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Forest fires: management, characteristics and prediction. Monograph for the Intensive Workshop “Analysis and Management of Anthropogenic Natural Hazards and Disasters, Faculty of Geology and Geoenvironment, National and Kapodistrian University of Athens, Naxos, Erasmus+ project: McAgenda, 29/08/2016 – 11/09/2016. 36 p.


Abstract and Figures

Wildfires (forest fires) are part of the ecological circle of Mediterranean ecosystems. However, during the last decades, they have become a problem due to the damages they cause. In the second part of the 20th century, the fire problem in the countries of Southern Europe has got worse. Abandonment of rural areas, long-term fire exclusion practices and expansion of fast growing species that are highly flammable (e.g. pines), have increased fire hazards.
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Forest fires: management,
characteristics and prediction
Athanasiou M. 2016. Forest fires: management, characteristics and prediction. Monograph for
the Intensive Workshop “Analysis and Management of Anthropogenic Natural Hazards and
Disasters, Faculty of Geology and Geoenvironment, National and Kapodistrian University of
Athens, Naxos, Erasmus+ project: McAgenda, 29/08/2016 11/09/2016. 36 p.
Synoptic description of the fire problem
Forest fires
are part of the ecological circle of Mediterranean ecosystems. However,
during the last decades, they have become a problem due to the damages they cause.
In the second part of the 20th century, the fire problem in the countries of Southern
Europe has got worse. Abandonment of rural areas, long-term fire exclusion practices
and expansion of fast growing species that are highly flammable (e.g. pines), have
increased fire hazards.
Fire hazard is directly related to fuel flammability and is a measure of that part of the
fire danger contributed by the fuels available for burning (FAO, 2006). The most
commonly accepted definition of fire danger is “the resultant descriptor of the
combination of both constant and variable factors that affect the initiation, spread and
difficulty to control a wildfire on an area” (Deeming et al., 1972; 1977).
The Mediterranean countries (Portugal, Spain, the south departments of France, Italy,
Greece and Cyprus) face a profound forest fire problem (Xanthopoulos et al., 2006)
and in common with Australia, Canada and U.S.A. they face a growing wildfire risk
from Wildland Urban Interface (WUI) expansion (Fig. 1 & 2).
Forest fire risk can be defined as a function of the wildfire hazard agent, the exposure
of the subject (e.g. forest or structures) reduced by the capacity of the responsible
organizations to mitigate and recover from loss. It is growing exponentially as a result
of high and increasing population density (Tedim et al., 2015) and the complexity of
the WUI and Rural Urban Interface (RUI) (Fig. 3). In Greece, a significant and recent
WUI fire event was the large and destructive wildfire of NE Attica, on 21-24 August
2009 (Xanthopoulos & Athanasiou, 2013) (Fig. 2).
Fire risk is socially constructed and needs to be resolved by social means (Pyne,
2007). Fire planning and risk assessment are concerned with how often fires burn,
what effects they have on wildland and urban values, and what opportunities exist to
improve the situation through management actions (Finney, 2005).
In Greece, fires started becoming a problem in the 1970s. Until the 1960s they were
considered a relatively minor problem, and were mainly fought by the people in the
villages, without even fire trucks until 1970 and with the guidance of the Forest
Service personnel. A few fire engines of the urban Fire Service were occasionally
available and there were no available aerial means (Xanthopoulos, 2008).
The term “forest fires” is mostly used in Europe. The terms “wildfires” or “wildland fires” and “bushfires” are equivalent to the
term “forest fires” and are commonly used in United States of America and in Australia, respectively.
Fig. 1: A moderate intensity WUI fire.
Fig. 2: A settlement - Wildland Urban Interface (WUI) after the NE Attica wildfire of 2009.
Fig. 3: A low intensity RUI fire.
Forest fuels were controlled through forest biomass utilization (for construction,
cooking, heating, animal feed) and agricultural cultivations were kept free of fine
fuels in the summer in order to reduce risk of fire damage. There were many
cultivated fields around the villages, interrupting forest continuity (Xanthopoulos,
2008). Grass was removed from vineyards that functioned as greenbelts (Fig. 4) and
olive groves were also properly cultivated and played the role of fuel breaks (Fig. 5).
Fig. 4: Vineyards function as green belts under correct maintenance (i.e. periodical removal
of grass) and may be used as safety zones by the firefighters.
In the 1950s and 1960s, the population of the villages was decreased sharply due to
the immigration abroad and migration to the big cities, resulting in a significant forest
fuel built-up (accumulation). The abandonment of rural areas (Fig. 6) and the poor
forest management by the Forest Service that further contributed to fuel build-up,
affected the wildfire problem.
Fig. 5: This olive grove plays the role of a fuel break.
Fig. 6: A not properly managed olive grove will facilitate fire spread.
Forest vegetation nowadays reaches the homes at the perimeter of villages allowing
fires to reach there as well (Fig. 7). Unfortunately, most of the people that live in WUI
and RUI areas do not understand the importance of fire prevention and they don’t
prepare their homes for the event of a forest fire (Fig. 7, 8 & 9). Most of them have
little knowledge about firefighting and fire safety and they often make wrong
decisions leaving their homes in panic and/or late, when they are about to be hit by
the fire. Errors in citizens’ response that have led to disasters, can lead to lessons
learned for dissemination to firefighters and the public (Xanthopoulos, 2015).
Forest fire (wildfire) management
Forest fire management is not only a technical matter of fire suppression but has also
important social, biological, political and financial aspects. It is a complex problem
that has to do with factors affecting the occurrence of fires, their characteristics and
their destruction potential.
