United States Department of Agriculture

Forest 

Service

Pacific Northwest 

Research Station

General Technical Report 

PNW-GTR-891

March 

2016

Synthesis of Knowledge of 

Extreme Fire Behavior:

Volume 2 for Fire Behavior Specialists, 

Researchers, and Meteorologists

Paul A. Werth, Brian E. Potter, Martin E. Alexander, Miguel G. Cruz, 

Craig B. Clements, Mark A. Finney, Jason M. Forthofer, Scott L. Goodrick, 

Chad Hoffman, W. Matt Jolly, Sara S. McAllister, Roger D. Ottmar, and 

Russel A. Parsons 
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution 

	 	 Pacific Northwest Research Station

 P.O. Box 3890 

Portland, OR 97208-3890

Authors

Paul A. Werth is a fire weather meteorologist (retired), Northwest Interagency Coordination Center, 150 SW Harrison Street, Suite 400, Portland, OR 97201. Brian E. Potter is a 

research meteorologist, U.S. Department of Agriculture, Forest Service, Pacific Wildland 

Fire Sciences Laboratory, 400 N 34th St., Suite 201, Seattle, WA 98103. Martin E. Alexander is an adjunct professor of wildland management, Department of Renewable Resources 

and Alberta School of Forest Science and Management, University of Alberta, Edmonton, 

AB T6G 2H1, Canada. Craig B. Clements is an assistant professor of meteorology, San 

Jose State University, San Jose, CA 95192. Miguel G. Cruz is a senior research scientist, 

Bush Fire Dynamics and Applications, CSIRO Ecosystems Sciences—Climate Adaptation 

Flagship, GPO Box 284, Canberra, ACT 2601, Australia. Mark A. Finney is a research forester, Jason M. Forthofer is a mechanical engineer, W. Matt Jolly is an ecologist, Sara S. 

McAllister is a research mechanical engineer, and Russel A. Parsons is a research ecologist, 5775 US West Highway 10, Missoula, MT 59808. Scott L. Goodrick is a research meteorologist, U.S. Department of Agriculture, Forest Service, Southern Research Station, 320 

Green Street, Athens, GA 30602. Chad Hoffman is an instructor, Wildland Fire Program, 

College of Natural Resources, University of Idaho, Moscow, ID 83844. Roger D. Ottmar

is a research forester, U.S. Department of Agriculture, Forest Service, Pacific Wildland Fire 

Sciences Laboratory, 400 N 34th St., Suite 201, Seattle, WA 98103. (Werth and Potter were 

lead co-authors on this project. Other authors are listed alphabetically per location.)

Funding for the printing of this volume was provided by the Joint Fire Science Program 

Project 09-5-03-1.

The use of trade or firm names in this publication is for reader information and does not 

imply endorcement by the U.S. Department of Agriculture of any product or service.

A SUMMARY OF

KNOWLEDGE FROM THE
Abstract

Werth, Paul A.; Potter, Brian E.; Alexander, Martin E.; Clements, Craig B.; Cruz, 

Miguel G.; Finney, Mark A.; Forthofer, Jason M.; Goodrick, Scott L.; Hoffman, 

Chad; Jolly, W. Matt; McAllister, Sara S.; Ottmar, Roger D.; Parsons, Russell 

A. 2016. Synthesis of knowledge of extreme fire behavior: volume 2 for fire behavior 

specialists, researchers, and meteorologists. Gen. Tech. Rep. PNW-GTR-891. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 258 p.

The National Wildfire Coordinating Group’s definition of extreme fire behavior indicates 

a level of fire behavior characteristics that ordinarily precludes methods of direct control 

action. One or more of the following is usually involved: high rate of spread, prolific crowning/spotting, presence of fire whirls, and strong convection column. Predictability is difficult as such fires often influence their environment to some degree and behave erratically, 

sometimes dangerously. Alternate terms include “blow up” and “fire storm.” Fire managers 

examining fires over the last 100 years have come to understand many of the factors necessary for extreme fire behavior development. This effort produced guidelines included in 

current firefighter training, which presents the current methods of predicting extreme fire 

behavior by using the crown fire model, which is based on the environmental influences of 

weather, fuels, and topography.

Current training does not include the full extent of scientific understanding nor does 

it include the most recent scientific knowledge. National Fire Plan funds and the Joint Fire 

Science Program have sponsored newer research related to wind profiles’ influence on fire 

behavior, plume growth, crown fires, fire dynamics in live fuels, and conditions associated 

with vortex development. Of significant concern is that characteristic features of extreme 

fire behavior depend on conditions undetectable on the ground, namely invisible properties 

such as wind shear or atmospheric stability.

No one completely understands all the factors contributing to extreme fire behavior 

because of gaps in our knowledge. These gaps, as well as the limitations as to when various 

models or indices apply should be noted to avoid application where they are not appropriate or warranted. This synthesis summarizes existing extreme fire behavior knowledge. It 

consists of two volumes. Volume 1 is for fire managers, firefighters, and others in the fire 

community who are not experts or specialists in fire behavior but need to understand the 

basics of extreme fire behavior. Volume 2 is more technical and is intended for fire behaviorists and fire researchers.
ii

The objective of this project is to synthesize existing extreme fire behavior knowledge 

in a way that connects the weather, fuel, and topographic factors that contribute to development of extreme fire behavior. This synthesis focuses on the state of the science but also 

considers how that science is currently presented to the fire management community, 

including incident commanders, fire behavior analysts, incident meteorologists, National 

Weather Service office forecasters, and firefighters. The synthesis seeks to delineate the 

known, the unknown, and areas of research with the greatest potential impact on firefighter 

protection.

Keywords: Extreme fire behavior, fuels, fire behavior.
iii

Preface

In 2008, the National Wildfire Coordinating Group (NWCG) Fire Behavior Committee (FBC) asked the Joint Fire Science Program (JFSP) to fund a synthesis and 

review of the scientific literature pertaining to extreme fire behavior. In September 

2008, the JFSP announced a call for proposals that included a request for “an 

examination of the state of the science underlying predictions of extreme fire behavior, and an assessment of the appropriate uses and limits of this information.” This 

document is the result of that request.

In performing the review, it became progressively clearer that the concept of 

extreme fire behavior is vaguely defined and means something different to everyone. The authors examined the official NWCG definition and solicited input from 

the management community to develop a definition that was both operationally 

useful and scientifically tractable. This definition and the initial stages of the review 

eventually led to the recognition that some relevant topics had not been included 

in the original outline. Other topics from the original outline expanded to include 

sections of their own.

The authors communicated these changes to both the JFSP and the FBC as 

they arose. In those conversations, it became apparent that these two groups had 

different needs. The JFSP needed something for fire managers and others without 

the technical background of a fire behavior analyst. The FBC needed a document 

for fire behavior analysts that would allow them to better understand the use and 

limitations of the tools they now have and may have in the near future. To meet 

these two needs, this review has two parts. Volume 1 summarizes the state of the 

science for fire managers and firefighters with pertinent references to scientific 

papers. It is intended to be of use to anyone who works at or near the fire line. 

Volume 2 covers the same topics (with one exception) in more detail and includes 

information necessary for fire behavior analysts to understand what is scientifically 

known, what science lies behind the tools they have, and what the limitations are on 

scientific knowledge and tools. It includes more references to scientific literature. 

The one difference in topical content between the volumes is that volume 2 includes 

a chapter on fuel dynamics. As the study progressed, the scope of this topic led to 

the need to include more experts, and the short time available precluded that section 

from publication in volume 1.

Summary

A working definition of extreme fire behavior was necessary to develop this synthesis. Because the subjective nature of the National Wildfire Coordination Group’s 

definition of extreme fire behavior makes it intractable for scientific purposes, the 
lead authors asked the fire behavior community for input on possible definitions 

of extreme fire behavior and examples of phenomena they considered extreme fire 

behavior. The only coherent theme was that extreme fire behavior is not steady 

state. After discussing responses, the authors agreed on the following working 

definition of extreme fire behavior: “Fire spread other than steady surface spread, 

especially when it involves rapid increases.” This definition does not emphasize any 

one element of the behavior triangle.

The state of the science at present can be summed up as follows:

• Fire is three dimensional and is not steady state.

• The tools available to us today are two dimensional and are predominantly steady state.

•	 Additional research into extreme fire behavior may one day result in

development of three-dimensional tools.

Complexity

It is imperative that fire managers understand that much extreme fire behavior 

happens where it cannot be seen. Multiple factors come into play, and not all factors 

need be present for extreme fire behavior to occur, nor must one factor be present 

in every case. Extreme fire behavior can occur on any scale, great or small, in any 

fuel type, and at any time of the day or night. At no time or under any circumstance 

should fire managers assume that extreme fire behavior will not occur.

A number of interactions among the elements were noted previously; however, 

it should be noted that the number of possible interactions between elements are 

practically unlimited, making research and the resulting tool development a key 

step in achieving successful forest management and safety. The state of the science 

at present can be summed up as follows: 

• Fire is three dimensional and is not steady state.

• The tools available to us today are two dimensional and are predominantly 

steady state.

• Additional research into extreme fire behavior may one day result in development of three-dimensional tools.

Overarching Gaps

The authors of this synthesis have identified areas in each chapter where understanding of the science is lacking and more research is needed. These knowledge 

gaps may pertain to just one chapter’s topic but are nonetheless important areas in 
which further research would be of value to the operational community. However, 

there are also certain overarching gaps where additional research of one element 

would advance the science for other elements as well:

• A greater recognition of the importance of plume dynamics to extreme fire 

behavior and spotting.

• Advances in the understanding of fuel dynamics and structure, especially 

fuel moisture dynamics and the importance of fuel heterogeneity as it relates 

to fire intensity, ember production, and crown fire.

• Better high-resolution observations of windflow in complex terrain to improve wind models used in fire behavior and spotting tools, and to identify 

fire whirl potential (e.g., upper air soundings on project-size fires).

• The influence of ambient winds or topography on fire interactions.

• Research to quantify the effects of atmospheric stability on fire behavior and 

move beyond the Haines Index.

New and expanded research into these areas will increase the understanding of 

the science on which they are based and is a necessary starting point for enhanced 

wildland fire management and advances in firefighter training and safety.

Operational Implications

Even the most advanced tools and models are limited by their design and assumptions. 

They can never, nor should they be expected to, take the place of direct observations one 

makes on the fireline—the “L” in LCES (Lookouts-Communications-Escape Routes-Safety 

Zones) and the concept of ”situational awareness.” Scientifically sound application of tools 

and models requires that the tools or models be used within their design limitations and in 

accordance with the tool assumptions.

Research can lead to development of additional or improved tools to help fire managers 

better identify those situations where extreme fire behavior may occur. The lack of a tool 

or model for a situation seen in the field does not mean extreme fire behavior cannot occur. 

Current training identifies circumstances that can result in extreme fire behavior, where 

increased awareness of multiple factors can guide fire managers to make decisions. Knowing what conditions can lead to extreme fire behavior, and knowing that you do not know 

whether those conditions exist, can be more important than any tools or models. Extreme 

fire behavior can occur on any fire.
Acknowledgements

The Extreme Fire Behavior synthesis (volumes 1 and 2) was funded by the Joint Fire Science Program (JFSP) Project Number 09-2-01-11 and a contribution from Project Number 

09-S-03-1. In addition to the funds contributed by JFSP, a considerable amount of time and 

effort was provided by the authors and their respective agencies in the form of “in kind” 

funding. The following individuals reviewed the entire volume 2 manuscript and provided 

insightful comments: Jason Loomis (National Park Service, Fire Management Analyst, 

Northwest Geographic Area Coordination Center) and Ross Wilmore (U.S. Forest Service, 

East Zone Fire Management Officer, Upper Colorado River Interagency Fire Management 

Unit) 

Each chapter was also peer reviewed, and we wish to acknowledge those individuals 

for their comments that significantly improved the document. Listed below are the chapter 

reviewers:

• Effects of Complex Terrain—Tom Rolinski (Fire Weather Program Manager, 

Southern California Geographic Area Coordination Center) and Kenn McCarty 

(California Department of Forestry and Fire Protection).

• Critical Fire Weather Patterns—Rick Ochoa (National Interagency Fire Center 

Predictive Services, retired), John Saltenberger (Fire Weather Services Program 

Manager, Northwest Geographic Area Coordination Center), and Nick Nimchuk 

(Fire Weather Section Head, Alberta Sustainable Resources, Alberta Canada).

• The Role of Fuels in Extreme Fire Behavior—Jason Loomis (National Park Serice, 

Fire Management Analyst, Northwest Geographic Area Coordination Center)

• Fire Interactions and Mass Fires—Two anonymous reviewers.

• Column/Plume Dynamics and Spot Fires—Nicholas Gellie (Australian Fire 

Behavior Analyst) and Marty Alexander (Alberta School of Forest Science and 

Management, University of Alberta, Edmonton, Canada). 

• Vortices and Wildland Fire—Don Haines and two anonymous reviewers.

• Crown Fire—Dave Thomas (U.S. Forest Service, retired), Phil Omi (Professor 

Emeritus, Colorado State University), Mike Jenkins and Wesley Page (Utah State 

University), Chad Hoffman (University of Idaho), John Kern (Florida Division of 

Forestry), David Weise (U.S. Forest Service), Nicole Vaillant (U.S. Forest Service), 

Bill Bereska (Alberta Sustainable Resource Development, retired), Terry Van Nest 

(Alberta Sustainable Resource Development, retired), Bill Droog (Ontario Ministry 

of Natural Resources), Steve Jakala (U.S. Fish and Wildlife Service), Brad Armitage 

(Ember Research Services Ltd.), Charley Martin (U.S. Geological Survey), Sonny 

Stiger (FireSafe Montana), and Mark Heathcott (formerly Parks Canada). The support of Mike Hilbruner (U.S. Forest Service, retired) is sincerely appreciated.

University, C. Hoffman (University of Idaho) and J. Kern (Florida Division of Forestry).

The authors also thank Victoria Werth for her help in writing the final document

summary, and for editing and formatting the manuscript in preparation for publication.
Contents

1 Chapter 1: Introduction

Brian E. Potter and Paul A. Werth

1 Definition

4 Methods

4 Literature Cited

5 Chapter 2: Effects of Complex Terrain on Extreme Fire Behavior

Craig B. Clements

5 Introduction

5 Wind Systems in Mountainous Terrain

5 Dynamically Driven Winds

10 Thermally Driven Winds

17 Wind Modeling Tools: WindNinja

18 Summary

19 Future Needs

19 Literature Cited

25 Chapter 3: Critical Fire Weather Patterns

Paul A. Werth

25 Introduction

25 Weather Elements That Promote Extreme Fire Behavior

27 Critical Fire Weather Patterns

29 Regional Critical Fire Weather Patterns

30 Northern Plains, Great Lakes, and the Northeastern United States

31 Southeastern United States

32 Southwestern United States

35 Rocky Mountain and Intermountain Regions

39 Pacific Northwest Region

41 California Region

43 Alaska Region

43 Canada

47 Australia

48 Models and Predictive Tools

49 The National Wildland Significant Fire Potential Outlook

49 GACC 7-Day Significant Fire Potential

49 Fuel and Fire Behavior Advisories

49 Other GACC Products and Services

49 Fire Weather Watches and Red Flag Warnings

49 Spot Weather Forecasts/Digital Web Services

50 Summary/Knowledge Gaps

50 Literature Cited
55 Chapter 4: The Role of Fuels in Extreme Fire Behavior

Russell Parsons, W. Matt Jolly, Chad Hoffman, and Roger Ottmar

55 Introduction

55 Key Aspects of Fuels: the Fuels Pentagon

56 Scaling From Particles to Landscapes

56 Chemistry

59 Geometry

59 Quantity

60 Density

60 Continuity 

61 Landscape-Scale Fuels and Extreme Fire Behavior

62 Landscape Fuel Distribution Patterns

63 Natural Fuel Changes

66 Anthropogenic (Human-Caused) Changes in Fuels

68 Climate Change and Other Agents of Fuel Change

70 Fuels Management Options That Reduce Extreme Fire Behavior Potential

70 Reduction of Surface Fuels

71 Increasing the Height to Live Crown

71 Decreasing Crown Density

71 Retaining Large, Fire-Resistant Trees

71	 Negative Influences of Fuels Treatments

71 Temporal- and Spatial-Scale Considerations for Hazardous Fuels Management

72 Future Directions

73 New Developments in Fuels Mapping 

73 New Developments in Fire Behavior Modeling 

73 Concluding Remarks

75 Literature Cited

83 Chapter 5: Fire Interactions and Mass Fires

Mark A. Finney and Sara S. McAllister

83 Introduction

84 Background: Time-Dependent Fire Behaviors

84 Fire Acceleration

85 Length of Fire Front

86 Flame Tilt

87 Spread Thresholds

87 Conditions Where Fire Interactions Occur

88 Specific Effects of Fire Interaction

88 Burning Rate

90 Flame Dimensions

92 Flame Temperatures and Pollutants

92 Indraft Velocity
95 Pulsation

96 Convection Column

97 Summary of Interaction Effects

97 Needs for Further Research and Application

98 Literature Cited

105 Chapter 6: Column/Plume Dynamics

Brian E. Potter

105 Introduction

106 Plume-Dominated and Wind-Driven Fires

109 Adverse Wind Profiles and Low-Level Jets

110 Stability and Instability

111 Downbursts and Plume Collapse

115 Summary

115 Literature Cited

119 Chapter 7: Spot Fires

Brian E. Potter

119 Introduction

119 The Spotting Process

121 Ember Generation

122 Ember Combustion and Fall Speed

125 Lofting and Transport of Embers

130 Ignition

130 Probability of Ignition

131 Ignition Delay Time

132 Spot Fire Prediction Tools

135 Knowledge Gaps

136 Literature Cited

141 Chapter 8: Vortices and Wildland Fire

Jason M. Forthofer and Scott L. Goodrick

141 Introduction

141 Vorticity Basics

142 Fire Whirls

143 Fire Whirl Physics

149 Fire Whirls in the Real World: Common Features

151 Horizontal Vortices

152 Transverse Vortices

152 Longitudinal Vortices

155 Tree Crown Streets

155 Summary

157 Literature Cited
163 Chapter 9: Crown Fire Dynamics in Conifer Forests

M.E. Alexander and M.G. Cruz

163 Introduction

169 Types of Crown Fires

172 Crown Fire Initiation

182 Crown Fire Propagation

183 Crown Fire Rate of Spread

191 Crown Fire Intensity and Flame Zone Characteristics

194 Crown Fire Area and Perimeter Growth

197 Crown Fire Spotting Activity

199 Models, Systems, and Other Decision Aids for Predicting Crown Fire Behavior

199 Rothermel Guide to Predicting Size and Behavior of Crown Fires

202 U.S. Fire Modeling System

205 Candian Forest Fire Behavior Prediction System

210 Crown Fire Initiation and Spread (CFIS) System

211 Some Other Empirically Based Approaches

216 Physics-Based Models

218 Example of a Practical Application of Linking Empirical and Physically Based Models

218 Implications for Fire and Fuel Management

224 Future Outlook 

226 Literature Cited

257 Acknowledgments

257 Common and Scientific Names

258 Unit Conversion Factors
1

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 1: Introduction

Brian E. Potter1 and Paul A. Werth2

The idea of “extreme fire behavior” is commonplace in 

the U.S. wildland fire community. It goes back, arguably, 

to the 1950s and the idea of a “blow-up” fire presented by 

George Byram. Byram (1954) listed the terms “blow up,” 

“conflagration,” and “erratic” as descriptors of “unusual 

high-intensity fires” (Box 1-1). He also used the phrase 

“extreme fire behavior” in both his 1954 paper and in his 

chapters in Davis (1959). Larger fires may be more likely to 

display these characteristics, he noted, but they can occur 

on a fire of any size. Since then, the concept and terms have 

become widely used.

In spite of this widespread use and implied understanding of what constitutes extreme fire behavior, there is no 

documented, critical examination of the types of fire behavior people consider “extreme.” Furthermore, whereas there 

is little question that the behavior labeled as extreme fire 

behavior by observers occurs, there are numerous explanations for that behavior that are now conventional wisdom, 

yet without any scientific support—the phenomenon is 

rarely in question, but the explanation may be. Actions 

based on incorrect explanations of extreme fire behavior can 

result in death or injury.

The primary goal of this synthesis is to summarize 

what is known scientifically about matters considered 

extreme fire behavior. The summary is presented to provide 

the most value possible to the operational fire management community. Research papers, although increasingly 

available to everyone, are not necessarily understandable 

by everyone. They contain substantial jargon and math 

and may only summarize their findings in terms of basic 

science. This synthesis distills the scientific information and 

provides references to the research papers. Note that science 

is a process of proposing possible explanations, and subsequently ruling out those explanations that contradict 

1 Brian E. Potter is a research meteorologist, U.S. Department 

of Agriculture, Forest Service, Pacific Wildland Fire Sciences 

Laboratory, 400 N 34th St., Suite 201, Seattle, WA 98103. 

2 Paul A. Werth is a fire weather meteorologist (retired), Northwest

Interagency Coordination Center, 150 SW Harrison Street, Suite 400, 

Portland, OR 97201. 

evidence. It is easy to propose explanations, but proving 

them wrong can be easy or difficult. An explanation that 

is repeatedly tested, compared to observations, and never 

contradicted is not necessarily true, but the more it is tested, 

the more confidence scientists have that it may be.

In the case of extreme fire behavior, hard, reliable data 

are rare, making it very difficult to confidently refute a proposed explanation. Rather, it is much more common to be 

able to cite scientific reasons for greater or lesser confidence 

in the proposed explanation. In this synthesis and review, 

the authors hope to present what hard evidence there is, 

and, when there is none, to provide an understanding of the 

strong and weak points in a given explanation.

Definition

A working definition was necessary to begin and to execute 

the synthesis. Without it, the task of gathering and summarizing would be unbounded and impossible to complete. 

There is no single scientific paper that lays out a scientific 

definition of extreme fire behavior. The only official or specific definition of extreme fire behavior is established by the 

National Wildfire Coordination Group (NWCG) glossary of 

wildland fire terminology: 

“Extreme” implies a level of fire behavior characteristics that ordinarily precludes methods of 

direct control action. One or more of the following 

is usually involved: high rate of spread, prolific 

crowning and/or spotting, presence of fire whirls, 

strong convection column. Predictability is difficult 

because such fires often exercise some degree of 

influence on their environment and behave erratically, sometimes dangerously.

Of the five properties “usually involved,” four are 

subjectively “high,” “prolific,” or “strong.” This makes the 

definition intractable for scientific purposes. Furthermore, 

the definition implies an inability to control in order to designate the fire behavior as “extreme.” This makes extreme 

fire behavior a function of control success or failure, not an 

objective, physical process.
2

GENERAL TECHNICAL REPORT PNW-GTR-891

Box 1-1

Byram’s (1954) Facts of Extreme Fire Behavior

1. Most severe fires and a considerable number of blowups occur during the middle of the 

afternoon on sunny days. On such days, the 

atmosphere is often turbulent and unstable to 

a height of several thousand feet. However, 

some of the worst forest conflagrations in the

United States have occurred either at night or 

reached to the peak of their intensity at night 

(usually between sundown and midnight). At 

this time, the lower layers of the atmosphere 

(up to 500 ft or more) are usually stable.

2. Some of the worst western fires in the past

15 years have been in rough country, which 

might indicate that topography is a dominating factor. On the other hand, there have 

been conflagrations, such as those that occurred in the Lake States many years ago, 

which burned in nearly flat or rolling country.

Some of the conflagrations have been compared to “tornadoes of fires.”

3. An intense fire may occasionally spread

rapidly across slope or downslope at night in 

the general direction of the cool downslope 

winds. Yet this same rapid downslope spread 

may happen in the middle of the afternoon 

when the surface winds, if any, would be 

upslope. Fires have travelled across drainages (upslope and downslope) as though these 

did not exist.

4. Turbulence in the atmosphere seems to be 

closely related to extreme fire behavior; yet

on a large proportion of warm, sunny days, 

the atmosphere is unstable. Often, fires do

not build up to extreme intensity on such 

days.

5. Many intense fires have been accompanied

by high winds; but some of the most dangerous and erratic fires have burned when the

windspeed was not especially high.

6. High temperatures and low relative humidity accompany a large proportion of severe 

fires, but some of the most intense and

rapid-spreading fires have burned when the

temperature was low and falling. The fires in

the East and Southeast in the fall of 1952 are 

examples.

7. Prolonged periods of drought and dry weather 

show a strong correlation with intense hot 

fires, but the Brasstown Fire in South Carolina

in March 1953 burned only a week after nearly 

2 in of rain had fallen on ground well charged 

with winter rainfall. However, both burning index and buildup index were high on this day.

8. The amount of fuel available to a fire is an

important factor in its behavior. At times, the 

effect of an increase in quantity of fuel on fire

intensity appears to be considerably greater 

than would be expected from the actual fuel 

increase itself. For example, doubling the 

amount of fuel might increase the apparent 

intensity four or five times.

9. Arrangement as well as quantity of fuel is 

important. Extreme fire behavior seems most

likely to occur in dense conifer stands. Intense 

fires also build up in stands of evergreen

brush, and in the South can readily cross 

swamps if the brush is dense enough.

10. On those fires to which one would be most

likely to apply the term “blowup” (owing to

the sudden and often unexpected buildup of 

turbulent energy), there is an obvious and 

well-developed convection column that may 

extend high into the atmosphere.

11. Large fires exhibiting extreme behavior have

been known to put up convection columns to 

a height of 25,000 ft or more. Since about 70 

percent of the total air mass is below the tops 

of such convection columns, these fires have

literally pierced the atmosphere. They are 

volume phenomena and have storm characteristics like certain other disturbances in the 

atmosphere. This in part seems to explain 

why they do not conform to the “rules” of fire

behavior. These “rules” are based on the farmore-frequent ordinary fire, which is pretty

much a surface phenomenon.
3

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

At the initiation of this project, the lead authors asked 

the fire behavior community for input on possible definitions of extreme fire behavior and examples of phenomena 

they considered extreme fire behavior, whether those 

examples matched the NWCG definition or not. Several 

people responded—mostly with examples—either via email 

or through MyFireCommunity.net, and the authors used that 

feedback in their initial discussion of the working definition. 

The phenomena listed in these responses included:

• Mass ignition.

• Actual plume dominance.

• Rapid exponential growth of spot fires.

• Spotting distances in miles.

• Things that just made me go, “Huh ... didn’t

expect that.”

• Fire activity that has that momentum feedback character, like Jimi Hendrix putting the guitar up to the 

amp, and it just builds and builds feeding back on 

itself.

• When the fire and convection column induce high 

levels of turbulence into the wind field; when the 

momentum flow into the convection column is of the 

same order of magnitude as the momentum in the 

wind field.

• Very rapid fire spread.

• Three-dimensional fire.

• Fire behavior in which large changes take place

rapidly.

• Flame attachment (the laying over and direct contact 

of flame with new fuels when there are steep slopes 

and string winds).

The responses made it quite clear that operational users 

had thought about extreme fire behavior well beyond any 

formal definition. They also recognized the difficulty of creating a precise definition that could be applied predictively, 

or a definition more concrete than “I know it when I see it.”

After reviewing and discussing practitioner responses, 

the authors felt that there were too many individual phenomena considered extreme fire behavior for a definition 

to include any sort of list. Furthermore, most tractable 

definitions included some level of subjectivity. In the end, 

the agreed definition for this project was:

Fire spread other than steady surface spread, especially when it involves rapid increases.

This definition includes most or all of the phenomena 

listed above, although admittedly indirectly in some cases. 

It includes some subjectivity, as “rapid” can be a matter of 

opinion. However, this is not the core of the definition—it 

is included to emphasize the safety and operational importance of increasing spread as opposed to decreasing or 

unusually slow spread. Furthermore, whereas the NWCG 

definition heavily leans toward atmospheric conditions and 

may underrepresent the importance of fuels and topography, 

this definition does not emphasize any one element of the 

behavior triangle (fig. 1-1).

This definition has other shortcomings important to 

bear in mind. The authors of the fuels chapter added for 

volume 2 (this volume) expressed two main concerns over 

the working definition. To a degree, these concerns were 

part of the original discussion. They are worth noting here 

to further emphasize the challenges of defining extreme fire 

behavior.

First, the working definition lacks a basis for evaluating 

the degree of extremity, which would be useful from an 

analytical perspective and would tend to lead towards more 

Figure 1-1—The fire behavior triangle.
4

GENERAL TECHNICAL REPORT PNW-GTR-891

specific scientific investigations regarding the nature of extreme fire behavior. For example, earthquake magnitude is 

characterized with the Richter scale, a base-10 logarithmic 

scale obtained by calculating the logarithm of the amplitude 

of waves measured by a seismograph (Richter 1935). This 

metric provides useful insights regarding the nature of the 

phenomenon and associated aspects across a wide range

of scales (Bak et al. 2002, Rundle 1989). No such metric

has yet been developed for wildland fire, but such a measure would be of great utility in characterizing and better 

understanding extreme fire behavior. 

Second, this definition ignores the context within which 

extreme fire behavior occurs. Despite its simplicity, the fire 

behavior triangle reminds us that several factors, including 

fuels, weather, and topography, interact to influence how a 

fire burns. For a given set of fuel conditions, fire behavior 

can range from none (where the fire fails to ignite) to a very 

active, high-energy fire. The other sides of the triangle 

constitute constraints on the magnitude of the role of fuels 

in influencing fire behavior. In many ecosystems, such as 

subalpine forests, fires may occur rarely, but, when they do, 

it is under extreme weather conditions, often on steep slopes, 

and consequently they burn intensely. In such cases, fire 

behavior is much more influenced by weather and topography 

than by the fuel, and can be relatively insensitive to fuel 

conditions (Bessie and Johnson 1995). If all fires that burn in 

a given ecosystem are, by definition, extreme, the concept is 

not of particular use in that ecosystem. Different ecosystems 

have very different fire regimes; fire behavior that is extreme 

in one ecosystem may be commonplace in another. 

All fire behavior must be considered within the context 

of fuels, weather, and topography, and none of these factors 

can be omitted when framing perceptions of extreme fire 

behavior. An ideal definition of extreme fire behavior would 

account for this and enable us to focus better on the particular aspects of each component of the fire behavior triangle 

and their contribution to extreme fire behavior.

Methods

The authors divided the work of synthesis and review based 

on expertise. The division was necessary to the synthesis, 

but it is also artificial, and the various sections overlap 

substantially. Many areas of overlap are explicitly noted, 

and readers will undoubtedly see other areas.

The review incorporated three primary sources of 

information. First and foremost was the peer-reviewed 

scientific literature. This is the most authoritative source 

of information to support or refute any explanation of 

what causes extreme fire behavior. Second was feedback 

from and interaction with practitioners. The project web 

site allowed reader comments and discussion, and, when 

appropriate, these guided the review. The third source was 

documents that are not peer reviewed—often referred to 

as “grey literature.” Peer review was the exception to the 

rule for many years in the field of forest fire research, so 

there is an extensive body of literature that was not peer 

reviewed. The problem with grey literature is that it has not 

been tested or widely available, so the scientific rigor of its 

content is unknown. It can, however, provide insight and 

information, and the authors did not want to ignore it.

Literature Cited

Bak, P.; Christensen, K.; Danon, L.; Scanlon, T. 2002.

Unfied scaling law for earthquakes. Physical Review Letters. 

88: 178051.

Bessie, W.C.; Johnson, E.A. 1995. The relative importance 

of fuels and weather on fire behavior in subalpine forest. 

Ecology. 76: 747–762.

Byram, G.M. 1954. Atmospheric conditions related to blowup 

fires. Sta. Pap. 35. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southeastern Forest Experiment 

Station. 34 p.

Davis, K.P. 1959. Forest fire: control and use. New York: 

McGraw-Hill. 584 p.

Richter, C.F. 1935. An instrumental earthquake magnitude 

scale. Bulletin of the Seismologists Society of America. 25-1.

Rundle, J.B. 1989. Derivation of the complete GutenbergRichter magnitude-frequency relation using the principle 

of scale invariance. Journal Geophysical Research. 94: 

12337–012312, 12342 

National Wildfire Coordinating Group. Glossary of wildland 

fire terminology. http://www.nwcg.gov/pms/pubs/glossary/

index.htm. (June 1, 2011).
5

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Introduction

Atmospheric processes in regions of complex terrain have 

received considerable interest in the research community for 

decades. Traditionally, the term “complex terrain” has been 

used to differentiate mountainous terrain from relatively 

flat and simple terrain. Research in mountain meteorology 

has its foundation in the Alps, and our present understanding of mountain circulations and the mountain atmosphere 

in general came from the early observational studies of 

Wagner (1938), Ekhart (1944), and Defant (1949).

The mountain meteorology research community most 

likely adopted the term “complex terrain” from the Atmospheric Studies in Complex Terrain (the ASCOT program), 

which focused on observational campaigns of thermally 

driven circulations in valleys and, in particular, Colorado’s 

Brush Creek Valley (Whiteman 1990). 

A new classification of mountainous terrain by

Meybeck et al. (2001) provided 15 relief patterns based on 

relief roughness and elevation. Relief roughness is defined 

as the difference between maximum and minimum elevation divided by half the length of cell used in the elevation 

data set (e.g., digital elevation model [DEM]). This terrain 

parameter is similar to the average slope typical of terrain 

classifications. Although Meybeck et al. determined many 

terrain types, they did not define any as complex terrain. 

Meybeck et al. classified mountains as terrain with elevations higher than 500 m and relief roughness greater than 

20 percent. One problem with this classification is that 

high plateaus are not mountains. Major river valleys can 

be incised into a high plateau such as the Grand Canyon. 

Although this is not “mountainous terrain,” it is complex. 

Most applicable to meteorological use of the term 

“complex terrain” is when defining the effect that land 

shape or topography has on meteorological measurements 

(Brode et al. 1987). These terrain effects include aerodynamic wakes, density-driven slope flows, channeling effects 

1 Craig B. Clements is an assistant professor of meteorology, San 

Jose State University, San Jose, CA 95192.

Chapter 2: Effects of Complex Terrain on Extreme Fire Behavior

Craig B. Clements1

of upper level winds, and flow accelerations over the crest 

of mountain ridges. These flows affect wind speed and wind 

direction measurements made in mountainous regions. 

For fire behavior applications, the term “complex

terrain” is used to describe regions of relative relief and,

in most cases, mountain topography. 

Wind Systems in Mountainous Terrain

Wind systems in mountainous terrain can be classified 

into two main types based on their forcing mechanisms: 

dynamically driven and thermally driven winds. Although 

thermally driven circulations occur more regularly in mountain terrain and are commonly experienced by hikers and 

climbers during fair weather conditions, it is the dynamically driven winds that can play a larger role in producing 

extreme fire behavior owing to their generally stronger surface wind velocities. However, thermally driven circulations 

are subject to diurnal transition periods where atmospheric 

stability changes twice daily, potentially leading to extreme 

changes in observed fire behavior. This chapter will review 

the main mesoscale and local-scale wind systems observed 

in mountainous terrain that can potentially lead to extreme 

fire behavior. 

Dynamically Driven Winds

Dynamically driven winds are generally considered the 

strongest of the wind systems in mountainous terrain and 

include downslope windstorms such as foehn and Santa 

Ana winds, strong surface winds associated with mountain 

wave development, gap winds, and channeling of synopticscale winds. The factors that affect these terrain-forced 

winds as summarized by Whiteman (2000) are (1) the 

stability of the air approaching the mountains, (2) the speed 

of the airflow, and (3) the characteristics of the underlying 

topography or mountain barrier. 

Foehn winds—

One of the most important dynamically driven winds

affecting fire behavior in mountainous terrain is the 

Chinook or foehn wind. Foehn winds (pronounced “firn”) 
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GENERAL TECHNICAL REPORT PNW-GTR-891

are downslope wind events and are often associated with 

extreme fire behavior because of their near-surface high 

windspeeds, warm temperatures, and low relative humidities (Durran 1990, Whiteman 2000). As a foehn develops, its onset can cause rapid changes in temperature and 

humidity because of adiabatic compression as air descends 

the lee side of mountain ranges. Extreme fire behavior can 

potentially occur during nighttime at the onset of a foehn 

event; strong winds will prevent nocturnal inversions from 

forming allowing nighttime temperatures to remain warmer 

(Whiteman 2000). Foehn winds can also start and stop suddenly, called a foehn pause (Whiteman 2000). The alternating wind break-in and cessation during a foehn event can 

cause air temperatures to oscillate sharply and can thus 

affect fire behavior. The foehn pause has been associated 

with changes in upstream conditions, including stability 

and cross-barrier windspeed that cause the wavelength of 

the waves to change (Whiteman 2000), and to lifting of the 

foehn wind by other local-scale drainage flows (BaumannStanzer and Piringer 2004). 

Foehn winds are common in most mountainous regions 

around the world. In the lee of the Rocky Mountains of 

North America, they are called chinooks. The chinook 

is most prevalent in winter months when strong westerly 

winds cross the Rockies (Whiteman 2000); however, when 

the synoptic conditions are right, chinooks do occur during 

fire season (see the next chapter, “Critical Fire Weather 

Patterns”).

In northern California, foehn winds that flow from the 

Great Basin over the Sierra Nevada to the Central Valley 

are known as north winds and in the region of Yosemite 

are called Mono winds (Ruscha 1976). Even more localized 

in the San Francisco Bay Area, these winds are known as 

diablo winds. Foehn winds in the Cascade Mountains of the 

Pacific Northwest are called east winds as they blow from 

east of the Cascades and descend becoming warmer and 

drier over the west slope of the mountain range. In Utah, 

the local foehn is known as the Wasatch wind as it descends 

from the higher elevations east of the Wasatch Mountains 

to the Salt Lake Valley. A comprehensive review of foehn 

winds of the Western United States is found in Whiteman 

(2000). 

Santa Ana winds—

The most notable foehn wind associated with extreme fire 

events is the Santa Ana of southern California. High windspeeds and extreme dryness associated with these episodes 

have been characterized as causing extreme fire behavior 

during fall in southern California. Barry (2008) stated that 

the Santa Ana develops as a result of high pressure over the 

Great Basin and development of a surface low off the southern California coast. An upper level trough to the east and a 

ridge in the eastern North Pacific causes the development of 

northerly flow.

Hughes and Hall (2009) suggested that the surface 

winds associated with Santa Ana events are produced by 

two mechanisms. When strong mid-tropospheric winds 

impinge on mountaintops in a stably stratified environment, 

gravity waves transfer midlevel momentum to the surface, 

causing strong lee-side surface winds. However, Hughes 

and Hall (2009) found strong variability in Santa Ana events 

with many days exhibiting strong offshore flow and weak 

synoptic forcing. They suggested local thermodynamic 

forcing must also cause offshore surface flow. When cold air 

is trapped in the Great Basin by topography, a hydrostatic 

desert-ocean pressure gradient forms, causing a negatively 

buoyant gravity current to flow through mountain gaps at 

the surface. 

