii Ponomarev ◽  
Nikita Yakimov ◽  
Tatiana Ponomareva ◽  
Oleg Yakubailik ◽  
Susan G. Conard
Keyword(s):  
Carbon Emissions ◽  
Current Trend ◽  
Fire Season ◽  
Fire Intensity ◽  
Fire Emissions ◽  
Active Fire ◽  
Radiative Power ◽  
Annual Carbon ◽  
Global Carbon ◽  
Fire Radiative Power

Smoke from wildfires in Siberia often affects air quality over vast territories of the Northern hemisphere during the summer. Increasing fire emissions also affect regional and global carbon balance. To estimate annual carbon emissions from wildfires in Siberia from 2002–2020, we categorized levels of fire intensity for individual active fire pixels based on fire radiative power data from the standard MODIS product (MOD14/MYD14). For the last two decades, estimated annual direct carbon emissions from wildfires varied greatly, ranging from 20–220 Tg C per year. Sporadic maxima were observed in 2003 (>150 Tg C/year), in 2012 (>220 Tg C/year), in 2019 (~180 Tg C/year). However, the 2020 fire season was extraordinary in terms of fire emissions (~350 Tg C/year). The estimated average annual level of fire emissions was 80 ± 20 Tg C/year when extreme years were excluded from the analysis. For the next decade the average level of fire emissions might increase to 250 ± 30 Tg C/year for extreme fire seasons, and to 110 ± 20 Tg C/year for moderate fire seasons. However, under the extreme IPCC RPC 8.5 scenario for Siberia, wildfire emissions might increase to 1200–1500 Tg C/year by 2050 if there were no significant changes in patterns of vegetation distribution and fuel loadings.

scholarly journals Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions

2017 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Fire Weather ◽  
Atmospheric Conditions ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Abstract. The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass burning fire emission estimates from the Global Fire Assimilation System (GFAS). GFAS converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence where observed FRP from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an overestimation of fire duration, which in turn translates into an overestimation of emissions from fires. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

scholarly journals Top-Down Estimation of Particulate Matter Emissions from Extreme Tropical Peatland Fires Using Geostationary Satellite Fire Radiative Power Observations

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7075
Author(s):  
Daniel Fisher ◽  
Martin J. Wooster ◽  
Weidong Xu ◽  
Gareth Thomas ◽  
Puji Lestari
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
El Niño ◽  
El Nino ◽  
Top Down ◽  
Fire Emissions ◽  
Se Asia ◽  
Particulate Matter Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Extreme fires in the peatlands of South East (SE) Asia are arguably the world’s greatest biomass burning events, resulting in some of the worst ambient air pollution ever recorded (PM10 > 3000 µg·m−3). The worst of these fires coincide with El Niño related droughts, and include huge areas of smouldering combustion that can persist for months. However, areas of flaming surface vegetation combustion atop peat are also seen, and we show that the largest of these latter fires appear to be the most radiant and intensely smoke-emitting areas of combustion present in such extreme fire episodes. Fire emissions inventories and early warning of the air quality impacts of landscape fire are increasingly based on the fire radiative power (FRP) approach to fire emissions estimation, including for these SE Asia peatland fires. “Top-down” methods estimate total particulate matter emissions directly from FRP observations using so-called “smoke emission coefficients” [Ce; g·MJ−1], but currently no discrimination is made between fire types during such calculations. We show that for a subset of some of the most thermally radiant peatland fires seen during the 2015 El Niño, the most appropriate Ce is around a factor of three lower than currently assumed (~16.8 ± 1.6 g·MJ−1 vs. 52.4 g·MJ−1). Analysis indicates that this difference stems from these highly radiant fires containing areas of substantial flaming combustion, which changes the amount of particulate matter emitted per unit of observable fire radiative heat release in comparison to more smouldering dominated events. We also show that even a single one of these most radiant fires is responsible for almost 10% of the overall particulate matter released during the 2015 fire event, highlighting the importance of this fire type to overall emission totals. Discriminating these different fires types in ways demonstrated herein should thus ultimately improve the accuracy of SE Asian fire emissions estimates derived using the FRP approach, and the air quality modelling which they support.

Remote classification of head and backfire types from MODIS fire radiative power and smoke plume observations

2005 ◽  
Vol 14 (3) ◽  
pp. 249 ◽  
Author(s):  
Alistair M. S. Smith ◽  
Martin J. Wooster
Keyword(s):  
Front Propagation ◽  
Pollutant Emissions ◽  
Radiative Energy ◽  
Burned Area ◽  
Remote Measurement ◽  
Radiative Power ◽  
Flaming Combustion ◽  
Order Of Magnitude ◽  
Fire Radiative Power

