The views expressed are purely those of the authors and may not, in any circumstances, be regarded as stating an official position of the European Commission

EUFIRELAB

EVR1-CT-2002-40028

D-03-09

http://eufirelab.org

EUFIRELAB: Euro-Mediterranean Wildland Fire Laboratory,

a “wall-less” Laboratory for Wildland Fire Sciences and Technologies

in the Euro-Mediterranean Region

Deliverable D-03-09

Behaviour Modelling of Wildland Fires: Final version of the State of the Art

Coordinated by Albert SIMEONI Jorge ANDRE, Didier CALOGINE, Pedro CUIÑAS, Jean Luc DUPUY,

Paulo FERNANDES, Michel LARINI, Ana Isabel MIRANDA, Dominique MORVAN, Josep PIÑOL, Olivier SERO-GUILLAUME

December 2006

EUFIRELAB

D-03-09.doc

CONTENT LIST 1 A review on empirical modelling of wildland fire behaviour................................................................................1

1.1 Introduction....................................................................................................................................................1 1.2 Empirical fire behaviour modelling in Australia and Canada.........................................................................2

1.2.1 Australian models.....................................................................................................................................2 1.2.2 Canadian models .....................................................................................................................................3 1.2.3 Input data and functional relationships.....................................................................................................3

1.3 Empirical fire behaviour modelling in Europe................................................................................................4 1.3.1 The different models.................................................................................................................................4 1.3.2 Fire regime models...................................................................................................................................5 1.3.3 Considerations about research priorities..................................................................................................5

1.4 References ....................................................................................................................................................6 2 A review on physical modelling of wildland fire behaviour ...............................................................................10

2.1 Introduction..................................................................................................................................................10 2.2 Semi-empirical models ................................................................................................................................10 2.3 Physical models...........................................................................................................................................10 2.4 Detailed physical and multiphase approach................................................................................................12

2.4.1 The fuel as a multiphase medium ..........................................................................................................12 2.4.2 The fuel as a fractal medium..................................................................................................................12 2.4.3 Changing spatial scale ...........................................................................................................................13 2.4.4 A system of multiphase, reactive and radiative equations .....................................................................13 2.4.5 Using asymptotic analysis......................................................................................................................13 2.4.6 Large Eddy Simulation ...........................................................................................................................14

2.5 Approximate models....................................................................................................................................14 2.6 Future prospects .........................................................................................................................................15

2.6.1 To characterise the fuel..........................................................................................................................15 2.6.2 To validate the fire behaviour.................................................................................................................15 2.6.3 To improve the metrology ......................................................................................................................15 2.6.4 To change the scale of prediction ..........................................................................................................15 2.6.5 To use larger numerical meshes............................................................................................................15 2.6.6 To develop approximate models ............................................................................................................15

2.7 Conclusion...................................................................................................................................................16 2.8 References ..................................................................................................................................................16

3 A review on wildland fire smoke dispersion modelling .....................................................................................20 3.1 Introduction..................................................................................................................................................20 3.2 Emission and dispersion of air pollutants....................................................................................................20

3.2.1 The major air pollutants..........................................................................................................................20 3.2.2 Emission of particulate matter................................................................................................................20 3.2.3 Modification of the mixture .....................................................................................................................21 3.2.4 Transportation of the mixture .................................................................................................................21

3.3 Numerical modelling of smoke production and dispersion..........................................................................22 3.3.1 To estimate the plume rise.....................................................................................................................22 3.3.2 To calculate the transport speed and direction ......................................................................................22 3.3.3 To simulate smoke dispersion................................................................................................................23

3.4 Final comments ...........................................................................................................................................26 3.5 References ..................................................................................................................................................27 3.6 Figures.........................................................................................................................................................31

4 Fire behaviour prediction..................................................................................................................................33 4.1 Introduction..................................................................................................................................................33 4.2 Fire development phase..............................................................................................................................33 4.3 Fire regime ..................................................................................................................................................34 4.4 Aspects of the fire behaviour predicted.......................................................................................................35

4.4.1 Main fire front .........................................................................................................................................35 4.4.2 Behaviour of a small section of the fire line (local prediction)................................................................35 4.4.3 Behaviour of the whole fire line (global prediction) ................................................................................36 4.4.4 Large crown fires and bushfires .............................................................................................................36 4.4.5 Spotting ..................................................................................................................................................37 4.4.6 Fire whirls ...............................................................................................................................................37

4.5 Applied research products...........................................................................................................................37 4.6 Future prospects .........................................................................................................................................38 4.7 References ..................................................................................................................................................38

EUFIRELAB

D-03-09.doc

SUMMARY

In this document, the authors summarise the two main approaches for modelling the behaviour of wildland fire classically followed by the scientific community: empirical and physical modelling.

Concerning the empirical modelling, the authors analyse the models developed in Australia since the 1960’s in the different type of ecosystems concerned by wildland fire: mainly grassland and eucalypt stands, and, more recently in moorlands and shrublands.

Then analyse also the models developed in Canada related to the Canadian Forest Fire Rating System in constant evolution since the 1920’s.

The authors pay attention to the required input data of these empirical models and the empirical functional relationships established between parameters like damping effect of the moisture content, wind and slope effects on wildland fire dynamic, or flame length and height and wildland fire intensity.

Afterwards, they analyse the empirical approach for modelling the wildland fire behaviour in Europe, in fact in the Euro-Mediterranean countries.

An original approach, called “fire regime modelling” is also presented. It aims to represent the impact of fires on a landscape scale over several decades, in terms of finally burnt area.

They add considerations about research priorities in the domain of the empirical approach in relation with the current state of the art.

Concerning the physical modelling, the authors compare the different physical models, which take into account one or several processes of energy transfer from the burning zone to the unburned fuel.

The authors describe largely the detailed physical and multiphase approach, indicating the nature of the solved equations.

Before developing some future prospects, they analyse the interest of approximate models.

The third part of the document is dedicated to a complete review of the smoke dispersion models used worldwide. The authors classify them in three groups: research models, planning models and screening models.

They underline the interest of fire assessment systems and expert systems in this domain.

In the whole document, the authors present the contribution of the European teams involved in EUFIRELAB. They are involved in all kinds of modelling and references are included in all sections.

At the end of each part, the authors provide a very large list of bibliographical references concerning the items developed in the three chapters of the document.

EUFIRELAB

D-03-09.doc 1

1 A REVIEW ON EMPIRICAL MODELLING OF WILDLAND FIRE BEHAVIOUR

1.1 INTRODUCTION

Reviews of fire behaviour models (BEER 1991a; BURROWS 1994; PERRY 1998; DUPUY 1999; ANDRÉ and VIEGAS 2001; WEBER 2001; PASTOR et al. 2003) usually recognize three categories, respectively empirical (or statistical), semi-empirical (semi-physical or laboratory models), and physical (theoretical or analytical).

The physical modelling of fire behaviour seeks mathematical solutions for the complex mechanisms involved in fire propagation.

A theoretical formulation capable of a direct, practical, and truly predictive response to the entire fire behaviour variability that can naturally occur is quite distant (ALEXANDER et al. 1998).

It will depend on the nature of its inputs (can they be readily acquired in the real world?) as well as on the availability of powerful computing resources.

Consequently, fire modelling for operational applications is currently restricted to semi-empirical or empirical models.

Empirical fire behaviour models are based on data collected in experimental fires or in well-documented wildfires or prescribed fires.

Although containing an element of uncertainty, the former are frequently used to expand the range of environmental conditions and fire characteristics.

Empirical models are built by correlating the observed fire characteristics with easily measured variables, which describe the so-called fire environment (fuel, weather and slope).

Observational evidences and statistical criteria guide the process, but the functional relationships employed in contemporary models try to be consistent with theoretical knowledge.

Empirical models should not be extrapolated beyond the data range used for their development, but this basic rule is seldom respected: it is better to use a (probably) poor model than no model at all.

This is not the sole weakness of empirical models, as environmental factors cannot be controlled in the field.

As a result, natural or circumstantial correlation between variables can hide their true effects and complicate the detection and quantification of relationships with the dependent variable, e.g. CHENEY et al. (1993).

Finally, yet importantly, various practical, economical and legal matters strongly restrain the number and size of field trials and the range of experimental burning conditions, which are acceptable, which poses constraints on the performance of the resulting models.

E.g., despite its long tradition of experimental wildland fires, it was only by the end of the 1990's that a vast and well-funded research program focused on high-intensity crown fires was undertaken in Australia.

Some of the advantages of empirical fire models are readily apparent, namely: - the absence of artificiality and scale problems

(present in the lab experiments associated to physically-based models), and

- the integration of numerous factors which operate in the real world, such as wind and moisture profiles (impossible to reproduce in the lab) and fuel heterogeneity.

This is probably why an empirical model developed for a given vegetation type usually performs better than ROTHERMEL's semi-physical model (LINDENMUTH and DAVIS 1973; MARSDEN-SMEDLEY and CATCHPOLE 1995; FERNANDES 1998; VEGA et al. 1998; BURROWS 1999; HÉLY et al. 2001).

Therefore, an empirical model can be viewed as a fast (and intrinsically incomplete) answer to a specific problem of fire behaviour prediction.

For the time being, the most apparent benefit of a physical approach to fire modelling is its contribution to understand fire propagation mechanisms, therefore helping the experimental design and interpretation of field trials (BURROWS 1994).

Nevertheless, the increasing knowledge on the fundamental fire behaviour allows developing simplified physical models useful for simulation tools (see sections 2.4 and 2.5).

EUFIRELAB

D-03-09.doc 2

1.2 EMPIRICAL FIRE BEHAVIOUR MODELLING IN AUSTRALIA AND CANADA

1.2.1 Australian models

Fire behaviour research in Australia is pragmatic: the objective is to derive relationships which are consistent with management necessities (fire danger rating and accurate prediction of prescribed fire behaviour, essentially) and are immediately usable, based in easily acquirable variables.

The existing diversity of climates and vegetation types has led to a variety of regional models (BURROWS 1994), which classically have been provided to end users in the form of circular meters (e.g. MCARTHUR 1967) or sequenced tables and graphs (e.g. SNEEUWJAGT and PEET 1985).

The fire behaviour relationships underlying the first generation of fire behaviour guides were the product of experimental fires lit by point ignition in specific fuel types and mild fire weather, with additional data collected in wildfires in order to make the predictions more respondent to severe conditions (CHENEY 1981; BURROWS 1994).

Fire spread in grassland (MCARTHUR 1966) and dry eucalypt forest (MCARTHUR 1962, 1967) in southeastern Australia was predicted from dead fuel moisture content and wind speed, and then adjusted for slope.

Forest situations considered also the fine fuel load and fuel availability (MCARTHUR 1962) or a drought factor (MCARTHUR 1967).

The importance of other fuel characteristics, namely the fuel bed compactness and particle size has been recognised, but their effects were disregarded, probably due to difficulties in obtaining exact measurements and the high field variability or simply because their effects were not quantifiable (CHENEY 1981; TOLHURST and CHENEY 1999).

The Forest Fire Behaviour Tables (FFBT) (SNEEUWJAGT and PEET 1985) were developed in the south-west of Australia by similar methods.

The Red Book, as its is often called, applies to 11 fuel complexes one of two models, respectively for dry eucalypt forest of Eucalyptus marginata with sparse and low understorey, and for humid eucalypt forest of Eucalyptus diversicolor with dense and tall understorey.

A basic spread rate is calculated from dead fuel moisture content and wind speed for a standard fuel type, and subsequently is adjusted for available fuel and terrain slope.

The above-mentioned predictive schemes are currently available as equations, which were fit to the original tables, graphs and meters by NOBLE et al. (1980), GOULD (1993), BECK (1995) and GRIFFITHS (1999) to facilitate calculation and comparison between systems.

Given their solid data background, these models perform well when applied to the situations for which they were developed (CHENEY 1981), but the agreement between observations and predictions is usually deficient outside the original range (UNDERWOOD et al. 1985; TOLHURST and CHATTO 1998; BURROWS et al. 2000).

The models for eucalypt forest tend to underestimate the rate of spread of fires initiated by a line, especially when wind is stronger and shrubs are taller (GOULD et al. 2001).

According to BURROWS (1994), the explanation lies not just in the original range of data but also in the methodology used to establish the relationships: some were inferred rather than derived directly, and there's a lack of descriptive and statistic information concerning the process of model development.

Fire management requirements are increasingly demanding and, consequently, more accurate and exact fire behaviour predictions are required.

A second generation of Australian fire models appeared in the 1990's, carefully examining the existing assumptions, enlarging the spatial scale and the meteorological range of experimental conditions, and based on sophisticated statistical analysis.

Some previously ignored influences were experimentally addressed, such as the effect of the ignition line length (CHENEY and GOULD 1995).

CHENEY et al. (1992) and BUCKLEY (1993) presented equations for eucalypt stands with an important shrub component.

CHENEY et al. (1993, 1998) qualitatively individualised two pasture types and introduced considerable modifications in the wind speed function, which has resulted in a new meter for grassland (CSIRO 1997).

The model of BURROWS (1999) for SW Australia eucalypt forest is much simpler than the cumbersome Red Book.

In the same forest environment, the VESTA project has expanded the weather conditions of experimentation to extreme fire danger levels, as well as examining in detail the polemic effects of fuel load, age and structure on spread rate (GOULD et al. 2001).

Fire behaviour models are now available for other Australian environments, namely discontinuous vegetation types in arid (BURROWS et al. 1991) and semi-arid (MCCAW 1991, 1998) regions, buttongrass moorland (MARDEN-SMEDLEY and CATCHPOLE 1995) and shrublands (CATCHPOLE et al. 1998).

New approaches include some physics in the second-generation models. They describe heat transfers (JOHNSTON et al. 2006) or buoyancy (DOLD et al. 2006) in order to represent the fire behaviour in various conditions (fuel heterogeneity or ignition line length).

EUFIRELAB

D-03-09.doc 3

1.2.2 Canadian models

The Canadian Forest Fire Behaviour Prediction System (FBP) is part of the Canadian Forest Fire Danger Rating System, whose concept has continuously evolved during the 1920's – 1990's period.

The system estimates fire behaviour as a function of indexes belonging to the FWI (Fire Weather Index) sub-system.

So, and contrarily to the Australian approach, there are no direct functional relationships between the descriptors of fire behaviour and the fire environment.

The FWI sub-system has temperature, relative humidity, precipitation and wind speed as inputs, variables, which allow the calculation of six components (VAN WAGNER 1987; STOCKS et al. 1989): - Fuel moisture content indexes: Fine Fuel Moisture

Code (FFMC), Duff Moisture Code (DMC) and Drought Code (DC);

- Fire behaviour indexes: Initial Spread Index (ISI), Buildup Index (BUI) and Fire Weather Index (FWI), respectively representing rate of fire spread, available fuel, and fire line intensity.

The FBP sub-system includes 89 equations, derived statistically (but with some theoretical background) from the analysis of an extensive quantitative database of experimental fires and wildfires (Forestry Canada Fire Danger Group 1992).

Data from wildfires (e.g. Stocks 1988) is more general and less reliable, but is important as it represents extreme events.

Fire behaviour predictions (which include crown fire propagation) are provided for 16 specific fuel types but, differently to other empirical models, structural variation within a vegetation type is not accounted for.

The ISI is the basic input for rate of spread estimation on each fuel type, followed by corrections for the BUI and terrain slope.

The FWI sub-system is increasingly being used in fire danger rating in several countries.

Because of its structure, the FBP system allows adaptation to fuel types outside Canada, e.g. FOGARTY et al. (1998).

The FBP database was analysed by CRUZ et al. (2002) in order to derive a model for the rate of spread of crown fires in conifer forest.

The independent variables in the equation are wind speed, canopy bulk density and dead fuel moisture content.

1.2.3 Input data and functional relationships

A particular type of models tries to describe the conditions under which a ignition succeeds, i.e. the environmental thresholds for sustained fire propagation.

This is because the moisture of extinction (ROTHERMEL 1972) in a given fuel type is not constant, and is affected by wind speed and the characteristics of the fuel complex.

The most common modelling approach is logistic regression, whose outcome is a probability of fire sustainability (LAWSON et al. 1994; MARSDEN-SMEDLEY et al. 2001).

Crown fire initiation models also rely on logistic models, with wind speed, fuel strata gap, surface fuel consumption class and dead fuel moisture content as independent variables (CRUZ et al. 2002) or, alternatively (CRUZ et al. 2003), with wind speed, crown base height and combinations of indexes from the FWI as predictors.

Models for fire spread typically combine in a multiplicative way functions for the individual effects of wind speed, fuel moisture and a fuel complex descriptor, with a subsequent correction for slope.

Wind speed measured or estimated at heights of 10m in open terrain is required for fire spread prediction by the Canadian system and a few other models (MCARTHUR 1966, 1967; CHENEY et al. 1998).

The remaining empirical models use surface wind speed, at the height of 1-2 m.

Wind speeds from weather forecasts (6 or 10 m height) can be converted to surface wind speed according to a logarithmic profile (ALBINI and BAUGHMAN 1979) or with specific simple equations (SNEEUWJAGT and PEET 1985; CHENEY et al. 1992; GOULD 1993; MARSDEN-SMEDLEY et al. 1999).

Most wind functions describe the wind influence on rate of spread by power or exponential curves.

The difference is unimportant (BEER 1991b), except at high wind velocities, where the option for an exponential equation tends to produce unrealistic high predictions, e.g. BURROWS (1999).

In the FBP system the wind component of the ISI is exponential, but rate of fire spread has an upper limit, usually based in wildfire data.

Few empirical functions are available to describe the effect of slope terrain in fire propagation (MCARTHUR 1962; VAN WAGNER 1977; CHENEY et al. 1992), possibly because experiments in flat terrain are prevalent.

All are exponential and provide similar results. The available information for shrubland suggests a

weaker effect of slope on rate of spread (GREEN 1981; CATCHPOLE et al. 1999).

The damping effect of dead fuel moisture on fire spread takes the form of an exponential function in most models (e.g. CHENEY et al. 1993; MARSDEN-SMEDLEY and CATCHPOLE 1995; MCCAW 1998; BURROWS 1999) which is well supported by theory and combustion experiments in controlled environments, but some Australian equations assume a linear effect.

EUFIRELAB

D-03-09.doc 4

It is difficult to quantify, under field conditions, the effect of fuel characteristics on fire spread, and nearly impossible to distinguish between the influences of different descriptors without manipulation of the fuel complex.

The dominant role of wind, slope and moisture, and natural heterogeneity and correlation between fuel properties are the main reasons for this problem.

Inputs concerning the physical properties of the fuel complex generally resort to variables with an apparent important variation within a fuel type and easily assessed at the management level.

Rate of spread increases linearly with available fuel quantity in the FBP system (Forestry Canada Fire Danger Group 1992) as well as in the eastern Australia models for eucalypt forest (MCARTHUR 1962, 1967; CHENEY 1978).

Other models use: - fuel age (MARSDEN-SMEDLEY and CATCHPOLE 1995), - vegetation height (CHENEY et al. 1992; CATCHPOLE

et al. 1998), - a fuel factor calculated from vegetation cover and

patchniness (BURROWS et al. 1991), or - a qualitative indicator of compactness (CHENEY et al.

1993).

Models for flame length or height and fire intensity are equally important, because these variables are directly related with fire suppression difficulty and fire effects.

BYRAM (1959) was the first to estimate flame length from a power function in fireline intensity, which by definition is the product of rate of spread, fuel available for flaming combustion and fuel heat content.

Various equations fit the model for different fuel types.

Inverse relations were also developed to estimate the fireline intensity from flame lengths (ALEXANDER 1982).

In Australia, flame height is generally preferred to flame length, and is predicted from fire line intensity (MARSDEN-SMEDLEY and CATCHPOLE 1995) or from the combination of rate of spread and fuel load in various linear and non-linear forms (NOBLE et al. 1980; CHENEY et al. 1992; GOULD 1993; BURROWS 1999).

1.3 EMPIRICAL FIRE BEHAVIOUR MODELLING IN EUROPE

1.3.1 The different models The fist attempt at an empirical fire behaviour model

in Europe comes from TRABAUD (1979), who presents an equation for the spread rate of fires in Quercus coccifera garigue.

However, few models have been developed up to now, due to the rarity of field experiments, the widespread use of ROTHERMEL's model in management applications, and the focus by several research teams on physically based models.

The statistical analysis of fire behaviour in Europe comprehends two types of equations based on field data, respectively simple descriptions of fire propagation (e.g. SZCZYGIEL 1988; PEREZ and VALETTE 1995; CARREGA and NAPOLI 1998), and models whose functional relationships are supported by physical reasoning whenever possible.

Within-fire analysis of the spatial and temporal variation of fire behaviour in response to wind and fuel variation has been examined by FERNANDES et al. (2000) in shrubland and FERNANDES et al. (in press) in a Pinus pinaster stand.

This type of studies can give insights on the development of empirical models, provide detailed data to test existing models, and is useful to analyse specific phenomena, e.g. the transition from surface to crown fire.

The main motivation for empirical fire behaviour modelling in Europe is, as in other regions of the world, to acquire a straightforward capacity of predicting fire behaviour, with input variables easily measured by fire managers, and using equations which reflect real-world conditions and produce reliable estimates within the environmental range of experimentation.

The overwhelming majority of the experiments has been conducted from Autumn to Spring, which means the application of the resulting models should be restricted to predict fire behaviour in the low to moderate range, namely in the prescribed burning decision-making process.

Models for shrubland fire behaviour in gorse and heath communities of NW Spain are presented in VEGA et al. (1998, 2001), including a slope effect and covering a relatively wide range in fire behaviour.

A small database from four shrubland types in Portugal has allowed the development of an interim fire spread model for no-slope conditions (FERNANDES 2001).

Finally, the most extensive experimentation was conducted in Pinus pinaster stands in northern Portugal, and has produced equations to predict the likelihood of sustained fire propagation and the characteristics of surface fires in fuel types dominated by litter, grass-ferns or a low shrub strata (FERNANDES 2002; FERNANDES et al. 2002).

The slope effect included in the head fire rate of spread model is similar to Australian models.

EUFIRELAB

D-03-09.doc 5

The fuel complex in these models is described by vegetation height or cover for the purpose of rate of spread estimation, or by fine fuel load, when the objective is the prediction of flame length / fireline intensity.

Current work includes the analysis of a joint database for shrubland fires from Spain, Portugal, Australia and New Zealand, which has already allowed the detection of a live fuel moisture content influence on rate of spread; such a report is exceedingly scarce in the literature.

