THERMAL DECOMPOSITION OF VEGETATIVE 

FUELS

Isaac T. Leventon Aand Morgan C. Bruns B

A National Institute of Standards and Technology, Fire Research Division,

100 Bureau Drive; Building 224, Room A265; Gaithersburg, MD, 20899; United States

B Virginia Military Institute, Department of Mechanical Engineering

710 Nichols Hall; Lexington, VA 24450; United States

ABSTRACT

This manuscript presents new measurement data from milligram-scale thermal decomposition 

experiments - thermogravimetric analysis (TGA) and microscale combustion calorimetry (MCC) –

conducted on stems and leaves of six plant species commonly found across the United States. For 

each fuel, measurement data from TGA experiments was analyzed to determine effective thermal 

decomposition mechanisms and the associated kinetics of their constituent reactions. MCC 

experiments were repeated under identical experimental conditions to determine the heats of complete 

combustion of all gaseous volatiles produced by these vegetative fuels and to validate the 

decomposition mechanisms and species char yields determined from TGA data. Through a coupled 

analysis of TGA and MCC measurement data, an estimate of the heats of combustion of the gaseous 

volatiles produced by individual reaction steps in the fuel’s decomposition was also made. Between 

different fuels, distinct differences were measured in the onset temperature of decomposition, the 

temperature range of decomposition, the number of apparent reactions, and the peak measured mass 

loss and heat release rates (as well as the temperatures at which they occur). To analyze the impact of 

these variations on predictions of wildfire behavior, a modeling study was then conducted in which 

simulations of wildland fire experiments were repeated using the thermal decomposition mechanisms 

and heats of combustion determined for six of the fuel species tested in this work. Model-predicted 

fire spread rate in these simulations varied between 0.50 m s-1and 1.09 m s-1.

INTRODUCTION

With a growing number of people moving to areas in or near fire prone wildlands 1and an 

increase in the number of large fires and total acres burned each year 2, wildland fires are an 

increasingly dangerous and costly problem. Accurate predictive modeling of current (or potential) 

uncontrolled wildland fires (i.e., quantitative prediction of fire intensity and spread rate) can mitigate 

the risk that these fires pose. Common models of wildland fire spread (e.g., BehavePlus 3and 

FARSITE 4) are relatively easy to use and can quickly provide fire spread predictions and 

deterministic assessments of fire hazard; however, they are based upon empirical relations (e.g., 

Rothermel 5) defining a constant rate of spread for given conditions of slope, weather, wind, moisture, 

and a user-selected fuel model. These fuel models define representative physical parameters (surface 

area to volume ratio, particle size, and fuel bed depth), moisture content, and heat content (prescribed 

as 18.6 kJ g-1for all but one, of 40 available, fuel models) 6. Such empirically based models of flame 

spread have valuable applications (e.g., they are used for operational predictions of flame spread rate)

but they do not incorporate the underlying processes controlling wildland fire spread behavior 7. 

Thus, they are unable to predict transient fire behaviors and they may not be able to provide accurate 

predictions of fire behavior under changing ambient conditions or when a mixture of fuel sources 

(vegetative and structural) is present (e.g., at the wildland urban interface, WUI).

More powerful physics-based models (e.g., FDS 8) can better capture the controlling mechanisms of 

wildland fires by solving governing equations for buoyant flow, heat and mass transfer, gas phase 
combustion, and condensed phase thermal decomposition of fuels. These more capable simulation 

tools may be particularly valuable at the WUI, where simulation of the burning behavior of vegetative 

and structural fuels could be used to better inform structural and community wildfire resilience. Such 

models require a large number of inputs (e.g., fuel heat of combustion and radiative fraction, 

thermophysical properties of the vegetation and soil, and ambient conditions) to provide accurate 

predictions of wildland fire behavior. It has been shown that model predictions of the rate of spread of 

wildland fires are particularly sensitive to windspeed and the thermal decomposition temperature of 

the burning vegetative fuel 9. Unfortunately, despite this sensitivity, comprehensive measurements of 

thermal decomposition are not readily available for a variety of common vegetative fuels and the fuel 

properties that are available from such experiments (i.e., the relevant data needed to parameterize 

physics-based models of wildfire spread) can be subject to large uncertainties 10.

Philpot conducted an early study on the pyrolysis of plant materials, using thermogravimetric analysis 

(TGA) and differential thermal analysis (DTA), to examine the relationship between a plant’s mineral 

content and pyrolysis behavior (rate, onset temperature, and residue yields) 11. Shafizadeh presented a 

thorough review of the pyrolysis of biomass, that described how the composition of its major 

components (cellulose, hemicellulose, and lignin) impacts its thermal properties and available 

decomposition pathways and that quantified the species yields of decomposition reactions how these 

vary with pyrolysis temperature 12. Through the 1980s, Sussot led a series of studies on a broad range 

of wildland fuels, identifying the temperature range of decomposition for various plant components, 

the total heat needed for their pyrolysis, and the total energy released by combustion of these gaseous 

pyrolyzates (using an experimental apparatus that was a precursor to modern microscale combustion 

calorimetry) 13-16. TGA experiments were also performed on multiple Mediterranean plant species to 

provide a ranking of their ‘potential combustibility’ 17 and other authors have thoroughly studied 

individual fuels (and their gaseous pyrolyzates) using multiple analytical methods 18. Most recently, 

Amini and Safdari have characterized the char, tar, and gaseous species yields, and their respective 

chemical compositions, of the pyrolysis products of (live and dead samples of) fifteen species of 

vegetation native to the Southern United States 19, 20.