Numerical modeling results by Huang et al. (2009) 

showed that a coupling between the synoptic scale and 

mesoscale exists leading to the development of Santa Ana 

winds. The coupling effects of the synoptic scale with the 

mesoscale are classified in three stages. During stage I, 

mesoscale subsidence occurs in the exit region of the jet 

stream causing an initial surge of dry air to the surface as 

a result of moisture divergence behind a surface cold front 

that was located in the Southwestern United States. During 

stage II, anticyclonic curvature of the jet stream increases, 

and strong northeasterly winds in the jet exit region advect 

dry air toward the California coast. During stage III, the 

extremely dry mid-tropospheric air is transported to the 

boundary layer by wave breaking and strong turbulence, 

which leads to the formation of a hydraulic jump on the east 

side of the Coast Range, creating the Santa Ana winds. 
7

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Many studies have focused on the large-scale dynamics 

of Santa Ana events, but few have investigated extreme fire 

behavior associated with these events. One recent study was 

made by Maranghides and Mell (2009) who conducted a 

postincident analysis of the fire behavior that occurred during the Witch and Guejito Fires near San Diego, California, 

in 2007. Surface winds in the region were about 11 m/s with 

gusts of 15 m/s. A home weather station in the region reported a maximum windspeed of 25 m/s. Relative humidity 

dropped from 90 to 8 percent during the onset of the Santa 

Ana wind event. Spread rates during the Guejito Fire were 

estimated between 1.7 and 2.5 m/s. Spotting distances were 

estimated to be approximately 4.5 km from the Guejito Fire 

front. The surface wind measurements were limited to just a 

few sites in the region of these fires, but indicate very strong 

surface winds and rapid fire spread. Better measurements of 

fire-atmosphere interactions during Santa Ana events would 

lead to improved understanding of extreme fire behavior 

during such events.

Esperanza Fire—The Esperanza Fire occurred on 26 

October 2006 near Cabazon, California, and was an event 

where extreme fire behavior was associated with five firefighter fatalities. The extreme fire behavior was caused by 

the fire spread up a narrow canyon enhanced by flow channeling created by the onset of a Santa Ana wind (Coen and 

Riggan 2010, Esperanza Investigation Team 2006). One key 

finding (finding 29, Esperanza Investigation Team 2006) 

was that none of the fire shelters for the five firefighters who 

were killed by the burnover were deployed, indicating that 

the head fire must have accelerated as it came up the creek 

drainage and caught all firefighters by surprise, leaving 

them no time to deploy their shelters.

One of the major contributing factors was the Santa 

Ana winds coming into alignment with the “unnamed 

creek drainage” as a channeled flow, which increased the 

surface winds in the canyon. Additionally, the inversion was 

penetrated by the convection column produced by the upcanyon fire run resulting in extreme fire behavior and area 

ignition. Coen and Riggan (2010) confirmed the presence of 

strong winds that aligned with the canyon; however, these 

surface winds were a result of atmospheric gravity waves 

bringing high-momentum east-northeasterly winds to the 

surface.

Sundowner winds—

Another foehn wind that has played a major role in observed extreme fire behavior is the sundowner wind of 

Santa Barbara, California. The sundowner is a localized 

downslope wind that flows from the Santa Ynez Mountains 

down to the narrow coastal plain of Santa Barbara. The 

topography is unique, as it is a section of coastline and 

mountains that are aligned west to east. The winds are a 

result of perpendicular flow at ridgetop, typically associated with warmer and drier air near the mountaintops and 

cooler, higher humidity air at the coast. The extreme effects 

of the winds include the onset of severe wind velocities and 

abrupt warming. The abrupt observed warming is a result of 

the adiabatic descent of mid-tropospheric air to the surface 

and the replacement of cooler marine air at the coast with 

the foehn wind (Blier 1998). The sundowner name is due 

to the time of onset, typically during the later afternoon or 

evening hours (Ryan 1994). One synoptic regime associated with sundowner events includes the alignment of an 

inverted ridge off the California coast and inverted trough 

in the interior of the Great Basin allowing for northerly 

winds along the California coast (Blier 1998). Additionally, 

as with other foehn events, the presence of a stable layer 

at ridge height enhances the flow and formation of mountain waves (Blier 1998). Sundowners have been associated 

with extreme fire behavior. For example, during the Painted 

Rock Fire in June 1990, an extreme sundowner event caused 

devastating winds and fire spread rates. Additionally, 

downslope winds can cause severe downslope fire spread as 

noted by Weise and Biging (1996).

Washoe zephyr—

The eastern Sierra Nevada is associated with strong 

chinook wind events in the winter and spring (Zhong et al. 

2008a). During the summer and fall, however, the Washoe 

zephyr occurs regularly. The Washoe zephyr is a daytime, 
8

GENERAL TECHNICAL REPORT PNW-GTR-891

down-canyon wind that occurs on the lee side of the Sierra 

Nevada (Clements 1999, Zhong et al. 2008a) often initiating afternoon thunderstorms in western Nevada (Hill 1980). 

Zhong et al. (2008a) defined the Washoe zephyr as a westerly wind with a sustained windspeed greater than 7 m/s starting after noon Local Standard Time (LST). Climatology 

of the zephyr indicates that 85 percent of the time these 

events start between 1300 and 2000 LST with 70 percent 

onset between 1500 and 1800 LST. Half of the events have 

a duration of 3 to 6 h, and few events last more than 9 h (5 

percent). Although zephyr events do occur all year, they 

are most frequent during the summer months. A frequency 

of less than 10 percent was observed from November to 

February.

The characteristics of the Washoe zephyr are somewhat 

opposite of what is generally observed in mountainous 

terrain where up-valley winds dominate in the afternoon. 

The zephyr develops in late afternoon during the summer 

and fall, and blows strongly down canyon with velocities 

regularly exceeding 5 m/s. The vertical wind profile of 

the zephyr is characterized by a strong low-level jet that 

produces strong vertical wind shear (defined as the change 

in windspeed or wind direction with height) and turbulence 

(Clements 1999, Kingsmill 2000) at the surface. The strong 

and gusty nature of the zephyr lasts throughout the night 

and finally diminishes, allowing thermally driven downvalley winds to persist until morning (Clements 1999). 

The dynamics of the Washoe zephyr have often been 

questioned. Zhong et al. (2008a) showed through mesoscale 

numerical modeling and climatological analyses that the 

Washoe zephyr is driven by the cross-barrier pressure 

gradient formed in response to the thermal low of the Great 

Basin. 

One incident in the lee of the Sierra Nevada that could 

be attributed to a Washoe zephyr-like event occurred 

during the Seven Oak Fire of the Inyo Complex (California 

Department of Forestry and Fire Protection 2008). On the 

afternoon of 7 July 2007 at 1400 Pacific Daylight Time 

(PDT), a strike team was assigned to burn out an area in 

order to protect the historical Mount Whitney Fish Hatchery 

on the western side of Owens Valley near the town of Independence, California. The site was just below the eastern 

escarpment of the Sierra crest. At 1430 PDT, the wind had 

changed and caused the fire to cross the planned control 

line. It is reported that at 1445 PDT, the fire intensified and 

the winds increased and began changing directions. At 

this time, the firefighters realized they were losing control 

and retreated toward their designated safety zone. They 

deployed their shelters while waiting out the burnover in a 

small pond. The entrapment resulted in burn and respiratory injuries to all nine firefighters and the total loss of one 

engine and damage to another. The incident report indicated 

that skies were clear with no cumulus buildup. The day 

before, when the fire started, there were frequent lightning 

strikes in the higher elevations of the Sierra with strong, 

gusty and erratic winds. A 26 m/s wind gust was recorded 

by fire personnel using a Kestrel handheld anemometer. 

Daytime temperatures on July 7th ranged from 32 to 38 °C 

at 1247 PDT. Relative humidity (RH) values ranged from 

a high of 13 percent to a low of 4 percent at 1447 PDT. At 

the Oak Creek remote automated weather station (RAWS), 

a wind gust of 22 m/s also occurred at 1447 PDT. Winds in 

the afternoon were sustained 4.5 to 6.7 m/s gusting to 13 

m/s. At the time of the burnover, winds were 9 to 13 m/s out 

of the southwest. 

Although the southeastern Sierra is not usually associated with Washoe zephyr events because of the higher terrain and fewer gaps in the crest, the observed characteristics 

have some similarities to the zephyr. Southwesterly winds 

with recorded velocities of 4.5 to 6.7 m/s are similar to what 

has been observed in Lee Vining and Reno to the north. 

The onset of the stronger winds occurring between 1400 

and 1500 PDT is typical for zephyr events. However, the 

Washoe zephyr typically has a more westerly component, 

but this could possibly be effects of flow channeling along 

the foothills of the Sierra eastern escarpment as found by 

Zhong et al. (2008b).

Terrain channeling effects—

Forced channeling or pressure-driven channeling of upper level, larger scale winds can cause drastic changes in 

windspeed and direction to occur in valleys (Whiteman 

2000). These high wind events can be produced by (1) 
9

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

downward momentum transport, (2) terrain channeling, and 

(3) pressure-driven channeling (Whiteman 2000, Zhong et 

al. 2008b). For a more detailed review on terrain channeling effects in mountainous regions, please refer to Sharples 

(2009).

The downward transport of momentum occurs when 

winds within a valley are strongly coupled to winds aloft 

(Zhong et al. 2008b). For this condition to occur, there must 

be vertical mixing associated with unstable or neutral stability allowing upper level winds to penetrate to the surface. 

When winds in a valley are driven by this mechanism, they 

are expected to align with the wind direction aloft. Downward transport of momentum in valleys occurs often. 

Another channeling effect is “forced channeling,”

which occurs when strong winds aloft blow directly along 

the valley’s axis (Whiteman 2000). According to Whiteman, forced channeling is more likely to occur during the 

daytime when the valley atmosphere is usually neutral 

or unstable. It typically begins in later morning after the 

breakup of the nocturnal inversion, resulting in abrupt 

changes in windspeed and gustiness. Forced channeling is 

strongest when the pressure gradient aloft is weak in the 

along-valley direction. Upper level winds can also be channeled when they blow at oblique angles to the valley axis, 

either flowing up or down the valley. 

Thirtymile Fire—The Thirtymile Fire investigative report 

indicates that fire-induced winds were associated with the 

deaths of four firefighters who deployed at a site located 30 

m upslope from the valley floor. The analysis suggests that 

the deployment site happened to be located at a point where 

the convection column had impinged on the valley sidewall, 

causing extensive convective heat to pass over the deployment site leading to the asphyxiation of the entrapped firefighters. Although early afternoon winds in the canyon

were relatively light, strong fire-induced winds were reported to be on the order of 22 m/s at the time of deployment 

(Brown 2002).

Tree needle heatset observations made at the deployment sites (Brown 2002) indicated that the fire-induced 

winds were in the up-canyon and upslope direction, 

suggesting that the convection column was being channeled 

up the canyon rather than rising vertically from the surface. 

The fact that the convection column near the surface was 

being advected up canyon suggests that the surface winds 

were blowing through the fire-front boundary. Additionally, 

observed spread rates at this time increased and caught the 

firefighters off guard (Brown 2002). The increase in fire 

spread rate was a result of the fire running in the crowns, 

driven by the up-canyon winds. At the same time, upper 

level winds were from the southwest and in alignment with 

the canyon’s axis, providing a source for increased wind 

velocities. The upper level winds may have been mixed 

downward from aloft to the surface owing to the dynamics 

of the convection plume. The downward mixing of horizontal momentum could help explain why the fire front accelerated and caused the burnover to happen so quickly. These 

events can be surge-like and last for only a few minutes. 

Another mechanism that could have been responsible for 

the convection column impinging on the canyon sidewall 

might be strong downdrafts that exist in plumes or convection columns. These downdrafts can be responsible for the 

strong fire-induced winds that are often observed at the fire 

front and may drive fire spread (Clark et al. 1996, Clements 

et al. 2007). 

Another mechanism possibly responsible for the intense 

fire-induced winds could be a developing low pressure field. 

This may have existed in the upper elevations of the canyon 

ahead of the fire front. This type of pressure perturbation 

ahead of the fire front has been found in numerical simulations done over flat terrain (Clark et al. 1996) and observed 

over slopes with crosswinds (Clements and Heilman 2010). 

A region of low pressure develops as a result of a hydrostatic pressure gradient that forms at the base of the convection column (Clark et al. 1996). Within a canyon during 

daytime, low pressure exists owing to the solar heating of 

the canyon volume causing up-canyon winds to occur. With 

the additional heating caused by the advection of the plume 

up the canyon, acceleration in the wind field could result 

and be the cause of the extreme fire-induced winds that 

blew through the fire front advecting hot gases along the 

sidewalls of the canyon. Although these mechanisms could 

be responsible for the plume impingement on the canyon 

sidewall, none has been confirmed.
10

GENERAL TECHNICAL REPORT PNW-GTR-891

Pressure-driven channeling—

Pressure-driven channeling occurs when there exists a 

larger scale pressure gradient above the valley that is superimposed on the valley below. The direction of the winds 

in the valley depends on the along-valley component of the 

horizontal pressure gradient. Pressure-driven channeling 

causes winds to always blow along the valley axis from the 

high pressure end of the valley to the low pressure end of 

the valley (Whiteman 2000, Zhong et al. 2008b). Pressuredriven channeling is strongest when the pressure gradient is 

strongest in the along-valley direction. 

Thermally Driven Winds

Thermally driven wind systems are very common because 

they are diurnally driven (daytime vs. nighttime) and are 

probably more experienced by wildland firefighters and 

backcountry hikers. These winds include the classic valley 

and slope winds. There is a distinct diurnal structure to the 

evolution of the thermally driven flows where their direction 

typically reverses daily owing to changes in the pressure 

gradient and buoyancy. 

Two main circulations exist in the valley atmosphere: 

valley winds and slope winds. Valley winds consist of two 

diurnal regimes: up-valley wind during the daytime and 

down-valley wind at night. Slope winds consist of a similar 

diurnal structure with downslope winds occurring during nighttime periods and upslope winds during daytime 

(Ekhart 1944; Vergeiner and Dreiseitl 1987; Whiteman 

1990, 2000). The strength of thermally driven circulations is 

a function of aspect, time of day, and time of year (Whiteman 2000). Of the two wind systems, valley winds play a 

larger role in fire behavior because of their overall stronger 

velocities and horizontal extent.

Slope winds—

Slope winds are the most intermittent of the thermally 

driven flows found in mountain environments (Vergeiner 

and Dreiseitl 1987, Whiteman 1990). This is due to both 

slope length and depth. Although there have been numerous 

studies focused on downslope flows (Horst and Doran 1986, 

Mahrt 1982, Manins and Sawford 1979, Papadopoulos and 

Helmise 1999, Whiteman and Zhong 2008), limited work 

has been focused on upslope winds. Vergeiner and Dreiseitl 

suggested that this is due to their intermittency and overall 

difficulty in obtaining useful measurements. They also concluded that any field study focused on measuring upslope 

flows will “give random inconclusive results from which 

representative values of mass and heat transport in the slope 

layer cannot be derived” (Vergeiner and Dreiseitl 1987). 

Fire behavior studies on slopes and especially field 

studies are limited, and therefore it is difficult to determine 

whether or not diurnal slope flows help drive the fire along 

the slope rather than being dominantly driven by the fuels 

and the effect of radiative and convective transfer from 

the fire front to the fuels (flame attachment). However, as 

will be discussed in a later section, the interaction of slope 

winds and valley winds can create shear layers, producing 

turbulence along the slopes that can potentially lead to 

extreme fire behavior scenarios.

Upslope winds—

According to Whiteman (2000), upslope flows have depths 

of 10 to 50 m above ground level (AGL) and velocities on 

the order of 1 to 5 m/s. Upslope flows react instantly to 

changes in insolation and begin immediately after sunrise 

(Vergeiner and Dreiseitl 1987). Two main forcing mechanisms drive the flow upslope: the pressure gradient force 

and the buoyancy force (Atkinson 1981). The air over a 

slope is heated by the sunlit ground causing an air parcel 

adjacent to the slope to have a higher potential temperature and lower density than air at the same altitude, but 

away from the slope. It is this temperature perturbation 

that drives the pressure gradient to force air toward the 

slope from the center of the valley (at the same altitude). 

Buoyancy drives the air parcel vertically above the slope, 

and the sum of both buoyancy and the horizontal pressure 

gradient causes the air parcel to accelerate up the slope 

while being replaced by air from over the valley center. This 

is the classic upslope circulation during ideal, fair weather 

conditions and is responsible for transporting heat and mass 

to the valley atmosphere (Vergeiner and Dreiseitl 1987). 

One of the more recent observations of upslope flows 

was made by Reuten et al. (2005), who observed upslope 
11

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

flows at the foot of a mountain range with a slope angle of 

19° and a ridge height of 780 m above sea level (ASL) in 

coastal British Columbia. Their observations indicate that 

the daytime upslope flows were strong with velocities up 

to 6 m/s and occurred over a depth of nearly 500 m AGL. 

Equally strong and deep return circulations occurred within 

the convective boundary layer (CBL). The transport of mass 

of the upslope flow and return flow approximately balanced 

during the morning period suggesting a closed-cell slope 

flow circulation within the boundary layer. This is the first 

observational evidence of the closed slope flow circulation. 

The intermittency of daytime upslope flows may 

influence the upslope fire behavior by possibly increasing 

upslope rate of spread (ROS) at random intervals. However, 

this influence is more likely limited owing to the weak 

nature of upslope velocities. Valley winds may have a larger 

impact on fire behavior on slopes owing to the cross-slope 

wind component of valley winds. As a valley wind develops, it can overcome the slope wind layer along the slope 

and create a cross-slope flow (Whiteman 2000). Fire spread 

will be upslope, but depending on the strength of the valley 

wind can likely be reduced and spread laterally along the 

slope. Synoptically forced winds that penetrate the valley 

atmosphere would intensify this effect. 

Fire behavior on sloped terrain—

Slope-driven fire spread has been studied for decades because many wildfires occur in regions of mountainous terrain, and fire spread on slopes is associated with increased 

acceleration leading to extreme fire behavior (Cheney and 

Sullivan 2008). Often the effect of slope on fire behavior 

can be visualized by the smoke column where smoke is entrained up and along the slope rather than producing a vertical column above the flame front (fig. 2-1). This observation 

suggests that the effect of terrain is more pronounced than 

that of wind. Understanding of fire behavior on slopes is derived mostly from laboratory-scale experiments conducted 

in wind tunnels (e.g., Weise and Biging 1996, Viegas 2005); 

however, recently a number of numerical simulations have 

been conducted (Linn et al. 2010). The effect of slope has 

been viewed as an added component of wind velocity since 

1946 (Weise and Biging 1997). There have been attempts to 

determine both the separate and combined effects of wind 

velocity and slope angle on spread rate and flame length 

(Weise and Biging 1997). Results from Weise and Biging 

indicate that as slope and wind velocity increase, fire behavior, including flame length and spread rate, increases significantly as compared to no-wind and downslope conditions. 

Backing fires on slopes can result in weak to no spread. 

Weise and Biging suggested that the wind acts to cool the 

unburnt fuel in advance of the fire front.

Figure 2-1—Fire behavior on a steep slope in a narrow canyon 

during a prescribed fire in chaparral fuels at Henry Coe State 

Park, California, November 2008. Ignition was made by a helitorch in a cross-slope pattern starting from the top and continuing 

with three to four passes until igniting the bottom of the canyon. 

Heat transport is up and along the slope as indicated by the smoke 

entrainment occurring from the bottom of the canyon up the slope 

into the base of the convection column at the ridge top. Background ambient winds were weak as determined by the vertical 

smoke column, and fire spread was caused by flame attachment 

with the fuels. 
12

GENERAL TECHNICAL REPORT PNW-GTR-891

Santoni et al. (1999) formulated a model to account for 

upslope fire spread and compared the solution to experimental results obtained using a tilted, combustion table. They 

suggested that the flame’s heat that is radiated ahead of the 

fire front toward the fuel is more important under slope conditions. They found that the ROS increases with slope. They 

also found that the fire front shape distorts toward the slope 

as the fire spreads upslope becoming more pointed. The fire 

front distortion increases with increasing slope angle.

Chimney effects—

An important aspect of upslope wind on fire behavior would 

be the effect that chimneys or steep gullies have on driving 

wind up the mountainside. Chimneys and steep gullies can 

help channel upslope flow if they are not lined with dense 

vegetation. Within the canopy, air is usually cooler than the 

free atmosphere and can result in drainage winds flowing 

below the canopy top while upslope winds occur above the 

canopy (Belcher et al. 2008, Whiteman 2000). However, 

few if any wind velocity observations in chimneys or steep 

gullies have been documented. 

Explosive fire behavior—

Eruptive fire behavior has been reviewed by Viegas and 

Simeoni (2010) where they define extreme fire acceleration 

as fire blowup characterized by a sudden change of spread 

rate and energy-release rate. This designation was first proposed by Viegas (2005, 2006), and such fire eruptions, especially those associated with canyons, are not rare (Viegas 

and Simeoni 2010). Laboratory studies using a combustion 

chamber and a fuel bed configured on a tilting, V-shaped 

table to replicate a steep chimney were conducted by Viegas 

and Pita (2004) and Viegas (2005). Their conclusions suggest that forest fire blowup depends mainly on fuel-bed 

properties and on the initial fire spread conditions dictated 

by topography or wind. Viegas (2005) also found that if the 

slope is not sufficiently long, blowup may not occur; however, a fire in the same fuel bed on a very steep slope will start 

with a high ROS, and blowup may occur quickly. Although 

laboratory studies in general do provide some insight, they 

are limited by the experimental design, as are most chamber-table studies owing to the limited table length, and the 

fact that atmospheric stability in these experiments is inherently limited to neutral conditions. However, one advantage 

of laboratory-scale experiments is that they remove many 

uncertainties that are found at the landscape scale such as 

wind and turbulence. Dold and Zinoviev (2009) and Dold 

(2010) suggested that when a fire is spreading upslope, 

heated air ahead of the fire front causes plume attachment 

with upslope fuels, leading to accelerated fire spread. This 

may potentially result in dangerous fireline conditions. They 

suggested that airflow is generated by the fire and is independent of the ambient wind. 

Wu et al. (2000) conducted a series of laboratory experiments and successfully visualized experimental fire plumes 

interacting with an inclined surface by using a grid schlieren system. They found that plumes were characterized by 

two parameters, plume attachment length and plume angle, 

and these were used to determine a critical inclination angle 

for flame attachment to occur. Their results suggested that 

24° is a critical angle for attachment to occur. Additionally, 

Wu et al. found that the critical inclination angle is not 

sensitive to the heat release rate or surface conditions. 

Dupuy and Maréchal (2011) conducted a series of 

laboratory fire experiments to determine the contribution 

of radiation and convection to fuel bed preheating on slopes 

of 0°, 10°, 20°, and 30°. Their results indicate that radiative 

heating is the dominant heat transfer mechanism on slopes 

between 0° and 20° that are close to the fireline. Convective heating was also found to be significant, becoming 

one-third of the total heat flux on the 20° slope. When the 

slope angle increased from 20° to 30°, the ROS increased by 

a factor of 2.5 owing to an increase in convective heating; 

at this angle, radiative heating stopped increasing. Their 

results also showed that far from the fireline, cooling by 

convection was found to be substantial except on 30° slopes 

Sharples et al. (2010a) suggested that the trench effect 

or flame attachment phenomena observed in structure fires 

of stairwells can be used as a surrogate for wildland fires 

exhibiting explosive behavior. The trench effect produces 

rapid fire spread in enclosed slopes such as escalator or 

stairwells by the interaction of the buoyant plume and an 

inclined trench of the stairwell. Plume impingement on 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

an inclined surface enhances preheating and pyrolysis of 

the fuel resulting in accelerated fire spread. Sharples et al. 

(2010a) suggested that the trench effect is a misnomer and 

the effect is really due to the trenchlike configuration of the 

fuels that limited lateral entrainment into the plume. They 

suggested that plume attachment or flame attachment are 

more appropriate to describe the phenomenon. This conceptual model applies to steep gullies or canyons, as these 

terrain features can potentially limit the lateral entrainment 

into the plume and result in eruptive or accelerated fire 

spread up the canyon. 

Sharples et al. (2010a) also noted that confined slopes 

over 25° are the most prone to flame attachment and the 

reason observed eruptive wildfire behavior is more prevalent on steep slopes and in steep canyons. This observation 

is in agreement with the results from Wu et al. (2000), who 

suggested 24° as a critical slope angle for flame attachment 

to occur.

Modeling of fire behavior on slopes—

To date, most studies aimed at determining the role of slope 

on fire behavior have based their models on wind tunnel 

experiments. More recently there have been attempts at 

using physics-based, coupled fire-atmosphere modeling 

systems to evaluate the role of slope on fire behavior (Linn 

et al. 2007, 2010). Using the FIRETEC modeling system 

(Linn and Cunningham 2005, Linn et al. 2002), Linn et al. 

(2010) simulated fire behavior on a 30° slope with different 

fuel types and found that slope alone has a significant effect 

on spread rate and spread pattern. This confirms the results 

of Weise and Biging (1997) and Santoni et al. (1999), but 

the most significant finding from the FIRETEC simulations 

was that the spread rate of all simulations is not the same at 

a point near the bottom of the hill and a point near the top, 

even though the slope is the same at each point. Linn et al. 

(2010) remarked that this result indicates that simply having a single value of local slope angle of a hill and a single 

nominal windspeed is not adequate to predict the spread 

rates on slopes. 

Linn et al. (2007) also showed that under certain 

conditions, the local slope had a more pronounced effect 

on spread rate than ambient wind. For example, numerical 

simulations showed that fire spread was dominated by the 

topography at locations on the middle of a slope when ambient winds were 6 m/s, whereas at other locations upwind 

of the slope, the fire behavior was strongly influenced by 

the coupling between the topography and ambient wind. 

This result indicates the importance of understanding the 

local winds that are influenced by the topography. Although 

the local wind field drives the fire behavior, topography 

has a more pronounced effect on the wind field rather than 

directly on the fire. Additionally, Linn et al. (2007) found 

a relationship among fire behavior, topography, and atmosphere that showed importance when the topographically 

influenced winds are not complementary to the slope effects 

such as those reported by Weise and Biging (1997). 

Because present knowledge of fire behavior on slopes 

and in gullies is a result of laboratory experiments and 

numerical modeling studies, there is still a large gap in 

understanding the role of slope-scale winds on fire spread 

Therefore, there is an immediate need for well-designed 

field experiments.

Downslope winds—

Downslope winds, also known as katabatic and drainage 

winds, develop once the slope becomes shaded as the sun 

sets. This reversal in heating causes a shallow layer of cold 

air to develop along the slope, and this cold layer of air is 

now denser than the surrounding air. As a result, it flows or 

drains downslope. As with upslope winds, downslope winds 

are driven primarily by temperature differences between 

the air on the surface of the slope and that at the same elevation away from the slope. Observations of downslope flows 

over simple slopes indicate that the velocities range from 1 

to 4 m/s and occur within a depth of 10 to 40 m above the 

slope (Horst and Doran 1986, Papadopoulos and Helmise 

1999, Whiteman 2000).

Because downslope winds have limited vertical extent 

and are typically much weaker in velocity, their effect on 

fire behavior may be limited. Down-valley winds are most 

likely to affect fire behavior on mountain valley slopes at 

night. Down-valley winds typically strengthen throughout 

the night and overrun the weaker downslope flows. 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Once surface winds become decoupled owing to the 

buildup of a nocturnal inversion at the valley floor, fire 

behavior can change dramatically with a change in direction 

or a decrease in spread rate, flame length, and intensity. 

These changes can also be attributed to relative humidity 

recovery near the surface.

Valley winds—

Valley winds, also known as along-valley winds, are a 

much more consistent wind regime than slope flows and 

are typically associated with much stronger velocities. The 

dynamic forcing is similar to that of slope winds with the 

exception that the forcing is driven by a valley volume 

effect. During daytime, air in the valley is warmer than 

over the plain because its volume is less and thus it warms 

faster than air over the plain (Schmidli and Rotunno 2010, 

Whiteman 1990). As a result, pressure is reduced in the valley while it is higher over the plain at an altitude that is the 

same elevation as the valley. The pressure gradient force is 

then directed from the plain to the valley (Whiteman 1990). 

During the night, the pressure gradient reverses and the 

winds blow down valley. Up-valley winds have velocities 

on the order of 3 to 8 m/s and down-valley winds about 3 to 

6 m/s. Typically, there exists an oscillation in the winds at 

night (Porch et al. 1991), which can affect fire behavior. The 

oscillations are thought to be caused by the interactions of 

air flowing out from tributary valleys into the main valley 

causing surges in the winds to occur at regular intervals on 

the order of 10 to 20 min. These surges can lead to changes 

in fire spread rate if the surface wind accelerates. However, 

there have been no quantitative studies on how the valley 

wind can affect fire behavior during daytime or night. 

Valley winds can sometimes be overcome by other 

mesoscale wind circulations especially in regions near 

coastlines. Seto and Clements (2011) observed the formation 

of a small fire whirl that formed during a prescribed fire 

when the prevailing up-valley wind was overcome by a sea 

breeze (fig. 2-2). Observations from a micrometeorological 

meas-urement tower placed in the burn unit showed that the 

fire whirl formed immediately after the sea breeze entered 

the valley at the surface. The fire whirl was first observed 

Figure 2-2—Time series of 5 minute averaged windspeed and direction observed during a wind reversal. Southeast up-valley winds 

reversed with the onset of a northerly sea breeze. Timing of fire whirl is indicated with dashed line. PDT = Pacific Daylight Time.
15

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

in the flaming front but moved behind the fire line as it 

stretched about 200 m in the vertical. The fire whirl caused 

the fire crew to quickly reposition themselves away from the 

fireline to remain safe. After the fire whirl dissipated, firing 

operations resumed. Seto and Clements (2011) ascertained 

that the fire whirl was caused by horizontal vorticity that 

was generated as a result of near-surface wind shear formed 

by the interaction of the sea breeze and the up-valley wind. 

Inversion destruction in valleys—

The diurnal evolution of vertical temperature structure in 

mountain valleys has been well established by extensive 

field and modeling studies (Whiteman 1982, Whiteman and 

McKee 1982). Inversion breakup and the transitional period 

that occurs afterward can produce significant changes in 

surface conditions such as windspeed, wind direction, temperature, and relative humidity. For these reasons, inversion 

breakup is likely to produce periods of extreme fire behavior. Whiteman (1982) identified three inversion destruction 

patterns in mountain valleys, but only two are applicable 

to fire behavior because the third is associated with snowcovered valleys. The first pattern is associated with one 

main mechanism, the development of the CBL from the 

surface. This pattern occurs in wide valleys and is similar 

to inversion breakup over flat terrain. The second pattern is 

associated with two processes and is the most common destruction pattern found in valleys around the world. In this 

pattern, (Whiteman’s pattern 3), the inversion top descends 

into the valley as the CBL continuously grows upward 

until the descending inversion top and CBL meet at some 

altitude. The subsidence that occurs is also responsible for 

transporting heat from the valley sidewalls to the entire valley atmosphere. 

Breakup of temperature inversions can occur within

2 to 3 h, depending on valley geometry and season

(Whiteman 1990). The most dangerous situation for 

increased fire behavior occurs when strong windflow above 

the valley is decoupled from the surface by the inversion’s 

capping top. Once the inversion breaks, momentum of the 

stronger winds aloft can quickly bring drier and warmer 

air to the surface. Windspeed can easily double and shift 

180o in direction. This situation is common in valleys and 

can be anticipated on fires, but the rate of inversion breakup 

and the decoupling of winds aloft should be estimated from 

smoke observations or a sounding taken on site. 

Whiteman’s (1982) inversion breakup model does not 

apply to every valley. The Riviera Project in the Swiss Alps 

(Rotach et al. 2004) found that the thermodynamic structure and evolution of inversion breakups in Swiss valleys 

differed from those studied by Whiteman in the Colorado 

Rocky Mountain valleys. Rotach et al. (2004) described a 

valley atmosphere that is stable throughout the afternoon 

rather than being well mixed as suggested by Whiteman 

(1982). However, a multilayered structure in temperature 

profiles has been found in other valleys of the Alps. Thus, 

the stability regime can be quite different from valley to 

valley. To determine local stability for fire behavior and fire 

weather predictions requires an on site sounding at the time 

of interest. 

An example of inversion breakup in a deep valley is 

shown in figure 2-3 where potential temperature, water 

vapor mixing ratio, and the up-valley component of windspeed are plotted up to 700 m AGL nearly to the valley 

crest height of 1000 m AGL. Potential temperature is the 

temperature that an air parcel would have if it were raised or 

lowered adiabatically to a reference pressure level, usually 

1,000 millibars (mb). Potential temperature (θ) is calculated 

using the measured temperature (T) in Kelvins (K), and 

pressure, P, where R is the gas constant for 

dry air, Cp is the heat capacity, and P0 is the reference pressure of 1,000 mb. When potential temperature is constant 

with height, the atmospheric stability is neutral. The atmosphere is considered unstable when potential temperature 

decreases with height and is stable when potential temperature increases with height. The up-valley wind component 

is a wind component that is rotated to align with the valley 

axis. Positive values represent up-valley winds and negative 

values down-valley winds. 

The evolution of this valley’s vertical temperature and 

wind profiles clearly illustrates decoupling of down-valley 
16

GENERAL TECHNICAL REPORT PNW-GTR-891

winds from the surface. This decoupling results from the 

strong capping inversion that formed at 80 m AGL while the 

entire valley was stable (increasing potential temperature 

with height). The strong and shallow surface inversion (0.07 

K/m) remained in nearly steady state during the morning, 

approximately 2 h, as the down-valley flow (with velocities of approximately 4 to 5 m/s) was decoupled from the 

surface. This decoupling caused very stagnant atmospheric 

conditions (cold air pool) at the surface as shown by the 

very weak winds (<0.5 m/s) within the layer. As the valley 

atmosphere destabilized (1035 Pacific Standard Time), the 

winds reversed and became up-valley, but were still weak at 

the surface. During the transition period between downvalley and up-valley wind regimes, winds can switch direction aloft while remaining constant or weak (fig. 2-2) at the 

surface. Missing in figure 2-3 is the period where stronger 

up-valley winds reached the surface. These missing data are 

a result of the strong turbulence that occurred at the surface 

making a balloon ascent extremely difficult. 

Another aspect of valley inversions is the role they have 

on the thermal belt. Thermal belts are areas along valley 

sidewalls that are warmer than the areas below and above 

them. This can have an effect on the fuel loading, moisture 

content, and temperature, and resulting fire behavior. 

Cross-valley winds—

Cross-valley winds result from either differential slope 

heating or dynamically forced flow over the terrain. 

Additionally, during valley inversion breakup, solar radiation that first illuminates one side of a valley causes a circulation to develop in the across-valley direction. Air within 

the center of the valley flows toward the heated sidewall and 

compensates for slope flow and convection that develops in 

response to solar heating (Colette et al. 2003, Whiteman et 

al. 2004). Rotach et al. (2004) found that valleys with bends 

can influence the location of the core of up-valley flow. In 

the Riviera Valley, the up-valley jet core was located closer 

to one valley sidewall because of the inertia of the flow as 

it came around a bend in the valley. This observation suggests that in valleys with sharp bends in the along-valley 

direction, the flow maxima can occur along one side of the 

valley. This characteristic can affect fire behavior in valleys 

by creating stronger surface wind on one side of the valley. 

If a fire were to cross the valley by spotting, the spread rate 

could potentially be much different than would be observed 

on the opposite valley sidewall.

Figure 2-3—Inversion breakup evolution observed by vertical profiles obtained from tethersonde measurements taken 16 March 1998 in 

Yosemite Valley California (adapted from Clements and Zhong 2004). AGL = above ground level. PST = Pacific Standard Time. Water 

vapor mixing ratio is defined as the ratio of the mass of water vapor in grams (g) to the mass of dry air in kilograms (kg), or (g/kg).
17

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Turbulence in mountainous regions—Turbulence is defined as the perturbation from the mean of wind velocity. 

Little is known about the characteristics of atmospheric 

turbulent processes in steep mountainous terrain (Weigel et 

al. 2007). The role of turbulence on fire behavior has been 

suggested as a critical driving force at the fire front (Taylor 

et al. 2004) and larger ambient scales (Sun et al. 2009). Both 

background ambient turbulence and the turbulence generated by the fire itself affect the resulting fire behavior (Sun 

et al. 2009).

Results from the Alps (Rotach et al. 2004, Weigel et al. 

2007) indicate significant spatial variability in surface turbulence characteristics throughout the valley atmosphere, 

which is largely determined by local topographical features 

such as slope. The maximum shear-induced turbulence was 

found to occur on the eastern valley sidewall (sunlit) and 

near the center of the valley at the core of the valley wind. 

Turbulence-producing slope surfaces have a significant 

influence on turbulence structure in large parts of the valley 

atmosphere. Consequently, fire behavior on slopes can be 

driven by a combination of slope effects and ambient turbulence that is generated by shear between the slope flow layer 

and valley wind. As found in the Riviera Valley, turbulence 

generation can often be dominated by wind shear. Intense 

turbulence is often associated with strong wind shear generated by strong surface winds such as foehn events (Sharples 

et al. 2010b). 

Wind Modeling Tools: WindNinja 

Determining real-time wind characteristics on incidents in 

complex terrain remains a challenge. This need has been 

partially addressed by the development of wind modeling 

systems by the U.S. Department of Agriculture Forest 

Service using in-house and commercially available computational fluid dynamics codes. The most popular modeling 

system is WindNinja (http://www.firemodels.org), which is 

similar to the more complex WindWizard model (Butler et 

al. 2006). WindNinja uses a wind observation at a location 

to compute a spatially varying high-resolution (100-m) wind 

field over the terrain, attempting to account for mechanical

modification of the flow by terrain. WindNinja is not a 

forecasting tool, but rather provides a “snap-shot in time”

of the wind for an area. WindNinja is becoming widely used 

on fire incidents by incident meteorologists and fire behavior analysts. This is due to the nature of the system—it can 

be run on a laptop computer, taking less than a minute to 

provide output. That is a big advantage as no forecasting 

system can provide this ease of use. Output from WindNinja 

has value for the user, but there are some major limitations 

of the system that users should be aware of. Numerics of 

the system are based on solving a rather simple set of mass 

continuity equations and optional slope flow equations. 