1.3.2 Fire regime models

Fire regime models act on a landscape scale over several decades.

Their goal is not to reproduce spread rates but rather the finally burnt area.

They can focus on the interaction between vegetation and fire regime, often use a cellular automata or gap structure and are normally spatially explicit: they are based on a grid structure.

The input data evidently cannot be very detailed and therefore the use of probabilistic equations is not rare. Typical output parameters are: annually burnt area, fire size distribution and stand age distribution.

They do not reproduce individual fires, but long-term averages of fire size and shape.

Fire regime models can generally be divided into three modules: vegetation build up, fire propagation and management.

The simplest form of the vegetation build up module is to determine for each cell the time elapsed since the last fire.

More sophisticated models consider biomass augment, ranging from simple linear increase until modelling of individual tree growth.

The fire propagation module is normally a neighbourhood process, i.e. fire spreads from one cell to its eight adjacent cells.

The propagation from one cell to another is not generally based on physical laws but probabilistic, and depends on the amount of accumulated fuel, weather conditions, slope, etc.

The parameters actually included in the propagation probability vary very much among the different models; some models also include spotting.

The management module is used to simulate the effect of fire suppression and/or extraction of biomass (mechanical or prescribed fires).

Models of this category often focus on the interaction between fuel build up and fire.

A few models put more attention to the theoretical aspects of fire regime itself, using simple vegetation growing modules on homogeneous terrain without considering wind and slope (PIÑOL et al. 2005), or considering them (HARGROVE et al. 2000).

Many concentrate: - on the effect of fuel heterogeneity on fire patterns, - pointing out the effect of spatial arrangement of fuel

on crown fires (TURNER and ROMME 1994), - the effect of connectivity of fuels on surface fire

regimes (MILLER and URBAN 2000), - how propagation probability scales with stand age

(LI et al. 1997), - testing a exponential stand age distribution due to

fire (BOYCHUK et al. 1997), - using vital attributes and fuzzy theory (ROBERTS

1996) or - on a broad scale, including other disturbances (HE

and MLADENOFF 1999).

Some models include: - human impacts on fire regime (BAKER 1992), - the effects of settlement and fire suppression on

landscape structure (FAVIER et al. 2004), on forest-savanna mosaic (including soil heterogeneity) (BRADSTOCK et al. 2006) or

- effects of different fire management strategies on one species (THONICKE et al. 2001)

One recent paper studies the influence of fire on the global vegetation dynamic equilibrium (BOND et al. 2006)

1.3.3 Considerations about research priorities

The current state of the art of empirical fire behaviour modelling suggests some strategies and priorities for the future research activities.

1.3.3.1 To reinforce the connection

To reinforce the connection between empirical and physical approaches to fire behaviour prediction arises as the first urgent need.

The partial findings obtained by each method could orientate the research effort in the complementary procedure.

We need to have in mind that our aim is to have models, which can be applied to real situations, and be useful to the fire-decision makers.

The interaction between both methodological approaches could accelerate fire propagation modelling, testing and validation processes.

1.3.3.2 To conduct experimental fires

To conduct experimental fires under a wide range of conditions.

Up to date most of the field experiments have been carried out in spring, winter and autumn, in mild weather conditions.

This limits the applicability of the empirical models to fire season.

It is necessary to expand the range of the meteorological variables to reflect typical summer meteorological scenarios.

At the same time, the range of certain variables like live fuel moisture needs to be also enlarged.

It is necessary to explore different combinations of environmental variables (fuel, topography and weather) to avoid co-linearity between variables, a common problem in the empirical approach.

EUFIRELAB

D-03-09.doc 6

1.3.3.3 To determine the effect of slope terrain

To determine the effect of slope terrain in fire propagation and its interaction with wind, to obtain the maximum fire propagation direction and the rate of spread for any given direction.

The influence of slope on fire propagation has been taken account by a few empirical models, but most of field research has been conducted on flat terrain.

Moreover, the interaction between wind and slope, a relevant factor in fire propagation, is still poorly understood.

It is necessary and effort to determine the combined effect of both variables on fire behaviour.

1.3.3.4 To identify new fuel descriptors

To identify new fuel descriptors with a relevant influence in fire behaviour and reflecting natural fuels heterogeneity.

Surprisingly, fuel variables explain a low percentage of variability in fire rate of spread in the current empirical models.

Furthermore, sometimes, dummy variables have to be used to reflect the influence of unknown characteristics of fuel types.

Fuel inventory and physical fuel particles properties assessment is an expensive and time-consuming task.

Consequently there is a challenge to find new fuel variables describing fuel particle array influence and the natural heterogeneity effects on fire behaviour variability.

1.3.3.5 To develop empirical models

To develop empirical models for fire behaviour prediction under different ignition techniques used in prescribed fire.

The available empirical models have been conceived to predict fire propagation in a quasi-steady state with fuels burning freely under natural conditions.

This limits its applicability to prescribed burning operations where the ignition techniques are a factor of a relevant influence on fire behaviour.

In fact, ignition pattern tries to avoid that fire develops a stationary state in equilibrium with environmental conditions.

Models taking account of the effect of ignition techniques can be very useful to refine fire prescription to achieve different management objectives.

1.3.3.6 To implement empirical model

To develop empirical models to predict fire behaviour in manipulated fuels.

Fuel treatments are commonly used in wild land areas for a great variety of purposes (fuel breaks, silviculture activities, wildlife habitat improvement, fuel accumulation reduction, etc.).

These actions result in a mosaic of fuel complexes with different characteristics.

These situations are not considered in the existing empirical models. It is necessary to develop new models, which take account of these fuel conditions, and to enable to simulate the potential effect of those fuel treatments on fire hazard.

1.4 REFERENCES

ALBINI, F.A., and R.G. BAUGHMAN. 1979. Estimating windspeeds for predicting wildland fire behavior. USDA For. Serv. Res. Pap. INT-221, Intermt. For. and Range Exp. Stn., Ogden.

ALEXANDER M.E. 1982. Calculating and interpreting forest fire intensities. Can. J. of Botany 60: 349-57.

ALEXANDER, M.E., B.J. STOCKS, B.M. WOTTON, and R.A. LANOVILLE. 1998. An example of multi-faceted wildland fire research: the International Crown Fire Modelling Experiment. In Proc. 3rd Int. Conf. on Forest Fire Research & 14th Conf. on Fire and Forest Meteorology, VIEGAS, D.X. (Ed.), ADAI. pp. 83-112.

ANDRÉ, J.C., and D.X. VIEGAS. 2001. Modelos de propagação de fogos florestais: estado-da-arte para utilizadores. Parte I: introdução e modelos locais. Silva Lusitana 9(2): 237-265.

BAKER W.L. 1992. Effects of settlement and fire suppression on landscape structure. Ecology 73: 1879-1887.

BECK, J.A. 1995. Equations for the forest fire behaviour tables for Western Australia. CALMScience 1(3): 325-348.

BEER, T. 1991a. Bushfire rate-of-spread forecasting: deterministic and statistical approaches to fire modelling. Journal of Forecasting 10: 301-317.

BEER, T. 1991b. The interaction of wind and fire. Bound.-Lay. Meteorol. 54: 287-308.

BOND W.F., WOODWARD F.I., MIDGLEY G.F. 2005. The global distribution of ecosystems in a world without fire. New Phytologist 165: 525.

BOYCHUK D., PERERA A., TER-MIKAELIAN M.T., MARTELL D.L., and LI C. 1997. Modelling the effect of spatial scale and correlated fire disturbances on forest age distribution. Ecological Modelling 95:145-164.

BRADSTOCK R.A., BEDWARD M., COHN J.S. 2006. The modelled effects of differing fire management strategies on the conifer Callitris verrucosa within semi-arid mallee vegetation in Australia. Journal of Applied Ecology 43: 281.

BUCKLEY, A.J. 1993. Fuel reducing regrowth forests with a wiregrass fuel type: fire behaviour guide and prescriptions. Dept. of Conserv. and Nat. Res., Fire Manage. Branch, Res. Rep. No. 40, Victoria.

BURROWS, N.D. 1994. Experimental development of a fire management model for jarrah (Eucalyptus marginata Donn ex Sm.) forest. PhD. thesis, Australian National University, Canberra.

BURROWS, N.D. 1999. Fire behavior in jarrah forest fuels: 2. Field experiments. CALMScience 3(1): 57-84.

EUFIRELAB

D-03-09.doc 7

BURROWS, N., B. WARD, and A. ROBINSON. 1991. Fire behaviour in spinifex fuels on the Gibson Desert Nature Reserve, Western Australia. J. Arid Environ. 20: 189-204.

BURROWS, N., B. WARD, and A. ROBINSON. 2000. Behaviour and some impacts of a large wildfire in the Gnangara maritime pine (Pinus pinaster) plantation, Western Australia. CALMScience 3(2): 251-260.

BYRAM, G.M. 1959. Combustion of forest fuels. In Forest Fire: Control and Use, Davis, K.P. (Ed.), McGraw-Hill, New York. pp. 90-123.

CARREGA, P., and A. NAPOLI. 1998. A quick protocol of in-the-field measurement for the study of forest and bush fire spread. In Proc. 3rd Intern. Conf. Forest Fire Research & 14th Fire and Forest Meteorology Conf., VIEGAS, D.X. (Ed.), ADAI. pp. 656-582.

CATCHPOLE, W., R. BRADSTOCK, J. CHOATE, L. FOGARTY, N. GELLIE, G. MCCARTHY, L. MCCAW, L., J. MARSDEN-SMEDLEY, and G. PEARCE 1998. Co-operative development of equations for heathland fire behaviour. In: Proc. 3rd International Conf. on Forest Fire Research & 14th Fire and Forest Meteorology Conf., VIEGAS, D.X. (Ed.), ADAI, University of Coimbra. pp. 631-645.

CATCHPOLE, W., R. BRADSTOCK, J. CHOATE, L. FOGARTY, N. GELLIE, G. MCCARTHY, L. MCCAW, L., J. MARSDEN-SMEDLEY, and G. PEARCE 1999. Co-operative development of prediction equations for fire behaviour in heathlands and shrublands. In Proc. Australian Bushfire Conf. http: //life. csu. edu. au / bushfire 99/papers.

CHENEY, N.P. 1978. Guidelines for fire management on forested watersheds based on Australian experience. FAO Conservation Guide 4, Rome.

CHENEY, N.P. 1981. Fire behaviour. In Fire and the Australian Biota, GILL, A.M., R.H. GROVES, e I.R. NOBLE (Eds.), Australian Academy of Science, Canberra. pp. 151-175.

CHENEY, N.P., and J.S. GOULD. 1995. Fire growth in grassland fuels. Int. J. Wildland Fire 5(4): 237-247.

CHENEY, N.P., J.S. GOULD, and I. Knight. 1992. A prescribed burning guide for young regrowth forests of silvertop ash. Forestry Commission of New South Wales, Res. Div., Res. Pap. No. 16, Beecroft.

CHENEY, N.P., J.S. GOULD, and W.R. CATCHPOLE. 1993. The influence of fuel, weather and fire shape variables on fire-spread in grasslands. Int. J. Wildland Fire 3(1): 31-44.

CHENEY, N.P., J.S. GOULD, and W.R. CATCHPOLE. 1998a. Prediction of fire spread in grasslands. Int. J. Wildland Fire 8(1): 1-13.

CRUZ, M.G., M.E. ALEXANDER, and R.H. WAKIMOTO. 2002. Predicting crown fire behavior to support forest fire management decision-making. In Proc. IV Int. Conf. on Forest Fire Research & 2002 Wildland Fire Safety Summit, VIEGAS, D.X. (Ed.), Luso, 18-23 Nov. Millpress Science Publishers, Rotherdam.

CRUZ, M.G., M.E. ALEXANDER, and R.H. WAKIMOTO. 2003. Assessing the probability of crown fire initiation based on fire danger indices. For. Chron. 79(5): 976-983.

CSIRO. 1997. The CSIRO Grassland Fire Danger and Fire Spread Meters.

http://www.ffp.csiro.au/nfm/fbm/grassmet.html.

DOLD J., ZINOVIEV A., WEBER R. 2006. Nonlocal Flow Effects in Bushfire Spread Rates. In Proc. V Int. Conf. on Forest Fire Research, VIEGAS, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

DUPUY, J.L. 1999. An analysis of semi-empirical and physical models for fire spread in wildland fuels. In Wildfire Management, Proc. of the Advanced Study Course, EFTICHIDIS, G., P. BALABANIS, e A. GHAZI (Eds.), Algosystems S.A., Athens. pp. 419-438.

FAVIER C., CHAVE J., FABING A., SCHWARTZ D., DUBOIS M.A. 2004. Modelling forest-savanna mosaic dynamics in man-influenced environments: effects of fire, climate and soil heterogeneity. Ecological Modelling 171: 85-102.

FERNANDES, P.M. 1998. Fire spread modelling in Portuguese shrubland. In Proc. 3rd Intern. Conf. Forest Fire Research & 14th Fire and Forest Meteorology Conf., VIEGAS, D.X. (Ed.), ADAI. pp. 611-628.

FERNANDES, P.M. 2001. Fire spread prediction in shrub fuels in Portugal. For. Ecol. Manage. 144: 67-74.

FERNANDES, P.M. 2002. Desenvolvimento de relações preditivas para uso no planeamento de fogo controlado em povoamentos de Pinus pinaster Ait. PhD thesis, UTAD. Vila Real.

FERNANDES, P., H. BOTELHO, and C. LOUREIRO. 2002. Models for the sustained ignition and behavior of low-to-moderately intense fires in maritime pine stands. In Proc. IV Int. Conf. on Forest Fire Research & 2002 Wildland Fire Safety Summit, VIEGAS, D.X. (Ed.), Luso, 18-23 Nov. Millpress Science Publishers, Rotherdam.

FERNANDES, P.M., W.R. CATCHPOLE, and F.C. REGO. 2000. Shrubland fire behaviour modelling with microplot data. Can. J. For. Res. 30: 889-899.

FERNANDES, P.M., C. LOUREIRO and H.S. BOTELHO. In press. Fire behavior and severity in a maritime pine stand under differing fuel conditions. Ann. For. Sci.

EUFIRELAB

D-03-09.doc 8

FOGARTY, L.G., H.G. PEARCE, W.R. CATCHPOLE, and M.E. ALEXANDER. 1998. Adoption vs. adaptation: lessons from applying the Canadian Forest Fire Danger Rating System in New Zealand. In Proc. 3rd Int. Conf. on Forest Fire Research & 14th Conf. on Fire and Forest Meteorology, VIEGAS, D.X. (Ed.), ADAI. pp. 1011-1028.

Forestry Canada Fire Danger Group. 1992. Development and structure of the Canadian Forest Fire Behavior Prediction System. For. Can. Inf. Rep. ST-X-3, Ottawa.

GOULD, J.S. 1993. Evaluation of MCARTHUR's control burning guide in regrowth Eucalyptus sieberi forest. Aust. For. 57: 86-93.

GOULD, J.S., N.P. CHENEY, and L. MCCAW. 2001. Project Vesta - research into the effects of fuel structure and fuel load on behaviour of moderate to high-intensity fires in dry eucalypt forest: progress report. In Bushfires 2001 Conf.

GREEN, L. 1981. Burning by prescription in chaparral. USDA For. Serv. Gen. Tech. Rep. PSW-51, Pacific Southwest For. and Range Exp. Stn., Berkeley.

GRIFFITHS, D. 1999. Improved formula for the drought factor in MCARTHUR's forest fire danger meter. Aust. For. 62(2): 202-206.

HARGROVE W.W., GARDNER R.H., TURNER M.G., ROMME W.H., DESPAIN D.G. 2000. Simulating fire patterns in heterogeneous landscapes. Ecological Modelling 135: 243-263.

HE H.S., MLADENOFF D.J. 1999. Spatially explicit and stochastic simulation of forest-landscape fire disturbance and succession. Ecology 80: 81–99.

HÉLY, C., M. FLANNIGAN, Y. BERGERON, and D. MCRAE. 2001. Role of vegetation and weather on fire behaviour in the Canadian mixedwood boreal forest using two fire behaviour prediction systems. Can. J. For. Res. 31: 430- 441.

JOHNSTON P., MILNE G., KELSO J. 2006. A heat transfer simulation model for wildfire spread. In Proc. V Int. Conf. on Forest Fire Research, VIEGAS, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

LAWSON, B.D., O.B. ARMITAGE, and G.N. DALRYMPLE. 1994. Ignition probabilities for simulated people-caused fires in British Columbia pine and white spruce-subalpine fir forests. In Proc. 12th Conf. Fire and Forest Meteorology, SAF Publ. 94-02, Soc. Am. For., Bethesda. pp. 493-505.

LI C., TER-MIKAELIAN M., PERERA A. 1997. Temporal fire disturbance patterns on a forest landscape. Ecological Modelling 99: 137-150

LINDENMUTH, A.W., and J.R. DAVIS. 1973. Predicting fire spread in Arizona's oak chaparral. USDA For. Serv. Res. Pap. RM-101, Rocky Mte. For. and Range Exp. Stn., Fort Collins.

MARSDEN-SMEDLEY, J.B., and W.R. CATCHPOLE. 1995a. Fire behaviour modelling in Tasmanian buttongrass moorlands. II. Fire behaviour. Int. J. Wildland Fire 5(4): 215-228.

MARSDEN-SMEDLEY, J.B., T. RUDMAN, A. PYRKE, and W.R. CATCHPOLE. 1999. Buttongrass moorland fire-behaviour prediction and management. Tasforests 11: 87-107.

MARSDEN-SMEDLEY, J.B., W.R. CATCHPOLE, and A. PYRKE. 2001. Fire behaviour modelling in Tasmanian buttongrass moorlands. IV Sustaining versus non-sustaining fires. Int. J. Wildland Fire 10: 255-262.

MCARTHUR, A.G. 1962. Control burning in eucalypt forest. Comm. Aust. For. Bur., Leaflet No 80, Canberra.

MCARTHUR, A.G. 1966. Weather and grassland fire behaviour. Aust. Forestry and Timber Bureau, Leaflet No. 100, Canberra.

MCARTHUR, A.G. 1967. Fire behaviour in eucalypt forest. Aust. Forestry and Timber Bureau, Leaflet No. 107, Canberra.

MCCAW, L. 1991b. Fire spread prediction in mallee-heath shrublands in South-Western Australia. In Proc. 11th Conf. Fire and Forest Meteorology. pp. 226-233.

MCCAW, L. 1998. Research as a basis for fire management in mallee-heath shrublands of South-Western Australia. In Proc. 3rd Inter. Conf. Forest Fire Research & 14th Conf. Fire and Forest Meteorology, VIEGAS, D.X. (Ed.), ADAI. pp. 2335-2348.

MILLER C., URBAN D.L. 2000. Connectivity of forest fuels and surface fire regimes. Landscape Ecology 15: 145-154

NOBLE, I.R., G.V. BARY, and A.M. GILL. 1980. MCARTHUR's fire-danger meters expressed as equations. Aust. J. Ecol. 5: 201-203.

PASTOR, E., L. ZÁRATE, E. PLANAS, J. ARNALDOS. 2003. Mathematical models and calculation systems for the study of wildland fire behaviour. Prog. Energy Comb. Sci. 29: 139-153.

PEREZ, J.Y., and J.C. VALETTE. 1995. L'éclosion et la propagation initiale du feu dans une lande a molinie en foret landaise mars a août 1995. INRA, Dept. Rech. For. Rech. Forest. Mediter., Doc. PIF9525, Avignon.

PERRY, G.L.W. 1998. Current approaches to modelling the spread of wildland fire: a review. Prog. Phys. Geog. 22(2): 222-245.

PIÑOL J, BEVEN K, VIEGAS DX. 2005. Modelling the effect of fire-exclusion and prescribed fire on wildfire size in Mediterranean ecosystem. Ecological Modelling 183: 397-409.

EUFIRELAB

D-03-09.doc 9

ROBERTS D.W. 1996. Landscape vegetation modelling with vital attributes and fuzzy systems theory. Ecological Modelling 90: 175-184.

ROTHERMEL, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. USDA For. Serv. Res. Pap. INT-115, Intermt. For. and Range Exp. Stn., Ogden.

SNEEUWJAGT, R.J., and G.B. PEET. 1985. Forest fire behaviour tables for Western Australia. 3rd Ed. West. Austr. Dept. Conserv. and Land Manage., Perth.

STOCKS, B.J. 1988. Forest fire close to home: Terrace Bay Fire no. 7/86. In Proc. Symp. and Workshop on Protecting People and Homes from Wildfire in the Interior West, Fischer, W. e S. Arno (Comps.), USDA For. Serv. Gen. Tech. Rep. INT-251. pp. 89-93.

STOCKS, B.J., B.D. LAWSON, M.E. ALEXANDER, C.E. VAN WAGNER, R.S. MCALPINE, T.J. LYNHAM, and D.E. DUBÉ. 1989. The Canadian Forest Fire Danger Rating System: an overview. For. Chron. 65(6): 258-265.

SZCZYGIEL, R. 1988. Forest litter burning in pine stands, laboratory and field studies. In Proc. Intern. Workshop on Prescribed Burning, INRA, Avignon. pp. 137-145.

THONICKE K., VENEVSKY S., SITCH S., CRAMER W. 2001. The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model. Global Ecology & Biogeography 10: 661.

TOLHURST, K.G., and K. CHATTO. 1998. Behaviour and threat of a plume driven bushfire in West-Central Victoria, Australia. In Proc. 13th Conf. Fire and Forest Meteorology, IAWF. pp. 321-331.

TOLHURST, K.G., and N.P. CHENEY. 1999. Synopsis of the knowledge used in prescribed burning in Victoria. Dept. of Nat. Res. and Environ. Fire Manage., Melbourne.

TRABAUD, L. 1979. Etude du comportement du feu dans la garrigue de chêne kermes à partir des températures et des vitesses de propagation. Ann. Sci. For. 36: 13-35.

TURNER M.G., ROMME W.H. 1994. Landscape dynamics in crown fire ecosystems. Landscape Ecology 9: 59-77.

UNDERWOOD, R.J., R.J. SNEEUWJAGT, and H.G. STYLES. 1985. The contribution of prescribed burning to forest fire control in Western Australia: case studies. In Fire Ecology and Management in Western Australian Ecosystems, Ford, J.R. (Ed.), WAIT Environmental Studies Group Rep. No. 14. pp. 153-170.