The classification of savanna fires into headfire and backfire types can in theory help in assessing pollutant emissions to the atmosphere via relative apportionment of the amounts of smouldering and flaming combustion occurring, and is also important when assessing a fire’s ecological effects. This paper provides a preliminary assessment of whether a combination of visible and thermal satellite remote sensing can be used to classify fires into head and backfire categories. Remote determination of the fire radiative power, alongside assessments of the prevailing direction of the wind (through identification of the fire-related smoke plumes) and the fire front propagation (through its relation to the previously burned area) were used to infer the fire type category and to calculate ‘radiative’ fireline intensity (FLI). The ratio of radiative FLI for the head and backfire categories was found similar to that of in situ fireline intensity measurements, but the magnitudes of the radiative FLI values were around an order of magnitude lower. This agrees with other data suggesting that a fire’s radiative energy is around an order of magnitude lower than the fuel’s theoretical heat yield, and suggests that the remote measurement of radiative FLI and classification of headfire and backfire types is a realistic proposition for large wildfire activity.

scholarly journals Using the Fire Weather Index (FWI) to improve the estimation of fire emissions from fire radiative power (FRP) observations

2018 ◽  
Vol 18 (8) ◽  
pp. 5359-5370 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Fire Weather ◽  
Atmospheric Conditions ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Abstract. The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

scholarly journals The empirical relationship between satellite-derived tropospheric NO<sub>2</sub> and fire radiative power and possible implications for fire emission rates of NO<sub>x</sub>

2014 ◽  
Vol 14 (5) ◽  
pp. 2447-2466 ◽  
Author(s):  
S. F. Schreier ◽  
A. Richter ◽  
J. W. Kaiser ◽  
J. P. Burrows
Keyword(s):  
Empirical Relationship ◽  
Fire Intensity ◽  
Burned Area ◽  
Emission Rates ◽  
Monthly Means ◽  
Fire Emissions ◽  
Vegetation Fires ◽  
Radiative Power ◽  
The Tropics ◽  
Fire Radiative Power

Abstract. Nitrogen oxides (NOx) play key roles in atmospheric chemistry, air pollution, and climate. While the largest fraction of these reactive gases is released by anthropogenic emission sources, a significant amount can be attributed to vegetation fires. In this study, NO2 from GOME-2 on board EUMETSAT's MetOp-A and OMI on board NASA's Aura as well as fire radiative power (FRP) from the measurements of MODIS on board NASA's Terra and Aqua satellites are used to derive fire emission rates (FERs) of NOx for different types of vegetation using a simple statistical approach. Monthly means of tropospheric NO2 vertical columns (TVC NO2) have been analyzed for their temporal correlation with the monthly means of FRP for five consecutive years from 2007 to 2011 on a horizontal 1° × 1° grid. The strongest correlation is found to be largely confined to tropical and subtropical regions, which account for more than 80% of yearly burned area, on average, globally. In these regions, the seasonal variation of fire intensity, expressed by the FRP data, is similar to the pattern of TVC NO2. As chemical models typically require values for the amount of NOx being released as a function of time, we have converted the retrieved TVC NO2 into production rates of NOx from fire (Pf) by assuming a constant lifetime of NOx. The comparison between Pf and NOx emissions from the Global Fire Emissions Database (GFEDv3.1) over 5 characteristic biomass burning regions in the tropics and subtropics shows good agreement. By separating the monthly means of Pf and FRP according to land cover type, FERs of NOx could be derived for different biomes. The estimated FERs for the dominating types of vegetation burned are lowest for open shrublands and savannas (0.28–1.03 g NOx s−1 MW−1) and highest for croplands and woody savannas (0.82–1.56 g NOx s−1 MW−1). This analysis demonstrates that the strong empirical relationship between TVC NO2 and FRP and the following simplified assumptions are a useful tool for the characterization of NOx emission rates from vegetation fires in the tropics and subtropics. Possible factors affecting the magnitude of the obtained values are discussed.

scholarly journals Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products

2020 ◽  
Vol 20 (17) ◽  
pp. 10687-10705
Author(s):  
Tianran Zhang ◽  
Mark C. de Jong ◽  
Martin J. Wooster ◽  
Weidong Xu ◽  
Lili Wang
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Crop Residue ◽  
Eastern China ◽  
Fire Emissions ◽  
Crop Residue Burning ◽  
Radiative Power ◽  
Agricultural Burning ◽  
Emissions Inventories ◽  
Fire Radiative Power

