Project no. FP6-018505
Project Acronym FIRE PARADOX
Project Title FIRE PARADOX: An Innovative Approach of Integrated Wildland Fire
Management Regulating the Wildfire Problem by the Wise Use of Fire: Solving the Fire
Paradox
Instrument Integrated Project (IP)
Thematic Priority Sustainable development, global change and ecosystems
D3.1-2a Review of knowledge gaps and proposal for fuel data collection
and test runs
Due date of deliverable: Month 14
Actual submission date: Month 20
Start date of project: 1st March 2006
Duration: 48months
Organisation name of lead contractor for this deliverable: AUTH
Revision (1000)
Project co-funded by the European Commission within the Sixth Framework Programme
(2002-2006)
Dissemination Level
PU
Public
PP
Restricted to other programme participants (including the Commission Services)
RE
Restricted to a group specified by the consortium (including the Commission Services)
CO
Confidential, only for members of the consortium (including the Commission Services)
X
Table of contents:
1. Introduction ................................................................................................................ 3
1.1 Among species variation ......................................................................................... 5
1.2 Within species variation .......................................................................................... 5
1.3 Temporal and spatial variation ................................................................................ 6
1.4 Seasonal variation .................................................................................................. 6
2. Fuel moisture of extinction ........................................................................................... 7
2.1 Extinction models ................................................................................................... 8
3. Methods to estimate Fuel Moisture Content ................................................................. 10
3.1 Field sampling and analogue samples .................................................................... 10
3.2 Fuel moisture and drought indexes ........................................................................ 12
3.3 Fuel moisture modelling ....................................................................................... 13
3.4 Remote sensing techniques for assessing fuel moisture ........................................... 20
4. Knowledge gaps in wildland fuel moisture research ...................................................... 21
5. References ............................................................................................................... 23
D3.1-2a-12-1000-1
Page 2 of 30
Knowledge gaps in fuel moisture
1. Introduction
Fuel moisture is one of the most important variables affecting fire behaviour. Nelson (2001)
identified three effects of fuel moisture on slowing the rate of fuel combustion, through
increasing ignition time, decreasing fuel consumption and increasing particle residence time.
Fuel moisture affects ignition by absorbing energy when being vaporized, and by diluting
flammable volatiles, which increases the ignition delay time. Fuel moisture also has the
effect of decreasing flame emissivity by reducing the percentage of carbon particles in the
flame mantle. Consequently, the literature in every aspect of dead and live fuel moisture
content is vast. Extensive reviews on fuel moisture content can be found in Nelson (2001)
and Kunkel (2001).
Fuel moisture content (FMC), generally expressed as percentage of oven-dry plant weight
(ODW), is measured either by direct oven drying and weighing or is calculated through
mathematical models (Viney 1991). Plant moisture determines whether a plant is prompt to
ignite and if so, how efficient the combustion behaviour will be after ignition (Pompe and
Vines 1966, Albini 1993). The intensity of wildland fires that behave fiercely during hot days
is not likely to be a function of the increase of the air or fuel temperature rather than a
reduction in fuel moisture content (Pompe and Vines 1966).
It is known that moist fuels are relatively non-flammable, especially when the combustion
interface is not vertical (buoyancy increases the penetration of water vapour into the
combustion zone giving rise to a slight cooling effect) so that the external emission of
radiant heat from the flame is reduced. Thus, the rate of combustion is reduced to less
intense fires in contrast to the dry fuels that burn more fiercely (Pompe and Vines 1966, Van
Wagner 1967, Trabaud 1976, Catchpole and Catchpole 1991).
Live and dead fuel moisture content is influenced by the phenological stage the plants have
reached in their life cycle, the environmental conditions through the seasonal changes in
weather conditions (rainfall, humidity, solar radiation), and the diurnal changes of air
temperature and air relative humidity. Equally, it varies with the type of fuel, fuel size and
orientation, as well as within and between species (Pompe and Vines 1966, Luke and
McArthur 1978, Tunstall 1988, Anderson 1990, Papio and Trabaud 1990, Pook and Gill
1993). Differences in the vapour concentration between the fuel surface and the
atmosphere and the resistance to flow govern the water loss from both living and dead fuels
(Tunstall 1988).
The moisture content of living fuels is generally known to be higher than that of dead fuels,
especially during periods of extreme drought (Papio and Trabaud 1990). The moisture
content of live fuels is governed by seasonal physiological regulations of water uptake by the
roots and transpiration from the leaves to avoid drought (Rothermel 1976).
Although the relative moisture content values for live plant leaves, green stems, and woody
stems, are available from phenological studies or site samples (Albini 1993), these values
varied considerably according to different authors.
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During periods of extreme drought, moisture content drops to a minimum limit for plant
survival (Luke and McArthur 1978). This moisture level is exhibited by most of the trees and
shrubs growing in arid climate.
Philpot (1977) confirmed it by stating that living fuel
moisture content of 50% ODW (Oven Dry Weight) is common in chaparral species during
the dry season. At such moisture content only few plants can maintain their live foliage. If
moisture drops to below 30%, the fuel is considered dead. This is usual for the annuals in
the late summer which start responding to moisture changes as dead fuels. Shrubs behave
in a similar way, but as long as the leaves and branches are live, the moisture content is in
the range of 80-100% (Rothermel 1983). Fires in Mediterranean shrubs burn intensively
when the foliage drops below 75% (Chandler et al. 1983). Crown fire potential is great
whenever moisture content of the foliage of live conifer trees is under 100% (Van Wagner
1967). In pine plantations, Pook and Gill (1993) stated that fine fuel moisture content of
dead aerial fuel influences the vertical propagation of fire and together with the moisture
content of live fine fuel influences crown fire potential. Within such fuel complexes,
consisting of both living and dead fuels, the fractional fuel moisture content will reflect their
water content, a reason for which large fires occur in the fall after curing of live fuel, in the
spring before flushing and when live fuels dry out and become less abundant (Rothermel
1976).
Dead fuels absorb moisture through condensation and adsorption of water vapour and
precipitation and lose to the surrounding atmosphere by desorption and evaporation. The
difference between adsorption (wetting) and desorption (drying) for any given type of fuel is
a practical problem in assessing fire behaviour (Luke and McArthur 1978).
The adsorption process increase the fuel's moisture content up to the fiber saturation point
around 35%, at which the water is bound in the cell wall of fuel. Condensation alone can
drive leaf moisture contents up to 150% oven dry weight.
