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Article

Wildfire Intensity and Fire Emissions in Siberia

by
Evgenii I. Ponomarev
1,2,3,*,
Andrey N. Zabrodin
4,5,
Eugene G. Shvetsov
1,5 and
Tatiana V. Ponomareva
1,2
1
V. N. Sukachev Institute of Forest, Federal Research Center, Russian Academy of Science, Siberian Branch, 660036 Krasnoyarsk, Russia
2
Institute of Ecology and Geography, Siberian Federal University, 660041 Krasnoyarsk, Russia
3
Laboratory of Biodiversity and Ecology, Tomsk State University, 634050 Tomsk, Russia
4
Institute of Engineering Physics and Radioelectronics, Siberian Federal University, 660074 Krasnoyarsk, Russia
5
Federal Research Center “Krasnoyarsk Science Center of the Siberian Branch of the Russian Academy of Sciences”, 660036 Krasnoyarsk, Russia
*
Author to whom correspondence should be addressed.
Fire 2023, 6(7), 246; https://doi.org/10.3390/fire6070246
Submission received: 22 May 2023 / Revised: 13 June 2023 / Accepted: 20 June 2023 / Published: 22 June 2023
(This article belongs to the Special Issue Integrated Vulnerability of Forest Systems to Wildfire: Implications on Forest Management Tools. VIS4FIRE Project)
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Abstract

:
An analysis of fire characteristics in the boreal forests of Siberia (50–75° N, 60–140° E) was performed for the period 2002–2022. We found a positive trend in the proportion of high-intensity fires in dominant forest stands of Siberia based on long-term series of variations in the Fire Radiative Power (FRP) measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our results showed that there was an increase in the proportion of areas of high-intensity fires over the past decade on about ~30% of the boreal forests of Siberia, including the Arctic zone. For the sample group of fires, the level of correlation (R2 = 0.80–0.94) between the fire impact, classified according to the NBR/dNBR technology, and the integral FRP values was revealed. The intensity of combustion in terms of FRP is associated with the volume of burned biomass and determines the dynamics of specific emissions values per unit area. The results suggest that further increase in fire emissions in Siberia will be determined not only by an increase of burned areas, but also by a redistribution of low- and high-intensity burning and an increase in specific emission values. Finally, we estimated that Siberian fires are responsible for about 5–20% of the total volume of greenhouse gas emissions in the Russian Federation, depending on the fire season scenario. The recurrence of extremely high emissions (296–350 Tg C/year) will make it possible to consider part of Siberian forests as a source of carbon in the nearest future.

1. Introduction

Siberian forests account for up to 20% of the world’s forest areas [1], where up to 45.05 ± 7.64 Pg of carbon is concentrated [2]. Currently, a positive carbon balance of Siberian forests is recorded (excess of carbon sink volumes over carbon emissions), varying according to various estimates from +0.56 Pg C year–1 [3] to +0.30 Pg C year–1 [4], or to +0.02 PgC year–1 [5]. At the same time, vegetation fires are one of the most significant factors regulating both the state of Siberian forests and the annual carbon balance.
Since the end of the 20th century, the territory of Siberia is characterized by an increase in the number of forest fires and the annual burned areas [6,7]. Such a scenario causes the formation of qualitatively new fire regimes in the region in the near future [8,9]. As a result, an increase in direct carbon emissions from fires into the atmosphere is predicted. According to the highest estimates [4,10,11], since the early 2000s, the annual amount of fire carbon emissions for the territory of Siberia is 300–500 Tg C/year. For comparison, Canadian fire emissions did not exceed 300 Tg C/year [12]. However, the adapted calculation methods show more conservative values of average long-term emissions in Siberia of ~100–150 Tg C/year with a positive trend [13]. This increasing trend is mainly associated with an increase of the burned area. At the same time, it is noted that the increasing emission trend can be also caused by an increase in the intensity of fires under a more warm and arid climate in the region. Modern estimates show a wide variability in the annual carbon sink for different forest types in Siberia ranging from 200 to 700 Tg C/year [2,14,15]. A further increase in average annual emissions from fires may be a primary reason for the transition of Siberian taiga forests from carbon sink to carbon source [3,5].
The problem of a reliable assessment and forecasting of wildfire emissions cannot be solved without considering the fire energy characteristics and combustion intensity, as a parameter that determines specific emissions per unit area. The variety of forest conditions, dominant forest stands, and the variability of forest fuels in Siberia determine a wide range of fire energy characteristics for each fire event. Therefore, new methods based on satellite measurements of high temporal resolution are required to control combustion characteristics in a near real-time mode [16].
In world practice, the determination of carbon fire emissions is based on the Seiler–Crutzen’s empirical relationship [17]. The parameters include the mass of forest fuels, the total burned area, and the coefficient of combustion completeness. The use of this approach implies expert assessments for all input parameters, while the combustion intensity, which determines the variation of all components in a wide range, is assumed to be constant.
Satellite-based measurements of Fire Radiative Power (FRP) allow instrumental assessment of the combustion intensity and subsequent classification of individual fire phases considering the variations of fire intensity [18,19,20]. Measurements made in the mid-IR and thermal IR wavelength ranges allow estimation of the energy released by the zone of active combustion [19]. The MODIS instrument on the Terra and Aqua satellites has become the first sensor that allows estimation of the Fire Radiative Power on a global scale [18]. The instantaneous FRP measurements from the zone of active combustion are directly related to the fireline intensity and are related to the biomass combustion rate [21,22]. At the same time, fire radiative energy (FRE) released during the fire life time is proportional to the amount of combusted biomass [23]. In addition, data on fire radiative power from wildfires are used in the analysis of the burning intensity [24], to detect crown fires [25], and also to assess the fire-related forest disturbance degree [26,27]. The dynamics of fire intensity in Siberia under the current climate conditions is usually described at a qualitative level. At the same time, there are very few quantitative estimates. In this regard, it is important to reliably assess the contribution of fires to the annual pool of carbon emissions [5], since this can reduce the level of uncertainty in the carbon emissions estimates [13].
We assumed that the increase in fire emission is associated with an increase in fire intensity. Therefore, the main hypothesis of the study was that the change in the intensity of combustion would result in a change of fire radiative power obtained from satellite imagery. We assumed that increasing trends in fire intensity would be less significant if we consider the entire set of data. However, we expected that the significance would increase if we consider fires in different types of vegetation, as well as if we preliminarily link fire areas to the degree of damage to vegetation by fire. Further, in our opinion, calculation of actual fire emissions will allow us to estimate the change in specific values of emissions for fires of different intensity.
In this work, we considered the following problems: (1) the dynamics of fire intensity in Siberia using fire radiative power estimates from the zone of active combustion, (2) the relationship between the integral FRP values and fire-related forest disturbance degree and values of specific emissions, and (3) the assessment of direct carbon emissions from fires considering the energy characteristics of combustion.