Abstract. Open burning of agricultural crop residues is widespread across eastern China, and during certain post-harvest periods this activity is believed to significantly influence air quality. However, the exact contribution of crop residue burning to major air quality exceedances and air quality episodes has proven difficult to quantify. Whilst highly successful in many regions, in areas dominated by agricultural burning, MODIS-based (MODIS: Moderate Resolution Imaging Spectroradiometer) fire emissions inventories such as the Global Fire Assimilation System (GFAS) and Global Fire Emissions Database (GFED) are suspected of significantly underestimating the magnitude of biomass burning emissions due to the typically very small, but highly numerous, fires involved that are quite easily missed by coarser-spatial-resolution remote sensing observations. To address this issue, we use twice-daily fire radiative power (FRP) observations from the “small-fire-optimised” VIIRS-IM FRP product and combine them with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. Using this we generate a unique high-spatio-temporal-resolution agricultural burning inventory for eastern China for the years 2012–2015, designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO2, CO, PM2.5, and black carbon. We calculate DMB totals 100 % to 400 % higher than reported by the GFAS and GFED4.1s, and we quantify interesting spatial and temporal patterns previously un-noted. Wheat residue burning, primarily occurring in May–June, is responsible for more than half of the annual crop residue burning emissions of all species, whilst a secondary peak in autumn (September–October) is associated with rice and corn residue burning. We further identify a new winter (November–December) burning season, hypothesised to be caused by delays in burning driven by the stronger implementation of residue burning bans during the autumn post-harvest season. Whilst our emissions estimates are far higher than those of other satellite-based emissions inventories for the region, they are lower than estimates made using traditional “crop-yield-based approaches” (CYBAs) by a factor of between 2 and 5. We believe that this is at least in part caused by outdated and overly high burning ratios being used in the CYBA, leading to the overestimation of DMB. Therefore, we conclude that satellite remote sensing approaches which adequately detect the presence of agricultural fires are a far better approach to agricultural fire emission estimation.

scholarly journals Hindcast experiments of tropospheric composition during the summer 2010 fires over Western Russia

2011 ◽  
Vol 11 (12) ◽  
pp. 31851-31909 ◽  
Author(s):  
V. Huijnen ◽  
J. Flemming ◽  
J. W. Kaiser ◽  
A. Inness ◽  
J. Leitão ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Quantitative Description ◽  
Atmospheric Composition ◽  
Fire Emissions ◽  
Radiative Power ◽  
The Impact ◽  
Assimilation System ◽  
Fire Radiative Power ◽  
Satellite Instruments

Abstract. The severe wildfires in Western Russia during July–August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to analyze large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 38% (O3) and 64% (NO2) during the first 24 h, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 38 and 80%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly depending on the assumptions made for forecasted fire emissions. This was well visible from a relatively rapid increase by the root mean square error with respect to ground-based data for AOD, and satellite based NO2. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.

scholarly journals Characterization of wildfire NO<sub>x</sub> emissions using MODIS fire radiative power and OMI tropospheric NO<sub>2</sub> columns

2011 ◽  
Vol 11 (12) ◽  
pp. 5839-5851 ◽  
Author(s):  
A. K. Mebust ◽  
A. R. Russell ◽  
R. C. Hudman ◽  
L. C. Valin ◽  
R. C. Cohen
Keyword(s):  
Ozone Monitoring Instrument ◽  
Fire Emissions ◽  
Active Fire ◽  
Radiative Power ◽  
Flaming Combustion ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Current Emission ◽  
Fire Radiative Power

Abstract. We use observations of fire radiative power (FRP) from the Moderate Resolution Imaging Spectroradiometer~(MODIS) and tropospheric NO2 column measurements from the Ozone Monitoring Instrument (OMI) to derive NO2 wildfire emission coefficients (g MJ−1) for three land types over California and Nevada. Retrieved emission coefficients were 0.279±0.077, 0.342±0.053, and 0.696±0.088 g MJ−1 NO2 for forest, grass and shrub fuels, respectively. These emission coefficients reproduce ratios of emissions with fuel type reported previously using independent methods. However, the magnitude of these coefficients is lower than prior estimates. While it is possible that a negative bias in the OMI NO2 retrieval over regions of active fire emissions is partly responsible, comparison with several other studies of fire emissions using satellite platforms indicates that current emission factors may overestimate the contributions of flaming combustion and underestimate the contributions of smoldering combustion to total fire emissions. Our results indicate that satellite data can provide an extensive characterization of the variability in fire NOx emissions; 67 % of the variability in emissions in this region can be accounted for using an FRP-based parameterization.

scholarly journals Daily global fire radiative power fields estimation from one or two MODIS instruments

2014 ◽  
Vol 14 (24) ◽  
pp. 13377-13390 ◽  
Author(s):  
S. Remy ◽  
J. W. Kaiser
Keyword(s):  
Dry Matter ◽  
Low Earth Orbit ◽  
Atmospheric Composition ◽  
Correction Algorithm ◽  
Fire Cycle ◽  
Fire Emissions ◽  
Radiative Power ◽  
Leo Satellite ◽  
Linear Regressions ◽  
Fire Radiative Power

Abstract. Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modelling enterprise. One method to estimate these emissions is to convert fire radiative power (FRP) analysis into dry matter burnt and emissions of smoke constituents using land-cover-dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments onboard the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. We develop a method to correct this bias in daily FRP observations from any low Earth orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, for example, GFAS in case of failure of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It decreases the bias in daily average FRP from Terra and Aqua by more than 95%, and RMSE by 75% for Aqua and 55% for Terra. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.

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