In the United States, four dead fuel size classes are standardized on the classification used
in 1978 National Fire Danger Rating System and recognized on the base of their time-lag
period (Deeming et al. 1977).
timelag size class (hours)
D3.1-2a-12-1000-1
Roundwood diameter (cm)
1
0
-
0.63
10
0.63
-
2.54
100
2.54
-
7.62
1000
7.62 - 20.32
Page 4 of 30
At each interval (1, 10, 100, 1000 hrs) the fuel looses 63% moisture of the difference
between the initial moisture content and the equilibrium moisture content. The biological
variation in the time-lag to reach equilibrium moisture content (EMC) in morphologically
similar conifer needles is significant and ranges from 4 to 21 hours for different species of
pine (Van Wagner 1969). After one year of weathering, all dead needles reached the
equilibrium state in moisture content very fast. The reason for such rapid response time is
that weathering has washed off coatings of waxes, oils or other material that normally slow
moisture diffusion in forest fuels (Anderson 1990). Hatton and Viney (1988) have shown
that fine fuels have most often a moisture level close to the EMC.
1.1 Among species variation
The curing process varies from species to species. Deciduous trees follow an annual cycle
with respect to moisture content. According to Pook and Gill (1993), live fuel moisture
content of conifers in North America, vary not only with seasonal foliar age but also with
species. Van Wagner (1967) illustrated such differences between eastern Canadian conifer
species with white pine (Pinus strobus) having the highest fuel moisture content, jack pine
(Pinus banksiana) and red pine (Pinus resinosa) following in order, while the two hardwoods
balsam fir (Abies balsamea) and white spruce (Picea glauca) were very similar to each other.
Trabaud (1976), added that for the same moisture content, the difference in response to fire
of combustible fuel are only due to factors linked to species such as the amount of minerals,
the shape of leaves etc.
1.2 Within species variation
Moisture content has been found to vary within the different plant parts (Luke and McArthur
1978). The tissues of most of the plants reflect an increase in dry weight with the age
coupled with lignification and suberization of these tissues (Tunstall 1988), thus, gravimetric
water content is expected to decrease as the plant ages. This pattern that varies with
species is reflected by organs having a limited number of cells such as leaves and needles.
The fuel moisture content of newly flushed conifer foliage presented a sharp decrease in
moisture content as they age and gradually levelled by early August (Van Wagner 1967,
Pook and Gill 1993). Van Wagner (1967) observed a spring drop in old needles of a variety
of conifer trees. This drop is assumed to be a physiological response of the tree due to an
increase in dry weight rather than an actual decrease in moisture content. On the other
hand, the increase in the foliar moisture content during the summer is caused by a loss in
dry weight rather than by increase in moisture content (Van Wagner 1974). The main
evidence suggests that the build-up of carbohydrates translocated to the new needles for
growth and development lags behind their increase in dry weight, causing the summer
decrease in the dry weight of the old needles which represented low moisture levels in
spring, and higher and more constant ones in the summer months.
Anderson (1990) reported a relative difference in moisture content between old and recently
dead foliage owing to the degree of weathering and corresponding contrast in water vapour
exchange characteristics of the fuels.
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Page 5 of 30
1.3 Temporal and spatial variation
Fine fuel moisture content is found to vary spatially and temporally within a given plant
species (Hatton and Viney 1988). As has been reported above, these temporal changes are
found to be a function of the plant phenology as well as physiological changes occurring
within the plants.
In fact, water uptake from the soil is along a negative water potential gradient that results
in diurnal and seasonal changes in leaf water content, related to water evaporative demand
and soil water availability and affected by irradiance and plant development (Tunstall 1988).
Variations in moisture content of most generations of live pine needles, in both shaded and
unshaded canopies, are proportional to the predicted water balance of the topsoil. Shaded
green foliage has higher moisture content than the needles of unshaded canopy (Pook and
Gill 1993).
Diurnal changes in fine fuel moisture content are the principal source of moisture variation
within a given type of fuel. Hourly sampling of surface Eucalyptus macrorhyncha bark,
leaves and twigs from different sites revealed large differences among these fine fuels
(Hatton and Viney 1988). Over a four-day period, the mean moisture content values of the
same fuel types ranged from 5 to 8% ODW. As far as the variation in large fuels moisture
content is seasonal, the changes of surface fuel moisture content over a day would trigger
the predicted variation over the entire season. Thus, fire control and planning must be
based on careful checking of fuel moisture changes on the basis of week to week, day to
day and hour to hour variation (Luke and McArthur 1978).
1.4 Seasonal variation
Generally, changes in the fire environment lag behind the seasonality of fire behavior
(Philpot and Mutch 1971, Trabaud 1976) owing to the relative fluctuations of fuel moisture
content from one month to the next (Trabaud 1976, Pompe and Vines 1966). Depending on
the Mediterranean Basin fire climate, Le Houerou (1974) associated the peak season of fire
occurrence to summer months where the drought conditions drop the live fuel moisture
content down to 65 and 45% of oven-dry weight. Van Wagner (1967) discovered that
seasonal trends of the foliar moisture content were similar from year to year despite
weather differences. Moreover, Van Wagner (1967) suggested that the year-to-year
differences in the foliage moisture content could be due to weather variations especially in
the summer where the foliage moisture content is largely influenced by rainfall and
evaporation.
The average composite foliar moisture content of conifers rose from 95% in late May and
early June to about 130% in mid-August. The seasonal trends of needle moisture
concentrations of old conifer foliage presented a marked spring minimum and then reached
a maximum and remained relatively constant throughout the summer months (Van Wagner
1967, Philpot and Mutch 1971).
Burgan and Sussott (1991), stated that the high initial moisture content of new leaves of
evergreen shrubs declined rapidly during the summer to about the same moisture content
as mature leaves, with the lowest moisture content recorded during the winter months.
D3.1-2a-12-1000-1
Page 6 of 30
2. Fuel moisture of extinction
Rothermel (1972) has formulated the effect of moisture content on the burning rate, by
defining a threshold moisture of extinction (ME), above which fire can not be sustained. The
concept of extinction moisture can not be defined in field experiments and it would be better
replaced by a probability of burning, given the fuel geometry and moisture content
conditions.
Mak (1988) inferred that in most cases with few exceptions, green live foliage is less
ignitable and sustainable than litter. Living plant parts of average moisture content 100%,
may or may not burn in a surface fire as opposed to dead plant parts having moisture
content fixed by the atmospheric humidity below the fibber saturation at about 30% (Albini
1993). Pompe and Vines (1966), reported that oven-dried leaves (do not contain essential
oils and waxes) with moisture content higher than 50% were hard to ignite. Trabaud (1976)
found that the threshold moisture content for different dry Mediterranean species
dominating maquis ecosystems is 32% ODW. Fire occurrence or ignition potential is
significantly related with fuel moisture content both in grasslands and forest stands (Pompe
and Vines 1966, Renkin and Despain 1992).
Moisture content is found to increase the specific heat and thermal conductivity of the fuel
so that more heat is absorbed by the surface layer to drive out moisture, delaying in this
way the pre-heating and ignition of fuel until it reaches ignition temperature (Pyne 1984).