This simplicity is what makes WindNinja operate so fast 

on a laptop. These same simplifications are reason for 

caution when using it in complex terrain. First, the model 

is a mass-consistent model requiring air to flow around 

mountains rather than over them. The major pitfall of this 

model type is the lack of thermodynamic fields to determine 

atmospheric stability, which would indicate whether air 

would flow around or over terrain. The lack of thermodynamics limits its use for situations where thermally driven 

circulations dominate. The exception is a simple slope flow 

submodel included in WindNinja. The model stability for 

flow computation is fixed for a neutral atmosphere (Butler et 

al. 2006), except in the initialization phase where WindNinja approximates lower atmosphere stability based on surface 

heat flux and subsequently uses a logarithmic vertical wind 

profile that includes adjustment for this stability. After the 

initialization phase, neutral stability is assumed for flow 

adjustment, but a method of relaxing this is currently being 

tested2 so the current version of WindNinja should not be 

expected to provide accurate simulations in situations where 

thermally driven flows dominate. For example, without the 

ability to run the model with specific stabilities such as a 

stable layer at crest height, it may not be able to accurately 

predict wind flow during foehn events because the crestlevel inversion is an important criterion for development of 

downslope windstorms. Also, WindNinja may fail during 

inversion breakup or when a valley atmosphere is slightly 

stable during the day. An enhancement currently being 

tested in WindNinja

2 Forthofer, J. 2011. Personal communication. Mechanical engineer, USDA Forest Service, Missoula Fire Sciences Laboratory, 

Missoula, MT.
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GENERAL TECHNICAL REPORT PNW-GTR-891

is to initialize it with available weather model forecasts 

from, for example, the National Weather Service (see 

footnote 2). Initializing WindNinja with coarse forecast 

model wind fields that already include thermal forcing may 

alleviate WindNinja’s thermal stability issues, resulting in 

improved downscaled (higher resolution) winds. 

Kochanski et al. (2009) used multiple meteorological modeling systems, including WindNinja, to simulate 

flow over a simple hill. The performance and accuracy of 

WindNinja were much less than the other models, primarily because the version of WindNinja used did not allow 

for a user-defined vertical wind profile. Note that the other 

models used were much more sophisticated and required 

extensive computing time and processors in order to 

complete their simulations, whereas WindNinja did not. 

Forthofer (2007) simulated the same hill using a research 

version of WindNinja that did specify the measured upwind 

vertical wind profile and showed much better results upwind 

and at the top of the hill. Flow on the lee side was less 

accurate, likely owing to the crude handling of momentum/

turbulence in WindNinja, which becomes most important 

on lee slope locations.

Although there are limitations with this type of modeling system in complex terrain, a user with an understanding 

of these limitations can run the model to get a general idea 

of the wind field over a fire area. This can be beneficial 

when there is a need to determine whether winds in an area 

are terrain forced and caused solely by topography. Because 

WindNinja provides a gridded wind field in under 1 minute 

of simulation time, it is a very capable tool, but users should 

have an understanding of the issues mentioned above. 

Finally, in the summer of 2010, a major field validation 

experiment was conducted to provide a comprehensive data 

set for testing and improving the WindNinja application (see 

footnote 2). It is likely to become an improved tool in the 

future. 

Summary

Atmospheric processes in complex and mountainous 

terrain produce a variety of phenomena that can affect 

fire behavior in unpredictable ways. Two main wind types 

should be considered for better predicting fire behavior in 

mountainous regions: large-scale dynamically driven winds 

and thermally driven winds. The most notable dynamically driven winds are the foehn winds that occur in most 

mountain ranges in the Western United States. Foehn winds 

are known for increasing surface winds dramatically and 

causing very rapid warming and drying. The Santa Anas of 

southern California, associated with extreme windspeeds 

and drying, have led to flow channeling in narrow canyons 

resulting in extreme fire behavior and accelerated down 

canyon fire spread. To date there are few observations of 

fire-atmosphere interactions and the resulting fire behavior 

during foehn events. More systematic observations are 

required to better understand extreme fire behavior during 

foehn wind.

Thermally driven winds in mountainous terrain occur 

regularly as they transition from up valley/upslope during 

the daytime to down valley/downslope at night. Thermally 

driven winds have weaker windspeeds than dynamically 

driven winds and can be overcome by synoptic-scale winds 

aloft when atmospheric stability permits the downward 

transport of higher momentum into the valley atmosphere. 

These situations lead to rapid increases in surface winds 

and fire spread rates. 

One of the most critical factors affecting fire behavior 

in valleys is inversion breakup during the morning transition period. During the morning transition period, stable air 

at the surface quickly mixes and becomes unstable owing 

to development of a convective mixed layer over the valley 

floor. When this occurs, winds above the inversion layer 

that were decoupled from the surface can mix down quickly 

bringing much stronger wind velocities to the surface, 

usually from a different direction. These situations can 

potentially lead to extreme fire behavior by affecting spread 

rates and direction. To better anticipate these rapid changes, 

vertical profiles of temperatures should be measured in 

real time using radiosonde soundings or remote-sensing 

temperature profilers. Real-time observations would allow 

fire crews to know the state of the atmosphere at a given 

instant. 

In addition to the valley inversion breakup, valley 

geometry can play a role in fire behavior. Valleys with sharp 

bends can have flow maxima along one side of the valley. 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

This characteristic can potentially affect fire behavior in 

valleys by creating stronger surface winds on one side of the 

valley. If the fire were to spot across the valley, the spread 

rate could potentially be much different than would be 

observed on the opposite valley side wall. 

Fire behavior on slopes is often explosive in nature as 

fire accelerates up slope. To date, most studies have used 

either wind tunnel experiments or coupled atmospherefire numerical modeling systems. Results of these studies 

indicate fire spread rates increase with increasing slope 

and the fire front shape distorts toward the slope, becoming 

more pointed. The fire front distortion also increases with 

increased slope angle. The increase in spread rate on slopes 

is caused by flame attachment to the fuel bed because the 

fuel is closer to the flame. Laboratory studies indicate that 

24° is a critical angle for flame attachment to occur. Radiative heat transfer is dominant on slopes up to 20°. When 

slope angle increased from 20° to 30°, convection caused 

the fire spread rate to increase by a factor of 2.5. Observations in mountainous terrain confirm that slopes with angles 

over 25° are most prone to flame attachment and explain 

why observed eruptive fire behavior is prevalent on steep 

slopes and in canyons. 

Because present knowledge of fire behavior on slopes 

is mainly a result of laboratory experiments and numerical 

modeling studies, there is still a large gap in understanding the role of slope-scale winds on fire spread on slopes. 

Numerical studies have shown that terrain has a more 

pronounced effect on fire spread on slopes than ambient 

wind. However, there are limited field data to support these 

results. Therefore, there is an immediate need for well-designed field experiments over sloped terrain to obtain a data 

set that can be used in model development and validation. 

Future Needs

Most fire behavior measurements have been limited to 

laboratory studies, wind-tunnel experiments, and numerical 

simulations. There are few, if any, field studies of fireatmosphere interactions during actual wildland fires (Clements et al. 2007). Therefore, comprehensive field experiments on slopes in mountainous areas need to be conducted 

to enhance the understanding of how complex terrain 

affects fire behavior. The data collected from these experiments can be used to test and develop fire behavior models. 

Specific experiments needed include: 

• Slope experiments with head fires starting on flat 

terrain and spreading upslope under various fuels 

and ambient meteorological conditions.

• Head fire experiments in chimneys and steep canyons.

• Experiments during inversion breakup on valley 

floors to investigate fire behavior during transition 

periods.

Idealized experiments are limited to smaller scales and 

do not account for true wildfire conditions. To overcome 

this, measurements can be made byincident meteorologists at incidents. The National Weather Service Incident 

Meteorologist program has begun implementing the use of 

radiosondes on incidents rather than pilot balloons. Having 

profiles of temperature, humidity, and wind at high temporal and spatial resolution (about 1 s, 2 m) will allow incident 

meteorologists and fire behavior analysts to determine 

changes in atmospheric stability on site. Additionally, the 

use of remote sensing technology should be considered a 

priority. These sensors include Doppler wind LIDAR and 

passive microwave temperature and humidity profiles. Although the cost of these technologies is high, the data would 

provide great insight into the mechanisms of atmospheric 

dynamics on fire behavior in complex terrain. 

Literature Cited

Atkinson, B.W. 1981. Meso-scale atmospheric circulations. 

Maryland Heights, MO: Academic Press. 495 p.

Baumann-Stanzer, K.; Piringer, M. 2004. Foehn signals 

detected by sodar wind and turbulence measurements in 

the Rhine Valley, Austria, during the MAP field phase. 

Meteorology Atmospheric Physics. 85: 125–139.

Barry, R.G. 2008. Mountain weather and climate. 3rd ed. 

Cambridge, United Kingdom: Cambridge University 

Press. 506 p.

Belcher, S.E.; Finnigan J.J.; Harman, I.N. 2008. Flows 

through forest canopies in complex terrain. Ecological 

Applications. 18: 1436–1453.
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Blier, W. 1998. The sundowner winds of Santa Barbara, 

California. Weather and Forecasting. 13: 702–716.

Brown, H. 2002. Thirtymile Fire: fire behavior and 

management response. Fire Management Today. 62(3): 

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Bennett, E.; Brode, R.; Dicke, J.; Eskridge, R.; 

Garrison, M.; Irwin, J.; Koerber, M.; Lockhart, 

T.; Method, T.; Perkins, S.; Wilson, R. 1987. Onsite meteorological program guidance for regulatory 

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Triangle Park: NC: U.S. Environmental Protection 

Agency. 

Butler, B.W.; Forthofer, J.M.; Finney, M.A.; Bradshaw, 

L.S.; Stratton, R. 2006. High resolution wind direction 

and speed information for support of fire 0perations. In: 

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S., eds. Monitoring science and technology symposium: 

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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 3: Critical Fire Weather Patterns

Paul A. Werth1

Introduction

Eyewitness accounts in journals and diaries have documented the relationship between weather and large wildland 

fire for hundreds of years. Survivor statements after the 

1871 Chicago, Peshtigo, and Michigan Fires, and the 1894 

Hinckley Fire identified hot, dry, and windy conditions as 

the primary weather elements contributing to the destruction caused by these fires. 

In the early 1900s, technological advances in meteorology permitted creditable scientific research into weather’s 

influence on wildland fire, most of which was closely tied to 

the study of historical wildland fires.

Even then it was recognized that there are short periods 

of one or several days in every fire season when wildland 

fuels (see chapter 4 for more information on wildland fuels) 

are unusually susceptible to large fire, and this was primarily dependent upon the weather. Show (1931) referred to 

these as “dangerous periods.”

However, it was not until the 1960s that critical fire 

weather patterns, producing high fire danger and large 

wildland fires, were identified for both the United States

and Canada.

Syverson (1962) documented the first definition of 

“critical fire weather patterns” as follows:

Crisis period is defined as the critical day, week or 

month during which blowup fires are experienced. 

Further, we might conclude that the period of critical 

fire weather is the result of that combination of 

weather patterns that have given rise to this condition and might further result in causing more fires or 

materially assist their spread.

Current fire behavior training courses define critical 

fire weather patterns as the atmospheric conditions that 

encourage extreme fire behavior resulting in large and 

destructive wildland fires.

1 Paul A. Werth is a fire weather meteorologist (retired), Northwest Interagency Coordination Center, 150 SW Harrison Street, 

Suite 400, Portland, OR 97201.

Critical Fire Weather Patterns are defined as the

atmospheric conditions that encourage extreme 

fire behavior resulting in large and destructive

wildland fires.

The four critical weather elements common to 

wildland fires exhibiting extreme fire behavior

are low relative humidity, strong surface wind, 

unstable air, and drought. 

Understanding weather’s influence on wildland fire is 

essential for safe and effective fire suppression activities. 

Fire managers and firefighters should be aware of critical 

fire weather patterns in their areas and how adverse weather 

associated with those patterns can produce extreme fire 

behavior conditions that put firefighters and the general 

public at risk. 

Weather Elements That Promote Extreme 

Fire Behavior

Early fire weather research focused on individual weather 

elements that occurred prior to and during large wildland 

fires. The culmination of these studies identified four critical weather elements common to wildland fires exhibiting 

extreme fire behavior extreme fire behavior: low relative 

humidity (or low atmospheric moisture), strong surface 

wind, unstable air, and drought.

Munns (1921) found that “In months with high vapor 

pressure (high relative humidity), very few fires occurred, 

while during months of low vapor pressure (low relative 

humidity) many bad fires occurred.” Separate studies by 

Hofmann (1923) in Washington and Weidman (1923) in 

Montana and Idaho concluded that relative humidity is the 

most important factor in development of dangerous forest 

fires because it significantly increases the flammability of 

forest material. In a study of southern Appalachian wildfires, McCarthy (1924) found that relative humidity was 
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GENERAL TECHNICAL REPORT PNW-GTR-891

unusually low on high fire risk days, and that this dry air 

was advected southward by winds from the interior of the 

continent. His was also the first study to connect the occurrence of low relative humidity to specific wind directions, 

and the warming and drying of air within high pressure 

systems owing to subsidence. A study of Massachusetts forest fires by Stickel (1928) stated, “Relative humidity appears 

to be the best single indication of forest fire hazard.” He also 

indicated that “The maximum forest fire hazard occurred 

between rainy periods, when the relative humidity is 40 percent or less.” Dague (1930) identified relative humidity of 20 

percent as the point below which bad fire weather situations 

were created east of the Cascade Mountains in Washington 

and Oregon. Since that time, numerous wildland fire reports 

have substantiated the importance of unusually low relative 

humidity in the development of extreme fire behavior. Regional threshold values for low relative humidity can range 

between 10 and 40 percent, depending on fuel model.

Low relative humidity (low atmospheric moisture) 

intensifies fire behavior by decreasing the moisture content 

of fine dead fuels, making them easier to ignite and carry 

fire. Fire line intensity (kW/m), rate of spread (ROS) (m/s), 

and the probability of spotting significantly increase when 

the relative humidity is low, sometimes so rapidly that there 

is little advance warning.

The relationship between strong surface wind and 

large fires exhibiting extreme fire behavior has been well 

documented for hundreds of years. The first scientific 

research connecting the two was conducted by Beals (1914). 

He researched surface atmospheric pressure patterns and 

associated weather conditions during four large fires (1881 

Michigan, 1884 Hinckley, 1902 Columbia, and the 1910 Great 

Idaho) and found that “The one striking feature of all large 

forest fires is the strong winds that prevail just before, during, 

and for a short period after the fire passes a given place.”

Subsequent fire weather research (Anderson 1968; 

Brotak 1979; Countryman et al. 1956; Dague 1930, 1934; 

Gisborne 1927; Goens and Andrews 1998; Hoenisch 2009; 

Hughs and Hall 2009; Jemison 1932; Joy 1923; Kauffman 

1937; Krumm 1954; Schaefer 1957; Simard et al. 1983; 

USDA, USDI, and USDC 1994) has documented strong 

cold front, thunderstorm, and foehn winds with the occurrence of extreme fire behavior conditions. (Note: For 

more information concerning foehn winds, see chapter 2.) 

Wind affects wildland fire in a number of ways. It supplies 

additional oxygen to the fire, increasing fire intensity. It also 

preheats the fuels ahead of the fire and increases ROS by 

carrying heat and burning embers to new fuels (spotting).

Until the U.S. Weather Bureau established a national 

network of radiosonde stations, fire weather research was 

limited to studying only the effects of surface weather on 

fire behavior. With the advent of radiosonde data, researchers were also able to investigate the influence of upper air 

temperature, relative humidity, and wind on wildland fire 

behavior. The concept of airmass stability was discovered 

through the analysis of vertical temperature profiles. When 

temperature decreases rapidly with height, the atmosphere 

is classified as unstable. If there is an increase, or only a 

slight decrease in temperature with height, the atmosphere 

is classified as stable. Crosby (1949) was the first to suggest 

the effect of atmospheric stability on fire behavior. He concluded that stable air dampened convection currents over a 

fire, whereas unstable air increased the speed and depth of 

the convection currents. Brown (1950) stated that the stability of the air at the location of a fire is as important to fire 

behavior as temperature and humidity. Byram ( 1954) and 

Byram and Nelson (1951) studied 17 severe fires around the 

county and identified unstable air and certain vertical wind 

profiles as being favorable for extreme fire behavior. Davis 

(1969) investigated 70 fires in the Southeastern United 

States and found that instability increases the chance of a 

big fire more often than low relative humidity. Haines (1988) 

developed a lower atmosphere severity index based on the 

stability and moisture content of the lower atmosphere. The 

drier and more unstable the airmass becomes, the higher the 

Haines Index, and the greater the threat of large wildland 

fire and extreme fire behavior. Brotak (1992–1993) found 

that in the Eastern United States, strong surface wind in 

conjunction with low fuel moisture caused more fire-control 

problems than unstable air. Werth and Ochoa (1990), 

Saltenberger and Barker (1993), and Goens and Andrews 

(1998) found good correlation between the Haines Index 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

and extreme fire behavior on fires in Idaho, central Oregon, 

and Arizona. 

In summary, unstable air amplifies the vertical growth 

of the smoke plume over a fire by enhancing the strength 

of the updrafts. This increases combustion rates by supplying more oxygen to the fire. As the height and strength of 

the smoke plume increases, the potential for gusty surface 

winds, dust devils, and fire whirls also increases. Spotting 

may become profuse all around the fire as large firebrands 

are lifted in the smoke plume. (Note: For more information 

concerning the effects of atmospheric stability on extreme 

fire behavior, see chapters 6 through 8.) Unstable air also 

increases the probability of thunderstorms and strong 

downdraft winds.

Beals (1916) defined drought as “Long-continued dry 

weather, especially so long continued as to cause vegetation to wither.” Beals also stated that while “Drought and 

periods of hot weather contribute to the fire hazard, these 

alone do not necessarily portend the occurrence of a great 

fire, as without wind an incipient fire would spread slowly.”

He recognized that drought and hot weather do not necessarily result in large fires, but a critical weather element, 

such as strong wind, is also needed to produce a large fire. 

Today drought is defined as a period of relatively long 

duration with substantially below-normal precipitation, 

usually occurring over a large area. Drought affects fuel 

availability by lowering the moisture content of both live 

and dead fuels, making them more combustible. Drought 

conditions are not a prerequisite for large fires, but there 

is a close relationship between drought conditions, large 

wildland fires, and extreme fire behavior when low relative 

humidity and either strong wind or unstable air are present.

Critical Fire Weather Patterns 

Critical fire weather patterns occur when atmospheric 

conditions combine to significantly increase the threat of 

destructive wildland fires that exhibit extreme fire behavior. 

Fire weather research has identified adverse atmospheric 

conditions as strong wind, unusually low relative humidity, 

and unstable air. Drought is also included as a significant 

factor, but is the result of a lack of precipitation over a 

period of weeks, months, or even years. 

Beals (1914) researched the September 1, 1894, Minnesota Hinkley Fire in which 418 people perished. He was 

a pioneer in studying synoptic weather maps depicting 

pressure, temperature, and wind patterns associated with 

large fires. On the Hinkley Fire, the weather map (fig. 3-1) 

showed a surface low pressure center in North Dakota and 

tightly packed isobars favoring strong wind in Minnesota. It 

should be noted that his map does not depict cold and warm 

fronts because frontal theory was not discovered until 1917 

by Norwegian meteorologists Vilhelm and Jacob Bjerknes.

In a much later study, Haines and Sando (1969) 

researched weather conditions during seven large fires in 

the Great Lakes States in 1871, 1881, 1894, 1910, and 1918. 

In their report, they included the 8 October 1871 surface 

weather map at the time the Chicago, Michigan, and 

Peshtigo Fires were actively burning. This map indicates a 

weather pattern similar to the one Beals found during the 

Hinkley Fire, but with the addition of surface fronts. The 

Haines and Sando map shows a surface low over southwestern Minnesota with a warm front across northern Wisconsin and Upper Michigan. A cold front extends southward 

from the low center across eastern Iowa. Their conclusion 

was that all of these fires actively burned in the warm sector 

of the surface low, in an area experiencing strong southerly 

winds and unusually low relative humidity. The Hinkley 

Fire likely also burned in the warm sector of a strong 

surface low pressure system (fig. 3-1). 

Figure 3-1—Surface pressure map, 01 September 1894 at 0800 

Central Standard Time. (Adapted from Beals 1914.)
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GENERAL TECHNICAL REPORT PNW-GTR-891

Show (1931) was the first to document weather being 

largely responsible for dangerous fire conditions when he 

wrote, “It was generally recognized that occasionally in 

every fire season there occurred short periods of one or 

several days when the forest cover was unusually flammable 

and at times seemed almost explosive.” He concluded, 

“Abnormal weather conditions were responsible for these 

periods.” 

The relationship between synoptic weather patterns and 

high fire danger was further advanced by Schroeder (1950). 

He noted that for the Great Lakes States in May, “Nearly 

all of the critical periods were associated with an area of 

high pressure which developed near the western shore of 

Hudson Bay and subsequently moved either southward or 

southeastward.” 

An early definition of a critical fire weather pattern 

was provided by Syverson (1962) when he described it as a 

“crisis period.” He stated, “A crisis period is defined as: the 

critical day, week or month during which blow-up fires are 

experienced.” 

Syverson (1963) expanded his concept of a crisis period 

in an investigation of synoptic fire weather types of the 

Northern Intermountain, Northern Rockies, and the Northwestern Plains regions. He selected synoptic weather types 

(upper air 500 hPa and surface) that contributed to high fire 

potential or large forest fires. The 500 hPa upper air patterns were divided into meridional, zonal, short-wave train, 

and high-low block categories. The surface patterns were 

classified according to the origin of the surface anticyclones 

(high pressure) affecting the area. Syverson concluded, 

“The greatest danger occurs just ahead of the upper trough 

in the area of the low pressure at the surface.”

The most complete research of critical fire weather patterns was published by Schroeder et al. (1964) in Synoptic 

Weather Types Associated With Critical Fire Weather. This 

study covered all the lower 48 states and determined: “Periods of critical fire weather are associated with relatively 

few synoptic weather patterns.” They concluded that east 

of the Rocky Mountains, most critical fire weather patterns 

are associated with the periphery of high-pressure areas, 

particularly in the prefrontal and postfrontal areas. Along 

the eastern slopes of the Rocky Mountains, weather patterns 

producing Chinook winds are the most important. In the 

intermountain West, critical fire weather is associated with 

upper troughs and overhead jet streams, or surface dry cold 

front passages. Along the Pacific Coast, from Washington 

to California, weather patterns producing offshore flow or 

foehn wind are the most important.

East of the Rocky Mountains, most critical 

fire weather patterns are associated with the

periphery of high-pressure areas, particularly in 

the prefrontal and postfrontal areas. Along the 

eastern slopes of the Rocky Mountains, weather 

patterns producing Chinook winds are the most 

important. In the intermountain West, critical fire

weather is associated with upper troughs and 

overhead jet streams, or surface dry cold front 

passages. Along the Pacific Coast, from Washington to California, weather patterns producing 

foehn wind are the most important.

Brotak and Reifsynder (1977b) detailed the relationship 

of Central and Eastern U.S. wildland fires with surface 

frontal systems and upper level troughs and ridges. They 

found that just prior to and after passage of cold fronts (fig. 

3-2) were favored areas for large fire growth to occur. At 

500 hPa, the favored area was between the upper ridge and 

trough axis (fig. 3-3).

Figure 3-2—Idealized surface map showing all fire runs. CFA= 

following cold frontal passage; CFB = preceding cold frontal passage; WSL= warm sector of low; and WS = warm sector of high. 

Source: Brotak and Reifsynder 1977b.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 3-3—Idealized 500 hPa geopotential height map showing 

all fire runs. CFA = following cold frontal passage; CFB = preceding cold frontal passage; WSL= warm sector of low; and WS = 

warm sector of high. Source: Brotak and Reifsynder 1977b.

Nimchuk (1983) documented the relationship between 

the breakdown of a blocking upper level ridge and severe 

fire behavior conditions in western Canada. He concluded 

that the trigger for extreme fire behavior was the breakdown 

of the upper ridge, rather than the presence of a persistent 

upper ridge. His statements concerning the fire behavior 

associated with the three stages in the life cycle of an upper 

ridge are of particular interest (fig. 3-4).

1. An establishment period characterized by warm, dry 

stable conditions, low humidity, light wind, rapidly 

decreasing fuel moisture, and low lightning risk.

2. Initial weakening of upper level disturbances, leading to decreased atmospheric stability and increased 

lightning activity, but little or no cooling or reduction in fire danger.

Figure 3-4—Life cycle stages of an upper level ridge.

3. Final breakdown, accompanied by a period of severe 

burning conditions, strong winds, and lightning followed by cooling and a reduction in fire danger.

In summary, these studies indicate that most periods 

of critical fire weather occur in transition zones between 

high- and low-pressure systems, both at the surface and in 

the upper air. The surface pressure patterns of most concern 

are those associated with cold fronts and terrain-induced 

foehn winds. Cold front passages are important to firefighters because of strong, shifting winds and unstable air that 

can enhance the smoke column, or produce thunderstorms. 

Foehn winds occur on the lee side of mountain ranges and 

are typically very strong, often occurring suddenly with 

drastic warming and drying. The area between the upper 

ridge and upper trough is the most critical upper air pattern 

because of unstable air and strong winds aloft that descend 

to ground level. 

Regional Critical Fire Weather Patterns

The following section will briefly describe critical fire 

weather patterns by region and season. Critical fire weather 

patterns can be separated into two primary categories:

• Those that produce strong surface wind.

• Those that produce atmospheric instability.

In both cases, an unusually dry airmass, for the region 

and season, must also occur. Strong wind with high relative humidity is not a critical fire weather situation nor is 

unstable air combined with high relative humidity. 

When critical fire weather patterns occur during 

periods of drought, the threat of extreme fire behavior significantly increases in brush and timber fuels. However, in 

grass fuels, some of the worst fire behavior has occurred in 

moist periods owing to increased fuel loadings. The key to 

identifying a critical fire weather pattern is the recognition 

that these patterns must also produce unusually low relative 

humidity for the region, along with strong surface wind or 

unstable air.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Northern Plains, Great Lakes, and the 

Northeastern United States

The fire season in this region primarily occurs before greenup in the spring and after leaf drop in the fall. The spring 

season can start as early as March in the Northern Plains 

and the Ohio River Valley and as late as April in the Great 

Lakes and Northeast States. The fall season can last through 

November. 

Critical fire weather patterns in this part of the country 

are identified by the source of surface high-pressure areas 

before or after the passage of cold fronts. That is because 

the source of these high-pressure areas determines the moisture content of the airmass and whether passing cold fronts 

will be wet or dry. There are three surface high-pressure 

types that can produce critical fire weather and extreme fire 

behavior in this region.

Pacific High—

This high pressure originates over the Pacific Ocean 

and loses much of its moisture as it crosses the Rocky 

Mountains. It moves into the Northern Plains and Great 

Lakes States with a dry continental airmass. This is the 

most common type and shows little preference for any particular month.

Northwest Canadian High—

This high pressure is normally warm and dry owing to its 

source region, subsidence warming, and southward movement over warmer land. Critical fire weather occurs on the 

periphery of the high, especially the north and northwest 

sides. This type occurs during the spring and fall.

Hudson Bay High—

This is similar to the Northwest Canadian High. The most 

critical fire weather is on the northwest side of the high. 

However, dry cold fronts can produce extreme fire behavior, both before and after frontal passage. Schroeder (1950) 

indentified the Hudson Bay High as the principal weather 

type associated with periods of very high fire danger for the 

Great Lakes States. 

Brotak (1979) analyzed the weather and fire behavior 

conditions during the 22 July 1977, Bass River Fire in New

Jersey. The fire claimed the lives of four firefighters when 

flames overran their position. Drought, strong wind, 

unusually low relative humidity, and extreme instability contributed to the extreme fire behavior experienced 

during the fire. The extreme fire behavior occurred after the 

passage of a cold front and in the southeast quadrant of a 

Hudson Bay high-pressure area (fig. 3-5). The 500 hPa map 

(fig. 3-6) shows an upper level trough over New Jersey and 

a northwesterly flow of subsiding air in the leading edge of 

high pressure over the Great Lakes. 

Figure 3-5—1400 Eastern Daylight Time, surface weather map. 

Source: Brotak 1979.

Figure 3-6—0800 Eastern Standard Time, 500 hPa geopotential 

height map. Source: Brotak 1979. 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 3-7—Cold frontal positions during the Mack Lake Fire. 

Source: Simard et al.1983.

Simard et al. (1983) researched the weather, topography, 

fuels, and fire behavior of the May 5, 1980, Mack Lake Fire 

in Michigan. They concluded that the extreme fire behavior 

observed on the Mack Lake Fire occurred as follows:

“Ahead of the weak cold front (fig. 3-7), relative 

humidity was low at 24 percent, and the temperature 

was unseasonably high at 26.7 deg C (80 deg F). 

Wind speed (at the Mio weather station) increased 

significantly to 24 km/h (15 mi/h), gusting to 40 

km/h (25 mi/h) plus as the front approached.” This 

is a classic prefrontal critical fire weather pattern 

during the spring months for the Great Lakes States.

The August 25, 1995, Sunrise Fire on Long Island, 

New York, is another example of a fire that burned during 

a postfrontal critical fire weather pattern, with north winds 

and a relative humidity of less than 20 percent reported. 

It burned approximately 2800 ha and damaged numerous 

homes and small businesses. 

Southeastern United States

The Southeastern United States encompasses an area from 

eastern Oklahoma and eastern Texas, eastward across the 

lower Mississippi Valley and the Gulf States, to the Atlantic 

coast from North Carolina to Florida. Fire season in the 

Southeast is typically during the spring and fall. However, 

wildland fires do occur at other times of the year. The 

spring fire season occurs in the weeks before green-up.

This usually begins during March near the Carolina and 

Georgia coast and the Gulf States. The fall fire season 

occurs in October and November, normally after the first 

frost. Oklahoma and Texas are typically dry in late winter, 

and large grass fires are not uncommon in February. The 

Florida season may extend through the winter and spring 

well into June, especially during periods of drought. Critical 

fire weather patterns in this region are those that produce 

low relative humidity, and either strong surface wind or 

unstable air. 

McCarthy (1923), in a study of fire weather in the 

southern Appalachian Mountains, observed “Low vapor 

pressure (related to low dew point and low relative humidity) usually accompanies high atmospheric pressure and 

seems to be induced by prevailing wind from the west or 

northwesterly directions, while south or easterly winds tend 

to increase the humidity.”

McCarthy (1924) further stated, “Winds, coming from 

the interior of the continent and warming as they move 

southward, are usually low in humidity, a condition which 

is increased by the downward convection of cold air in 

the high pressure zone which warms as it approaches the 

surface.” 

Williams and Smith (1962) documented the weather 

and fire behavior associated with the March 1953 Brasstown 

Fire in South Carolina. They determined that the fire’s large 

growth and extreme fire behavior occurred after the passage 

of a cold front when northwesterly winds brought dry air 

from Canada and the Great Lakes.

Early fire case studies concluded that high fire activity in the Southeast is more often associated with surface 

high-pressure systems that originate in Canada or those that 

move across the Rocky Mountains from the Pacific Ocean. 

The important characteristic of these high-pressure systems 

is the dry air that replaces the moist Gulf of Mexico or 

Atlantic Ocean airmass, which normally covers this part of 

the country.

The movement of surface high-pressure systems is 

dependent upon the upper level windflow. For that reason, 

it is difficult to discuss critical fire weather patterns without 
32

GENERAL TECHNICAL REPORT PNW-GTR-891

linking the surface features to upper level pressure patterns. Three upper level patterns are effective in keeping 

the Southeast under the influence of high pressure at the 

surface. If the antecedent condition of below normal rainfall 

is in place, a critical fire weather pattern emerges.

Strong westerly flow—

During the spring and fall, strong westerly winds aloft 

result in a rapid succession of Pacific fronts traversing 

the Southeast. Little, if any, moisture from the Gulf of 

Mexico is able to return to the region in advance of these 

cold fronts. Rainfall with the front is sparse and light. 

Exceptionally low relative humidity may occur the day after 

frontal passage, and little recovery can be expected before 

the next front arrives. Strong and gusty winds are a distinct 

possibility.

Northwesterly flow—

Dry air, associated with Canadian high-pressure systems, 

can spread across the Southeast during the spring and fall. 

The initial Canadian cold front moves through the Southeast 

and remains stationary far south of the region until the upper level pattern changes. A large and stagnant high-pressure system settles over the region. Weak fronts from the 

north may reinforce the dry airmass. Relative humidity may 

not be quite as low as with Pacific fronts, and better humidity recovery can be expected at night. Strong northwest to 

north winds often occur as the surface high pressure pushes 

into the Southeast. 

Blocking ridge aloft—

This pattern occurs when high pressure aloft persists near 

the Atlantic coast for an extended period of time, possibly 

for a few weeks. Weather systems from the west or north 

are blocked from moving through the region. Little or no 

rainfall is produced during the period that the upper level 

ridge is in place.

In addition to the upper level patterns, extreme fire 

behavior can also occur in advance of a tropical storm owing to subsidence-produced dry air and a strong wind area 

that extends beyond the cloud and rain shield.

Critical fire weather patterns should be carefully examined for the presence of strong low-level jets (i.e., reverse 

wind profile). Research conducted by Byram (1954) showed 

a strong connection between low-level jets and extreme 

fire behavior in the Southeast. (Note: For more information 

concerning low-level jets and adverse wind profiles, see 

chapter 6.)

The combination of extreme drought and critical fire 

weather patterns was a major factor in the severe 1998 

Florida wildland fire season. Fires in the northern and 

central portions of the state experienced major fire runs on 

July 4, driven by strong westerly winds and unusually low 

relative humidity of 30 percent or less (fig. 3-8). The source 

of the dry air was the Great Plains, the dry air being pushed 

into Florida by a northwesterly upper level windflow

(fig. 3-9).

Southwestern United States

The Southwestern region includes the states of Arizona, 

New Mexico, and west Texas. The normal fire season spans 

the months of May to October but can extend throughout 

the year in the grasslands of eastern New Mexico and 

western Texas.

Figure 3-8—Visible satellite picture 2132 Coordinated Universal 

Time, 01 July 1998, shows strong, dry westerly wind pushing 

smoke columns from central Florida wildfires out over the Atlantic 

Ocean. (National Oceanic and Atmospheric Administration and 

University Corporation for Atmospheric Research.)
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Crimmins (2005) examined the seasonal climatology 

of extreme fire weather conditions across Arizona and New 

Mexico during the period 1988–2003. He found that there 

are three key upper level patterns associated with over 80 

percent of the extreme fire-weather days identified in this 

study. These upper level patterns represent broad southwesterly flow and large geopotential height gradients and are 

very similar to the critical fire weather patterns identified

by Schroeder et al. (1964). All three of these upper level patterns are consistent with the breakdown of the upper level 

ridge critical fire weather pattern defined earlier.

The major critical fire weather patterns of the Southwest are listed below.

Breakdown of Upper Ridge—

This is the most prevalent pattern in the Southwest, as a 

mean 500 hPa ridge is frequently positioned over the area 

during the fire season. From late spring through the early 

summer, upper level troughs moving inland from the Pacific 

Ocean are strong enough to temporarily push the upper 

ridge east and south of the area. These upper troughs are 

Figure 3-9—500 hPa geopotential height 

map indicating northwesterly flow aloft over 

Florida. (National Oceanic and Atmospheric 

Administration, National Centers for Environmental Prediction/National Center for 

Atmospheric Research reanalysis.)

Figure 3-10—07 May 2000, 500 hPa geopotenital height map. 

(National Oceanic and Atmospheric Administration, National 

Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis.) 
34

GENERAL TECHNICAL REPORT PNW-GTR-891

manifest at the surface as dry, cold fronts, which produce 

strong winds, very low relative humidity, and isolated dry 

lightning. The airmass becomes unstable as the upper level 

trough approaches, resulting in moderate to high Haines 

Index values. Strong upper level winds will frequently mix 

down to the surface, producing winds of 60 to 80 km/h. 

The peak fire season ends when these upper troughs stay 

well to the north and the southwest monsoon becomes fully 

developed.

The May 7, 2000, Cerro Grande Fire in New Mexico 

exhibited extreme fire behavior owing to a critical fire 

weather pattern known as the breakdown of the upper level 

ridge. A strong upper level trough (fig. 3-10) was moving into Arizona and New Mexico, pushing a ridge that 

had been over the area into Texas and Oklahoma. Strong 

southwest surface winds (fig. 3-11) were experienced on 

the fire with gusts up to 120 km/h. Drought conditions and 

Figure 3-11—07 May 2000, visible satellite picture showing well-defined smoke plume driven by strong southwest winds. 

(National Oceanic and Atmospheric Administration, National Weather Service.) 

extremely low relative humidity also contributed to the 

extreme fire behavior. The final size of the fire was 20 000 

ha and 235 homes were burned.

Early stage monsoon—

The onset of the southwest monsoon can present an opportunity for extreme fire behavior owing to the combination 

of gusty wind, low relative humidity, and dry lightning-induced fire starts. As the mean 500 hPa ridge builds north in 

June and early July, moisture begins to increase at mid-levels while surface conditions remain hot and dry. The speed 

and strength at which the monsoon develops determine 

the severity of this pattern. If the monsoon starts slowly, 

there may be enough dry lightning to overwhelm local fire 

management resources. If it develops quickly, dry storms 

will rapidly become rain producers and effectively end the 

fire season. When surface dew points rise to 10 to 15 °C, the 

majority of storms will be wet.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 3-12—1200 Coordinated Universal Time, 27 February 2011, 

500 hPa geopotential height map showing an upper level low system 

centered along the California/Arizona border and strong jet stream 

winds over New Mexico and West Texas. (National Oceanic and 

Atmospheric Administration, National Weather Service.)

Figure 3-13—1800 Coordinated Universal Time, 27 February 

2011, surface pressure map showing a cold front in extreme eastern New Mexico and a dryline in central Texas. The dryline marks 

the boundary between very dry air in western Texas and moist air 

located across eastern Texas (National Oceanic and Atmospheric 

Administration, National Weather Service.) 

Lee surface trough/dryline—

This pattern occurs in eastern New Mexico and western 

Texas in advance of an approaching upper level trough. 

Well ahead of the upper trough, a north-south dryline develops in the surface pressure pattern that sharply divides 

moist air to the east and dry air to west. The passage of a 

dryline is similar to that of a dry cold front. Strong, gusty 

southwest winds develop and surface dewpoint temperatures drop from 10 to 20 °C to -5 to -10 °C. This results in 

very low relative humidity and rapidly drying fuels. Dry, 

windy conditions behind a dryline can last for hours until 

the trailing cold front moves through with much cooler temperatures, higher relative humidity, and decreasing west to 

northwest winds. 