2. Materials and Methods

We used data on wildfires in Siberia (50–75° N, 60–140° E) for the period from 2002 to 2022. Initial fire data with a spatial resolution of 1000 m was obtained from the satellite imagery obtained from NOAA/AVHRR and TERRA/AQUA/MODIS that were processed into a geospatial database of satellite monitoring at the Federal Research Center (Krasnoyarsk, Russia). We also used standard MODIS (Moderate Resolution Imaging Spectroradiometer) burned area product MCD64A1 [28] to refine the spatial locations of burned areas with a spatial resolution of ~500 m. Fire radiative power (FRP) was estimated using MODIS thematic thermal anomaly product MOD14/MYD14 [28]. The product contains data on coordinates, the detection time of a thermal anomaly, and FRP values with a spatial resolution of ~1000 m. To refine the spatial reference of individual fire pixels we additionally used MODIS geolocation product MOD03/MYD03.
FRP values in standard MODIS products MOD14/MYD14 of collection 6 (https://ladsweb.modaps.eosdis.nasa.gov/, accessed on 1 May 2023) are calculated using the following ratio [28]:
F R P = S × σ a L 4 − L 4 ¯ ,
where L 4 and L 4 ¯ —are the 4-μm radiances (MODIS band #21) of the fire pixel and background, respectively; σ = 5.6704 × 10−8 W × m–2 × K–4—is the Stefan–Boltzmann constant; S—is the area of the MODIS pixel, and a = 3.0 × 10–9 W × m–2 × sr–1 × μm–1 × K–4—is a sensor-specific empirical constant.
To test the hypothesis that an increase in the fire frequency and in the burned area in Siberia is accompanied by an increase in the intensity of fires, we compared mean and integral FRP values for the first and second half of our study period (2002–2012 vs. 2013–2022). FRP data for each year between 2002 and 2022 were converted into raster GIS layers using a regular grid with a cell size of 0.5° × 0.625° (Figure 1). Following we calculated mean and integral/total FRP values for each grid cell considering two time periods (2002–2012 and 2013–2022). Then we created raster layer containing a relative change in FRP between two periods as follows:
FRP = 100% × (FRP2013–2022 − FRP2012–2002)/FRP2012–2002,
where FRP2002–2012 is the total FRP value for 2002–2012 and FRP2013–2022 is the total FRP value for 2013–2022.
Thus, we assessed how the values of the FRP have changed in different regions of Siberia as well as the spatial redistribution of both average and integral FRP values.
FRP threshold values determining the categories of combustion intensity were obtained from statistical data, which are mean FRP value (FRPm) and standard deviation (σ) of fire pixels within the fire polygon [29]. Such a threshold method enabled estimation of the fire areas burned by three categories of fires (of low, medium, and high intensity), and then, the long-term statistics were used to identify trends in the redistribution of the proportion of fires of each category of intensity in the overall fire statistics in Siberia between 2002 and 2022.
At an intermediate stage, a sample of fires was processed to determine the degree of fire impact on vegetation considering the dominant forest stands. We have studied 35 fires with a total area of ~20 thousand km2 within 7 types of forest stand, including: larch stands and sparse larch forests (Larix sibirica), pine stands (Pinus sylvestris), tundra vegetation and Cedar Elfin Wood (Pinus pumila), Siberian pine (Pinus sibirica) and spruce stands (Picea obovata).
At this stage, we used Landsat-8/OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) images obtained from open access catalogs (https://earthexplorer.usgs.gov/, accessed on 1 May 2023). We used Landsat images for the selected fire events (with burned areas > 2000 ha) in different tree stands of Siberia to evaluate fire impact on vegetation based on the normalized burn ratio method, described below. Area threshold was selected for reliable classification results. Information on the dominant forest stand was assigned to fire data based on an open access vegetation map (Vega service, ISR RAS, Moscow (http://pro-vega.ru/maps/, accessed on 1 May 2023).
For a representative sample of fires (Table 1), the level of fire impact on vegetation was assessed using the normalized burn ratio (NBR/dNBR) and standard threshold classification procedures (Table 2) [30,31]. Post-fire polygons were classified in terms of NBR/dNBR by per-pixel calculations:
N B R = N I R − S W I R N I R + S W I R ,
where NIR—is reflection in near-infrared (NIR) band (λ = 0.85–0.89 μm), and SWIR—is reflection in short wave-infrared (SWIR) band (λ = 2.10–2.30 μm);
d N B R = N B R p r e − N B R p o s t ,
where N B R p r e —is pre-fire NBR value, N B R p o s t —is post-fire NBR value.
We obtained the dates of fires using the geospatial database of satellite monitoring by the Federal Research Center (Krasnoyarsk, Russia) in 1996–2022. Pre- and post-fire images in the Landsat data catalog were selected with reference to the date of the fire event. The time interval between pre- and post-fire Landsat images was 1 year on average, in accordance with the requirements of the method [30,31]. Longer intervals in several cases were due to the lack of a cloud-free image (Table 1).
Using the Dnbr ranges, 4 categories of fire disturbance degree of vegetation are distinguished [30,31]. Further, in this work, we defined 3 classes of fire impact (Table 2): low level of disturbance (Dnbr < 0.269), moderate level (Dnbr < 0.659), significant level of disturbance (Dnbr > 0.659).
The relationship between the NBR/Dnbr and FRP was estimated taking into account the dominant forest stands of Siberia (Figure 2 and Figure 3). For this purpose, we considered the integral FRP value over all fire pixels for each fire disturbance class identified using the NBR/Dnbr values. We analyzed the diagrams of the correlation field (Pearson correlation) of the dependence of the integral FRP on the Dnbr category for different types of forest stands with an estimate of the coefficient of determination R2.
In current conditions, an increase in fire emissions is caused not only by an increase in burned areas but also by an increase in the specific value of emissions per unit area during high-intensity combustion. Burned areas of low-, moderate-, and high- combustion intensity in Siberia have characteristic specific values of emissions per unit area of ~8.7, 12.0, and 15.4 t C/ha, respectively [13,16].
At the final stage, we classified “fire” pixels taking into account categories of combustion intensity and calculated fire emissions using the threshold method. The total emissions were determined using the modified [29] relation of Seiler–Crutzen [17], where the combustion intensity determines the variation of the input parameters:
C = C E × ∑ i S i F R P × β i F R P × B i F R P
where C is direct carbon emissions (g); S i F R P is total burned area (m2), calculated as the sum of different burning intensity areas; β i F R P is a combustion efficiency coefficient; B i F R P is fuel load (kg/m2), and CE is emission factor relating the amount of combusted fuels and carbon emission (g/kg). Input parameters were considered as functions of fire radiative power (FRP) for each i-th burned plot of fire.
Considering the results of field studies [19,32], the coefficient β i F R P varied between 0.35 and 0.40 for low-intensity fires, for moderate-intensity fires it was 0.40–0.55, and it was 0.55–0.65 for high-intensity fires.
Fuel loads B i F R P in various forest stands of Siberia are estimated in a wide range of 5.0–60.0 t/ha [33,34,35,36,37,38,39,40]. At the same time, in Equation (4), the specific values of combusted fuels (kg/m2) were taken as follows: 0.11–0.97 kg/m2, 0.86–2.15 kg/m2, and 2.25–5.36 kg/m2 for low-intensity, moderate-intensity and high-intensity combustion, correspondingly [10,41]. The recalculation of these values gives estimates of specific emissions from fires of different intensity in the range of 5.4, 15.1, 38.1 t C/ha. Variation of combusted fuel loads in fires of various characteristics determines a significant dispersion in the values of the integral FRP [22] and subsequent estimates of fire emissions by method (5).
Finally, we analyzed long-term emissions variety for 2002–2022 from the point of view of the dynamics of fire intensity in Siberia, characterizing the general patterns of trends and the predicted proportion of fire emissions in the total annual volume of greenhouse gas emissions in the Russian Federation.