Fig. 7: The grass reaches the homes of this settlement allowing a fire to reach there as well.
Fig. 8: The wildfire reached the village, destroyed several houses and caused damages to
Fire suppression oriented policy seems to be effective in Central European countries
where forest fires are not a main environmental factor. In spite of that, some recent
large fires could be considered as early warnings or signs of climate change that tends
to affect forest fire risk in Central and Northern Europe. In 2008, a wildfire spread
across Southern Norway burning 2,600 ha mainly of pine forest and in 2014 a wildfire
burned 15,000 ha in Central Sweden (Tedim et al., 2015).
Fig. 9: The 2009 WUI fire is burning down a house in Palaia Penteli (Attica).
In Southern Europe, suppression is successful in “easy” fire seasons which means that
under “easy” conditions (Fig. 10), most fires are stopped immediately. The doctrine
“keep every fire as small as possible” which was the main objective of the forest fire
policy in Europe during the twentieth century, combined with the poor forest fuel
management, have led to fuel accumulation so when fire danger is extreme (that is
under hot, dry and windy conditions) the stage is usually set for disaster (Fig. 11).
It is obvious that such an approach is not sustainable over the long run. What is
needed is well designed, comprehensive forest fire policies based on a good
understanding of all the interacting factors and, of course, adapted to each country’s
environment and conditions (Xanthopoulos, 2007b). Those policies should take into
consideration the underlying causes of fire incidence, the fire regime (frequency of
fire occurrence, fire intensity, etc.) and the ecological role of the fire in the ecosystem.
In the Mediterranean region, wildfires occasionally erupt from natural causes (mainly
lighting) but the majority of the wildfires, are human-caused (Leone et al., 2009;
Tedim et al., 2015), being also associated with the increasing WUI (Martınez et al.,
2009; Badia et al., 2011; Marino et al., 2014). The human-caused starts, fall into three
categories: a) negligence, b) accident and c) intentional arson.
Fig. 10: Firefighting in low maquis under relatively “easy” conditions.
Fig. 11: Extreme fire behavior in a lowland pine forest under hot, dry and windy conditions.
Negligent behavior causes the majority of the wildfires in the Mediterranean
countries. Wildfires often start from unattended campfires in forested areas,
irresponsibly discarded cigarettes, cars parked on grasses in WUI areas and burning
debris in agricultural or RUI areas (e.g. annual crop residues which are mainly dead
fine fuels such as wheat stubble or pruning). Sparks by train brakes, powerlines and
machinery as well as car accidents, often cause accidental fires. Regarding arsonists,
some of them are pyromaniacs, some others want to cause terror and some others
focus on relatively small areas (Xanthopoulos, 2010).
The common Fire Cause classification that was established in 2012 and can be used
by all European countries to report national fire causes to the European Fire Database
(Camia et al., 2013), is expected to improve knowledge on this subject (Tedim et al.,
2015). The Mediterranean countries contribute 94% of the total burned area in
Europe, according to an analysis of the 1975-2000 statistics by the European Forest
Institute. Analysis of fire statistics (causes, burned areas, etc.) shows that a small
number of fires contribute most to the total area burned (Xanthopoulos, 2010).
Wildfire Prevention aims at minimizing the incidence of destructive fires. Forest fuel
management plays a key role in successful wildfire prevention (Marino et al., 2014)
and reduces wildfire hazard. The need for reducing fire hazard through active fuel
management is becoming more and more obvious (Xanthopoulos et al., 2006) and
should be integrated in sustainable long-term forest planning and in other rural
development activities, taking into consideration fire risk, land use and fire causes.
Apart from reducing fire potential, fuel management activities and agroforestry
practices can directly involve and benefit the rural population (Marino et al., 2014).
As there is no single ideal technique for wildfire prevention, fuel management plans
should commonly combine different practices (Fernandes & Botelho, 2003) and
techniques, including mechanical treatments, prescribed burning and controlled
grazing depending on the particular characteristics of each forest or rural area.
Applying fuel treatments at an appropriate landscape scale is critical to reducing
wildfire damage (Agee & Skinner, 2005), in the context of prevention and pre-
suppression planning. Areas with low surface fuel loads (Fig. 12) and properly
cultivated agroforestry fields (Fig. 5) that may function as fuel breaks, as well as
constructed fuel breaks along strategic locations (Fig. 13), can support suppression
actions. Fuel break construction is common in Spain, France and Italy, while it is less
common in Portugal and quite uncommon in Greece (Xanthopoulos et al., 2006).
Nevertheless, vegetation removal is not a panacea as fuel breaks and fire breaks don’t
stop a fire but they only provide a starting line for firefighters because they create an
area of reduced fire intensity.
Estimation of fire hazard in broad areas may provide general guidance on forest type
selection when planning re-vegetation and restoration of a burned site. (Xanthopoulos
et al., 2012). One such example is that of the site of ancient Olympia, in Peloponnese
Greece which was burned in August 2007 (Xanthopoulos et al., 2012). In that case,
the scientists of Mediterranean Forest Ecosystems and Forest Products Technology in
Athens, re-introduced the broadleaved species that occupied the site in ancient times,
reducing fire hazard for the regenerating forest (Lyrintzis et al., 2010).
Fig. 12: A Pinus brutia stand which can function as a fuel break.
Fig. 13: A stripe-shaped fuel break in Chios, constructed by the volunteer team Omikron.