VAN WAGNER, C.E. 1977. Effect of slope on fire spread rate. Can. For. Serv. Bi-Mon. Res. Notes 33: 7-8.

VAN WAGNER, C.E. 1987. Development and structure of the Canadian Forest Fire Weather Index System. Can. For. Serv., Forestry Tech. Rep. 35, Ottawa.

VEGA, J.A., P. CUIÑAS, M.T. FONTÚRBEL, P. PÉREZ-GOROSTIAGA, and C. FERNÁNDEZ. 1998. Predicting fire behaviour in Galician (NW Spain) shrubland fuel complexes. In Proc. 3rd Int. Conf. Forest Fire Research & 14th Conf. Fire and Forest Meteorology, VIEGAS, D.X. (Ed.), ADAI. pp. 713-728.

VEGA, J.A., P. PÉREZ-GOROSTIAGA, P. CUIÑAS, M.T. FONTÚRBEL, and M.C. FERNÁNDEZ. 2001. Manual de queimas prescritas para matogueiras de Galicia. Xunta de Galicia, Consellería de Medio Ambiente, Centro de Información e Tecnoloxía Ambiental.

WEBER, R.O. 2001. Wildland fire spread models. Pp. 151-169 In Forest Fires Behaviour and Ecological Effects, E.A. JOHNSON and K. MIYANISHI (Eds.). Academic Press, San Diego.

EUFIRELAB

D-03-09.doc 10

2 A REVIEW ON PHYSICAL MODELLING OF WILDLAND FIRE BEHAVIOUR

2.1 INTRODUCTION

Forest fire spread models are usually classified into two types (PITTS 1991): - Stochastic models consisting to predict the more

probable fire behaviour from average conditions and accumulating acknowledges obtained from laboratory and outdoor experimental fires,

- Deterministic models (Semi-empirical and physical) in which the fire behaviour is deduced from the resolution of the physical conservation laws (mass, energy, momentum…) governing the evolution of the system formed by the flame and its environment,

The main purpose of these models is to predict the local rate of spread of a fire front, when parameters characterising the condition of spread (vegetation, meteorology, terrain) are given.

It is reminded that stochastic models are only based on the observation of field fires (experimental fires and wildfires) from which the fire rate of spread (ROS) is related to relevant parameters in a purely statistical way (fuel type, fuel loading, fuel moisture, wind, ...).

These empirical relations depend strongly from the very specific conditions from which the statistical study was performed.

Without systematic parametric studies, it is very difficult to extract a general behaviour for the fire.

2.2 SEMI-EMPIRICAL MODELS

Semi-empirical models are based on a global energy balance (FRANDSEN 1971) and on the assumption that the energy transferred to the unburned fuel is proportional to the energy released by the combustion of the fuel, several terms of the model being fitted to laboratory fire experimental results (ROTHERMEL 1972).

The simplicity of this approach has allowed to develop operational tools such as BEHAVE and FARSITE.

Other models have been developed in order to represent the fire-line evolution under slope and wind conditions (Viegas 2002) or fire blow-ups (Viegas and Pita 2004).

2.3 PHYSICAL MODELS

Physical models take into account one or several process of energy transfer from the burning zone to the unburned fuel.

They describe how a part of the energy released by the fire preheats the unburned fuel, providing its own propagation.

The fire front is generally supposed to be an infinite straight line perpendicular to the direction of spread.

These models are based on a simplified representation of each of the included phenomena.

Generally, the flame is assimilated as a radiating panel at a fixed temperature (flame temperature) transferring its energy toward the vegetation by radiation.

This leads to writing one equation of energy balance for the unburned fuel in a frame attached to the flame (steady-state regime of propagation) and in one space dimension (the direction of fire spread x).

For this, these models consider a homogeneous and uniform fuel bed made of one type of particle of plant material.

The unburned fuel is separated from the burning zone by a surface where the fuel ignites.

On this surface, named ignition interface, the fuel particles have been raised to the ignition temperature, which is a given property of the fuel.

The differences between physical models are essentially due to the choice of the control volume used to establish the energy balance equation and due to the processes of energy transfer they take into account.

Most of these models calculate the energy balance in one dimension (x), and consider: - either a control volume over the whole fuel bed

depth (e.g. HOTTEL et al. 1965) or - a small control volume located at the top of the fuel

bed (e.g. HOTTEL et al. 1965).

ALBINI (1981,1985,1986): - calculates the energy balance in two space

dimensions (x, z) and - uses a small control volume around a point of co-

ordinates (x,z) located inside the fuel bed.

In the most sophisticated models of this type, the energy transfers for preheating the unburned fuel are: - the radiation from embers (often calculated as

radiation from the ignition interface), - the radiation from the flame, the radiation from the

unburned particles themselves (ALBINI 1985, 1986), and

- one or several terms of a convective type rendering the thermal exchange between the fuel particles and the gas (PAGNI and PETERSON 1973).

EUFIRELAB

D-03-09.doc 11

Some authors add a term of energy loss to the ambient medium, which can be of radiative or convective nature (or both).

A recent approach provides the main characteristics of a 3D fire front (Balbi et al. 2005).

Some empirical relations are combined with mass, momentum and energy balances to obtain a very simple quasi-analytical model (after making some strong assumptions).

The model provides: - the fire rate of spread, - the temperature in the fuel layer and - the fire front geometry: flame tilt angle, flame height

and fire front width. It has to be validated at the field scale.

Hence, physical models often permit a simple calculus of the fire rate of spread, however only when some properties are given as input parameters: embers, flame, hot gases at the vicinity of the ignition interface.

These properties directly depend on the fire itself, thus they are not parameters, which only characterise the combustible medium and the environmental conditions.

It is a weak point of this modelling approach, which will be now called a phenomenological approach.

Nevertheless, it represents the first generation of models that are at the root of physical modelling.

For better understanding the difference with other approaches, it is worth noting that in the previous models, the energy balance does not contain any term of energy source due to the combustion of the plant material.

This kind of term is implicitly contained into the properties of the flame, the embers or the hot gases, used to calculate the energy transfers.

Other models are, in their principle: - similar to the models previously described and - planned to be integrated in operational management

tools. This kind of simulators necessitates simple and

robust models, which are able to provide information on the fire spread, within a given margin of error and with a short calculation time.

They consist in using one equation of energy balance, generally applied to a surface (terrain surface), which is of a generic mathematical form and may include convective, diffusive and/or radiative processes.

In addition, the balance equation contains a term of energy source, which often depends on the temperature.

It describes the consumption of fuel but without any reference to a chemical reaction (global heat release).

The balance equation is: - sometimes one-dimensional and time dependent

(CEKIRGE 1978), - often two-dimensional and time dependent – x, y on

a plane or ξ,η on a generalised surface representing a terrain (WEBER 1991a, BALBI and SANTONI 1999).

They can also take into account slope and wind conditions (SANTONI et al. 1999, SIMEONI et al. 2001, MORANDINI et al. 2003, 2005).

The energy balance equation may be coupled with a model of atmospheric air flow (FERRAGUT et al. 1996, LYMBEROPOULOS et al. 1998).

Using these models, if the empirical coefficients that calibrate each term representing each physical process in the balance equation, are known, one can calculate the fire rate of spread and a temperature field, sometimes with the help of hard numerical methods (SANTONI 1998, SIMEONI et al. 2002).

Up to this point, none of the reviewed models took into account the basic phenomena, which explain the behaviour of a forest fire (pyrolysis, oxygen transport, gaseous phase combustion, embers combustion).

Starting from a description of these basic phenomena, the properties of the flame, the embers and the hot gases must be known.

In others words, the only input parameters of the model must be the initial characteristics of the medium; the model itself calculates these properties.

This family of models, based on a detailed physical and multiphase approach, is presented in the following chapter.

EUFIRELAB

D-03-09.doc 12

2.4 DETAILED PHYSICAL AND MULTIPHASE APPROACH

The main groups of work based on a detailed physical and multiphase approach of forest fire behaviour modelling found in the literature are described hereafter.

2.4.1 The fuel as a multiphase medium

GRISHIN (1997) reports in a book the main results of the researches carried out in Russia (and ex-USSR) on the modelling of forest fire behaviour (see also GRISHIN et al 2002).

Among these works, a general mathematical model of forest fire behaviour has been described at a first time in 1992.

The numerical solutions of this detailed model are not presented.

However, numerous approximate models based on this approach have been produced before this date and are described in the same book (GRISHIN 1997).

LINN (1997) presents a PhD dissertation entitled “a transport model for prediction of wildfire behaviour”, which is a first step of a wider project aiming to develop the abilities of “self-determined” models of fire propagation through forest fuels.

The practical interest of the physical approach to study wildfire behaviour was demonstrated in a paper (HANSON et al 2000) comparing: - numerical predictions obtained for historical fires

using various physical formulations - with those obtained with classical empirical (or semi-

empirical) approach.

Finally, LARINI et al. (1998) (and in a more teaching form LARINI, 1998), PORTERIE et al (2000), MORVAN et al (2000,2001,2002,2003) provide the bases for the formulation of a complete model for forest fire propagation.

They describe how the usual equations of continuum media mechanics can be transformed following a rigorous method into equations well-suited to a multiphase medium, a vegetation sustaining a fire.

We summarise here after the last two approaches, because only these authors completely describe how the multiphase equations for forest fire modelling are obtained.

Then, we briefly mention the main differences with the works of Grishin and Linn.

When a combustible medium is observed at a sufficiently small scale, a gaseous phase that surrounds solid particles of combustible vegetation can be seen.

These particles can be leaves, small twigs, needles, grasses, ...

The combustible medium is considered as a multiphase medium composed of a gaseous phase and several solid phases.

Each solid phase consists of the same fuel particles, which have the same shape, the same size, the same physical-chemical properties, thus the same behaviour with respect to a fire.

In particular, each family of particle is characterized by its Surface Area / Volume (SA/V) ratio.

In order to achieve a spatial description of the medium, the fraction of space volume occupied by each family or each solid phase, at each point and each time, must be known.

2.4.2 The fuel as a fractal medium

In the approach of SÉRO-GUILLAUME et al. (2002), the vegetal phase is considered as a fractal medium, which can be described mathematically.

Moreover, the different parts of plants are considered as a porous medium, in order to take into account the internal structure of vegetation.

Therefore, this model is more complex than the preceding one, and the difference of viewpoint leads to substantial change in the mathematical formulation.

If the medium is known, what are the mechanisms that will govern the propagation of a fire through this medium?

Observing the medium at a very small scale, which allows distinguishing one fuel particle from surrounding gas, the behaviour of this particle is described in the case of an approaching fire: - The particle receives energy from this fire by

convection and radiation. - Its temperature is then raised from the ambient

temperature to the boiling water temperature, at which it looses its water.

- As soon as the particle is dried, its temperature can raise again.

- The plant material is decomposed gradually releasing pyrolysis gases.

- The reactive part of these gases is combined with the oxygen of the air (gaseous phase chemical reactions).

- The gas temperature then increases, causing its expansion and, due to buoyancy forces, the gas moves.

- These movements play a very important role in the necessary transport of oxygen and in the energy transfers.

- At the end of the pyrolysis, the particle is mainly composed of chars.

- Oxygen may reach the surface of the particle and react with the remaining chars.

- It’s the combustion of chars, which causes the regression of the particle surface.

Now the problem is to know how to describe the evolution of such a medium at the scale of a particle.

The medium is known when at each point of the gas, the velocity, the temperature, the mass fraction of chemical species and the pressure are known, and when at each point of the motionless particle, the temperature, the mass fraction of chemical species and the density are known.

EUFIRELAB

D-03-09.doc 13

At this scale, for calculating the state of the medium: - at each point of the gas, the balance equations of

reactive fluid mechanics are available: mass, momentum, energy and chemical species, and

- at each point of the particle, a system of balance equations (mass, chemical species, energy) is available, which describes the behaviour of a solid conductive medium that can loose its water and be decomposed into pyrolysis products.

2.4.3 Changing spatial scale

In addition, at the interface between the solid particle and the gas, some conditions must be verified (local interface conditions).

Solving all these equations at this scale can be done only in very simple geometric configurations, but cannot be done in real configurations with numerous particles distributed in a gas.

In this last case, and at this scale, it is impossible to obtain solutions of the strongly coupled balance equations at each point of the gas, each point of each particle, which in addition must verify all local interface conditions.

Thus, it is necessary to increase this scale of observation.

From a mathematical point of view, operating this change of scale is equivalent to average the equations established at a point of the gas or at a point of the particle over some volume around these points.

It is a usual operation for modelling multi phase multi constituent materials and several methods of averaging are available.

In both approach, using a spatial weighting function (ANDERSON and JACKSON 1969), balance equations for the gaseous phase and balance equations for each solid phase have been integrated over: - the whole volume occupied by the gas, and - the whole volume occupied by each solid phase.

After some mathematical transformations, a system of equations is obtained for each phase (gaseous phase and solid phases).

These equations establish the relations in time and space between the weighted average of the different variables (temperature, velocity, density,...).

2.4.4 A system of multiphase, reactive and radiative equations

It is worth noting that the averaging operation leads to the appearance of new terms in the balance equations, which are due to the interactions between the different phases (here between the gaseous phase and each solid phase).

Thus, systems of partial derivative equations are obtained that are strongly coupled through these terms of interaction.

For instance, these terms of interaction are due to the mass transfers related to: - the drying and pyrolysing process of the particles, - the drag forces, - the heat transfers by convection and by radiation.

A system of multiphase, reactive and radiative equations, rigorously deduced from the instantaneous point equations (generalised NAVIER-STOKES equations for the gas and point balance equations for the solid), is now available.

Before solving these equations, it is necessary to close them.

The closure operation differs for both approaches.

LARINI’s approach (1998) made some assumptions and sub-models describing the basic phenomena at the multiphase scale are provided.

Among these phenomena, there are chemical reactions and different interactions between phases, which appear after the transformation of point equations into multiphase (i.e. averaged) equations.

In the other approach, closure is obtained by thermodynamics argument.

Let us pinpoint that the used thermodynamic is an extended one allowing the averaging on very large representative element volume REV; the concept of REV is equivalent to the notion of fluid particle.

For this, it is necessary to make some assumptions and furthermore, to establish sub-models describing the basic phenomena at the multiphase scale.

These systems of partial derivative equations have been yet solved in one and two space dimensions (plane symmetry and axi-symmetry) (MORVAN et al. 1998, PORTERIE et al. 1998, 2000).

These approaches have been used with success to study various fire propagating through: - a dead fuel bed (PORTERIE et al. 1998, 2000)

(MORVAN et al. 2000, 2001), - a living Mediterranean shrub (MORVAN et al. 2002), - a complex shrub/canopy Mediterranean forest, - a fuel break (MORVAN et al. 2003) (EFAISTOS and

FIRESTAR research programmes).

Some physical properties such as the ratio between the respective contribution of the two modes of heat transfer (radiation and convection) between the flame, the burned hot gases and the vegetation has been quantified for the two regimes of propagation of a surface fire: plume-dominated and wind-driven fires.

Calculating numerical solutions of these equations is hard and time consuming.

This is the reason why simplified models have been also deduced from the complete sets of equations (GIROUD 1997, DUPUY and LARINI 1999).

2.4.5 Using asymptotic analysis

MARGERIT et al. (1997, 2002), proposed a new approach using in order to derive or relate the detailed physical approach to 2D propagation models.

They obtained a model very similar to the so-called “physical model”, see above.

Propagation of fire is modelled by a reaction diffusion system of partial differential equations.

This model has been coded and numerical experiments of propagation have been provided at laboratory and terrain scales.

EUFIRELAB

D-03-09.doc 14

This effort is continuing and recent results show the clear relation between different approaches of modelling: SÉRO-GUILLAUME et al. (2002), SÉRO-GUILLAUME (2003), INFLAME and SPREAD research programmes.

The multiphase approach has also been used to improve phenomenological models (SIMEONI et al. 2001) and to develop simplified flow sub-models (SIMEONI et al. 2003).

2.4.6 Large Eddy Simulation

A recent approach is worthwhile to mention. It involves “Large Eddy Simulation” LES modelling to describe the turbulence in the fire plume.

It has been developed stating that the evolution of large eddy structures characteristic of most fire plumes is lost with the classical Reynolds-Average Navier-Stokes RANS methods.

The application of LES techniques to fire is aimed at extracting greater temporal and spatial fidelity from simulations of fire performed on the more finely meshed grids allowed by ever-faster computers.

The assumptions made to model the turbulence must ultimately be justified by comparison to experiments.

A first model is the extension to wildland fires of the “Fire Dynamics Simulator” FDS (MCGRATTAN 2004) developed by the NIST for fire-driven fluid flow in buildings and industry.

This new simulation tool is called WFDS (MELL et al. 2006a and b).

The LES approach was: - applied to crown fire initiation (TACHAJAPONG et al.

2006) and - validated by experiments at the laboratory scale.

2.5 APPROXIMATE MODELS

Thus, two kinds of approximate models are available: those of the phenomenological approach presented at the beginning of this review, and those, which are directly deduced from the complete physical and multiphase approach, described above.

Linn (1997) aims at simulating large-scale forest fires and introduces a sophisticated turbulence model, including the dissipation of turbulent energy due to the solid particles.

However, because of the large scale of space, he had to make strong assumptions on some of the basic mechanisms (especially the combustion process in the gaseous phase is not really described).

The approach remains very similar to the previous one (LARINI et al 1998).

In the different models proposed by GRISHIN (1997), there is only one solid phase made of different components (dry material, water, chars, ashes).

In addition, for most of these models, the solid phase is assumed to be in thermal equilibrium with the gas (one-temperature models).

Hence, this approach differs from the previous ones.

Physical and semi-empirical approaches are complementary ones; they can be mixed to describe wildfire behaviour at large scale (see CRUZ et al. 2006 a and b for crown fires).

Recent studies have shown the interest to compute the local atmospheric flow using a physical approach coupled with a heat released curve representing the fire.

In this case the ROS of the curve fire is evaluated from a modified ROTHERMEL model taking into account the local conditions of propagation (state of the vegetation, wind velocity, slope…) (CLARK et al. 1996, HANSON et al. 2000).

EUFIRELAB

D-03-09.doc 15

2.6 FUTURE PROSPECTS

In terms of future prospects, we want to underline that every modelling approach must be accompanied by experiments.

The reverse is true, except if one chooses to follow a statistical or purely empirical approach of the phenomena.

2.6.1 To characterise the fuel

First, it is necessary to characterise the fuel: - on one hand the particles, which compose the

medium (morphological and physical-chemical properties), and

- on the other hand, the combustible medium, in other words the spatial arrangement of the particles.

One attempt has been done (CALOGINE et al. 2001, 2002) to establish models describing the vegetation in details from parameters that should be easy to obtain on the field, this work should be pursued and improved, see report of WP 02.

2.6.2 To validate the fire behaviour

Second, of course, experiments must permit the validation of fire behaviour models, by carrying out both laboratory and field fires.

Conversely, this kind of experiment can show phenomena, which cannot be easily explained, and thus make questions to the modellers arise.

This kind of experiment is yet widespread at the international level (studies of wind effect, slope effect, fuel type effect... on fire behaviour).

But, faced with the increasing requirement of varied validation data, these experiments should be encouraged.

Finally, in particular in the frame of the complete physical and multiphase approach, the closure of forest fire behaviour models requires physical sub-models, which can be directly established only with the help of specific laboratory experiments.

In this case, the experiment must sufficiently isolate the studied physical mechanism.

Up to now, this kind of experimental work has been scarce in the forest fire-modelling domain, and it is necessary to develop them.

This lack has been partially addressed in the frame of the current project (see the deliverables of Unit 7 “Metrology”).

2.6.3 To improve the metrology

All these experiments must be accompanied by an improvement of the metrology, in both laboratory and field conditions.

For instance, gas temperature measurements using thermocouple sensors have shown limits, and other physical variables are still difficult to be measured: - field of gas velocity, - solid phase temperature, - gas and particles radiation

It seems that optical methods should be a relevant solution to numerous arising problems (see also unit 7).

2.6.4 To change the scale of prediction

Today, the computer resources, which are available for solving the partial derivative equations derived from the detailed physical and multiphase approach, permit the correct calculation of the physical phenomena only at the local fire scale (~100-200 m) and during a relatively short time (~10 minutes), and in addition, with still high duration of calculation.

The objective is to solve these equations in larger spatial domains, in three space dimensions, and more generally, to change the scale of prediction.

For this, the most obvious way is to use “ super-computers ” (e.g. massively parallel computers) with increasingly efficient numerical methods (algorithms).

2.6.5 To use larger numerical meshes

Another idea is to use larger numerical meshes without transgressing too much the physics of the model.

This has been initiated by LINN who used PDF (Probability Density Function) for describing the source terms of equations.

This kind of work must continue, but new research ways, which would be more specific of the forest fire problem, should be investigated.

2.6.6 To develop approximate models

In order to change of scale of prediction, developing approximate (simplified) models can be also envisaged; some examples are described in section 2.4.

We remind that these models can be either phenomenological models or models directly deduced from the complete physical and multiphase approach.

Their interest is that they have a physical base and that they are easier to solve and less time consuming than complete models.

Of course, in order to achieve such models, it is necessary to often make strong hypothesis.

Thus, in order to evaluate the relevance of this hypothesis in a given studied situation, the detailed approach may be used.

EUFIRELAB

D-03-09.doc 16

2.7 CONCLUSION

In the face of the thematic diversity and the extent of the work to carry out, it is essential to group together and co-ordinate the research means, and in particular, to not forget that modelling and experimental works are closely related.

The work to carry out implies long-term researches.

For basic reasons that cannot be developed here, one can expect that researches conducted in the field of forest fire behaviour modelling bring up very relevant scientific questions and leads to producing results of a more general significance.

However, it will be necessary to reply to some of the questions of the users of research results as soon as possible.

For this, it seems that users and researchers should define together: - which applied problems must be investigated in

priority and - which researches are expected to provide the best

solutions.

Indeed, these studies will: - directly answer to the questions of users, and - consist in an essential step to progress toward a

complete solution of the problem.

2.8 REFERENCES

ALBINI, F.A. 1981. A model for the wind-blown flame from a line fire . Combustion & Flame 43:155-174.

ALBINI, F.A. 1985. A model for fire spread in wildland fuels by radiation. Combustion Science and Technology 42:229-258.