3. Results

A positive trend (R2 = 0.47, for p < 0.05) was revealed for high-intensity fires (HIF) in terms of FRP (Figure 4) for the territory of Siberia in 2002–2022. The result shows the percentage of the total area of fires without taking into account differences that may occur in different dominant tree stands. Therefore, the trend for HIF shows a slight increase. The redistribution is associated with a reduction of the areas of low-intensity fires (LIF) (Figure 4a,c). At the same time, the proportion of medium-intensity fires (MIF) actually remained unchanged with slight decreasing trend (R2 = 0.0002) (Figure 4b). According to our data (Table 3), the proportion of high-intensity burning increased from 10% to 15.9% of the total area of wildfires, which is 0.63 ± 0.31 million ha/year, respectively, in 2002–2011, and 1.40 ± 0.49 million ha/year in 2012–2019. Seasons 2020 and 2021 with a proportion of high-intensity fires about ~2.5 ± 0.95 million ha should be considered as sporadic extremes of the last decade.
At the same time, the dynamics of the average and integral FRP values also demonstrated positive trends (R2 = 0.24–0.28, at p < 0.05) against the background of rapidly periodic interseasonal variations (Figure 4d,e). This trend is even more reliable (R2* = 0.33–0.42, at p < 0.05) without the outlier of 2022 for average and integral FRP, which were marked on the graph with (*). Average values increased from FRPm = 45 × 106 W in 2002 to FRPm = 65 × 106 W, i.e., by ~150% from the initial level. The integral FRP values calculated for all fires of the season showed a threefold increase with FRPint = 2.42 ± 0.93 × 1012 W in 2002–2011 to FRPint = 7.69 ± 3.18 × 1012 W in 2012–2022 (Figure 4e).
For the considered sample of fires, the integral values of FRP varied at the level of 10,000–75,000 MW in light coniferous tree stands, 5000–30,000 MW in the sparse larch forests and in tundra zone, and 1800–7000 MW in dark coniferous stands. It was instrumentally confirmed that high-intensity fires (integral FRP values within a single fire event exceed 50 × 103 MW) are mainly recorded in light coniferous (Larix sibirica, Pinus sylvestris) stands, where the fire impacts of medium and high degree were also the largest and amounted to ~46% of the total damage area per year [31]. The results are consistent with the available estimates of the degree of fire impact on vegetation in Siberia [26,31]. Thus, according to Krylov et al., 2014 [42], light conifer stands comprised 65% of all non stand-replacement and 79% of all stand-replacement fire in Russia. Spatially, these estimates are detected on the Landsat-based global forest cover change product (https://glad.earthengine.app/view/global-forest-change/, accessed on 7 June 2023) by Hansen et al., ver. 1.10, updated in 2022 [43]. For the class of Deciduous Needle-Leaf Forests (i.e., larch forests of Siberia), the highest probability of annual fire impact [26] and post-fire mortality [7] was also recorded. The majority of fires in Siberia are detected in light coniferous forests [6]. The level of fire impact in these forests is twofold excess of the intensity in comparison with other forest stands (Figure 5). At the same time, the linear dependence of FRP vs. dNBR is the same in all types of forest.
We found a direct correlation (R2 = 0.80–0.94) between FRP intensity and vegetation disturbance categories in terms of dNBR (Figure 5). This result is also consistent with the idea that an increase in the intensity of combustion determines an increase in the volume of fuel loads burning and the corresponding volumes of direct emissions from fires [16,21].
Interpolation of integral FRPint using a regular grid with a cell of 0.5° × 0.625° (Figure 6) demonstrates the spatial distribution of the increase in FRP values over a large area, on about ~30% of the boreal forests of Siberia, including the Arctic zone. At the same time, in several regions of Siberia, the integral FRPint increased by 2.5–50.0 times relative to the values for the previous decade.
The spatial redistribution of both average and integral FRP values in the grid cells is shown in Figure 7. We note an increase in the average/integral FRP in large areas north of 60° N in the northeastern part of Siberia, attributing this to two factors: (1) an increase in the number (and areas) of fires, and (2) an increase in the level of burning intensity.
Thus, the zone where fires intensity increases is located to the north of 60° N, mainly in the territory of larch (Larix sibirica, L. gmelinii) forests and sparse larch forests. Fires here and in the adjacent areas of peatlands and tundra (>67° N) can potentially provide a significant additive contribution to the annual volumes of emissions, as it was recorded during the season of extreme burning in the Arctic in 2020 [44]. The sporadic maximum (Figure 8c) demonstrates a significant excess of fire emissions in the northern part of the region in 2020 (~3.7 Tg C/year), which is 5 times higher than the long-term average (~0.59 Tg C/year). The recurrence frequency of such events is estimated at 1 time per 8–10 years [44].
The values of direct fire emissions for the considered period varied from 58 Tg/year (2006) to 120 Tg/year (2014) and reached extreme values of ~350 Tg/year (in 2020, 2021). The average long-term level of fire emissions increased three times from 60.0 ± 25.8 Tg/year in 2002–2011 up to 137.0 ± 60.0 Tg/year in 2012–2019 and up to 296.0 ± 102.0 Tg/year in the last three seasons of 2020–2022 (Figure 8a).
Filtering high-frequency variations after averaging over five-year intervals allows us to speak of an exponential trend in the growth of emissions (Figure 8b). Projected maximum annual emissions from fires can be ~325 Tg C/year in the short term of 2022–2027.