This manuscript presents new measurements from milligram-scale thermal decomposition experiments -

thermogravimetric analysis (TGA) and microscale combustion calorimetry (MCC) – conducted on 

stems and leaves of six plant species commonly found in the United States. These fuels were 

collected in the summer of 2017 by the US Forest Service in Missoula, Montana (Lodgepole Pine, 

and Douglas-Fir) and in the North Mountain experimental area in Southern California (Chamise, 

Bigberry Manzanita, Desert Ceanothus, and Chaparral Whitethorn). All tests were conducted in 

nitrogen (i.e., in an anaerobic environment). Although the rate of degradation of these fuels may be 

affected by oxidation, it has been noted 21 that thin vegetative fuels will not ignite by pure radiative 

heating thus convective heating and flame ‘bathing’ is critical. Such direct flame impingement 

presents a ‘fuel rich’ (and thus a largely anaerobic) environment at the fuel’s surface as it pyrolyzes. 

For each fuel, sample mass and mass loss rate measured in TGA experiments were analyzed to 

determine effective thermal decomposition mechanisms and associated kinetics of these reactions. 

MCC experiments were repeated under the same experimental conditions to determine the heats of 

complete combustion of all gaseous pyrolyzates and species char yields. Additionally, by a coupled 

analysis of TGA and MCC measurement data, an estimate of the heats of combustion of the gaseous 

volatiles produced by individual reaction steps was made. To analyze the impact of variations in the 

degradation behavior of these fuels, a study was then conducted in which simulations of grassland fire 

experiments 8,9 were repeated using the unique thermal decomposition mechanisms and heats of 

combustion determined in this work. The sensitivity of model-predicted development of fire spread 

rate to these variations is discussed.

MATERIALS AND METHODS

Materials

The vegetative fuels studied in this work were obtained between May and July of 2017 from 

the United States Forest Service Pacific Southwest and Rocky Mountain Research Stations (which are 

located in Southern California and Western Montana, respectively). Species locations of origin, 
scientific names, and common names are provided in Table 1. The selected species represent 

vegetation types commonly found in the regions in which they were picked. For each species, a bulk 

sample – consisting of small branches with leaves attached – was picked from a series of randomly 

selected individual plants. Milligram-scale experiments conducted in this work were performed on 

both leaves and stems of all six plant species (except for Douglas-fir) thus creating a test matrix of 

eleven unique fuel species. Thermal analysis experiments were conducted on Douglas-fir leaves only.

Table 1. Vegetative fuels tested in this study

Origin Scientific Name Common Name

Pacific Southwest 

Research Station

(North Mountain

Experimental Area, 

California)

Adenostoma Fasciculatum Chamise

Arctostaphylos Glauca Bigberry Manzanita

Ceanothus Greggii Desert Ceanothus

Ceanothus Leucodermis Chaparral Whitethorn

Rocky Mountain 

Research Station

(Missoula, Montana)

Pinus Contorta Lodgepole Pine

Pseudotsuga Menziesii Douglas-Fir

A preliminary series of TGA experiments was conducted on samples in one of two states: fresh 

(tested within 1-2 weeks after being picked) and after being microwaved three times (60 s each), 

sealed in plastic sample bags, and stored in a refrigerator. This treatment ensured the stability of 

samples (i.e., prevented decay, degradation, and/or molding) without affecting the chemical or 

physical structure of the foliage. Differences in measured sample mass and mass loss rate (MLR) of 

fresh and microwaved samples during these TGA experiments were negligible, thus all experimental 

measurements presented in this work were performed on samples that had been microwaved.

After this treatment, samples were cut into thin (< 0.75 mm thick) flat sections, less than 5 mm in 

length and between 4.5 mg – 6.5 mg in mass. For each test, leaves were kept whole/intact and stems 

were cut through their middle to create a flat surface that allowed for good thermal contact during 

experiments. All samples were stored in a desiccator (in the presence of Drierite) for a minimum of 48 

hours prior to testing. Immediately before testing, samples were removed from the desiccator, pressed 

flat into the base of alumina test crucibles, and weighed using a Mettler M3 analytical balance.

Thermal Analysis Experiments

Thermogravimetric Analysis (TGA) experiments were conducted in a Netzsch STA 449 F1 Jupiter. 

This apparatus continuously measures mass (using a microbalance with a 0.025 μg precision) and 

temperature (using an S-type thermocouple positioned directly beneath the sample crucible) of 

samples as they are heated through a well-defined temperature program in an anaerobic environment. 