ALBINI, F.A. 1986. Wildland fire spread by radiation - a model including fuel cooling by natural convection. Combustion Science and Technology 45:101-113.

ANDERSON, T.B. and JACKSON R. 1967. A fluid mechanical description of fluidised bed. Industrial Engineering Chemistry Fundamentals 6:527-539.

ANDERSON, H.E. 1969. Heat transfer and fire spread. USDA Forest Service, Research Paper (INT-69), Ogden, Utah, 20 pages.

BALBI, J.H., P.A. SANTONI,. DUPUY J.L 1999. Dynamic modelling of fire spread across a fuel bed. International Journal of Wildland Fire 9 275-284.

BALBI J.H., ROSSI J.L., MARCELLI T., SANTONI P.A. 2005. Propagation de feux de végétation : un modèle 3d temps réel. In proc. of Journées Internationales de Thermique, Tanger.

CALOGINE D, SÉRO-GUILLAUME O, 1998, Characteri-sation of Vegetation in Forest Fire Models, 3rd International Conference on Forest Fire Research, Coimbra 16-20 November.

CALOGINE D, SERO-GUILLAUME O., 2001, Sur un modèle fractal de végétation, application à la modélisation de la lutte contre les feux de forêts, Récents progrès en génie des procédés, Vol. 15, pp. 11-18.

CALOGINE D, SERO-GUILLAUME O., 2002a, L’équation de NAVIER-STOKES avec mémoire et le transfert de quantité de mouvement dans un milieu poreux, Comptes Rendus à l’Académie des Sciences Mécanique, Vol. 330, pp. 383-389.

CALOGINE D, SÉRO-GUILLAUME O., 2002b, Air flow model in a tree crown”, IV International Conference on Forest Fires Research, Coimbra.

CATCHPOLE, E.A. and N.J. DE MESTRE. 1986. Physical models for a spreading line fire. Australian For. 49:102-111.

CEKIRGE, H.M. 1978. Propagation of fire fronts in forest. Comp. and Maths. with Appls. 4:325-332.

CLARK T.L., M.A. JENKINS, J. COEN, D. PACKHAM 1996, A Coupled Atmospheric-Fire Model: Convective Feedback on Fire Line Dynamics, J. Appl. Meteor, Vol.35, pp.875-901.

CRUZ, M.G., BUTLER, B.W., ALEXANDER, M.E., FORTHOFER, J.M., WAKIMOTO, R.H. 2006a. Predicting the ignition of crown fuels above a spreading surface. fire I: Model idealization. Int. J. Wildland Fire 15, 47-60.

EUFIRELAB

D-03-09.doc 17

CRUZ, M.G., BUTLER, B.W., ALEXANDER, M.E. 2006b. Predicting the ignition of crown fuels above a spreading surface fire. Part II: Model behavior and evaluation. Int. J. Wildland Fire 15, 61-72.

DE MESTRE, N.J., E.A. CATCHPOLE, D.H. ANDERSON, and R.C. ROTHERMEL. 1989. Uniform propagation of a planar fire front without wind. Combustion Science and Technology 65:231-244.

DUPUY, J.L. 1997. Towards a better understanding and prediction of forest fires propagation : experimentation, testing and model development (In French). Thesis (Doctorate of University). University Claude-Bernard, Lyon-I, 272p.

DUPUY, J.L. and M LARINI 1999. Fire spread through a porous forest fuel bed : a radiative and convective model including fire-induced flow effects. Accepted in the International Journal of Wildland Fire.

EMMONS, H.W. 1964. Fire in the forest. Fire Research Abstracts and Reviews 5:163-178.

FERRAGUT, L.F., ASENSIO M.I., MONTENEGRO R., PLAZA A., WINTER G., SERON F.J. 1996. A model for fire simulation in landscapes. Computational Fluid Dynamics, pp 111-116.

FERRAGUT, L.F., TIMÒN S.M., ASENSIO M.I. 2006. Radiation modelling in fire spread. In Proc. V Int. Conf. on Forest Fire Research, VIEGAS, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

FRANDSEN, W.H. 1971. Fire spread through porous fuels from the conservation of energy. Combustion and Flame 16:9-16.

FUJII, N., J. HASEGAWA, L. PHALLOP, and Y. SAKAWA. 1980. A non-stationary model of fire spreading. Appl. Math. Modelling 41:76-180.

GIROUD, F. 1997. Contribution to fire propagation modelling : multiphase approach of forest fires, development of a propellant fire in a semi-confined medium (In French). Thesis (Doctorate of University). University of Provence, IUSTI, Aix-Marseille-I, 198 p.

GRISHIN A.M., O.V. SHIPULINA 2002. Mathematical model for spread of crown fires in homogeneous forests and along openings. Combustion, Explosion & Shock Waves, Vol. 38(6): 622-632p.

GRISHIN, A.M. 1997. Mathematical modelling of forest fires and new methods of fighting them. Publishing House of the Tomsk State University, Russia. 390 pages.

HANSON H.P., M.M. Bradley, J.E. BOSSERT, R.R. LINN, L.W. YOUNKER 2000, The potential and promise of physics-based wildfire simulation, Environmental Science & Policy Vol. 3: 161-172p.

HOTTEL, H.C., G.C. WILLIAMS, and F.R. STEWARD. 1965. The modelling of fire spread through a fuel bed. Tenth Symposium (International) on Combustion, the Combustion Institute, Pittsburgh, 1965, pages 997-1007.

HOTTEL, H.C., G.C. WILLIAMS, and G.K. KWENTUS. 1971. Fuel preheating in free-burning fires. Thirteenth Symposium (International) on Combustion, U.S.D.A. Forest Service, Washington, D.C. The Combustion Institute, Pittsburgh, 1971, pages 963-970.

LARINI, M. 1998. The complete physical models. Short Course on Forest fire Behaviour Modelling. Luso-Coimbra, Portugal, 21-22 November 1998.

LARINI, M., F. GIROUD, B. PORTERIE, and J.C. LORAUD. 1998. A multiphase formulation for fire propagation in heterogeneous combustible media. International Journal of Heat and Mass Transfer 41(6-7):881-897.

LINN, R.R. 1997. A transport model for prediction of wildfire behaviour. PhD dissertation, New Mexico State University, Dept. of Mechanical Engineering, Las Cruces, New Mexico; also published as Los Alamos National Laboratory Report, number LA-13334-T. 195p.

LINN, R.R. and F. HARLOW 1998. Use of transport models for wildfire behaviour simulations. Proceedings of the 3rd International Conference on Forest Fire Research, Coïmbra, Portugal. Vol. 1: 363-372p.

LYMBEROPOULOS, N., TRYFONOPOULOS, and F.C. LOCKWOOD 1998. The study of small and mesoscale wind field. Proceedings of the 3rd International Conference on Forest Fire Research, Luso, Coïmbra, Portugal. Vol 1 pp405-418.

MARGERIT J., O. SERO-GUILLAUME 2002. Modelling forest fires. Part 2 :Reduction to two-dimensional models and simulation of propagation. Int. J. of Heat & Mass Transfer Vol. 45: 1723-1737p.

MCGRATTAN, K.B. 2004. Fire Dynamics Simulator (Version 4), Technical Reference Guide, NISTIR Special Publication 1018, McGrattan, K., editor

http://fire.nist.gov/bfrlpubs/

MELL, W.E., JENKINS, M.A., GOULD J., CHENEY, P., 2006a. A Physics-Based Approach to Modeling Grassland Fires. International Journal of Wildland Fire, in press.

MELL, W.E., MANZELLO S.L., MARANGHIDES A. 2006b. Numerical Modeling of Fire Spread through Trees and Shrubs. In Proc. V Int. Conf. on Forest Fire Research, VIEGAS, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

MORANDINI F., P.A. SANTONI, J.H. BALBI 2001. The contribution of radiant heat transfer to laboratory-scale fire spread under the influences of wind and slope. Fire Safety Journal. Vol. 36: 519-543p.

MORANDINI F., SIMEONI A., SANTONI P.A, BALBI J.H. 2005. A model for the spread of fire across a fuel-bed incorporating the effects of wind and slope. Combustion Science and Technology 177, 1381-1418.

EUFIRELAB

D-03-09.doc 18

MORVAN D., J.L. DUPUY 2001. Modelling of fire spread through a forest fuel bed using a multiphase formulation. Combust. & Flame, Vol.127: 1981-1994p.

MORVAN D., J.L. DUPUY 2002. Numerical simulation of the propagation of a surface fire through a Mediterranean shrub. 7th International Symposium on Fire Safety Science, Boston-Worcester, Vol. 7: 557-568p.

MORVAN D., J.L. DUPUY 2003. Wildfire spread simulation through a Mediterranean shrub in the urban/forest interface. Proc. Multiphase and Complex Flow Simulation for Industry, Cargèse 13p.

MORVAN D., V. TAULEIGNE 2002. Wind effects on wildfire propagation through a Mediterranean shrub. Proc. 4th International Conference on Forest Fire Research, Coimbra 14p.

MORVAN D., V. TAULEIGNE, J.L. DUPUY 2002. Flame geometry and surface to crown fire transition during the propagation of a line fire through a Mediterranean shrub. Proc. 4th International Conference on Forest Fire Research, Coimbra 11p.

MORVAN, D, J.L. DUPUY, M. LARINI 2000. Multiphase formulation applied to the modeling of fire spread through a forest fuel bed. Proc. 28th Symposium (International) on Combustion, The Combustion Institute, Vol. 28: 2803-2809p.

MORVAN, D., B. PORTERIE, M. LARINI and J-C LORAUD 1998. Numerical simulation of the behaviour of turbulent diffusion radiating in a cross wind during a surface fire. Proceedings of the 3rd International Conference on Forest Fire Research, Coïmbra, Portugal. Vol. 1: 389-404p.

MORVAN, D., M. LARINI 2001. Modelling of one dimensional fire spread in pine needles with opposing air flow. Combust. Sci. & Tech., Vol.164: 37-64p.

PAGNI, P.J., and T.P. PETERSON. 1973. Flame spread through porous fuels. 14th Symposium (International) on Combustion, U.S.D.A. Forest Service, Washington, D.C. The Combustion Institute, Pittsburgh, pages1099-1107.

PITTS, W.M. 1991. Wind effects on fires, Prog. Energy Combust. Sci., 17: 83-134.

PORTERIE B, D. MORVAN, J-C LORAUD and M. LARINI 2000. Fires spread through fuel beds: modelling of wind-aided fires and induced hydrodynamics. Physics of Fluid, Vol.12(7): 1762-1782p.

PORTERIE, B., D. MORVAN, M. LARINI and J-C LORAUD 1998. A multiphase model for predicting line fire propagation. Proceedings of the 3rd International Conference on Forest Fire Research, Coïmbra, Portugal. Vol 1: 343-360p.

RAMEZANI S, CALOGINE D, SÉRO-GUILLAUME O. January 2004, Numerical Model of Combustion of Vegetation, Line and Point Ignition. Deliverable D. 2.12,. Spread Report.

ROTHERMEL, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Research Paper (INT-115), Ogden, Utah, 40 pages.

SANTONI P.A. 1998. Elaboration of an Evolving Calculation Domain for the Resolution of a Fire Spread Model. Num. Heat Transfer 33, 279-298.

SANTONI, P.A., BALBI J.H. 1998. Numerical simulation of a fire spread model. Proceedings of the 3rd International Conference on Forest Fire Research, Luso, Coïmbra, Portugal. Vol 1 pp295-310.

SANTONI, P.A., BALBI J.H., DUPUY J.L. 1999. Dynamic modelling of upslope fire growth. International Journal of Wildland Fire 9, 285-292.

SIMEONI A., SANTONI P.A., LARINI M. BALBI J.H. 2001. Proposal for theoretical contribution for improvement of semi-physical forest fire spread models thanks to a multiphase approach: application to a fire spread model across a fuel bed. Combustion Science and Technology 162, 59-84.

SIMEONI A., SANTONI P.A., LARINI M., BALBI J.H. 2001. On the wind advection influence on the fire spread across a fuel bed: modelling by a semi-physical approach and testing with experiments. Fire Safety Journal 36, 491-513.

SIMEONI A., SANTONI P.A., BALBI J.H. 2002. A strategy to elaborate forest fire spread models for management tools. International Journal of Modelling and Simulation 4, 213-224.

SIMEONI A., SANTONI P.A., LARINI M. BALBI J.H. 2003. Reduction of a multiphase formulation to include a simplified flow in a semi-physical model of fire spread across a fuel bed. International Journal of Thermal Science 42, 95-105.

SÉRO-GUILLAUME O. 2003, Presentation and comparison of forest fires propagation models, Workshop on forest fire modelling, invited conference, State University of Florida, Tallhasee, pp. 12-14 March.

SERO-GUILLAUME O., J. MARGERIT 2002. Modelling forest fires. Part 1 : a complete set of equations derived by extended irreversible thermodynamics. Int. J. of Heat & Mass Transfer Vol. 45: 1795-1722p.

SERO-GUILLAUME O., MARGERIT J. 2002. Modelling forest fires. Part 1 : a complete set of equations derived by extended irreversible thermodynamics. Int. J. of Heat & Mass Transfer, Vol. 45, pp. 1795-1722.

SERO-GUILLAUME O., MARGERIT J., BOTELLA O., 2002. Asymptotic analysis: the way from complete physical models to propagation models, IV International Conference on Forest Fires Research. Coimbra.

STEWARD, F.R. 1971. A mechanistic fire spread model. Combustion Science and Technology Vol.4:177-186.

EUFIRELAB

D-03-09.doc 19

TACHAJAPONG W., ZHOU X., MAHALINGAM S. 2006. Numerical Modeling of Fire Spread through Trees and Shrubs. In Proc. V Int. Conf. on Forest Fire Research, Viegas, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

TELISIN, H.P. 1974. Flame radiation as a mechanism of fire spread in forests. In: Heat Transfer in Flames (Edited by John Wiley and Sons). Chapter 29, pages 441-449.

THOMAS, P.H. 1967. Some aspects of the growth and spread of fire in the open. Forestry 40:139-164.

THOMAS, P.H. 1971. Rates of spread of some wind-driven fires. Forestry 44:155-175.

VAN WAGNER, C.E. 1967. Calculations on forest fire spread by flame radiation. Forestry Branch Publication No 1185, Canadian Department of Forestry, 14 pages.

VIEGAS D.X. 2002. Fire line rotation as a mechanism for fire spread on a uniform slope. International Journal of Wildland Fire 11(1): 11-23.

VIEGAS D.X., Pita L.P. 2004. Fire Spread in Canyons. International Journal of Wildland Fire 13(3):1-22.

WEBER, R.O. 1989. Analytical models for fire spread due to radiation. Combustion and Flame 78:398-408.

WEBER, R.O. 1991a. Toward a comprehensive wildfire spread model. International Journal of Wildland Fire 1(4) :245-248.

WEBER, R.O. 1991b. Modelling fire spread through fuel beds. Progress in Energy and Combustion Science 17:67-82.

EUFIRELAB

D-03-09.doc 20

3 A REVIEW ON WILDLAND FIRE SMOKE DISPERSION MODELLING

3.1 INTRODUCTION

Biomass burning is: - a locally, regionally, and globally important

biospheric phenomenon, and - a significant source of various environmentally

significant gases and solid particulate that can produce severe degradations of air quality on a local and/or regional scale.

As a consequence of the increase of frequency and severity of forest fires, in particular in the wildland-urban interface (WUI), populations are frequently exposed to unhealthy concentrations of toxics.

It is worsey when fire occurs in the vicinity of large urban agglomerations, as recently in Southeast Asia, Australia, South America, Russia, or Southern Europe.

In fact, there is a growing awareness that smoke from wildland and prescribed fires can expose individuals and populations to hazardous air pollutants.

This represents an important public health issue for the affected communities and the personnel involved in firefighting operations (BRUSTET et al. 1991, Ward et al. 1993, MIRANDA et al. 1994, 2005, REINHARDT et al. 2001).

According to the literature in the subject (e.g., ROTHMAN et al. 1991), fire workers can experience subchronic, and chronic effects of exposure to forest fires.

Adverse health effects begin with acute, instantaneous eye and respiratory irritation and shortness of breath but can develop into headaches, dizziness, and nausea lasting up to several hours.

Longer-term health effects, lasting days to perhaps months, have also been identified among wildland fire-fighters.

These decrements in lung function include: - a slightly diminished capacity to breathe, - constriction of the respiratory tract, and - hypersensitivity of the small airways.

The spatial scale of the problem gives it an even larger dimension.

Notwithstanding that even major wildfires are limited to some hundreds of hectares, their impacts, knowing no natural or political boundaries, can be felt and reported far beyond the physical limits reached by fire spread.

Depending on meteorological conditions, biomass-burning plumes and haze layers can persist at the atmosphere for long periods of time influencing the chemical and optical characteristics of the atmosphere.

NASA mission conducted at South Pacific found approximately 20 days old smoke plumes (BLAKE et al. 1999): - travelling up to 1000 km or more (BROWELL et al.

1996) and - extending horizontally over 100 km as observed

over the central Amazon Basin (ANDREAE et al. 1988).

3.2 EMISSION AND DISPERSION OF AIR POLLUTANTS

3.2.1 The major air pollutants

Since their formation to final impacts, forest fires-generated air pollutants undergo in an extremely complex process: - it begins with the emission from the biomass

combustion, - continues through the transport phase, during which

pollutants are transformed and dispersed, and the subsequent deposition and penetration into the canopy, and

- ends at the exposed organisms, with consequent effects on health, especially among individuals from groups at risk.

Biomass smoke is a complex mixture that contains a large and diverse number of chemicals, including both particulates and gaseous compounds.

Following LEVINE (1999), typical major air pollutants resulting from forest fires are: - PM: particulate matter - CO2: carbon dioxide - CO: carbon monoxide - CH4: methane - NMHC: non-methane hydrocarbons - NO: nitric oxide - N2O: nitrous oxide, and - NH3: ammonia

CO, CH4, NMHC, and NO are chemically active gases that strongly influence the local/regional concentrations of the major atmospheric oxidants ozone O3 and the hydroxyl radical OH.

Some measurements suggest that biomass burning may be a significant global source of methyl bromide (LEVINE et al. 1995), which leads to the chemical destruction of O3 in the stratosphere.

Production of aerosols is also very important, giving rise to local pollution, and affecting the radiation budget of the Earth and, hence, influencing global climate.

3.2.2 Emission of particulate matter

In some large wildfires, source strengths exceeding 0.6 tons of particles per second can be attained (WADE and WARD 1973).

PM emission factors range from 2 to 90 g.kg-1, depending on forest fuel types, fuel arrangement and combustion characteristics (MAHAFFEY and MILLER 2001).

Although the distribution of PM depends on the combustion conditions, the available data indicates that the sizes of most particles produced during a fire can be described through a bimodal distribution, similarly to the emissions from any other combustion source.

A fine particle mode can be defined with a maximum aerodynamic diameter of 2.5 µm and a mean value of 0.3.

EUFIRELAB

D-03-09.doc 21

Additionally, ultra-fine particles, that rapidly agglomerate to form fine particles, can be defined as those smaller than 0.1 µm.

A coarse particle mode is characterized by a mean diameter greater than 10 µm and agglomerates all the particles larger than 2.5 µm.

The size distribution of biomass burning smoke has been measured by several investigators, indicating that in terms of number the majority of particles emitted are ultra-fine, with a peak in the distribution between 0.15 and 0.4 µm and only a small fraction in the larger size range (BRAUER 1998, MORAWSKA et al., 1998).

This was evidenced by SANDBERG and MARTIN (1975) works, which indicated that: - 82% of the particle mass was constituted by

particles smaller than 1 µm and - 69% smaller than 0.3 µm.

This was also evidenced by the ground-based particles samplings conducted by WARD and HARDY (1989), which showed the bimodal distribution with only a small fraction of the total mass (less than 10%) between 2 and 10 µm.

3.2.3 Modification of the mixture

The overall mixture of combustion products is also affected by the combustion phase.

In fact, the smouldering phase, known to be a very low-intensity combustion process, releases several times more fine particles than flaming combustion.

In this last, the percentage of fine particles produced ranges from 80 to 95%, depending, among other factors, on the turbulence intensity in the combustion zone (WARD 1999).

Moreover, also fuel properties can induce a substantial effect on the percentage of vegetation consumed by smouldering combustion: - from 10% in savannah ecosystems - to 90% in peat, rotten logs or rotten wood residues

(WHO/UNEP/WMO, 1999).

Also the addition of fire retardant chemicals can affect the type and quantity of air pollutants emitted.

In fact, an increase on the amount of particles produced was already reported as a consequence of the diminishing of the pyrolysis temperature (GEORGE and SUSOTT 1971, PHILPOT et al. 1972, Kalabokidis 2000).

Fire retardant chemical formulation is also known to affect the amount of smoke produced.

In fact, ammonium phosphate retardants reduce glowing combustion and are much more effective in decreasing the rate of weight loss and increasing residue and smoke production than ammonium sulphate retardants (GEORGE and SUSOTT 1971, PHILPOT et al. 1972).

3.2.4 Transportation of the mixture

After formation, the transport of the highly dynamic mixture of combustion compounds away from the emission site depends on the velocity and thermodynamic fields of the atmosphere.

The transport patterns are ultimately influenced by the interactions of the intense turbulent and convective circulations induced by the heat released with the surrounding atmosphere.

During transport, air pollutants undergo in physicochemical transformation processes in which the compounds change their chemical composition, physical characteristics and concentration.

The residence time of combustion compounds in air: - depends on the nature of these processes and,

depending on the size range of particles and meteorological conditions,

- varies from seconds or minutes for very large particles, to days or weeks in particles smaller than 1 µm.

This fact was reported by MORAWSKA et al. (1998), who observed that at a distance of 20 km from a fire there is a significant increase only in the concentration of particles with less than 1 µm, indicating that, while travelling over this distance, coarser particles have been removed from the atmosphere.

In fact, due to high gravitational settling velocities of large particles, only fine particles can remain suspended in the atmosphere the necessary time to permit their transportation over long distances.

Also smaller particles are transformed, mainly by coagulation and condensation into larger particles, and are subsequently removed through gravitational settling or by in-cloud scavenging during precipitation.

The time of day or night is of extreme importance in the transportation of smoke plumes.

In the presence of nocturnal jets with speeds of 25 m.s-1 smaller particles can be transported up to 1000 km during a 10-hour period (GARSTANG 1999).