The time needed to reach ignition temperature will depend not only on moisture content but
also on other physical parameters, such as material density (specific gravity), thickness
(surface area-to-volume ratio, S/V), specific heat and heat source intensity (Anderson 1970,
Brown 1970, Montgomery and Cheo 1971, Wright and Bailey 1982, Papio and Trabaud
1990). The higher the S/V (surface to volume ratio) the easier the absorption or loss of
atmospheric water for dead material.
The understanding of the concept of flammability in forest fire science remained equivocal
until Anderson (1970) defined flammability to be comprised of three elements: ignitability,
sustainability and combustibility. Ignitability was defined as the measure of the ease of
ignition of the fuel, sustainability as the measure of how well the fuel continues to burn and
combustibility of how rapidly the fuel burns. In this context, plants with low ignitability do
not necessary rival a critically lower flammability than plants with higher ignitability, since
they may have higher combustibility or sustainability than the highly ignitable plants
(Montgomery and Cheo 1971). The foliage of such plants may burn readily once ignited due
to the contribution of highly combustible volatiles.
Trabaud (1976), defined moisture of extinction (ME) as the maximum moisture content (MC)
above which a fire cannot be sustained. The experimental results allowed the author to
conclude that a value of 45% (fresh weight) can be considered as threshold. Above that
value the inflammation did not occur or it was delayed more than 15 min. Another limit of
moisture of extinction was 32% of plant fresh weight, above which the inflammation delay
started to show very large values. Trabaud (1976) remarked that Cistus monspeliensis was
very flammable if its moisture content was less than 25%.
D3.1-2a-12-1000-1
Page 7 of 30
Sylvester and Wein (1981), examined the influence of a certain value of moisture of
extinction on the relative fuel-potential ratings of the live foliage of different arctic plant
species. The authors accepted ME = 200% (dry weight), considering that the minimum
moisture content (MC) for the majority of the species was 80 - 200 %. A linear relationship
was observed to exist between MC and simulated Byram's (1959) intensity for the range 80200 % of moisture content. For values of MC below 80%, the relationship was found to be
negative exponential. The chosen ME of 200% caused MC of some of the species to fall in
the exponential region of the expanded moisture range.
2.1 Extinction models
Fire extinction is affected by similar processes to ignition and probably occurs at higher
moisture contents than ignition from a point source (Catchpole 2002). McCaw (1986)
observed fires in eucalypt regrowth to ignite at a FMC of 15% and lower, but sustain when
the moisture content was 19%. Ignition from a line source could have a critical moisture
threshold similar to that of an extinguishing fire with a headfire width of comparable
proportions supposing that there is comparable heat to that required for sustaining fire.
Luke and McArthur (1978) claimed that fires are self extinguishing at moisture contents of
16-20% in dry eucalypt fuels and 25-30% in dry pine fuels.
Extinction models are relatively rare. The most comprehensive extinction model was given
by Wilson (1985; 1990), and is based on laboratory data, using milled wood sticks and
shaved excelsior.
Wilson (1985) gave Equation 2.1 as a rule of thumb when fires will rarely burn, where δ is
the depth of the fuel bed, β the fuel packing ratio and σ the surface area to volume ratio.
nx 
ln( h v / Qm )
0.01m  Q f / Qm
(Equation 2.1)
m  25 ln( 2 )
Wilson (1985) suggested that extinction is primarily an energy balance phenomenon and
introduced the concept of an extinction index nx :
nx 
ln( h v / Qm )
0.01m  Q f / Qm
(Equation 2.2)
where m is the moisture content, hv is the heat of combustion of pyrolysis gases (kJ/kg), Qm
the heat of water (the amount of energy required to heat a unit mass of water to 100 0C and
then vaporize it) is given by the value of 2550 kJ/kg and Q f the heat of pyrolysis (the
heat required to raise the dry fuel from the ambient temperature to 400 0C). Wilson
(1990) later gave a probability function to predict extinction using nX :
P(n x )  (1 /(1  exp(  (n x  n  ) / 1.2 3 ))) (Equation 2.3)
D3.1-2a-12-1000-1
Page 8 of 30
where variables hv and Qf are not known for most fuel types. Wilson (1985) found these to
vary among his experimental fuels and used values determined by Susott (1982a; 1982b).
n x is the mean nx calculated from Wilson's experiments. Wilson (1990) gave nx a value of
3.0 and suggested that it and the coefficient 1.2 can be varied for calibration. Catchpole
(2002) noted that Wilson's models are really testing ignition from a source, since his
experiments were done with a fixed ignition source, a burning line of alcohol. The
extinction index therefore assumes that the ignition source simulates heat required for
sustainable burning if it were possible (Catchpole 2002).
Bradshaw et al. (1983) give the equation used in the 1978 US National Fire Danger Rating
System NFDRS (Deeming et al. 1977), which is based on earlier work by Fosberg and
Schroeder (1971). In this equation a is the fraction of living fuel, md is the dead fuel moisture
content (%), and mxd is the dead fuel moisture content of extinction (%). This equation
indicates that mxl (live fuel moisture content of extinction, %) is highly dependent on the
amount of standing dead fuel. The dead fuel moisture content of extinction is usually set at
30%, based on Fosberg and Schroeder (1971).
m
1
mx1  290(
)(1  d )  22.6

mxd
(Equation 2.4)
Marsden-Smedley and Catchpole (1995, 2001) have constructed and tested a more practical
model of extinction in standing live vegetation (Tasmanian buttongrass moorland). Their
empirical model is based on field data and was constructed for the purpose of predicting
conditions suitable for unbounded burning. Unbounded burning is the use of prescribed fire
without fuel breaks or suppression, and relies on fires self extinguishing. Marsden-Smedley
and Catchpole (2001) observed the sustainability of fires lit with ignition lines of 50 or 100
meters in length (Marsden-Smedley and Catchpole 1995) representing a case where the
ignition and extinction threshold are assumed to be the same. The model is specific to
buttongrass moorlands and is given in Equation 2.5. Pb is the probability of sustained
burning and U1.7 is the wind speed at 1.7 meters height (m/s). Site productivity (p s)
was used as a surrogate for fuel mass and fuel continuity, and was given a value of 0 for low
productivity sites and 1 for medium productivity sites. Marsden-Smedley and Catchpole
(1995) gave a table containing prescription conditions suited for unbounded burning in
buttongrass moorlands.
Pb 
1  exp( 1  0.68U 1.7
1
 0.07md  0.0037U 1.7 md  2.1 p S )
(Equation 2.5)
An empirical approach was presented by de Groot et al (2005), who determined the
probability of ignition using the Fine Fuel Moisture Code (FFMC) of the Canadian Forest Fire
Weather Index System.
