3.2
COUPLED ATMOSPHERE-FIRE MODEL SIMULATIONS IN VARIOUS FUEL TYPES IN
COMPLEX TERRAIN
1
Janice L. Coen* 1, Terry L. Clark 1, and Don Latham 2
National Center for Atmospheric Research, Boulder, CO
2
USDA Forest Service, Missoula, MT
1. INTRODUCTION
Fire behavior is inextricably linked to the fuel type
and loading and topography, among many other factors.
These experiment show simulated fire behavior for a
setup that reduces such a situation to its simplest
elements. The experiment described here involves the
propagation of a simple fireline over a small hill and on
flat ground in two different fuel types in (steady)
constant environmental conditions.
NCAR's wildfire simulation model is an important
component of our studies to understand the dynamics
of wildfires. This atmospheric prediction model, coupled
to a wildfire model, has been developed to represent the
complex interactions between the fire and local winds.
In previous studies,
(Clark et al. 1996a,b) it
demonstrated the dynamic causes of the bowing of fire
lines, the formation of vortices within the fireline, and
sudden outbursts of small fingers of flame from the
fireline.
Observations, laboratory experiments (Weiss and
Biging, 1996), and computer models (Linn, 1997; Coen
et al 1998) show that when fire spreads on sloped
terrain, it generally spreads faster uphill, particularly on
steeper slopes. This is due to a combination of dynamic
effects, including the fire's convection increasing
momentum and wind speeds near the ground, the
dynamics of flow over hills that leads to accelerations
and decelerations in wind velocities, even without a fire,
and (although this effect is not captured in these
simulations) the fuel effectively being brought closer to
the fire, preheating and drying the fuel ahead of the fire
(Rothermel, 1972).
Fuel characteristics contribute to fire behavior not
only by the total amount of heat that is released (related
to the mass burned) but the rate at which they carry the
flaming front and the rate at which, once ignited, the fire
consumes the fuel.
We first describe the current status of the wildfire
simulation model, give an overall view of the features of
the modeling results, and discuss implications for fire
behavior raised by the simulation.
2. NUMERICAL MODEL
NCAR's coupled atmosphere-fire model is
described in detail in Clark et al. (1996a,b). A threedimensional, nonhydrostatic atmospheric prediction
model (Clark, 1977, 1979; Clark and Hall, 1991; Clark
and Hall, 1996) has been coupled with an empirical fire
spread model such that sensible and latent heat fluxes
* Corresponding author address: Janice L. Coen,
NCAR. P. O. Box 3000, Boulder, CO 80307; email:
janicec@ncar.ucar.edu; phone: 303 497-8986; fax: 303
497-8181
from the fire feed back to the atmosphere to produce fire
winds, while the atmospheric winds drive the fire
propagation. This wildfire simulation model can thus
represent the complex interactions between a fire and
local winds.
The meteorological model is a three-dimensional
non-hydrostatic numerical model based on the NavierStokes momentum, thermodynamic, and conservation
of mass equations using the anelastic approximation.
Vertically-stretched terrain-following coordinates allow
us to simulate in detail the airflow over mountainous
topography. It can ingest a changing mesoscale
atmospheric environment. Its two-way interactive nested
grids capture the outer forcing domain scale of the
environmental mesoscale winds while allowing us to
telescope down to the meter-sized fine dynamic scales
of vortices in the fireline through horizontal and vertical
grid refinement. Cloud physics are approximated using
a warm rain parameterization. The ice phys ics of the
model are turned off.
Since Clark et al. (1996a,b) the fire code has
improved considerably. Local fire spread rates depend
on the modeled wind components through an
application of the BEHAVE fire spread rate formula
(Rothermel, 1972). A BURNUP-type algorithm (Albini,
1994) characterizes how the fire consumes fuels of
different sizes over time. Four tracers, assigned to each
fuel cell, identify burning areas of fuel cells and define
the fire front. A local contour advection scheme avoids
any ghosting effects (Richards, 1994). The fire model
has a simple formulation for canopy drying and ignition
and a simple radiation treatment for distributing the
sensible and latent heat in the atmosphere.