A classic example of the lee surface trough/dryline 

critical fire weather pattern occurred on February 27, 2011, 

in west Texas when wildfires burned thousands of hectares 

and numerous structures, and caused the evacuation of 

thousands of people. The upper level pattern consisted of a 

500-hPa low pressure center along the southern California/

Arizona border with a jet stream ahead of the low over 

New Mexico and west Texas (fig. 3-12). The surface pattern 

(fig. 3-13) indicated a cold front across New Mexico and a 

dryline in west Texas, both moving toward the east. Figure 

3-14 displays both the cold front and dryline superimposed 

on the visible satellite picture. Very low relative humidity values of 5 to 15 percent were observed in west Texas 

between the dryline and the cold front. The Haines Index 

map that morning (fig. 3-15) indicated values of 6 across 

west Texas and extreme eastern New Mexico. This critical 

fire weather pattern produced strong southwesterly winds, 

very low relative humidity and dry, unstable air over west 

Texas resulting in extreme fire behavior on many of these 

wildfires. 

Rocky Mountain and Intermountain Regions

These two regions cover much of the interior Western 

United States. The Rocky Mountain region includes the 

states of Montana, Wyoming, Colorado, and northern Idaho. 

The Intermountain region comprises the states of Nevada, 

Utah, and southern Idaho. The fire season ranges from May 

through October in the southern and June through October 

in the northern portions of these regions. However, in the 

grasslands of eastern Colorado, eastern Wyoming, and 

eastern Montana, it may start as early as February or March 

prior to green-up.

A considerable amount of fire weather research has 

been conducted in these regions, beginning with the historic 
36

GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 3-14—1800 Coordinated Universal Time, 27 February 2011, visible satellite picture with superimposed 

cold front and dryline. Relative humidity in the teens and subteens and zero cloud cover indicate the presence of 

very dry air over western Texas, an area between the dryline (yellow dashed line) and cold front (blue line with 

triangles). (National Oceanic and Atmospheric Administration, National Weather Service, and the University of 

Wyoming.) 

Figure 3-15—1200 Coordinated Universal Time, 27 

February 2011, Haines Index map. An area of Haines 

Index 6 (dry, unstable air) covers extreme eastern New 

Mexico and western Texas. (USDA Forest Service, 

Wildland Fire Assessment System.) 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

1910 Great Idaho Fire. Beals (1914) studied this fire that 

burned over 800 000 ha in Idaho and Montana and caused 

85 fatalities. He noted, “There were many fires burning in 

northern Idaho, but they were kept under fair control until 

August 20, when a hot, high wind from the southwest began 

to blow. They burned so furiously that nothing could be 

done to stop them.”

Syverson (1962, 1963, 1964) researched and identified 

a number of critical fire weather patterns in the Northern 

Rocky and Intermountain regions as part of a “Nationwide 

Study of Synoptic Fire Weather Types” project spearheaded 

by Schroeder, Glovinsky, Hendricks, and others. He studied 

weather patterns on days when the fire danger was high or 

days of large fire activity and concluded that:

• The area of high fire danger is almost always on the 

southwest or west side of the high-pressure cell at 

the surface.

• The greatest danger occurs just ahead of the upper 

trough in the area of the low pressure at the surface.

• The breakdown of this type (high pressure) comes 

with a strong upper air impulse of cooler air moving 

through from the Pacific.

Syverson’s conclusions agree very well with what

occurs during the breakdown of the upper level ridge critical fire weather pattern.

Anderson (1968) examined the weather and fire environment conditions during the September 1, 1967, major 

run of the Sundance Fire in northern Idaho. He found that 

the extreme fire behavior on this fire occurred with strong 

winds and low relative humidity in the prefrontal area ahead 

of an advancing cold front. 

Werth and Ochoa (1993) documented the weather and 

fire behavior that occurred on the 1988 Willis Gulch and 

1989 Lowman Fires in central Idaho. The breakdown of the 

upper level ridge critical fire weather pattern was identified as significantly contributing to extreme fire behavior 

observed on both fires. They concluded that this pattern 

consisted of both upper level and surface pressure pattern 

components (fig. 3-16) that resulted in high Haines Index 

values. These index values correlated well with the ROS for 

both fires, validating the usefulness of the Haines Index. 

Figure 3-16—Typical 500 hPa and surface pressure pattern associated with the breakdown of the upper ridge critical fire weather 

pattern. Pattern is also favorable for moderate to high Haines 

Index values. Source: Werth and Ochoa 1993. 

The transition zone, between an upper level ridge and upper 

trough, and in an area defined by the surface thermal lowpressure trough, is a favored location for moderate and high 

Haines Index values.

Gibson (1996) found that the breakdown of the upper 

ridge critical fire weather pattern was present with major 

increases in area burned on wildland fires in the Northern 

Rocky region. Gibson stated, “The pattern develops as the 

normal upper level ridge is shoved east by an approaching 

shortwave trough.”

In a study of lightning-induced wildfires in Nevada, 

Miline (2006) found that two weather patterns account for 

all 17 of the major outbreaks over a 9-year sample period. 

The “monsoon pattern” accounted for 7 of the 17 events and 

is characterized by high pressure centered over northern 

Utah and southern Idaho. With this pattern, warm, moist air 

originating in the Gulf of California is advected northward 

into Nevada, triggering thunderstorms. The second and 

more significant pattern involves a negatively tilted shortwave trough moving northeastward from the eastern Pacific 

Ocean into north-central California and through northern 

Nevada and southern Oregon.

The following is a brief summary of critical fire 

weather patterns in the Rocky Mountain and Intermountain 

regions.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Upper ridge-surface thermal trough—

This is the most significant pattern for these regions. It is 

characterized by a strong north-south upper ridge along 

105 to 110 degrees west longitude and a hot, dry surface 

thermal trough extending from central California to eastern 

Washington or Idaho. High fire danger results when a weak 

mid- to upper level trough moves up the west side of the 

ridge, producing dry lightning in the vicinity of the thermal 

trough. If the upper trough is strong enough, the upper ridge 

will break down and the thermal trough will shift eastward 

across the area. A dry and windy surface cold front then 

follows the thermal trough, producing very high fire danger 

and increasing the threat of extreme fire behavior on ongoing wildland fires.

Early stage monsoon—

This pattern occurs with an upper level ridge around 105 

degrees west longitude and an upper trough off the Pacific 

coast. It results in dry lightning and gusty winds over the 

southern parts of these regions.

Foehn wind/Chinook wind—

These strong downslope winds, along the eastern slopes of 

the Rocky Mountains, are unusually warm and dry for the 

season. This pattern occurs when strong jet-stream winds 

blow perpendicular to the mountains and the airmass is stable. They are most pronounced in the winter and spring, but 

can occur during the fall. When the upper level windflow 

is from the southwest, the onset of Chinook winds is often 

prior to the passage of a weak cold front. When the flow is 

northwesterly, the strong wind begins after frontal passage.

The 1994 South Canyon Fire in western Colorado is a 

good example of a fire that burned during a breakdown of 

the upper ridge critical fire weather pattern. On the afternoon of July 6, the fire rapidly transitioned from a surface 

to a crown fire during the passage of a dry cold front. 

Tragically, 14 firefighters perished when the fire overran 

their position. The upper level pattern that afternoon (fig. 

3-17) showed a low center in northwestern Wyoming and a 

trough southward along the Colorado/Utah border. This low 

pressure system replaced an upper ridge that had been previously over Colorado. A surface cold front moved across 

the fire site earlier in the afternoon and at 1800 Mountain 

Figure 3-17—1800 Mountain Daylight Time, 06 July 1994, 500 

hPa map. Source: USDA et al. 1994. 

Figure 3-18—1800 Mountain Daylight Time, 06 July 1994, surface 

map. Source: USDA et al. 1994. 

Daylight Time (MDT) was located in eastern Colorado (fig. 

3-18). This weather pattern not only produced strong, gusty 

winds and unusually low relative humidity (<10 percent), 

but also very unstable air. Fuels were also especially dry 

owing to long-term drought.

The September 6–7, 1988, extreme fire behavior exhibited on the Yellowstone National Park (northwest Wyoming) 

and Canyon Creek (Montana) Fires also occurred during a 

breakdown of the upper level ridge. An upper level trough 

and a strong west-to-northwest jet stream (fig. 3-19) pro-
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 3-19—1200 Coordinated Universal Time 07 

September 1988, 500 hPa geopotential height map 

showing a trough (red line) over Alberta, Canada, 

and a strong northwesterly jet stream (blue line) 

over Idaho, Montana and Wyoming. (National 

Oceanic and Atmospheric Administration, National 

Weather Service.) 

duced winds in excess of 80 km/h, unusually low relative 

humidity, and major crowning on both of these wildland 

fires. The passage of two cold fronts (fig. 3-20) added to the 

severity of the weather pattern. A Chinook wind developed 

in Montana, pushing the Canyon Creek Fire well east of the 

Continental Divide. Long-term drought was also a major 

factor.

Pacific Northwest Region

The Pacific Northwest region comprises the states of 

Washington and Oregon. The typical fire season is short 

compared to other regions and extends from June through 

early October. 

There are two critical fire weather patterns in this 

region, foehn or east winds in western Washington and 

western Oregon, and the breakdown of the upper ridge from 

the crest of the Cascade Mountains eastward across eastern 

Washington and eastern Oregon. 

East winds were recognized as a fire problem west of 

the Cascades from the beginning of fire weather research. 

Beals (1914) and Joy (1923) noted that large fires west of 

the Cascades were caused by strong east winds that were 

unusually hot and dry for the area. They also noted that 

these strong winds occurred when there was high pressure 

east of the Cascades and low pressure west of the Cascades.

Dague (1934) documented weather during the August 

1933 Great Tillamook Fire that burned 105 880 ha in 

western Oregon. He stated, “Low relative humidity, fresh to 

strong easterly winds, and high temperatures were responsible for this huge fire.” Dague also observed that a surface 
40

GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 3-20—1200 Coordinated Universal 

Time, 07 September 1988, surface pressure 

map showing cold fronts over northwestern Wyoming along the Canadian/United 

States border. Source: National Weather 

Service 1993.

low-pressure trough west of the Cascades contributed to the 

strength of these winds, and the trough pushed northward 

from the interior of California.

Saltenberger and Barker (1993) researched weather and 

extreme fire behavior conditions during the August 4–5, 

1990, Awbrey Hall Fire in central Oregon. They concluded 

that the plume-dominated wildfire became severe owing to 

a combination of fuels and weather, noting, “The Haines 

Index performed well. When the index indicated moderate 

to high growth potential the fire displayed extreme behavior 

and rapid growth.”

In a study of lightning-induced wildland fires in the 

Pacific Northwest, Rorig and Ferguson (1999) discovered 

that there were distinctly different weather patterns between 

dry and wet thunderstorm days. The pattern for dry days 

showed an upper trough near the coast and a pronounced 

thermal trough at the surface in eastern Washington and 

eastern Oregon (near the Idaho border). Wet-pattern days 

show a deeper upper trough (much lower geopotential 

heights) and a weak surface thermal trough in southern 

Idaho and eastern Nevada.

Critical fire weather patterns of the Pacific Northwest 

are detailed below.

Foehn wind/east wind—

Severe east wind patterns occur when surface high pressure 

pushes inland behind the passage of a cold front and becomes centered over eastern Washington, Idaho, or western 

Montana. Meanwhile, the California surface thermal trough 

pushes northward along the Oregon and Washington coasts 

(fig. 3-21). This pressure pattern produces strong pressure 

differences (gradients) across western Washington and 

western Oregon, resulting in offshore flow and northeastto-east winds of 80 to 100 km/h through the Columbia 

Gorge and the ridges and passes of the Cascade and coastal 

mountains. Subsidence also results in warming and drying 

of the airmass, and relative humidity can drop to 10 percent 

or lower. The combination of strong wind and unusually 

low relative humidity often results in wind-driven fires and 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

extreme fire behavior. The upper level pattern (fig. 3-22) 

shows a strong high amplitude ridge off the coast between 

130 and 140 degrees west longitude. The east wind pattern 

normally ends when the upper ridge moves inland and the 

surface thermal trough either dissipates or pushes east of the 

Cascades. This pattern typically occurs during September 

and early October and often represents the peak of the fire 

season west of the Cascades.

Upper ridge breakdown—

This is similar to the type previously described for the 

Rocky Mountain and Intermountain regions. In this case, 

the pattern is shifted farther west so the southwest flow is 

over Oregon and Washington. This pattern occurs when an 

upper level trough approaches the coast pushing the upper 

ridge to the east. Cooling aloft results in unstable air and an 

increased risk of lightning. If the airmass is dry, moderate 

to high Haines Index values and dry lightning are possible. 

The upper level winds will frequently mix to the surface, 

resulting in strong gusty winds. Meanwhile, the surface 

thermal trough will shift eastward across the area increasing the threat of extreme fire behavior on new and ongoing 

wildland fires.

California Region

The fire season extends from mid-May through October in 

northern California and from late March through December 

in southern California. However, during drought years, the 

season in southern California can extend throughout the 

year. 

Krumm (1954) examined the meteorological conditions 

that affected the July 9, 1953, Rattlesnake Fire in northern 

California. Fifteen firefighters were killed on this fire. 

He determined that strong downslope winds occurred on 

Figure 3-21—East wind surface 

pressure pattern with a thermal 

trough just off the Coast and a 

high centered over Idaho and 

western Montana. Pattern produces strong pressure gradients 

and strong winds from the crest 

of the Cascades to the Coast. 

(National Oceanic and Atmospheric Administration, National 

Weather Service.)
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 3-22—Typical east wind 

500 hPa geopotential height 

pattern with strong ridge off the 

Washington and Oregon coasts. 

(National Oceanic and Atmospheric Administration, National 

Weather Service.)

the fire after sunset, caused by a strong pressure gradient 

between surface high pressure along the Pacific coast and 

a thermal trough over the Sacramento Valley. This wind 

develops and descends to the surface similar to other foehn 

winds with low relative humidity and warm temperatures.

Weather, fuels, and fire behavior of the 1956 Inaja Fire 

were researched by Countryman et al. (1956) to determine 

what caused the firefighter fatalities during the fire’s major 

run. They determined that the fire burned during a Santa 

Ana wind event in a very wind-prone canyon in the San 

Diego area.

Ortel (1964) studied serious fire weather conditions 

in northern and central California as part of a nationwide 

study of synoptic fire weather types. He identified five 

weather patterns of concern: an upper level high over the 

Southwestern States, an upper high over the Pacific Ocean, 

an upper trough offshore near 130 degrees west longitude, 

surface cold fronts, and easterly winds from surface highpressure systems over the Great Basin.

The following is a summary of critical fire weather 

patterns in California. 

Foehn winds/north and mono winds—

This is the most common critical fire weather pattern in 

northern and central California. These strong, dry winds 

occur when surface high pressure builds into the Pacific 

Northwest, resulting in large pressure differences (gradients) across northern California. Dry air moves from 

Oregon southward into the Sacramento Valley with additional warming and drying. Relative humidity of 10 percent 

or less with temperatures of 43 °C (110 °F) can occur in the 

valley under these conditions. Windspeed strength depends 

on the pressure gradient, upper level windflow, and local 

topography. When the upper wind flow is from the north

or northeast, windspeed values in excess of 65 km/h often 

occur. Mono winds are strong easterly winds that occur 

along the western slopes of the Sierra Nevada Mountains. 

They are similar to the above-mentioned North winds, but 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

weather pattern. These massive wildfires burned hundreds 

of thousands of hectares, displaced nearly a million people, 

destroyed thousands of homes, and resulted in 10 fatalities. 

The surface and upper level pressure patterns are 

shown in figures 3-23 and 3-24. Strong surface high pressure was centered over Utah and Nevada, and an upper 

ridge was located off the California coast. A satellite picture 

(fig. 3-25) shows numerous smoke plumes being driven 

off the coast by northeast to east winds. Surface winds in 

excess of 80 km/h and relative humidity of less than 10 

percent were reported on the fires. 

Subtropical high aloft—

This pattern occurs when the westerlies shift northward, 

causing a closed subtropical high to become centered over 

the Southwest. The upper ridge axis extends far enough off 

the coast to block subtropical moisture from the area. This 

pattern produces heat waves in California. When a weak 

upper trough pushes into the western portion of the upper 

ridge, instability can result in a significant outbreak of dry 

lightning (fig. 3-26).

Alaska Region

The fire season in Alaska extends from May through 

August but is most active during June and July. 

The primary critical fire weather pattern in Alaska is 

the breakdown of the upper level ridge. 

Breakdown of the upper ridge with southwest flow—

This pattern occurs when southeasterly winds push moist, 

unstable air into the retreating upper level ridge (fig. 3-27). 

This can bring gusty winds and dry lightning to the interior 

of Alaska. The June 1998 Carla Lake Fire burned under 

these conditions caused by wind gusts of 56 km/h and

relative humidity of less than 25 percent.

Canada

Critical fire weather pattern research in Canada has primarily concentrated on the relationship between large fire 

occurrence and the 500 hPa upper level pattern. Newark 

(1975) researched the 1974 Ontario fire season, one of the 

worst on record in terms of the number of fires and area 

burned, and found that a persistent long-wave 500 hPa ridge 

in this case, the center of the surface high pressure is located in Nevada and Utah. This is primarily a late summer 

and fall pattern, but can occur at other times during the year 

if the fuels are dry.

Foehn winds/Santa Ana and sundowner winds—

This is the primary critical fire weather pattern for southern 

California. The pattern develops when surface high pressure builds over Nevada, Utah, and northern Arizona after 

the passage of an upper level trough. Meanwhile, an upper ridge of high pressure builds off the Pacific Northwest 

coast. North to northeasterly flow around the upper ridge results in cold air advection and strengthening of the surface 

high over the Great Basin. High pressure over Nevada and 

low pressure along the California coast result in strong pressure gradients over southern California. As a result, strong 

north to east winds develop from the crest of the mountains 

into the coastal areas. Air descending from higher to lower 

elevations causes compressional heating, which results in 

dramatic heating and drying of the air. When Santa Ana 

winds occur, extreme fire behavior conditions can suddenly 

develop as relative humidity drops to 10 percent or less and 

winds increase to 80 km/h or more. Winds can be substantially stronger in mountain passes and canyons. These 

winds are typically strongest at night and during the morning hours, and diminish somewhat during the afternoon 

owing to surface heating. This pattern occurs most often 

during the fall and winter months. 

A sundowner wind is an offshore northerly foehn 

wind that occurs in the lee of the Santa Ynez Mountains, 

which rise directly behind Santa Barbara and the surrounding coastal area. They develop when high pressure at the 

surface is centered over the Pacific Northwest and northern 

California and pressure gradients are perpendicular to the 

east-west axis of the Santa Ynez Mountains. These winds 

often precede Santa Ana events by a day or two. The normal 

progression is for the surface high pressure to migrate into 

the Great Basin causing pressure gradients and winds to 

shift more to the northeast and east ending the sundowner 

winds. (for more information on Santa Ana and sundowner 

winds, see chapter 2.) 

The October 2007 siege of wildland fires in southern 

California is a good example of a Santa Ana critical fire 
44

GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 3-23—1200 Coordinated Universal Time, 22 

October 2007, surface pressure map. High centered 

over Utah with strong pressure gradients over southern California. (National Oceanic and Atmospheric 

Administration, National Weather Service.

Figure 3-24—1200 Coordinated Universal Time, 22 

October 2007, 500 hPa geopotential height map. A 

strong high was centered off the northern California 

coast. (National Oceanic and Atmospheric Administration, National Weather Service.)
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 3-25—22 October 2007 visible satellite picture showing smoke from numerous southern California wildfires blowing out over the Pacific Ocean. (National Oceanic and Atmospheric Administration, National Weather Service.) 

Figure 3-26—Critical California lightning pattern with subtropical 500 hPa ridge over the Great Basin and short-wave trough 

(red dashed line) moving inland from the Pacific Ocean. (National Oceanic and Atmospheric Administration, National Centers 

for Environmental Prediction/National Center for Atmospheric 

Research reanalysis.)
46

GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 3-27—Breakdown of the upper ridge (dashed red line) 

critical fire weather pattern in Alaska. Source: National Weather 

Service 1993.

Figure 3-28—0600 Mountain Daylight Time, 27 August 1981, 

500 hPa geopotential height map showing a strong ridge over the 

Canadian Prairie Provinces and a low pressure system along the 

British Columbia coast. Large northern Alberta wildland fires 

were burning within a transition zone between the ridge and low 

pressure system, a pattern known as the breakdown of the upper 

level ridge. Source: Nimchuk 1983. 

was located over northwestern Ontario. This ridge produced 

an extended period of dry weather from mid-June through 

early August resulting in extreme fire danger. He also noted 

that the ridge exhibited orderly oscillating behavior and that 

the fire danger generally increased during the building of 

the ridge, decreased during the collapse, and peaked during 

maximum ridging.

Nimchuk (1983) studied fire weather conditions during 

the severe 1981 Alberta, Canada, fire season to determine 

whether there was a relationship between surface and 500 

hPa pressure patterns and the occurrence of large wildland 

fires. He identified the lifetime of an upper level ridge in 

three distinct stages: establishment, progressive weakening, 

and final breakdown (fig. 3-4). He concluded that the trigger 

for extreme fire behavior was the breakdown, rather than 

the presence of a persistent upper level ridge.

In support of his conclusion, Nimchuk detailed the upper level and surface pressure patterns during the extreme 

fire behavior conditions experienced on wildland fires in 

northern Alberta on August 27, 1981. On that day, winds of 

40 to 65 km/h and relative humidity as low as 30 percent 

caused fire to spread across 376 000 ha of forest land. The 

500 hPa pattern (fig. 3-28) showed a strong upper ridge east 

of the fires in Saskatchewan. An upper low pressure system 

was centered along the British Columbia coast. Meanwhile, 

a surface low pressure center (fig. 3-29) was located in 

northwestern Alberta with a cold front extending southward into northwestern Montana. Both the upper level and 

surface pattern exhibited strong temperature and pressure 

gradients across northern and western Alberta resulting in 

strong southerly surface winds. 

Skinner et al. (1999) examined 500 hPa pressure patterns over North America to see if there was a correlation 

between anomalous height values and wildland fire severity 

in Canada. Their results showed statistically significant 

correlations between regional total area burned and clusters 

of anomalous 500 hPa geopotential height values over and 

immediately upstream of the affected region. The high 

burned area years coincided with positive 500 hPa height 

anomalies while the low burned area years coincided with 

negative height anomalies. 
47

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

In a subsequent study, Skinner et al. (2002) determined 

that large Canadian wildland fires were associated with 

strong meridional flow at the 500 hPa geopotential height 

level rather than zonal, or weak westerly windflow. This 

substantiates the upper level patterns that Nimchuk (1983) 

identified during the severe 1981 Alberta fire season.

In general, Canadian critical fire weather patterns are 

very similar to those experienced in the United States. 

The Hudson Bay and Northwest Canadian high pressure 

systems, containing modified arctic air and very low relative humidity, are the primary critical fire weather pattern 

in the eastern Canadian provinces, such as Ontario and 

Quebec. Critical fire weather and extreme fire behavior 

can occur on the periphery of these high pressure systems, 

especially prior to, during, and after the passage of dry cold 

fronts. The east slopes of the Rocky Mountains in Alberta 

are also susceptible to Chinook winds. Timber fires in the 

Alberta Rocky Mountains typically require an extended 

drought period, usually from late June into late August and 

September, followed by a Chinook strong wind and low 

relative humidity event. Large grass/brush fires exhibiting 

extreme fire behavior because of Chinook events are possible throughout the winter months of November through 

March if snow-free conditions exist. On November 27, 2011, 

five homes were lost in the Palliser triangle area in southern 

Alberta to a prairie grass/brush fire during a Chinook 

event.1 Foehn-type winds also affect the British Columbia 

coastal regions and Vancouver Island through a similar 

mechanism as the east winds in Washington and Oregon 

(strong surface ridge building rapidly from the north over 

the British Columbia interior, particularly in fall). This situation is often referred to as an easterly outflow event.

Australia

Fire season extends from June to October in northern 

Australia (winter months) and November through April (late 

spring through early fall) in southern Australia. During periods of drought, the threat of bushfires exhibiting extreme 

fire behavior significantly increases. Outbreaks of historical large bushfires have burned hundreds of thousands of 

hectares, thousands of homes, and caused many fatalities. 

Long (2006) studied fire seasons in the state of Victoria 

from 1970 to 1999 to determine the frequency of extreme fire 

weather days and the synoptic weather patterns associated 

with extreme days. The author found a high percentage of 

extreme fire weather days occurred when there was a northwest to northerly windflow over Victoria advecting hot, dry 

air from the Australian interior. Winds from this direction 

are associated with surface pressure patterns ahead of cold 

fronts. In the Northern Hemisphere prefrontal winds blow 

from the south or southwest, but in the Southern Hemisphere 

these winds blow from the northwest or north. Long also 

evaluated the Haines Index and found that it correlated well 

with extreme fire weather days, but at a higher frequency.

The February 16, 1983, Ash Wednesday bushfires 

in Victoria were the most destructive to that date, taking 

the lives of 75 people and burning over 335 000 ha. Mills 

(2005) researched surface and 500 hPa patterns during the 

fire event and found that the depth of the cool air behind the 

surface cold front was a critical factor in the severity of the 

fires. His analysis of 40 years of data indicated the temperature gradient at 850 hPa on Ash Wednesday was the third 

strongest experienced in any February to date, and the

1 Nimchuk, N. 2012. Critical fire weather patterns—a few comments. NickNimchuk@gov.ab.ca (7 February 2012?)

Figure 3-29—1200 Mountain Daylight Time, 27 August 1981, 

surface pressure map showing high pressure over eastern Saskatchewan and Manitoba and a low pressure center and cold front 

in Alberta. The large wildland fire in northern Alberta exhibited extreme fire behavior prior to the passage of the cold front. 

Source: Nimchuk 1983.
48

GENERAL TECHNICAL REPORT PNW-GTR-891

10th strongest during any summer month. The strong 

temperature gradient likely resulted in stronger surface 

winds that significantly increased the ROS and severity of 

the fires.

The surface and upper air weather charts Mills examined were indicative of the breakdown of the upper ridge 

critical fire pattern. The 500 hPa pattern on February 16, 

1983 (fig. 3-30), showed a strong upper level high pressure 

area centered over the Australian interior with a ridge 

extending across southeastern Australia. At the same time, 

an upper level trough over the Southern Ocean was moving eastward across southern Australia, pushing the upper 

ridge farther off the southeastern Australian coastline. 

Meanwhile, the surface pressure pattern (fig. 3-31) showed 

a surface high off the eastern Australian coast and a surface 

low over the Southern Ocean with a cold front extending 

northward into southern Australia. This cold front was moving eastward and would eventually move across Victoria 

(southeastern Australia) during the day. Between the 

surface high and the cold front a strong pressure gradient 

developed, which produced strong northwesterly prefrontal 

winds across Victoria. These northwesterly winds also 

brought hot, dry air from the interior of Australia across 

southeastern Australia. Strong surface wind, very low 

relative humidity and long-term drought created conditions 

that resulted in extreme fire behavior on bushfires across 

Victoria on February 16, 1983. 

Models and Predictive Tools

The Predictive Services Program is national in scope. It 

supports the wildland fire community and others with 

information and decision-support products. The program 

encompasses meteorologists and intelligence coordinators 

at each geographic area coordination center (GACC) and 

Figure 3-30—1200 Coordinated Universal Time, 16 February 

1983, 500 hPa geopotential height map showing a large upper 

high over the Australian interior with a ridge off the southeastern 

coast. A well- defined upper trough, over the Southern Ocean, was 

moving eastward toward New South Wales and Victoria. (National Oceanic and Atmospheric Administration, National Centers 

for Environmental Prediction/National Center for Atmospheric 

Research reanalysis.) 

Figure 3-31—0600 Coordinated Universal Time, 16 February 

1983, surface pressure map showing high pressure off the eastern 

Australian coast and a cold front moving eastward across New 

South Wales toward Victoria. Strong, dry northwesterly winds 

in advance of the cold front. (National Oceanic and Atmospheric 

Administration, National Centers for Environmental Prediction/

National Center for Atmospheric Research reanalysis.) 
49

Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

the National Interagency Coordination Center (NICC). 

Fire behavior or long-term analysts are detailed to GACCs 

during the fire season.

The following is a list of products produced by Predictive Services units that are useful in determining areas of 

greatest concern in relation to large fire potential and the 

possibility of extreme fire behavior.

The National Wildland Significant Fire Potential

Outlook

This product is prepared by NICC on the first business day 

of each month. The report consists of national maps and 

associated text that depict areas of below normal, normal, 

and above normal significant fire potential. 

GACC 7-Day Significant Fire Potential

This GACC product is produced daily during the primary 

fire season under the direction of a qualified fire weather 

meteorologist. The report contains projected fire weather, 

fuel dryness, fire danger, fire potential, and resource status 

information for the next 7-day period. A short discussion accompanies the report detailing weather of concern 

through the period. 

Fuel and Fire Behavior Advisories

These advisories are issued to inform fire managers and 

firefighters of safety concerns owing to existing or predicted 

fuel and fire behavior conditions.

Other GACC Products and Services

The GACC Predictive Services units provide a wide variety 

of products and services in support of wildland fire operations. These include weather/intelligence briefings, situation 

reports, and resource summaries. 

The National Weather Service (NWS) provides fire 

weather products and services in support of fire management decisions. Some of the best tools in assessing the 

potential for critical fire weather situations are the Fire 

Weather Watch and Red Flag Warning program, and Spot 

Weather Forecasts.

Fire Weather Watches and Red Flag Warnings

Fire Weather Watches and Red Flag Warnings are issued 

when the combination of dry fuels and weather conditions indicate the possibility of extreme fire danger or 

fire behavior. These conditions alert land management 

agencies to the potential for widespread new ignitions that 

could overwhelm initial attack activities, or conditions that 

could cause control problems on existing fires, etc. Any 

of these outcomes could pose a threat to life and property. 

Fire Weather Watches are issued when there is a high 

potential for the development of a Red Flag Event. Red Flag 

Warnings are used to warn of an impending, or occurring 

Red Flag Event. Their issuance denotes a high degree of 

confidence that weather and fuel conditions consistent with 

local Red Flag Event criteria will occur in 24 hours or less.

Spot Weather Forecasts/Digital Web Services

A spot forecast is a site-specific 24- to 36-h forecast issued 

to fit time, topography, and weather of a specific location. 

The spot forecast can be requested for wildfires, prescribed 

burns, spray projects, and other special projects. Other 

products available include FARSITE data streams and point 

forecast matrix forecasts from the National Digital Forecast 

Database. The NWS issues thousands of spot forecasts per 

year, and there is extensive use of digital Web services in 

diagnosing fire risks resulting from critical fire weather 

patterns.

The Storm Prediction Center’s (SPC) Fire Weather program issues a daily national fire weather guidance product 

for use by the NWS, as well as other federal, state, and local 

government agencies. The product is intended to delineate 

areas of the contiguous United States where preexisting 

fuel conditions, combined with forecast weather conditions 

during the next 8 days, may result in a significant threat of 

wildfires. 

There are three types of Fire Weather Outlook areas:

• Critical Fire Weather Area for wind and relative

humidity.

• Extremely Critical Fire Weather Area for extreme 

conditions of wind and relative humidity.

• Critical Fire Weather Area for dry thunderstorms.
50

GENERAL TECHNICAL REPORT PNW-GTR-891

The SPC Fire Weather Outlook comprises a day 1 and a 

day 2 forecast, in addition to a day 3 through 8 forecast. 

Summary/Knowledge Gaps

Fire weather research has been ongoing for nearly a century, and many advances have been made during that time 

concerning weather’s effect on wildland fire behavior. 

Wind and relative humidity have been effectively incorporated into the fire behavior models. However, the effect of 

atmospheric stability on fire behavior is not modeled and 

remains subjective at best. More research is needed, beyond 

the Haines Index, to quantify the effects of atmospheric 

stability on fire behavior.

The concept of critical fire weather patterns has been 

in existence for 50 years. It has been successfully applied to 

fire case studies, but rarely has it been used in conjunction 

with weather forecast models to predict periods when large 

fires or extreme fire behavior are likely to occur.

Future research into the climatology of critical fire 

weather patterns would be helpful in determining the 

frequency, duration, and strength of these events during 

a typical fire season. Because there are large variances in 

fire season severity, research is also needed to determine 

whether there are relationships among the occurrence 

of critical fire weather patterns, sea surface temperature 

anomalies, and atmospheric teleconnection indices. Sea 

surface temperature anomalies include El Nino-Southern 

Oscillation, the Pacific Decadal Oscillation, and the Atlantic 

Multidecadal Oscillation, for example. Atmospheric 

teleconnection indices include the Pacifc North American 

Index, the East Pacific/North Pacific Oscillation, North 

Atlantic Oscillation, the Arctic Oscillation, and others. 

Research into the strength, or dynamics, of these critical 

fire weather patterns could be accomplished by studying 

the temperature and moisture gradients (horizontal and 

vertical) associated with these systems. Synoptic-scale 

temperature and moisture gradients significantly affect the 

surface wind, relative humidity, and atmospheric stability 

on wildland fires, and ultimately the observed fire behavior. 

Research concerning the effects of surface thermal troughs 

on fire behavior also need to be better defined.

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Krueger, D.W.; Mallory, L.P.; Oertel, A.G.; Reese, 

R.H.; Sergius, L.A.; Syverson, C.E. 1964. Synoptic 

weather types associated with critical fire weather. 

Berkeley, CA: U.S. Department of Agriculture, Forest 

Service, Pacific Southwest Forest and Range Experiment 

Station. 492 p.

Show, S.B. 1931. Meteorology and the forest fire problem. 

Monthly Weather Review. 59: 432–433.

Simard, A.J.; Haines, D.A.; Blink, R.W.; Frost, J.S. 

1983. The Mack Lake Fire. Gen. Tech. Rep. NC-83. 

St. Paul, MN: U.S. Department of Agriculture, Forest 

Service, North Central Forest Experiment Station. 36 p.

Skinner, W.R.; Flannigan, M.D.; Stocks, B.J.; Martell, 

D.L.; Wotton, B.M.; Todd, J.B.; Mason, J.A.; Logan, 

K.A.; Bosch, E.M. 2002. A 500 Mb synoptic wildland 

fire climatology from large Canadian forest fires, 1959–

1996. Theoretical and Applied Climatology. 71: 157–169.

Skinner, W.R.; Stocks, B.J.; Martelli, D.L.; Bonsal, B.; 

Shabbar, A. 1999. The association between circulation 

anomalies in the mid-troposphere and area burned 

by wildland fire in Canada. Theoretical and Applied 

Climatology. 63: 89–105.

Stickel, P.W. 1928. Forest-fire weather in Central 

Massachusetts. Monthly Weather Review. 56(4):

134–136.

Syverson, C.E. 1962. Prediction of the crisis period and the 

use of Red Flag Forecasts. Unpublished report. On file 

with: U.S. Department of Agriculture, Forest Service, 

Pacific Wildland Fire Sciences Laboratory, 400 N 34th

St., Suite 201, Seattle, WA 98103.

Syverson, C.E. 1963. Fire weather synoptic types of the 

Northern Intermountain, Northern Rockies and the 

Northwestern Plains Regions. Unpublished report. 

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Service, Pacific Wildland Fire Sciences Laboratory, 400 

N 34th St., Suite 201, Seattle, WA 98103.
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Syverson, C.E. 1964. The meridional ridge–Pacific high, 

and fire weather of Northern Intermountain, Northern 

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Seattle, WA 98103.

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USDI, and USDC]. 1994. Report of the South Canyon 

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1994-573-183 / 84023 Region No. 8. Washington, DC: 

U.S. Government Printing Office. 243 p. 

Weidman, R.H. 1923. Relation of weather forecasts to the 

prediction of dangerous forest fire conditions. Monthly 

Weather Review. 51(11): 563–564.

Werth, P.A.; Ochoa, R. 1990. The Haines Index and Idaho 

fire growth. Fire Management Notes. 51(4): 9–13.

Werth, P.A.; Ochoa, R. 1993. The evaluation of Idaho 

wildfire growth using the Haines Index. Weather and 

Forecasting. 8(2): 223–234.

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National Fire Behavior School, San Dimas, CA 91773.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 4: The Role of Fuels in Extreme Fire Behavior

Russell Parsons1, W. Matt Jolly2, Chad Hoffman3, and Roger Ottmar4

Introduction

Fuels are central to the problem of extreme fire behavior. 

While it is possible to have fires without the influence of 

topography and under diverse weather conditions, it is 

impossible to have fire without fuels. Numerous instances 

of fatalities or close calls have occurred in deceptively light 

fuels where extreme fire behavior might not have been 

expected. Virtually all of the critical fire management issues 

facing land managers in years to come are fuels related. 

Changes in fuels because of climate change, disturbances 

such as insect pathogen outbreaks, windthrow, and invasive 

species have already changed fire management and will 

only continue to do so. Furthermore, of the three components of the fire behavior triangle (fig. 4-1), fuels are the 

only component that we have some capacity to manipulate 

through management actions. Fuels are also an area of fire 

science where advances in knowledge and technology offer 

new hopes and possibilities. Looking forward, advances in 

our ability to quantify and map fuels, using new tools such 

as laser imaging and high-resolution imagery will help develop fuels maps with more detail than was available before. 

Additionally, new physics-based fire models are emerging 

that will enable us to examine and improve our understanding of the sensitivities of fire behavior to fuels with greater 

detail than was possible before. For these reasons, there is a 

compelling need to characterize the role of fuels in extreme 

fire behavior. 

This chapter summarizes the role of wildland fuels in 

extreme fire behavior with a “big picture” perspective that 

captures the salient properties of fuels, both at fine scales

1 Russel A. Parsons is a research ecologist, U.S. Department of 

Agriculture, Forest Service, Missoula Fire Sciences Laboratory, 

5775 US West Highway 10, Missoula, MT 59808.

2 W. Matt Jolly is an ecologist, U.S. Department of Agriculture, Forest 

Service, Missoula Fire Sciences Laboratory, 5775 US West Highway 10, 

Missoula, MT 59808.

3 Chad Hoffman is an Instructor, Wildland Fire Program, College of Natural Resources, University of Idaho, Moscow, ID 83844.

4 Roger D. Ottmar is a research forester, U.S. Department of Agriculture, 

Forest Service, Pacific Wildland Fire Sciences Laboratory, 400 N 34th St., 

Suite 201, Seattle, WA 98103.

and across landscapes. We hope to inform readers of the 

current state of the science and to highlight knowledge gaps, 

with the idea of complementing, rather than duplicating, 

material presented elsewhere in this review. 

Key Aspects of Fuels: the Fuels Pentagon

Fuels are probably the most complex component in the 

fire environment triangle, yet are in many ways the least 

understood. While commonly conceptualized as being 

homogenous (dead) material, fuels are actually vegetation and span the full spectrum from live and vigorously 

growing vegetation to dead and decomposed material. 

Even simple fuel beds such as grasslands can have multiple 

types of fuels in multiple states or conditions. Under the 

right circumstances, all fuels are potentially available for 

combustion, but more often, different types of fuels will 

interact with fire in different ways. 