D3.1-2a-12-1000-1
Page 9 of 30
Using models of probability of extinction in the field can be risky in some situations where
smoldering combustion in logs and peat can continue for long periods after flaming has
ceased and be an ignition source when conditions have become favourable.
3. Methods to estimate Fuel Moisture Content
Estimation of FMC has typically involved such methods as field sampling, mathematical
models, calculation of meteorological indices and application of remote sensing techniques.
3.1 Field sampling and analogue samples
There are many different methods used to measure or estimate the moisture content of fine
fuels. These include gravimetric sampling (oven drying), the use of analogue samples, and
portable methods such as the leaf bending method, the speedy moisture meter, and the
Wiltronics TH Fine Fuel Moisture Meter. Gravimetric sampling is the most common and
reliable method used to calculate fuel moisture (Viney 1992, Chatto and Tolhurst 1997).
What is peculiar about ovendrying is their great variety:
Reference
Year
T (oC)
hours
Van Wagner
1967
100
24
Trabaud
1976
80
48
Caramelle and Clément*
1978
60
24
Loomis and Main*
1980
105
24
Sylvester and Wein
1981
65
till constant weight
Chrosciewicz*
1986
100
24
Valette
1986
60
36
Vega and Casal*
1987
80
24
Mak
1988
85
24
Hatton et al.*
1988
80
48
D3.1-2a-12-1000-1
Page 10 of 30
Martin and Lara*
1989
60
24
Valette
1992
60
24
Viegas et al.
1992
100
4
Lara et al.
1994
60
24
Dimitrakopoulos
1994
105
48
*from Viegas et al (1992)
However with this method there is a time lag between sample collection and fuel moisture
calculation, due to the requirement for oven drying, which renders this method impractical
for use during fire operations (Buck and Hughes 1939). This problem has inspired the
development of a number of portable fuel moisture measurement methods and fuel
moisture models. The leaf bending method (Burrows 1991) uses the maximum angle that a
pine needle can bend without breaking to estimate the FMC. It is only applicable to pine
needles and does not appear to have been used much. The speedy moisture meter (Dexter
and Williams 1976) measures the pressure of acetylene gas evolved from mixing finely
divided fuels with calcium carbide. It requires careful preparation of materials and has fallen
into disuse (Marsden-Smedley and Catchpole 2001). The Wiltronics TH Fine Fuel Moisture
Meter (Chatto and Tolhurst 1997) estimates FMC from the electrical resistance of a fine fuel
sample.
Methods involving oven drying, such as gravimetric sampling and hypsometric methods are
more suited for data collection for the development of models due to their high accuracy.
Gravimetric sampling involves comparing the difference in weight of a sample from the field
with its oven dry weight, to calculate the percentage FMC. Random samples of fuel are
collected by hand and put into sealed containers. A number of samples are required to get
an accurate measure of the field as there can be significant variation in the moisture content
of samples collected from the same site, particularly when fuels have free surface moisture.
There are many combinations of temperature and time that have been used in the drying
process, ranging from 4 hours at 100°C (Viegas et al. 1992) to 48 hours at 105°C (Viney
and Hatton 1990). The most common method appears to be 24 hours with oven
temperatures between 95 and 105°C (Viney 1992, Pook 1993, Pook and Gill 1993, Cheney
and Sullivan 1997). Pook (1993) equilibrated samples to room temperature in desiccators
after removal from the oven and prior to weighing. This facility was not available, though
dried samples in tins were resealed and allowed to cool before reweighing.
Methods involving the oven drying of fuel samples can give a slight overestimate of moisture
content because of the loss of volatiles during oven drying (Buck and Hughes 1939, Simard
1968, Chatto and Tolhurst 1997). These errors are likely to be negligible in dead fuels,
however, as they are likely to have lost most volatiles already. Another method, xylene
distillation, was developed to measure fuels without loss of volatiles. The method is based
D3.1-2a-12-1000-1
Page 11 of 30
on the principle that the boiling characteristics of a liquid are changed when it is boiled in a
liquid with which it is immiscible (Buck and Hughes 1939). It involves a laboratory procedure
and gives precise estimates of moisture of live and dead fuels. However, it is expensive and
time consuming. Van Wagner (1963) found this method to be less precise, require larger
samples, and take more time per sample than oven drying.
Analogue methods of estimating FMC involve the repeated weighing of a sample exposed to
field conditions. The use of analogue methods avoids sample errors that plague other
methods, including gravimetric sampling, and also avoids the problem of stripping the site
bare of fuel. The most common analogues are hazard sticks and samples of real fuels.
Hazard sticks are pieces of dowel (usually pine), with a known dry weight, that are usually
placed on small stands and reweighed in the field (Burgan1987). Hazard sticks have slower
response rates than fine fuels such as leaves and grass, and they have a limited useful life
due to weathering (Haines and Frost 1978). The dry weight of hazard sticks is known before
placement in the field (usually 100 g) so that FMC can be calculated once its field weight is
known.
Samples of real fuel are usually in the form of small branchlets (Viney and Hatton 1990),
loose particles in a bag (Pook 1993, Pook and Gill 1993, Marsden-Smedley and Catchpole
2001) or tray (McCaw 1997). The major disadvantage of these methods is that the FMC is
not known until after oven drying. Containing the fuel in a bag or tray limits error from
weight loss due to breakages. Fuel bags are sometimes used for aerial fuels, and are usually
made of terylene or shade cloth. The bags have been found to reduce the absorption of
overnight dew of fuels and to increase the day time temperature to greater absorption of
solar radiation (Marsden-Smedley and Catchpole 2001). Litter trays have an open top and a
mesh base which allows litter to have contact with the soil. They are essentially a
representative sample bed of litter, and are based on the same principles as lysimeters,
which are used for measuring soil moisture loss by monitoring change in weight (Daamen et
al. 1993). The validity of analogous methods depends on whether the changes in weight
(moisture content) from the isolated body are essentially the same as that from a
comparable non-isolated body (Simard 1968). To give an accurate estimate of fuel
moisture, the analogues must be placed in the field long enough to equilibrate with local
conditions and should have Equilibrium Moisture Content (EMC) and rates of response
similar to those of the fuel being estimated (Simard 1968).
3.2 Fuel moisture and drought indexes
Most operational fire danger rating systems base their estimation of FMC on meteorological
data. Such data express the state of the atmospheric variables related to FMC and is
routinely collected by national or regional weather services. The main atmospheric variables
affecting FMC are solar radiation, air temperature, air humidity, precipitation, and wind.