A temperature calibration was conducted as per the manufacturer’s recommendations 22- using a set 

of 6 pure metals, with melting points between 156.6 °C and 961.8 °C - to provide a relation between 

measured and actual sample temperature. The calibration was performed using the same crucible type,

heating rate, and gaseous environment as was used during thermal analysis experiments on vegetative 

fuel samples. All TGA experiments were conducted within three months of this calibration. 

The temperature program used for TGA experiments included an initial heating at 10 °C min-1to 75 °C

followed by a 20-minute-long isotherm at that temperature, during which time the chamber was 

continuously purged with nitrogen. This conditioning period ensured that the system was completely 

free of oxygen and that any residual moisture in samples was removed prior to dynamic heating and 

thermal decomposition. Following this conditioning period, samples were heated at a constant rate of 

10 °C min-1to 700 °C (approximately 200 °C above the highest temperature at which a mass loss

event was observed). Throughout this program, the test chamber was continuously purged with ultrahigh purity (UHP) nitrogen at 50 mL min-1

to ensure thermal decomposition of samples occurred 

without oxidation. All tests were conducted in open alumina crucibles to allow gaseous pyrolyzates to 

escape unimpeded.

At the start of each day of testing, a baseline test was performed in which an empty alumina crucible 

was subjected to the same heating program as was used during thermal analysis experiments. This 
baseline history (mass vs. temperature) was subtracted from the corresponding data obtained during 

experiments on vegetative fuel samples; all TGA measurement data presented in this work has been 

baseline-corrected in this manner. For each test, measured sample mass, m, was normalized by initial 

sample mass, m0. Normalized sample mass loss rate d(m/m0

)

dt

[s-1] was calculated as the numerical 

derivative of time-resolved sample mass curves and, prior to further analysis, noise in mass loss rate

curves was reduced using a Savitzky-Golay filter. For each fuel species and sample type (stem and 

leaf), tests were repeated five times to accumulate necessary statistics; mass history curves from 

repeated experiments were averaged together prior to further analysis. 

The relatively low heating rate (10 °C min-1) used in these experiments was selected, in combination 

with the small sample masses used during testing, to ensure that samples did not experience 

significant temperature or composition gradients during heating 23,24. Further, it has been 

demonstrated that an inverse analysis of total mass and mass loss rate data measured in TGA 

experiments conducted under these conditions can be used to determine effective reaction 

mechanisms, and associated reaction kinetics, that accurately describe the thermal decomposition of 

combustible solids

25

. 

Effective reaction mechanisms for all fuels were calculated assuming that measured decomposition 

behavior could be captured by a series of parallel, first order, Arrhenius rate reactions of the form

dm

dt

= − ∑ (1 − νi

)miAi exp (

Ei

RT i ) [1]

where m is the total sample mass, miis the mass of component i, T is the sample temperature, R is 

the gas constant, and Ai and Ei are the kinetic parameters describing the reaction. Assuming a parallel 

reaction mechanism is reasonable in this case since it is likely that the vegetative fuels are composed 

of distinct components (cellulose, hemicellulose, and lignin) 12, 14. TGA data does not allow for the 

determination of either the stoichiometric coefficient, νi, or the initial amount of component i present 

in the material, m0,i. However, it is possible to determine the amount of mass lost as volatiles in each 

reaction from the TGA data. This quantity is simply Δmi ≡ m0,i

(1 − νi). The solid residue yield is 

related to the reaction mass losses through μ = 1 − ∑i Δmi. For each of the fuels considered, the 

kinetic parameters Ai and Ei along with the reaction mass losses, Δmi were determined using the 

algorithm developed in a recent work 26.

Microscale Combustion Calorimetry (MCC) experiments were conducted in an apparatus built in 

accordance with the relevant standard, ASTM D7309 27. In this test, specimens of known mass are 

thermally decomposed in an anaerobic environment at a constant heating rate. Gaseous volatiles 

released by pyrolyzing samples are mixed with an inert carrier gas and transported to a high 

temperature combustion chamber where they are forced to complete combustion in an oxygen rich 

environment. The heat released by combustion of these volatiles is computed from the rate of oxygen 

consumption in the gas stream exiting the combustion furnace. Sample temperature is continuously 

monitored during tests using a K-type thermocouple positioned directly beneath the sample crucible.