EUFIRELAB

D-03-09.doc 22

3.3 NUMERICAL MODELLING OF SMOKE PRODUCTION AND DISPERSION

The study of the effect of forest fire emissions on air quality requires an integrated approach of different components, such as emissions, atmospheric flow, fire progression, pollutants dispersion and associated impacts, as visibility reduction or human health effects.

The numerical modelling of this entire complex process, which is intended to provide useful information for managing or interpreting fire effects, is not, however, a trivial task.

The knowledge of fire characteristics and the spatial and temporal distribution of products originated by a fire require an overall understanding of all the atmospheric processes involved, which can have extremely distinct spatial scales: - from motions smaller than those of the fire itself, - to circulations occupying a significant fraction of the

planet (GARSTANG 1999).

To estimate the effects of smoke on air quality the emission production calculation is insufficient.

Beyond the knowledge of the emission characteristics, air pollution assessment requires the calculation of the concentration of a given pollutant some distance from its source, taking into account all the phenomena that affect transport and dispersion of smoke.

In practice, this procedure can be considered to have three main components, which are not necessarily independent of each other: - to estimate the plume rise, - to calculate the transport speed and direction of the

emitted pollutants, and finally, - to simulate smoke dispersion.

3.3.1 To estimate the plume rise

Fire emission products vary greatly with the type of fuel consumed and its moisture content, the fire intensity or rate of energy released, and other factors such as humidity and wind speed.

Therefore, in order to estimate the emission rate of a pollutant, variables as the fuel load, combustion rate, and the emission factor are needed.

Fire, as an intense heat source, induces the formation of heat-driven turbulence and convective motions, which interact with the atmospheric flow field according to a complex and non-linear process.

The heat released originates the formation of a convection column that forces the emitted pollutants to ascend.

Upward velocities within the fire can range from 20 to 40 m.s-1, determining the height attained by the centre of the smoke plume.

In fact, there is a considerable uncertainty on the knowledge of the mean height reached by fire products, which is a critical factor to determinate the distance through which these combustion products will be transported (GARSTANG 1999).

With the diminishing of the rate of energy released the plume looses its columnar shape to a point where lift of emissions results mainly from vertical atmospheric mixing.

This fact demonstrates that plume rise is a function of not only heat release rate, but also atmospheric stability and wind speed.

Therefore, its modelling requires to calculate the rates of heat release and emission for each gas species and particle size, and also to know the local meteorological parameters (temperature, wind and humidity) and mixing height of the atmosphere.

PHURO et al. (1976) presented a set of equations to calculate the plume height, based on the plume rise relations developed by BRIGGS (1969, 1971 and 1972) for stack emissions and in the stability classes of PASQUILL (1975)

Over the years, different procedures and tools have been developed to: - estimate fuel loading and consumption, and - characterize the resulting emissions from prescribed

burns and wildfires.

Three models should be emphasized within this global research effort (SANDBERG et al. 1999, SESTAK et al. 2002): - the fuel consumption models FOFEM First Order

Fire Effects Model (REINHARDT et al. 1997) - CONSUME (OTTMAR et al. 1993a and 2000) and - the source strength model EPM Emissions

Production Model (SANDBERG and PETERSON 1984), They are used to provide emission and heat release

data for most dispersion models.

A number of other models should also be referred, namely: - SMSINFO (OTTMAR et al. 1993b); - FETM Fire Emissions Tradeoff Model (SCHAAF et al.

1996); - ACOST Automatic Calculation of Slash Tonnage

model and PCOST Pile Tonnage Calculation Worksheet (ACOST/PCOST 2000); and

- FASTRACS Fuel Analysis, Smoke Tracking, and Report Access Computer System (url 1).

3.3.2 To calculate the transport speed and direction

The vertical distribution of local air temperature and wind fields governs the recirculation of fire-generated products.

Once the smoke plume has reached its maximum rise height, it becomes completely dominated by the local wind field.

Thus, the accurate determination of the three-dimensional wind field is of fundamental importance, especially in areas of complex topography where mountain-valley or slope-valley winds are expected to occur.

Factors that affect transport and dispersion are, in that stage, atmospheric stability, temperature inversions, mixing height, wind speed and direction and local-scale systems as land and sea breezes (PHURO et al. 1976).

EUFIRELAB

D-03-09.doc 23

In very simple models, the trajectory and dispersion of the smoke plume can be simulated through straight-line trajectories.

Others, assuming that the atmosphere is neutrally buoyant, compute the trajectories of air parcels that are transported by the three-dimensional wind field, which is calculated by a numerical weather prediction NWP model.

This so-called “trajectory technique” is considered to be simple in its concept, and requiring modest computer resources.

Trajectories can be run: - forward in time to determine receptor areas, or - backwards in order to determine the pollutant

source areas. In general, multiple trajectories are required due to

the instability of the atmospheric flow.

Another technique used to simulate the behaviour of the smoke plume is to apply an atmospheric transport model ATM, based on the conservation of mass for the considered pollutant.

Its movement through the atmosphere, resulting from the mean wind field provided by the NWP model and the turbulent mixing processes (parameterised in the ATM), is balanced by the difference between the emission inputs and pollutant losses by wet and dry deposition (also parameterised in the ATM).

Two main modelling approaches are applied in this kind of numerical tools: - either Lagrangian models that follow the trajectories

of segments, puffs or particles; or - Eulerian models, which solve the diffusion equation

at every point on a fixed grid.

In Lagrangian models, the dispersion is accomplished by Gaussian (or an equivalent probability function) diffusion for segments and puffs and by Monte Carlo (Langevin-Markov) techniques for particles.

For Eulerian models, the dispersion is usually performed by first order turbulent closure, but higher-order turbulent closures could be used (TAPPER and HESS 1999).

3.3.3 To simulate smoke dispersion

According to the type of application, smoke dispersion models can be classified as follow (BREYFOGLE and FERGUSON 1996, MILLER 2001):

Research models They are capable to model many chemical

compounds, concentrations, and a large radius of receptor sites and their concentrations.

Regulatory/Planning models They could be used for permit approval and would

be capable to show dispersion over large geographic areas.

They offer the possibility to run various weather scenarios in advance of the actual fire.

However, because any model has been yet officially validated for biomass burning, regulatory use would be inappropriate at this time.

Screening models They are use by field users concerned with smaller

prescribed burns. They would aid to visualise what fuel and weather

conditions are best suited. They could be used as a planning tool before fire

and at the time of the fire with the real-time meteorological data.

Smoke dispersion models are becoming increasingly valuable tools in smoke management, especially for screening and planning.

The expected result of all models is the ability to estimate the variation in time and space of particle and gas concentrations that affect human health and alter visibility.

Several models have been developed for the simulation of smoke transport and dispersion (BREYFOGLE and FERGUSON 1996, MIRANDA 1999), namely: - SASEM Simple Approach Smoke Estimation Model

(SESTAK and RIEBAU 1988), - HYSPLIT Hybrid Single-Particle Langrangian

Integrated Trajectory model (DRAXLER 1992, DRAXLER and HESS 1997),

- TSARS Plus (HUMMEL and RAFSNIDER 1995), - CALPUFF (SCIRE et al. 1995 and 2000), - AIRFIRE (MIRANDA 1998 and 2004), - DISPERFIRE (MIRANDA 1998), - VALBOX Ventilated Valley Box model (SESTAK et al.

1988), - TAPAS (FOX et al. 1987), - VSMOKE (LAVDAS 1996), - VSMOKE-GIS (HARMS and LAVDAS 1997), - NFSPUFF (HARRISON 1996), - REMSAD Regional Modelling System for Aerosols

and Deposition (ICF Consulting 2002), which is based on the variable-grid UAM-V Urban Airshed Model,

- a regional-scale photochemical modelling system (SAI 1999), or

- CMAQ Community Multi-scale Air Quality modelling system (USEPA 1999).

Some of the referred models integrate the determination of the three-dimensional wind field through the interpolation of surface wind measurements.

In this case, the interpolation becomes more accurate as the number of input observation sites increase.

Others, on the contrary, assume a constant value of wind velocity and direction, which can be considered as a very rude approach, since wind fields occurring in nature are far from being uniform, especially in areas of complex topography.

EUFIRELAB

D-03-09.doc 24

In the generation of the necessary meteorological fields, the coupling with meteorological models assumes a particular importance, both - local, as is the case of NUATMOS (ROSS et al.

1988), or - mesoscale ones, as MM5 (DUDHIA, 1993), MEMO

(FLASSAK and MOUSSIOPOULOS 1987) or - RAMS (PIELKE et al. 1992).

Mesoscale circulation can be very important because they: - directly affect the dispersion of smoke by causing

abrupt changes in local atmospheric stability and - can influence the direction in which smoke will be

transported.

Despite the large number of smoke dispersion models, some management systems include the efficiency of the atmospheric dispersion of smoke using dispersion indexes.

An example of this type of tools is the FFMIS Florida Fire Management Information System (BRENNER et al. 1997).

It is a fire management system that simulates the current and forecast state of the: - surface weather, - atmospheric stability, - fire danger, - fire weather index and - fire behaviour potential.

In this system, the efficiency of the atmospheric dispersion of smoke is estimated using the ADI Atmospheric Dispersion Index, which indicates the efficiency of the atmosphere at carrying gaseous or small PM away from its source.

ADI is based on the Gaussian Plume statistical model (LAVDAS 1986), considering a Gaussian distribution of the pollutant concentration in a finite box downwind of the source.

In terms of visibility modelling, the optical effects in the atmosphere due to air pollution can be adequately described by examination of the atmospheric light extinction.

This approach assumes that the increased extinction due to an increment in a specific air pollutant species is directly proportional to the increase in mass of the species.

Two basic approaches have been used to establish the relationships between atmospheric composition and optical properties that dictate the visibility impairment.

One is based in the estimation of light extinction using extinction efficiencies derived theoretically, while in the other extinction efficiencies are obtained by empirical methods.

The most reliable estimates of extinction due to individual chemical species are those computed using theoretical light scattering models (Mie-theory) fitted to the size-resolved composition of the aerosols observed in the region of interest.

Such models explicitly simulate the physical cause-and-effect relationship and best utilize all of the data on the aerosol's properties that affect scattering efficiencies (TRIJONIS et al. 1991).

However, even the most fundamental of these models incorporate important assumptions concerning particle structure, condensed water, and other unobserved aspects of the aerosol.

Many researchers have developed versions of Mie scattering models (GRAY and KLEINHESSELING 1996).

A significant limitation to the use of theoretically derived extinction efficiencies is that they have not been developed and tested for many locations.

A much wider range of extinction efficiency estimates is available from multiple regression analyses of the empirical relationship between extinction and aerosol composition (TRIJONIS et al. 1991).

In this approach, the observed light scattering is statistically compared to observed aerosol concentration and composition to estimate the scattering efficiencies of each particulate species.

The hygroscopic species (e.g., sulphate and nitrate) concentrations are usually corrected to account for water uptake using a function of the ambient relative humidity.

This approach has generated statistically significant extinction relationships for many applications, and the resulting coefficients have been used to accurately estimate light scattering from aerosol concentration data (GRAY and KLEINHESSELING 1996).

The computational tools used to estimate visibility from aerosol concentrations and compositions are often found as post processing modules attached to air quality models.

Various modelling approaches that have been used to relate air quality to visibility, namely: - VASM / ASTRAP (DOE 1994), - MESOPUFF II (USEPA, 1993), - CALPUFF (SCIRE et al. 1995), - NPAQMS (RIM II) (GRAY et al. 1993), - VISCREEN / PLUVUE II (USEPA 1992), - STAGHAZE (LATIMER 1993), - RIVAD (LATIMER 1990), - GCVTC (USEPA 1995), - ELSIE (SLOANE et al. 1991) and - ROME (GABRUCK et al. 1999).

A third approach consists in developing a composite set of extinction efficiencies from a variety of empirical and theoretical analyses.

Empirically derived extinction efficiencies obtained using multiple linear regression models are subject to a number of uncertainties.

Errors in the resulting extinction efficiencies can be related to errors in light scattering (bsp) measurements, correlations between chemical concentrations, and non-linearities caused by variations in size distribution and mixing state from sample to sample ‘GRAY and KLEINHESSELING 1996).

In the United States, for the assessment of values of air quality and visibility at risk from wildland fires, a 40-year database was generated, providing the first nationally consistent map of surface wind and ventilation index, which represents the product of wind speed and mixing height.

EUFIRELAB

D-03-09.doc 25

An interactive VCIS ventilation climate information system was produced (FERGUSON et al. 2003), allowing users to assess risk based on frequency patterns of poor, marginal, fair, and good ventilation conditions through internet (url 2).

In spite of the number of available models to simulate smoke dispersion and although the existence of some systems already covering the main questions to be taken into account, a lack of integration concerning fire progression stills remains.

In general, the wind field distribution, the fire line advance, and the interaction between fire and the ambient wind are not taken into account.

In this context the Universities of Coimbra and Aveiro, in Portugal, produced the fire behaviour system DisperFireStation (VALENTE et al. 2007), which was developed to estimate: - fire progression, - smoke dispersion and - visibility impairment at a local scale.

This system results from the improvement and integration of two already available numerical tools, DISPERFIRE (MIRANDA et al. 1994) and FireStation (LOPES et al., 2002).

FireStation is a software system aimed at the simulation of fire spread over complex topography.

DISPERFIRE is a real time system developed to simulate the dispersion in the atmosphere of the pollutants emitted during a forest fire.

In addition, a model for the estimation of visibility impairment, based on the relationship between the air pollutants concentration and visibility was included.

The whole system was developed under a graphical interface, previously created for FireStation, allowing a friendly user access and providing easily readable output to facilitate its application under operational conditions.

A detailed description of the system can be found in Deliverable D-03-04.

Figure 1 presents an example of an output of the model obtained from the simulation of the flow field and the CO dispersion in an area centred in Gestosa, during the 2004 fire experiments.

The simulation domain is, in this case about 2 km2.

Current fire assessment systems are starting to be planned in order to integrate all the needed variables in a common and user-friendly tool.

In the scope of the development and implementation of advanced methods for high-resolution fire and smoke modelling, USDA-FS U.S. Department of Agriculture Forest Service recently established the FCAMMS Fire Consortia for Advanced Modelling of Meteorology and Smoke (url 3), involving all the USDA-FS Research Stations and their partners, as represented in figure 2.

Under this collaborative and coordinated national effort to model smoke impacts, BlueSky Smoke Modelling Consortium is specially devoted to the development and application of real-time smoke modelling to support fire operations and smoke management.

This automated smoke modelling framework, designed to rely upon real-time meteorological forecast data and existing air quality models, tracks daily emissions and predicts the cumulative concentrations of smoke from prescribed fires and wildfires (FERGUSON et al. 2001, SESTAK et al. 2002).

In BlueSky, fire emission estimates are obtained through the coupling of CONSUME and EPM with real-time fire activity reports.

Real-time estimates and forecasts of smoke impacts for active smoke management programs are generated through the integration of the high-resolution mesoscale meteorological model MM5 with EPM and the dispersion model CALPUFF.

In the near future, it is expected that the BlueSky framework will also be running the CMAQ Eulerian grid model.

Another effort in the development of appropriate emissions models for wildland burning is CSEM Community Smoke Emissions Model; it: - which couples CONSUME with EPM - utilises national fuels coverage in GIS format, and - is specifically designed to provide historical fire

emissions estimates for use in CMAQ and REMSAD.

Smoke forecasts are produced by the NOAA-ARL National Oceanic and Atmospheric Administration's Air Resources Laboratory using HYSPLIT HYbrid Single-Particle Lagrangian Integrated Trajectory model, which is the newest version of a complete system for computing simple air parcel trajectories to complex dispersion and deposition simulations.

Forecasts are available in url 4, and an example is shown in figure 3.

This tool is integrated in the web-based system called READY Real-time Environmental Applications and Display sYstem, developed for: - accessing and displaying meteorological data and - running trajectory and dispersion model products on

the web server. This system brings together dispersion models,

graphical display programs and textual forecast programs.

WFAS Wildland Fire Assessment System, which was first made available in 1994 and is still under development.

Although it does not simulate the dispersion of smoke, it constitutes a valuable tool for modellers as an internet-based (url 5) integrated information system planned to provide information on weather and fire potential for the entire territory of the U.S.

An example is shown in figure 4.

EUFIRELAB

D-03-09.doc 26

INTERFACE Project, funded by the Portuguese National Foundation for Science and Technology, addresses the assessment of the effects of WUI fires on air, meeting the growing need for information related to smoke impacts management.

The proposed work involves the development and application of an air quality modelling system for the simulation and forecast of smoke dispersion on an integrated mesoscale / microscale basis.

An innovative and substantial aspect is the integration of all the steps involved within this global process: - fire behaviour description; - emission modelling systems; - plume transport, - dispersion, - reaction and deposition mechanisms; and - visibility impairment.

3.4 FINAL COMMENTS

Depending on the nature of transport, dispersion and removal processes, the residence time of the emitted combustion compounds is highly variable, varying from few seconds to some weeks.

The study of the changes in chemical composition, physical characteristics and atmospheric concentration in which this highly dynamic mixture undergoes requires an integrated approach of all the involved components: - fire progression and emissions, atmospheric flow

and - air pollutants dispersion, reaction and deposition.

However, a lack of integration between all the components of the process, since emission to health effects and visibility reduction, still compromises the accuracy of the result.

The development of powerful and accurate codes for the numerical simulation of the involved phenomena still represents a challenge to science.

EUFIRELAB

D-03-09.doc 27

3.5 REFERENCES

ACOST/PCOST. 2000. Oregon Department of Forestry, Fuels and Meteorological Services Section. Available in: http://www.odf.state.or.us.

ANDREAE, M., BROWELL, E.V., GARSTANG, M., GREGORY, G.L., HARRISS, R.C., HILL, G.F., JACOB, D.J., PEREIRA, M.C., SACHSE, G.W., SETZER, A.W., SILVA DIAS, P.L., TALBOT, R.W., TORRES, A.L. and WOFSY, S.C. 1988. Biomass-Burning Emissions and Associated Haze Layers Over Amazonia, Journal of Geophysical Research, 93(D2). pp 1509–1527.

BLAKE, N.J., BLAKE, D.R., WINGENTER, O.W., SIVE, B.C., MCKENZIE, L.M., LOPEZ, J.P., SIMPSON, L.J., FUELBERG, H.E., SACHSE, G.W., ANDERSON, B.E., GREGORY, G.L., CARROLL, M.A., ALBERCOCK, G.M. and ROWLAND, F.S. 1999. Influence of Southern Hemispheric Biomass Burning on Mid Tropospheric Distributions of Nonmethane Hydrocarbons and Selected Halocarbons Over the Remote South Pacific. Journal of Geophysical Research, 104. pp. 16213-16232.

BRAUER, M. 1998. Health Impacts of Biomass Air Pollution. Report prepared for the Biregional Workshop on Health Impacts of Haze-related Air Pollution, 1-4 June, Kuala Lumpur, Malaysia.

BRENNER, J., ARVANITIS, L., BRACKETT, D., LEE, B., CARR, R. and SUDDABY, R. 1997. Integrating GIS, Meso-Scale Fire Weather Prediction, Smoke Plume Dispersion Modeling, and the Internet for Enhanced Open Burning Authorizations and Wildfire Response in Florida. In: Proceedings from the 1997 ESRI International Users Conference. 8-11 July, San Diego, California, USA.

BREYFOGLE, S. and FERGUSON, S.A. 1996. User Assessment of Smoke-Dispersion Models for Wildland Biomass Burning. Gen. Tech. Rep. PNW-GTR-379. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR, EUA. 30 p.

BRIGGS, G.A. 1969. Plume Rise. Air Resour Atmos. Turbul. and Diffus. Lab., Environ. Sci. Serv. Adm., U.S. Atomic Energy Comm., Oak Ridge, Tenn., USA. 81 p.

BRIGGS, G.A. 1971. Some Recent Analyses of Plume Rise Observations. In: Proceedings from the 2nd Int. Clean Air Congr., Acad. Press. New York, USA. pp. 1029-1032.

BRIGGS, G.A. 1972. Discussion on Chimney Plumes in Neutral and Stable Surroundings. Atmos. Environ. 6(7). pp: 507-510.

BROWELL, E.V., FENN, M.A., BUTLER, C.F., GRANT, W.B., CLAYTIN, M.B., FISHMAN, J., BACHHMEIER, A.S., ANDERSON, B.E., GREGORY, G.L., FUELBERG, H.E., BRADSHAW, J.D., SANDHOLM, S.T., BLAKE, D.R., HEIKES, B.G., SACHSE, G.W., SINGH, H.B. and TALBOT, R.W. 1996. Ozone and Aerosol Distributions and Air Mass Characteristics Over the South Atlantic Basin During Burning Season. Journal of Geophysical Research, 101. pp. 24 043-24 068.

BRUSTET, J., VICKOS, J., FONTAN, J., PODAIRE, A. and LAVENU, F. 1991. Characterisation of active fires in West African savannahs by analysis of satellite data: Landsat thematic mapper. In: LEVINE, J. (Ed.), Global biomass burning: atmospheric, climatic, and biospheric implications. The Massachussets Institute of Technology Press, Cambridge, Massachussets, London, pp. 53-60.

DOE. 1994. VASM Q's and A's: An Overview of the Visibility Assessment Scoping Model (VASM) and Model Set. Quick Reference Fact Sheet, U.S. Department of Energy, Washington, D.C., USA.

DRAXLER, R.R. 1992. HYbrid Single-Particle Langrangian Integrated Trajectories (HY-SPLIT): version 3.0 - User’s Guide and Model Description. NOAA Tech. Memo. ERL ARL-195.

DRAXLER, R.R. and HESS, G.D.1997. Description of the HYSPLIT-4 Modeling System. NOAA Technical Memorandum ERL ARL-224.

DUDHIA, J. 1993. A Nonhydrostatic Version of the Penn State – NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front. Mon. Wea. Rev., 121. pp. 1493-1513.

FERGUSON, S.A., MCKAY, S.J., NAGEL, D.E., PIEPHO, T., RORIG, M.L., ANDERSON, C. and KELLOGG, L. 2003. Assessing Values of Air Quality and Visibility at Risk from Wildland Fires. Res. Pap. PNW-RP-550. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR, USA. 59 p.

