simulating global fire regimes & biomass burning with vegetation-fire models kirsten thonicke 1,...
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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 modelsEx = 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
Vegetation-fire model:Our approach
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
• 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 Index
No. ignitions
Spread
Effects
Emissions
d
r
N
mmdifPdewd dTdTdTNNI
3maxmax
Litter moisture index = e(- *NI)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1000 2000 3000 4000 5000 6000 7000
Nesterov Index
1-hr fuel 10-hr fuel 100-hr fuel
(Nesterov 1949)
“Frame” for potential fires Fuel availability (as simulated by LPJ) Climate
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
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
a) Human-caused ignitions per region:- Intentional > negligence
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
b) Estimated for case study regions (grid cell)
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
Canada: LFDB
+ small fires+ grassland fires
Siberia
NorthernAustralia
• 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
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
Per PFT
Fuel class
• Human-dominated fire regimes (regional estimate) & constant wind speed
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
• Surface fire intensity
Isurface=H*ROS*(fuel consumed)
• Scorch height per PFT
• Crown scorch (CK) per PFT
SH of fire vs. tree height & crown length
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
3/2surface* IFSH
• Low intensities in savannahs• High intensities in forest ecosystems
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 l / critical time for cambial damage c
c = 2.9 * BT2 with BT- Bark thickness
– Biomass of killed trees to litter pool available for burning in the following year
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
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
• Carbon release to atmosphere– Surface fire
– Crown scorch
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
Fire Danger Index
No. ignitions
Spread
Effects
Emissions
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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