A temperature calibration was conducted as per best practices 27, 28 to provide a relation between 

measured and actual sample temperature. The calibration was performed using the same crucible type, 

heating rate, and gaseous environment as was used during thermal analysis experiments on vegetative 

fuel samples. All MCC tests were conducted within three months of this calibration. At the start of 

each day of testing, the MCC oxygen sensor was calibrated using a prepared gas mixture (19.19 % 

oxygen in nitrogen) and the total system calibration was checked using a reference material, 

polystyrene (Styron 665 GP). MCC experiments were conducted in accordance with ASTM D7309 27

using 4.5 mg to 6.5 mg material samples that were pyrolyzed in UHP nitrogen. Samples were placed 

into open alumina crucibles, introduced into the pyrolysis chamber, and allowed to reach equilibrium 

at a temperature of 75 °C, at which point the chamber temperature was increased to 700 °C at a 

constant heating rate of 10 °C min-1. Although the standard 27 recommends heating rates between 

12_°C min-1and 120 °C min-1, a heating rate of 10 °C min-1 was selected in this study to provide 

measurements of sample heat release rate under comparable conditions to those used in TGA 

experiments.
The heat of complete combustion of all gaseous pyrolyzates (ΔHc,total) released by the pyrolyzing 

sample was determined as the integral of heat release rate, HRR, measured throughout the duration of 

tests divided by final volatilized mass, mvol (i.e., initial sample mass minus the mass of char remaining 

after each test: mvol = m0 - mchar). Char yield, μchar, was calculated by dividing mchar by m0. Each fuel 

species was tested in the MCC at least three times to ensure reproducibility; ΔHc,total and μchar were 

calculated for each repeated experiment to accumulate necessary statistics. Values of ΔHc,total and μchar

reported in this manuscript represent average values of repeated measurements.

Heats of complete combustion of the gases species produced during each reaction step, ΔHc,i, were 

determined by comparing heat release rate measured in MCC experiments and mass loss rate 

predicted by the decomposition model (which was developed on the basis on TGA experiments). The 

model was used to simulate sample mass loss rate under the conditions matching TGA and MCC 

experiments and a predicted heat release rate curve was generated by scaling the instantaneous rate of 

gaseous volatile production attributed to each reaction step by its corresponding ΔHc,i. These heats of 

combustion were adjusted in an iterative process until acceptable agreement between model-predicted 

and experimentally-measured heat release rate was obtained.

Numerical Simulations of Wildfire Spread 

Computational Fluid Dynamics (CFD) simulations of wildfire spread were conducted in the NIST 

Fire Dynamics Simulator (FDS version 6.7.1) 29 to determine the sensitivity of flame spread rate 

predictions to measured variations in fuel decomposition behavior. Selected as a case study for this 

sensitivity analysis was a controlled burn of a 100 m by 100 m plot of kerosene grasslands conducted 

by the Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia between 

July and August of 1986 (Case C064) 30. Measured properties of this case are reported in Table 2.

Table 2. Measured properties of CSIRO Grassland Fire Case C064 30

Property Value

Wind Speed 4.6 m s-1

Ambient Temperature 32 °C

Surface Area to Volume Ratio 9770 m-1

Grass Height 0.21 m

Bulk Mass per Unit Area 0.283 kg m-2

Moisture Fraction 6.3%

The computational domain in this case is 120 m by 120 m by 20 m. This domain is subdivided into 36 

meshes, each with 0.5 m cubic grid cells. Increasing or decreasing grid size by a factor of 2 yields 

approximately a 5% deviation in model-predicted flame spread rate. In these simulations, Lagrangian 

particles are used to simulate blades of grass, which are modeled as slender cylinders whose diameters 

are inferred from the measured surface area to volume ratio. Each grid cell contains one simulated

blade of grass; by applying a weighting factor, each explicitly modeled blade of grass represents 

approximately 5000 actual blades, thus matching the experimentally measured bulk mass per unit 

area. Blades of grass are rigidly fixed, perpendicular to the wind and the source of thermal radiation. 

Further detail on model assumptions concerning heat transfer and drag around blades of grass is 

provided elsewhere 9.

Wildfire simulations were repeated using the reaction mechanisms and associated kinetics and heats 

of combustion determined for six of the vegetative fuels tested in this work. Two additional 

simulations were also defined using decomposition models that represent the degradation of a typical

leaf or stem; these models are referred to as ‘Average Leaf’ and ‘Average Stem’. These cases provide 

insight into whether a given vegetative fuel’s degradation mechanism can be estimated (based on 

existing knowledge) or if it must be uniquely measured to provide reasonable predictions of wildfire 

spread. Requisite parameters defining these decomposition models are reported, in further detail, in 

the ‘Results and Discussion’ section of this manuscript. All other relevant soil, vegetation, and 

combustion parameters used in these simulations are reported in Table 3; these values have been taken 

from a recent modeling study 9and are typical of wood or cellulosic fuels. Ignition was defined to 

match experimental conditions, as described in a recent work 9.
Table 3. Assumed Fuel and Soil Properties for Wildfire Simulations 9

Property Value

Fuel Properties

Chemical Composition C6H10O5

Radiative Fraction 0.35

Soot Yield 0.015

Specific Heat 1.5 kJ kg-1 K-1

Conductivity 0.1 W m-1 K-1

Density 512 kg m-3

Heat of Pyrolysis 418 kJ kg-1

Soil Properties

Soil Specific Heat 2.0 kg-1 K-1

Soil Conductivity 0.25 W m-1 K-1

Soil Density 1300 kg m-3

RESULTS AND DISCUSSION

Thermal Analysis Experiments

Figure 1 plots the results of TGA experiments conducted on stem and leaf samples of all plant 

species tested in this work. Solid black lines represent the mean of repeated experiments; the shaded 

area is calculated as one standard deviation. Also shown in Fig. 1, as red lines, are model-predictions 

of sample behavior during TGA experiments (dashed and dotted lines represent total and reactionstep-specific model-predicted residual mass loss rates, respectively). Optimized kinetic parameters for 

each of these reaction mechanisms are provided in Table 4. The details of this fitting process are 

provided in a related work 26. As seen in Fig. 1, all models capture experimentally measured mass loss 

rate data with reasonable accuracy. 