Five general characteristics influence how fuel particles 

will burn: chemistry, quantity, density, geometry, and 

continuity. We highlight these characteristics in the concept 

of the fuels pentagon in figure 4-2. The term geometry is 

broadly used here to include both the shape and size of 

Figure 4-1—The fire behavior triangle. 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 4-2–The fuels pentagon.

a given fuel particle, and its spatial relationship to other 

particles. Because we are generally interested in how fire 

spreads between particles, continuity is included as a key 

fuels characteristic and relates to the arrangement and 

distances between particles at various scales. While one 

characteristic may dominate how fuels burn, all characteristics interact to influence how fuels burn at a given time.

Scaling From Particles to Landscapes

Fires can exist across a wide range of spatial scales, thus 

it is useful to consider fuels across a series of hierarchical 

scales, starting with individual particles, such as a leaf 

or twig. This concept of scales more precisely identifies 

what aspects of fuels are influencing fire behavior, and by 

extension, what might contribute to extreme fire behavior. 

For example, an individual shrub or tree can be considered 

as a collection of particles. Under most conditions, only a 

subset of that collection (typically the drier, smaller diameter particles) will burn. The collection of shrubs in an area 

constitutes the shrub component within the larger fuel bed, 

which may include several vegetation components (e.g., 

understory and overstory trees, and litter). The diversity of 

fuels within most wildland fuel beds makes them difficult to 

classify or describe. One strategy to address this complexity 

is to describe fuels as a series of components, each with its 

own properties, such as is done in the Fuel Characteristic 

Classification System (FCCS) (Ottmar et al. 2007, Riccardi et al. 2007). The FCCS considers fuel beds as being 

composed of canopy, shrubs, nonwoody vegetation, woody 

dead material, and other basic components (fig. 4-3). Scaling 

up, a landscape can be viewed as a collection of fuel beds 

with different properties. 

Below, we describe the five fuels characteristics that 

compose the fuels pentagon and how each contributes to extreme fire behavior, with an emphasis on fundamental fuels 

properties that are measureable at the scales of individual 

particles or small collections of particles. In subsequent sections, we address how these fine-scale fuels characteristics 

are influenced by the landscape context. 

Chemistry

Fuels particles are composed of five broad chemical 

categories: water, carbohydrates, fats, proteins, and ash 

(mineral) content. In downed, dead sound woody fuels, 

water can compose only a small fraction of the total mass of 

the fuels, while in dead, rotten woody fuels’ and live fuels, 

water can make up 50 percent or more of the fuels total 

mass. The remainder of the fuel’s total mass is distributed 

disproportionately across the remaining four categories. A 

fuel’s fractional allocation to different categories of chemical compounds varies substantially depending on whether 

the fuel is alive or dead and for different parts of the fuel 

such as leaves or branches. We will detail the roles of each 

of the compounds on wildland fuels’ heat content and flammability with the exception of proteins, which are not likely 

a large contributor to combustion.

Fuel water or moisture content—

Fuel moisture content has long been considered a factor in 

driving fire behavior (Fons 1946, Hawley 1926, Richards 

1940). The specific heat of water is approximately 4000 J/(kg 

°C), over four times that of any other chemical component of 

wildland fuels. This suggests that a lot of energy goes into 

raising the water to boiling temperature that could otherwise be used to raise the fuel to ignition temperature and 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

create the gases that support flames. Fuels are ignited by 

raising their temperature to a point where sufficient volatile 

compounds are produced and are ignited either spontaneously or through a pilot heat source. When fuels are heated, 

some of the moisture is evaporated and pyrolysis is taking 

place. Conventional wisdom suggests all the moisture must 

be removed from a fuel particle before significant pyrolysis 

can occur (Rothermel 1972), but both theoretical (Catchpole 

and Catchpole 1991) and experimental (Pickett et al. 2010) 

results suggest that not all water in a fuel particle must be 

removed prior to ignition. However, many studies have 

shown that it takes longer to ignite a fuel particle with a 

higher moisture content (Dimitrakopoulos and Papaioannou 

2001, Pellizzar et al. 2007, Xanthopolous and Wakimoto 

1993) and thus moisture content must account for at least a 

portion of the ignitability of fuels. 

Figure 4-3—Horizontal stratification of an Fuels Characteristic Classification System fuel bed by strata and 

categories for improved fire behavior prediction.

Carbohydrates— 

Carbohydrates make up a large percentage of wildland 

fuels and can generally be classified as either structural or 

nonstructural (fiber and nonfiber). Generally, live fuels are 

equally split between fiber and nonfiber-based compounds, 

while dead fuels are mostly composed of only fiber-based 

compounds such as holocelluloses and lignin. Structural 

carbohydrates give fuels form, strength, and stability and 

the nonstructural carbohydrates serve as energy reserves in 

living plants. These carbon-based compounds provide the 

primary substrates for the gases that contribute to flaming combustion. Fuel combustion studies have primarily 

focused on structural compounds because it has been assumed that fuels are mostly composed of those compounds 

(Philpot 1970, Shafizadeh 1968). While that may be true for 

commercial lumber, it is not true for wildland fuels.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Fats—

Fat-based compounds make up 10 percent or more of a 

fuel’s dry mass. These compounds are generally composed 

of waxes, oils, resins, and isoprenes. In literature, this 

group of compounds is often referred to as crude fat, ether 

extractives, or simply extractive content. Highly flammable 

leaves are often found to contain large amounts of crude 

fats; eucalyptus leaves’ crude fat content is nearly 20 percent of their dry mass (Mutch 1970). Fats are an important 

fuels component because they can potentially release twice 

as much heat as other leaf compounds (Merrill and Watt 

1973). Thus small changes in fat can cause large changes 

in a fuel’s flammability. Much attention has been given 

to changes in a fuel’s flammability in relation to changes 

in extractive content. However, no studies have shown a 

conclusive link between changes in isoprenes and changes 

in flammability. Owens et al. (1998) suggested that changes 

in the terpene limonene can increase flammability of Ashe 

juniper (Juniperus ashei) by as much as 30 percent while 

the presence of other terpenes decreased its flammability. In 

contrast, Alessio et. al. (2008) found only a few weak correlations between terpene concentrations and flammability. 

Burgan and Sussot (1991) found that the amount of volatile compounds present was dependent on leaf age and that 

foliar flammability might therefore differ between old and 

new foliage. It is likely that the interactive nature of high 

moisture contents and high terpenoid concentrations may 

combine to limit their ability to influence combustion (De 

Lillis et al. 2009). 

Mineral (ash) content— 

Mineral, or ash, content is a measure of the amount of

fuel that is composed of unburnable compounds. It is generally quantified as the ash content or ash fraction of a fuel 

measured as the remaining biomass after complete combustion of a fuels sample. Ash content is sometimes corrected 

by removing silica from the ash sample because silica is 

considered to be completely inert to combustion processes 

(Philpot 1970). The remaining ash, however, can alter fuel 

combustion by increasing the proportion of charring to 

flaming combustion. Small changes in ash content induced 

large changes in the combustion of wildland fuels; however,

it is likely that not all of the ash content is involved in shifting combustion pathways. Ash content can vary greatly 

between species and is believed to contribute to the flammability of different species or of the same species at different 

times of the year (Broido and Nelson 1964).

Heat content— 

The heat content (heat of combustion) of wildland fuels is a 

measure of the amount of thermal energy produced during 

the complete combustion of fuels and is strongly related to 

the percentage of the fuel composed of carbon (Sussott et al. 

1975). Although not highly variable, heat content has been 

shown to vary among species, especially when the inorganic ash fraction of the samples is removed (Williamson 

and Agee 2002). Heat contents are usually reported as either 

high (gross) or low (net) heat content depending on whether 

or not the measured values have been corrected for the heating of the water vapor during the measurement. Total heat 

content of a wildland fuel is likely the sum of the individual 

heat contents of the chemical compounds that make up the 

fuel, weighted by their respective dry mass percentages. 

This may be important because compounds such as crude 

fats have twice the heat content per unit mass of carbohydrates such as cellulose, and thus as these compounds vary 

from species to species or seasonally within a species, they 

may influence the apparent fuel heat content. 

However, all of these values report the total heat of 

combustion and not the effective heat of combustion available to preheat adjacent fuels and help a fire spread. The 

effective heat of combustion is always lower because char 

has a high heat content but generally remains unburned 

during a wildland fire (Babrauskas 2006).

Flammability—

Anderson (1970) suggested a standardized method for rating wildland fuel flammability, which is broken into three 

categories: ignitability, sustainability, and combustibility. 

Ignitability is measured in terms of the ignition delay time, 

or time to ignition; sustainability measures how well the 

fuel can burn when the heat source is removed; and combustibility quantifies the rate of the combustion reaction. 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Attempts have been made to use foliar chemistry and moisture content to characterize flammability, but these studies 

are rarely standardized in a format that would permit their 

intercomparison.

Most studies focus on fuels ignitability, most likely 

because it is the easiest to measure. Many studies have 

shown that ignitability is highly correlated to changes in 

foliar moisture content (Dimitrakopoulos and Papaioannou 

2001, Pellizzaro et al. 2007, Xanthopoulos and Wakimoto 

1993), but these studies rarely explain more than half of 

the ignitability variation, suggesting that there are many 

unknown drivers that influence flammability. Other studies 

suggest that a more physical approach is needed to assess 

flammability or fire hazard and recommend metrics such as 

the fuel’s heat release rate as a way to rank a fuel’s flammability (Babrauskas and Peacock 1992). Heat release rate may 

be more related to combustibility than ignitability and thus 

both metrics may help to elucidate the true factors that are 

important to fuel flammability. 

So far we have discussed fuels’ different chemical 

components, which influence all fire behavior. This is 

the first, and perhaps most fundamental leg of the fuels 

pentagon. In addition to these chemical components, there 

are four key physical and structural characteristics of fuels, 

which constitute the other four legs of the fuels pentagon: 

geometry, mass, density, and continuity. Each of these key 

aspects can influence fire behavior and each is discussed in 

more detail below.

Geometry

Surface area-to-volume ratio is a fuel property that describes individual fuel particle geometry and strongly 

influences flammability, fuel temperature, and moisture 

dynamics. Surface area-to-volume ratio is often assessed 

based on measurements of diameter. However, assignment 

of fuel size classes based on diameter is only valid for fuels 

that have a cylindrical or rectangular geometric shape 

(Brown 1971). Where the geometric shape of the fuels 

particle departs from either a cylindrical or rectangular 

shape, more advanced measurement methods are required 

to estimate this parameter. Accuracy of surface area-tovolume ratios greatly depends on the degree of departure 

from the assumed geometry (Brown 1971). However, other 

methods based on water immersion do not need to assume 

a geometric shape and have shown good agreement with 

standard measurements (Fernandes and Rego 1998). The 

size of fuel particles influences the amount of heat required 

to ignite and combust the particle. Smaller particles require 

less heat exposure to ignite and combust compared to larger 

fuel particles. 

Surface area-to-volume ratio also forms the basis for 

the concept of timelag size classes (Fosberg 1970). The 

concept of timelag size classes is based on the amount of 

time required for dead fuel to lose approximately 63 percent 

of the difference in between its initial moisture content 

and the equilibrium moisture content in the atmosphere. 

Four timelag classes are currently used to characterize 

dead fuels: 0 to 2 h, 2 to 20 h, 20 to 200 h, and >200 h. 

Class midpoints were then used as the title for each timelag 

class: 1-h, 10-h, 100-h, 1,000-h. Timelag size classes have 

been widely used to assist in describing the fuel mass and 

distribution of fuel sizes within an area. The fuel mass by 

timelag size classes is used in mathematical predictions of 

fuel moisture, fire behavior, fire danger ratings, and in descriptions of fuels complexes. In general, fine fuels tend to 

combust more rapidly and have been associated with several 

firefighter fatalities and close calls (NWCG 1997). 

In addition to information regarding particle size, the 

distribution of fuel particles, estimates of fuel mass within 

an area, and the arrangement of fuel are other important 

variables that influence fire behavior. Fuel arrangement pertains to the packing ratio or compactness of fuel particles 

as well as the vertical and horizontal continuity of the fuels. 

In most empirical and semiempirical fire models, fuels are 

assumed to be homogeneously and continuously arranged; 

however, in real world situations, this seldom occurs. The 

effects of heterogeneous arrangements and deviations from 

continuity on fire behavior are not well understood. 

Quantity

The quantity (mass) of fuel available for combustion is a key 

factor in how fires burn. Fuel quantities are often expressed 

as a “loading” in terms of mass per unit area, such as tons 

per acre. Determination of quantity of fuels by size classes 
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GENERAL TECHNICAL REPORT PNW-GTR-891

is one of the fundamental assessments conducted in fuels 

inventory and, in conjunction with fuel moisture, meteorological parameters, and topography, helps to determine the 

availability of fuels to consume, generate fire intensity, and 

spread the flaming front along the ground or in the crowns 

of trees and shrubs. 

There are several definitions commonly used in the 

assessment and communication of fuel mass. The three 

most common are total fuel mass, potential fuel mass, and 

available fuel mass. Total fuel mass, or total biomass, is 

often considered the entire amount of combustible material 

present in an area. Potential fuel mass is the amount of the 

entire fuel mass that could burn in the hottest possible fire. 

Available fuel mass is the amount of the total fuel mass 

that will be consumed for a particular situation. In some 

cases, these three values may be very similar; however, 

in most forested fuels complexes, these values will differ 

greatly. Differences among the three fuel mass definitions 

arise from weather conditions, the distribution of fuel size 

classes, and other properties of the fuels complex. For 

example, in a moist temperate rain forest of the Pacific 

Northwest, total fuel mass can be in the hundreds of Mg/ha;

of this, however, the potential fuel mass will typically be 

considerably less, as much of the larger diameter material, 

while flammable, is difficult to ignite. The available fuel 

mass is often a much smaller quantity because of the generally moist conditions. In each case, the fuel masses include 

a combination of living and dead fuels. In many cases, the 

proportion or mass of fuels by categories (i.e., living vs. 

dead or timelag size classes) are reported in addition to one 

of the above. 

Live surface fuels are often further grouped into herbaceous and woody fuel components, while dead surface fuels 

are often grouped into timelag size classes, and canopy 

fuels are often grouped into foliage and woody timelag 

size classes. The result of such groupings allows for total, 

available, and potential fuel masses to be quantified and the 

distribution of fuels based on size classes to be described. 

Fuel masses by size classes or one of the above mentioned 

groups are often required as inputs for mathematical fire 

models, such as Rothermel (1972) and for fire behavior 

modeling systems such as BehavePlus (Andrews et al. 

2003), FCCS (Ottmar et al. 2007), Nexus (Scott 1999, Scott 

and Reinhardt 2001), First Order Fire Effects (Reinhardt et 

al. 1997), and Consume (Ottmar et al. 2005) to predict fire 

behavior and effects. 

Density 

The compactness of fuel particles in a fuel bed influences 

several processes related to combustion, including heat 

transfer and oxygen diffusion within the fuel bed. The 

simplest measure of fuel compactness commonly used to 

describe the canopy fuels complex is bulk density. Bulk 

density is a measure of the weight of fuel per unit volume, 

and includes not just the fuel particles but also the space 

between fuel particles. The concept of bulk density can be 

applied to either surface or crown fuels. In mathematical 

fire behavior models, the bulk density is often converted to 

a packing ratio. Rothermel (1972) defined the packing ratio 

as the fuel bulk density divided by the particle, or material 

density (such as a solid block of wood), thus the packing 

ratio is a dimensionless number. In addition to the actual 

fuel bed packing ratio, a theoretical optimum packing ratio 

can be calculated from the surface area-to-volume ratio of 

the fuel particles within the fuel bed. The quotient of the 

packing ratio divided by the optimum packing ratio is an 

important concept within the Rothermel (1972) fire behavior 

model as this value affects the optimum reaction velocity 

and the wind adjustment factor term. In terms of crown 

fuel assessment, density is most commonly represented as 

canopy bulk density. Canopy bulk density has been incorporated as a primary variable influencing crown fire spread 

in several fire behavior prediction systems (see chapter 9 

of this review) and is commonly included in fuel hazard 

assessments. 

Continuity 

Another fuel characteristic with important implications for 

extreme fire behavior is continuity. Continuity refers to the 

degree to which fuels are uninterrupted. Continuity cannot 

be considered at the scale of a single particle but is quite 

important to how fire burns across collections of particles. 

At the scale of individual particles, such as surface beds of 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

leaves or needles, fuels are often considered to be continuous even though gaps between particles exist because the 

size of the gaps are small compared to typical flame sizes as 

fires burn through the fuel bed (Finney et al. 2010). 

At larger scales, horizontal and vertical fuel continuity 

is very important to fire spread. Horizontal continuity is 

primarily related to the spread of a fire across a landscape, 

while vertical continuity influences vertical fire movement 

among fuel layers such as surface-to-crown fire transition. 

One important aspect of continuity is the spatial scale at 

which we consider this variable. 

In mathematical fire behavior models such as Rothermel (Rothermel 1972, 1991) and Van Wagner (Van Wagner 

1977), fuel is assumed to be homogenous and no spatial 

variations are considered. In the Rothermel (1972) fire 

spread model, the effect of decreased continuity of surface 

fuels is expressed through alterations to the packing ratio. 

In terms of canopy fuels, vertical continuity is expressed 

through the bulk density of the fuel layer. Although often 

not included in mathematical descriptions of fire spread, 

a common measure of continuity in the field is estimated 

percentage or fractional cover. 

In terms of surface-to-crown fire transition, continuity 

is captured through an estimation of the canopy base height. 

Canopy base height is defined as the lowest height above 

the ground at which there is sufficient canopy fuel for fire to 

propagate vertically. This is, however, difficult to directly 

measure. Several methods have been used to estimate 

canopy base height, including the height at which a given 

amount of canopy bulk density exists, the mean crown base 

height, the lowest crown base height, and a percentile crown 

base height. 

Operational fire behavior models do not explicitly 

account for spatial variation in vertical and horizontal 

continuity. However, more recent models have been 

developed that can allow for both vertical and horizontal 

continuity to be included (Linn et al. 2002, 2005; Mell et 

al. 2009; Morvan and Dupuy 2001). Despite difficulties in 

quantification of spatial continuity and a lack of modeling 

methodologies able to account for spatial continuity, a lack 

of horizontal continuity can reduce fire rate of spread, or 

completely prevent spread in extreme cases. Conversely, 

increased horizontal and vertical continuity can increase the 

potential for fire spread and crown fire initiation (chapter 9). 

The disruption or alteration of vertical and canopy 

fuel continuity has been used to guide fuel management 

operations and fire suppression operations. The removal of 

fuels through the construction of fire line in fire operations 

reduces horizontal fuel continuity with the goal of preventing further horizontal spread of the fire. Thinning operations in forested ecosystems often focus on reducing both 

horizontal and vertical continuity to reduce the likelihood of 

fire transition into the crown and the ability of fire to move 

horizontally through an area.

As we have shown in the preceding paragraphs, each 

of the different characteristics in the fuels pentagon can 

individually contribute to extreme fire behavior. But the 

potential for extreme fire behavior is further heightened 

when multiple fuel pentagon characteristics interact. When 

an adequate quantity of dense, continuous fuels with 

favorable geometry (high surface area to volume ratio), and 

chemistry (high fat and low moisture content) is exposed to 

ignition, extreme fire behavior should be expected. In the 

next section, we discuss how broader patterns of fuels can 

lead to such combinations at landscape scales. 

Landscape-Scale Fuels and Extreme 

Fire Behavior

The role of fuels in extreme fire behavior is complex 

because all of the fuel characteristics in the fuels pentagon 

interact with one another, within the fire environment 

context, to determine how fuels burn. 

So far, our discussion of fuels has focused on the fuel 

characteristics that influence flammability at fine scales 

(particles to small collections of particles). At landscape 

scales, the distribution and spatial arrangement of fuels 

significantly affect the potential for fires to spread as well 

as the mechanisms and rates of spread and associated fire 

intensities (Miller and Urban 2000). Changes in fundamental fuel characteristics over time, arising from one or 

more factors, can lead to changes in likelihood of ignition, 

tendency and rate of spread, or intensity (fig. 4-4). Fuels are 
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GENERAL TECHNICAL REPORT PNW-GTR-891

highly susceptible to changes arising from interactions with 

the environment, often in complex ways and across multiple 

spatial and temporal scales. 

Landscape Fuel Distribution Patterns

Fuel patterns on the landscape are complex because they 

result from interactions between the processes of growth 

and succession, which typically cause fuel accumulations; 

and natural disturbance processes such as fire, windthrow, 

insects, and pathogens, which kill individual plants and alter 

vegetation composition and structure. Disturbance processes are integral parts of natural systems and play a key role 

in regulating fuel masses and other characteristics as well as 

opening up resources for new plants to grow. We begin with 

a discussion on landscape fuel distribution patterns and fuel 

changes that are driven by natural ecological processes. We 

then discuss how fuels distributions and characteristics can 

be affected by management actions such as fire suppression, 

fuel treatment, or other land management activities, or by 

other factors such as climate change or exotic species, all of 

which can significantly affect fire behavior.

In general, spatial and temporal environmental gradients in temperature, water availability, light, and soil 

nutrients interact to determine which plants can grow in 

a given location (Ohmann and Spies 1998, Waring and 

Running 1998, Whittaker 1967), although specific characteristics of particular vegetation such as seed dispersal 

mechanisms, competitive relationships with other species, 

and other factors are also important. Current patterns are 

also significantly influenced by past patterns. These gradients play a key role not only in the distribution patterns of 

vegetation but also in how fuels respond to the environment. 

The pattern and diversity of fuel states at landscape scales 

can result in conditions that are favorable to the growth 

of large fires or can disrupt fire growth depending on the 

circumstances (Viedma et al. 2009).

Figure 4-4—Interrelationships between the 

fire behavior triangle, the fuels environment 

pentagon, and the change agents that can 

influence fuel characteristics and subsequent 

fire behavior. 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Natural Fuel Changes

Natural systems are in a constant state of flux as plants 

respond to the environment, grow, and compete for space 

and other resources. Many fuel changes are directly related 

to the weather. Live understory grasses, which may be 

unlikely to burn, can greatly increase in flammability following a killing frost. Diurnal shifts in fine woody surface 

fuels on a hot day can rapidly change ignition potentials 

and surface fire rates of spread. Fires have been observed 

to significantly increase in intensity over a few seconds 

simply because the sun comes out from behind a cloud. 

Desert ecotones in which fire has historically been limited 

by low fuel continuity can burn extensively after cheatgrass 

invasions. High-elevation forests, often too wet to burn, can 

burn with great intensity following a drought. 

Fuels interact with weather and climate in several 

important ways spanning different temporal scales, ranging from diurnal (within a day) to seasonal (across a few 

months) to interannual (across a few years). These interactions can significantly influence extreme fire behavior as 

they can result in marked changes in fire behavior.

Temporal dynamics—

One of the most important characteristics of fuels is their 

temporal dynamics. The physical and chemical characteristics of fuels vary strongly from time scales as short as a few 

minutes to as long as decades and centuries (Davis 1959). 

Fuel dynamics play a key role in determining the likelihood 

and intensity of a fire burning at a given location at a given 

point in time; significant changes in fuel dynamics often 

result in extreme fire behavior. We discuss four major fuel 

dynamic timeframes: diurnal, seasonal, interannual, and 

successional.

Daily (diurnal)—

Temperature, relative humidity, and wind fluctuate diurnally, affecting the dead fuel moisture content, one of the 

most critical variables determining whether extreme fire 

behavior will occur (Brackebusch 1975, Britton et al. 1973, 

Byram 1940, Fosberg 1970, Fosberg et al. 1981, Pyne et al. 

1996, Simard 1968, Steen 1963). The rate at which the dead 

fuel will respond to these factors depends on the intrinsic 

properties of the fuel including particle size, surface-tovolume ratio, and state of decay (Gisborne 1933). For example, a stem of dead grass or a small twig will have a large 

surface area compared to its volume, and moisture can be 

absorbed and evaporated rapidly in response to fluctuations 

in temperature, relative humidity, and wind. On the other 

hand, large logs have a smaller surface area compared to 

the volume, and the internal moisture content will respond 

slower to fluctuations of temperature and relative humidity. Consequently, rapid rates of spread and large releases 

of energy during extreme fire events often occur during 

the warmest, driest, and windiest part of the diurnal cycle 

when the fuels with high surface-to-volume ratios are at 

their driest. Live fuel moisture content also is important 

in determining fire behavior and has been shown to vary 

substantially over the course of a single day (Philpot 1965). 

These daily variations are not a direct result of changes in 

temperature and relative humidity but are driven more by 

physiological plant processes such as transpiration (Nelson 

2001). These diurnal fuel fluctuations can strongly influence 

fire behavior. Generally, the lowest diurnal moisture contents for both live and dead fuels are observed during the 

hottest and driest parts of the day.

Seasonal—

Fuels change throughout the year because of the cumulative effects of weather and their biophysical cycles. Early 

in the spring, prior to greenup, last year’s grass fuels may 

be dry enough to burn and fires might ignite and spread. 

Shortly after greenup, live surface fuels, with extremely 

high moisture contents may impede the spread of fires until 

later in the summer when those live fuels cure and are drier. 

On the other hand, freshly fallen leaves from a deciduous 

forest often contain large air spaces between individual fuel 

particles; however, as the leaves become compressed or 

decay, their surface fire potential decreases. In some cases, 

standing dead fuels can burn while leaving the live fuels 

unburned.

Interannual— 

Over longer time scales, climate fluctuates owing to the 

influence of myriad factors. Climate scientists have identified a number of different oscillatory climate patterns such 
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GENERAL TECHNICAL REPORT PNW-GTR-891

as the Pacific Decadal Oscillation, the El Niño/Southern 

Oscillation, and the Arctic Oscillation (Higgins 2007). 

These interannual cycles can reinforce each other when 

they are in phase or counteract each other when they are 

out of phase, so analysis of their interactions is complex. 

Predictions of the influence of these climate patterns for 

fire management are often difficult for short time periods 

because the nature and magnitude of the different cycles are 

often difficult to determine until after they are well under 

way (Latif et al. 1998). 

In many cases, climate-related influences on fuels may 

span 2 or more years. Prolonged droughts can debilitate 

overstory species, drying out foliage and increasing flammability over extensive areas (Allen 2007). Reduction in 

leaf production can also reduce litter quantities. Depending 

on the ecosystem, climatic conditions, and site productivity, 

surface fuel loads can increase rapidly if deposition exceeds 

decomposition. Patterns can emerge in which wet years spur 

higher production of surface fuels such as grasses, which 

then increase the risk of extreme fire behavior in subsequent 

dry years; this is common in the Southwestern United 

States (Westerling et al. 2003). 

Decadal—

Over longer time scales, fuels change along with vegetation 

through succession, the process by which ecological communities change in species composition and structure over 

time following disturbance. Over time, our understanding 

of this phenomenon has changed from a view as a highly 

deterministic process (Clements 1936) to a more complex 

process with numerous interacting influences and multiple 

potential outcomes (Connell and Slatyer 1977). Although 

the nature of the underlying dominant processes may vary 

among ecosystems and ecological communities, it is nearly 

always the case that vegetation changes over time, modifying the microclimate and other factors affecting growth at 

the same time. 

Succession in forest ecosystems has been studied 

extensively. Given our focus on fuels and desire for brevity, 

we touch only on the general concepts. In forest ecosystems, 

it is common for grasses and forbs to occupy a disturbed 

site, giving way to shrubs and small trees over time (Wittinger et al. 1977). As trees get taller, shrub components 

tend to diminish, and canopy structure changes over time 

with distinct patterns (Oliver 1980). Openings created by 

death of individual overstory trees provide opportunities 

for recruitment of new individuals (Hanson et al. 2011). In 

most forest ecosystems, succession trends towards a shift in 

species composition and structure that favors longer lived 

and typically more shade-tolerant species. For example, 

pine barrens in the Eastern United States will typically shift 

to an oak-hickory-dominated forest in the absence of fire 

(Little and Forman 1998). 

Fuels shift along with succession, typically with an 

accumulation of surface fuels, dependent on the balance 

between production and decomposition rates (Ryu et al. 

2004), often arriving at a semiequilibrium state in the 

absence of disturbance (Schimmel and Granström 1997). In 

some cases, shifts in fuel loads and other characteristics of 

the fuels pentagon may be different at different points along 

a successional time series; different fuel components also 

may change at different rates (Agee and Huff 1987).

Vegetation changes occurring through succession can 

exhibit feedback mechanisms affecting future fires; changes 

in the light environment occurring as trees grow can alter 

the surface fuel moisture regime and associated ignition 

potentials (Tanskanen et al. 2005). Similarly, forest canopies 

alter the vertical profile of windspeeds and turbulence at 

spatial scales often several times the canopy height (Amiro 

1998); wind acceleration can occur in openings or along 

edges (Stavey et al. 1994). These canopy-wind field effects 

are not random or chaotic in nature but rather, coherent 

and organized (Raupach et al. 1996), which thus likely has 

significant effects on fire spread rates and intensities.

Disturbance—

Disturbances can promote rapid changes in fuel composition and structure. Many disturbances, such as fire 

and pathogen attacks, are regular occurrences in a given 

ecosystem and may generally be limited in extent. Other 

disturbances, such as climatic changes, windthrow, management action-related impacts, and nonnative invasive species 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

may also significantly affect the fuel complex. These types 

of disturbances alter some or all components of the fuels 

pentagon and thus significantly affect expected fire behavior. Some of the more important natural and human-caused 

disturbances are discussed below.

Fire— 

Disturbance processes influence the accumulation of fuel 

and the nature of fuel characteristics on the landscape. The 

interactions between fire, climate, fuels, and the landscape 

are complex, with influence from both top-down (broad 

climate patterns) and bottom-up (local fuel and topographic 

influences) processes (Falk et al. 2011, Heyerdahl et al. 

2001). In most regions of the world where fire is a dominant 

disturbance process, vegetation shows evidence of adaptation to fire, with characteristics that tend to promote fire under conditions favorable to the adaptive strategies of those 

species (Habeck and Mutch 1973). 

Forest pathogens and insects—

Forest pathogens and insects, such as sudden oak death 

and the mountain pine beetle, are affecting large areas of 

North American forests (Tkacz et al. 2008). Many of these 

pathogens and insects are natural parts of the disturbance 

cycle of forests and endemic attacks are common and help 

to maintain uneven-aged stand structures. The interaction 

of pathogens and insects with their host as well as their ability for spread, influence the rate at which mortality occurs 

and therefore influence the resulting fuels complex. In some 

cases, the mortality resulting from insects and diseases is 

a slow process affecting relatively small areas across the 

landscape; however, in other cases, epidemic outbreaks may 

occur that can cause widespread mortality and affect large 

areas. Such events are particularly important in fuels owing 

to the rapid and widespread change in the fuels complex 

that is associated with the outbreak. Often, our understanding of how fire behavior changes in insect and pathogen-infested stands is still limited but is an area of active research. 

Current concerns have particularly arisen regarding the 

effect of insect outbreaks in the Western United States. 

Current bark beetle outbreaks throughout the Western 

United States have infested a large number of hectares in a 

relatively short time. However, fuel conditions continuously 

change over several years following bark beetle attacks. 

Early during an outbreak, foliar moisture content decreases 

rapidly as leaves senesce, significantly increasing flammability (Jolly et al. 2012); similar effects have been observed 

as a result of pathogens such as sudden oak death (Kulijan 

and Varner 2010). Although this increased flammability 

likely diminishes in a given tree after a few years as foliage 

falls, increased flammability at stand or landscape scales 

may persist for several years as the attack continues to unfold (Jenkins et al. 2012). Different components of the fuel 

bed continue to change for decades after an attack (Jenkins 

Figure 4-5—Mountain pine beetle-attacked lodgepole pine 

tree torching from a single point source ignition of the lowest 

branch during a prescribed burn on the Seeley Lake Ranger 

District, Lolo National Forest, in 2011. 

W.M. Jolly
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GENERAL TECHNICAL REPORT PNW-GTR-891

and the United States support these conclusions. For example, figure 4-5 shows a mountain pine beetle attacked tree, 

before any foliar loss has occurred, readily torching from a 

single point source ignition of its lowest branch. Regardless 

of the specific influences on fire behavior, forest pathogens 

and insects greatly affect the temporal fuel dynamics of infested stands, and these changes are likely manifested over 

decades.

Wind—

Wind disturbances, such as hurricanes, tornados, blowdowns and micro-bursts, can greatly affect the fuel structure of a given ecosystem and can alter those fuels in 

minutes. In general, leaves may be stripped from standing stems; branches and whole stems may be broken and 

et al. 2008; Page and Jenkins 2007a, 2007b). Studies have 

reported an assortment of changes in fire behavior in forests 

infested with bark beetles including increases in spread 

rates, flame lengths, and fire intensities (Page and Jenkins 

2007b, Valachovic et al. 2011), and increase in crown fire 

potential (Hoffman et al. 2012, Kuljian and Varner 2010). 

Other studies report a reduction in the crown fire risk from 

reductions in the crown bulk density of infested stands 

(Simard et al. 2011). Intuitively, if the foliar moisture content of leaves or needles decreases by an order of magnitude 

and no other structural changes occur, one would expect 

that infected trees would be more flammable during or immediately following attack. Although research reports are 

contradictory, observations of free-burning fires in Canada 

Figure 4-6—Blowdown of sparsely stocked pines in southern Mississippi after Hurricane Katrina, 

September 2005. 

Jon Wallace
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

deposited as surface fuels, which significantly increases 

surface fuel masses (Loope et al. 1994, Myers and van Lear 

1998). An example of rapid fuel alterations after Hurricane 

Katrina is shown in figure 4-6. These events change the 

continuity, mass, and arrangement of the surface and aerial 

fuels and eventually will change the surface fuel moisture 

regime as the green foliage of broken stems dies and more 

solar radiation can penetrate through the canopy to the 

surface (Loope et al. 1994). In the Boundary Waters Canoe 

Area Wilderness of Minnesota, a blowdown event affected 

over 150 000 ha of the forested land and vastly altered the 

fuel complex. After that event, 100-h and 1,000-h surface 

fuel masses were found to be almost twice as high as those 

found in adjacent, undisturbed stands (Woodall and Nagel 

2007). Additionally, these events may preferentially affect 

larger diameter, shade-intolerant trees and thus substantially 

alter subsequent fuel structures (Rich et al. 2007). These 

surface fuel alterations promote fires that burn intensely

and are hard to contain because of restricted access into

affected stands. After this event, large fires ensued such

as the 13 000-ha Cavity Lake Fire of 2006, the 29 000-ha 

Ham Lake Fire of 2007, and the 38 000-ha Pagami Creek 

Fire in 2011.

Anthropogenic (Human-Caused) Changes in Fuels

While many fuel changes are natural processes, some fuel 

changes have been induced by human action or inaction. 

Some of these changes are discussed below.

Human activities can affect extreme fire behavior by changing the dynamic interactions between succession and natural disturbance processes. Past management activities or 

land use practices have resulted in lasting impacts on fuel 

characteristics. In many parts of the Western United States, 

episodes of heavy grazing over several decades essentially 

eliminated surface fuels, facilitating the development of 

a thick understory of shade-tolerant species, which would 

have likely been killed or substantially thinned by fire

otherwise (Heyerdahl et al. 2006). In southwestern ponderosa pine forests, forest structure has changed from a 

fire-dominated, low-density stand structure characterized 

by large trees to vast expanses with thickets of small trees 

owing to the combined effects of logging, grazing, and 

fire suppression (Allen et al. 2002). In both cases, these 

changes represent a landscape-scale shift in fuel continuity 

and geometry with significant implications for extreme fire 

behavior. Anthropogenic influences can also disrupt fuel 

continuity, reducing the likelihood of fire spread. A simulation study assessing changes in fuel connectivity in the 

Southeastern United States suggests that a small proportion 

of landscape area, such as roads, can significantly impede 

fire spread with certain fuel types (where spotting distances 

are shorter) at landscape scales (Duncan and Schmalzer 

2004). This can produce unintended consequences but can 

also be used as a management strategy in certain circumstances. 

Fire suppression plays a role in altering fuel masses 

and continuity. Fire suppression is often considered to 

have resulted in increases in fire intensity and resistance 

to control efforts. This appears to be the case in the Southwestern United States and in many dry forest types, where 

fuel accumulations related, at least in part, to fire suppression, have resulted in intense fires uncharacteristic of those 

frequent fire regime areas (Allen et al. 2002).

Suppression limits the reductions in surface fuels that 

are common in areas with short fire-return intervals and 

allows the growth of shade-tolerant species in the understory that often serve as ladder fuels to carry fires from the 

surface into the crowns of trees that might not normally 

be affected by low-intensity surface fires. Fire plays an 

important role in opening up growing space for seedlings, 

so suppression may affect regeneration, particularly for fireadapted species (Lunan and Habeck 1973). Other changes 

may occur as well. For example, in a mixed-conifer forest 

in California, small tree density and surface fuels increased 

with fire suppression (Parsons and DeBenedetti 1979). 

However, fire suppression does not alter all fuel complexes equally. In some cases, the role of fire suppression is 

more difficult to estimate as it can be obscured by climate or 

its effects may not be immediately perceived. Keeley et al. 

(1999) demonstrated that fire suppression had little impact 

on the frequency and extent of wildfires burning in the 

California chaparral, and Johnson et al. (2001) suggested that 
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GENERAL TECHNICAL REPORT PNW-GTR-891

the same may be true for closed-canopy coniferous forests in 

the boreal regions. Overall, decades of fire suppression may 

differentially affect the fuel dynamics of various ecosystems, 

and we must understand how these changes might manifest 

themselves as changes in fire behavior. 

A century of fire ecology research has established 

that fire is a natural part of wildland ecosystems, and fire 

management practices are changing to facilitate a broader 

recognition of this. Rather than requiring suppression in 

all cases, fire management policies now support a more 

nuanced and flexible view of fire’s role on the landscape, 

and allow for portions of a fire to be managed for resource 

benefit while other portions may be suppressed. Decision 

support systems that support this process are currently in 

use in the United States and continue to be revised. Recognition of the natural role of fire in the landscape helps guide 

proactive management practices that will be helpful in years 

to come as we continue to adapt to a changing environment.

Climate Change and Other Agents of 

Fuel Change

Fire managers face the prospect of ecosystems that are 

changing rapidly and often in several ways at once. Many of 

these changes are exacerbated by climatic changes. Climatic 

changes can influence extreme fire behavior by altering both 

the short-term fire weather environment and by influencing 

longer term changes in vegetation composition, condition, 

and structure (fig. 4-7). Understanding the implications of 

these changes is essential for determining, and potentially 

mitigating, the role of fuels in extreme fire behavior. 