Post fire forest regeneration depends on meteorological conditions, soil type and slope
steepness, number of seeds, mortality of seedlings, competition among species, fire
regime (frequency of fire occurrence, fire intensity and the amount of fuel consumed),
grazing intensity and land use changes. Restoration strategies may consist of a
combination of techniques that may be used both immediately after a fire, and may
continue to be necessary for years’ post-fire. Soil stabilization to control erosion and
flood events is crucial and can be applied by utilizing suitable mechanical techniques.
Fire Danger
A Fire Danger Rating level takes into account: current and antecedent weather, fuel
types, and both live and dead fuel moisture. Fine dead fuel moisture content and wind
speed are major components in the fire danger rating calculation. Since these
components change daily, the fire danger rating of an area also changes daily, giving
the fire manager a tool to help with the day-to-day “fire business” decisions (Fig. 14).
Following are the terms and definitions for adjective fire danger.
Low (L) (Green) Fuels do not ignite readily from small firebrands although a more
intense heat source, such as lightning, may start fires in duff or punky wood. Fires in
open cured grasslands may burn freely a few hours after rain, but woods fires spread
slowly by creeping or smoldering, and burn in irregular fingers. There is little danger
of spotting.
Moderate (M) (Blue) Fires can start from most causes but, with the exception of
lightning fires in some areas, the number of starts is generally low. Fires in open
cured grasslands will burn briskly and spread rapidly on windy days. Timber fires
spread slowly to moderately fast. The average fire is of moderate intensity, although
heavy concentrations of fuel, especially draped fuel, may burn hot. Short distance
spotting may occur, but is not persistent. Fires are not likely to become serious and
control is relatively easy.
High (H) Yellow All fine dead fuels ignite readily and fires start easily from most
causes. Unattended brush and campfires are likely to escape. Fires spread rapidly and
short distance spotting is common. High intensity burning may develop on slopes or
in concentrations of fine fuels. Fires may become serious and their control difficult
unless they are attacked successfully while small.
Very High (VH) (Orange) Fires start easily from all causes and, immediately after
ignition, spread rapidly and increase quickly in intensity. Spot fires are a constant
danger. Fires burning in light fuels may quickly develop high intensity characteristics
such as long range spotting and fire whirlwinds when they burn in heavier fuels.
Extreme (E) (Red) Fires start quickly, spread furiously, and burn intensely. All fires
are potentially serious. Development into high intensity burning will usually be faster
and occur from smaller fires than in the very high fire danger class. Direct attack is
rarely possible and may be dangerous except immediately after ignition. Fires that
develop headway in heavy slash or conifer stands may be unmanageable while the
extreme burning conditions last. Under these circumstances the only effective and
safe control actions are on the flanks until the weather changes or the fuel loading
Fig. 14: An updated Fire Danger Prediction Map is issued and posted every day during the
fire season (from 1 May to 30 October) by the General Secretariat for Civil Protection of
Wildfires behaviour descriptors and prediction
Success in pre-suppression planning and actual suppression of wildfires is directly
related to how well Fire Managers understand and are able to predict fire behavior.
The safety of all firefighting personnel also depends on this knowledge and all actions
taken on a fire during suppression, depend on how it “behaves”.
Fire behaviour is the manner in which fuel ignites, flame develops, and fire spreads
and exhibits other related phenomena as determined by the interaction of fuel,
weather and topography.
Fire managers use various fire behavior descriptors such as rate of spread, flame
length, fire line intensity, spotting, the onset of crowning (or crowning initiation
and/or propagation), etc. The term Rate of Spread (ROS, km/h or m/min) refers to the
linear rate of advance of a wildfire either it is a head fire or not (Fig. 15). Head of the
fire is the segment of the fire perimeter oriented in the direction of maximum spread
(fanned by the wind or/and burning upslope) whereas heel of the fire is that segment
of the fire perimeter that spreads against the wind or/and burning downslope (Fig. 15).
Flame length (FL, m), has been used to describe suppression difficulty (Alexander,
1982; Anderson et al., 2006). Flame size is directly related to the frontal fire intensity
(Forestry Canada Fire Danger Group, 1992) or fire line intensity (Byram, 1959;
Albini, 1976) (I, kW/m) which is defined as the energy output rate per unit length of
fire front (Fig. 16). Depending on the fuel biomass which is available for
consumption, the general weather conditions and the time of the year, the fire
intensity may vary, influencing the heat per meter of fire line that the soil is subjected
Fig. 15: The anatomical parts (segments) of a wildfire.
Fig. 16: Flame characteristics of a head grassfire on a level terrain: Flame length (FL), flame
height (hF), flame tilt angle (AT), flame angle (A).
The effects of fire on soil depend on the temperature and the residence time of the fire
as well as the soil moisture content (Canu et al., 2007). The degree of change in both
the chemical and biological properties of soil are strongly linked with fire intensity
(Flinn et al., 1984). Exposure of the mineral soil may lead to loss of nutrient
(Chesterfield, 1984) and organic matter. Some of these events may be further
aggravated by the weather conditions following fire (Canu et al., 2007).
Spotting occurs when a fire produces sparks or embers that are carried by the wind
and start new fires (spot fires) beyond the zone of direct ignition by the main fire (Fig.
17, 18, 19, 20 & 21). This phenomenon often threats the safety of human life,
exacerbates fire suppression activities and causes structural losses in WUI fires
(Alexander, 2009). Spotting can jeopardize the safety of the firefighters (Athanasiou
& Xanthopoulos, 2013) (Fig. 19).
