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11 - Natural Sources

11.B - Forest Fires

Short Description

In Germany’s forests prescribed burning is not applied. Therefore, all forest fires are categorized as wildfires (include emissions from forest fires occurring naturally or caused by humans). - Note that emissions reported here are not accounted for the national totals.

Method AD EF Key Category
CS, T2, T1 CS D not included in key category analysis

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Legend T = key source by Trend / L = key source by Level


Methods D: Default RA: Reference Approach T1: Tier 1 / Simple Methodology * T2: Tier 2* T3: Tier 3 / Detailed Methodology * C: CORINAIR CS: Country Specific M: Model as described in the EMEP/CORINAIR Emission Inventory Guidebook - 2019, in the group specific chapters.


AD:- Data Source for Activity Data NS: National Statistics RS: Regional Statistics IS: International Statistics PS: Plant Specific data AS: Associations, business organisations Q: specific questionnaires, surveys


EF - Emission Factors D: Default (EMEP Guidebook) C: Confidential CS: Country Specific PS: Plant Specific data


Methodology

For calculating the emissions of wildfires a country specific Tier2 approach was used. The mass of carbon emitted M(C) was calculated using the adapted equation follows the methodology of Seiler and Crutzen (1980) 1).

M(C) = 0.45 * A * B * β

where:

0.45 = average fraction of carbon in fuel wood;

A = forest area burnt in [m²];

B = mean above-ground biomass of fuel material per unit area in [kg/m²];

β = burning efficiency (fraction burnt) of the above-ground biomass.

The data on forest areas burnt for the period 1990 to 2021 have been taken from the German forest fire statistic (BLE, 2022)2) managed by the Federal Agency for Agriculture and Food. The mean above-ground biomass of fuel material was determined from the pools above ground biomass, dead wood and litter. The mean above-ground biomass and dead wood biomass was derived for each year by linear extrapolation and interpolation between the

Biomass of Litter was derived for each year by linear interpolation between 1990 and 2006 and extrapolation from 2007 based on the both Forest soil inventories (BZE I Wald (1990)3) and BZE II Wald (2006)4)).

Pursuant to König (2007) 5), 80% of the forest fires in Germany are surface fires and 20% crown fires. In accordance to the IPCC Good Practice Guidance for LULUCF (2003) a burning efficiency of 0.15 was used for surface fires and an efficiency of 0.45 was used for crown fires.

The emissions for the pollutants were calculated by multiplying the mass of carbon with the respective emission factors from table 3-3 (EMEP/EEA, 2019)6).

For the calculation of particulate emissions (TSP, PM10 and PM2.5) the burnt biomass was multiplied with the respective emission factors from table 3-5 (EMEP/EEA, 2019). Those particulate emission factors have been estimated by averaging the emission factors from the US Environmental Protection Agency (USEPA, 1996) 7) methodology, since no better information is available. Those emission factors are assumed to be the same for all types of forest.

The Guidebook does not indicate whether EFs have considered the condensable component (with or without).

Activity data

The data on forest areas burnt for the period 1990 to 2021 are based on the German forest fire statistic (BLE, 2021) managed by the Federal Agency for Agriculture and Food.

Table 1: Area of forest burnt from 1990 until the latest reporting year, in [ha]

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
1,606 920 4,908 1,493 1,114 592 1,381 599 397 415 581
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
122 122 1,315 274 183 482 256 539 757 522 214
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
269 199 120 526 283 395 2,349 2,711 368 148

Emission factors

For the year 2021 the estimated emission factors from table 2 were applied.

Table 2: Emission factors applied for 2021

Pollutant EF2021
NOx 155.19
CO 5,535.19
NMVOC 488.86
SOx 37.25
NH3 41.9
TSP 879.42
PM10 569.04
PM2.5 465.58
BC 41.90

In addition, a large-scale fire, which occurred in September 2018, is reported under 11.B. A detailed description can be found in the NIR 2020 in Chapter 6.8.2.5 8), because a large amount of CO2 emissions were released.

The burned area of the drained moor, which is used as a military facility, covered 1,221 ha. This fire was extensively investigated and documented by the Federal Office for Infrastructure, Environmental Protection and Services of the German Armed Forces. The emissions are calculated according to IPCC GL (2006), chapter 2, form 2.27 9).

The product MB×Cf is set to 336 t dm ha-1 according to Table 2.6 and formula 2.7, 2013 IPCC Wetlands Supplement 10), i.e. it is assumed that the moor was completely drained during the fire.

For the calculation of CO emissions the EF according to Table 2.7, 2013 IPCC Wetlands Supplement 207 g (kg dm)-1, is taken into account. This results in 85 kt CO. For other emissions from land fires on drained organic soils no Tier-1 emission factors exist and are therefore not reported (NO).

Recalculations

Recalculations were made for the complete time series due to the methodology changes (the inclusion of the burning biomass of dead wood and litter, which has not been considered until now). No recalculation was made for the large-scale fire in 2018.