Table 4. Kinetic Parameters Describing Decomposition of Vegetative Fuels

Sample 

Name

A1

(s-1)

E1

(kJ kmol-1) Δm1

A2

(s-1)

E2

(kJ kmol

-1

) Δm2

A3

(s-1)

E3

(kJ kmol-1) Δm3

Leaves

Chamise 9.98×102 5.67×104 0.30 1.21×104 7.69×104 0.32 3.39×108 1.37×105 0.11

Bigberry 

Manzanita 2.12×103 5.91×104 0.34 3.64×109 1.39×105 0.12 1.07×103 7.23×104 0.27

Desert 

Ceanothus 2.22 3.32×104 0.64 9.99×1010 1.52×105 0.01 3.80×1014 2.14×105 0.02

Chaparral 

Whitethorn 9.85×103 6.68×104 0.17 1.15×105 8.68×104 0.24 1.53 4.36×104 0.21

Lodgepole 

Pine 2.38×105 8.99×104 0.38 2.85×108 1.12×105 0.11 6.39×101 5.67×104 0.23

Douglas-Fir 3.35×104 7.07×104 0.24 1.45×10

7 1.09×105 0.26 2.13×101 5.17×104 0.26

Stems

Chamise 9.56×106 1.08×105 0.38 3.40×1012 1.58×105 0.07 1.43×102 6.04×104 0.24

Bigberry 

Manzanita 4.85×105 7.41×104 0.10 2.14×106 1.01×105 0.53 2.06×1014 1.79×105 0.07

Desert 

Ceanothus 1.16×108 1.20×105 0.64 5.05×1010 1.39×105 0.13

Chaparral 

Whitethorn 3.23×109 1.26×105 0.07 9.56×105 9.86×104 0.69

Lodgepole 

Pine 3.45×106 8.91×104 0.30 5.97×107 1.15×105 0.22 1.51×101 4.96×104 0.26

Average 

Stem* 8.58×105 9.64×104 0.49 1.03×1016 1.95×105 0.07

Average 

Leaf* 1.22×103 5.75×104 0.23 2.46×105 9.03×104 0.23 1.32×102 6.02×104 0.25

* Effective values representing the thermal decomposition of a typical leaf or stem tested in this work
Figure 1. Experimentally measured and model predicted mass loss rate data of stem (left) and leaf 

(right) samples of Chamise, Bigberry Manzanita, Desert Ceanothus, Chaparral Whitethorn, Lodgepole 

Pine, and Douglas-Fir in TGA tests conducted in Nitrogen at 10 °C min-1.
Temperature-resolved mass loss rate measurements obtained during TGA experiments on all 

vegetative fuels tested in this work are plotted together in Fig. 2, to allow for a qualitative comparison 

of the thermal degradation behavior of each of these fuels. As seen here, between each fuel, there are 

distinct differences in the onset temperature of degradation, the number of apparent reactions, and the 

peak measured mass loss rate (as well as the temperature at which it occurs). Across all samples 

tested, the temperature corresponding to each of these reaction peaks varies between 220 and 485 °C. 

In general, two reaction peaks (local maxima in d(m/m0

)

dt

) are observed for stem samples. Stem 

samples demonstrated higher peak mass loss rates than leaves and their decomposition generally 

occurred over a narrower temperature range, with little mass loss above 400 °C. Leaf samples were 

characterized by a series of overlapping reactions, typically at least three, that occurred over a wider 

temperature range. These results support previous observations by Sussot 14, who noted that the 

relative proportions of extractives (i.e., volatile hydrocarbons), hemicellulose, cellulose, and lignin 

appear to explain the mg-scale thermal degradation behavior of typical forest fuels. Also plotted in 

this figure (black lines) are curves representing the degradation behavior of an idealized ‘Average’ 

stem or leaf. It should be stressed that these curves do not represent actual measurement data: rather, 

they define a curve representative of typical stem or leaf degradation, which is used in this work to 

help interpret wildfire simulation results. Kinetic parameters used to define these curves are reported 

in Table 4 under the sample names ‘Average Stem’ and ‘Average Leaf’.

Figure 2. Measured mass loss rates of all vegetative fuels tested in this work when heated (in ultrahigh purity nitrogen) at 10 °C min-1

in the TGA. Each curve represents an average of five repeated 

experiments; for clarity, measurement uncertainty is not plotted (this is shown in Fig. 1).