However, these variables do not properly estimate FMC by themselves, but need to be
combined in different indices. Most commonly, meteorological danger indices try to estimate
the FMC of dead materials lying on the forest understory, which are the driest and most
likely to ignite. The diameter of the fuel expresses the ignition potential, and therefore many
danger rating systems classify the fuels according to size (Bradshaw et al. 1983). In spite of
the relevance of also estimating the FMC of live fuels, few models explicitly do. The US
National Fire Danger Rating System (NFDRS) uses the 1000-hr timelag moisture content
D3.1-2a-12-1000-1
Page 12 of 30
code as an estimation of moisture conditions of live fuels. The revised version of NFDRS
produced in 1988 included two factors, which varied according to the current state of live
herbaceous and woody fuels (Burgan 1988). Other authors have attempted to obtain an
indirect estimation of live FMC values from drought indices (such as the Canadian Drought
Code and the Keetch & Byram index), which take into account the medium- to long-term
trends of atmospheric variation and are more related to soil water availability. These indices
have been successfully correlated to the FMC of live Mediterranean herbaceous species
using regression models. However, the correlation between drought indices and moisture
content of shrubby and forest species has shown poor results (Viegas et al. 2001,
Dimitrakopoulos and Bemmerzouk 2003, Castro et al. 2003).
3.3 Fuel moisture modelling
According to Chandler et al. (1983) "the history of attempts to accurately predict fuel
moisture contents of forest fuels have been an endless series of beautiful theories
demolished by ugly facts". Fuel moisture models have been reviewed by Viney (1991, 1992),
who classed them as EMC and vapour exchange models. Viney (1991) also listed a number
of models that give the effects of precipitation and dewfall. As the name suggests, EMC
models estimate the moisture content of fuels in equilibrium with the input conditions. They
have generally been formulated using laboratory experiments and can include physical
processes (eg: Nelson 1984). Most fuel moisture models are vapour exchange models (Viney
1991), which have mainly been devised using the results of field experiments. Vapour
exchange models assume that screen level temperature and relative humidity are
proportional to those at the fuel particle level, and ignore the effects of solar radiation and
wind (Marsden-Smedley and Catchpole 2001). Vapour exchange models are typically
empirical and some include book-keeping methods for determining FMC. Book-keeping
methods can be unnecessary for very fine fuels as they have rapid response rates (Viney
1991; 1992, Marsden-Smedley and Catchpole 2001).
Byram (1963) approximated the change in the moisture content of a fuel particle over time
using Equation 3.1, where m is the moisture content of the fuel particle at time t, τ is the
response of the fuel particle, and q is the EMC. The FMC of a fuel particle at the next time
step is given by Equation 3.2 (McCaw1997), where m0 is the FMC at the start of the time
step. McCaw (1997) used error statistics to determine which value of τ resulted in the best
fit to his data, and found a response time of one hour best approximated litter FMC and
three hours for dead aerial fuels.
dm
(m  q)

(Equation 3.1)
dt
τ
m=
(m0  qdt)
(Equation 3.2)
(τ  dt)
Simard (1968) developed a simple regression model for EMC using values from desorbing
wood (Equations 3.3-3.5, converted to °C by Viney (1991)), where HR is the relative
humidity (%) and Ta is the ambient temperature (°C).
D3.1-2a-12-1000-1
Page 13 of 30
q  0.03  0.2526H R  0.001040H RTa
q  1.76  0.1601H R  0.02660 Ta
HR < 10 (Equation 3.3)
10 ≤ HR ≤ 50 (Equation 3.4)
2
q  21.06  0.4944H R  0.005565H R  0.00063H RTa 50 ≤ HR (Equation 3.5)
The equilibrium moisture content varies depending on whether the fuel is experiencing
adsorption or desorption (Viney 1991). Van Wagner (1972) derived separate equations for
the EMC of adsorping and desorping fuels for a range of forest litter types. These equations
were later corrected by Van Wagner (1977; 1987) so that the EMC is 0 when the relative
humidity is 0 regardless of temperature, and are given by Equations 3.6 and 3.7. Anderson
et al. (1978) adapted Van Wagner's (1972) adsorption and desorption EMC models for Pinus
ponderosa needles (Equations 3.8 and 3.9).
qde  0.942H R
0.679
 11e ( H R 100) / 10  0.18(21.1  Ta )(1  e 0.115H R ) (Equation 3.6)
0.753
 10e ( H R 100) / 10  0.18(21.1  Ta )(1  e 0.115H R ) (Equation 3.7)
qad  0.618H R
qde  1.651H R
0.493
qad  0.891H R
0.612
 0.001972e 0.092H R  0.101(23.9  Ta ) (Equation 3.8)
 0.000234e 0.112H R  0.101(23.9  Ta ) (Equation 3.9)
Nelson (1984) developed a semi physical EMC model based on the thermodynamics of
sorption and the empirical relationship between EMC and the change in Gibbs free energy
function of sorbed water. Nelson's model formulates EMC with a time step procedure to
account for fuel response time. It is given by Equation 3.10, where ΔG is the change in
Gibbs function of the adsorbed water with changes in temperature and relative humidity, R g
is the universal gas constant (1.987cal/mol K) and also includes the molecular mass of water
(18, as the denominator of the logarithm). Experimental data relating EMC to relative
humidity was used to determine coefficients (b0 and b1) by regression analysis.
  R g (273.15  Ta
q  b0  b1 log G  b0  b1 log  
18
 

HR
 log 

100


(Equation 3.10)
Nelson (1984) gave values b0 and b1 for adsorption and desorption of five different fuels
valid for relative humidities between 10 and 90 % (at fixed temperatures). Anderson (1990)
has modelled b0 and b1 as quadratic functions of fuel temperature for a range of fuel
particles. Nelson's (1984) model can predict the moisture content of different dead fuel
components, provided that appropriate inputs for response time, surface level temperature
and relative humidity are used (McCaw 1997).
McArthur (1962, 1966, 1967) made predictions of FMC using data collected in eucalypt
forests and grasslands. McArthur (1962) predicted the moisture content of litter in his guide
D3.1-2a-12-1000-1
Page 14 of 30
for controlled burning in eucalypt forests (CBEF). This guide was presented in graphical form
and gave estimates of FMC for desorption and adsorption. McArthur's (1962) FMC graphs
were converted into equations by Viney and Hatton (1989) (Equations 3.11 and 3.12),
though a slightly different version of Equation 3.12 was given by Gill et al. (1987) (Equation
3.13). mde and mad are the moisture contents of desorption and adsorption respectively and
Hs is the relative humidity measured at screen level. McArthur (1962) noted that the
relationships for the CBEF are typical of fuels receiving about 25% of full sunlight.
mde  12.5  0.113H S  0.281Ta (Equation 3.11)
mad  6.8  0.132 H S  0.168Ta (Equation 3.12)
mad  6.9  0.134 H S  0.18Ta (Equation 3.13)
McArthur later developed a nomogram for predicting the moisture content of cured grass, as
part of his Grassland Fire Danger Meter (GFDM) (McArthur 1966). The GFDM was converted
to equations by Noble et al. (1980), where the prediction of fuel moisture is given by
Equation 3.14. C is the degree of curing (%) and is considered as 100% for the calculation
of dead FMC. McCaw and Catchpole (1997) found a discrepancy between predictions from
this equation and the original meter, which is greatest in cool and humid conditions.