Fig. 17: Massive spotting in phrygana
Fig. 18: A downwind spot fire in a pine forest
Fig. 19: Spotting in shrub lands jeopardizing the safety of firefighters
Fig. 20: Spot fires spreading through a rough landscape
Analysis of field observations and measurements of spotting on maquis, phrygana and
grass, that were made during the evolution of a large number of wildfires in Greece,
in eight fire seasons (2007-2014), has led to some preliminary conclusions. There was
no spotting at air relative humidity (RH) values higher than 40,3 % but it was
documented that for RH values lower than 17% there was massive spotting that
triggered extreme fire behavior (Athanasiou, 2015). Those results represent the first
approach of this type to the spotting phenomenon in Greece.
Fig. 21: Surface spot fires spreading rapidly upslope late in the evening
Surface fires (Fig. 22, 23 & 24) burn through surface fuels (the maximum height of
surface fuels is roughly 2.5 meters).
Fig. 22: A surface fire spreading in phrygana, downslope, fanned by strong wind
Fig. 23: A surface fire spreading in short maquis, upslope
Fig. 24: A low intensity surface fire spreading beneath a pine forest canopy
The phenomenon that occurs when a fire transitions from a surface fire into the
crowns of individual trees or small groups of trees and burns briefly and vigorously
but not necessarily from one crown to another is called torching (Albini, 1983;
Andrews, 1996) (Fig. 25). Tree flammability, presence of ladder fuels, the height of
lower branches (i.e. Canopy Base Height, CBH) and the entire surface fire behavior,
may help torching.
Fig. 25: A small group of pine trees torching
A fire in which the crowns of individual trees or small groups of trees torch and burn,
but solid flaming in the canopy cannot be maintained except for short periods is called
a passive crown fire: (Scott & Reinhardt, 2001) (Fig. 26).
As burning conditions get worse and winds increase, a passive crown fire may
become an active crown fire (Fig. 27 & 28) in which the surface and crown “phases”
of the fire travel together as a linked unit (Van Wagner, 1977).
A preliminary finding about active crown fires in Aleppo pine forests with tall maquis
understory in Greece, is that active crown fire ROS tends to be two times greater than
surface fire ROS (Athanasiou & Xanthopoulos, 2010; Athanasiou, 2015). This finding
is in general agreement with the suggestion of Cruz et al. (2005) that rates of spread
in crown fires are often twice the spread of the surface fire.
Fig. 26: A passive crown fire in a pine forest
Fig. 27: An active crown fire in a pine forest
Fig. 28: Strong winds drive the flames of an active crown fire. It is a wind driven (Rothermel,
1991) active crown fire
The fires that are associated with the development of a strong convection column or
plume, that towers above the fire rather than leaning over before the wind, are called
plume dominated fires (Rothermel, 1991) (Fig. 29 & 30). The power of a plume
dominated fire is higher than the power of the wind, and spot fires are produced
massively and are drawn back towards the huge convection column by strong indrafts.
In Greece, it has been found that the behavior of the powerful, plume dominated fires
is affected by the conditions that the plume generates rather than by the characteristics
of the fuels that the fire spreads in (Athanasiou, 2015).
Forest fuels and fire behavior in Greece
Vegetation in Greece mainly consists of Mediterranean shrubs (phrygana and
maquis), and pine, oak and fir forests. In Greece, wildfires mainly occur in the lower
elevation Mediterranean vegetation. In 2007, the worst fire season in its recent
history, many fires burned high elevation and low flammability forests of Northern
Greece, due to the extreme drought. They kept burning for more than ten days until
that part of Greece received a significant quantity of rain (Athanasiou &
Xanthopoulos, 2010). Phrygana (Fig. 31 & 22) are low, often thorny, xeric shrubs up
to 0.5 m height (Sarcopoterium spinosum, Phlomis fruticosa, Cistus spp., etc.) which
are adapted to high fire frequency and occupy lower elevation areas.
Fig. 29: A plume dominated fire heading towards Ancient Olympia, in 2007
Fig. 30: A plume dominated fire spreading vigorously in mountainous Arcadia, in 2007
Fire in phryganic areas is characterized by low to medium fire intensity and can reach
very high rates of spread under high wind conditions. Phrygana have been the cause
of many firefighting accidents in Greece They are fine, quite flammable, flashy fuels
and respond very quickly to changes of the environmental conditions (Xanthopoulos
Fig. 31: Phryganic area where the dominant species is Sarcopoterium spinosum
Maquis (Fig. 32 & 33) are tall or short typical Mediterranean evergreen shrublands
(Arbutus unedo, Pistacia lentiscus, Pistacia terebinthus, Phillyrea latifolia, Quercus
coccifera, etc.). Those shrublands consist of drought-resistant broadleaved species,
their fuel height ranges from 0.5 to more than 2.5 m and fuel load varies accordingly.
Fire behavior varies significantly and depends on species composition, site
characteristics, fuel loads, intensity of livestock grazing, etc. Most maquis species re-
sprout vigorously after fire.
Fig. 32: Tall maquis
Most of the phryganic species (e.g. Sarcopoterium spinosum, Phlomis fruticosa) and
maquis (Quercus coccifera, Pistacia lentiscus, Arbutus unedo, Phillyrea sp. Erica
arborea) resprout after the fire while some phryganic species (e.g. Cistus spp.) are
obligate seeders.
Low-elevation pine forests mainly comprise of Pinus halepensis or Pinus brutia,
usually with evergreen shrub understory that is short or tall maquis (Fig. 34). In those
lowland coniferous forests, high intensity crown fires occur under high fire danger
conditions, and quite often their behavior is extreme (Fig. 35 & 36).