There are conceptual problems with how to apply a
spread rate formula such as BEHAVE in a coupled
atmospheric model, for which it was not designed. In
principle, one wishes to choose a representative wind
(the component normal to the fireline) that is driving the
fire. Such spread rate formulas, developed in laboratory
wind tunnels containing burning fuel, require input of the
local horizontal wind (the "mean wind" or "midflame"
wind speed) that (for practical reasons) could be
measured in the field as one of several key inputs;
usually 0.2 times the free stream wind speed, or 0.2
times the 10 m wind is used. In the numerical model,
atmospheric winds are located at fixed points on a
three-dimensional grid, and must be interpolated to the
point where the spread rate is required. However, more
fundamentally, it is not practical to identify a background
wind, since the fire dramatically alters the winds in its
environment. And, since we are trying to capture the
interaction of the fire with the winds, a wind closer to the
fire should be more representative of the winds driving
the fire. It is possible to refine the grid to a point and
interpolate to identify the wind speed normal to the
Dry fuel load
Tall grass
0.674 kg m -2
Chaparral
3.584 kg m -2 (16 ton/acre)
Moisture content
Fuel depth
Surface area to volume ratio (SAVR)
Oven dry fuel density
7.0 %
0.762 m
4921 m -1 (1500 ft-1)
32 lb ft-3
20.0 %
2.0 m
(not used)
(not used)
Fuel particle effective mineral content (SE)
Fuel particle total mineral content (ST)
Weighting parameter for mass consumption
0.010
0.0555
7.
(not used)
(not used)
180.
Table 1. Fuel characteristics used in these experiments.
fireline very close to the line itself, however, since the
fireline is a point of convergence of winds from ahead of
and behind the fire, the horizontal wind is effectively
zero. Thus, we have chosen to proceed by allowing the
model user to select a distance behind the fireline
(along a line normal to the local fireline front) (we
choose 5 m in these calculations) at a specifiable height
(we choose the fuel height) at which wind speeds for
use in the spread rate calculation will be taken.
Two types of fuel were used in these experiments,
tall grass and chaparral. Some fuel characteristics
distinguishing each are contained in Table 1. The
characteristics for some chaparral fuel parameters are
not used because the following formula was used to
calculate the fire spread rate:
Sf = 1,294 * U 1.41
(1)
which is capped at a maximum spread rate of 6 m/s.
When the component of wind normal to the fireline is 0,
the backing rate of spread (Sfo) for chaparral is specified
as 0.033 m/s. (In other fuel types, Sfo is calculated using
BEHAVE as the spread rate for no wind on flat ground.
Using the parameterized spread rate, the rate at
which fuel is consumed once ignited is described using
a mass loss parameterization, where the mass
remaining as a function of time was assumed to
decrease exponentially, an approximation to the general
curve produced by the BURNUP algorithm, according to
the formula:
1-F = exp (-t/W)
(2)
where F is the fraction of fuel that has been burned, t is
time since ignition, and W is a weighting factor
determining how fast the fuel mass is consumed. W is
currently selected to best fit the analogous BURNUP
mass loss curve. The mass loss curves for the two fuel
types used here are shown in Figure 1.
The propagation of the fire line through a fuel cell
means that points within the cell will have been burning
different lengths of time. To determine the fractional
mass loss over a time step, we estimate the time history
of the area burned in the fuel cell and integrate to
calculate the currently remaining fuel mass.
3. EXPERIMENT
These idealized experiments examine a fireline
propagating up the relatively sharp slope of a small
Gaussian hill (height 200 m, half-width 300 m). The hill
extends north-south over the entire modeling domain.
We initialized a 409 m fireline to the west of this hill.
The ambient flow had constant 3 m/s wind from the
west (left). The atmospheric temperature structure was
stable such that the potential temperature lapse rate
was 10 deg per km.
The simulations use 3 nested domains. The
domains, from outer to inner, are 8.4 x 8.4 km, (with 120
m grid spacing), 3.36 x 3.36 km, with (40 m grid
spacing), and 2.8 x 2.8 km (20 m). There is 2:1 vertical
grid refinement between each domain.
The heat from both fuels is deposited into the
atmospheric over a 50 m extinction depth
Four experiments are compared (Table 2).
4. RESULTS
Figure 2 shows the fire perimeter for experiment
GRHL at 8 times. What appear to be odd variations in
Figure 1. Mass loss curve for (a) tall grass (W=7), and (b)
chaparral (W=180.).
Grass
Flat ground
GRFT
Hill
GRHL
Table 2. List of experiments.
Chaparral
CHFT
CHHL
Figure 2. Fireline as fire crosses north -south oriented hill (topography contours every 50 m) at 8 times.
the shape are responses to the fire's effects on its local
winds. Although all simulations will be presented in
more detail at the meeting, several items of interest
arose:
•
Well-known regions of the fires are apparent
and behave as known. The head of the fire
propagates quickly generally in the direction of
the environmental wind. The flanks of the fire
propagate at an intermediate speed growing
a
b
e
f
•
c
g
until the winds created by the fire blow parallel
to the line. The slowest spreading part is the
back, which creeps against the wind.