Climate change—

Although climate change is represented as controversial 

in the popular media, there is little debate in the scientific community that climate change is occurring, that it is 

Figure 4-7—Changes in climate have significant implications for fire management in the future. A climate shift towards 

hotter and drier conditions can greatly alter the likelihood of events. Hot and dry events that may have been rare historically (area under curve, A) may become much more common (area under curve, B; note that B includes A). 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

anthropogenic in nature, and that it has profound consequences for ecosystems and human communities throughout the globe (Pachauri 2007). Globally, climate change is 

resulting in increasing temperatures, widespread melting of 

both polar and continental ice, and sea level rise. Changes 

in precipitation vary among regions (Pachauri 2007). 

Many semiarid areas, such as the Western United States, 

are anticipated to undergo substantial decreases in water 

resources as changes in precipitation have more pronounced 

effects than temperature changes in arid regions (Conant et 

al. 1998). Additionally, extreme weather events such as heat 

waves and subsequent droughts may be more variable and 

less predictable (Schar et al. 2004).

Earlier spring snowmelt dates combined with higher 

temperatures have lengthened fire seasons in the Western 

United States (Westerling et al. 2006), and this trend is 

expected to continue. This shift towards increased temperatures may significantly alter the likelihood of occurrence 

of weather patterns associated with extreme fire behavior. 

At regional scales, climate change effects are expected to 

spur increased fire activity (Flannigan et al. 2000). In many 

cases, these increases in fire activity are expected to accompany increased severity of ecological effects. In recent 

years, large, stand-replacing wildfires have occurred in 

Southwestern ponderosa pine stands where such fires were 

extremely rare in the past; these fires likely result from both 

climate and fuel-change induced effects (Allen et al. 2002).

A shifting landscape: spatial shifts in species 

distributions—

Changes in temperature and water alter growing conditions 

across the landscape. These shifts favor some species while 

harming others, eventually altering the spatial distribution 

of different species (Iverson and Prasad 2002). For example, 

tree lines might shift upwards in elevation as temperature 

conditions become more favorable to tree growth (Grace et 

al. 2002). During this change, existing species at treeline 

might experience competition from lower elevation species expanding into their range (MacDonald et al. 2000). In 

some cases, temperature changes may differentially affect 

fuels at different elevations. For example, during an extreme 

heat wave event, vegetation at low elevations was severely 

water stressed while vegetation at higher elevations grew 

more vigorously because the snow-free period was lengthened (Jolly et al. 2005).

Shifts in species distributions are typically expected 

to take place slowly (Grace et al. 2002). However, recent 

evidence suggests that they can occur very rapidly when 

thresholds of environmental conditions are passed. Widespread tree mortality has been observed in temperate forests 

in the Western United States (Van Mantgem et al. 2009), 

and similar rapid, landscape-scale pinyon pine die-off under 

drought conditions has been reported at lower elevations 

in pinyon-juniper woodlands (Breshears et al. 2005, 2008). 

In the short to medium term, these die-off situations can 

represent a potentially disastrous increase in flammability 

leading to increased risk of extreme fire behavior. Over 

longer time periods, if persistent, these climate change 

effects may result in a wholly different species composition 

and structure, such as a shift from forest to lower density 

woodlands or from shrublands to desert. 

Invasive species—

Fire managers must also adapt to the spread of invasive

species (Vitousek et al. 1997). Invasive species can be either 

native or exotic. “Exotic species” generally refers to any 

species that falls outside of its native range. They are usually from other continents, but many native species can also 

be considered invasive when they move into areas that they 

otherwise would not occupy. Both native and exotic invasive 

species can significantly alter the fuel dynamics. More than 

2,000 exotic species have been introduced into the United 

States and the majority of these introductions have been human caused (Vitousek et al. 1997). The resulting changes in 

the fuel complex can have far-reaching impacts on the disturbance cycle of a given ecosystem (Mack and D’Antonio 

1998). Where invasive species are physically similar to 

other species, such as the invasion of a grass into an ecosystem already dominated by grasses, they tend to alter fire 

behavior by changing fuel masses (D’Antonio 2000). In 

contrast, when the invasive species have no analogous life 

form within a given ecosystem, they can significantly influence the disturbance cycle of the system into which they are 

introduced (D’Antonio 2000). While these species also alter 
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GENERAL TECHNICAL REPORT PNW-GTR-891

fuel masses, they can also change other fuel characteristics 

such as fuel continuity, density, and chemistry. These fuel 

changes are generally important to fire managers, but, frequently, management tools such as prescribed burning may 

actually promote or maintain fire-adapted invasive species 

to the detriment of native species (Kerns et al. 2006).

Epidemic insect and pathogen outbreaks—

While frequent, low-intensity pathogen and insect attacks 

are common, occasionally pathogens or insects may rapidly 

reproduce and attack vast areas, killing vegetation and altering fuel structures. These attacks can induce widespread, 

rapid changes in the fuel complex that can last for decades. 

One example of an epidemic outbreak is the current widespread infestation of mountain pine beetles (Dendroctonus 

ponderosae) throughout the Western United States. These 

outbreaks generally occur when there is an abundance of 

host vegetation, high concentrations of pathogens, and favorable weather (Raffa et al. 2008). Fuel changes following 

an outbreak are very dynamic, and these changes can either 

increase or decrease expected fire behavior by changing all 

five sides of the fuel pentagon to some degree (Jenkins et al. 

2008). For example, foliar moisture content drops rapidly 

after a successful attack (Kuljian and Varner 2010) altering 

the fuels chemistry and little else. Later, when dead, aerial 

vegetation falls to the ground, surface fuel masses, continuity, and geometry are altered (Page and Jenkins 2007a). 

All of these changes manifest themselves as changes in the 

potential for extreme fire behavior that can span millions of 

hectares in some instances.

Climatic changes can strongly affect both the spread of 

invasive species and the likelihood and extent of epidemic 

pathogen outbreaks. For example, plant species distributions 

are strongly related to temperature and precipitation. Both 

temperature and precipitation interact to define this species 

envelope and thus no single component of climate change 

can be considered in isolation. Research has suggested that 

expected climatic changes can vastly alter the land area 

suitable for invasion by species such as Bromus techtorum

(cheatgrass) (Bradley 2009). However, not all climatic 

changes will promote invasive species. Some work suggests 

that future climates may be less favorable for exotic species 

and thus may present opportunities for restoration (Bradley et al. 2009). Climatic changes may also significantly 

influence the potential for pathogen or insect epidemics. For 

example, warmer temperatures might permit insects such as 

the mountain pine beetle to attack trees at higher latitudes 

and higher elevations (Bentz et al. 2010). Climatic changes 

are a strong secondary driver of many types of fuel changes 

and thus should always be considered when attempting to 

characterize long-term expectations in fuel dynamics.

Fuels Management Options That Reduce 

Extreme Fire Behavior Potential

Fuels, weather, and topography interact to dictate how fires 

burn. However, fuels can be manipulated to lessen extreme 

fire behavior potential through different fuels management 

strategies. The terms fuel management and fuel treatment 

are often used to denote activities that are undertaken to either meet some land management or ecological objective or 

to reduce the potential for extreme fire behavior (Reinhardt 

et al. 2008). Various fuel treatment actions are undertaken 

to change one or more of the elements of the fuels pentagon 

and subsequently change some component of fire behavior 

such as rates of spread or intensity of a fire burning in a 

given fuel stratum (fig. 4-4). Fuel treatment projects are 

objective-driven, where a particular problem is identified 

and many options are evaluated to meet that objective. 

Ultimately, some strategy must be developed that alters fuel 

characteristics and achieves specific, and ideally measureable, reductions in the potential for extreme fire behavior. 

Primarily, there are four types of fuel treatment actions 

that are undertaken to achieve these objectives: reduction or 

redistribution of surface fuels, increasing the height to live 

crown, decreasing crown density, and retaining large trees 

of fire-resistant species (Agee and Skinner 2005). 

Reduction of Surface Fuels

The reduction of surface fuels influences many factors of 

the fuels pentagon such as continuity, quantity, geometry 

and density, with the potential to alter both surface fire 

rates of spread and fire intensity as well as the likelihood 

that fires will carry from the surface into the crowns (Van 

Wagner 1977). For this reason, surface fuel reductions 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

have the potential to affect many characteristics of extreme 

fire behavior. Many activities can be employed to reduce 

surface fuels, such as prescribed fire, chaining, mastication, 

and grazing. One main consideration for these types of fuels 

treatment activities is the longevity of the treatment. Fuels 

are dynamically influenced by climate and thus areas where 

surface fuels can rapidly accumulate may require frequent 

treatments to maintain effectiveness. In some cases, rapid 

fuel accumulations, like those found in the Southeastern 

United States, may limit effectiveness to less than a decade 

(Brose and Wade 2002).

Increasing the Height to Live Crown

Crown fire is generally thought to be a two-stage process 

(see chapter 9 of this volume). The first part is called 

initiation, where a surface fire is carried into the crowns; 

the second part is the propagation, where crown fires are 

carried from tree crown to tree crown (Van Wagner 1977). 

Increasing the height to live crown effectively increases the 

amount of heat that must be sustained from a given surface 

fire to preheat and ignite the crown fuels above. Thus, this 

fuels treatment principle is primarily used to reduce crown 

fire potential.

Decreasing Crown Density

The second component of crown fire potential, propagation, 

is primarily influenced by canopy density. Therefore, fuels 

treatments aimed at reducing crown fire spread potential 

must focus on reducing crown continuity and density. These 

objectives are generally met using some form of mechanical 

thinning. However, thinning alone is generally an insufficient 

fuels treatment option because postthinning residuals can 

increase surface fuel masses, thereby significantly increasing 

surface fire intensities and the potential for passive crown fires. 

Because of this, thinning is generally combined with some 

form of surface fuel reduction such as prescribed fire or pile 

burning. Not only can thinning influence canopy density, it can 

also influence the height to live crown of the entire stand if tree 

selection is based on diameter classes. Low thinning, where 

trees are removed from the smallest diameter classes, can 

simultaneously increase stand-scale canopy base heights while 

decreasing canopy density (Agee and Skinner 2005). 

Retaining Large, Fire-Resistant Trees

The fourth principle of fuels treatment is the development 

of strategies for retaining the largest and most fire-resistant 

trees. Large trees generally have thick bark and high crown 

base heights. Additionally, stocking densities of large 

trees are generally low and thus reduce crown density and 

continuity. These fuel conditions can reduce the likelihood 

of crown fire and potentially promote a fire regime of more 

frequent and lower intensity fires than would be common in 

these cover types.

Negative Influences of Fuels Treatments

Fuels and weather are strongly coupled and interact to influence the microclimate of a given stand. These couplings 

and interactions are important to consider when developing 

strategies for fuels management. Activities such as thinning, while effective at reducing canopy bulk density, may 

also increase surface windspeeds because of the reduction 

in aerodynamic drag (Landsberg and James 1971). Additionally, removing trees may increase the amount of solar 

radiation that reaches the forest floor, potentially influencing surface fuel moistures. Ultimately, strategies aimed at 

effectively reducing the likelihood of extreme fire behavior 

must be considered within the context of both the direct 

changes in fuel characteristics and their indirect influences 

on other components of the fire environment.

Temporal-and Spatial-Scale Considerations for 

Hazardous Fuels Management

It is important to consider the temporal and spatial scale 

of fuels management efforts to ensure that treatments 

are targeted where they are likely to have the largest and 

longest impact. When available data permit, it is helpful to 

consider treatments within the context of the historical fire 

regimes for a given area. Ideally, fuels treatments should 

be comparable to both the spatial and temporal scales of 

natural disturbances. As such, small and scattered treatments, while potentially effective for small areas, may be 

ineffective in reducing extreme fire behavior at landscape 

scales, particularly in areas in which larger fires have historically dominated landscape patterns. Within the context 

of reducing extreme fire behavior potential, landscape-scale 
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GENERAL TECHNICAL REPORT PNW-GTR-891

fuel reduction activities, such as fire managed for resource 

benefit, offer certain advantages as these activities can often 

be accomplished over larger areas than is feasible with 

comparable mechanical or prescribed fire fuels treatments. 

Generally, it has been recommended that hazardous fuels 

treatments be focused on restoring areas where low-severity 

fires historically dominated but that now have substantial 

fuel accumulations owing to fire exclusions (Brown et al. 

2004).

Future Directions

The primary take-home message from volume 1 of the 

extreme fire behavior review (Werth et al. 2011) and this 

volume, as well, is that fire is quintessentially a threedimensional and nonsteady state phenomenon. In many 

ways, the current limits in our capacity to understand, 

assess, and predict extreme fire behavior arise from the fact 

that we describe and model fuels and fire in overly simplistic, one- or two-dimensional frameworks that fail to include 

the three-dimensional nature of fire.

Sampling methods for measuring many aspects of 

different fuel bed components have been developed (Lutes 

et al. 2006), and large ecosystem databases such as Forest 

Inventory and Analysis (Miles et al. 2001) provide objective 

and scientifically credible information on fuels for specific 

ecosystems. Yet, the science of fuels sampling, measurements, and modeling is still in early stages because current 

approaches fail to adequately capture fuel heterogeneity or 

address discontinuities within the fuel bed. 

One of the main reasons for this is that systems for 

describing fuels have largely been oriented towards providing inputs for fire models that can only accept a limited 

scope of fuel complexity. The systems used operationally in 

the United States such as FARSITE (Finney 2004), Nexus 

(Scott 1999), Fire and Fuels Extension to the Forest Vegetation Simulator (Reinhardt et al. 2003), BehavePlus

(Andrews et al. 2008), and the Fuel Characteristic Classification System (Ottmar et al. 2007, Sandberg et. al. 2007) 

are based primarily on a semiempirical surface fire spread 

model (Rothermel 1972). Although these systems model 

crown fire spread, this is still carried out through links 

to Rothermel’s empirical crown fire rate of spread model 

(Rothermel 1991) via Van Wagner’s crown fire initiation 

and propagation models (Van Wagner 1977, 1993). In these 

modeling systems, surface fuels are assumed to be homogeneous, continuous, and contiguous to the ground, and crown 

fuels are considered as a homogeneous layer of uniform 

height above the ground, depth, and bulk density. Different 

mechanisms of heat transfer (i.e., radiative, convective, or 

conductive) are not explicitly modeled, nor are transitory 

fire behaviors. Fuel models used as inputs to this modeling 

system consist of sets of parameters (e.g., surface area to 

volume, heat content, and fuel load) describing homogeneous fuel beds (Anderson 1982, Scott and Burgan 2005), 

in which fuel heterogeneity or discontinuities are not well 

described. 

Thus, while detailed fuels data, such as tree lists with 

individual tree attributes or fuels transect data with multiple 

measurements of litter depth might be available, only summarized quantities such as averages are used to represent 

the homogeneous case used in fire behavior calculations. 

For example, a single value for canopy base height is used to 

represent a stand of trees when in fact there is usually much 

variability in individual tree crown base heights. 

The use of these averaged values results in lower sensitivity to fuels and fuel changes because the geometry and 

spatial configuration of fuels are not accounted for. Forest 

stands that are quite different in composition and structure 

could appear very similar with respect to their representation. For most operational purposes, spatially explicit fire 

simulations are carried out with models such as FARSITE 

with spatial resolution of cells 30 m on a side (Rollins and 

Frame 2006). Although this cell size is very small compared 

to most landscapes, it is very large with respect to the 

spatial scale at which fire tends to interact with wildland 

fuels (Finney et al. 2010). This homogenization of fuels 

inputs arising from use of these summarized quantities, thus 

imposes a spatial scale on fuels that may not be realistic. 

An additional disadvantage to these simplified fuel and fire 

models is that potentially important fuel/fire interactions, 

such as the effect of tree canopies on winds approaching 

and around a fire, cannot be addressed in detail. 

Future improvements in our capabilities to model fuels 

and fire in three dimensions will increase our understanding 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

of the role of fuels in extreme fire behavior, and vice versa. 

While these capabilities are not yet fully developed, recent 

advances show promise and provide a potential view of 

things to come.

New Developments in Fuels Mapping 

New technologies are emerging, such as airborne light 

detection and ranging (LiDAR) systems that can provide 

detailed measurements of forest canopies over large areas 

with accuracies comparable to on-the-ground field measurements in a fraction of the time that field crews would take 

(Lefsky et al. 1999). Such data are increasingly common 

and available for use in developing detailed fuels maps. 

Other recent developments include the use of ground-based 

LiDAR systems, which provide very detailed views of 

canopy structure (Parker et al. 2004). Such systems provide 

an unprecedented depth of data for characterizing the 

spatial structure, heterogeneity of forest canopies (Seidel et 

al. 2012), and associated fuels quantities (Garcia et al. 2011). 

New Developments in Fire Behavior Modeling

Mechanistic physics-based fire behavior models have 

emerged, capable of addressing many aspects of fire 

behavior currently not addressed by operational fire models. 

These computational fluid dynamics (CFD) models simulate 

fire behavior dynamically over time within a three-dimensional spatial domain, describing the dynamics according to 

equations for the conservation of mass, momentum, energy, 

and chemical species. Unlike operational models, which 

assume steady-state rates of fire spread (Rothermel 1972), 

CFD models are self-determining and are thus capable 

of addressing fire-fuel interactions arising from spatial 

variability within the fuel bed, and fire-atmosphere interactions (fig. 4-8). There are several such models, but the most 

developed at this point are FIRETEC (Linn and Harlow 

1997, Linn et al. 2002) and the Wildland Fire Dynamics 

Simulator (Mell et al. 2009). 

Of particular significance to this chapter is the capacity 

of these models to address fuel heterogeneity. Computational fluid dynamics models have been used to model 

fire from the scale of individual trees (Mell 2006, 2009) 

in laboratory experiments to larger scale landscape fuels 

environments such as grasslands and woodlands (Cunningham and Linn 2007, Linn et al. 2005). These models can 

deal with very detailed fuels scenarios at the spatial scales 

where fire is very sensitive to fuel heterogeneity and spatial 

configuration. As each tree, or even different portions of 

each tree, within a stand could have different fuel characteristics, these models have great potential for examining 

and improving our understanding of the complex roles of 

fuels in extreme fire behavior, as well as to elucidate the 

range of weather or topographic conditions under which fire 

is less sensitive to fuels. An additional important aspect of 

these models is that they can address the dynamic interactions between the fuels, fire, atmosphere, and topography 

in extreme fire behavior (Cunningham and Linn 2007). No 

other modeling frameworks can address these critical and 

complex interactions. 

One potential limitation is that these complex fire models require detailed three-dimensional fuels inputs, which 

are difficult to directly measure. Standard forestry inventory data only provide lists of trees and basic attributes, 

such as height and diameter, and lack the more fundamental 

fuel characteristics such as bulk density. Although methods 

have been developed to estimate bulk density at the stand 

scale through indirect measurements (Keane et al. 2005), 

more sophisticated approaches, typically involving modeling, are required to address this need at finer spatial scales. 

To accommodate this need, Parsons (Parsons et al. 2011) has 

developed and is continuing to improve the FUEL3D model. 

This model uses forest inventory data and incorporates the 

pipe model theory and a simple three dimensional recursive 

branching approach to model the distribution of fuel within 

individual tree crowns. This model will address new fuel 

characteristic requirements from current mechanistic 

physical-based fire models coming on line and will improve 

our ability to assess the potential of extreme fire behavior. 

Concluding Remarks

Throughout this chapter we have attempted to paint, in 

broad strokes, the role of fuels in extreme fire behavior. We 

demonstrate that fuels play a critical, but complex, role in 

which key characteristics of fuels, summarized in the fuels 

pentagon, and their composition, arrangement, and land-
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GENERAL TECHNICAL REPORT PNW-GTR-891

scape pattern, can change, often rapidly, in response to both 

natural and human influences. These changes can result 

in significant increases in flammability that can affect the 

nature and magnitude of how fires burn at landscape scales. 

We bring in the big picture view of a dynamic landscape, 

changing in many ways at the same time and across scales, 

in response to climate change, anthropogenic activities, 

and feedbacks with other agents of change such as invasive 

species or beetle epidemics. All of these factors play a role 

in the occurrence of extreme fire behavior.

We concluded with discussion of technological changes, in fuels mapping and modeling, and in fire behavior 

modeling, that offer new perspectives on the nature and 

drivers of extreme fire behavior. With hotter, drier and 

longer fire seasons; prolonged drought; invasive; species 

and large-scale beetle attacks all affecting wildland fuels, 

looking ahead, it is apparent that extreme fire behavior is 

not going to disappear. Faced with these challenges, our 

best answer is to build stronger ties between the fire science 

and fire management communities and work together for a 

better future.

Figure 4-8—View from two perspectives: (a) 

oblique and (b) overhead of a dynamic fire 

simulation with the FIRETEC, physics-based fire 

model (graphic developed by Eunmo Koo, Los 

Alamos National Laboratory). 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 5: Fire Interactions and Mass Fires

Mark A. Finney1 and Sara S. McAllister2

Introduction

Some interactions of wildland fires are experienced routinely under field conditions. Firefighters and prescribed 

fire personnel see flames tilting toward adjacent ignition 

points or fire edges, particularly as the sources advance 

closer together (Martin and Dell 1978, Rothermel 1984). In 

the extreme case, interactions occurring when large areas 

are ignited and burning simultaneously are described as 

mass fires, area fires, or “fire storms” (Countryman 1964). 

Hundreds or thousands of individual fires may interact 

over an area and exhibit some “unified” behavior. Such 

fires are generally described as having such strong indrafts 

that outward propagation is minimal. They have extremely 

tall convection columns or smoke plumes and burn for 

long durations until all the fuel within their perimeter is 

consumed. Good reviews of mass or large area fires can 

be found in Williams (1982), Pitts (1991), and Heskestad 

(1998). Mass fires were responsible for tremendous burning rates and tornado-strength winds (Carrier et al. 1985) 

witnessed after the fire bombings of cities in Germany and 

Japan during World War II (Hewitt 1983, Schmalz 1992) 

and have been studied mainly in relation to consequences 

of nuclear attacks (Balwin and North 1967; Chandler 1963; 

Countryman 1964, 1965, 1969; Eggleston 1968; German 

1968; Hewitt 1983; Larson et al. 1982; Larson and Small 

1982a, 1982b; Lee 1969a, 1969b; Lommasson et al. 1967, 

1968; Nielsen 1970; Nielsen et al. 1963; Parker 1967; Penner 

et al. 1986; Pryor and Yuill 1966; Quintiere 1993; Sanderlin 

et al. 1981; Wood et al. 1971). Many of these studies were 

through “Project Flambeau,” a joint effort between the U.S.

Office of Civil Defense, Defense Atomic Support Agency 

and the U.S. Department of Agriculture Forest Service in 

1 Mark A. Finney is a research forester, U.S. Department of 

Agriculture, Forest Service, Missoula Fire Sciences Laboratory, 

5775 US West Highway 10, Missoula, MT 59808.

2 Sara S. McAllister is a research mechanical engineer, U.S. Department 

of Agriculture, Forest Service, Missoula Fire Sciences Laboratory, 5775 US 

West Highway 10, Missoula, MT 59808.

the mid-1960s. These fires were designed to mimic a 

suburban fire. Each square fuel bed was constructed with 

a mixture of pinyon pine and juniper (see “Common and 

Scientific Names” section) and was approximately the same 

size and fuel load as a typical suburban house (185.8 m2 and 

about 18 000 kg of fuel). The spacing between fuel beds 

was either 7.6 m or 35.1 m and fire sizes were 2, 6, 12, and 

20 ha. Airflow velocities and temperatures were measured 

inside and just outside the fire area along with thermal radiation just outside the fire area, oxygen and carbon dioxide 

concentrations inside the fire area, and the mass loss rate of 

the fuel beds (Countryman 1964, 1965, 1969).

Wildland fire interactions are intentionally manipulated 

for ignition or firing operations (see figs. 5-1 and 5-2) to 

orient spread directions (Johansen 1987), use indrafts for 

backfire operations (Miralles et al. 2010), increase the development of convection columns on prescribed fires through 

center-firing techniques (Martin and Dell 1978), and limit 

spread and intensity with spot fire ignitions (Johansen 

1984, Luke and McArthur 1978, Weatherspoon et al. 1989). 

Rapid increases in fire growth and energy release—termed 

“blowup”—are sometimes associated with fire interactions 

(Arnold and Buck 1954). Yet, despite the common usage 

and practical familiarity with interactions that fire personnel often acquire, very little quantitative information exists 

about the physical processes involved and there are no 

operational models that can predict them. By comparison to 

other fire behavior characteristics, such as fire spread rates, 

fire interactions at any scale have been subject to limited 

study. 

In this review, we endeavored to obtain literature from 

many sources, including wildland fire, and structural fire, 

as well as combustion engineering and fluid dynamics, to 

cover the range of research on fire-fire interactions and the 

state of knowledge. Our search revealed that the topic of fire 

interactions overlaps considerably with other fire behaviors 

that are distinguished individually, such as vortices and 

terrain effects. These behaviors will be mentioned when 
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GENERAL TECHNICAL REPORT PNW-GTR-891

appropriate, but their full discussion is beyond the scope of 

this review.

Background: Time-Dependent Fire 

Behaviors 

For a constant set of environmental conditions, fire 

behavior is known to change with time. These changes 

are not expressly considered interactions, but spread and 

intensity changes within individual fires are also affected 

during interaction among fires and may contribute to later 

development of interactions. Thus, such behaviors provide 

useful background material for discussion of fire-fire 

interactions, although studies of fire acceleration have not 

directly addressed interactions of multiple fires. Many of 

the time-dependent changes in fire behavior are associated 

with fire growth or expansion in two dimensions. Changes 

are observed in spread rates (acceleration), frontal geometry (width, curvature), and heat transfer indicated by the 

orientation and size of flames. These fire characteristics are 

interrelated with spread processes, and the literature does 

not discern the causes of observable features as distinct 

from their probable effects. 

Fire acceleration

Fire acceleration is defined as the time-dependent changes

in spread and intensity occurring under constant weather

and uniform fuel conditions. The notion of acceleration is

implicitly applied to fires that are already capable of 

Figure 5-1—Prescribed fire ignition patterns designed to restrict 

or enhance fire front interactions (from Martin and Dell 1978). Figure 5-2—Indrafting and flame response of sequential line fires 

in prescribed burning (from Rothermel 1984).

spreading as compared to combinations of threshold conditions where spread only occurs above some limit. Various 

mathematical representations of acceleration (fig. 5-3) have 

been proposed from a theoretical standpoint that express 

spread rate from a point-source fire as a negative exponential 

function of time (Cheney 1981, Cheney and Bary 1969). 

Parameters of these equations were fit to empirical data from 

wind tunnel experiments by McAlpine and Wakimoto (1991). 

These functions tend toward a final equilibrium rate and 

are, thus, commonly communicated in terms of the time to 

reach some fixed fraction of equilibrium (e.g., 90 percent). A 

similar result was developed by Weber (1989), who represented acceleration of fires expanding as a circle from a point 

ignition and depended on the curvature of the fire front.

Studies of acceleration typically report time elapsed 

from ignition to a near-steady spread rate. Values of 20 to 

30 min for point-source ignitions in slash fuels for prescribed fire conditions (McRae 1999) and in pine litter and 

feather moss (Kucuk et al. 2007) have been reported. Winddriven grass fires in Australia (Albini 1982) showed large 

variation in acceleration times (about 6 min under slow 

wind conditions to over 45 min with faster winds) and a 

strong dependency on the width of the fire front. Wind tunnel burns of shallow (8 cm deep) pine needle and excelsior 

beds suggested time to equilibrium of only a few minutes 

(McAlpine and Wakimoto 1991) and largely independent of 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 5-3—Theoretical fire spread rate acceleration curves from 

point-source ignitions show asymptotic increase in spread rate 

over time toward an equilibrium (from McAlpine and Wakimoto 

1991). ROS = rate of spread. 

windspeed. Data from point ignitions in pine needle litter 

reported by Curry and Fons (1938) suggested windspeed 

affected acceleration rate (increased time to equilibrium) 

as well as a final spread rate. Windspeed may also affect 

acceleration times for conflagrations involving structures 

at urban densities. Chandler et al. (1963) referenced much 

longer time estimates than for wildland fuels, including 1 hr 

to achieve near-steady spread rates with windspeed up to 7 

m/s, 2 h for winds to 18 m/s and possibly much longer times 

for stronger winds. A long acceleration period, exceeding 

the 36-min observation time, was described for line ignitions in heavy fuel loadings associated with felled eucalyptus slash (McArthur 1969a). By contrast, rapid acceleration 

to near-steady burning after line ignition was reported for 

experimental crown fires in jack pine forests (Stocks 1989). 

A theoretical analysis by Albini (1982) suggests that line 

ignitions in surface fuels could accelerate very rapidly, 

initially overshooting the steady rate, but then slow and 

exhibit damped oscillations toward the steady value as the 

increasing vertical buoyancy of the combustion zone offsets 

horizontal wind force. From the existing literature, it is not 

clear what influences the various factors of fuel loading, 

fuel sizes, burning duration, and final spread rates have on 

acceleration time, nor more complicated interactions among 

multiple flame zones or heat sources.

In addition, acceleration of fires can occur when air 

inflow is asymmetrically restricted by surface topography, 

either in canyons (Viegas and Pita 2004), or inclined channels (Woodburn and Drysdale 1998) and slopes (Dold and 

Zinoviev 2009, Wu et al. 2000). Detailed treatment of these 

important fire-topographic interactions, however, is beyond 

the scope of this review of fire-fire interactions.

Length of Fire Front

Fire acceleration and final spread rate appear to be dependent on fire size. Fires accelerate slowly from point-ignition 

sources (Cheney and Gould 1995, McAlpine and Wakimoto 

1991, McRae 1999) relative to line-source ignitions (Cheney 

and Gould 1995, Johansen 1987). At the small scale of 

laboratory stick arrays, fuel bed width and proportion of 

edge on the curvature of the head fire had significant effects 

on spread rate (Fendell and Wolff 2001). In wind-driven 

grass fires, fire spread rates were found to be dependent on 

the length of the ignition for lines shorter than 50 to 75 m 

(Cheney and Gould 1995) and required longer acceleration 

times for higher winds (fig. 5-4). Experiments and modeling 

by Wotton et al. (1999) for fires in red pine litter, however, 

showed no increase in radiation from flames for ignition 

lines longer than about 2 m and no effect of line width on 

spread rate beyond about 1 m. Dold et al. (2006) offered 

an explanation for fire size effect on forward spread rate. 

As fires expand in two dimensions, the distance between 

the fire edges increases, meaning that buoyancy-induced 

inflow along segments of flaming front comes from a wider 

area. This allows ambient winds from behind the front to 

penetrate to the heading portion of the flame zone. Such 

effects on narrow combustion zones of expanding fires are 

presumably different than for mass fires or large-area ignitions, which create indrafts from all directions (Baum and 

McCaffrey 1989, Smith et al. 1975) and strong buoyancydriven convection may deflect ambient airflow around the 

column (Countryman 1964).
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 5-4—Fire spread rates 

in grass fuels were found to 

increase with the width of head 

fires and depend on the final 

spread rates determined by 

windspeed (from Tolhurst and 

Cheney 1999).

Flame Tilt

Flame angle orientation relative to the unburned fuel is related to acceleration and is affected by fire size and stage of 

growth. Flames can tilt owing to wind, slope, or the interaction with other fires. Flames tilted away from the direction 

of spread are referred to as backing fires, and flames tilting 

toward the direction of spread are referred to as heading 

fires. Flames tilt toward the interior of the burned area in 

small fires or point-source fires, producing backing spread 

(Fendell and Wolff 2001, Luke and McArthur 1978, Tolhurst 

and Cheney 1999). Spread rate of backing fires spreading 

downslope has been shown to be only weakly diminished 

as slope increases (Van Wagner 1988) and little affected 

by wind (Beaufait 1965, McAlpine and Wakimoto 1991). 

Backing fires have been reported to increase fuel consumption and residence times. As fires grow larger, backing 

fire remains only at the rear of the perimeter (upwind or 

downslope) and flames for the heading portion of the fire 

tilt toward the unburned fuel. The very large differences 

in spread rate and intensity between backing and heading 

fires (and flanking fires) can be estimated assuming elliptical fire shapes (Catchpole et al. 1982). Numerous studies of 

flame tilt angle in a wildland fuel bed on flat terrain in wind 

have consistently found a strong relationship to the Froude 

number calculated from ratios of windspeed to intensity or 

flame length (Albini 1981, 1982; Nelson and Adkins 1986; 

Weise and Bigging 1996). Similar experimental results were 

found using liquid pool fires (Martin et al. 1991, Pipkin and 

Sliepcevich 1964, Welker and Sliepcevich 1966, Welker et 

al. 1965) and were explained as the counteraction of upward 

buoyant forces by crossflow, including flame trailing (lateral 

deflection of combustion products and flames) with high 

windspeeds. Recent numerical modeling (Nmira et al. 2010) 

has also reported Froude number relationships for both 

line-source and point-source simulated fires. Although slope 

effects were deemed significant (Weise and Bigging 1996), 

they are not accounted for in such formulations. When 

fires are in proximity, the interaction between them can 

change the flame tilt angle and rates of spread (Pitts 1991, 

Rios 1966, Welker et al. 1965). In these cases, the flame tilt 

angles can be correlated with a modified Froude number 

that includes the separation distance of the fires (Pitts 1991, 

Rios 1966, Welker et al. 1965). In the case of no wind, a 

modified Grashof number is used (Gebhart et al. 1976, Pera 

and Gebhart 1975) to describe the flame tilt purely owing to 

flame interaction.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Spread Thresholds

Thresholds describe a point of near-instantaneous acceleration that delineates when fire will and will not spread. 

Threshold-crossing for fire spread has been documented for 

many discontinuous fuel types including grasses (MarsdenSmedly et al. 2001), shrubs (Barrows et al. 1991, Bradstock 

and Gill 1993, Brown 1982, Weise et al. 2005), and trees 

(Bruner and Klebenow 1979, Van Wagner 1977). Laboratory-scale fires reveal similar spread thresholds in arrays of 

small sticks (Beer 1995, Vogel and Williams 1970, Weber 

1990) and taller beds of excelsior (Finney et al. 2010). These 

studies reveal threshold dependencies on multiple environmental, fuel, and fire variables, such as windspeed, fuel 

moisture, slope, horizontal fuel gap dimensions, fuel bed 

depth, fuel combustion rate, and flame size. Chandler (1963) 

proposed combinations of ranges of windspeed, humidity, 

and rainfall by fuel type to define spread thresholds for significant growth of large fires. Recent studies of fire spread 

sustainability provide empirical evidence on the importance 

of fuel moisture, wind, and fuel loading (Beverly and 

Wotton1997, Leonard 2009). As described in later sections 

of this chapter, fire interactions exert strong influences over 

many of these same environmental and fire variables, and 

thus, may elicit threshold-crossing spread for fires burning 

in discontinuous fuels.

Conditions Where Fire Interactions Occur

Interactions are possible when many separate fires grow 

together or multiple segments of a single continuous fire are 

oriented in proximity. In natural wildland fires, multiple 

fronts often occur because of spotting from a single main 

fire. Spot fires are relatively common under dry and windy 

conditions and even long-distance spotting contributes to 

fire movement (Anderson 1968). But massive deposition of 

firebrands at relatively short distances from the fire front 

(a few kilometers) can substantially increase spread rate 

and create simultaneous area ignition (Cheney and Bary 

1969). On wildfires, Cheney and Bary observed that the 

highest concentration of fire brands fell within a fan-shaped 

zone about 9 degrees in angle on either side of the primary 

wind direction and theorized that mass fire behavior could 

be achieved for certain unspecified combinations of fire 

brand density and acceleration time for individual ignitions. Johansen (1984) made similar observations for spot 

ignition patterns on prescribed burns where higher spot 

densities increased the numbers and frequencies of junction 

or merger zones. The increase in intensity at such junction 

zones has been documented empirically (Johansen 1984, 

McRae et al. 2005) and modeled (Morvan et al. 2009) 

leading to recommendations for wide separation of ignitions (Marsden-Smedley 2009, Tolhurst and Cheney 1999) 

unless area ignition is desired (Taylor et al. 1973). Mass 

ember deposition and area ignition has been documented by 

McArthur (1969b) for Tasmanian fires, where it resulted in 

near-simultaneous ignition of hillsides. A similar process 

was proposed for the Air Force Bomb Range Fire (Wade 

and Ward 1973), periodically causing area ignition ahead 

of the main front and vertical development of a convection 

column. Modeling by Weihs and Small (1986) showed 

that interactions between large mass fires can even cause 

these typically nonspreading fires to propagate toward one 

another.

How close together fires must be before flames visibly interact and subsequently merge is not clear. There 

have been many empirically derived merging criteria in 

the literature. Correlations exist for the critical parameters 

for both flame interaction (Baldwin 1968, Liu et al. 2007, 

Sugawa and Takahashi 1993 ) and merging (Delichatsios 

2007, Fukuda et al. 2004, Putnam and Speich 1963, Wood et 

al. 1971). These correlations take many forms—some define 

a critical ratio between the fire spacing and fire diameter 

(Sugawa and Takahashi 1993, Wood et al. 1971) or flame 

height (Baldwin 1968, Delichatsios 2007, Liu et al. 2007), 

some define a critical ratio between the flame height and 

fire diameter (Wood et al. 1971), and some define a critical 

dimensionless heat release rate (Fukuda et al. 2004, Putnam 

and Speich 1963). Upon close examination, however, it 

becomes clear that fire spacing, fire diameter, flame height, 

and dimensionless heat release rate have interdependencies, 

and, thus, these different correlations are not necessarily 

contradictory. The discussion here will focus on the relations between spacing, diameter, and flame height because 

they are the most intuitive.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Using both gas diffusion burners and pool fires, 

Sugawa and Takahashi (1993) reported that flames begin 

to interact when the ratio of the spacing distance to the 

fire diameter is less than four. In other words, flames can 

interact, here defined as visually tilting, over distances four 

times their diameter. Baldwin (1968) considered the onset 

of flame interaction in terms of flame height. Flames were 

considered to be interacting if the flame heights increased 

more than 10 percent above the independent flame height. 

Using square and round gas burners, wood cribs, and large 

timber yard fires, Baldwin (1968) (and Baldwin 1966, 

Baldwin et al. 1964, Thomas 1968) correlated experimental 

data over a wide range of scales and configurations found in 

the literature and determined that the flames would interact 

if the spacing was less than 0.22 times the flame length. For 

a characteristic dimension D and height L, this correlation 

holds for 1 < L/D < 300. Liu et al. (2007) also found the 

same dependency but with a slightly different constant of 

proportionality for merging of round pool fires. In their 

experiments, flame merging was likely to occur when 

closer than 0.29 to 0.34 times the merged flame length. 