Fig. 33: Short maquis
Fig. 34: Aleppo pine forest (Pinus halepensis) with tall maquis understory
Fig. 35: A high intensity active crown fire is spreading in an Aleppo pine forest (Pinus
Fig. 36: High rates of energy release as shown by the flame lengths in an Aleppo pine forest
Deciduous and semi-deciduous oak forests (Quercus spp.) (Fig. 37 & 38), at altitude
800 m, and fir forests (Fig. 39) at higher altitude, usually burn with less intensity than
the other types of Mediterranean vegetation. High elevation forests may consist of
deciduous broadleaved species (Fagus orientalis, etc.) or cold tolerant conifers (Pinus
nigra, Pinus sylvestris, Abies spp., etc) which are not adapted to fire (Fig. 40). Fire
events are rare in the high elevation forests but they are more destructive.
Fig. 37: Oak forest in Southern Peloponnese
Fig. 38: Oak forest in Central Peloponnese
Fig. 39: A moderate intensity passive crown fire in a fir forest
Fig. 40: Black pine forest (Pinus nigra) in Northern Greece
Weather (meteorological conditions) and topography
When the fire spreads through a rough landscape (steep slopes, ravines, gorges,
narrow valleys, canyons, saddles, ridges) the interaction between the general wind,
the terrain and the energy that is released by the fire, affects its propagation, leading
to erratic fire behaviour and making firefighting very difficult and dangerous
(Athanasiou & Xanthopoulos, 2016) (Fig. 41, 42 & 43). The local wind field at the
fire area often reflects the interaction between the topography and the general wind.
Fig. 41: Extreme fire behavior was documented in this gorge
Fig. 42: A wind-driven fire is spreading rapidly downslope, through an agroforestry mosaic
Fig. 43: Eruptive fire behaviour in a box canyon
Wildfire behaviour prediction
Wildfire behaviour models can be adopted in wildfire management (forest
management, fire prevention and suppression) only if their degree of reliability is well
known. Broad operational adoption of such systems can be achieved and benefits can
be maximized only if their strengths and generalizations, assumptions, weaknesses or
limitations are well known. Continuous and extensive testing of fire behaviour
prediction models, is necessary since it shows their limitations, documents their
proper use and increases, eventually, their contribution to firefighting safety and
efficiency (Athanasiou & Xanthopoulos, 2014; Athanasiou, 2015). Minor or
significant disagreements between fire behaviour observations and predictions, are
often attributed to the inadequacy of stylized fuel models to represent the existing
forest fuel conditions. In those cases, specific custom fuel models can be developed to
better describe heterogeneous forest fuel situations and mixed fuel complexes.
A significant number of studies have tested the prediction performance of BehavePlus
(Andrews et al., 2005) for various types of fuels (Athanasiou & Xanthopoulos, 2014).
It is based on Rothermel’s model (1972) which is the most widely used model of this
kind in the world. It is practical, documented and the most comprehensive and robust
to date. It’s not a spatial system but its ‘point-based’ fire modeling approach, provides
a quick and easy way to do initial fire behavior assessments and allows easier ‘what
if’ gaming. Measurements of weather conditions and topography as well as
information about the forest fuels (fuel models), are necessary as inputs for using the
BehavePlus prediction system.
It has been found that for Greece, BehavePlus can be a useful tool for predictions of
surface wildfire ROS in tall maquis (Fig. 44), short maquis (Fig. 45 & 23), phryganic
areas where the dominant species is Sarcopoterium spinosum (Fig. 22), and grass
(Fig. 46) (Athanasiou & Xanthopoulos, 2014; Athanasiou, 2015).
The analysis of the flame length (FL) for the same fuel types in Greece, showed that
BehavePlus predictions are not reliable. The finding that FL is seriously under-
predicted when using BehavePlus to predict fire behaviour in Sarcopoterium
spinosum dominated phrygana fields, is an important result that can be very useful for
the safety of firefighters (Fig. 47 & 48). It should be seriously taken into consideration
in operational firefighting in the country as the underestimation takes place in a
narrow band of FL values that includes the FL threshold value of 1.2 m which is
considered as the limit for direct attack on the flames with hand tools. The reliability
of FL predictions is crucial since FL affects extinguishing capacity with hand tools
and inaccuracies in FL predictions could jeopardize the safety of the firefighters
(Athanasiou & Xanthopoulos, 2014; Athanasiou, 2015).
Fig. 44: A passive crown fire spreading in tall maquis, downslope
Fig. 45: A surface fire spreading in short maquis, upslope
Fig. 46: A surface fire spreading in grass, on flat terrain
Fig. 47: Plot of observed (FLobserved ) and predicted flame length for Sarcopoterium spinosum
dominated phrygana fields. The pairs of values are sorted in ascending order of FLobserved.
Fig. 48: A surface fire spreading in phrygana, downslope and against the wind
Agee, J., Skinner, C., 2005. Basic principles of forest fuel reduction treatments. Forest
Ecology and Management 211(1-2), 83-96.
Albini, F.A., 1976. Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-30.
Ogden, UT: USDA, Forest Service, Intermountain Forest and Range Experiment
Station. 92 p.
Albini, F.A., 1983. Potential spotting distances from wind-driven surface fires. Res.
Pap. INT-309. Ogden, UT: U.S. Department of Agriculture, Forest Service,
Intermountain Forest and Range Experiment Station. 27 p.
Alexander, M.E., 1982. Calculating and interpreting forest fire intensities. Can. J. Bot.
60: 349-357.
Alexander, M.E., 2009. Some pragmatic thoughts on the prediction of spotting in
wildland fires. MITACS/GEOIDE Conference on Forest Fire Modelling, June 22-
23, 2009 Hinton, AB.