FERGUSON, S.A., PETERSON, J. and ACHESON, A. 2001. Automated, Real-Time Predictions of Cumulative Smoke Impacts From Prescribed Forest and Agricultural Fires. In: Proceedings from the 4th Symposium on Fire and Forest Meteorology, 13-15 November 2001, Reno, Nevada, American Meteorological Society. pp. 168-175.

FLASSAK, T. and MOUSSIOPOULOS, N. 1987. An Application of an Efficient Non-Hydrostatic Mesoscale Model. Boundary Layer Meteorology, Vol. 41. pp. 135-147.

FOX, D., ROSS, D., DIETRICH, D., MUSSARD, D. and RIEBAU, A. 1987. An Update on TAPAS and its Model Components. Proceedings of the Ninth Conference on Fire and Forest Meteorology. April, 21-24, San Diego, California. pp. 1-7.

EUFIRELAB

D-03-09.doc 28

GABRUCK, R.S., SYKES, R.I., SEIGNEUR C., PAI P., GILLESPIE P., BERGSTROM, R.W. and SAXENA, P. 1999. Evaluation of the Reactive and Optics Model of Emissions (ROME). Atmospheric Environment, Vol. 33. pp. 383 -399.

GARSTANG, M. 1999. The Role of the Atmosphere in Fire Occurrence and the Dispersion of Fire Products. In: Health Guidelines for Vegetation Fire Events - Background Papers. Kee-Tai-Goh, Schwela D., Goldammer J.G. and Simpson O. (Eds). United Nations Environment Programme, Nairobi, World Health Organization, Geneva, World Meteorological Organization, Geneva, Institute of Environmental Epidemiology, WHO Collaborating Centre for Environmental Epidemiology, Ministry of the Environment, Singapore. pp 101-121.

GEORGE, C.W. and SUSOTT, R.A. 1971. Effects of Ammonium Phosphate and Sulfate on the Pyrolysis and Combustion of Cellulose. Research Paper INT-90. U.S. Department of Agriculture, Forest Service. 27 p.

GRAY, H.A. and KLEINHESSELING, D. 1996. Evaluation of Existing Information of the Effects of Air Pollutants on Visibility in the Southern Appalachians. Systems Applications International.

GRAY, H.A., STOECKENIUS, T.E., EMERY, C.A. and RICHARDS L.W. (1993) Assessment of the Effects of the 1990 Clean Air Act Amendments on Visibility in Class I Areas. Systems Applications International, San Rafael, California (SYSAPP-93/162).

HARMS, M.F. and LAVDAS, L.G. 1997. Users Guide to VSMOKE-GIS for Workstations. Research Paper SRS-6. U.S. Department of Agriculture, Forest Service, Southern Research Station. Asheville, NC, USA. 41 p.

HARRISON, H. 1996. A User’s Guide to “NFSPUFF”: A Dispersion Model for Smoke Management in Complex Terrain. Seattle, WA. A final report available from Air Program Manager, USDA For. Ser., Northern Region, Missoula, MT. 42 pp.

HUMMEL, J. and RAFSNIDER, J. 1995. TSARS Plus Smoke Production and Dispersion Model User's Guide. Preliminary Draft 7.95. National Biological Service and the Interior Fire Coordination Committee. 96 p.

ICF CONSULTING. 2002. User’s Guide to the Regional Modeling System for Aerosols and Deposition (REMSAD). Version 7. July 2002. 153 pp.

KALABOKIDIS, K.D. 2000. Effects of Wildfire Suppression Chemicals on People and the Environment - A Review. In: Global Nest, the Int. J. Vol. 2, No 2, Greece. pp 129-137.

LATIMER, D.A. 1990. Visibility Modeling: The State of the Technology and New Directions. In: Visibility and Fine Particles. Mathai, C.V. (Ed.), Air and Waste Management Association, Pittsburgh, Pennsylvania.

LATIMER, D.A. 1993. Development of Regional Haze Screening Models. Air and Waste Management Association 86th Annual Meeting and Exhibition, Denver, Colorado (93-TP- 49.04).

LAVDAS, L.G. 1986. An Atmospheric Dispersion Index for Prescribed Burning. Research Paper SE-256, US Department of Agriculture, Macon, Georgia, USA.

LAVDAS, L.G. 1996. Program VSMOKE - Users Manual. Gen. Tech. Rep. SRS-6. U.S. Department of Agriculture, Forest Service, Southern Research Station. Asheville, NC, USA. 147 p.

LEVINE, J., COFER, W., CAHOON, D. and WINSTEAD, E. Biomass Burning: A Driver for Global Change, Environmental Science and Technology, 29, 3, pp. 120A-125A, 1995.

LEVINE, J.S. 1999. Gaseous and Particulate Emissions Released to the Atmosphere From Vegetation Fires. In: Health Guidelines for Vegetation Fire Events - Background Papers. Kee-Tai-Goh, Schwela D., Goldammer J.G. and Simpson O. (Eds): United Nations Environment Programme, Nairobi, World Health Organization, Geneva, World Meteorological Organization, Geneva, Institute of Environmental Epidemiology, WHO Collaborating Centre for Environmental Epidemiology, Ministry of the Environment, Singapore. pp 280-294.

LOPES, A.M.G., CRUZ, M.G. and VIEGAS, D.X. 2002. FireStation – an integrated software system for the numerical simulation of fire spread on complex topography. Environmental Modelling & Software, 17, 269-285.

MAHAFFEY, L. and MILLER, M. 2001. Air Quality. In: Fire Effects Guide, National Wildfire Coordinating Group, Fire Use Working Team. Chapter IV. Available in: http://fire.r9.fws.gov/ifcc/monitor/FEG.pdf.

MILLER, M. 2001. Computer Software. In: Fire Effects Guide, National Wildfire Coordinating Group, Fire Use Working Team. Chapter XII. Available in: http://fire.r9.fws.gov/ifcc/monitor/FEG.pdf.

MIRANDA, A. I. 1998. Efeito dos Incêndios Florestais na Qualidade do Ar. PhD. Thesis. Department of Environment and Planning, University of Aveiro, Portugal. 213 pp.

MIRANDA, A.I. 1999. Review Commenting on the State-of-the-Art on Smoke Dispersion. DELFI knowledge data base. 8 p.

MIRANDA A.I. 2004. An integrated numerical system to estimate air quality effects of forest fires. Int. J. Wildland Fire 13(2), pp. 217-226.

MIRANDA, A.I., BORREGO, C., and VIEGAS, D. 1994. Forest fire effects on the air quality. In ‘Air Pollution II, Computer Simulation.’(Eds JM Baldasano, CA Brebbia, H Power, P Zannetti) pp. 191-199. (Computational Mechanics Publications: Southampton).

EUFIRELAB

D-03-09.doc 29

MIRANDA, A.I., MARTINS, H., CARVALHO, A. and BORREGO, C., 2005. Modelling Smoke Effects on Lisbon Air Quality. In Sixth Fire and Forest Meteorology Symposium and the19th Interior West Fire Council Meeting, 25-27 October 2005 Canmore, AB, Canada. Proceedings in CD-Rom.

MORAWSKA, L., THOMAS, S., BOFINGER, N.D., WAINWRIGHT, D. and NEALE, D. 1998. Comprehensive Characterisation of Aerosols in a Subtropical Urban Atmosphere: Particle Size Distribution and Correlation with Gaseous Pollutants. Atmospheric Environment, 32. pp. 2461-2478.

OTTMAR, R.D., ANDERSON, G.K., DEHERRERA, P.J. and REINHARDT, T.E. 2000. CONSUME User’s Guide, Version 2.1. USDA Forest Service Pacific Northwest Research Station, Fire and Environmental Research Applications Group, Seattle, Washington, USA.

OTTMAR, R.D., BURNS, M.F., HALL, J.N. and HANSON, A.D. 1993a. CONSUME Users Guide. Gen. Tech. Rep. PNW-GTR-304. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR, USA. 118 p.

OTTMAR, R.D., BURNS, M.F., TEESDALE, D.R. and HALL, J.N. 1993b. SMSINFO Users Guide. USDA Forest Service, Pacific Northwest Research Station, Fire and Environmental Research Applications Group, Seattle, Washington, USA.

PASQUILL, E. 1975. Atmospheric Diffusion. 2nd ed. John Wiley and Sons, New York. 368 p.

PHILPOT, C.W., GEORGE, C.W., BLAKELY, A.D., JOHNSON, G.M. and WALLACE JR., W.K. 1972. The Effect of Two Flame Retardants on Particulate and Residue Production. Research Paper INT-117. Intermountain Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, Ogden, Utah, USA.

PHURO, J.A., LAVDAS, L.G. and BAILEY, P.M. 1976. Smoke Transport and Dispersion. In: Southern Forestry Smoke Management Guidebook, Chapter V, Gen. Tech. Rep. SE-10, U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. Ashville, NC, USA. 140 p.

PIELKE, R.A., COTTON, W.R. and WALKO, R.L. 1992. A Comprehensive Meteorological Modeling System - RAMS. Meteor. Atmos. Phys., 49. pp. 69-91.

REINHARDT, E., KEANE, R. and BROWN, J. 1997. First Order Fire Effects Model: FOFEM 4.0, User's Guide. General Technical Report INT-344, USDA Forest Service, Intermountain Research Station, Ogden, UT.

REINHARDT, E., OTTMAR, R. and CASTILLA, C. 2001. Smoke Impacts from Agricultural Burning in a Rural Brazilian Town. Journal of the Air & Waste Management Association 51, 443-450.

ROSS, D., SMITH, I., MANINS, P. and FOX, D. 1988. Diagnostic Wind Field Modelling for Complex Terrain: Model Development and Testing. Journal of Applied Meteorology, Vol. 27. pp. 785-796.

ROTHMAN N., FORD, D.P., BASER, M.E., HANSEN, J.A., O'TOOLE, T., TOCKMAN, M.S. and STRICKLAND, P.T. 1991. Pulmonary function and respiratory symptoms in wildland firefighters. Journal of Occupational Medicine 33(11): 1163-1169.

SAI. 1999. User’s Guide to the Variable-Grid Urban Airshed Model (UAM-V). Systems Applications International, San Rafael, California.

SANDBERG, D. and MARTIN, R. 1975. Particle Sizes in Slash Fire Smoke. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR, USA.

SANDBERG, D.V. and PETERSON, J.L. 1984. A Source Strength Model for Prescribed Fires in Coniferous Logging Slash. Annual Meeting, Air Pollution Control Association, Pacific Northwest Section. Reprint #84.20. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR, USA. 10 p.

SANDBERG, D.V., HARDY, C.C., OTTMAR, R.D., SNELL, J.A.K., ACHESON, A.L., PETERSON, J.L., SEAMON, P., LAHM, P. and WADE, D. 1999. National Strategic Plan: Modeling and Data Systems for Wildland Fire and Air Quality. Gen. Tech. Rep. PNW-GTR-450. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, OR, USA. 60 p.

SCHAAF, M., SNELL, K., Carlton, D., OTTMAR, R., WIITALA, M., HILBRUNER, M. and NESBITT, J. 1996. Development of the Fire Emissions Tradeoff Model (FETM) and Application to the Grande Ronde River Basin, Oregon. Technical Report prepared for USDA For. Ser., PNW Reg., Contract Nr. 53-83FT-03-2.

SCIRE, J., STRIMAITIS, D.G., YAMARTINO, R.J. and XIAOMONG, Z. 1995. A User's Guide for CALPUFF Dispersion Model. Doc. 1321-2. Concord, MA: Sigma Research/Earth Tech. 315 p.

SCIRE, J., STRIMAITIS, D.G., YAMARTINO, R.J. and XIAOMONG, Z. 2000. A User’s Guide for CALPUFF Dispersion Model (Version 5). Concord, MA: Earth Tech, Inc. 512 p.

SESTAK, M., O'NEILL, S., FERGUSON, S., CHING, J. and FOX, D.G. 2002. Integration of Wildfire Emissions into Models-3/Cmaq with the Prototypes: Community Smoke Emissions Modeling System (CSEM) and BLUESKY. 2002 Models-3 User’s Workshop, October 21-23, EPA, Research Triangle Park, NC, USA.

SESTAK, M.L. and RIEBAU, A.R. 1988. SASEM - Simple Approach Smoke Estimation Model. Tech. Note 382. U.S. Department of the Interior, Bureau of Land Management. Denver, CO, USA. 31 p.

EUFIRELAB

D-03-09.doc 30

SESTAK, M.L., MARLATT, W.E. and RIEBAU, A.R. 1988. VALBOX: Ventilated Valley Box Model. Unpublished draft. U.S. Bureau of Land Management. 32 p.

SLOANE, C.S., WATSON, J., CHOW, J., PRITCHETT, L. and RICHARDS L.W. 1991. Size-Segregated Fine Particle Measurements by Chemical Species and their Impact on Visibility Impairment in Denver. Atmospheric Environment., 25A(5-6):1013-1024.

TAPPER, N.J. and HESS, G.D. 1999. Forest Fire Emissions Dispersion Modelling for Emergency Response Planning: Determination of Critical Model Inputs and Processes. In: Health Guidelines for Vegetation Fire Events - Background Papers. Kee-Tai-Goh, Schwela D., Goldammer J.G. and Simpson O. (Eds). United Nations Environment Programme, Nairobi, World Health Organization, Geneva, World Meteorological Organization, Geneva, Institute of Environmental Epidemiology, WHO Collaborating Centre for Environmental Epidemiology, Ministry of the Environment, Singapore. pp 122-146.

TRIJONIS, J.C., MALM, W.C., PITCHFORD, M., WHITE, W.H., CHARLSON, R. and HUSAR, R. 1991. Visibility: Existing and Historical Conditions - Causes and Effects. In: Irving, P.M. (Ed.). Acidic Deposition: State of Science and Technology, Volume III: Terrestrial, Materials, Health and Visibility Effects. The U.S. National Acid Precipitation Assessment Program (State of science and technology report no. 24). Washington, DC, USA.

USEPA. 1992. User's Manual for the Plume Visibility Model (PLUVUE II) (Revised). U.S. Environmental Protection Agency, EPA-454/B-92-008.

USEPA. 1993. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 1 Report: Interim Recommendation for Modeling Long Range Transport and Impacts on Regional Visibility. U.S. Environmental Protection Agency, EPA-454/R-93-015.

USEPA. 1995. Interim Findings on the Status of Visibility Research. Office of Research and Development, U.S. Environmental Protection Agency, February.

USEPA. 1999. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. U.S. Environmental Protection Agency, EPA/600/R-99/030.

VALENTE, J., MIRANDA, A.I., LOPES, A., BORREGO, C. and VIEGAS, D.X. 2007. A local-scale modelling system to simulate smoke dispersion. International Journal of Wildland Fire. Accepted for publication.

WADE, D.D. and WARD, D.E. 1973. An Analysis of the Air Force Bomb Range Fire. Research Paper SE-105, U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. Ashville, NC, USA. 38 p.

WARD, D., ROTHERMEL, R. and BUSHEY, C. 1993. Particulate matter and trace gas emissions from the Canyon Creek Fire of 1988. In: Society of American Foresters (Eds.), Proceedings of the 12th Fire and Forest Meteorology, Georgia, United States of America, pp. 62-76.

WARD, D.E. 1999. Smoke From Wildland Fires. In: Health Guidelines for Vegetation Fire Events - Background Papers. Kee-Tai-Goh, Schwela D., Goldammer J.G. and Simpson O. (Eds). United Nations Environment Programme, Nairobi, World Health Organization, Geneva, World Meteorological Organization, Geneva, Institute of Environmental Epidemiology, WHO Collaborating Centre for Environmental Epidemiology, Ministry of the Environment, Singapore. pp 70-85.

WARD, D.E. and HARDY, C. 1989. Organic and Elemental Profiles for Smoke from Prescribed Fires. In: Watson J.G. (Ed.). Receptor Models in Air Resources Management: transactions of an international specialty conference of the Air and Waste Management Association, San Francisco, CA. Pittsburg, PA, USA. pp. 299-321.

WHO/UNEP/WMO. 1999. Health Guidelines for Vegetation Fire Events - Guideline Document. SCHWELA D., GOLDAMMER J.G., MORAWSKA L., SIMPSON O. (Eds.). United Nations Environment Programme, Nairobi, World Health Organization, Geneva, World Meteorological Organization, Geneva, Institute of Environmental Epidemiology, WHO Collaborating Centre for Environmental Epidemiology, Ministry of the Environment, Singapore.

url 1: http://www.fs.fed.us/r6/fire/fastracs/

url 2: http://www.fs.fed.us/pnw/fera/vent/

url 3: http://www.fs.fed.us/fcamms/

url 4: http://www.arl.noaa.gov/smoke/index.html

url 5: http://www.fs.fed.us/land/wfas/

EUFIRELAB

D-03-09.doc 31

3.6 FIGURES

Figure 1 – CO concentration (µg.m-3) and wind fields obtained for the burning of a experimental fire

during GESTOSA 2004 experiments.

Figure 2 – Fire Consortia for the Advanced Modelling of Meteorology and Smoke (FCAMMS).

Available online in url 3.

EUFIRELAB

D-03-09.doc 32

Figure 3 – HYSPLIT archived smoke forecasts for South Central U.S. fires. Available in url 4.

Figure 4 – Example of a fire danger map obtained from the Wildland Fire Assessment System (WFAS).

Available online in url 5.

EUFIRELAB

D-03-09.doc 33

4 FIRE BEHAVIOUR PREDICTION

4.1 INTRODUCTION

Before stepping to the more specific subject of this review, it is perhaps worthwhile to mention the broad synthesis of André et al. (1992), covering virtually all aspects of research on the physics of forest fires, namely, mattering for /1.1-2/, /1.3/, /2.1-5/ and /3.1-3/.

In this review, four main criteria are used to organise the state-of-the-art of research, based, respectively, on: - the concept of fire development phase; the concept

of fire regime 1; - the main aspect of the fire behaviour that is

predicted; and - the more or less applied character of the research.

Although they are used essentially in parallel, there is some cross linking among these criteria.

4.2 FIRE DEVELOPMENT PHASE

Following ANDRÉ (1996), we can distinguish eight main potential development phases in the history of a general forest fire, named after: - (1) ignition; - (2) build-up; - (3) full development of a specific fire regime; - (4) (eventual) transition of fire regime; - (5) decay; - (6) extinguishing of the flame (for a flaming fire

front); ( - (7) smouldering combustion, till its extinction; and - (8) cooling of the combustion residues to ambient

temperature.

The study of ignition (1st phase) is relevant for /1.3-5/.

As it is a rather self-contained research area, bearing few relations with fire spread studies; we do not review it here (see, for instance, André et al. 1992).

The exceptions apply to: - the influence of ignition conditions on the build-up

phase of the fire history (see below); - the concept of temperature of ignition, which is used

by most semi-empirical fire spread models; and, - in a less extent, the concept of ignition time delay,

which is seldom used (for instance, in the semi-empirical fire spread model of Fons 1946).

During the build-up phase (2nd phase), the fire progressively loses the memory, so to say, of the ignition conditions, before attaining a fully developed steady regime.

The duration of this phase is a matter of interest for initial dispatch operations /1.2/ and for performing fire experiments.

1 Terms in italic are defined in the DELFI Vocabulary.

From a theoretical point of view, three effects that are responsible for the time variation of the rate of spread of the fire during this phase, have been studied, although not very deeply: - the establishing of the fire front depth (Emmons

1964); - the establishing of a steady temperature field in the

part of the fuel bed (or fuel complex) still non-burned, ahead of the fire line, in a reference frame linked to the latter (Fujii et al. 1980); and the effect of the fire line curvature characteristic of a point-ignited fire (Cekirge 1978, Weber 1989).

Relevant experimental studies on this subject are: MC ARTHUR (1966), JOHANSEN (1987) and MC ALPINE (1988).

After the build-up phase, the fire becomes fully developed (3rd phase), being characterised by a (quasi-)steady behaviour which is exclusively determined by the fuel bed and ambient conditions.

This is, by far, the most studied phase of forest fires. Indeed, throughout the remaining sections of this review, when not mentioned otherwise, it should be understood that the works cited deal with the behaviour of fire during this phase of its history.

It is also the richest phase of the fire history in what concerns the diversity of possible behaviours of the fire.

In the latter regard, the concept of fire regime introduced in the following Section (Section 3), can be envisaged as a very convenient way of subdividing the study of the fire in this phase.

Some fires pass through a fourth phase, which consists in, a transition of regime caused by a significant change of the fuel bed or ambient conditions.

This phase is usually rather brief due to strong non-linear effects that come into play.

Its complexity explains the relatively few research done on it.

Below, two particularly important fire regime transitions are mentioned: flare up from a smouldering fire, and blow-up of a large crown or bush fire from a less intense surface fire.

When the conditions of the fuel bed (i.e. high fuel moisture or very low fuel bed porosity) or the ambient (i. e. strong opposing wind) turn it difficult for the fire to spread, the fire may enter into an unstable decay phase (5th phase).

The rate of spread shows a very high sensitivity to the fuel bed or ambient pertinent parameter.

Although the fire is non-steady in this phase of its history, for brief periods of time, it behaves in a way that is similar to a steady fire regime that belongs to a class of, so called, marginal fire regimes, the research of which is reviewed in Section 3.

EUFIRELAB

D-03-09.doc 34

The decay phase may ultimately lead to the flame extinction (6th phase).

The study of the fire in this phase is especially interesting associated with the use of fire suppression means (see /1.2/ and /1.7/, and also ANDRÉ et al. 1992).

Usually, the flame extinction phase is followed by a seventh phase, characterised by a smouldering combustion regime in the fire front, which - proceeds at the surface of the solid fuel bed

particles (usually, during this phase, already practically reduced to char), and

- requires much less oxygen (air) and - has a much lower burning rate, and, so, a much

higher residence time, than flaming combustion (cf. CHANDLER et al. 1983).

The study of this phase of the fire is relevant for (see /1.2/, /1.5/, /2.3/, /3.1/): - the soil heating (p.e. DIMITRAKOUPOULOS and

MARTIN 1990); - the smoke generation (p.e. KANURY 1976, RASBASH

and DRYSDALE 1982); and - the rekindling of flame (flare up) due to sudden

changes of the fuel bed or ambient conditions (FONS 1950, Chandler et al. 1983).

The final cooling of the combustion residues (8th phase) has not attracted much research attention (see, however, /3.1/).

4.3 FIRE REGIME

The concept of fire regime is thoroughly defined and explored in ANDRÉ (1996).