4. Discussion

Currently, remote sensing is the most important tool for obtaining data on wildfires in Siberia [6]. First of all, this is due to the low population of the region and the lack of alternative methods for detecting and controlling fires over large areas. Therefore, modern technologies for both studying fires and assessing fire effects are based on the use and generalization of satellite data. In particular, this applies to the issues of assessing the fire impact on vegetation cover [7,26,27,31] or calculating fire emissions [10,13]. Satellite estimates complement and allow extrapolation of single ground-based experiments [11,32,37,45] in matters of wildfire and post-fire processes.
According to various estimates the characteristic specific emissions values per unit area in Siberia vary from ~8.7, 12.0, and 15.4 t C/ha [16] to 5.0, 11.0, 27.4 t C/ha [10,41] for low-, medium-, and high-intensity combustion, respectively. In our opinion, for the considered period 2002–2022, an increase of fire emissions in Siberia was determined by: (1) an increase in annual burned areas; (2) redistribution of low- and high-intensity combustion (Figure 4a,c); and (3) increasing the values of specific emissions.
Based on the results of instrumental data processing, we found a change in the areas burned by fires of various intensities (Table 3) for three time periods, as well as average long-term estimates of fire emissions (Figure 8a). Thus, the calculation of specific emissions for fires of various intensities comes down to the analytical solution of the system in matrix form, which has the form
A × x = B A = 2.3 2.5 0.6 3.4 3.6 1.4 6.6 6.9 2.5 ,     x = x 1 x 2 x 3 ,     B = 59.7 136.6 296.0  
where variables x1, x2, x3 determine the specific emission for fires of low, medium, and high intensity, respectively, Bi are the average long-term estimates of fire emissions (see Figure 8a), and Aij are coefficients equal to the average long-term values of fire areas in accordance with intensity categories (see Table 3).
The exact solution of system (6) with respect to the variables x1, x2, x3 does not correspond to the characteristic limits of possible values of specific emissions due to the uncertainty of the used coefficients Aij for areas and Bi for average annual emissions. Nevertheless, approximate solutions of specific emissions for three time ranges were obtained using the least squares method (standard plugin of MS Office Excel, ver. 2013) with optimization of Residual Sum of Squares (RSS) of model calculations of annual emissions (Table 4).
It is indicative that specific emissions for high-intensity fires at the present stage (38.8 t C/ha) more than doubled (Table 4). A significant increase in high-intensity fire areas from 0.63 ± 0.31 million ha/year in 2002–2011 up to 1.70 ± 0.49 million ha/year in 2012–2019 and up to 2.5 ± 0.92 in 2020–2021 is spatially typical for the zone of light coniferous forests of Central/Eastern Siberia (Larix sibirica, L. gmelinii, Pinus sylvestris) (Figure 6). According to published data (Table 5), the fuel loads can reach 45–60 t/ha in these territories.
Indirectly, the volumes of burning biomass are confirmed by the conjugate analysis of satellite estimates of the degree of post-fire ground cover disturbance and the spatial variation of FRP, where a high (R2 = 0.80–0.94) positive correlation was recorded (Figure 5). At the same time, the combustion intensity which depends on the amount of forest fuels consumed, determines both the overall level of fire impact [26,31] and the variety of post-fire effects on all components of forest ecosystems. In particular, deep burning of the ground cover in high-intensity fires is indirectly confirmed by long-term thermal anomalies observed on soils at the sites of degradation of heat-insulating covers [45].
Most modern forecasts of forest fire regimes in Siberia [6,8,9,41] identify climate change as the most important driver of the increase in fire areas. Nowadays, it is noted that the high burning of Siberian forests directly depends on the increasing anthropogenic factor. Human disturbances, such as forest logging, may further intensify the fire activity in the Siberian forests [46]. It can be assumed that an increase in the fuel loads could affect the probability of high-intensity fires under the conditions of increased climate aridity of the region in the future [47]. In this vein, an increase in fire emissions of carbon compounds in the nearest perspective is a generally accepted and justified point of view. The results of our study show that, in addition, the dynamics of annual emissions will be determined by the increase in high-intensity combustion, as well as an increase in the values of specific emissions per unit area. Such effects can be typical for a large area of Siberia, which makes up to ~30% of all forests in the region. Potentially, this conclusion is also applicable to a significant part of the Arctic in the conditions of extreme fire seasons.
A separate issue for discussion is the problem of assessing the carbon balance in the forests of Siberia [2,15]. In this matter, it is very important to have instrumentally confirmed characteristics of fire emissions.
It can be expected that the increasing trend of high-intensity combustion will continue further, resulting in additional volumes of fire emissions. If we take into account the volume of greenhouse gas emissions in the Russian Federation, which is estimated at 1584–2155 Tg C/year [4,48], then fires are responsible for 5% of the total emissions during low fire seasons and up to 20% during extreme seasons as demonstrated in 2020 and 2021 (~350 Tg/year). At the same time, the annual carbon sink for various forests of Siberia is about 200–700 Tg C/year [2,14,15], which can be compensated by fire emissions of the same order, even if the average level of the last decade (~296.0 ± 102.0 Tg/year) is maintained. A further increase in average annual emissions from fires may be the primary reason for the transition of Siberian taiga forests from an atmospheric carbon sink to the source of carbon [3,5].

