Simulating global fire regimes biomass burning with vegetation fire models
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Simulating global fire regimes & biomass burning with vegetation-fire models. Kirsten Thonicke 1 , Allan Spessa 2 & I. Colin Prentice 1 1 2. to estimate global fire emissions: Wildfire emission models

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Simulating global fire regimes biomass burning with vegetation fire models

Simulating global fire regimes & biomass burning with vegetation-fire models

Kirsten Thonicke1, Allan Spessa2 &

I. Colin Prentice1

1 2


Challenges

to estimate global fire emissions: Wildfire emission models vegetation-fire models

Ex = Area burnt*Fuel load*Combustion Efficiency*EFx

to simulate vegetation - fire interactions: Mechanistic fire models in DGVMs

Vegetation dynamics & composition on fuel characteristics

Burning conditions (fire behaviour & intensity) determine biomass burnt, thus trace gas emissions

Actual vs. potential vegetation (Human impact)

  • Reduce uncertainties

    • Inventory & satellite data

      • Inter-annual variability

  • Different climate conditions

  • Burning conditions

    • Affected vegetation

Challenges


Vegetation fire model our approach
Vegetation-fire model: vegetation-fire modelsOur approach


Sp read and i ntensi t y of fire spitfire
SP vegetation-fire modelsread and IntensiTy of FIRE(SPITFIRE)

  • Embedded in Lund-Potsdam-Jena DGVM

    • litter carbon pool (leaves, sapwood, heartwood) reclassified into dead fuel classes (1, 10, 100, 1000-hr)

    • live grass (higher moisture content than dry fuel)  fire spread

    • Tree architecture  fire behaviour & post-fire mortality

    • Post-fire mortality  Vegetation composition & fuel availability

    • More fire processes = more PFT parameters  fuel characteristics & fire traits

  • Resolution:

    • 0.5° x 0.5° grid cell

    • Daily: fire processes

    • Monthly: calculating trace gas emissions

    • Annual: update of vegetation dynamics


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

(Nesterov 1949)

  • Distribution of precipitation according to no. wet days (Gerten et al. J.Hydr. 2004)

     daily estimation of fire danger

  • Fire danger index FDI = Probability that an ignition leads to a spreading fire

  • Litter moisture per fuel class = f(NI)


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

“Frame” for potential fires

  • Fuel availability (as simulated by LPJ)

  • Climate


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Expected number of fires

    E[nf]=E[Nig]*FDI with E[nig]=E[nl,ig]+E[nh,ig]

    • Lightning

    • Human-caused ignitions (after Venevsky et al. 2002)

      • Depending on human population density

      • Population growth 1950-2000: RIVM Database (NL)

      • Spatial: rural vs. urban lifestyle

      • Temporal: average no. ignitions per grid cell or region (intentional & negligence)

  • Minimum intensity to sustain a fire


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Human-caused ignitions per region:

    - Intentional > negligence


Canada: LFDB vegetation-fire models

Siberia

Fire Danger

Index

No. ignitions

Northern

Australia

Spread

Effects

Emissions

+ small fires

+ grassland fires

b) Estimated for case study regions (grid cell)


Fire Danger vegetation-fire models

Index

Fuel class

No. ignitions

Spread

Effects

Emissions

  • Conditions of an average fire

  • Fire spread after Rothermel

    • Potential fuel load

    • Fuel characteristics

      • Litter moisture

      • Surface-area-to-volume ratio

      • Fuel bulk density

    • Wind speed (NCEP re-analysis data)

  • Fuel consumption after rate of spread

    • Litter moisture

  • Assume elliptical fire shape

Per PFT


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Human-dominated fire regimes (regional estimate) & constant wind speed


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Surface fire intensity

    Isurface=H*ROS*S(fuel consumed)

  • Scorch height per PFT

  • Crown scorch (CK) per PFT

    SH of fire vs. tree height & crown length


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Low intensities in savannahs

  • High intensities in forest ecosystems


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Post-fire mortality Pm= Pm(CK) & Pm(cambial damage)

    • Mortality from crown scorch = r(CK)*CK3

    • Cambial damage = residence time of fire tl / critical time for cambial damage tc

    • tc = 2.9 * BT2 with BT- Bark thickness

    • Biomass of killed trees to litter pool  available for burning in the following year


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Carbon release to atmosphere

    • Surface fire

    • Crown scorch

  • Plant material from killed plants to respective dead fuel classes

  • Emission factor (Andreae & Merlet 2001, Andreae pers. comm. 2003)

    • CO2, CO, CH4, VOC, NOx, Total Particulate Matter


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Carbon release to atmosphere

    • Surface fire

    • Crown scorch


Fire Danger vegetation-fire models

Index

No. ignitions

Spread

Effects

Emissions

  • Emission factor (Andreae & Merlet 2001, Andreae pers. Comm. 2003)

    • CO2, CO, CH4, VOC, NOx, Total Particulate Matter


Next steps
Next steps vegetation-fire models

  • Evaluation of interannual variability & seasonality

  • Variability in area burnt, fire intensity in relation to biomass burning

  • Comparison of biomass burning estimates

    • Methods

    • Uncertainties


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