Figure 3 plots experimentally measured heat release rate, HRR, (normalized by volatilized sample 

mass, mvol) of stem and leaf samples of all plant species tested in this work in the MCC. Experimental

measurements are plotted as solid black lines. Measured values of ΔHc,total and μchar are reported in

Table 5. Uncertainties are reported for μchar as one standard deviation and for ΔHc,total as the maximum 

of either: one standard deviation of repeated measurements or 5% of the average value (which 

represents the inherent uncertainty in oxygen consumption measurements of heat release 31). 

Measured values of ΔHc,total vary between 8.9 14.4 kJ g-1and 14.4 kJ g-1, which is a significant 

deviation from the value of 18.6 kJ g-1that is prescribed for most (39 of 40) fuels in the Standard Fire 

Behavior Fuel Models 6. For the fuels tests in this work, exclusive of Lodgepole Pine stems, measured 

values of ΔHc,total are, on average, 17% greater for leaves than for stems. The heat of combustion of 

Lodgepole Pine stems is neglected in this comparison because it is, 42% greater than the average 

ΔHc,total of the other stems tested in this work. 

Also shown in Fig. 3 are model predictions of heat release rate (i.e., model-predicted mass loss rate, 

scaled by the heats of combustion, ΔHc,i). Optimized values for ΔHc,i are presented in Table 5; these 

values are calculated based on the ratio of experimentally measured heat release rate (in MCC tests) 

and mass loss rate (in TGA tests) at the peak mass loss rate of each of the reaction steps defined by 

the fuel’s decomposition model (Table 4). This calculation accounts for relative fraction of gaseous 

volatiles produced by each reaction step at the temperature of interest. The uncertainty in reported 

values of ΔHc,i is estimated to be 15% based on a propagation of the 5% uncertainty in heat release 
measurements and the differences between of model-predicted and experimentally-measured sample 

mass loss rate (see Fig. 1), which was used to generate the HRR curves shown in Fig. 3. 

Figure 3. Experimentally-measured and model-predicted heat release rate of stem (left) and 

leaf (right) samples of Chamise, Bigberry Manzanita, Desert Ceanothus, Chaparral Whitethorn, 

Lodgepole Pine, and Douglas-Fir in MCC tests conducted in Nitrogen at 10 °C min-1.
Table 5. Heats of Complete Combustion and Char Yields of Vegetative Fuels

Sample Name

ΔHc,1

(kJ g-1)

ΔHc,2

(kJ g-1)

ΔHc,3

(kJ g-1)

ΔHc,total

(kJ g-1)

μchar

(-)

Leaves

Chamise 17.3±2.6 12.9±1.9 22.9±3.4 11.7±1.2 0.25±0.04

Bigberry Manzanita 15.4±2.3 14.9±2.2 21.8±3.3 12.4±0.9 0.22±0.06

Desert Ceanothus 17.1±2.6 30.7±4.6 47.4±7.1 12.3±1.1 0.32±0.03

Chaparral Whitethorn 7.9±1.2 20.7±3.1 19.0±2.8 10.4±1.8 0.33±0.04

Lodgepole Pine 10.8±1.6 16.1±2.4 23.6±3.5 12.6±0.6 0.24±0.04

Douglas-Fir 18.3±2.7 8.7±1.3 21.1±3.2 12.2±0.6 0.25±0.04

Average Leaf *- - - 11.9±0.8 0.27±0.05

Stems

Chamise 17.5±2.6 7.3±1.1 5.5±0.8 8.9±0.6 0.27±0.04

Bigberry Manzanita 9.0±1.4 12.9±1.9 21.9±3.3 8.9±0.9 0.37±0.06

Desert Ceanothus 13.2±2.0 11.7±1.8 9.1±0.5 0.25±0.06

Chaparral Whitethorn 6.1±0.9 16.1±2.4 11.5±2.6 0.23±0.05

Lodgepole Pine 20.8±3.1 15.4±2.3 18.6±2.8 14.4±2.0 0.22±0.04

Average Stem* - - - 10.9±2.3 0.27±0.05

*Calculated as the mean value of ΔHc,total or μchar measured for all stem or leaf species

Numerical Simulations of Wildfire Spread 

Figure. 4 plots time-resolved predictions of fire front location in FDS simulations using the 

decomposition models developed in this work for stems and leaves of Bigberry Manzanita, Chamise, 

and Chaparral Whitethorn. The fire front is defined as the location of the maximum gas temperature in 

a 1 m wide, 1 m tall strip along the centerline of the grass field. As seen in Fig. 4, propagation of this 

fire front across the length of the field occurred at a fairly constant rate, R, which varied between 0.50

and 1.09 m s-1(factor of two difference) when using the decomposition models developed in this work. 

Figure 4. Comparison of predicted fire front position of CSIRO C064 Grassland Fires simulated in 

FDS 6.7.1 using the thermal decomposition models developed for three stem (left) and leaf (right) 

vegetative fuels tested in this work.