As it is said above, this concept allows a finer discrimination of the fire behaviour during its fully developed phase.

Although it can also be used, for short periods of time, during other, non-steady phases of the fire history, such as the build-up and the decaying phases.

Below, four main criteria of subdivision of the fire regimes, not entirely independent, are used to review the research.

These criteria are based, respectively: - on: the combustion regime in the fire front, which

can be glowing or smouldering, with laminar or turbulent diffusion flames, or even, although seldom, with a premix flame (cf. ROTHERMEL 1991, on tree torching);

- the type of forest fuels that carry the front, which can give place to ground fires, surface fires and crown fires that are either free or dependent on surface fires spreading underneath;

- the sensitivity of the fire response to a change in a given input parameter of the fuel bed or of the ambient;

- the order of magnitude of the values of certain properties of the fire line, such as, the rate of spread (slow and fast fire regimes) or the fire line intensity (low, medium and high intensity fire regimes).

The class of, so called, marginal fire regimes (ANDRÉ 1998b) deserves a special reference, for its interest in the study of: - the transition between the ignition and build-up

phases, and - the decaying phase of the fire history.

An interesting open question concerning forest fire regimes is the (eventual) existence of multiple fire regimes for the same conditions of the fuel bed and ambient, depending on the history of fire prior to the fully developed phase and, in particular, depending on its ignition conditions (EMMONS and SHEN 1971, STEWARD 1974, PALMER and NORTHCUTT 1975, THOMAS 1967,1971).

EUFIRELAB

D-03-09.doc 35

4.4 ASPECTS OF THE FIRE BEHAVIOUR PREDICTED

4.4.1 Main fire front

4.4.1.1 Ground fires

Ground fires, perhaps because they are the least intense of all the forest fire regimes (due to the smouldering combustion regime that characterises it, differently from the much more energetic flaming combustion regimes that characterise the surface and crown fires), have not attracted much research attention.

On this subject, besides the references given above about the smouldering combustion phase of the history of a more general fire (7th phase), see: Mc MAHON et al. (1980), WILLIAMS (1982) and WADE (1984).

4.4.1.2 Surface fires

This is the area of forest fire physics research in which more theoretical and experimental work has been done.

A useful reference covering practically all types of fire spread models mostly dealing with the main fire front behaviour of surface forest fires, is the recent review of ANDRÉ and VIEGAS (1999), which is addressed to non-specialists.

A major division that ought to be done in the research in this area respects the distinction between the modelling of a small section of the fire line during small periods of time, and the modelling of the whole fire line for long time periods.

The former type of predictions are said to have a local character while the latter have a global character.

4.4.2 Behaviour of a small section of the fire line (local prediction)

Almost all studies included in this first group apply to a 2D (i.e., straight line and infinite), fully developed and flaming fire front, spreading through a statistically homogeneous fuel bed, on plane but inclined terrain, under a uniform wind field.

Moreover, all the properties of the fuel bed and the ambient are constant and, if they exist, the directions of maximum slope and of wind velocity are both perpendicular to the straight fire line.

Closest to the empirical bottom of the modelling scale, we have the fire behaviour models based on wildfire observations and field tests, typically with a scope of application rather restrict, such as: - in Australia, the works reported in LUKE and MC

ARTHUR (1978), GILL and NOBLE (1989) and CATCHPOLE (1998);

- in Canada, large parts of the work underlying the Canadian Forest Fire Behaviour Prediction System (STOCKS et al. 1989); and,

- in the USA, the early work of CURRY and FONS (1940).

Upper, in a second level of the modelling scale, there are the empirical models based on laboratory experiments.

However, in the first place, it should be stressed that most of the laboratory work has other goals, such as: - studying the phenomena that control the fire spread

in certain fire regimes; - investigating the influence of given input parameters

of the fuel bed and ambient (such as: the thickness and moisture of the fuel bed particles, the fuel load and the fuel bed porosity, the wind speed or the slope), either in an isolated fashion or in small groups, on given output parameters characterising the fire front behaviour (such as: the rate of spread, the depth and residence time of the fire front, and the flame length and inclination),

- sometimes leading to the proposal of particular empirical correlation; or validating any fire behaviour model of the semi-empirical (physical incomplete) or comprehensive (physical complete) type.

STEWARD (1974), ANDRE et al. (1992), VAZ (1997) and CATCHPOLE and CATCHPOLE (1998) refer numerous studies of this kind.

NELSON and ADKINS (1988) and CARRIER et al. (1991) deal specifically with Dimensional Analysis and Similitude

Theory problems posed by these type of experiments.

One of the few, and, clearly, the most outstanding laboratory empirical fire spread model is the model of ROTHERMEL (1972), which is the core of the well known software BEHAVE (ANDREWS 1986, 1989).

Going up the modelling scale, we find, at a third level, the physical-incomplete or semi-empirical fire spread models.

These models are back-up on a set of physical laws, the most important of which expresses the conservation of energy in a control volume located in the part of the fuel bed not yet burned, ahead of the fire line

The rate of spread of the fire, which is the main output of the models, appears as an integral parameter (albeit, eventually hidden) in this equation.

The equation explicitly includes two sets of terms associated, respectively, with heat sink and heat flux phenomena, but the positive heat flux terms, representing an income of energy to the control volume, depend on a third set of heat source phenomena occurring at the fire front.

In fact, even ROTHERMEL model is based on such a law, written in the simplest possible (space-time integral) form, but, contrary to the semi-empirical models, does not model in a physical way the heat flux terms.

However, the semi-empirical models cannot be considered as physical-complete models because they do not model the heat source terms.

EUFIRELAB

D-03-09.doc 36

The following are review works specifically dealing with this class of fire spread models: STEWARD (1974), CATCHPOLE and DE MESTRE (1986), WEBER (1991) and CATCHPOLE (1994).

ANDRE et al. (1992) contains also interesting material on this regard.

A first group of models of this type, treating the fire spread in a space-time continuous way, is: EMMONS (1964), THOMAS et al. (1964), HOTTEL et al. (1965), FANG (1966), VAN WAGNER (1967), ALBINI (1967), THOMAS (1967), FANG (1969), ANDERSON (1969), BERLAD (1970), SANDHU (1970), FRANDSEN (1971), THOMAS (1971), STEWARD (1971), HOTTEL et al. (1971), PAGNI and PETERSON (1973), TELISIN (1974), PAGNI (1975), CEKIRGE (1978), FUJII et al. (1980), ALBINI (1985,1986), DE MESTRE et al. (1989) and WEBER (1989).

There is a second group of models in which the fire spread is modelled in a discontinuous way, as a succession of jumps from particle to particle along the fuel bed.

Although these models seem more adapted to cope with very low porosity fuel beds, in any case, they can always be converted into formally continuous fire spread models.

Here is a list of works in this group: FONS (1946), VOGEL and WILLIAMS (1970), EMMONS and SHEN (1971), STEWARD and WAIBEL (1973) and WEBER (1990).

The software FIRELAB (GUARNIERI et al. 1998), under development, implements some of the former models in an easy-to-use way.

Finally, in a fourth level of the modelling scale, there is still another group of fire spread models that can be already classified as physical complete models (see the DELFI review of LARINI).

It is a striking fact that the rate of production of new semi-empirical models practically comes to zero in the nineties, while the one of physical complete models begins to rise.

4.4.3 Behaviour of the whole fire line (global prediction)

Let us now step to the global scope of prediction of the fire behaviour, in which the whole fire line is followed for long periods of time, paying attention to the significant space and time changes of the fuel bed and ambient conditions along the fire line, during the time interval of prediction.

In this area, not accounting for the models of the physical complete type (see the DELFI review of LARINI), practically all the research can be cast into the theoretical framework of ANDRÉ and VIEGAS (1998b), which is summarised in ANDRÉ and VIEGAS (1999) (see also the critical proposal of VIEGAS et al. 1998).

However, there also exist some works with a deep statistical nature, using concepts and tools of Chaos Theory, such as: - percolation models, - cellular automata models and - fractal geometry.

They have been applied to study the behaviour of forest fires at a global level, with the purpose, for instance: - of identifying critical behaviours (eventually

associated with marginal fire regimes) or - of determining the fractal dimension of the burned

area.

These works, essentially distinct from the works of the main stream, are not reviewed here (see the early work of VON NIESSEN and BLUMEN 1988, and the recent review of DUARTE 1997).

4.4.4 Large crown fires and bushfires

Large crown fires and bushfires are much less studied than lower intensity surface fires.

See ANDRE et al. (1992) for a more extensive review of this subject.

The initiation of a fire regime of this class is usually associated with a regime transition from lower intensity surface fires, named blow-up.

It is studied in the following works: BYRAM (1954), SHAEFER (1957), MOLCHANOV (1957), VAN WAGNER (1964, 1977), EMMONS (1966), BROWN and DAVIS (1973), DIETERICH (1976), BROTAK (1978), LUKE and MC ARTHUR (1978), WILLIAMS (1982), CARRIER et al. (1985), HAINES (1988), SIMARD and EENIGENBURG (1990).

Tentative identifications of different fire regimes within this class are done by: BYRAM (1966), LEE (1972), PALMER and NORTHCUTT (1975) and ROTHERMEL (1991).

Dimensional Analysis and Similitude Theory considerations relevant to the modelling of large forest fires can be found in BYRAM (1966) and WILLIAMS (1969).

Some authors studied the strong flows induced by this type of fires, such as, the main fire plume and different vortex flow structures, but as this subject pertains more to /2.4/, we do not review it here.

Regarding the fire spread models that have been proposed specifically to model large forest fires, we have: - the empirical models of VAN WAGNER (1989) and

ROTHERMEL (1991), which are incorporated, respectively, in the Canadian Forest Fire Behaviour Prediction System and in the software package BEHAVE (see Section 5);

- the semi-empirical model of ALBINI (ALBINI and STOCKS 1986); and, finally,

- some models of the physical complete type (see the DELFI review of LARINI).

An on-going important field experimental program on crown fires is reported in ALEXANDER et al. (1998).

EUFIRELAB

D-03-09.doc 37

4.4.5 Spotting

Spotting, albeit being present in any type of flaming forest fire, even in the least intense ones (cf. STEWARD 1974), only in medium-to-high intensity fires becomes a really important phenomenon in the context of /1.2/ and /2.5/, which ought to be predicted in addition to the behaviour of the main fire front (see Section 4.1).

However, this is a relatively self-contained research problem seldom addressed.

Following ANDRÉ et al. (1992), we can distinguish three main sub-problems in the study of spotting: - the production of firebrands in the main fire front (or,

more generally, in any spotting source, such as can be an isolated torching tree);

- the transport of the firebrands by the flow of hot gases and wind, from the fire line towards the still non-burned part of the fuel bed, beginning with the lifting of a firebrand in the fire plume and ending with its gravity fall across the wind flow; and

- the (eventual) ignition of a secondary fire spot, ahead of the main front.

The study of the transport problem, obviously, benefits from independent modelling both of the fire plume (see, p. e., LEE and EMMONS 1961, MORTON 1965, NIELSON and TAO 1965, SMITH 1967, BYRAM and NELSON 1974, WILLIAMS 1982), and of the wind flow over complex terrain /2.4/.

Similarly, the investigation of secondary fire spots can take profit of the research on the more general subject of ignition of forest fires (see Section 2).

Specifically addressing the spotting issue, we have: the wildfire observations reported by LUKE and MC ARTHUR (1978); the experimental studies of TARIFA et al. (1965) and STEPHEN and WRIGHT (1974); and the semi-empirical models of ALBINI (1979, 1982).

4.4.6 Fire whirls

Fire whirls are large scale coherent vortex flow structures – according to EMMONS (1964).

These vortexes can be assimilated to cylinders with (diameter x height): - ranging from (10 cm x 30 cm), for a small fire whirl, - to (100 m x 300 m), for a strong fire tornado 2 - carrying inside flames and firebrands, that

sometimes detach from the fire front and wander around for a while till they dissipate.

As this seems to be a rather rare and fortuitous phenomenon in most forest fires, besides seldom posing fire safety problems 3, we do not review its research here (such a review can be found in ANDRÉ et al. 1992).

2 Probably, forest fires can never become intense enough

to generate such a fire tornado, which, on the other hand, has been observed in large industrial and urban fires.

3 Personal communication done to the author by Richard ROTHERMEL, at the 1st International Conference on Forest Fire Research (19-22 November 1990, Coimbra, Portugal).

4.5 APPLIED RESEARCH PRODUCTS

To simulate with a particular model the behaviour of a fire front in natural dynamic scenery of fuel bed and ambient conditions, either on virtual, historical, real-time or prognostic grounds, one must solve two main problems: - to get the input information required by the model,

and - to perform all the due mathematical computations.

In accordance, to facilitate these tasks for the operational user, an applied research effort has been done by the forest services of some countries (particularly the USA), by enterprises and by research groups, ultimately leading to the construction of more or less elaborated and user-friendly software packages.

Some of these software systems are in the public domain, others are commercial products, and still others have an indefinite statute.

Many of these software systems, in addition to a module specifically dealing with the prediction of the fire behaviour, which incorporates some of the models reviewed above, contain other modules that help the user: - in organising activities of fire management /1.1/, - fire suppression /1.2/, - fire detection /1.3/, - fire risk /1.4/, or - even other more or less specialised fire activities

(/1.5/, /3.6/); - in the fuel characterisation /2.1/ or - in wind modelling /2.4/; or - in the managing of smoke (/2.3/, /3.1/).

As all these functions pertain to other topics of DELFI Index, we concentrate here on systems possessing an important or original fire behaviour module.

The most important systems that give only local fire behaviour predictions are: - CFBPS Canadian Forest Fire Behaviour Prediction

System and - the software package BEHAVE.

Regarding the numerous Australian subsystems see (CATCHPOLE 1998).

Although aimed at laboratory simulations more than at operational applications, the software FIRELAB should also be mentioned.

All the existing software systems that produce fire behaviour predictions with a global character and incorporate empirical or semi-empirical fire spread models can ultimately be framed within the theory (see Section 4.1.2).

In fact, a rather general software kernel of implementation of this theory is presently under development.

Moreover, practically all systems use BEHAVE as Local Fire Spread Model.

EUFIRELAB

D-03-09.doc 38

Here is an illustrative list of such systems: - FMISTM, FIRESTATIONTM, GEOFOGO and

CARDIN, using slightly different variants of the algorithm of DIJKSTRA to implement a Global Fireline Propagation Model based on the principle of the fastest path of propagation; and

- FARSITE, which implements HUYGENS' principle as the basis of a Global Fireline Propagation Model.

Some attempts were made to adapt it to European shrublands (ARCA et al. 2006, see also TECNOMA-FSE in deliverable D-03-06).

These systems are in constant evolution to take better into account external and vegetation conditions (BUTLER et al. 2006).

Concerning detailed physical modelling, the WFDS simulator is in under development, as stated in section 2.4.

(Review prepared under an Associated Contract to the implementation of the CONCERTED ACTION DELFI)

4.6 FUTURE PROSPECTS

To simulate with a particular model the behaviour of a fire front in a natural dynamic scenery of fuel bed and ambient conditions, either on virtual, historical, real-time or prognostic grounds, one must solve two main problems: - to get the input information required by the model,

and - to perform all the due mathematical computations.

As the authors stated in section 2.4, physical and semi-empirical approaches are complementary ones and they should be mixed to describe such a situation (at this point of our knowledge on the fire behaviour).

4.7 REFERENCES

ALBINI, F. A. 1967. A physical model for fire spread in brush. Proc. 11th International Symposium on Combustion, pp. 553-560.

ALBINI, F. A. 1976a. Combining wind and slope effects on spread rate. United States Department of Agriculture, Forest Service, Northern Forest Fire Laboratory (Missoula). Memorandum to R. C. ROTHERMEL (January 19).

ALBINI, F. A. 1976b. Computer-based models of fire behaviour: a user's manual. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station (Ogden, Utah). 68 pp..

ALBINI, F. A. 1979. Spot fire distance from burning trees – A predictive model. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station (Ogden, Utah). General Technical Report INT-56.

ALBINI, F. A. and R. G. BAUGHMAN. 1979. Estimating windspeeds for predicting wildland fire behaviour. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-221.

ALBINI, F. A. 1982. Response of free burning fires to non-steady wind. Combustion Science and Technology 29: 225-241.

ALBINI, F. A., LATHAN, D. J. and R. G. BAUGHMAN. 1982. Estimating up-slope convective windspeeds for predicting wildland fire behaviour. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-257.

ALBINI, F. A. 1985. A model for fire spread in wildland fuels by radiation. Combustion Science and Technology 42:229-258.

ALBINI, F. A. 1986. Wildland fire spread by radiation – A model including fuel cooling by natural convection. Combustion Science and Technology 45:101-113.

ALBINI, F. A. and B. J. STOKES. 1986. Predicted and observed rates of spread of crown fires in immature jack pine. Combustion Science and Technology 48:65-76.

ALBINI, F. A. 1996. Iterative solution of the radiation transport equations governing spread of fire in wildland fuel. Physika Goreniya I Vzryva 32(5): 71-82.

ALEXANDER, M. E., STOCKS, B. J., WOTTON, B. M. and R. A. LANOVILLE. 1998. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 83-112.

ANDERSON, D., CATCHPOLE, E., DE MESTRE, N. and T. PARKES. 1982. Modelling the spread of grass fires. Journal of the Australian Mathematical Society, Series B, 23:451-466.

EUFIRELAB

D-03-09.doc 39

ANDERSON, H. E. 1968. Sundance fire: an analysis of fire phenomena. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station (Ogden, Utah). General Technical Report INT-122. 39 pp..

ANDERSON, H. E. 1969. Heat transfer and fire spread. United States Department of Agriculture, Forest Service. Research Paper INT-69. 20 pp..

ANDERSON, H. E. 1982. Aids to determining fuel models for estimating fire behaviour. United States Department of Agriculture, Forest Service. General Technical Report INT-122.

ANDERSON, H. E. 1983. Predicting wind-driven wildland fire size and shape. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-305. 26 pp..

ANDRÉ, J. C. S., LOPES, A. G. and D. X. VIEGAS. 1992. A broad synthesis of research on physical aspects of forest fires. Fluid Mechanics Group, Mechanical Engineering Department, Faculty of Science and Technology, University of Coimbra. Cadernos Científicos sobre Incêndios Florestais 3. 148 pp..

ANDRE, J. C. S. 1996. A theory on the propagation of surface forest fire fronts. PhD. Thesis (in portuguese). Mechanical Engineering Department, Faculty of Science and Technology, University of Coimbra. 330 pp..

ANDRÉ, J. C. S. and D. X. VIEGAS. 1998a. Modelling of the propagation of a forest fire front: appraisal of an algorithm in common use (DIJKSTRA'S algorithm). Proc. 2nd Numerical Meeting on Partial Differential Equations (25-27 Feb, Coimbra, Portugal).

ANDRÉ, J. C. S. and D. X. VIEGAS 1998b. An unifying theory on the propagation of the fire front of surface forest fires. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 259-279.

ANDRÉ, J. C. S. and D. X. VIEGAS 1999. Forest fires spread models: state-of-the-art for non-specialists. (In portuguese. Draft version.)

ANDREWS, P. L. 1986. BEHAVE: fire behaviour prediction and fuel modelling system – – BURN subsystem, Part 1. United States Department of Agriculture, Forest Service (Ogden, Utah). General Technical Report INT-194. 130 pp..

ANDREWS, P. L. and G. A. MORRIS. 1987. Equations for wildland fire behaviour prediction: a summary. United States Department of Agriculture, Forest Service, Intermountain Fire Sciences Laboratory (Missoula, Montana).

ANDREWS, P. L. and C. H. CHASE. 1989. BEHAVE: fire behaviour prediction and fuel modelling system - BURN subsystem, Part 2. United States Department of Agriculture, Forest Service (Ogden, Utah). General Technical Report INT-260. 93 pp..

ANDREWS, P. L. and L. S. BRADSHAW. 1990. RXWINDOW: defining windows of acceptable burning conditions based on desired fire behaviour. United States Department of Agriculture, Forest Service, Intermountain Research Station (Ogden, Utah). General Technical Report INT-273. 54 pp..

ANDREWS, P. L. and C. D. BEVINS. 1998. Update and expansion of the BEHAVE fire behaviour prediction system. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 733-740.

ARCA B., DUCE P., PELLIZZARO G., LACONI M., SALIS M., SPANO D. 2006. Evaluation of FARSITE simulator in Mediterranean shrubland. In Proc. V Int. Conf. on Forest Fire Research, VIEGAS, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

BARROS, F. J. and G. L. BALL. 1998. Fire modelling using dynamic structure cellular automata. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 879-888.

BERLAD, A. L. 1970. Fire spread in solid fuel arrays. Combustion and Flame 14:123-136.

BRADSHAW, L. S., DEEMING, J. E., BURGAN, R. E. and J. D. COHEN. 1984. The 1978-National Fire Danger Rating System: Technical documentation. United States Department of Agriculture, Forest Service. General Technical Report INT-169. 44 pp..

BROTAK, E. A. 1978. The Bass-River fire – Weather conditions associated with a killer fire. Proc. 5th Symposium on Forest Meteorology (Ottawa, Ontario, Canada).

BROWN, A. A. and K. P. DAVIS. 1973. Forest fire: control and use (2nd Ed.). Ed. McGraw-Hill.

BRUCE, H. D., PONG, W. Y. and W. L. FONS. 1961. The effect of density and thermal diffusivity of wood on the rate of burning of wood cribs. United States Department of Agriculture, Forest Service. Technical Paper N. 63.

BURGAN, R. E. and R. C. ROTHERMEL. 1984. BEHAVE: fire behaviour prediction and fuel modelling system - FUEL subsystem. United States Department of Agriculture, Forest Service (Ogden, Utah). General Technical Report INT-167. 126 pp.

BUTLER B., FORTHOFER J., FINNEY M., MCHUGH C., STRATTON R., BRADSHAW L. 2006. The impact of high resolution wind field simulations on the accuracy of fire growth predictions. In Proc. V Int. Conf. on Forest Fire Research, VIEGAS, D.X. (Ed.), Figueira da Foz, 27-30 Nov. Elsevier Publishers.

BYRAM, G. M. 1954. Atmospheric conditions related to blow-up fires. United States Department of Agriculture, Forest Service, Southeast Forest and Range Station. Paper SE-35.

