This document provides an overview of the history and evolution of fire modeling. It begins with a basic conceptual model proposed by Byram in 1959 to describe fireline intensity. It then discusses the development of empirical behavior models and semi-mechanistic models. The document outlines several influential spatial fire emission models and notes how later models incorporated remote sensing data and continuous fuel fields. It concludes by explaining how savanna fires differ from forest fires in being more frequent, lower intensity, and influenced by mixed grass-tree fuels, grazing, and human ignition sources rather than solely fuel availability.

1. The History of Fire Modelling
with comments on
How Savannas are Different
Bob Scholes
CSIR Environmentek
Bscholes@csir.co.za

2. A taxonomy of fire models
S p a t ia l p a t t e rn
R a te o f s p r e a d I n t e n s ity
F ir e b e h a v io r
C a t e g o r ic a l C o n t in u o u s
F ir e e m is s io n s F ir e I m p a c t
W ild f ir e m o d e ls

3. 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
L
t2 t1
F, E
Byram, GM 1959 Combustion
of forest fuels. In Davis, KP
(ed) Forest fire control and
use. McGraw-Hill, NY 61-89

4. 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, Canberra
Trollope, WSW and Potgieter ALF 1985 Fire behavior in the Kruger National Park.
J Grassld Soc Sthn Af 3:148-52

5. 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

6. 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

7. Seiler & Crutzen emission
model
E = A*F*C*EF
E = emission of a gas or particle (g) [g x 105
]
A = area burned (m2
) [km2
]
~ Total area/mean fire return time
F = 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 between
The biosphere and the atmosphere from biomass burning Clim Change 2, 207-247
Crutzen, PJ and Andreae MO 1990 Biomass burning in the tropics:
impact on atmospheric chemistry and biogeochemical cycles.
Science 250: 1669-1678

8. Note on emission factors
Atmospheric chemists
(sampling plumes)
Molar emission ratio
∆X/∆CO2 (mol/mol)
Emission modellers
(controlled lab burns)
Hurst et al 1994 J Atm Chem 18,35-56
gX/kg burned fuel
gN-NOx/kg fuel
Ward: USFS
(Fire Atmosphere Sampling
System)
Ward & Radke In Fire in the
Environment. Wiley. 53-76
gX/kg fuel =
ƒ(C-CO2/(C-CO+C-CO2)

9. 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

10. 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 relation
Scholes 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 aerosol
Particles due to vegetation burning in southern hemisphere Africa.JGR 101:23677-23682

11. Classify or model
continuously?
 As nclasses⇒∞ or npoints⇒2 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

12. 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)

13. Burn completeness using
satellite data
Fires near Skukuza,
South Africa. The black
areas are scars from before
the assessment period. The
Shades of blue represent
degrees of completeness
as assessed using Landsat
and Modis
Landmann (in prep)

14. 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*ε *P/ET
 Fueltypei = f(NPP, tree cover fraction,decay rate)
 Context-sensitive completeness and
emission-factors
Eg Scholes (in prep)

15. Evolution of fire models
Combustion chemistry
and physics
~1900
Byram
1959
MacArthur
1966
Rothermal
1972
Fire spread
Seiler &Crutzen
1980
Hao
1990
Scholes
1996 Hybrid
Remote
sensing
Intensity, ROS
Cellular automatons
Ignition
Fuel types
Emission
factors
Burned area
Industrial
Emission
concepts

16. How are savannas different?
 Frequent, low intensity, surface fires
 Mixed tree and grass fuels
 Grazing and human habitation
 Fine fuel constrained, not ignition
constrained

17. 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

18. 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

19. ‘Bootstrapping’ completeness
0.00
0.20
0.40
0.60
0.80
1.00
0 2000 4000 6000 8000
Fireline intensity (kW/m)
Plot-scalecompleteness
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
Intensity
F(fuel,H2O)
Cplot = a(1-exp(-b(I-I0)))

20. 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)

21. 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