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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

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
sp read and i ntensi t y of fire spitfire
SPread 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
slide5

Fire Danger

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)
slide6

Fire Danger

Index

No. ignitions

Spread

Effects

Emissions

“Frame” for potential fires

  • Fuel availability (as simulated by LPJ)
  • Climate
slide7

Fire Danger

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
slide8

Fire Danger

Index

No. ignitions

Spread

Effects

Emissions

  • Human-caused ignitions per region:

- Intentional > negligence

slide9

Canada: LFDB

Siberia

Fire Danger

Index

No. ignitions

Northern

Australia

Spread

Effects

Emissions

+ small fires

+ grassland fires

b) Estimated for case study regions (grid cell)

slide10

Fire Danger

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

slide11

Fire Danger

Index

No. ignitions

Spread

Effects

Emissions

  • Human-dominated fire regimes (regional estimate) & constant wind speed
slide12

Fire Danger

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

slide13

Fire Danger

Index

No. ignitions

Spread

Effects

Emissions

  • Low intensities in savannahs
  • High intensities in forest ecosystems
slide14

Fire Danger

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
slide15

Fire Danger

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
slide16

Fire Danger

Index

No. ignitions

Spread

Effects

Emissions

  • Carbon release to atmosphere
    • Surface fire
    • Crown scorch
slide17

Fire Danger

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
  • 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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