5. Conclusions

According to the results, ~30% of Siberian boreal forests, including the Arctic zone, are characterized by an increase in the proportion of areas of high-intensity fires over the last decade. A significant increase in areas burned by high-intensity fires is observed in the zone of light coniferous forests of Central/Eastern Siberia (Larix sibirica, L. gmelinii, Pinus sylvestris). Fires here and in the adjacent areas of peatlands and tundra (>67° N) can potentially provide a significant additive contribution to the annual volumes of emissions. Direct correlation (R2 = 0.80–0.94) between FRP intensity and vegetation disturbance categories in terms of dNBR was typical for all types of tree stands in Siberia.
At the same time, a further increase in fire emissions in Siberia will be determined not only by an increase of burned areas, but also by a redistribution of low- and high-intensity burning and an increase in specific emission values. Currently, we estimated that specific emissions for high-intensity fires in 2020 (38.8 t C/ha) increased more than twice since 2000 (~15.0 t C/ha).
We estimated the predicted maximum annual fire emissions at ~325 Tg C/year for the short term 2022–2027. Siberian fires are responsible for about 5–20% of the total volume of greenhouse gas emissions in the Russian Federation. Probably, in the perspective of available climatic scenarios, the regular recurrence of extreme emissions will make it possible to consider part of Siberian forests as a source of carbon in the near future.

Author Contributions

Conceptualization, E.I.P.; methodology, E.I.P.; software, A.N.Z., E.G.S. and E.I.P.; validation, E.I.P., T.V.P. and E.G.S.; resources, E.I.P.; data curation, E.I.P. and E.G.S.; writing—original draft preparation, E.I.P.; writing—review and editing, E.I.P., T.V.P. and E.G.S.; visualization, E.I.P., A.N.Z. and E.G.S.; supervision, E.I.P.; funding acquisition, T.V.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was performed using the subject of project no. 0287-2021-0008 (IF SB RAS) and no. FWES-2022-0001 (KSC SB RAS). The investigation of fire impact on ground vegetation cover was supported by the Russian Science Foundation (RSF) no. 23-14-20007, https://rscf.ru/project/23-14-20007/ (accessed on 22 May 2023) and Krasnoyarsk Regional Fund of Science. The study of long-term wildfire’s distribution over Siberia was supported by the Tomsk State University Development Program «Priority-2030».

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. These data can be found here: https://earthexplorer.usgs.gov/ (accessed on 1 May 2023), https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 1 May 2023), http://pro-vega.ru/maps/ (accessed on 1 May 2023), and https://worldview.earthdata.nasa.gov/ (accessed on 1 May 2023).