However, Noble et al.'s (1980) equations have now become more accepted than the original
meters (McCaw 1997). The GFDM has been found to perform well in predicting the moisture
content of aerial fuels in pine forests (Pook 1993), mallee-heath (McCaw 1997) and
buttongrass moorlands (Marsden-Smedley and Catchpole 2001).
97.7  4.06 H a
3000
m
 0.00854 H s 
 30
Ta  6.0
C
(Equation 3.14)
McArthur (1967) also developed the Forest Fire Danger Meter (FFDM), which contained a
table of fuel moisture content values calculated at a range of temperatures and relative
humidities. This table was converted to a regression equation (Equation 3.15) by Viney
(1991). Equations 3.11 – 3.15 are models of vapour exchange and refer to the actual
moisture content rather than the EMC.
3
0.000315 H S
0.77
m  5.658  0.04651H s 
 0.1854Ta
Ta
(Equation 3.15)
The measurements of temperature and relative humidity for McArthur's equations were
made at screen level (1.5m) and ranged from 10-32°C and 20-70% for the CBEF, 10-43°C
and 5-80% for the GFDM and 10-41°C and 5-70% for the FFDM. McArthur's models have
been found to perform better than more sophisticated North American models in eucalypt
litter (Viney and Hatton 1989). Pook (1993) found McArthur's models to under-predict the
moisture content of pine litter, and demonstrated that the CBEF could be calibrated for the
heavily shaded fuels of pine plantations to perform as well models made specifically for
these fuels. McCaw (1997) found the CBEF and GFDM models to consistently over-predict
D3.1-2a-12-1000-1
Page 15 of 30
moisture content of surface litter in mallee-heath, but show better agreement with profile
litter.
The Forest Fire Behaviour Tables (FFBT) (Sneeuwjagt and Peet 1985) used for fire
prediction in Western Australia contains tables for the prediction of litter surface and profile
fuel moisture content (SMC and PMC). Equations for the FFBT were derived by Beck (1995).
The prediction of FMC uses a book-keeping method that requires an estimate of moisture
content at an earlier time. Moisture contents are assumed to decrease from a daily
maximum at 08.00 hours to a daily minimum at 15.00 hours. The FFBT are not intended to
model FMC through the diurnal cycle, although a simple nomogram is given to predict it
during daylight hours (McCaw 1997). The potential drying of fuel is expressed in the “basic
drying unit” which is derived as a function of the expected daily maximum temperature and
minimum relative humidity. The FFBT also includes a "night wetting correction", which is
calculated from the amount of rainfall in the 24 hours before 08.00 or by a count of a
thermo-hygrograph trace that exceeds 70% relative humidity, in the absence of rain. The
night wetting correction is added to the previous days minimum FMC to calculate a
maximum SMC for the next day. The procedure to calculate FMC is cumbersome and specific
to six different forest associations endemic to south-western Australia. Beck (1995) gave 26
equations in total for the entire FMC component of the FFBT. Beck's equations describe each
individual component including calibrations for different fuel types. Viney (1991) summarized
part of the FFBT into four general equations (Equations 3.16-3.19). The first two equations
describe the maximum FMC (mmax) when rain has fallen in the 24 hours before 08.00
(Equation 3.16) or when no rain has fallen (Equation 3.17). Here r is the amount of rainfall
(mm), mo is the previous days minimum FMC, and H c is the overnight humidity count, which
represents the area enclosed by an overnight thermohygrograph trace between 15.00 hours
and 08.00 hours that exceeds the 70% humidity level, and can be determined by Equation
3.18, where t is time in hours (Viney 1991). Viney (1991) also determined the afternoon
minimum moisture content (mmin) using the maximum temperature (Tmax, °C) and minimum
humidity (Hmin, %) in Equation 3.19.
The basic drying unit is incorporated in the first set of parentheses. McCaw (1997) found
surface moisture content predictions from the FFBT to be unsuitable for use in mallee-heath
as they consistently over-predicted the FMC of litter.
mmax  m0  50r 0.26 e 0.0042m0  30.9 (Equation 3.16)
mmax  9.4  m0  0.116H C  2.36m0
0800
H C  0.25

0.65 0.003H C
max  H S  70, 0 dt
e
(Equation 3.17)
(Equation 3.18)
1500
mmin  mmax  3.58(0.614Tmax  0.163H min  10.7) 0.3 ln( mmax )  0.0000246(mmax ) 2.6  18.0
(Equation 3.19)
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Page 16 of 30
The fine fuel moisture code (FFMC) used by the Canadian Forestry Service represents the
moisture content of litter in a forest stand in a layer of about 0.25 kg/m2 (Van Wagner
1987). The Canadian Fire Weather Index System also includes two other moisture measures,
the Duff Moisture Code and the Drought Code. These represent the moisture content of the
loosely compacted decomposing organic matter (duff) and the deep layer of compact
organic matter respectively (Van Wagner 1987). The FFMC was originally given in tabular
form (Forestry Canadian Fire Danger Group 1992), but has since been given as equations
(Van Wagner and Pickett 1985). The FFMC itself is an index rather than a prediction of FMC,
although it contains a prediction of FMC within it, and contains a rainfall input. The FFMC
can reach equilibrium within a few days after rain, which would suggest that a surface fire
could spread at the same rate within days of fuels being saturated as it could after a month
of continuous fire weather. The moisture content in the FFMC is calculated for either drying
or wetting fuels (Van Wagner 1987) using Equations 3.20 and 3.21. As with the FFBT, the
FFMC uses FMC from the previous day (mo). However, in this case mo is calculated using the
previous day's moisture code, which is adjusted with set equations if there is rainfall greater
than 0.5mm (Van Wagner 1987). The EMCs for drying (qde) and wetting (qad) are calculated
using Equations 3.6 and 3.7 (Van Wagner 1972). Log drying (kd e) and wetting (kad) rates
are calculated with Equations 3.22 and 3.23, where U n is the noon wind speed (km/h).
Van Wagner (1977) developed a model to predict hourly changes in moisture content
predictions from the FFMC, although this was based on earlier sets of tables (Muraro et al.