Delichatsios (2007) also found that flames began to merge 

at spacing less than 0.33 times the actual flame length for 

gaseous burners. The discrepancy in these constants may 

be due to different definitions of flame interaction (tilting 

versus change in flame height) and flame merging (using 

completely merged flame height versus actual flame height), 

different fuels, and possibly uncertainty of measuring flame 

dimensions. In comparing the results of the Project Flambeau fires to those using a sand-filled pan burner, Wood et 

al. (1971) reported that flames merged if the flame height 

was at least half of the fire diameter. Heskestad (1998) 

clarified that this occurs when the nondimensional group 

N ~Q2/D5 is near 10-5 (Q is the heat release rate and D is 

the fire diameter). Clearly there is no definitive criterion for 

when flames begin to interact and merge, and these relations 

will remain qualitative guidelines until there is some sort of 

unifying theory.

An opposing effect may occur with area fires over 

large homogenous fuel beds (small flame height compared 

to fire diameter). For a sufficiently large fuel bed, it may be 

impossible for a continuous flame to exist over the entire 

bed. Instead of one continuous flame, the fire may break 

up into many distributed flamelets (Countryman 1969, 

Heskestad 1991, Wood et al. 1971). Heskestad (1991) showed 

that the breakup of continuous flames occurs when the 

nondimensional group N ~Q2/D5 is near 10-6. The convection column for these cases has been described as having 

two modes: Bénard cell convection near the surface, which 

then merges and transitions to a more organized convective 

plume (Fosberg 1967).

Specific Effects of Fire Interaction

Studies of fire interactions involve specific types of behavior of the combustion and observable fire characteristics. 

Most of the research on these behaviors comes from laboratory experiments with artificial fuel sources and attempts to 

isolate the particular response of interest.

Burning Rate

When fire fronts are close enough to interact and merge, 

such as in a mass fire, the mass of fuel burned as a function 

of time, or burning rate, of the fire can change dramatically. 

Much of the research on fire interactions has been done using gas burners with a fixed burning rate, but there has been 

some work on the interaction of flames over liquid pool 

fires and wood crib fires. Although the geometry and heat 

transfer mechanisms inside the fuel bed are different, liquid 

pool fires are much like fires burning over solid fuel in that 

the heat transfer from the fire back to the fuel controls the 

burning rate. In contrast, the burning rate of a gas burner 

is controlled by using a fixed fuel supply rate. Results from 

pool and crib fire experiments can often be extended to 

larger fuel beds using appropriate scaling laws (Emori and 

Saito 1983). 

The experiments by Huffman et al. (1969) clearly 

revealed the effect of spacing on the burning rate of pool 

fires. In this work, the burning rate of an array of liquid 

pools was measured while keeping a constant fuel depth 

and varying the number of pools, pool diameter, fuel, and 

pool separation distance. In general, the burning rate of 

each individual pool burner increases as the burners are 

brought closer together and the flames began to interact. In 

particular, the pools in the middle of the array show a very 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

dramatic increase. For example, figure 5-5 shows that the 

burning rate of 4-in diameter pools of cyclohexane experienced over a 400-percent increase in burning rate when 

the separation distance was halved. At the onset of flame 

merging, the burning rate is at its maximum. As the flames 

merge, the burning rate decreases as the separation distance continues to decrease. In the limit of zero separation 

distance, however, the burning rate of the individual fires is 

still larger than if they were burning independently with no 

interaction effects. These trends were also seen by Grumer 

and Strasser (1965) with solid fuel beds.

Kamikawa et al. (2005) studied the effect of flame 

merging on heat release rates (heat released per time). Heat 

release rate is calculated by multiplying the burning rate 

(mass of fuel burned per time) by the heat of reaction (heat 

released per mass of fuel burned). However, the heat of 

reaction is dependent on the fuel and the mixture ratio of 

fuel to air. In large fire arrays, the inner regions of the array 

typically experience a shortage of air. Without sufficient air, 

the fuel cannot completely react and release the full potential heat; i.e., the combustion efficiency is low and less heat 

is released per mass of fuel. Not surprisingly, Kamikawa et 

al. saw the same trend with heat release rates as Huffman et 

al. (1969) with burning rates. When the flames are merged, 

the heat release rate increases with separation distance. As 

the burners are moved farther apart, more air can penetrate 

into the inner regions of the array. More air entrainment 

means greater combustion efficiency and greater heat 

release. This, in turn, heats up and evaporates the unburned 

fuel more quickly, increasing the burning rate. 

Liu et al. (2009) explained the mechanisms behind 

these trends in burning and heat release rate with separation 

distance. The nonmonotonic behavior seen in figure 5-5 

is the result of two competing mechanisms: heat feedback 

enhancement and air entrainment restriction. As the burners 

are moved closer, the view factor between neighboring fires 

increases. In other words, the fires can “see” each other 

better, increasing the radiative heat transfer in addition to 

the convective heat transfer (Grumer and Strasser 1965). 

Because the burning rate is dictated by the heat feedback 

from the flame, this increased radiative heat seen by the

fuel will evaporate the fuel more quickly and increase 

Figure 5-5—Burning rate as a 

function of separation distance 

for 10.1-cm-diameter cyclohexane burners (from Huffman 

et al. 1969). 
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GENERAL TECHNICAL REPORT PNW-GTR-891

the burning rate. Conversely, as the fires get sufficiently 

close, there is less room to entrain air inside the array and 

the flames become “choked.” When the flames are merely 

interacting, the heat feedback mechanism is more important 

than the air restriction and the burning rate increases. When 

the flames have merged, the air restriction is the dominant 

mechanism and the burning rate decreases.

Because the experiments by Kamikawa et al. (2005) 

used wood crib fires, they were also able to examine the 

release rate as a function of time for merged flames. As 

with most wildland fires, the heat release rate (and burning 

rate) of wood crib fires increases as the fire builds, reaches 

a maximum, then begins to decrease as the fuel is depleted. 

Kamikawa et al. (2005) observed that as the number of fires 

increases, the peak heat release rate increases above that 

expected by multiplying the independent fire heat release 

rate by the number of fires. This discrepancy grows as the 

number of fires increases. So the burning and heat release 

rates of interacting and merging fires not only are dependent 

on the spacing of the fires, but also on the total number of 

fires (see also Liu et al. 2009).

Fire interactions can increase burning rates by another 

mode as well. If the fires interact such that vorticity is generated, fire whirls can form. Although not discussed further 

here, it has been shown that fire whirls have dramatically 

increased burning rates in comparison to an equivalent, 

nonrotating fire (see, for example, Emmons 1965, Grishin

et al. 2004 and chapter 8 of this volume).

Flame Dimensions

Flame height trends for a nonpremixed flame, such as those 

in a wildfire, are usually discussed in terms of two dimensionless parameters: the dimensionless flame height and the 

dimensionless heat release rate. The dimensionless flame 

height is usually defined as the flame height divided by the 

characteristic burning area diameter (D). The characteristic 

burning area diameter is a dimensioned parameter frequently introduced in fire arrays and is usually some function of 

the number of fires, fire diameter, and the fire arrangement 

(separation distance). The dimensionless heat release rate 

(Q*) is usually defined as the total heat release rate of the 

group divided by the characteristic burning area diameter 

to the five-halves power (material property constants are 

used to make the ratio dimensionless: Q* ~ Qtot/D5/2). The 

dimensionless heat release rate for natural fires tends to fall 

between 0.05 and 5 (McCaffrey 1995).

Much of the research on flame height has been performed using gas burners. However, two regimes of flow 

from a gas burner can be identified. When the flow velocity 

is low or the burner diameter is large, the momentum of 

the gaseous fuel is due primarily to its buoyancy. When the 

flow velocity is high or the burner diameter is small, the 

flow is like a jet. Putnam and Speich (1963) have a method 

for determining whether the flow from a gas burner is a 

high-momentum jet or buoyancy controlled. The discussion 

here will be limited to turbulent, buoyancy-driven flames, 

as this situation better describes what occurs during a 

wildfire. 

In general, the flame height increases as the fires are 

moved closer. When the flames begin to merge, the flame 

height will dramatically increase with further decreases 

in separation distance. However, once the flames are fully 

merged, further decreases in separation distance will 

have little effect (Chigier and Apak 1975, Fukuda et al. 

2004, Putnam and Speich 1963). The dimensionless flame 

height has successfully been correlated to the dimensionless heat release rate raised to some power, a. Because 

the dimensionless heat release rate can range over at least 

seven orders of magnitude, this power “a” can take on three 

different values depending on the range of the dimensionless heat release rate. As shown in figure 5-6 (Quintiere and 

Grove 1998), the dimensionless flame height increases with 

the dimensionless heat release rate. These correlations were 

originally developed for the flame height of a single independent burner where the characteristic dimension is the 

burner diameter, and hold for buoyancy-driven gas burners, 

liquid pool fires, and wood crib fires. However, there is an 

indication that these correlations also apply to interacting 

flames when the characteristic burning area dimension is 

given as discussed above. For example, for the interaction of 

relatively tall flames compared to the actual burner diameter 

(Lf

/D > 1, or high values of Q*), Putnam and Speich (1963) 

and Sugawa and Takahashi (1993) showed that the dimensionless flame height correlates well with the dimensionless 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

heat release rate to the two-fifths power (Lf/D ~ Q*2/5). 

Delichatsios (2007) successfully correlated the dimensionless flame height to the dimensionless heat release rate to 

the two-thirds power (Lf/D ~ Q*2/3) for Q* between 0.1 and 

1. On the other hand, Weng et al. (2004) and Kamikawa et 

al. (2005) showed that the data for merged flame height is 

better correlated with the exponent “a” varying with the 

number of burners. 

With all else remaining constant, these correlations 

suggest that an increase in either the number of fires or the 

individual fire heat release rate will increase the interacting 

or merged flame height. Increases in the separation distance 

or the fire diameter will result in a decrease in the interacting or merged flame height. An interesting caveat to these 

correlations is that the burning rate for individual pool or 

crib fires is not constant, but is a function of the separation 

distance as discussed above. This trend is not necessarily 

captured in figure 5-6 or by Putnam and Speich (1963) (gas 

burners), Kamikawa et al. (2005), Fukuda et al. (2004), or 

Figure 5-6—Dimensionless flame height (Lf/D) correlations with dimensionless heat release rate

(Q*) (Quintiere and Grove 1998).

Delichatsios (2007) (all fully merged flames). Also, vorticity 

can greatly increase flame height as well (Emmons 1965).

This literature suggests that in a mass fire situation, as 

the flames grow closer together, the heat release rate and 

characteristic “burner” diameter should increase. The net 

effect is most likely an increase in the flame height. If more 

spot fires were ignited in the burning area, for example, 

the flame height would increase further. This is consistent 

with the observations of spot ignitions on prescribed burns 

(Johansen 1984) and mass spotting in wildfires (Cheney 

and Bary 1969). However, for a sufficiently large area or 

mass fire, when the nondimensional group N ~Q2/D5 is near 

10-6, the fire is not expected to burn as a continuous flame 

but will break up into many distributed flamelets (Countryman 1969, Heskestad 1991, Wood et al. 1971). In this case, 

the flame height will be less than that predicted for a fully 

merged, continuous flame but larger than that of isolated 

flames (Thomas 1963). 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Flame Temperatures and Pollutants

In relation to flame height, as fires are moved closer 

together, air entrainment is blocked and the gaseous fuel 

must travel higher to find sufficient air for combustion. 

Experiments by Chigier and Apak (1975) indicated that 

a fuel particle journeying from the base to the tip of an 

interacting turbulent flame would experience delayed combustion compared to an independent flame (see fig. 5-7a). 

The delay means that the maximum temperature of the 

interacting flames would occur farther from the flame base. 

With limited mixing of fresh air into the flame to provide 

cooling, the temperatures inside an interacting flame decay 

more slowly with height so the flame is hot over a greater 

portion. In addition, limited mixing of air into the flames 

causes the formation of more carbon monoxide inside the 

flame zone. This prompted Countryman (1969) to speculate 

that the lack of oxygen in conjunction with elevated carbon 

monoxide could be fatal to ground personnel trapped inside 

the burning area.

Chigier and Apak (1975) also showed that the maximum temperature achieved by interacting turbulent flames 

is also a function of the separation distance and the number 

of burners (see fig. 5-7b). When the flames are close enough 

to interact, they lose less heat from radiation (the surroundings are at the same temperature) and by mixing with cool, 

fresh air. The maximum temperatures inside interacting 

flames therefore increase as the number of fires increase and 

as the burners get closer together. These increased temperatures could produce more of the smog-forming nitrogen 

oxide emissions (Tarr and Allen 1998).

Indraft Velocity

In typical fire situations where the flame height is relatively 

tall compared to the fire diameter, standard correlations 

exist to predict the mass of air entrained by the fire and its 

plume owing to the velocity difference between the plume 

gases and the ambient air. This air entrainment causes an 

inflow into the fire and is generally responsible for the 

bending of two flames in relative proximity. However, 

the standard correlations of plume theory are valid only 

above the flame. Although several plume theories exist 

in the literature (see review in Heskestad 2008), there is 

general agreement that the total mass of air entrained can be 

estimated as proportional to the convective heat release rate 

(heat release rate minus radiative and other losses) raised to 

the one-third power and to the height above the fire source 

to the five-thirds power. Fires with greater heat release rate 

entrain more air, and the total amount of air entrained increases with height above the plume. Note, however, that the 

velocity of the flow inside the plume decreases with height, 

so at some point near the top of the plume no further air is 

entrained (no velocity difference). Current research on the 

indraft caused by entrainment as related to fire interactions 

is focused mainly on providing better quantitative predictions with computational fluid dynamics modeling (Morvan 

et al. 2009, Roxburgh and Rein 2010).

However, plumes from wildfires can interact with local 

meteorology (Weber and Dold 2006) such as wind and 

atmospheric conditions. Additionally, classic plume theory 

for entrainment rates may not hold for small ratios of the 

flame height to fire diameter (Lf /D). Although the exact 

threshold is not known, Heskestad (2008) contended that 

the standard plume theory falls apart for Lf /D somewhere 

between 0.14 to 0.9. The perimeter of the plume where 

entrainment occurs becomes too small in relation to the 

volume of air inside and the slow-moving entrained air will 

not have much effect on the momentum of the entire plume. 

Mass fires by definition fall into the range of flame heightto-fire diameter ratios where classic plume theory does not 

hold. The results of the Project Flambeau burns confirm that 

there is little entrainment into the plume core (Palmer 1981). 

Many authors (e.g., Adams et al. 1973, Small et al. 1983, 

Smith et al. 1975) also argue that the entrainment of plume 

theory does not account for the reported high-velocity 

winds associated with mass fires. As discussed earlier, 

mass fires are characterized by such strong indrafts that the 

fire does little outward propagation. In their review of the 

range of possible indraft velocities, Trelles and Pagni (1997) 

showed that indraft velocities of large fires can range from 

about 2 to 40 m/s. In the Project Flambeau burns, Countryman (1964, 1965, 1969) also reported complicated airflow 

patterns and strong downdrafts that cannot be accounted for 

with simple plume theory.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 5-7a—Effect of nearby burners 

on flame temperature (from Chigier and 

Apak 1975). DT is throat diameter, DE is 

exit diameter, a is separation distance. 

Figure 5-7b—Temperature compared 

to independent flame for varying axial 

distance along flame, number of burners, burner arrangement, and burner 

spacing (from Chigier and Apak 1975). 

Tm is merged flame temperature, and Ts

is single flame temperature.
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GENERAL TECHNICAL REPORT PNW-GTR-891

There seem to be two main theories in the literature 

as to what causes the high-velocity inflows. One theory, 

advanced by Baum and McCaffrey (1989) and Carrier et al. 

(1985) is that large-scale vorticity in conjunction with heat 

release is responsible. These models contend that the entire 

fire plume slowly rotates. Note, however, that Church et 

al. (1980) and McRae and Flannigan (1990) characterized 

this type of motion as one type of fire whirl. In Baum and 

McCaffrey’s model (also used by Trelles and Pagni 1997 

and Ohlemiller and Corley 1994), this rotation is caused 

by density gradients from the high heat release, and not 

necessarily by any imposed swirling caused by the ambient 

environment. The slow rotation of such a large mass of air 

above the ground translates to high-velocity, purely horizontal, and nonrotating flow at the ground. One unique feature 

of the Baum and McCaffrey model is that it treats the large 

area fire as an ensemble of randomly distributed individual 

fires of varying strengths. Because of the method chosen 

to represent the fire, the model is only valid for heights 

above the fuel bed where the plumes of the individual fires 

have not merged. The model of Carrier et al. (1985) was 

intended to determine how long it would take to spin up the 

convective column and under what conditions this occurred. 

Based on the fact that the fire in Hamburg, Germany, took 

2 h to develop, they concluded that the growth of swirl, at 

least in this case, was most likely due to the intensification 

of a preexisting vortex from earlier fires and bombings. 

Although this contradicts the Baum and McCaffrey model, 

the experiments and discussion by Church et al. (1980) 

support this argument. The spatial orientation of individual 

fires may cause a swirling flow owing to the interaction of 

the indrafts to each fire (Soma and Saito 1991). Carrier et 

al. (1985) found that large-diameter plumes spin up faster, 

and proposed a set of four criteria that must be met for 

a “firestorm” to develop: heat release of 106 MW over a 

localized area for 2 to 3 h, a preexisting weak vortex, low 

ambient winds, and a nearly dry-adiabatic lapse rate over 

the first few kilometers of the atmosphere. 

Because it seems unlikely that all the criteria for 

spin-up of a convective column will be met, another theory, 

advanced by Smith et al. (1975) and Small et al. (1983) is 

proposed. These authors claimed that buoyancy-induced 

pressure gradients are responsible for the large indrafts. 

Smith et al. (1975) used a simple two-dimensional model of 

a convective column over a hot area to effectively show that 

near the fire, a dynamic pressure gradient can cause highvelocity inflow. This dynamic pressure gradient is caused 

by a balance between hydrostatic pressure and buoyancy. 

Buoyancy pushes the hot gases up while atmospheric 

pressure pushes fresh air at the ground in toward the fire 

horizontally to fill the gap left by the rising gases. Smith 

et al. (1975) also suggested that the traditional “weakly 

buoyant” plume theories described above may be valid for a 

small range of plume heights sufficiently far away from the 

fire and any inversion layer above. Small et al. (1983) used 

a similar model to Smith et al. (1975) but included a volume 

heat addition and large density and temperature gradients. 

Small et al. (1983) also numerically matched their model 

results of the area near the fire to the results of traditional 

plume theory for the region far from the fire. In both the 

Smith et al. (1975) and Small et al. (1983) models, the fire 

is treated as a single large heat source (fig. 5-8). Small et al. 

(1983) used their model to demonstrate how the maximum 

indraft velocity varies with fire radius, burning rate, and 

fire height (fig. 5-8). They showed that the maximum indraft 

velocity at first increases but eventually levels off (to about 

40 m/s) with increases in both the fire radius and the burning rate. On the other hand, the maximum indraft velocity 

appears to be linear with fire height.

A third, yet not well-explored, explanation was 

proposed by Carrier et al. (1984). In this work, they used 

classic plume theory but assumed that the fire does not burn 

as a single fire, but a collection of individual fires. They 

hypothesized that the high indraft velocities are then due to 

the increased fire perimeter from this “multicellular burning 

zone.” This hypothesis was not further developed, and in 

later works, these authors treated the fire as a subterranean 

point source. Interestingly, both the Baum and McCaffrey 

(1989) and Small et al. (1983) models reasonably replicate 

what little experimental data are available. However, the 

theories differ slightly in their predictions of the distance 

away from the fire that these indrafts extend (Pitts 1991). 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

The model of Baum and McCaffrey (1989) predicts that the 

high-velocity indrafts will extend much farther from the fire 

compared to the model of Small et al. (1983). Without more 

detailed experimental data, it is impossible to say which 

model more accurately portrays the physics. 

Pulsation

Although not an effect of flame interactions, flame pulsation 

(or puffing) is an interesting phenomenon that can occur in 

stationary fires, such as a mass fire. This pulsation typically 

occurs in circular or axisymmetric fires in weak ambient 

wind and is periodic in nature. Flame pulsation is important 

to many researchers because it can have a great influence 

on air entrainment rates and therefore heat release rates and 

pollution formation (Ghoniem et al. 1996). Observations of 

this phenomenon reveal the expansion of the flame near the 

base of the fire as a toroidal vortex, about the size of the fire 

diameter. As this vortex is shed and propagates upward, the 

flame necks inward giving the appearance of a “mushroom” 

shape. Figure 5-9 illustrates the process with time sequence 

of photos. Not all circular flames pulsate, however. Using 

dimensional analysis, Byram and Nelson (1970) attempted 

to describe what type of fires will pulsate. They defined a 

dimensionless “buoyancy” number, , 

where Ԛc is the rate of convective heat release per area, g is 

the gravitational acceleration, D is the fire diameter, and ρ, 

cp, and T are the density, specific heat, and temperature of 

the ambient air. Although no quantitative values were given, 

they argued that a fire will not pulsate if π2 is either too 

small (low heat release rate relative to large fire diameter) 

or too large (large heat release rate relative to small fire 

diameter).

Because this puffing also occurs in nonreacting helium 

plumes, it is actually not caused by a combustion instability, but instead is produced by a fluid dynamic instability 

(Cetegen and Ahmed 1993). There is disagreement about the 

actual cause of the instability (Tieszen 2001), but the vortex 

is generally thought to be formed because of the interaction 

between gravity and the density gradient between the flame 

and ambient air temperatures (Ghoniem et al. 1996). 

Most of what has been learned about the characteristics 

of pulsation has been learned through experiments. Cetegen 

Figure 5-8—Model results for flow-field streamlines for three fires in proximity (from Weihs and Small 1986). 

p c [ ] ( ) ρ pTcgDQ 5.0

2  =
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GENERAL TECHNICAL REPORT PNW-GTR-891

and Ahmed (1993) showed that the toroidal vortex forms 

within one fire diameter above the flame base and that the 

frequency of the puffing is insensitive to the fuel or the heat 

release rate. By plotting the available data in the literature, 

Cetegen and Ahmed (1993), and later Malalasekera et al. 

(1996), showed that the pulsation frequency is proportional 

to the fire diameter raised to the negative one-half power

(f ~D-1/2) so that large fires pulsate at a much lower 

frequency than small fires. Though this correlation was 

developed using data from fires ranging from 0.1 to 100 m 

in diameter (four orders of magnitude) using gaseous, liquid 

and solid fuels, Baum and McCaffrey (1989) suggested that 

it may well hold for much larger fires as well. For a large fire 

with a diameter on the order of 20 km, Larson et al. (1982) 

estimated that the pulsation will occur every 20 minutes. 

Although it is not accounted for in the above correlation, 

Malalasekera et al. (1996) showed that increasing fuel flow 

rates also result in a small increase in puffing frequency, 

especially for small fire sizes. Because of this, Malalasekera 

et al. (1996) correlated the puffing frequency in a slightly 

different manner using the dimensionless Strouhal number 

(ratio of oscillation frequency to 1 over the characteristic 

time of convection) and Froude number (ratio of inertia 

force to gravitational force), which retains the same dependency on fire diameter but allows for a correction owing to 

changes in fuel flow velocity.

Convection Column 

Mass fires are also described as having very tall convection columns, or smoke plumes with large cloud structures 

because of the moisture release from combustion (Small 

and Heikes 1988). As discussed in the section on indraft 

velocities, the entrainment of cold, ambient air slows the 

rise of the hot gases by cooling them. Additionally, the 

density of the ambient air itself decreases with elevation. As 

the hot gases rise and cool, the density difference driving 

their upward motion disappears. It follows then that the top 

of the smoke plume corresponds to the height where the 

combustion products stop rising. As the fire diameter grows, 

however, the entrainment predicted by classic plume theory 

becomes less effective. Entrainment occurs at the perimeter 

of the plume, and with large fire sources, there is such a 

large core of hot gases that entrainment is less effective at 

slowing the rise of the combustion products (Palmer 1981). 

Thus, it takes longer to entrain enough cold air to slow 

the combustion products, and therefore the smoke plume 

becomes taller. For example, a lack of entrainment to the 

convection column was noted and discussed by Taylor et 

al. (1973) on a large prescribed burn. In fact, the plume 

from a sufficiently large mass fire may be almost as wide 

as it is tall, so Brode and Small (1986) and Palmer (1981) 

contended that air entrainment is not likely to be a major 

influence on plume height and that it is the structure of the 

Figure 5-9—Thermal images of flame pulsation (from

Malalasekera et al. 1996).
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

atmosphere itself that is the limiting factor. The plume of 

large mass fires is therefore more sensitive to atmospheric 

gradients, inversion heights, and upper atmosphere crosswinds (see also Penner et. al 1986). Brode and Small (1986) 

showed that the tropopause/stratosphere transition may be 

what actually caps the smoke plume. Note, these theories 

contradict the suggestion of Smith et al. (1975) that the 

traditional plume theory holds at some intermediate height 

above the ground. Perhaps the scale of the fires modeled by 

Smith et al. (1975) was not large enough to see this effect.

Palmer (1981) described the interesting structure of 

the convection columns that formed during the Project 

Flambeau tests. In the first few minutes of these large-scale 

burns, the majority of the gaseous combustion products 

were contained in a “bubble” near the fire. Once the 

“bubble” got sufficiently hot, the associated buoyancy was 

enough to overcome the surface drag forces and the bubble 

rose. As the bubble rose, a vortex ring would form in a 

similar manner described above with respect to flame pulsations. Regardless of the atmospheric stability, this vortex 

ring would rise until it encountered a region of vertical wind 

shear. The vertical wind shear weakened the vortex enough 

for the plume to then follow the prevailing horizontal winds. 

Palmer (1981) also noted that the “exterior form of the 

convection column at a particular altitude was determined 

by the initial vortex bubble as it passed that altitude.” Most 

of the plumes in these fires began to rotate as a single vertical vortex, as suggested by the Baum and McCaffrey (1989) 

model. This rotation further inhibits entrainment, which 

would also prevent the use of classic plume models for mass 

fires (Banta et al. 1992). 

Summary of Interaction Effects

As the individual spot fires grow together, they will begin 

to interact. This interaction will increase the burning rates, 

heat release rates, and flame height until the distance between them reaches a critical level. At the critical separation 

distance, the flames will begin to merge together and burn 

with the maximum rate and flame height. As these spot fires 

continue to grow together, the burning and heat release rates 

will finally start to decrease but remain at a much elevated 

level compared to the independent spot fire. The flame 

height is not expected to change significantly. The more 

spot fires, the bigger the increase in burning rate and flame 

height.

Needs for Further Research and 

Application

The characteristics of many fire interactions have been 

examined and reported in the research literature, leaving 

little doubt that local spread and behavior experienced by 

wildland fire personnel can be greatly influenced by fire 

configurations at larger scales. The ignition patterns and 

“suppression fire” tactics used in firefighting (Castellnou 

et al. 2010, Miralles et al. 2010) depend on understanding 

these interactions. However, questions remain about how 

to extend the findings of fundamental research to the field 

scale for wildland fires and mass fires. In particular, there 

is no clear method to determine the minimum separation 

distance between two fires for interaction and merging to 

occur. The influence of ambient winds or topography on 

interactions is directly relevant to wildfire management 

activities and tactics but has not been explored. Large-area 

fires were discussed as an extreme case of fire interactions 

and often behave quite differently than propagating line 

fires. Just how much area must be ignited to display “mass 

fire” characteristics is unknown. Even in the Project Flambeau experiments, Countryman (1964) argued that these 

large fires were not large enough to be considered mass 

fires. Both Byram (1966) and Thomas et al. (1968) developed scaling laws in an attempt to answer this question, but 

many potentially limiting assumptions were made in the 

development and the laws were not validated. Baldwin and 

North (1967) attempted to quantify the minimum area for 

urban applications based on city layout and historical fires, 

but their estimations are admittedly crude. As discussed, 

there is no consensus in the literature about the convection 

column dynamics of mass fires and what mechanism is 

responsible for the reported strong indrafts. These suggestions are merely a starting point, as the subjects of fire 

interactions and mass fires clearly involve a great deal of 

physics and require the union of many fields of study.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Literature Cited

Adams, JS.; Williams, D.W.; Tregellas-Williams, 

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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 6: Column/Plume Dynamics

Brian E. Potter1

Introduction

“Plume dynamics” refers to the airflow related to a fire’s updraft and the way that updraft changes over time. In terms 

of extreme fire behavior, plume dynamics matter because 

they can accelerate surface winds and can bring the wind, 

moisture, and temperature conditions above the ground 

down to the ground, where the fire is. These aboveground 

conditions may or may not be the same as the conditions 

at the ground, and the differences can produce unexpected 

changes in fire behavior. The updraft is part of a fire-driven 

circulation that includes downward air motion in other areas 

and modifies the horizontal winds near the fire. The updraft 

also lifts burning embers that can ignite spot fires. Any of 

these can lead to nonsteady state, or for the purposes of this 

review, extreme fire behavior. This section examines several 

concepts and tools related to plume dynamics that are well 

known in the fire management community, noting their 

foundations, strengths, weaknesses, and limitations. Spot 

fires are addressed in more detail in chapter 7.

Over the past 40 years, several studies have reported information related to plume structure in wildland fires. These 

studies combined with some complementary knowledge of 

the structure of convective thunderstorms make it possible 

to create a qualitative description or idealized model of 

the plume and associated airflow produced by a wildland 

fire. This model is a simplification and does not include the 

transient features of a real plume, especially features that 

come and go on very short time scales. However, it serves 

as a useful foundation for discussing how plume dynamics 

can lead to extreme fire behavior. Before addressing specific 

relationships between plumes and basic or extreme fire 

behavior, it is necessary to briefly establish this qualitative 

description and its origins.

Countryman (1969) summarized the Project Flambeau 

experiment and the data collected there. This study involved 

1 Brian E. Potter is a research meteorologist, U.S. Department 

of Agriculture, Forest Service, Pacific Wildland Fire Sciences 

Laboratory, 400 N 34th St., Suite 201, Seattle, WA 98103.

slash piles laid out in grids, with the intent of simulating 

mass ignitions following a nuclear attack. In spite of the 

artificial fuel loads and geometry and the fact that the fires 

were stationary, the observed airflow in and around the 

plume provides insight into natural wildland fire situations. 

Reid and Vines (1972) used a sequence of photographs and 

radar scans of a fire plume to track individual smoke turrets 

and infer both vertical and horizontal velocities. Taylor et al. 

(1968, 1971, 1973) flew aircraft through plumes on prescribed fires to measure vertical motions and temperatures 

in the plumes at heights up to about 3 km above ground 

level (AGL). Banta et al. (1992) used Doppler radar to look 

through smoke into the fire plume and captured detailed 

airflow in and around the plume. Schroeder and Buck (1970) 

and Goens and Andrews (1998) identified the possibility of 

intense downdrafts associated with plume updrafts and the 

potential implications of these downdrafts. Clements et al. 

(2006, 2007) directly measured airflow and temperature as 

a fire front passed instrument towers, yielding insight into 

the near-ground winds associated with a propagating fire.

Although these papers address fire-related plume 

studies, there are many more studies of thunderstorm 

plume dynamics in the absence of fire. There are critical 

differences between the two types of plumes—intensity 

of the energy source, horizontal movement of that energy 

source, and vertical distribution of the energy source, to 

name a few—but there are also important similarities that 

can provide valuable insight into fires. The understanding of 

storm dynamics comes from decades of expensive research 

that would be markedly more expensive and dangerous 

to perform on fire plumes, so it would be unwise and 

irresponsible to ignore it. (Not only can thunderstorms be 

more easily measured and observed, but they are also easier 

to simulate in computer models to identify features not 

directly observed.) Key publications in the storm literature 

include Browning (1976), Klemp et al. (1981), Foote and 

Frank (1983), Hobbs and Rangno (1985), Houze et al. (1989), 

and McCaul and Weisman (2001).
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Figure 6-1 illustrates a composite structure constructed 

from these various studies (this structure includes only the 

features most clearly observed in fire studies and/or those 

observed in fires as well as thunderstorm studies). The most 

consistently observed, best recognized characteristics are 

the updraft column, and the rear and lateral inflow to the 

fire at the ground (Countryman 1969, Palmer 1981). Less 

documented are the descending rear inflow and the accelerating character of the updraft. These have been observed 

(Banta et al. 1994; Clements et al. 2006, 2007) and are 

consistent with both basic fluid mechanics and the storm 

literature, but they are more difficult to observe, and details 

of their character are very poorly known. This descending 

rear inflow is not the same as the downbursts discussed 

by Schroeder and Buck (1970) or Goens and Andrews 

(1998)—those are more intense, short-lived, and localized 

currents, while the descending rear inflow appears to be 

a persistent and moderate speed feature. The updraft may 

consist of a sequence of puffs or turrets that separate from 

the fire front and move downwind (Reid and Vines 1972). 

These successive turrets (each one an updraft) could move 

forward or backward relative to the fire, but typically move 

forward. Stability of the atmosphere also influences the 

acceleration and magnitude of the updraft, with an unstable 

atmosphere allowing stronger, deeper updrafts. The wind 

profile influences the inclination of the updraft, its strength, 

and the relative forward motion of the turrets compared to 

the fire front.

Plume-Dominated and Wind-Driven Fires

The most recognized connection between extreme fire 

behavior and plume dynamics is the concept of a plumedominated or wind-driven fire. Generally, wind-driven 

fires are more predictable because the fire spreads with the 

wind (Bryan 1959). When someone labels a fire “plumedominated,” it is because the smoke plume is standing 

almost vertically and there is little visible influence of the 

horizontal wind on the fire. In common usage, the implication is that the fire’s behavior may change rapidly and that 

the fire’s direction of spread could change unexpectedly.

Byram’s original presentation did not provide any 

derivation, but Nelson (1993) did so based on Nelson’s 

conversations with Byram and Byram’s own notes on the 

equations. Nelson’s derived forms and Byram’s original 

forms differ slightly, with Byram’s equations having units 

of mass per distance per unit time (or as Byram stated, foot 

pounds per second per square foot) and Nelson’s having 

units of mass per unit time cubed. Units aside, both authors 

actually emphasize the use of what they call the convection 

number, Nc, which is the ratio of the power of the fire to the 

power of the wind–the units cancel out and both authors’ 

equations for Nc are the same:

Nc = 

2gI

ρcpT0(v - r)

3 

where g is the gravitational acceleration (9.8 m/s2), I is the fire 

intensity per unit length of the fire front (Joules per second 

per meter), ρ is the density of air (kilograms per cubic meter), 

cp is the specific heat of air (Joules per kilogram per degree 

Celsius), T0 is the environmental temperature of air at the 

ground (Kelvins, equal to degrees Celsius plus 273), v is the 

horizontal windspeed (m/s), and r is the fire rate of forward 

spread (m/s). The power equations and the equation for Nc

assume a neutrally stable atmosphere. Nelson (1993) provides 

Figure 6-1—Conceptual model of the airflow in and around a 

fire’s plume (This structure includes only the features most clearly 

observed in fire studies or those observed in fires as well as thunderstorm studies).
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

a brief discussion of how an atmosphere that is not neutrally 

stable and mixing of plume air with the environment affect 

the equations, and Nelson (2003) expanded on these two 

points. 

Byram (1959) provided one example of applying the 

power equations to an actual fire, using the Wood River 

Valley Fire in May 1951 in Rhode Island. He stated:

The energy criterion cannot give directly any 

information as to what [power of the fire dominating 

power of the wind and an inferred three-dimensional 

structure] means in terms of specific fire behavior 

characteristics, but the case studies can. These 

studies have shown that extreme fire behavior and 

blowup characteristics occur when Pf > Pw for a 

considerable height above the fire–usually at least 

1000 feet and more, often greater than 3000 or 4000 

feet. Possibly one of the most erratic conditions is in 

the transition zone where Pf and Pw are nearly equal.

He presents a valuable discussion of the evolution of a 

wildland fire in terms of these powers. He rightly observes 

that for every fire, Pf starts out small. For intensity and Pf

to increase, he states moderate to high surface winds are 

necessary to fan the flames. However, it is plausible that 

terrain or fuels could contribute to increasing intensity even 

without high surface winds. Regardless of what causes intensity and Pf to increase, they may grow to exceed Pw over 

a layer of the atmosphere deep enough to create a “chimney 

or convection column” over the fire.

Wade and Ward (1973) considered Nc in their study 

of the 1971 Air Force Bomb Range fire in North Carolina. 

They noted that computing Nc required several assumptions, 

including neutral stability, no long-distance spotting, and 

“adequate data on fuels, weather and fire behavior.” Making 

these assumptions, they determined that Nc exceeded a 

value of 1 from the surface to just below 300 m AGL during 

the period of the fire’s major run and as it increased, so 

did rate of spread. Their calculations for the peak spread 

period indicated Nc greater than 1 up to 2000 m AGL with 

the exception of the layer between 300 to 900 m AGL. The 

highest value of Nc in their analysis was approximately 4.6, 

at the surface during the peak spread period. The highest 

value of Nc in the layer from 900 to 2000 m AGL was 1.4,

at 1500 m.

Applying the necessary assumptions forced Wade 

and Ward to contradict other evidence from the fire. For 

example, the assumption of neutral stability contradicts 

their observations of an unstable atmosphere reaching up 

to 2300 to 2700 m AGL during the day. Instability would 

combine with the fire’s energy output to increase Pf and 

therefore Nc. Including the impacts of instability would thus 

have increased Nc during the daytime, most likely amplifying the changes Wade and Ward observed in Nc during the 

fire’s blowup period.

The assumption of no long-distance spotting contradicts their narrative summary of the fire’s behavior, as 

well as their centerpiece figure illustrating the fire’s spread 

by spotting. It is not clear why Wade and Ward state that 

the calculation of Nc requires assuming no long-distance 

spotting; Byram’s sole claim about Nc was that fire behavior became more erratic when Nc is close to 1 and more 

extreme when Nc is greater than 1, but spotting does not 

necessarily contradict that claim. Strong spotting does 

increase the effect of wind on fire spread, but spotting is 

also enhanced by greater lofting in a strongly developed 

convection column—which would result from high Pf. The 

end result is that spotting and its potential role in rapid fire 

spread (i.e., extreme fire behavior) make Nc a less helpful 

tool for extreme fire behavior prediction.

