The History of Fire Modelling

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How Savannas are Different

Bob ScholesCSIR EnvironmentekBscholes@csir.co.za

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

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

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

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

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

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

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)

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

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

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

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)

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)

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)

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

How are savannas different? Frequent, low intensity, surface

fires Mixed tree and grass fuels Grazing and human habitation Fine fuel constrained, not ignition

constrained

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

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

‘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)))

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)

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