Anderson, W., Pastor, E., Butler, B., Catchpole, E., Dupuy, J.L., Fernandes, P.,
Guijarro, M., Mendes-Lopes, J.M., Ventura, J., 2006. Evaluating models to
estimate flame characteristics for free-burning fires using laboratory and field data.
In ‘Proceedings, V International Conference on Forest Fire Research’, 2730
November 2006, Figueira da Foz, Portugal. (Ed. DX Viegas) (CD-ROM). Forest
Ecology and Management. 234S. S77.
Andrews, P.L., 1996. Fire behavior. In: Pyne, S.J.; Andrews, P.L.; Laven, R.D.
Introduction to Wildland Fire. 2d ed. New York, NY: John Wiley and Sons:
Chapter 2.
Andrews, P.L., Bevins, C.D., Seli, R.C., 2005. BehavePlus fire modeling system,
Version 3.0: User’s Guide. General Technical Report RMRS-GTR-106WWW
revised. Ogden, UT: U.S. Department of Agriculture, Forest Service,
Intermountain Forest and Range Experiment Station. 132 p.
Athanasiou, M., Xanthopoulos, G., 2010. Fire behaviour of the large fires of 2007 in
Greece. In proceedings of the 6th International Conference on Forest Fire
Research. 15-18 November 2010, Coimbra, Portugal. D.G. Viegas, Editor.
ADAI/CEIF, University of Coimbra, Portugal. Abstract p. 336, full text on CD.
Athanasiou, M., Xanthopoulos, G., 2013. Observations of the spotting phenomenon,
in wildfires in Greece. 30-40 p. In proceedings of the 16th Hellenic Forestry
Conference, October 6-9, 2013, Thessaloniki, Greece. Hellenic Forestry Society.
1144 p. (in Greek with English abstract).
Athanasiou, M., Xanthopoulos, G., 2014. Wildfires in Mediterranean shrubs and
grasslands, in Greece: In situ fire behaviour observations versus predictions. In
proceedings of the 7th International Conference on Forest Fire Research: Advances
in Forest Fire Research, 17-20 November 2014, Coimbra, Portugal. D. G. Viegas,
Editor. ADAI/CEIF, University of Coimbra, Portugal. Abstract p. 488, full text on
Athanasiou, M., 2015. Development of an optimal methodology for forecasting forest
fire behaviour in Greece. PhD dissertation, Department of Geology and
Geoenvironment, National and Kapodistrian University of Athens, 408 p.
Athanasiou, M., Xanthopoulos, G., 2016. The wildfire of 17 July 2015, at the Faraklo
village, Lakonia, Greece and its suppression. International Conference on Forest
Fires and WUI Fires. May 25-27th 2016, Aix en Provence, France (in Press).
Badia, A., Serra, P., Modugno, S., 2011. Identifying dynamics of fire ignition
probabilities in two representative Mediterranean wildlandurban interface areas.
Applied Geography (Sevenoaks, England) 31(3), 930940.
Byram, G.M., 1959. Combustion of forest fuels; Forest fire behavior. in Davis, K.P.,
ed. Forest fire: Control and use. New York: McGraw-Hill: 61-89, 90-123.
Camia, A., Durrant, T., San-Miguel-Ayanz, J., 2013. In: Harmonized Classification
Scheme of Fire Causes in the EU Adopted for the European Fire Database of
EFFIS Executive Report. JRC, European Commission, Ispra, Italy.
Canu, A., Arca, B., Ghiglieri, G., Pittalis, D., Deroma, M., Ventura, A., Arca, A.,
2007. Fire intensity in moderate drought conditions: effect on topsoil properties in
Mediterranean shrubland vegetation in Seventh Symposium on Fire and Forest
Meteorology, Bar Harbor, ME American Meteorological Society.
Chesterfield, E.A., 1984: Effects of Fire on the Flora of dry Sclerophyll Forest. In:
EHM Ealey (Eds.) Fighting Fire with Fire, Proc. Symposium on Fuel Reduction
Burnibg, Monash University, Victoria, September 17-18, 1983, pp.129-145.
Cruz, M. G., Alexander, M. E., Wakimoto, R. H., 2005. Development and testing of
models for predicting crown fire rate of spread in conifer forest stands. Canadian
Journal of Forest Research 35, 1626-1639.
Deeming, J.E., Lancaster, J.W., Fosberg, M.A., Furman, R.W., Schroeder, M.J., 1972.
The National Fire Danger Rating System, Report No. RM-84. USDA, Forest
Service,, Ogden, UT.
Deeming, J.E., Burgan, R.E., Cohen, J.D., 1977. The National Fire-Danger Rating
System -1978. USDA, Forest Service, General Technical Report INT-39,
Intermountain Forest and Range Experiment Station, Ogden Utah. 63 p.
FAO, 2006. Fire management: voluntary guidelines. Principles and strategic actions.
Fire Management Working Paper 17. Rome. Available at
Fernandes, P. M., Botelho, H.S., 2003. A review of prescribed burning effectiveness
in fire hazard reduction. International Journal of Wildland Fire 12, 117128.
Finney, M.A., 2005. The challenge of quantitative risk analysis for wildland fire.
Forest Ecology and Management 211:97-108.
Flinn, D.W., Farrell, P.W., Steward, H.T.L., Leitch, C.J., Hopmans, P., 1984: The
Effects of Fire in Eucalypt Forest on Soil, Nutrient Cycling, Tree Growth and
Catchment Hydrology: a Review with Particular Reference to Fuel reduction
Burning. In: EHM Ealey (Eds.) Fighting Fire with Fire, Proc. Symposium on Fuel
Reduction Burnibg, Monash University, Victoria, September 17-18, 1983, pp.146-
Forestry Canada Fire Danger Group, 1992. Development and structure of the
Canadian Forest Fire Behavior Prediction System. For. Can. Info. Rep. ST-X-3.
