The History of Fire Modelling with comments on How Savannas are Different Bob Scholes CSIR...
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![Page 1: The History of Fire Modelling with comments on How Savannas are Different Bob Scholes CSIR Environmentek Bscholes@csir.co.za.](https://reader035.fdocuments.in/reader035/viewer/2022062314/56649e735503460f94b7379b/html5/thumbnails/1.jpg)
The History of Fire Modelling
with comments on
How Savannas are Different
Bob ScholesCSIR [email protected]
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A taxonomy of fire models
S p atia l p a tte rn
R a te o f sp re ad In ten s ity
F ire b eh a v io r
C a te go rica l C o ntin uo us
F ire e m iss io ns F ire Im p a ct
W ild fire m o d e ls
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Byram’s conceptual ‘model’
I = F*H*ROS
I=fireline intensity (kW/m/s)F=fuel load (kg/m2)H=energy content of fuel (~18 MJ/kg)ROS=rate of spread = L/(t2-t1) = m/s
Lt2 t1
F, E
Byram, GM 1959 Combustion of forest fuels. In Davis, KP (ed) Forest fire control and use. McGraw-Hill, NY 61-89
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Empirical behavior models Predict RoS and/or Intensity on the basis of
fuel load and meteorology McArthur 1966
CSIRO grass fire spread nomogram (humidity,temperature, degree curing, wind speed, slope)
Trollope and Potgieter (1985) (fuel load, humidity, wind speed, temperature)
Used for risk prediction and fire control
McArthur, AG 1966 Weather and grassland fire behavior. Dept Forests, CanberraTrollope, WSW and Potgieter ALF 1985 Fire behavior in the Kruger National Park. J Grassld Soc Sthn Af 3:148-52
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Intensity and flame length
Van Wilgen (1986) SAJBot 52,384-385
Hflame
•Simple way to estimate intensity in the field•Fuel available
•Easy way to predict tree mortality
if Htree<Hflame then die
I = 401 H 1.95
I~400 H2
or H ~ sqrt (I)/20
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Rothermel’s semi-mechanistic fire behavior model Concept of fuel components
Based on time to reach moisture equilibrium with the air
Eg ‘1 hour fuels’ such as fine dry grass, 10 hr twigs etc
Concept of ‘fuel packing’ Arrangement of fuels in 3-D space
Relatively complex models with many parameters
Basis of most modern fire behavior models and some fire risk prediction systems
Rothermel, RC 1972 A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service Res Pap INT 115
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Seiler & Crutzen emission model
E = A*F*C*EFE = emission of a gas or particle (g) [g x 105]A = area burned (m2) [km2] ~ Total area/mean fire return timeF = fuel load (g/m2) [t/ha]C = combustion completeness (gburned/gexposed)EF = emission factor (ggas/gfuel) [g/kg]
Seiler,W & Crutzen, PJ 1980 Estimates of gross and net fluxes of carbon betweenThe biosphere and the atmosphere from biomass burning Clim Change 2, 207-247Crutzen, PJ and Andreae MO 1990 Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science 250: 1669-1678
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Note on emission factors
Atmospheric chemists(sampling plumes)
Molar emission ratioX/CO2 (mol/mol)
Emission modellers(controlled lab burns) Hurst et al 1994 J Atm Chem 18,35-56
gX/kg burned fuelgN-NOx/kg fuel
Ward: USFS(Fire Atmosphere Sampling System)Ward & Radke In Fire in the Environment. Wiley. 53-76
gX/kg fuel =(C-CO2/(C-CO+C-CO2)
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Hao et al: spatial application
5 x 5 grid over tropical Africa, Asia and America
Applied Seiler & Crutzen formula to each
Assumed one fuel load for all savannas (~5 t/ha) and
A high burned area fraction (~0.8)
Hao WM, Liu MH & Crutzen PJ 1990 In Goldammer, PJ Fire in the Tropical Biota. Springer. Ecological Studies 84,440-462
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Scholes et al: continuous fields 0.5 x 0.5 degree grid over southern
Africa Crutzen formula, but fuel loads
modelled from climate, vegetation and herbivory fields 5 fuel categories: green & dry grass, litter,
twigs, wood Burned area from calibrated AVHRR Emission factors from Ward relationScholes RJ Kendall, J and Justice CO 1996 The quantity of biomass burned in
southern Africa. JGR 101:23667-23676.Scholes, RJ, Ward, DE and Justice, CO 1996 Emissions of trace gases and aerosolParticles due to vegetation burning in southern hemisphere Africa.JGR 101:23677-23682
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Classify or model continuously?