1969). This model is similar to the daily FFMC model and is given by Equations 3.24 and
3.25 for drying and wetting fuels respectively. Rothermel et al. (1986) used the Canadian
FFMC calibrated for the lower latitudes of the United States for FMC input in the BEHAVE fire
model.
mde  q de  (m0  q de ) x10  kd e (Equation 3.20)
mad  q ad  (q ad  m0 ) x10  kad (Equation 3.21)
k de  
k ad
  H S 1.7 
  H S 8 
0.5
0.4241      0.694U n 1     x0.581e 0.0365Ta
  100  
  100  
(Equation 3.22)
  100  H S 1.7 
  100  H S  8 
0.0365Ta
0.5
  0.4241  
   0.694U n 1  
  x0.581e
  100  
  100  
(Equation 3.23)
mde  q de  (m0  q de )e 2.303k d e (Equation 3.24)
mad  q ad  (q ad  m0 )e 2.303kad (Equation 3.25)
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Page 17 of 30
Pech (1989) presented the Reindeer Lichen Moisture Code (RLMC) which predicts moisture
content in the upper 3-4 cm of reindeer lichen (Cladina rangiferina), a common understorey
fuel in northern Canadian woodlands. The RLMC estimates a pseudo equilibrium moisture
content (q) as it incorporates a term for solar heating when Hs<75 (assuming that solar
radiation is associated with lower humidities) to account for differences between screen level
and surface level conditions (Viney 1991). This model is given by Equations 3.26 - 3.28, and
is based on the FFMC model and Van Wagner (1972) (Equation 3.6 and 3.7).
q  0.136 H s1.070  0.000590e 0.1H S
(if HS ≤ 40) (Equation 3.26)
q  0.2772 H S  4.013  0.18(21.1  Ta )(1  54.6e 0.1H S ) (if 40<HS<75) (Equation 3.27)
q  0.618H S0.753  0.18(21.1  Ta )(1  54.6e 0.1H S )  0.000454e 0.1H S
(if 75 ≤ HS) (Equation
3.28)
Pook and Gill (1993) developed empirical models predicting the moisture content of dead
aerial and litter fuels in Pinus radiata plantations in dry conditions (FMC<fibre saturation
(35%)). They found that moisture regimes were different in stands that were pruned and
thinned compared to those that were not. They produced a list of models using
temperature, relative humidity, the duff moisture and the available moisture in the topsoil as
predictors. Their model for aerial fuel moisture in thinned and pruned plantations is given by
Equation 3.29 and model for litter moisture content in unthinned and unpruned plantation is
given by Equation 3.30. Pook (1993) used the same dataset to devise models using the
difference between temperature and relative humidity. These included models for aerial
fuels (Equation 3.31), fuels measured in the same screen as the instruments (Equation
3.32), and pine litter (Equations 3.33 and 3.34). Pook (1993) found that adding the effect of
the available moisture in the topsoil (ms) for the prediction of pine litter moisture improved
the prediction from R2 =0.78 (Equation 3.33) to R2 =0.88 (Equation 3.34).
m  8.38  0.17Ta  0.17 H S (Equation 3.29)
m  6.87  0.19Ta  0.30 H S (Equation 3.30)
m  8.56  0.18(Ta  H S ) (Equation 3.31)
m  9.11  0.14(Ta  H S ) (Equation 3.32)
m  9.67  0.27(Ta  H S ) (Equation 3.33)
m  8.1  0.13(Ta  H S )  0.69mS (Equation 3.34)
Marsden-Smedley and Catchpole (2001) developed a regression model (Equation 3.35) using
an exponential function of relative humidity and dew point temperature (T d) for elevated
fuels of Tasmanian buttongrass moorlands. Dew point temperature was used because it is
D3.1-2a-12-1000-1
Page 18 of 30
not correlated with relative humidity, so avoided problems of multi-colinearity experienced
when using temperature with relative humidity.
m  exp(1.66  0.0214H S  0.0292Td )
(Equation 3.35)
Catchpole et al. (2001) proposed a method for predicting fuel moisture content that can be
calibrated using field data. This method estimates response times and utilizes Nelson's
(1984) prediction of EMC (Equation 3.10) and Byram's (1963) approximation of moisture
change in a fine fuel particle (Equation 3.1). Catchpole et al. (2001) solved Byram's (1963)
moisture change approximation to give Equation 3.36, where m i and qi are the values of m
and q at time i, and λ is given by Equation 3.37, with δt being the sampling interval for the
moisture content. Catchpole et al.'s (2001) method uses Nelson's (1984) formula (Equation
3.10) to calculate q. τ (from Equation 3.1), bo and b1 (from Equation 3.10) are estimated by
minimizing the sum of square errors (SSE) using Equation 3.38, where m i is given by the
right hand side of Equation 3.36, Mi is the observed moisture content and n is the number of
observations. This requires fuel moisture data collected over several days. Catchpole et al.
(2001) tested their model on data from Western Australian mallee-heaths and Tasmanian
buttongrass moorlands data (Marsden-Smedley and Catchpole 2001) and found it to perform
well. They claimed that it fitted the buttongrass moorland data better than MarsdenSmedley and Catchpole's (2001) model (Equation 3.35). Catchpole et al.'s (2001) model also
requires a starting point, from a measured or estimated value of FMC, as each estimate is
calculated from a previous estimate. This may be a problem in some field situations where
measurements cannot be made. The first observed FMC was used as the starting point for
predictions from this model in each run.
mi  2 m i-1   (1   )q i-1  (1   )q i (Equation 3.36)
  exp(
n
-δt
)
2τ
(Equation 3.37)

SSE   ( M i  mi ) 2
i 1
(Equation 3.38)
EMC and vapour exchange models only account for fuel moisture up to the fibre saturation
point. Precipitation effects are accounted for in some of the book-keeping methods (FFBT
and FFMC) and have been investigated by Fosberg (1972). Viney and Hatton (1990) noted
that the lack of consideration of the effects of condensation was a major shortcoming in the
prediction of FMC and presented a physical model to quantify the effects of nocturnal
condensation on the moisture content of leaf litter. Their model was complex, containing
many parameters that are not normally measured in the field for fire prediction and is given
by Equation 3.39. Here, Δm is the increase in leaf litter moisture content due to nocturnal
condensation (%), w is the surface fuel mass (kg/m2), Qg is the soil heat flux (W/m2), Q* is
the net all-wave radiation flux (W/m2), Lv is the latent heat of vaporisation or sublimation
(J/kg), cp is the specific heat of air at constant pressure (J/kgK), T f is the fuel temperature
(K) and qs and qf are the specific humidities at screen and surface levels, respectively. This
D3.1-2a-12-1000-1
Page 19 of 30
model is based on several micrometeorological principles and assumes that all condensation
remains on the surface until it is either soaked into the fuel or evaporated. Viney and Hatton
(1990) found this model to predict dew fall well in open areas overnight, except when fog
was present. It is unlikely that this model would suit data with dense vegetation near the
ground or with different shaped litter particles.
m 
QG  Q*
100
dt
w t Lv  c p (Ta  T f ) /( q S  q f )
(Equation 3.39)
Figure 1.
