1 / 18

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

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

lucio
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Simulating global fire regimes & biomass burning with vegetation-fire models Kirsten Thonicke1, Allan Spessa2 & I. Colin Prentice1 1 2

  2. 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

  3. Vegetation-fire model:Our approach

  4. 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

  5. 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)

  6. Fire Danger Index No. ignitions Spread Effects Emissions “Frame” for potential fires • Fuel availability (as simulated by LPJ) • Climate

  7. 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

  8. Fire Danger Index No. ignitions Spread Effects Emissions • Human-caused ignitions per region: - Intentional > negligence

  9. 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)

  10. 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

  11. Fire Danger Index No. ignitions Spread Effects Emissions • Human-dominated fire regimes (regional estimate) & constant wind speed

  12. 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

  13. Fire Danger Index No. ignitions Spread Effects Emissions • Low intensities in savannahs • High intensities in forest ecosystems

  14. 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

  15. 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

  16. Fire Danger Index No. ignitions Spread Effects Emissions • Carbon release to atmosphere • Surface fire • Crown scorch

  17. 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

  18. 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

More Related