As nclasses or npoints2 the approaches become equivalent
Classification reduces computational effort without loss of accuracy if variation within the class is less than variation between
Known relationships between model inputs and some spatially continuous field (eg climate, RS), even if weak help to reduce uncertainty relative to using a class mean
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Remote sensing approaches Long history of fire detection and
mapping from satellite (eg Setzer AW & MC Periera 1991 Ambio 20, 19-22)
True burned area needs high resolution, frequent overpass
Emission modeling using RS inputs (eg Kaufman et al 1990 JGR 95, 9927-9939)
Fuel load estimation (eg Tomppa et al 2002 RS&Env)
Fire completeness (Landmann in prep)
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Burn completeness using satellite data
Fires near Skukuza, South Africa. The black areas are scars from before the assessment period. TheShades of blue representdegrees of completenessas assessed using Landsatand Modis
Landmann (in prep)
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Hybrid approaches Use remote sensing for burned area and to
constrain plant production Use FPAR, climate, soil and vegetation-
driven models to generate fuel load NPP = FPAR* *P/ET Fueltypei = f(NPP, tree cover fraction,decay rate)
Context-sensitive completeness and emission-factors
Eg Scholes (in prep)
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Evolution of fire models
Combustion chemistry and physics
~1900
Byram1959
MacArthur1966
Rothermal1972
Fire spread
Seiler &Crutzen1980
Hao1990
Scholes1996 Hybrid
Remotesensing
Intensity, ROS
Cellular automatons
Ignition
Fuel types
Emissionfactors
Burned area
Industrial Emissionconcepts
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How are savannas different? Frequent, low intensity, surface
fires Mixed tree and grass fuels Grazing and human habitation Fine fuel constrained, not ignition
constrained
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Frequent, low intensity fires Not ‘stand transforming’
Rapid regrowth of fuels means High observation frequency needed for fire scar
detection Fuel load dynamic at sub-annual scale
At regional scale, annual fraction of area burned is much less variable than in forests
~2 fold variation vs 10 fold
Fuelavailable<<aboveground phytomass+necromass
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Mixed tree and grass fuels Need 5 or more fuel categories to capture
fuel dynamics and combustion characteristics Dead grass, live grass, tree leaf litter, twig litter,
large downed woody, [live tree leaf, dung] Fuel composition varies spatially and
temporally f(tree cover, day of year, time since last fire)
Strong influence on emission factors and combustion completeness
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‘Bootstrapping’ completeness
0.00
0.20
0.40
0.60
0.80
1.00
0 2000 4000 6000 8000
Fireline intensity (kW/m)
Plo
t-scale
co
mp
lete
ness
Grass
Litter
Twigs
Wood
a b Io
Grass 1 .004 100
Litter .95 .002 100
Twig .55 .001 300
Wood .8 .0002 900
Grass
Litter
Twig
Wood
IntensityF(fuel,H2O)
Cplot = a(1-exp(-b(I-I0)))
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Grazing and human use Carbon has alternate fates, each
with emission consequences Burned in wildfire; or in domestic fire;
or eaten by herbivore or termite; or accumulates on land or in structures
In fertile savannas, up to 80% of aboveground NPP is grazed (10-20% is more typical)
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Fuel vs ignition constrained In Africa, humans have been the
dominant ignition agent for ~1 million years, in Australia for ~50 000 yr and in America for ~15 000 yr Lightning only ~10% of ignitions
Burned area and emissions go down in fire seasons following a drought growing season, not up as in forests