Table 3: Recalculation of air pollutant emisssions from 1990 until the latest reported year, in kg and %

Pollutant Submission Unit 1990 1995 2000 2005 2010 2015 2016 2017 2018 2019 2020
Black Carbon Sub 2023 kt 0,061 0,022 0,022 0,007 0,020 0,021 0,011 0,016 0,096 0,112 0,015
Black Carbon Sub 2022 kt 0,047 0,017 0,017 0,005 0,016 0,017 0,009 0,013 0,078 0,091 0,013
Black Carbon Difference kt 0,014 0,005 0,005 0,001 0,004 0,004 0,002 0,003 0,018 0,020 0,003
Black Carbon Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
CO Sub 2023 kt 8,043 2,949 2,879 0,911 2,641 2,771 1,506 2,114 12,684 14,762 2,019
CO Sub 2022 kt 6,165 2,284 2,253 0,722 2,125 2,257 1,228 1,724 10,357 12,067 1,652
CO Difference kt 1,878 0,665 0,627 0,189 0,515 0,515 0,278 0,390 2,327 2,695 0,367
CO Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
NH3 Sub 2023 kt 0,061 0,022 0,022 0,007 0,020 0,021 0,012 0,016 0,098 0,112 0,015
NH3 Sub 2022 kt 0,047 0,017 0,017 0,005 0,016 0,017 0,009 0,013 0,078 0,091 0,013
NH3 Difference kt 0,014 0,005 0,005 0,001 0,004 0,004 0,002 0,003 0,020 0,020 0,003
NH3 Difference % 30,0% 28,7% 27,7% 26,8% 27,2% 25,8% 25,5% 25,7% 25,5% 22,3% 22,2%
NMVOC Sub 2023 kt 0,710 0,260 0,254 0,080 0,233 0,245 0,133 0,187 1,120 1,304 0,178
NMVOC Sub 2022 kt 0,545 0,202 0,199 0,064 0,188 0,199 0,108 0,152 0,915 1,066 0,146
NMVOC Difference kt 0,166 0,059 0,055 0,017 0,046 0,045 0,025 0,034 0,205 0,238 0,032
NMVOC Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
NOx Sub 2023 kt 0,226 0,083 0,081 0,026 0,074 0,078 0,042 0,059 0,356 0,414 0,057
NOx Sub 2022 kt 0,173 0,064 0,063 0,020 0,060 0,063 0,034 0,048 0,290 0,338 0,046
NOx Difference kt 0,053 0,019 0,018 0,005 0,014 0,014 0,008 0,011 0,065 0,076 0,010
NOx Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
PM 10 Sub 2023 kt 0,827 0,303 0,296 0,094 0,271 0,285 0,155 0,217 1,304 1,518 0,208
PM 10 Sub 2022 kt 0,634 0,235 0,232 0,074 0,218 0,232 0,126 0,177 1,065 1,241 0,170
PM 10 Difference kt 0,193 0,068 0,064 0,019 0,053 0,053 0,029 0,040 0,239 0,277 0,038
PM 10 Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
PM 2.5 Sub 2023 kt 0,677 0,248 0,242 0,077 0,222 0,233 0,127 0,178 1,067 1,242 0,170
PM 2.5 Sub 2022 kt 0,519 0,192 0,189 0,061 0,179 0,190 0,103 0,145 0,871 1,015 0,139
PM 2.5 Difference kt 0,158 0,056 0,053 0,016 0,043 0,043 0,023 0,033 0,196 0,227 0,031
PM 2.5 Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
SO2 Sub 2023 kt 0,054 0,020 0,019 0,006 0,018 0,019 0,010 0,014 0,085 0,099 0,014
SO2 Sub 2022 kt 0,041 0,015 0,015 0,005 0,014 0,015 0,008 0,012 0,070 0,081 0,011
SO2 Difference kt 0,013 0,004 0,004 0,001 0,003 0,003 0,002 0,003 0,016 0,018 0,002
SO2 Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
TSP Sub 2023 kt 1,278 0,469 0,457 0,145 0,420 0,440 0,239 0,336 2,015 2,345 0,321
TSP Sub 2022 kt 0,980 0,363 0,358 0,115 0,338 0,359 0,195 0,274 1,646 1,917 0,262
TSP Difference kt 0,298 0,106 0,100 0,030 0,082 0,082 0,044 0,062 0,370 0,428 0,058
TSP Difference % 30,5% 29,1% 27,8% 26,3% 24,2% 22,8% 22,7% 22,6% 22,5% 22,3% 22,2%
1)
Seiler, Wolfgang, and Paul J. Crutzen. “Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning.” Climatic change 2.3 (1980): 207-247.
2)
BLE (Bundesanstalt für Landwirtschaft und Ernährung), (2022, 30. Juni), 2022: Waldbrandstatistik der Bundesrepublik Deutschland für das Jahr 2021, Bonn: 21 p. Retrieved July 2022, https://www.ble.de/DE/BZL/Daten-Berichte/Wald/wald_node.html
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WOLFF, B. & RIEK, W. (1997): Deutscher Waldbodenbericht 1996 - Ergebnisse der bundesweiten Bodenzustandserhebung in Wald (BZE) 1987 - 1993. Hrsg.: Bundesministerium für Ernährung, Landwirtschaft und Forsten, Bonn, Bd. 1 u. 2., 144 S.,https://www.bmel-statistik.de/fileadmin/daten/FHB-0320205-1996.pdf
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7)
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8)
NIR (2020): National Inventory Report 2020 for the German Greenhouse Gas Inventory 1990-2018. Available in April 2020
9)
IPCC (Intergovernmental Panel on Climate Change) (2006): 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and Other Land Use. Eds.: Eggleston S., Buendia L., Miwa K., Ngara T., Tanabe K. (Eds). IEA/OECD, IPCC National Greenhouse Gas Inventories Programme, Technical Support Unit, Hayama, Kanagawa, Japan. http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html
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IPCC (Intergovernmental Panel on Climate Change) (2014b): 2013 Supplement to the IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands. Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M. and Troxler, T.G.(eds). Published: IPCC, Switzerland http://www.ipcc-nggip.iges.or.jp/public/wetlands/index.html