The calculated fire spread rates for ‘Average Stem’ and ‘Average Leaf’ models are Ravg

stem = 0.75 m s-1

and Ravg

leaf= 0.61 m s-1

, respectively (a relative difference of 24%). This indicates that measured 

differences in the decomposition behavior of stems and leaves (e.g., in heats of combustion or 

decomposition temperature, range, and peak mass loss rate) produce distinct differences in the global 

behavior of wildfires in FDS simulations. To better understand how well average stem or leaf models 

capture the behavior of fuel-specific stem or leaf models, a simple sensitivity analysis was performed. 

Model sensitivity of predicted fire spread rate was calculated as S =

(Ri−Ravg)

Ravg

where Ri and Ravg

represent the fire spread rates calculated using a specific (stem or leaf) fuel model or that of the 
average (stem or leaf) fuel model. Calculated sensitivity varied between 0.22 and 0.45 for the three 

stem species and -0.17 and 0.37 for the three leaf species simulated here. This indicates that, not only 

is the predicted wildfire spread rate of leaves different from that of stems, but there are distinct 

differences between the predicted fire spread rates of individual fuels of the same type.

CONCLUSIONS

In this work, the thermal degradation behavior of stem and leaf samples of six vegetative 

fuels commonly found in the United States was examined through a series of thermogravimetric 

analysis (TGA) and microscale combustion calorimetry (MCC) experiments. Measurement data from 

TGA experiments was used to determine effective thermal decomposition mechanisms – consisting of 

a series of parallel, first order, Arrhenius rate reactions – and associated kinetics. MCC experiments 

were repeated under the same experimental conditions as TGA tests, thus allowing for the 

determination the heats of complete combustion of all gaseous pyrolyzates released by the degrading 

sample, ΔHc,total, and heats of complete combustion of the gases species produced during each reaction 

step, ΔHc,i. MCC data was also used for the validation of the decomposition mechanisms and species 

char yields, μchar, determined from TGA experiments. 

Distinct differences were measured in the onset temperature of degradation, the number of apparent 

reactions, and the peak measured mass loss and heat release rates (as well as the temperatures at 

which they occur). Across all samples tested, the temperatures corresponding to the peaks of each 

reaction step in the determined degradation mechanisms varied by more than 250 °C. In general, stem 

samples demonstrated higher peak mass loss rates than leaf samples and their decomposition 

generally occurred over a narrower temperature range, with little mass loss above 400 °C. Leaf 

samples were characterized by a series of overlapping reactions that occurred over a wider 

temperature range and generally had higher heats of combustion. FDS simulations were run to 

examine the sensitivity of model predictions of wildfire spread rate to these measured variations in the 

thermal decomposition behavior of these fuels. Six fuel models were selected for this sensitivity 

analysis: simulations demonstrated a clear dependence on fuel decomposition mechanism, with 

predictions of wildfire spread rate varying between 0.50 m s-1and 1.09 m s-1.

ACKNOWLEDGEMENTS

The authors would like to thank Dr. Sara McCallister of the Rocky Mountain Research 

Station and Dr. David Weise of the Pacific Southwest Research Station for collecting and sharing the 

vegetative fuel samples tested in this work and for helpful discussions during the planning stages of 

this project. Preliminary TGA experiments and related analysis were conducted at NIST by David 

Hoddinott; the authors are grateful for his assistance. Wildfire simulations and a final review of this 

manuscript were assisted by Dr. Kevin McGrattan of NIST – this was greatly appreciated, especially 

given the condensed timeframe in which this help was offered.

REFERENCES

1. Government Accountability Office (GAO), “Technology Assessment: Protecting Structures and Improving 

Communications During Wild-Land Fires,” Technical Report GAO-05-380, United States Government 

Accountability Office: Washington, DC. (2005)

2. National Interagency Fire Center (NIFC). Total wildland fires and acres, 1983–2017. Accessed October 

2018. www.nifc.gov/fireInfo/fireInfo_stats_totalFires.html.

3. Heinsch, F. A., Andrews, P.L., “BehavePlus fire modeling system version 5.0: Design and Features,” Gen. 

Tec. Rep., RMRS-GTR-249, US Department of Agriculture, Rocky Mountain Research Station, Fort 

Collins, CO. (2010)

4. Finney, M. A., “FARSITE: Fire Area Simulator – Model Development and Evaluation,” RMRS-RP-4, US 

Department of Agriculture, Rocky Mountain Research Station, For Collins CO. (2004)

5. Rothermel, R. C., “A Mathematical Model for Predicting Fire Spread in Wildand Fuels,” USDA Forest 

Service, Intermountain Forest and Range Experiment Station, Research Paper INT-115. (1972)
6. Scott, J.H., Burgan, R.E., “Standard Fire Behavior Fuel Models: A Comprehensive Set for Use with 

Rothermel’s Surface Fire Spread Model,” RMRS-GTR-153, US Department of Agriculture, Rocky 

Mountain Research Station, Fort Collins, CO. (2005)

7. Finney, M. A., Cohen, J.D., McAllister, S.S., Jolly, M., “On the Need for a Theory of Wildland Fire 

Spread,” International Journal of Wildland Fire 22: p. 25-36. (2013)