Acknowledgments

The satellite data-receiving equipment used was provided by the Center of Collective Usage of the Federal Research Center “Krasnoyarsk Science Center, Siberian Branch of Russian Academy of Sciences”, Krasnoyarsk, Russia. Since 2017, we have been monitoring wildfires as an activity of The Regional Eurasian Fire Monitoring Center (Krasnoyarsk, Russia) under an agreement between V.N. Sukachev Institute of Forest (Krasnoyarsk, Russia) and The Global Fire Monitoring Center (GFMC, Germany, https://gfmc.online/, accessed on 1 May 2023).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Keenan, R.J.; Reams, G.A.; Achard, F.; de Freitas, J.V.; Grainger, A.; Lindquist, E. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manag. 2015, 352, 9–20. [Google Scholar] [CrossRef]
  2. Zamolodchikov, D.G.; Grabowsky, V.I.; Chestnykh, O.V. Dynamics of Carbon Budget in Forests of Federal Districts of Russian Federation. For. Sci. Issues 2018, 1, 1–24. [Google Scholar] [CrossRef]
  3. Shvidenko, A.Z.; Schepaschenko, D.G. Carbon Budget of Russian Forests. Sib. For. J. 2014, 1, 69–92. (In Russian) [Google Scholar]
  4. Ptichnikova, A.V.; Shvartsa, E.A.; Kuznetsova, D.A. On the potential of greenhouse gas absorption by forests of Russia to reduce the carbon footprint of domestic products export. Rep. Russ. Acad. Sci. Earth Sci. 2021, 499, 181–184. (In Russian) [Google Scholar] [CrossRef]
  5. Fan, L.; Wigneron, J.P.; Ciais, P.; Chave, J.; Brandt, M.; Sitch, S.; Yue, C.; Bastos, A.; Li, X.; Qin, Y.; et al. Siberian carbon sink reduced by forest disturbances. Nat. Geosci. 2023, 16, 56–62. [Google Scholar] [CrossRef]
  6. Kharuk, V.I.; Ponomarev, E.I.; Ivanova, G.A.; Dvinskaya, M.L.; Coogan, S.C.P.; Flannigan, M.D. Wildfires in the Siberian taiga. Ambio 2021, 50, 1953–1974. [Google Scholar] [CrossRef]
  7. Bartalev, S.A.; Stytsenko, F.V. An assessment of the forest stands destruction by fires based on the remote sensing data on a seasonal distribution of burnt areas. Contemp. Probl. Ecol. 2021, 2, 115–122. [Google Scholar] [CrossRef]
  8. Hayasaka, H. Rare and Extreme Wildland Fire in Sakha in 2021. Atmosphere 2021, 12, 1572. [Google Scholar] [CrossRef]
  9. Hayasaka, H. Fire Weather Conditions in Boreal and Polar Regions in 2002–2021. Atmosphere 2022, 13, 1117. [Google Scholar] [CrossRef]
  10. Soja, A.J.; Cofer, W.R.; Shugart, H.H.; Sukhinin, A.I.; Stackhouse, P.W., Jr.; McRae, D.J.; Conard, S.G. Estimating fire emissions and disparities in boreal Siberia (1998–2002). J. Geophys. Res. 2004, 109, D14S06. [Google Scholar] [CrossRef]
  11. McRae, D.J.; Conard, S.G.; Ivanova, G.A.; Sukhinin, A.I.; Baker, S.; Samsonov, Y.N.; Blake, T.W.; Ivanov, V.A.; Ivanov, A.V.; Churkina, T.V.; et al. Variability of fire behavior, fire effects, and emissions in Scotch pine forests of Central Siberia. Mitig. Adapt. Strateg. Glob. Chang. 2006, 11, 45–74. [Google Scholar] [CrossRef]
  12. de Groot, W.J.; Cantin, A.S.; Flannigan, M.D.; Soja, A.J.; Gowman, L.M.; Newbery, A. A comparison of Canadian and Russian boreal forest fire regimes. For. Ecol. Manag. 2013, 294, 23–34. [Google Scholar] [CrossRef]
  13. Ponomarev, E.I.; Yakimov, N.D.; Ponomareva, T.V.; Yakubailik, O.E.; Conard, S.G. Current Trend of Carbon Emissions from Wildfires in Siberia. Atmosphere 2021, 12, 559. [Google Scholar] [CrossRef]
  14. Sergienko, V.G. The expected impact of climate change on carbon balance and ecosystem productivity in the forest sector of the Russian Federation. Proc. St. Petersburg For. Res. Inst. 2018, 1, 74–90. (In Russian) [Google Scholar] [CrossRef]
  15. Vaganov, E.A.; Porfiryev, B.N.; Shirov, A.A.; Kolpakov, A.Y.; Pyzhev, A.I. Assessment of the contribution of Russian forests to climate change mitigation. Econ. Reg. 2021, 17, 1096–1109. [Google Scholar] [CrossRef]
  16. Ponomarev, E.I.; Shvetsov, E.G.; Kharuk, V.I. The Intensity of Wildfires in Fire Emissions Estimates. Russ. J. Ecol. 2018, 49, 492–499. [Google Scholar] [CrossRef]
  17. Seiler, W.; Crutzen, P.J. Estimates of Gross and Net Fluxes of Carbon between the Biosphere and the Atmosphere from Biomass Burning. Clim. Chang. 1980, 2, 207–247. [Google Scholar] [CrossRef]
  18. Justice, C.O.; Giglio, L.; Korontzi, S.; Owens, J.; Morisette, J.T.; Roy, D.; Descloitres, J.; Alleaume, S.; Petitcolin, F.; Kaufman, Y. The MODIS fire products. Remote Sens. Environ. 2002, 83, 244–262. [Google Scholar] [CrossRef]
  19. Wooster, M.J.; Roberts, G.; Perry, G.L.W.; Kaufman, Y.J. Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. J. Geophys. Res. 2005, 110, D24311. [Google Scholar] [CrossRef]
  20. Kumar, S.S.; Roy, D.P.; Boschetti, L.; Kremens, R. Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations. J. Geophys. Res. 2011, 116, 1–18. [Google Scholar] [CrossRef]
  21. Wooster, M.J.; Zhukov, B.; Oertel, D. Fire radiative energy for quantitative study of biomass burning. Derivation from the BIRD experimental satellite and comparison to MODIS fire products. Remote Sens. Environ 2003, 86, 83–107. [Google Scholar] [CrossRef]
  22. Ponomarev, E.I.; Shvetsov, E.G.; Litvintsev, K.Y. Calibration of Estimates on Direct Wildfire Emissions from Remote Sensing Data. Izv. Atmos. Ocean. Phys. 2019, 55, 1065–1072. (In Russian) [Google Scholar] [CrossRef]
  23. Vermote, E.; Ellicott, E.; Dubovik, O.; Lapyonok, T.; Chin, M.; Giglio, L.; Roberts, G. An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power. J. Geophys. Res. 2009, 114, 1–22. [Google Scholar] [CrossRef]
  24. Shvetsov, E.G.; Ponomarev, E.I. Estimating the Influence of External Environmental Factors on Fire Radiative Power Using Satellite Imagery. Contemp. Probl. Ecol. 2015, 8, 337–343. [Google Scholar] [CrossRef]
  25. Ponomarev, E.I.; Shvetsov, E.G.; Usataya, Y.O. Determination of the Energy Properties of Wildfires in Siberia by Remote Sensing. Izv. Atmos. Ocean. Phys. 2018, 54, 979–985. [Google Scholar] [CrossRef]
  26. Loupian, E.A.; Lozin, D.V.; Balashov, I.V.; Bartalev, S.A.; Stytsenko, F.V. Study of the dependence of forest fire damage degree on burning intensity based on satellite monitoring data. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosm. 2022, 19, 217–232. (In Russian) [Google Scholar] [CrossRef]
  27. Shvetsov, E.G. Study of the influence of fire radiative power of forest fires on forest disturbance degree in southern regions of Central Siberia using satellite data. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosm. 2022, 19, 136–146. (In Russian) [Google Scholar] [CrossRef]
  28. Giglio, L.; Schroeder, W.; Justice, C. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 2016, 178, 31–41. [Google Scholar] [CrossRef]
  29. Ponomarev, E.I. A Method for Calculating Direct Fire Carbon Emissions, Taking into Account the Threshold Classification of Vegetation Fire Intensity from Satellite Imagery in the IR Range. Russian Patent RU 2755936 C1, 28 January 2021. (In Russian). [Google Scholar]