EUFIRELAB

D-03-09.doc 40

BYRAM, G. M. 1959. In: DAVIS, K. P. 1959. Forest fire: control and use. (1st Ed.) Ed. McGraw-Hill. Chapters: Combustion of forest fuels and Forest fire behaviour.

BYRAM, G. M., CLEMENTS, H. B., ELLIOT, E. R. and P. M. GEORGE. 1964. An experimental study of model fires. United States Department of Agriculture, Forest Service, South-eastern Forest and Experiment Station, Southern Forest Fire Laboratory (Macon, Georgia). Technical Report N. 3.

BYRAM, G. M. 1966. Scaling laws for modelling mass fires. Pyrodynamics 4: 271-284.

BYRAM, G. M., CLEMENTS, H. B., BISHOP, M. E. and R. M. NELSON. 1966. An experimental study of model fires – Project Fire Model. United States Department of Agriculture, Forest Service, South-eastern Forest and Experiment Station, Southern Forest Fire Laboratory (Macon, Georgia). Final Report.

BYRAM, G. M. and R. M. NELSON. 1974. Buoyancy characteristics of a fire heat source. Fire Technology 10(1): 68-79.

CALL, P. T. 1997. Run-time optimisation of a radiation driven crown fire model. MSc. Thesis. Montana State University (Bozeman, Montana). 86 pp..

CALL, P. M. and F. A. ALBINI. 1997. Aerial and surface fuel consumption in crown fires. International Journal of Wildland Fire 7: 259-264.

CARRIER, G. F., FENDELL, F. E. and P. S. FELDMAN. 1985. Firestorms. Journal of Heat Transfer 107: 19-27.

CARRIER, G. F., FENDELL, F. E. and M. F. WOLFF. 1991. Wind-aided fire spread across arrays of discrete fuel elements. I theory. Combustion Science and Technology 75: 31-51.

CATCHPOLE, E.A., DE MESTRE, N. J. and A. M. GILL. 1982. Intensity of fire at its perimeter. Australian Forestry Research 12:47-54.

CATCHPOLE, E. A. and N. J. DE MESTRE. 1986. Physical models for a spreading line fire. Australian Forestry 49(2):102-111.

CATCHPOLE, E. A. 1995. A review of physical fire models, 1945-1975. In: Elements of Bushfire Science. Vol. 1: Fire Behaviour Models (PACKHAM, D. R., CATCHPOLE, E. A., CATCHPOLE, W. R., KRUSEL, N. and N. VINEY, Eds.). 44 pp..

CATCHPOLE, W. C. 1998. Fire behaviour research in Australia. Photocopies of the transparencies presented at the Short Course on Forest Fire Behaviour Modelling (Luso, 21/22 November). 25 pp..

CATCHPOLE, W. C. and T. CATCHPOLE. 1998. Laboratory based models. Photocopies of the transparencies presented at the Short Course on Forest Fire Behaviour Modelling (Luso, 21/22 November). 26 pp..

CATCHPOLE, W. A., CATCHPOLE, E. A., ROTHERMEL, R. C., MORRIS, G. A., BUTLER, B. W. and D. J. LATHAM. 1998. Rate of spread of free-burning fires in woody fuels in a wind tunnel. Combustion Science and Technology 131: 1-37.

CEKIRGE, H. M. 1978. Propagation of fire fronts in forests. Computational and Mathematical Applications 4:325-332.

CHANDLER, C., CHENEY, P., THOMAS, P., TRABAUD, L. and D. WILLIAMS. 1983. Fire in forestry - Forest fire behaviour and effects. Ed. John Wiley & Sons. Vol. 1.

CHENEY, N. P., GOULD, J. S. and W. R. CATCHPOLE. 1998. Prediction of fire spread in grasslands. International Journal od Wildland Fire 8 (1): 1-13.

COUNTRYMAN, C. M. 1983. Ignition of grass fuels by cigarettes. Fire Management Notes 44: 3-7.

CURRY, J. R. and W. L. FONS. 1940. Forest fire behaviour studies. Mechanical Engineering 62:219-225.

DE MESTRE, N., CATCHPOLE, E., ANDERSON, D. and R. ROTHERMEL. 1989. Uniform propagation of a planar fire front without wind. Combustion Science and Technology 65:231-244.

DIETERICH, J. H. 1976. Jet stream influence on the Willow fire. Fire Management Notes 37: 6-8.

DIMITRAKOPOULOS, A. P. and R. E. MARTIN. 1990. Measuring and modelling soil temperatures during simulated wildland fire conditions. Proc. International Conference on Forest Fire Research (Coimbra). Communication B.21. 17 pp..

DUARTE, J. A. M. S. 1997. Bushfire models and their phase transitions. International Journal of Modern Physics C (...)

EFTICHIDIS, G., MARGARITIS, E., SFIRIS, A. and V. VARELA. 1998. Fire management information systems: FMIS. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. II, pp. 2641-2642.

EMMONS, H. W. 1964. Fire in the forest. Fire Research Abstracts and Reviews 5(3):163--178.

EMMONS, H. W. 1966. Fundamental problems of free burning fire. Fire Research Abstracts and Reviews 8(1):1-17.

EMMONS, H. W. and T. SHEN. 1971. Fire spread in paper arrays. Proc. 13th International Symposium on Combustion. pp. 917-926.

FANG, J. B. 1966. Fire spreading studies. MSc. Thesis. Chemical Engineering Department, University of New Brunswick (New Brunswick, Canada). 115 pp..

EUFIRELAB

D-03-09.doc 41

FANG, J. B. 1969. An investigation of the effect of controlled wind on the rate of fire spread. PhD. Thesis. Chemical Engineering Department, University of New Brunswick (New Brunswick, Canada). 169 pp..

FERNANDEZ-PELLO, A. C. and T. HIRANO. 1983. Controlling mechanisms of flame spread. Combustion Science and Technology 32 (1-4): 1-32.

FEUNEKES, U. 1991. Error analysis in fire simulation models. MSc. Thesis. Department of Forest Research, University of New Brunswick (New Brunswick, Canada). 75 pp..

FINNEY, M. A. 1998. FARSITE: Fire Area Simulator – Model development and evaluation. United States Department of Agriculture, Forest Service, Rocky Mountain Research Station. Research Paper RMRS-RP-4. 47 pp.

FINNEY, M. A. and P. L. ANDREWS. 1998. Application and status of the FARSITE fire area simulator. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 755-760.

FONS, W. L. 1946. Analysis of fire spread in light forest fuels. Journal of Agricultural Research 72:93-121.

FONS, W. L. 1950. Heating and ignition of small wood cylinders. Industrial Engineering Chemistry 42(10):2130-2133.

FONS, W. L., BRUCE, H. D., PONG, W. Y. and S. S. RICHARDS. 1960. Project Fire Model - An experimental study of model fires. United States Department of Agriculture, Forest Service, Pacific Southwest Forest and Experiment Station (Macon, Georgia). Summary Progress Report.

FONS, W. L., CLEMENTS, H., ELLIOT, E. R. and P. M. GEORGE. 1962. Project Fire Model - An experimental study of model fires. United States Department of Agriculture, Forest Service, South-eastern Forest and Experiment Station (Macon, Georgia). Summary Progress Report II.

FONS, W. L., CLEMENTS, H. B. and P. M. GEORGE. 1963. Scale effects on propagation rate of laboratory crib fires. Proc. 9th International Symposium on Combustion, pp. 860-866.

Forestry Canada Fire Danger Group. 1992. Development and structure of the Canadian Forest Fire Behaviour Prediction System. Forestry Canada (Ottawa, Ontario). Information Report ST-X-3. 63 pp..

FRANDSEN, W. H. 1971. Fire spread through porous fuels from the conservation of energy. Combustion and Flame 16:9-16.

FRANDSEN, W. H. 1973. Effective heating of fuel ahead of a spreading fire. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-140.

FRANDSEN, W. H. and P. L. ANDREWS. 1979. Fire behaviour in nonuniform fuels. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-232.

FUJII, N., HASEGAWA, J., PALLOP, L. and Y. SAKAWA. 1980. A non-stationary model of fire spreading. Applied Mathematics Modelling 4:176-180.

GILL, A. M. and I. R. NOBLE. 1989. Bibliography of fire ecology in Australia. Bushfire Council of New South Wales (2nd Ed.).

GREEN, D. G. 1983. Shapes of simulated fires in discrete fuels. Ecological Modelling 20: 21-32.

GRISHIN, A. M., GRUZIN, A. D. and V. G. ZVEREV. 1983. Mathematical modelling of the spreading of high-level forest fires. Sovietic Physical Doklady 28:328-330.

GRISHIN, A. M. 1984. Steady state propagation of the front of a high level forest fire. Sovietic Physical Doklady 29: 917-919.

GUARNIERI, F., ANDERSEN, C. K., OLAMPI, S. and N. CHAMBINAUD. 1998. FIRELAB: towards a problem solving environment to support forest fire behaviour modelling. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 483-496.

HAINES, D. A. 1988. A lower atmosphere severity index for wildland fire. Natural Weather Digest 13 (2): 23-27.

HOTTEL, H. C., WILLIAMS, G. C. and F. R. STEWARD. 1965. The modelling of fire spread through a fuel bed. Proc. 10th International Symposium on Combustion, pp. 997-1007.

HOTTEL, H. C., WILLIAMS, G. C. and G. K. KWENTUS. 1971. Fuel preheating in free-burning fires. Proc. 13th International Symposium on Combustion, pp. 963-970.

JOHANSEN, R. W. 1987. Ignition patterns and prescribed fire behaviour in southern pine stands. Georgia Forest Commission (USA), Research Division. Georgia Forest Research Paper.

JORBA, J., ANDRÉ, J. C. S., J., MARGALEF, T., LUQUE, E. and D. X. VIEGAS. 1999. A framework for forest fire propagation simulation. Extended abstract submitted to the International Symposium on Forest Fires: Needs & Innovations (18-19 November 1999, Athens, Greece). (In preparation.)

KANURY, A. M. 1976. The science and engineering of hostile fires. Fire Research Abstract and Reviews 18:72-96.

KONEV, E. V. and A. I. SUKHININ. 1977. The analysis of flame spread through forest fuel. Combustion and Flame 28: 217-223.

EUFIRELAB

D-03-09.doc 42

KOURTZ, P. H. and W. G. O'REGAN. 1971. A model for a small forest fire to simulate burned and burning areas for use in a detection model. Forest Science 17 (2): 163-169.

LEE, S. L. and H. W. EMMONS. 1961. A study of natural convection above a line fire. Journal of Fluid Mechanics 11 (3): 353-368.

LEE, S. L. 1972. Fire research. Applied Mechanics Review (May): 503-509.

LOPES, A. M. G., CRUZ, M. G. and D. X. VIEGAS. 1998. FIRESTATION – An integrated system for the simulation of wind flow and fire spread over complex topography. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 741-754.

LUKE, R. H. and A. G. MCARTHUR. 1978. Bushfires in Australia. Australian Government Publishing Service (Canberra), pp. 31-118.

MARGERIT, J. and O. SÉRO-GUILLAUME. 1998. Richards’ model, Hamilton-Jacobi equation and temperature field equations of forest fires. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 281-294.

MARTINEZ-MILLÁN, J. and S. SAURA. 1998. CARDIN 3.2: forest fires spread and fighting simulation system. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 907-926.

MC ALPINE, R. S. 1988. The acceleration of point source fire to equilibrium spread. MSc. Thesis. University of Montana (Missoula, Montana). 130 pp..

MC ALPINE, R. S., STOCKS, B. J., VAN WAGNER, C. E., LAWSON, B. D., ALEXANDER, M. E. and T. J. LYNHAM. 1990. Forest fire behaviour research in Canada. Proc. International Conference on Forest Fire Research (Coimbra, Portugal). Communication A.02. 12 pp..

MC ARTHUR, A. G. 1966. Weather and grassland fire behaviour. Australian Forest and Timber Bureau. Leaflet n. 100.

MC ARTHUR, A. G. 1967. Fire behaviour in Eucalypt forests. Australian Forest and Timber Bureau. Leaflet n. 107.

MC ARTHUR, A. G. 1973. Forest fire danger meter, Mark 5. Forest Research Institute, Forest and Timber Bureau (Canberra, ACT, Australia).

MC MAHON, C. K. et al. 1980. Combustion characteristics and emissions from burning organic soils. Proc. 73rd Annual Meeting of the Air Pollution Control Association.

MORTON, B. R. 1965. Modelling fire plumes. Proc. 10th Symposium on Combustion, pp. 973-982.

NELSON, R. M. and C. W. ADKINS. 1988. A dimensionless correlation for the spread of wind-driven fires. Canadian Journal of Forest Research 18: 391-397.

NIELSON, H. J. and L. N. TAO. 1965. The fire plume above a large free-burning fire. Proc. 10th Symposium on Combustion, pp. 965-972.

PAGNI, P. J. and T. G. PETERSON. 1973. Flame spread through porous fuels. Proc. 14th International Symposium on Combustion, pp. 1099-1107.

PAGNI, P. J. 1975. Flame spread over thin solid fuels. Journal of Heat Transfer 97:153-155.

PALMER, T. Y. and L. I. NORTHCUTT. 1975. Convection columns above large experimental fires. Fire Technology 11:111-118.

PHILPOT, C. W. 1968. Mineral content and pyrolysis of selected plant materials. United States Department of Agriculture, Forest Service. Research Note INT-84. 4 pp..

PHILPOT, C. W. 1970. Influence of mineral content on the pyrolysis of plant materials. Forest Science 16(4):461-471.

PUTNAM, A. A. 1965. A model study of wind-blown free-burning fires. Proc. 10th Symposium on Combustion, pp. 1039-1046.

RASBASH, D. J. and D. D. DRYSDALE. 1982. Fundamentals of smoke combustion. Fire Safety Journal 5:77-86.

RAY, S. R. and I. GLASSMAN. 1983. The detailed processes involved in flame spread over solid fuels. Combustion Science and Technology 32:33-48.

RICHARDS, G. D. 1990. An elliptical growth model of forest fire fronts and its numerical solution. International Journal for Numerical Methods in Engineering 30:1133-1149.

RICHARDS, G. D. 1995. A general mathematical framework for modelling two-dimensional wildland fire spread. International Journal of Wildland Fire 5(2):63-72.

ROBERTS, S. 1989. A line element algorithm for curve flow problems in the plane. Centre for Mathematical Analysis, Australian National University. CMAR5889. 20 pp..

ROTHERMEL, R. C. and ANDERSON, H. E. 1966. Fire spread characteristics determined in the laboratory. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-30.

ROTHERMEL, R. C. 1972. A mathematical model for predicting fire spread in wildland fuels. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-115. 40 pp..

EUFIRELAB

D-03-09.doc 43

ROTHERMEL, R. C. 1983. How to predict the spread and intensity of forest and range fires. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. General Technical Report INT-143. 161 pp..

ROTHERMEL, R. C. and G. C. RINEHART. 1983. Field procedures for verification and adjustment of fire behaviour predictions. United States Department of Agriculture, Forest Service (Ogden, Utah). General Technical Report INT-142.

ROTHERMEL, R. C., WILSON, R. A., MORRIS, G. A. and S. S. SACKETT. 1986. Modelling moisture content of fine dead wildland fuels: input to BEHAVE fire prediction system. United States Department of Agriculture, Forest Service. Research Paper INT-359.

ROTHERMEL, R. C. 1990. Modelling fire behaviour. Proc. International Conference on Forest Fire Research (Coimbra, Portugal). Closing Conference. 19 pp..

ROTHERMEL, R. C. 1991. Predicting the behaviour and size of crown fires in the Northern Rocky Mountains. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experimental Station (Ogden, Utah). Research Paper INT-438.

SANDHU, S. S. 1970. Mechanisms of heat transfer to fibrous fuel ahead of an advancing flame. MSc. Thesis. Chemical Engineering Department, University of New Brunswick (New Brunswick, Canada). 85 pp..

SCHROEDER, M. J. and C. C. BUCK. 1970. Fire weather - A guide for application of meteorological information to forest fire control operations. United States Department of Agriculture, Forest Service. Handbook 360.

SHAEFER, V. J. 1957. The relationship of jet streams to forest wildfires. Journal of Forestry 55 (6): 419-425.

SIMARD, A. J. and J. E. EENIGENBURG. 1990. METAFIRE: a system to support high level fire management decisions. Fire Management Notes.

SMITH, R. K. 1967. Radiation effects on large fire plumes. Proc. 11th Symposium on Combustion, pp. 507-515.

STEPHEN, C. H. and H. A. WRIGHT. 1974. Ignition capabilities of non-flaming firebrands. Journal of Forestry (Oct.): 646-649.

STEWARD, F. R. 1964. Linear flame heights for various fuels. Combustion and Flame 8: 171-178.

STEWARD, F. R. 1971. A mechanistic fire spread model. Combustion Science and Technology 4:177-….

STEWARD, F. R. and WAIBEL, R. T. 1973. Fire spread through uniform fuel matrices. West State Section Meeting on Combustion (Tempe, Arizona). WSC1-73-3.

STEWARD, F. R. 1974. Fire spread through a fuel bed. In: Heat transfer in fires: thermophysics, social aspects, economic impact. Blackshear, P.L. (ed.). Ed. Scripta Book Company (Washington DC), pp. 317-378.

STOCKS, B. J., LAWSON, B. D., ALEXANDER, M. E., VAN WAGNER, C. E., MC ALPINE, R. S., LYNHAM, T. J. and D. E. DUBÉ. 1989. The Canadian Forest Fire Danger Rating System: an overview. Forestry Chronicle 65:450-457.

SUSOTT, R. A. 1982a. Characterisation of the thermal properties of forest fuels by combustible gas analysis. Forest Science 28(2):404-420.

SUSOTT, R. A. 1982b. Differential scanning calorimetry of forest fuels. Forest Science 28(4):839-851.

SUSOTT, R. A. 1984. Heat of preignition of three woody fuels used in wildfire modelling research. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experimental Station (Ogden, Utah). Research Note INT-342. 4 pp..

TARIFA, C. S., DEL NOTARIO, P. P. and F. G. Moreno. 1965. On the flight paths and lifetimes of burning particles of wood. Proc. 10th Symposium on Combustion, pp. 1021-1037.

TELISIN, H. P. 1974. Flame radiation as a mechanism of fire spread in forests. In: Heat transfer in flames. Afgan, N. H. and J. M. Beer (ed.). Ed. John Wiley & Sons. pp. 441-449.

THOMAS, P. H. 1963. The size of flames from natural fires. Proc. 9th Symposium on Combustion, pp. 844-859.

THOMAS, P. H. 1967. Some aspects of the growth and spread of fire in the open. Journal of Forestry 40(2):139-164.

THOMAS, P. H., SIMMS, D. L. and G. H. WRAIGHT. 1964. Fire spread in wooden cribs. Fire Research Station (Borehamwood, Hertsfordshire, England). Fire Research Note No. 537. 38 pp..

THOMAS, P. H. 1971. Rates of spread of some wind-driven fires. Journal of Forestry 44(2):155-175.

VAN WAGNER, C. E. 1964. History of a small crown fire. Forest Chronicle 40 (2): 202-205, 209.

VAN WAGNER, C. E. 1967. Calculations on forest fire spread by flame radiation. Canadian Forestry Service, Department of Forestry and Rural Development, Petawawa Forest Experiment Station (Chalk River, Ontario). Report N.1185. 13 pp..

VAN WAGNER, C. E. 1969. A simple fire-growth model. Forest Chronicle 45:103-104.

VAN WAGNER, C. E. 1977. Conditions for the start and spread of crown fires. Canadian Journal of Forestry Research 7 (1): 23-34.

EUFIRELAB

D-03-09.doc 44

VAN WAGNER, C. E. 1987. Development and structure of the Canadian Forest Fire Weather Index System. Canadian Forest Service (Ottawa). Technical Report 35.

VAN WAGNER, C. E. 1989. Prediction of crown fire in conifer stands. Proc. 10th Fire and Forest Meteorology Conference (Ottawa, 17/21 April), pp. 207-212.

VASCONCELOS, M. J. 1988. Simulation of fire behaviour with a geographic information system. MSci. Thesis. School of Renewable Natural Resources, University of Arizona (Tucson, Arizona).

VASCONCELOS, M. J. P., PAÚL, J. C. U. and A. GONÇALVES. 1998. GEOFOGO – Testing a forest fire simulation system. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 889-890.

VAZ, G. C. 1997. Prediction of the properties of a fire front spreading through a solid porous fuelbed: critical analysis of the state-of-the-art. MSc. Thesis (in portuguese). Mechanical Engineering Department, Faculty of Science and Technology, University of Coimbra.

VAZ, G. C., ANDRÉ, J. C. S. and D. X. VIEGAS. 1998. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 547-564.

VIEGAS, D. X., RIBEIRO, P. R. and L. MARICATO. 1998. Proc. III International Conference on Forest Fire Research / 14th Conference on Fire and Forest Meteorology (Luso, 16/20 November 1998), Vol. I, pp. 325-342.

VOGEL, M. and F. A. WILLIAMS. 1970. Flame propagation along matchstick arrays. Combustion Science and Technology 1:429-436.

VON NIESSEN, W. and A. BLUMEN. 1988. Dynamic simulation of forest fires. Canadian Journal of Forestry Research 18: 805-812.

WADE, D. D. 1984. Fire science adaptations for the Southern US – A research update 1980-1984. Proc. Prescribed Fire and Smoke Management in the South Conference (12/14 Sept., Atlanta), pp. 101-112.

WEBER, R. O. 1989. Analytical models for fire spread due to radiation. Combustion and Flame 78:398-408.

WEBER, R. O. 1990. A model for fire propagation in arrays. Mathematical and Computer Modelling 13(12):95-102.

WEBER, R. O. 1991. Modelling fire spread through fuel beds. Progress in Energy and Combustion Science 17:67-82.

WILLIAMS, F. A. 1969. Scaling mass fires. Fire Research Abstract and Reviews 11(1):1-23.

WILLIAMS, F. A. 1982. Urban and wildland fire phenomenology. Progress in Energy and Combustion Science 8:317-354.

WILSON, R. A. 1985. Observations of extinction and marginal burning states in free burning porous fuel beds. Combustion Science and Technology 44:179-193.

WILSON, R. A. 1990. Re-examination of ROTHERMEL'S fire spread equations in no wind and no slope conditions. United States Department of Agriculture, Forest Service (Ogden, Utah). Research Paper INT-434. 13 pp..