Leone, V., Lovreglio, R., Martın, M.P., Martınez, J., Vilar, L., 2009. Human factors
of fire occurrence in the Mediterranean. In: Chuvieco, E. (Ed.), Earth Observation
of Wildland Fires in Mediterranean Ecosystems. Springer-Verlag, Berlin
Heidelberg, pp. 149e170.
Lyrintzis, G., Baloutsos, G., Karetsos, G., Daskalakou, E.Ν., Xanthopoulos, G.,
Tsagari, C., Mantakas, G., Bourletsikas, A., 2010. Olympic Rebirth. Wildfire
Marino, E., Hernando, C., Planelles, R., Madrigal, J., Guijarro, M., Sebastian, A.,
2014. Forest fuel management for wildfire prevention in Spain: a quantitative
SWOT analysis. International Journal of Wildland Fire, 23, p. 373-384.
Martınez, J., Vega-Garcıa, C., Chuvieco, E., 2009. Human-caused wildfire risk rating
for prevention planning in Spain. Journal of Environmental Management 90,
12411252. doi:10.1016/J.JENVMAN.2008.07.005.
Pyne, S.J., 2007. Problems, paradoxes, paradigms: triangulating fire research. Int. J.
Wildland Fire 16, 271-276.
Rothermel, R.C., 1972. A mathematical model for predicting fire spread in wildland
fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, Forest
Service, Intermountain Forest and Range Experiment Station. 40 p.
Rothermel, R.C., 1991. Predicting behaviour and size of crown fires in the Northern
Rocky Mountains. Res. Pap. INT438. Ogden, UT: USDA Forest Service,
Intermountain Research Station.
Scott, J. H.; Reinhardt, E. D., 2001. Assessing crown fire potential by linking models
of surface and crown fire behavior Res. Pap. RMRS-RP-29. Fort Collins, CO: U.S.
Department of Agriculture, Forest Service, Rocky Mountain Research Station. 59
p.Tendim F., Xanthopoulos G. and Leonne V. 2015. Forest fires in Europe: Facts
and Challenges. p.p. 77-93. In Wildfire Hazards, Risks, and Disasters, Paton et al
2015 283 p.
Tedim, F., Xanthopoulos, G., Leone, V., 2015. Forest Fires In Europe: Facts and
Challenges, In book: Wildfire Hazards, Risks and Disasters, Edition: 1st, Chapter
5, Publisher: Elsevier, Editors: Paton Douglas, Mccaffrey Sarah, Tedim Fantina,
Büergelt Petra, pp. 77-99.
Van Wagner, C. E., 1977. Conditions for the start and spread of crown fire. Canadian
Journal of Forest Research. 7: 2334.
Xanthopoulos, G., Caballero, D., Galante, M., Alexandrian, D., Rigolot, E., Marzano,
R., 2006. Forest Fuels Management in Europe. pp. 29-46. In proceedings of the
International Conference on “Fuels Management - How to Measure Success”,
March 28-30, 2006, Portland, Oregon, USA. Andrews, P. L, and B. W. Butler,
compilers. USDA Forest Serv., Rocky Mountain Research Station, Fort Collins,
CO. RMRS-P-41. 809 p.
Xanthopoulos, G., 2007a. Forest fire related deaths in Greece: confirming what we
already know. p. 339. In book of abstracts of the “IV International Wildland Fire
Conference”, May 13-17, 2004, Seville, Spain. Full paper on the CD
accompanying the book of abstracts.
Xanthopoulos, G., 2007b. Forest fire policy scenarios as a key element affecting the
occurrence and characteristics of fire disasters. p. 129. In book of abstracts of the
“IV International Wildland Fire Conference”, May 13-17, 2007, Seville, Spain.
Full paper on the CD accompanying the book of abstracts.
Xanthopoulos, G., 2008. People and the Mass Media during the fire disaster days of
2007 in Greece. pp. 494-506. In proceedings of the International Bushfire Research
Conference on “Fire, Environment and Society” of the Bushfire Cooperative
Research Centre and the Australasian Fire Emergency Service Authorities Council
(AFAC), September 1-3, 2008, Adelaide, Australia. 570 p.
Xanthopoulos, G., Viegas, D. X., Caballero, D., 2009. The fatal fire entrapment of
Artemida (Greece) 2007. pp. 65-75. In “Recent Forest Fire Related Accidents in
Europe”. Domingos Xavier Viegas (Editor). European Commission, Joint Research
Centre, Institute for Environment and Sustainability. EUR 24121 EN. 75 p.
Xanthopoulos, G., 2010. Examining the causes of large forest fires in mediterranean
countries. pp 22-23. In proceedings of the international workshop on “Assessment
of Forest Fire Risks and Innovative Strategies for Fire Prevention”, 4-6 May, 2010,
Rhodes, Greece. Ministerial Conference on the Protection of Forests in Europe. 46
Xanthopoulos, G., Fernandes, P., Calfapietra, C., 2012. Fire hazard and flammability
of European forest types pp. 79-92. In Moreira, F., M. Arianoutsou, P. Corona, and
J. De las Heras (Eds.). Post-Fire Management and Restoration of Southern
European Forests. Springer, Heidelberg. 329 p.