  30. dos Santos, S.M.B.; Bento-Gonçalves, A.; Franca-Rocha, W.; Baptista, G. Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices. Geosciences 2020, 10, 106. [Google Scholar] [CrossRef]
  31. Ponomarev, E.; Zabrodin, A.; Ponomareva, T. Classification of Fire Damage to Boreal Forests of Siberia in 2021 Based on the dNBR Index. Fire 2022, 5, 19. [Google Scholar] [CrossRef]
  32. Tsvetkov, P.A. Adaptation of Larix gmelinii to Fires in the Northern Taiga of Central Siberia. Sib. Ecol. J. 2005, 1, 117–129. (In Russian) [Google Scholar]
  33. Pleshikov, F.I.; Vaganov, E.A.; Vedrova, E.F. Lesnye Ekosistemy Eniseyskogo Meridiana (Forest Ecosystems of the Yenisei Meridian); Izd-vo: SO RAN, SB RAS: Novosibirsk, Russia, 2002; 356p. (In Russian) [Google Scholar]
  34. Prokushkin, S.G.; Abaimov, A.P.; Prokushkin, A.S.; Masyagina, O.V. Biomass of the Soil Cover and Underbrush in Larch Associations of the Cryolithozone of Middle Siberia. Sib. Ecol. J. 2006, 2, 131–139. (In Russian) [Google Scholar]
  35. Vedrova, E.F. The Intensity of Organic Matter Decomposition in Gray Soils of Forest Ecosystems in the Southern Taiga of Central Siberia. Soil Sci. 2008, 8, 973–982. [Google Scholar] [CrossRef]
  36. Vedrova, E.F.; Evdokimenko, M.D.; Bezkorovainaya, I.N.; Mukhortova, L.V.; Cherednikova, Y.S. Carbon Reserves in Organic Matter of Postfire Pine Forests in Southwestern Lake Baikal Basin. For. Sci. 2012, 1, 3–13. (In Russian) [Google Scholar]
  37. Chevychelov, A.P. Forest fires in Yakutia and their impact on soil cover in terms of predicted climate change. NEFU Bull. Earth Sci. Ser. 2019, 13, 1889. [Google Scholar]
  38. Sergeeva, O.V.; Mukhortova, L.V.; Krivobokov, L.V. Distribution of forest litter and live ground cover biomass in northern taiga of the central Evenkia depending on relief. Sib. For. J. 2020, 1, 38–46. (In Russian) [Google Scholar]
  39. Gerber, A.A.; Loiko, S.V.; Lim, A.G.; Istigichev, G.I.; Kulizhsky, S.P. Estimation of carbon stocks in tundra landscapes of Western Siberia. In Proceedings of the Materials IV All-Russian Conference with International Participation, Ulan-Ude, Russia, 15–18 June 2021; pp. 102–104. (In Russian). [Google Scholar]
  40. Ivanova, G.A.; Kukavskaya, E.A.; Bezkorovaynaya, I.N.; Bogorodskaya, A.V.; Zhila, S.V.; Ivanov, V.A.; Kovaleva, N.M.; Krasnoshchekova, E.N.; Tarasov, P.A. The Impact of Fires on the Light Coniferous Forests of the Lower Angara Region; Nauka (Science): Novosibirsk, Russia, 2022; 204p. (In Russian) [Google Scholar]
  41. Conard, S.G.; Sukhinin, A.I.; Stocks, B.J.; Cahoon, D.; Davidenko, E.P.; Ivanova, G.A. Determining effects of area burned and fire severity on carbon cycling and emissions in Siberia. Clim. Chang. 2002, 55, 197–211. [Google Scholar] [CrossRef]
  42. Krylov, A.; McCarty, J.L.; Potapov, P.; Loboda, T.; Tyukavina, A.; Turubanova, S.; Hansen, M.C. Remote sensing estimates of stand-replacement fires in Russia, 2002–2011. Environ. Res. Lett. 2014, 9, 1–8. [Google Scholar] [CrossRef]
  43. Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R. High-resolution global maps of 21stcentury forest cover change. Science 2013, 342, 850–853. Available online: https://glad.earthengine.app/view/global-forest-change/ (accessed on 7 June 2023). [CrossRef]
  44. Conard, S.G.; Ponomarev, E.I. Fire in the North—The 2020 Siberian Fire Season. Wildfire 2020, 29, 26–32. [Google Scholar]
  45. Ponomareva, T.V.; Ponomarev, E.I.; Litvintsev, K.Y.; Finnikov, K.A.; Yakimov, N.D. Thermal state of disturbed soils in the permafrost zone of Siberia according the remote data and numerical simulation. J. Comput. Technol. 2022, 27, 16–35. [Google Scholar] [CrossRef]
  46. Bowd, E.J.; Banks, S.C.; Strong, C.L.; Lindenmayer, D.B. Long-term impacts of wildfire and logging on forest soils. Nat. Geosci. 2019, 12, 113–118. [Google Scholar] [CrossRef]
  47. Kirillina, K.; Shvetsov, E.G.; Protopopova, V.V.; Thiesmeyer, L.; Yan, W. Consideration of anthropogenic factors in boreal forest fire regime changes during rapid socio-economic development: Case study of forestry districts with increasing burnt area in the Sakha Republic, Russia. Environ. Res. Lett. 2020, 15, 035009. [Google Scholar] [CrossRef]
  48. Strategy of Socio-Economic Development of the Russian Federation with Low Greenhouse Gas Emissions Until 2050. Order of the Government of the Russian Federation dated 29.10.2021.—No. 3052-R. Available online: http://publication.pravo.gov.ru/Document/View/0001202111010022 (accessed on 1 May 2023). (In Russian)
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Figure 1. Scheme of the FRP raster layers generation: (a) Spatial alignment of the centers of “fire” pixels with various FRP values and cells of the raster grid (0.5° × 0.625°); (b) Color gradient of the output grid cells corresponds to integral values of FRP.
Figure 1. Scheme of the FRP raster layers generation: (a) Spatial alignment of the centers of “fire” pixels with various FRP values and cells of the raster grid (0.5° × 0.625°); (b) Color gradient of the output grid cells corresponds to integral values of FRP.
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Figure 2. Burned area in Siberian larch (Larix sibirica) forest on Landsat-8 image of 16 August 2020: (a) Original image of burned area; (b) Variation of dNBR values with 4 categories of fire impact on vegetation; (c) FRP values.
Figure 2. Burned area in Siberian larch (Larix sibirica) forest on Landsat-8 image of 16 August 2020: (a) Original image of burned area; (b) Variation of dNBR values with 4 categories of fire impact on vegetation; (c) FRP values.
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Figure 3. Burned area in Siberian pine (Pinus sibirica) forest on Landsat-8 image of 10 September 2019: (a) Original image of burned area; (b) Variation of dNBR values with 4 categories of fire impact on vegetation; (c) FRP values.
Figure 3. Burned area in Siberian pine (Pinus sibirica) forest on Landsat-8 image of 10 September 2019: (a) Original image of burned area; (b) Variation of dNBR values with 4 categories of fire impact on vegetation; (c) FRP values.
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Figure 4. Trends in the proportions of fire areas, taking into account the intensity of burning in terms of FRP: (a) Low-intensity, (b) medium-intensity, (c) high-intensity fires. Dynamics of average FRPm values (d) and integral FRPint values (e), calculated from all detected fires in Siberia. R2* values are obtained without taking into account the minima of 2022, which were marked on the graph with (*).
Figure 4. Trends in the proportions of fire areas, taking into account the intensity of burning in terms of FRP: (a) Low-intensity, (b) medium-intensity, (c) high-intensity fires. Dynamics of average FRPm values (d) and integral FRPint values (e), calculated from all detected fires in Siberia. R2* values are obtained without taking into account the minima of 2022, which were marked on the graph with (*).