Depending on the rate of heat release in the
interior of the fire and whether external winds
displace the location of surface convergence
under the convective updraft over the fire or
ahead of it, the fires experience a range of
behavior from "plume" to "wind-driven".
d
h
Figure 3. Buoyancy (contours every 2 degrees) at 9 times (61 - 69 minutes at 1 minute intervals) in
experiment CHHL. The fire area is approximately 0.6 km north to south and east to west at this point. The
solid lines are topography contours at 50 m intervals; the thickest line at the right of each image is the hill
crest.
•
Fire behavior is very sensitive to local changes
in the low-level winds that the fire itself
produced. For example, the winds along a
steadily propagating flank are parallel to the
fireline at low levels, not contributing to
outward spread. A perturbation of outward
growth near the back end of a flank (whatever
its source) creates a locally stronger
convective updraft that causes some surface
winds to be directed outwards across the fire,
causing a local enhanced burning feature,
directing the winds nearby to be directed
outward, propagating this feature forward to
the head of the fire (as a vortex moving along
the edge) where it bursts forward (Figure 3).
This may be a mechanism of forward bursts
observed in crown fires on slopes (Radke et
al, 2000).
•
The hill (even a symmetric north-south ridge)
acts to funnel the convective updraft toward
the center of the fire's head, where the
convergence creates strong surface winds at
the updraft base that spread the fire faster,
increasing the fuel that is burned there,
increasing the updraft strength, etc., leading to
a narrow, rapidly spreading fire head.
As pointed out by Albini (1992) "It is a remarkable
fact that the general shape is the same for a savanna
fire, a shrub fire, or a timber crown fire". Although not all
the complexity of a fire has been represented in the
model, we can see the fundamental shape
characteristics arise in even simple experiments and are
a direct consequence of fire/atmosphere interactions.
6. Acknowledgements
This work was supported in part by funds provided
by the Rocky Mountain Research Station, Forest
Service, U.S. Department of Agriculture.
7. REFERENCES
Albini, F. A., 1992: Dynamics and modeling of vegetation fires:
observations, pp. 39-52 in Crutzen and Goldhammer
(eds.) Fire in the Environment (New York: Wiley).
Albini, F. A., 1994: PROGRAM BURNUP: A simulation model
of the burning of large woody natural fuels. Final Rept.
On Research Grant INT-92754-GR by U.S.F.S. to
Montana State Univ., Mechanical Engineering Dept.
Clark, T. L., 1977: A small-scale numerical model using a
terrain following coordinate transformation. J. Comput.
Phys., 24, 186-215.
Clark, T. L., 1979: Numerical simulations with a threedimensional cloud model: lateral boundary condition
experiments and multi-cellular severe storm simulations.
J. Atmos. Sci., 36, 2191-2215.
Clark, T. L. and W. D. Hall, 1991: Multi-domain simulations of
the time dependent Navier Stokes equation: Benchmark
Error analyses of nesting procedures. J. Comp. Phys., 92,
456-481.
Clark, T. L. and W. D. Hall, 1996: On the design of smooth,
conservative vertical grids for interactive grid nesting with
stretching. J.. Appl. Meteor., 35:1040-1046.
Clark, T. L., M. A. Jenkins, J. Coen and David Packham,
1996a: A Coupled Atmospheric-Fire Model: Convective
Feedback on Fire Line Dynamics. J. Appl. Meteor. 35,
875-901.
Clark, T. L., M. A. Jenkins, J. Coen and David Packham,
1996b: A Coupled Atmospheric-Fire Model: Convective
Froude number and Dynamic Fingering. Intl. Journal of
Wildland Fire. 6:177-190.
Coen, J. L., T. L. Clark, and W. D. Hall, 1998: Simulations of
the effect of terrain on fire behavior: Experiments using a
coupled atmosphere-fire model. Preprints 2nd Symposium
on Fire and Forest Meteorology, Jan. 11-16, Phoenix,
Arizona, Amer. Meteor. Soc., 87-90.
Linn, R. L., 1997: A transport model for prediction of wildfire
behavior. Los Alamos National Laboratory thesis LA13334-T (New Mexico State Univ.). 195 pp.
Radke, L. R., T. L. Clark, J. L. Coen, C. Walther, R. N.
Lockwood, P.J. Riggin, J. Brass, and R. Higgins, 2000:
The Wildfire Experiment (WiFE): Observations with
airborne remote sensors. Canadian J. Remote Sensing.
26:406-417.
Richards, G. D., 1994: The properties of elliptical wildfire
growth for time dependent fuel and meteorological
conditions. Combust. Sci. and Tech., 95, 357-383.
Rothermel, R. C., 1972: A mathematical model for predicting
fire spread in wildland fuels. USDA Forest Service
Research Paper INT-115. 40 pp.
Weise, D. R. and G. S. Biging, 1997: A qualitative comparison
of fire spread models incorporating wind and slope effects.
Forest Science 43:170-180.