Xanthopoulos, G., Athanasiou, M., 2013. The evolution and the suppression of the
NorthEast Attiki wildfire of August 21 to 24, 2009: Lessons for the future. 73-83 p.
In proceedings of the 16th Hellenic Forestry Conference, October 6-9, 2013,
Thessaloniki, Greece. Hellenic Forestry Society. 1144 p. (in Greek with English
Xanthopoulos, G., 2015. Wildfires and Safety Issues in Greece. p.p. 157-176. In
Current International Perspectives on Wildland Fires, Mankind and the
Environment, Leblon Alexander 2015. 262 p.
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The Mediterranean region accounts the larger proportion of human caused fires in the world (95%) followed by South Asia (90%), South America (85%) and Northeast Asia (80%) (FAO 2007). Socio-economic changes which are occurring in Europe along with global warming result in an augment of fire risk. Systematic and reliable information on fire causes is necessary in order to improve wildland fire management. However, collection of information on forest fire causes and motivations is still quite restricted in most countries around the world. The unknown cause is still too frequent in many wildfire statistics. A promising technique to overcome this shortcoming is the Delphi technique which uses a panel of carefully selected experts to improve the knowledge on fire motivations in a specific area. Understanding more about why people start fires would help to reduce the impacts of deliberate fire lighting. Spatial and temporal analysis of wildland fire occurrence data and the interaction with explanatory geographical variables is a critical part of fire management activities. Geographic Information Systems (GIS) are appropriate tools to create, transform, combine and integrate variables related to fire risk in order to find geographical and analytical relationships which help to discriminate areas where risk factors are most severe in order to adopt the appropriate preventive actions
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This paper presents a testing of surface wildfire rate of spread (ROS) field observations (ROS observed(surface)) versus rate of spread predictions (ROS predicted(surface)) from BehavePlus (Andrews et al., 2005) for tall and short Mediterranean shrublands, Sarcopoterium spinosum (small xeric shrub, up to 0.5 m height) and grass. In order to evaluate the degree of their agreement and analyse the results of their correlations, surface or passive crown fire behaviour data as well as meteorological, topography and forest fuels data had to be prepared according to specific criteria in order to ensure their quality and compatibility. Ninety five fire behaviour data records were created from field observations and measurements made during the evolution of specific wildfires in Greece, in the last seven fire seasons (2007-2013). The data were classified depending on the fuel model that describes the fuel types they had spread in and, thus, four data subsets were generated that correspond to the following four fuel models for Greece which had been developed in 2001 (Dimitrakopoulos et al 2001, Dimitrakopoulos 2002): a) "Evergreen-schlerophyllous shrublands (1.5 -3 m)" for tall maquis (38 cases), b) "Evergreen schlerophyllous shrublands (up to 1.5 m)" for short maquis (13 cases), c) "Phrygana II (Sarcopoterium spinosum)" for phryganic areas where the dominant species was Sarcopoterium spinosum (26 cases) and d) "Mediterranean grasslands" for grass (18 cases). After creating the database, the BehavePlus system was used to produce rate of spread predictions for each of the ninety five cases and flame length (FL) predictions (FL predicted values) for the twenty six cases of Sarcopoterium spinosum dominated phrygana fields. The main finding is that for the four Greek fuel models tested, BehavePlus can be a useful tool for predictions of fire behaviour. However, there is a relatively consistent over-prediction of ROS for the models "Evergreen-schlerophyllous shrublands (1.5 -3 m)" for tall maquis, "Evergreen schlerophyllous shrublands (up to 1.5 m)" for short maquis (13 cases), and "Phrygana II (Sarcopoterium spinosum)", while there a significant under-prediction for the "Mediterranean grasslands" fuel model. Four linear regression equations describing mathematically the relation of the predicted to the observed ROS were developed. They are statistically significant and can be used for adjusting BehavePlus predictions to match "real world" fire behaviour. A further finding was that flame length is seriously under-predicted when using BehavePlus with the Phrygana II fuel model to predict fire behaviour in Sarcopoterium spinosum dominated phrygana fields. This is an important result that can be very useful for the safety of firefighters.
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This paper focuses on determining ignition probabilities of forest fires in Wildland-Urban Interface (WUI) areas to more effectively develop prevention plans. Multivariate Logistic Regression methodology was used to identify the most important biophysical and human variables to explain the emergence of ignition points, incorporating spatial analysis from Remote Sensing and Geographical Information Systems (GIS) data. To test this model we used two representative Wildland-Urban Interface landscapes in a Mediterranean environment, located in Catalonia (northeast Spain): an example of dispersed housing in a forested area associated to metropolitan processes and an agro-forestry mosaic connected with tourism development. For a better understanding a temporal comparison has been made, analyzing data from 1990s and from 2000s. Results show differences in the explicative models; in the former study area, high ignition probabilities are associated to human activity, mainly distance to urban areas and road networks, whereas in the latter they are related with land-use (scrubland and coniferous forest) and mean maximum temperatures. As a consequence, prevention tasks seem to be less difficult in the more metropolitan study area because the spatial model is further disperse in the agro-forestry mosaic. Finally, temporal analysis indicates that both areas were more prone to forest fires in the most recent decade than in the 1990s.
Some theory and observations are presented on the factors governing the start and spread of crown fire in conifer forests. Crown fires are classified in three ways according to the degree of dependence of the crown phase of the fire on the ground surface phase. The crown fuel is pictured as a layer of uniform bulk density and height above ground. Simple criteria are presented for the initiation of crown combustion and for the minimum rates of spread and heat transfer into the crown combustion zone at which the crown fire will spread. The theory is partially supported by some observations in four kinds of conifer forest.