8. Mell, W., Jenkins, M.A., Gould, J., Cheney, P., “A physics-based approach to modeling grassland fires,” 

International Journal of Wildland Fire 16: p. 1-22. (2007)

9. McGrattan, K.B., “Progress in Modeling Wildland Fires using Computational Flud Dynamics,” 10th US 

Combustion Meeting, College Park, MD. (2017)

10. Gollner, M., Trouvé, A., “Towards Data-Driven Operational Wildfire Spread Modeling,” WIFIRE 

Workshop, San Diego, CA. (2015)

11. Philpot, C.W., “Influence of Mineral Content on the Pyrolysis of Plant Materials,” Forest Science 16: p. 

461-471. (1970)

12. Shafizadeh, F., “Introduction to Pyrolysis of Biomass,” Journal of Analytical and Applied Pyrolysis 3: p. 

283-305. (1982)

13. Sussot, R. A., “Thermal Behavior of Conifer Needle Extractives,” Forest Science 26: p. 347-360. (1980)

14. Sussot, R. A., “Characterization of the Thermal Properties of Forest Fuels by Combustible Gas Analysis,” 

Forest Science 28: p. 404-420. (1982)

15. Sussot, R. A., “Differential Scanning Calorimetry of Forest Fuels,” Forest Science 28: p. 839-851. (1982)

16. Rogers, J.M., Sussott, R.A., Kelsey R.G., “Chemical Composition of Forest Fuels Affecting Their Thermal 

Behavior,” Canadian Journal of Forest Research 16: p. 721-726. (1986)

17. Dimitrakopoulos, A.P., “Thermogravimetric Analysis of Mediterranean Plant Species,” Journal of 

Analytical and Applied Pyrolysis 60: p. 123-130. (2001)

18. Statheropoulos, M., Liodakis, S., Tzamtzis, N., Pappa, A., Kyriakou, S., “Thermal Degradation of Pinus 

Halepensis Pine-Needles Using Various Analytical Methods,” Journal of Analytical and Applied Pyrolysis 

43: p. 115-123. (1997)

19. Safdari, M-S., Rahmati, M., Amini, E., Howarth, J.E., Berryhill, J.P., Dietenberger, M., Weise, D.R., 

Fletcher, T.H., “Characterization of pyrolysis products from fast pyrolysis of live and dead vegetation 

native to the Southern United States,” Fuel 229: p. 151-166. (2018)

20. Amini, E., Safdari, M-S., DeYoung, J.T., Weise, D.R., Fletcher, T.H., “Characterization of pyrolysis 

products from slow pyrolysis of live and dead vegetation native to the Southern United States,” Fuel 235: p. 

1475-1491. (2019)

21. McAllister, S., Finney, M., “Convection Ignition of Live Forest Fuels,” Fire Safety Science 11: p. 1312-

1325. (2014)

22. NETZSCH, “Software Manual (STA 449 F1 & F3) Temperature and Sensitivity Calibration,” 

Wittelsbacherstrasse 42, 95100 Selb, Germany: NETZSCH Gerätebau GmbH. (2012)

23. Lyon, R.E., Safronava, N., Senese, J., Stoliarov S.I., “Thermokinetic model of sample response in 

nonisothermal analysis,” Thermochimica Acta 545: 82-89. (2012)

24. Vyazovkin, S., Chrissafis, K., Di Lorenzo, M.L., Koga, N., Pijolat, M., Roduit, B., Sbirrazzuoli, N. Sunol, 

J.J., “ICTAC Kinetics Committee Recommendations for Performing Kinetic Computations on Thermal 

Analysis Data,” Thermochimica Acta 590: p. 1-23. (2014)

25. Stoliarov S.I., Li J., “Parameterization and Validation of Pyrolysis Models for Polymeric Materials; Fire 

Technology 52: p. 79-91. (2016)

26. Bruns, M.C., Leventon, I.T., “Automated Fitting of Thermogravimetric Analysis Data,” Interflam 2019

27. ASTM D7309, “Standard Test Method for Determining Flammability Characteristics of Plastics and Other 

Solid Materials Using Microscale Combustion,” ASTM International: West Conshohocken, PA, USA. 

(2013)

28. Lyon, R. E., Walters, R. N., Stoliarov, S. I., Safronava, N., “Principles and Practice of

Microscale Combustion Calorimetry,” FAA Report, DOT/FAA/TC-12/53 R3 (April 2013)

29. McGrattan, K., Hostikka, S., McDermott, R., Floyd, J., Vanella, M., “Fire Dynamics Simulator Technical 

Reference Guide,” NIST Special Publication 1018-1, Sixth Edition. (2019)

30. Cheney, N.P., Gould, J.S., Catchpole, W.R., “The Influence of Fuel, Weather and Fire Shape Variables on 

Fire-Spread in Grasslands,” International Journal of Wildland Fire 3: p. 31–44, 1993. 

31. Hugget, C. “Estimation of Rate of Heat Release by Means of Oxygen Consumption Measurements,” Fire 

and Materials 4: p. 61-65. (1980)