Grishin et al. (2001) developed a physical model of the drying of a layer of combustible
forest materials by solving the equations of a binary boundary layer and the equations of
heat and mass transfer in a layer of combustible forest material (CFM) with corresponding
boundary and initial conditions. Figure 1 presents an explanatory diagram of heat and mass
exchange between the CFM layer and the ground atmospheric layer: ue, Te, and C1e are,
respectively, the wind speed, the air temperature, and the mass concentration of water
vapour in air (subscript 1 denotes solar radiation), (ρυ)w is the mass rate of supply of
moisture from the soil due to moisture conduction, and h is the height of the CFM layer
(after Grishin et al. 2001).
3.4 Remote sensing techniques for assessing fuel moisture
The spatial and temporal coverage provided by remote sensors makes remote sensing
techniques a useful alternative to estimating FMC for a whole area at regular time intervals.
Unlike meteorological indices, which refer to the specific conditions where the weather
stations are located, remote sensing data are spatially comprehensive, covering the whole
territory at various spatial resolutions, from few meters to few kilometres, depending on the
sensor. Moreover, remote sensing data are directly derived from vegetation conditions
D3.1-2a-12-1000-1
Page 20 of 30
(reflectance or temperature), whereas meteorological indices measure FMC indirectly,
through the analysis of atmospheric characteristics from which vegetation water status is
estimated. The disadvantages of these satellite-derived data over meteorological indices are
related to temporal frequency, cloud coverage, and calibration.
In recent years, several authors have shown good empirical relations between FMC and
satellite-derived variables in several ecosystems (Paltridge and Barber 1988, Chladil and
Numez 1995, Chuvieco et al. 1999; 2003). FMC for grasslands was more efficiently
estimated than other fuels (Paltridge and Barber 1988, Hardy and Burgan 1999), because
water variations in grasslands have a greater influence on other variables that critically
affect plant reflectance (such as chlorophyll content or leaf area index) and are more
sensitive to seasonal variations than shrubs or trees. Experiences with shrubs have been less
successful, with trends varying by species analyzed.
Satellite variables most commonly used are the normalized difference vegetation index
(NDVI) and surface temperature or a combination of the two (Leblon 2001, Chuvieco et al.
2003). Most of these have been based on NOAA-AVHRR images (with daily coverage), which
until recently have been the only data to provide enough temporal frequency for operational
FMC estimation. Recent sensors, such as SPOT-Vegetation and Terra-MODIS, provide an
alternative for FMC estimation, since they collect data in the short-wave infrared band
(1200-2100 mm), which is the most sensitive spectral band to estimate plant water content,
as shown in several theoretical and practical studies (Chuvieco et al. 2003; 2004).
Live fuel moisture content can be estimated from satellites with the normalized difference
vegetation index (NDVI) which operates by sensing red and near infrared radiation reflected
from surfaces of green or curing vegetation. Burgan et al (1991) showed that the NDVI is
useful for calculating overall site moisture content, whereas Pinol et al. (1998) developed a
rapid method for measuring the moisture content of individual plants. Chladil and Nunez
(1995) used AVHRR to follow curing of grasslands in Tasmania and concluded that the NDVI
gives good results for fuel and soil moisture contents but is not a suitable standalone system
for fire managers.
The estimation of FMC for dead fuels from remotely sensed data is complex for two reasons:
(i) the dead fuels are under the vegetation canopy and, therefore, cannot be directly sensed
remotely; and (ii) dead fuels do not show changes in green coloration from water variations
and, consequently, are less sensitive to changes in radiance. However, some estimation may
be obtained through indirect estimation of weather parameters relevant to FMC assessment,
such as air temperature, water vapour pressure deficit, or rainfall amount (Camia et al.
2003).
4. Knowledge gaps in wildland fuel moisture research
In view of the above synopsis on the different aspects of fuel moisture research, the
following knowledge (research) gaps can be identified:
D3.1-2a-12-1000-1
Page 21 of 30
a. The horizontal spatial variability of dead fuel moisture in relation to vegetation (e.g.,
stand structure, crown closure, stem density, litter and duff depth, etc.) and topographic
characteristics (aspect, slope, soil depth and type, etc.) needs to be measured and modelled
in the field.
b. The temporal (diurnal and seasonal) variation in dead and live fuel moisture content as
related to changes in meteorological parameters (air relative humidity and temperature,
insolation and cloudiness, wind speed and duration, etc) needs to be measured in the field
for all the dominant Mediterranean fuel types at the species level, to the extent possible. For
fire-stricken geographical regions of the Mediterranean Basin, extensive data bases of
seasonal fuel moisture data per species or fuel type must be created and, subsequently,
converted through statistical analysis to empirical models of fuel moisture prediction, refined
for every species or fuel complex.
c. Equilibrium moisture content (EMC) sorption (adsorption and desorption) curves of dead
fuels as a function of air relative humidity and temperature need to be created for the fuels
of all the dominant species.
d. The fuel moisture timelag (TL) concept needs to be reassessed and measured in dead
fuels from different species in relation to the fuel moisture sorption phase (adsorption or
desorption), in order to account for the moisture hysteretic effects of dead fuels during the
wetting or drying process.
e. A physical model that predicts canopy (crown) live fuel moisture content variations in
terms of stand and tree phenological and physiological characteristics and soil water balance
has to be formulated.
f. A comparison between actual measurements of dead fuel moisture with the moisture
content of fuel analogues (i.e., fuel moisture sticks) is necessary in order to validate the
precision of the analogues in fuel moisture assessment.
g. The relationship between dead and live fuel moisture and drought (prolonged period of
high temperatures and low air and soil humidity) needs to be further investigated, and in
particular, the response of shrub and tree species moisture content to drought. The use of
the newly formed SPI (Standard Precipitation Index) drought index might be useful in the
correlation with fuel moisture, in addition to the traditionally used KBDI and Palmer indexes.
In view of the expected global warming and climatic change, this research aspect of fuel
moisture could be very significant in the future.
h. The moisture of extinction (ME) of dead and live fuels must be measured in the field (in
situ) with a long series of ignition experiments in different fuel types and, subsequently,
correlated with the existing fuel moisture content and meteorological parameters in the
field, into regression and probabilistic models.
ME values of the most significant
Mediterranean fuel types must be measured in the laboratory and in the field.
i. The condensation (water vapour that originates from the atmosphere in the form of dew
on the surface of dead fuels) and the latent heat of vaporisation of free water from the fuel
D3.1-2a-12-1000-1
Page 22 of 30
particle surface are two terms that are currently neglected and must be taken into account
in future physical models of dead fuel moisture content.
j. The optimal temporal (time) step for monitoring vegetation moisture content (greenness)
via satellite imagery needs to be determined in dead and live Mediterranean fuel complexes.
5. References
Albini F.A. 1993. Dynamics and modelling of vegetation fires: observations. In: Crutzen P.J.,
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