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Figure 5. Correlation between FRP and categories of vegetation disturbance in terms of dNBR for the prevailing types of forest stands and vegetation cover in Siberia. Prevailing types of vegetation: (a) Larch forests (Larix sibirica), (b) larch sparse, (c) pine stands (Pinus sylvestris), (d) cedar elfin wood (Pinus pumila), (e) siberian pine (Pinus sibirica) and (f) tundra vegetation.
Figure 5. Correlation between FRP and categories of vegetation disturbance in terms of dNBR for the prevailing types of forest stands and vegetation cover in Siberia. Prevailing types of vegetation: (a) Larch forests (Larix sibirica), (b) larch sparse, (c) pine stands (Pinus sylvestris), (d) cedar elfin wood (Pinus pumila), (e) siberian pine (Pinus sibirica) and (f) tundra vegetation.
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Figure 6. Changes in the integral values of FRP (in %) for fires in Siberia detected between 2013 and 2022 relative to integral FRP values detected in 2002–2012. FRP values were gridded using 0.5° × 0.625° cell size.
Figure 6. Changes in the integral values of FRP (in %) for fires in Siberia detected between 2013 and 2022 relative to integral FRP values detected in 2002–2012. FRP values were gridded using 0.5° × 0.625° cell size.
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Figure 7. Regular grids (cell size 0.5° × 0.625°) of mean (a,b) and integral (c,d) FRP values for the territory of Siberia. Panels (a,c) correspond to 2002–2012 and panels (b,d) correspond to 2013–2022.
Figure 7. Regular grids (cell size 0.5° × 0.625°) of mean (a,b) and integral (c,d) FRP values for the territory of Siberia. Panels (a,c) correspond to 2002–2012 and panels (b,d) correspond to 2013–2022.
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Figure 8. Estimates of direct emissions from fires in Siberia for 2002–2022: (a) Variation of the average level; (b) Forecast until 2027 with interannual fluctuation filtering; (c) Extreme emissions in 2020 against the background of long-term average values of emissions from fires in the peatlands and tundra zone (>67° N) of Siberia.
Figure 8. Estimates of direct emissions from fires in Siberia for 2002–2022: (a) Variation of the average level; (b) Forecast until 2027 with interannual fluctuation filtering; (c) Extreme emissions in 2020 against the background of long-term average values of emissions from fires in the peatlands and tundra zone (>67° N) of Siberia.
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Table
Table 1. Dates of Landsat images used.
Table 1. Dates of Landsat images used.
Tree StandNumber of Fire EventPre-Fire Image DataPost-Fire Image Data
Larch (Larix sibirica)72 July 201714 September 2018
27 June 201916 August 2020
15 June 20194 August 2020
21 July 202021 September 2021
9 September 202012 September 2021
21 May 202028 August 2021
23 August 202018 September 2021
Sparse larch512 July 201828 July 2018
21 July 201712 September 2018
4 July 201916 August 2020
1 September 20196 September 2021
22 June 202126 September 2021
Pine
(Pinus sylvestris)		65 August 201725 September 2018
6 August 201710 September 2018
22 July 202011 September 2021
5 June 20219 September 2021
24 August 202012 September 2021
17 June 20191 September 2021
Tundra vegetation58 June 201911 August 2019
8 June 201918 August 2019
25 June 201913 September 2019
25 June 201913 September 2019
9 August 201913 September 2019
Cedar elfin wood (Pinus pumila)520 August 201816 August 2020
9 June 201814 August 2019
28 June 20171 September 2019
28 June 20171 September 2019
9 September 202012 September 2021
Siberian pine
(Pinus sibirica)		523 July 201710 September 2018
6 August 201710 September 2018
16 June 201619 June 2017
23 July 201511 September 2016
23 August 202025 July 2021
Table
Table 2. Threshold Dnbr values for classification of fire disturbance degree.
Table 2. Threshold Dnbr values for classification of fire disturbance degree.
CategoryDnbrDisturbance Degree
1<0.269Low
20.270–0.439Low-moderate
30.440–0.659Moderate
4>0.660High
Table
Table 3. Long-term average values of areas (million ha) of low-intensity (LIF), medium-intensity (MIF), and high-intensity (HIF) burning in Siberia.
Table 3. Long-term average values of areas (million ha) of low-intensity (LIF), medium-intensity (MIF), and high-intensity (HIF) burning in Siberia.
YearsAverage Area, mln ha
LIFMIFHIF
2002–20112.3 ± 1.52.5 ± 1.80.6 ± 0.4
2012–20193.4 ± 3.03.6 ± 3.01.4 ± 0.5
2020–20226.6 ± 2.96.9 ± 2.42.5 ± 1.0
Table
Table 4. Solutions for the values of specific emissions (t C/ha) for three times.
Table 4. Solutions for the values of specific emissions (t C/ha) for three times.
YearsSpecific Emissions, t C/ga
x1x2x3
2002–20119.012.015.0
2012–201910.012.420.3
2020–202210.014.538.8
Table
Table 5. Data on the fuel loads in the forests of Siberia.
Table 5. Data on the fuel loads in the forests of Siberia.
Tree standsSubregionLoads, t/haReference
Light coniferous forests
(Larix sibirica, L. gmelinii,
Pinus sylvestris)		Larch forests/Eastern Siberia, Yakutia14.5–23.0Chevychelov, 2019 [37]
Larch forests/Central Siberia18.0–18.8Pleshikov et al., 2002 [33],
Prokushkin, 2006 [34]
Larch forests/waterlogged taiga of Central Siberia21.0–60.0Sergeeva et al., 2020 [38]
Larch forests/southern taiga of Central Siberia48.6–65.0Ivanova et al., 2022 [40]
Pine forests/Eastern Siberia, Yakutia5.7–7.8Chevychelov, 2019 [37]
Pine forests/Central Siberia6.6–14.5Vedrova, 2012 [36], Pleshikov, 2002 [33]
Pine forests/southern taiga of Central Siberia37.5–49.5Ivanova et al., 2022 [40]
Dark coniferous forests
(Pinus sibirica, Picea obovata,
Abies sibirica)		Southern taiga of Central Siberia5.0–7.5Vedrova, 2012 [36]
Central Siberia9.0–14.0Pleshikov, 2002 [33]
Deciduous forests
(Betula spp.,
Populus tremula)		Central Siberia5.0–6.0Pleshikov, 2002 [33]
Tundra and
cedar elfin wood (Pinus pumila)		Tundra of Western Siberia15.0Gerber, 2021 [39]
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Ponomarev, E.I.; Zabrodin, A.N.; Shvetsov, E.G.; Ponomareva, T.V. Wildfire Intensity and Fire Emissions in Siberia. Fire 2023, 6, 246. https://doi.org/10.3390/fire6070246

AMA Style

Ponomarev EI, Zabrodin AN, Shvetsov EG, Ponomareva TV. Wildfire Intensity and Fire Emissions in Siberia. Fire. 2023; 6(7):246. https://doi.org/10.3390/fire6070246

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Ponomarev, Evgenii I., Andrey N. Zabrodin, Eugene G. Shvetsov, and Tatiana V. Ponomareva. 2023. "Wildfire Intensity and Fire Emissions in Siberia" Fire 6, no. 7: 246. https://doi.org/10.3390/fire6070246

APA Style

Ponomarev, E. I., Zabrodin, A. N., Shvetsov, E. G., & Ponomareva, T. V. (2023). Wildfire Intensity and Fire Emissions in Siberia. Fire, 6(7), 246. https://doi.org/10.3390/fire6070246

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