Simard et al. (1983) applied Byram’s Pf and Pw calculations as part of their case study for the 1980 Mack Lake 

Fire. They found that during the period of most rapid 

spread, Pf

 exceeded Pw up to at least 1300 m AGL. (The 

lowest value for the convection number, Nc, between the 

ground and that height was 2.4.) Like Wade and Ward 

(1973), Simard et al. had to make numerous assumptions 

about the weather. They assumed, for example, that the 

wind profile was constant over time, though the surface 

observations and regional variations in the vertical wind 

profile suggest quite a bit of variability. The winds used 

in their calculation of Pw are actually the highest winds 

observed at given levels, and any other windspeeds would 

have substantially reduced Pw. 
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Aronovitch (1989) applied Byram’s equations to two 

fires, the Sundance Fire from 1967 and the Butte Fire of 

1985. For the Sundance Fire, Aronovitch’s treatment of wind 

variability with height is unclear. The one figure depicting 

Nc shows no height dependence, only a time series, and 

this indicates Nc greater than 1 for several hours during the 

fire’s major run. Aronovitch’s calculations show Nc near 20 

at 1500 Local Standard Time (LST), then dropping sharply 

to near 1 at 1700 LST and remaining between 0.02 and 2.8 

for the remainder of the period. Aronovitch’s calculations of 

fire intensity and narrative of the run show the peak growth 

between 2000 and 2300 LST, including the comment “fire 

storm” at 2000 LST—but Nc peaks at 1500 LST and is only 

1.7 at 2000 LST. If Nc directly indicates “blow up” fire 

conditions, it did not work in this instance. For the Butte 

Fire, Aronovitch examined Nc (as the two components, Pw

and Pf) using two different values of Pf —one “prior to 

blow up” and the other “after blow up.” Using the “prior” Pf, 

Nc exceeds 1 between approximately 150 and 1100 m. Using 

“after” Pf, Nc exceeds 1 from the ground to 1400 m above 

the fire. For both values of Pf, the peak value of Nc occurs 

at 60 m above the fire, where Pw drops to less than 0.2, 

yielding Nc of 20 (prior) and 60 (after). It is a circular argument, however, to say that when the fire was most intense 

(“after,” according to Aronovitch’s narrative), it was most 

powerful—for management purposes, the simple knowledge that the fire’s intensity more than tripled between the 

two periods provides as much insight as the more detailed 

calculation (requiring many more assumptions) of Nc. 

Rothermel (1991) incorporated Byram’s equations into 

his model for predicting behavior and size of crown fires in 

the Northern Rockies. The nomogram calculations of Pf

 and 

Pw do not influence any other calculations in this crown fire 

system, rather their comparison is used to suggest the possibility of a plume-dominated fire. In incorporating them, 

however, Rothermel had to rely solely on winds measured 

at 6.1 m above canopy and could not require any further 

information on vertical variations in windspeed. This and 

the fact that the nomograms are only available for fuel types 

in the Northern Rockies lead to limitations in the application of the crown fire behavior calculations. (Only one of the 

four fires discussed in the aforementioned studies, the Butte 

Fire, was in the Northern Rockies.)

Byram’s equations, or Nelson’s more general equations, 

represent the energy produced by combustion and that 

contained in the wind field. Application of the equations for 

case studies has a very low precision. Small uncertainty (or 

errors) in determining the windspeed or the rate of spread, 

especially when they are comparable, can lead to very large 

uncertainty (or errors) in Pw and Nc because the difference 

in windspeed and fire rate of spread is cubed and because 

this difference appears in the denominator of Nc.

The scientific strength of any claimed relationship 

between Pf, Pw, or Nc and fire behavior suffers from an 

additional, very important limitation. To truly be meaningful in indicating potential for “extreme fire behavior and 

blowup characteristics,” it is important to not only show 

that this behavior has happened when Pf > Pw, but to also 

show that the behavior does not happen when Pf < Pw. This 

cannot be done by looking only at blowup fires, it requires 

application of a uniform computation of Pf and Pw for a 

large set of random fires, some of which showed extreme 

behavior and others that did not. Similarly, there is no 

published study considering whether Pf > Pw guarantees 

extreme fire behavior, or what proportion of the time this 

might be true. Byram’s statement that fire behavior is possibly most erratic when Pf

 and Pw are nearly equal makes 

any analysis still more challenging.

With respect to operational use, obtaining the necessary 

data to apply the equations is a daunting task. Aronovitch 

simplified the application somewhat by assuming density, 

specific heat, and gravity are constant. Rothermel (1991) 

went one step further and specified temperature at 80 °F (40 

°C). The remaining unknowns are windspeed, fire intensity, 

and rate of spread. The two fire measures can be obtained, 

either for current or future conditions, from observations or 

a model like BEHAVEPlus. They depend solely on surface 

conditions. To allow proper calculation and application 

of Nc, windspeed must be known as a function of height. 

(The inconsistency of assuming constant winds over height 

with Byram’s adverse wind profiles is discussed in the next 

section.) Because of high variability across the landscape, 

especially in rugged terrain, and the importance of changes 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

over time, this is perhaps the most difficult information to 

obtain—and yet, it is arguably the most critical because it 

is cubed in computing Nc. Current weather forecast models 

provide information on a scale down to 1.3 km, at the very 

best, and finer resolution models typically only provide surface windspeeds. These challenges may explain why there 

are no reports of Fire Behavior Analysts applying Nc, Pf ,or 

Pw in an operational context. Although the NWCG S-490 

stated objectives include estimation of Pf and Pw, there is 

no quantitative exercise or explicit discussion of estimating 

these, and it is not listed in the instructor handbook among 

the topics to be tested.

Adverse Wind Profiles and Low-Level Jets

Closely related to the power of the fire and the power of the 

wind is Byram’s other well-known contribution to the science of extreme fire behavior, the idea of the “adverse wind 

profile” (Byram 1954) This is often simplify referred to as 

the “low-level jet.” Byram’s adverse profiles or the low-level 

jet concept are taught in all of the NWCG fire behavior 

classes, and are cited in numerous case studies. 

This paper discusses turbulence, instability, and the 

jetstream’s location in addition to wind profiles, but it is the 

wind profiles that remain widely known. Byram examined 

the wind profiles measured near 17 fires. He identified 

eight wind profile types he felt indicated various degrees of 

blowup potential, associating as many as four profile types 

with individual fires. 

The technique Byram used is extremely important. He 

truncated the soundings at the height of the fire when the 

sounding location elevation was lower than the fire elevation, and for cases where the fire occurred below the height 

of the sounding base, he simply raised the sounding and left 

the lower portion blank. In doing this, many of his “jets” 

that occurred at higher elevations appear to be closer to the 

ground. For example, Byram used observations from Great 

Falls, Montana (elev. 1100 m) for the Mann Gulch Fire (estimated elev. 1700 m) and so any jet present in that sounding 

appears 580 m closer to the surface than it actually was 

observed. Similarly, the “surface” wind cited for the McVey 

Fire was actually observed at 640 m above the ground at 

Rapid City, South Dakota. He includes the full soundings at 

the end of the paper, but these appear to have been largely 

ignored in his and others’ subsequent discussion of his 

work. Excluding the effects of stability and terrain makes 

it impossible to know whether the atmosphere would flow 

over or around terrain, and therefore whether it would have 

been more appropriate to assume the ground-level winds at 

the observation site were also the ground-level winds at the 

fire site, or to truncate the soundings as he did. 

One feature Byram mentioned has been largely 

forgotten. He states “The direction profile is an extremely 

important part of the complete wind profile” and provides 

the full wind direction profiles for each case he examined—

without truncation, but he does not provide any discussion 

or explanation of how the directional profiles may have 

influenced the case studies, or exactly how they were 

important. The figures show a variety of directional profiles, 

as well as changes in those profiles during the fires. 

Steiner (1976) raised the question of what physical 

process might explain the role of a low-level jet in blowup 

fires. While the discussion centered on momentum, convergence, and divergence and how they affect the fire column, 

it was in general terms and did not reach any particular 

conclusions.

Brotak (1976) considered Byram’s low-level jet along 

with a variety of other atmospheric measures in examining 62 “large and extremely serious” wildfires. These fires 

were predominantly in the Eastern United States. Brotak’s 

criterion for a jet was a windspeed 2 m/s greater than that 

300 m above or below it. Overall, only one third of the fires 

considered displayed such a jet.

Even disregarding the concerns noted above about 

the truncation of the soundings used by Byram, nothing is 

known about how often a low-level jet is present at times 

when a fire does not behave erratically or blowup. This is 

essential for knowing what the false alarm rate might be, or 

whether the low-level jet might be a regular feature in some 

parts of the country.

The low-level jet seen in Byram’s most dangerous 

profiles directly repudiates the use of height-invariant winds 

to compute Pw, noted in the previous section. Using such 
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GENERAL TECHNICAL REPORT PNW-GTR-891

winds assumes no jet, underestimates Pw, and therefore 

underestimates the value of Pf necessary to yield Nc > 1. As 

noted earlier, because the windspeed is cubed in computing 

Pw, small errors in the windspeed produce larger errors in 

Pw. There is no published study applying both Byram’s Pf, 

Pw, or Nc ,and his adverse wind profiles to a common set of 

fires.

Presently, the only operational application of the 

Byram wind profiles is subjective assessment of observed 

profiles by fire behavior analysts or incident meteorologists. If Byram’s truncation technique is not problematic 

and the low-level jet primarily occurs on blowup fires, then 

this assessment is appropriate. However, if the technique 

misrepresented the winds over the actual fires or the jet 

occurs on many days, with or without blowup fires, then 

the assessment provides no real insight and may lead to a 

misperception of the actual risk of extreme fire behavior. 

Stability and Instability

Few who currently work with wildland fires doubt that 

there is a connection between atmospheric stability and 

fire behavior. The concept first appears in Foley (1947) 

followed by Crosby (1949), Byram and Nelson (1951), and 

Davis (1969), and several other, less pivotal papers. These 

papers discussed the relationship in general terms but 

were not scientific studies. Davis (1969) examined stability 

accompanying 70 fires in Arkansas, Alabama, Louisiana, 

Mississippi, and Tennessee using broad stability and fire 

size classes. Later, Brotak and Reifsnyder (1977) found that 

unstable air above a fire was present for a number of large 

fires. Haines (1988a) used these observations as the basis of 

his Lower Atmospheric Severity Index, now known widely 

as the Haines Index. More recently, Potter (2002) discussed 

how atmospheric stability may reflect the potential circulation created by a fire, which includes the winds at the 

surface influencing fire behavior. 

A stable atmosphere resists the rising motion of hot air 

in the fire’s plume and saps some of the energy from that air 

in doing so. In contrast, an unstable atmosphere supports 

vertical movement of air, promoting general mixing of air 

between the ground and regions higher up even when no 

fire is present. Instability itself cannot directly influence the 

combustion process—it must be converted into wind to do 

this. Instability (a form of potential, or stored, energy) can 

be converted into an organized circulation including the 

fire’s updraft, on a scale of a kilometer or more, or it may 

produce turbulent wind energy on scales down to meters or 

tens of meters. Even in the absence of a fire, strong instability promotes mixing and turbulence and can bring air aloft 

down to the ground where it can interact with fuels and any 

combustion that does occur. For a given fire at a given time, 

all of these processes can be occurring simultaneously. 

Instability cannot directly start a fire, either. It can foster 

thunderstorms that produce lightning, but that is an indirect 

connection and is not related to extreme fire behavior.

Foley (1947), Crosby (1949), and Byram and Nelson 

(1951) all postulated that instability influences fire behavior 

primarily through the turbulence and high winds it can 

transport downward to the fire. They did not differentiate 

between mixing resulting from the instability alone and 

mixing resulting from the fire’s interaction with instability. Because terrain and surface features like lakes, rivers, 

and changes in vegetation can promote mixing, it would 

be very difficult to determine whether ambient mixing or 

fire-induced mixing plays a stronger role. In any case, the 

instability enables it, and there is a straightforward physical 

explanation of at least one way instability can contribute to 

extreme fire behavior.

Brotak and Reifsnyder (1977), Haines (1988a), and 

Potter (2002) all included some measure of moisture aloft 

in their discussions of instability. Brotak and Reifsnyder 

(1977) found a correlation between low moisture aloft

and large fire occurrence at the ground, which led to the 

inclusion of this element in the Haines Index and suggested it may be important for the circulation considered 

by Potter (2002). Because of this mixture of moisture and 

stability—and the intrinsic physical influence of moisture 

on stability—one must be careful in interpreting the results 

or implications of these research studies.

Davis (1969), Brotak and Reifsnyder (1977), and Haines 

(1988a) did not examine extreme fire-behavior qualities—

intensity, rate of spread, or flame length. Rather, Davis 

considered stability at the times of fires over 120 ha (300 

ac) provided to him by state fire control staff. Brotak and 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Reifsnyder looked at atmospheric properties present at the 

time of “large” fires, defined as larger than 2000 ha. Haines’ 

fire data set included 74 fires reported by wildland fire management units as “their worst situations over 20 [years].” 

How to translate these fire characteristics to the concept of 

extreme fire behavior, if it is even possible to do so, is not 

clear. At present, the Haines Index is the only quantitative 

measure of stability used in wildland fire management. 

There are questions of scale and the role moisture 

plays in interactions between stability or instability and 

fire. Nonetheless, the use of the Haines Index to indicate 

the regional potential for fires to display erratic behavior 

is consistent with its derivation. There are other stability 

indices used for thunderstorms (such as the K, Lifted, 

Showalter, SWEAT, and the Total Totals Indices) or smoke 

dispersion (such as the Lavdas Atmospheric Dispersion 

Index), but none of these have been scientifically evaluated 

for use in predicting extreme fire behavior, or fire behavior 

of any kind.

Much remains unknown about instability’s influence 

on extreme fire behavior. Is it possible to differentiate 

instability’s influence on plume strength (and subsequently 

ground-level inflow winds) from the relationship between 

instability and turbulence? Heilman and Bian (2010) showed 

that multiplying the Haines Index by the surface turbulent 

kinetic energy differentiates fires bigger than 400 ha from 

smaller fires better than just the Haines Index does alone. 

This suggests that it is indeed the turbulence generated by 

the instability that matters for fire size, but does not rule 

out plume strength as an additional contributing factor. 

In addition, the questions noted above regarding moisture 

interactions with instability and which measures of fire 

behavior are influenced by instability are subjects needing 

further research. While answers to these questions may 

prove useful from an operational perspective there is sufficient evidence of instability’s ties to extreme fire behavior 

to justify great caution when unstable conditions exist. 

Downbursts and Plume Collapse

In meteorology, a downburst is both a broad description of 

a family of related phenomena and a specific member of 

that family. The fire behavior community generally uses 

the broader definition, “an area of strong, often damaging 

winds produced by a convective downdraft over an area 

from less than 1 to 400 km in horizontal dimensions” (Allen 

Press 2000). The physical processes driving a downburst 

come fundamentally from the condensation and evaporation 

of moisture in the plume, but depend as well on the vertical wind profile. There must be sufficient moisture in the 

convective updraft to produce rain droplets, snow, and hail. 

The updraft must lift the precipitation to a height where 

there is a deep layer of dry air beneath it. The updraft must 

lean over enough so that when the precipitation falls, it does 

not fall directly down into the updraft but falls into the dry 

air—either to the side of the updraft or else beneath the base 

of the cloud. Details of the downburst process can be found 

in Houze (1993) and other books describing the dynamics of 

severe storms. 

The term “plume collapse” (sometimes called column 

collapse) evokes vivid images of towering smoke plumes 

roiling upward and then falling back towards the ground. 

There is no official definition in the fire community for 

plume collapse, nor does there appear to be any generally 

agreed upon standard. The idea of plume collapse is taught 

in S-290 and in more detail in S-390, but there is no single 

stated definition in those curricula, nor is there a definition 

in the NWCG Glossary of Wildland Fire Terminology. The 

S-390 precourse work still references the Fire Weather 

Handbook (Schroeder and Buck 1970) that includes a 

discussion of the air mass thunderstorm concept presented 

above, suggesting that is the intended use of the term for 

wildland fire management.

For the present synthesis, plume collapse is a special 

case of a downburst. In plume collapse, the energy source 

is cut off or ceases, and the updraft decays or reverses in 

motion, producing a downburst. A downburst, more generally, is a downdraft that occurs near the continuing updraft, 

not necessarily including the loss of the driving energy 

source or the cessation of the updraft. This definition of 

plume collapse is compatible with the equivalence of plume 

collapse and dissipating convection in the current S-390 

course.
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At the ground, the symptoms of a downburst are a 

sudden but brief period where the wind subsides, followed 

by a sudden gust of winds radiating out from the center of 

the sinking air. There may be precipitation at the ground 

or signs of precipitation aloft, such as virga. The area 

underneath the precipitation is the most likely candidate 

for the center of the sinking air. Air temperatures may drop 

suddenly at the ground, but this may be difficult to detect 

near a fire and will occur at the same time as the arrival of 

the wind gust. Rothermel (1991) notes, 

Another indicator [of a downburst] is the rapid development of a strong convection column above the 

fire, or nearby thunder cells. This is a poor indicator 

because all crown fires have a convection column 

located above them in some form, and a person 

beneath the cell cannot see its vertical development; 

but observers around the fire periphery could call 

attention to any large column growing vertically 

above the fire front.”

The column development Rothermel mentions is the 

precursor to the downburst, it is the strong updraft that can 

produce and lift the necessary precipitation that may then 

fall and produce the downburst.

Downbursts associated with fire behavior appear in

the fire literature going back to Cramer (1954)—though this 

paper calls them thundersqualls. Here and in the references 

to downbursts in Schroeder and Buck (1970) and Haines 

(1988b), the discussion focuses on downbursts generated by 

nearby thunderstorms—not by the fire column itself. Haines 

(1988b) noted that downbursts with heavy precipitation are 

more common in the Eastern United States, labeling them 

“wet downbursts.” Dry downbursts, in contrast, are more 

common in the arid regions of the United States where the 

precipitation may evaporate before reaching the ground. 

Downbursts generated by convection separate from the 

fire’s plume may be easier to anticipate because spotters 

can see the thunderstorm directions more clearly, but they 

can also be more difficult to anticipate if thunderstorms are 

numerous and widespread.

Goens and Andrews (1998) hypothesized that the fatalities on the Dude Fire in 1990 resulted from a downburst that 

drove the fire behind the retreating fire crew. They presented both fire behavior observations and meteorological observations consistent with the development of a downburst. 

While “consistent with” is not the same as “definitively 

identified,” their analysis presented stronger evidence than 

many other claims that have made their way into wildland 

fire lore. The observations included light precipitation at the 

ground, a strong convection column, and a calm just before 

the downburst. The downburst, when it came, brought 

winds of 20 to 30 m/s and lasted only a few minutes. In this 

instance, topography added to the danger of the downburst. 

The air in a downburst is denser than the air around it, and 

so it will flow downhill. If that flow runs into the fire, it 

will carry the fire downhill at speeds more typical of an 

uphill run. In support of their analysis, Goens and Andrews 

showed the local temperature and moisture profiles at the 

time of the downburst. The hot, dry air extending upwards 

to 500 mb shows clearly the conditions that can yield a dry 

downburst, whether from a thunderstorm or fire plume.

The only reference to plume (column) collapse in 

the scientific literature on wildland fires is Fromm and 

Servranckx (2003). They referred to the Chisolm Fire in 

2001, and the use of the term “convective collapse” is not 

clarified; it appears to mean the plume top, which had been 

well above the tropopause, sank down to be closer to the 

tropopause. Because the reported surface winds at this time 

during the fire were between 30 and 50 km/h, the top of 

the convective plume would have been well downwind of 

the fire when this occurred, and the event does not qualify 

as plume collapse under the definition stated above. In 

addition, while this and the “collapse” of other tropopause 

or inversion-penetrating fire plumes are well documented, 

there is no evidence that their collapse at the top led to fire 

behavior changes at the ground.

Without additional scientific papers examining the 

influence of plume collapse on fires, the extent or existence 

of the process is a legitimate subject for debate. The air 

mass thunderstorm model presented in the Fire Weather 

Handbook (USDA 1970) is still used in meteorology, but 

only as an idealized model. It requires a decrease in the 
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energy feeding the storm cell with precipitation subsequently falling straight back into that updraft—this will 

not occur if there is any significant wind shear. When it 

does occur, it marks the end of the storm, and after the 

brief surge in surface winds, the event is over. If this type 

of energy cutoff were to occur on a wildland fire and cause 

extreme fire behavior, there would presumably be documented observations that the fire’s plume was destroyed at 

the time or the plume would have to have separated from 

the energy source. The present review of scientific literature 

did not yield any descriptions of such events associated with 

extreme behavior on wildfires.

Plume collapse in a low- or no-wind situation may 

be relevant to broadcast burning situations. Those events 

would typically occur with light winds (less than about 8 

km/h), with concentrated fuels, and would produce nearly 

vertical columns. The energy output drives the updraft, 

which in turn produces the converging winds at the surface. 

As the energy output diminishes, so do the surface winds. 

At some point, although the energy output has not dropped 

to zero, the natural stability of the atmosphere will inhibit 

the updraft to the point that any embers or other debris that 

were held aloft by the updraft will fall back to the ground. 

Concurrently, the inflow of winds at the base will diminish 

and the smoke still produced by the low-intensity burning 

will stop being drawn inward and upward, and will remain 

near the ground. No further “collapse” or downward rush of 

air is necessary, though it may occur.

Foote and Frank (1983) and Yang and Houze (1995) 

described the separation of storm updrafts from the leading 

edge of the storm (roughly analogous to the fire front), with 

new updraft cells forming at the leading edge as old, now 

detached cells drift rearward (fig. 6-2) Conceivably these 

detached cells, now deprived of their energy source, would 

dissipate in a way that would produce winds and ember 

showers similar to those attributed to plume collapse—even 

though the main plume at the fire front is still robust. Drawing analogies between fires and storms requires caution, as 

noted earlier. For example, while separated cells are often 

observed on fires (e.g., Banta et al. 1994, Reid and Vines 

1972), they are typically downwind of the fire instead of 

upwind (fig. 6-3). This would significantly alter their possible influence on fire behavior.

Clearly the processes involved in plume collapse are 

not known, but this does not negate the importance of the 

observations frequently attributed to plume collapse—firsthand observations of showers of embers, increasing smoke, 

or sudden changes of wind and fire spread are not in question. Many people have observed these phenomena. What 

is questionable or unknown is what precursors or processes 

caused these things to happen, whether they in any way 

relate to the idea of plume collapse as defined here, or what 

factors control the timing and location of these processes.

Haines (1988b) listed several fires where thunderstorm 

downbursts were considered responsible for firefighter 

fatalities and extreme fire behavior. The Dude Fire study by 

Figure 6-2—Evolution of 

updraft cells in multicell 

thunderstorm. The storm is 

moving from right to left. 

Frames progress in time from 

top to bottom. Source: Foote 

and Frank (1983).
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Figure 6-3—Progression of smoke plumes away from fire front (the fire is on the left side of the

figure). Source: Reid and Vines (1972).

Goens and Andrews (1998) appears to be the only fire case 

study specifically documenting a downburst created within 

the fire’s plume. There is no doubt that downbursts can 

cause extreme fire behavior. 

Unresolved scientific questions about downbursts 

do not concern whether they occur or can cause extreme 

fire behavior. Nor is it particularly important from an 

operational perspective to determine whether fire plumes 

actually collapse. The useful and interesting questions about 

downbursts center on understanding when conditions favor 

the occurrence of downbursts and whether those conditions 

can be predicted. At the least, dry downbursts require a 

deep, dry layer of air near the surface at the same time they 

require sufficient rising air and moisture to produce the 

necessary precipitation. The vertical wind profile interacts 

with temperature and moisture in complex ways, and while 

it influences the strength of the updraft and convection 

(McCaul and Weisman 2001) somewhat, it has a greater 

influence on where any downdraft occurs, relative to the 

updraft. The latter question is more difficult to answer, and 

of limited value for operational purposes—if downdrafts 

are possible at all, extreme fire behavior is also possible. 

As for wet downbursts, Haines (1988) noted that “the wet 

downburst will be a difficult problem for some time to 

come” and this is still true. 

While there is no scientific study of plume collapse 

(as defined here) in wildland fires, management anecdotes 

and physics both support it as a sound explanation for some 

situations, notably occasions when the fire’s energy output 

ceases or drops off rapidly, such as slash burns. The stated 
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significance of plume collapse in the NWCG fire behavior 

courses, and the fact these courses include outdated scientific models of storm behavior, bespeaks the importance of 

answering these questions. 

Furthermore, the multiple uses, ambiguity, and strong 

imagery inherent in the phrase “plume collapse” are 

problematic. The multiple communities using the term in 

different ways creates potential for confusion. Eliminating 

the term “plume collapse” in the context of fire behavior 

and just discussing “downbursts” could reduce confusion.

Operationally, the science behind downbursts suggests that fire managers and forecasters need to pay close 

attention to local forecasts for thunderstorm potential, and 

assume that any time there is a thunderstorm in the region, 

there is potential for a downburst from that storm or the 

fire’s plume to influence fire behavior. Individuals engaged 

in operations at the fireline cannot see the early warning 

signs of a potential or developing downburst—all, vigorous 

convection column, virga, or strong surface winds bending 

surrounding vegetation as they approach—and therefore 

must rely on lookouts who can see those signs. 

Summary

The winds associated with a fire’s plume influence fire behavior on both short and long time scales. The influence also 

varies along the fire front at any given time. Research clearly 

shows that these winds depend on the vertical temperature, 

wind, and moisture profiles of the atmosphere. Furthermore, 

there is strong scientific support for the notion that certain 

profiles, when combined with a fire’s intensity, produce what 

this synthesis defines as extreme fire behavior.

The scientific literature does not provide enough information or insight to allow reliable quantitative relationships 

or tools for determining when or to what degree extreme 

fire behavior might occur. The Haines Index provides an 

indication of regional potential, but is untested on smaller 

spatial scales or timeframes shorter than a day. Byram’s 

adverse wind profiles and power equations, however, are 

not practical for operational use, and too sensitive to the 

assumptions necessary to use them, to be helpful. 

There are many unanswered questions related to plume 

dynamics’ effects on fire behavior. These range from the 

basics of how fire intensity and surface winds interact to 

the complex interactions of vertical temperature, wind, and 

humidity profiles. Are there truly “adverse” wind profiles? 

What types of profiles are most conducive to downbursts 

from fires? Is it possible to document and measure cases of 

plumes collapsing on wildfires? In a more applied vein, one 

could attempt to rigorously validate Byram’s convective 

number, including examining its false alarm rate.

Operationally, use of even the qualitative relationships 

between plume dynamics and extreme fire behavior is 

limited by the need to know the three-dimensional structure 

of the atmosphere. How that structure varies across the fire 

and how it is changing with time are also important. This 

means practitioners must either have intuition that allows 

them to artfully understand and predict the dynamic nature 

of the atmosphere in three dimensions, or else they must 

have access to numerical model data that tell them what that 

structure is.

This is not to say that there is no practically valuable 

understanding of plume dynamics and extreme fire behavior. 

Even the highly simplified model presented at the beginning 

of this chapter, combined with modest and approximate 

information on wind and moisture profiles can be valuable. 

For example, a pi-ball sounding can reveal wind variations in 

the lowest 1 to 3 km of the atmosphere, which can indicate 

possible gusts and runs by the fire. Similarly, elevated cloud 

bases and a bright white smoke plume (indicating high moisture) can warn of possible downburst potential. These sorts of 

general ideas are already taught in fire behavior classes and 

could be expanded. 

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Hobbs, P.V.; Rangno, A.L. 1985. Ice particle 

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plume from a forest fire. Tech. Paper No. 2. Clayton 

South, VIC, Australia: Commonwealth Scientific 

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Packham, D.R.; Vines, R.G. 1971. Some meteorological 

aspects of three intense forest fires. Tech. Pap. No. 21. 

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Packham, D.R.; Vines, R.G. 1973. Convective 

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NC: U.S. Department of Agriculture, Forest Service, 

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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 7: Spot Fires

Brian E. Potter1

Introduction 

Byram (1959) considered spotting “the worst behavior characteristic, both from the standpoint of fire suppression and 

the effect on fire intensity.” When a wildland fire produces 

embers that can be lofted and carried outside the main fire 

perimeter, the difficulty of managing the fire or containing 

it increases significantly. Ember showers can get into roofing shingles, gutters, and attics on houses and other buildings, igniting them in spite of any surrounding defensible 

space. Embers can start new fires across firebreaks, rivers, 

highways, and other unburnable areas and can produce a 

dangerous, distracting assault on ground crews. Even when 

the embers do not cross barriers, if they are numerous and 

dense enough, spot fires created by the embers can merge 

and create a separate, new fire front ahead of the previous 

fire front. Byram observed that “Although the spot fires 

occurring at long distances are spectacular and effective in 

spreading fire over large areas, the spot fires nearer the main 

flame front have a much greater effect on fire behavior. 

Showers of burning embers within a quarter or half mile of 

the main fire front occasionally produce disastrous firestorm 

effects by igniting large areas almost simultaneously.” 

Cheney and Bary (1969) stated that concentrated spotting 

near the fire front increases spread rate by a factor of three 

to five. Spot fires, for all of these reasons, are regularly 

considered a type of extreme fire behavior even though they 

are a normal characteristic of fires in some fuel types. 

This section discusses scientific studies of the spotting 

process, the tools available to predict properties related to 

spotting, and the limitations of both the science and the 

tools. The primary areas for further research and tool development are also examined. While there are some tools for 

assessment of spot fire potential, they apply under specific 

circumstances. There is, however, much information that 

is not in a “tool” that will be valuable to a fire behavior 

practitioner.

1 Brian E. Potter is a research meteorologist, U.S. Department 

of Agriculture, Forest Service, Pacific Wildland Fire Sciences 

Laboratory, 400 N 34th St., Suite 201, Seattle, WA 98103.

Readers interested in more scientific detail should 

consult either the papers cited here, or one of three recent 

scientific papers: Koo et al. (2010), Gould et al. (2007), and 

Ellis (2000). Koo et al. (2010) is a scientific review of the 

literature on spot fires, emphasizing the physical equations and observations related to ember lofting, burning, 

and transport. Gould et al. (2007) is a detailed report from 

Australia’s Project Vesta, which studied high-intensity fires 

in dry eucalypt forests. The report discusses far more than 

just spotting, and the observations in the study are largely 

specific to eucalypt forests and spotting, but this is the most 

readily available report of field observations on spotting to 

be found. The discussion and some of the observations are 

widely applicable. Ellis (2000) examined the aerodynamic 

and burning properties of several species of eucalypt bark 

and contains a substantial discussion of episodic longdistance spotting. All three papers also contain many 

relevant references.

The Spotting Process

In its simplest form, spotting is the creation and transport 

of embers or fire brands followed by ignition of fuels where 

those embers land. Considering the details of that simple 

description reveals the complexity behind the process. Most 

scientific research frames spotting as a three-stage process: 

ember production, lofting and transport, and ignition. In 

practice, the discussion of the middle stage—lofting and 

transport—is commonly split into two smaller stages. Lofting is the vertical portion and depends more on the fire’s 

plume, while transport is primarily the horizontal portion 

and depends more on the environmental winds. The ignition 

stage includes the stage of the ember’s life spent on the 

ground after transport, part of which is spent burning before 

it ignites any other fuels. These stages are each relatively 

tractable in terms of the scientific questions they pose. 

Implicit in the stages, though, is the fact that the ember is 

burning and losing mass, which changes how the winds 

drive it and how fast it falls.
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An individual ember starts as a leaf, twig, seed, nut, 

pine cone, piece of bark, or small fragment of a larger piece 

of fuel that was partially consumed. It may originate on the 

ground, in the understory, or in the canopy. The air currents associated with the fire must lift the ember up into the 

fire’s plume, until the air currents or gravity take it out of 

the updraft. As the ember falls to earth, the winds continue 

to push it horizontally—and perhaps vertically, if it gets 

caught in an eddy. All through this journey, the ember 

continues to burn, losing mass (if it stops burning for some 

reason, it is no longer a spotting hazard). As the ember’s 

mass decreases, the wind pushes or carries it more easily, 

and it settles toward the ground more slowly. Eventually, 

however, the ember reaches the ground or perhaps it comes 

to rest in a tree canopy or understory vegetation. If it lands 

in flammable fuel, it may ignite that fuel if the ember has 

enough energy to dry and heat the fuel to the combustion point. That drying and heating may take some time, 

resulting in an ignition delay. At this point, the ember has 

established a spot fire.

Some embers, especially large ones, land relatively 

close to the main fire front. The closer they land to the 

front, and the longer they take to ignite the recipient fuels, 

the more likely it is that the main fire front will overrun 

them and the less likely that they will cause the fire front to 

hop forward. The zone where overrunning occurs has no 

definite size—it depends on the fire’s rate of spread, the fuel 

moisture, and, again, the fuel type (fig. 7-1). Even in these 

cases, however, heavy showers of embers can gradually dry 

and heat the fuels so that the fire spreads more rapidly when 

the main front arrives. 

Cheney and Bary (1969) described two distinct patterns 

of spotting in eucalypt fires, and similar patterns have been 

reported for other vegetation types (e.g., Countryman 1974, 

Wade and Ward 1973). The first pattern includes a profusion of embers a short distance from the fire, with numbers 

decreasing as distance from the fire front increases. The 

second pattern includes embers landing in groups at varying 

distance from the fire front, with few embers observed 

between the groups. This second pattern is associated more 

with long-distance spotting. 

Muraszew and Fedele (1976) referred to the “zone of 

fire front over-take” and the “zone of spot fire hazard,” 

the former identical to the overrun zone in figure 7-1. In 

discussing the spotting process, they echo Byram’s (1959) 

comments about the profusion of spot fires that can occur 

within a quarter to a half mile of the fire, or just beyond the 

Figure 7-1—Conceptual diagram (not to scale) of the spotting process, illustrating the influence of ember size and shape, lofting height, 

and ignition. Thin arrows are ember trajectories, those ending in dashed lines indicate the ember burned completely and never reached 

the ground. Round embers represent woody embers, and the stick-shaped ember leaving the top of the plume represents aerodynamic 

embers (typically bark) or large embers lofted extremely high by fire whirls or intense surges in the fire. 
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overrun zone. In this area relatively close to the fire, spot 

fires can establish and merge, leading to sudden surges in 

fire progress or intensity similar to what McArthur (1967) 

described as a typical spotting behavior in eucalypt fires, 

Wade and Ward (1973) documented for the Air Force Bomb 

Range Fire in North Carolina, and Gellie et al. (2010) 

described for the 2009 Black Saturday fires in Australia.

Ember Generation

Most studies of embers and spot fires simply state that there 

is not enough information on the number or size of embers 

generated by burning vegetation. The term “ember generation” is common usage but is not particularly accurate. 

Embers that burn out in the air are clearly not relevant to 

actual spotting, and no study has documented the number 

or size distribution of these embers quantitatively. While the 

number of embers landing on the ground is not the same as 

the number produced, it is much more easily measured, and 

arguably the quantity of greater interest. 

Modeling studies by Muraszew and Fedele (1976), Koo 

et al. (2007), Sardoy et al. (2008), and Perryman (2009) each 

assumed a number, size, and time distribution of embers. 

Muraszew and Fedele (1976) started from nine fuel models 

included in the 1974 (revised) version of the National Fire 

Danger Rating System (A through I) and divided the dead 

fuels with moisture content below 20 percent into size 

classes. Assuming cylindrical fire brands with a lengthto-diameter ratio of 10, they produced an equation that 

converted the dead fuel mass to a number of fire brands 

produced every second along unit length of fire front. 

They stated that this approach cannot be applied without 

field observations of spot fires and spot fire coalescence to 

guide the value of some of the parameters they require. To 

date, there are no published field observations to serve this 

purpose.

Koo et al. (2007) assumed that at any location that 

was burning in their model domain, one ember would be 

produced each second for every 2- by 2-m area. The size of 

that ember was the largest that could be lifted by the model 

updraft at that location and time. These embers ranged in 

size up to 8 mm thick and 36 mm radius (disks), or 3.5 mm 

radius and 21 mm long (cylinders). Sardoy et al. (2008) took 

a similar approach, releasing 10,000 disk-shaped fire brands 

of random size (1 to 3 mm thick and 4 to 10 cm in diameter) 

and density (50 to 300 kg/m3) into a modeled plume. Perryman (2009) used what was essentially a tree-level model of 

fire spread, in which any torching tree produced 50 embers. 

Rather than computing the size of each ember, Perryman 

specified landing distances using a probability model.

In their discussion of the European SALTUS project, 

Colin et al. (2002) briefly mentioned some of the characteristics of embers observed during observations on 48 

wildfires that occurred in France, Greece, Italy, Portugal, 

and Spain between 1998 and 2001. They reported that one 

third of the embers collected (a total of 30) on these fires 

were leaves or needles, and the next most abundant embers 

were from pine bark fragments or twigs (The only indication of the vegetation species given in this paper is reference 

to Pinus pinaster). Leaves and bark fragments traveled the 

farthest, and a third of the particles were larger than 5 cm

The very low number of embers collected, 30, out of 48 

wildfires, reflects the difficulty of studying ember production on wildland fires.

Two recent studies examined the number of embers 

produced during experimental wildland fires. Racher (2003) 

studied embers produced in saltcedar (Tamarix spp.) and 

juniper/oak (Juniperus spp./Quercus spp.) stands in Texas 

by laying out plastic sheets downwind of the fires, where 

landing embers burned holes in them. The field crew also 

recorded distance for spot fires observed outside the study 

area. The author did not report the number of embers generated, but did document that saltcedar yielded significantly 

more embers that reached the ground than did juniper/oak. 

One point Racher specifically noted was that among the 

saltcedar fires, those producing the most numerous or distant landing embers were not those with the greatest rate of 

spread, flame height, or flame depth zone—fireline behavior 

was a poor predictor of spotting number or distance. Too 

few embers were captured in the juniper/oak experiments to 

make a similar statement.

Gould et al. (2007) applied similar techniques during 

Project Vesta. Plastic sheets were laid out on the forest floor 
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GENERAL TECHNICAL REPORT PNW-GTR-891

downwind of the burn plots, and the number of holes burned 

during the fires was recorded in terms of holes per unit area 

and location. The results are presented as spatial contour 

plots (number per unit area), and focus largely on the spatial 

distribution of the landing embers. This will be discussed 

shortly, but there are three points of note for the present 

discussion. First, the authors found that the number of embers was proportional to the time since the (eucalypt) stand 

had last burned. Second, the authors document cases where 

large embers broke into multiple small embers on landing, 

scattering apart and burning distinct holes in the plastic 

sheeting. Third, when the fires reached the established firebreaks on the downwind sides of the burn plots, the loss of 

fuel and energy caused a visible decrease in plume energy, 

and an accompanying shower of many embers near the fuel 

boundary. While the first of these points may be primarily 

relevant to eucalypt forests, the second could apply to other 

types of vegetation, and the third is likely to be universal. 

Any fire with embers in the updraft, should it encounter an 

abrupt decrease in available fuels, will likely exhibit similar 

behavior.

Two laboratory studies examined the number and size 

distribution of embers produced by vegetation. Manzello et 

al. (2007a) burned individual 2.6- and 5.2-m tall Douglas-fir 

(Pseudotsuga menzeisii) in a windless laboratory and collected the embers in pans of water. Manzello et al. (2007b) 

repeated this exercise with Korean pine (Pinus koraiensis). 

These papers clearly illustrated that the ember size distribution depends on the tree size and species (fig. 7-2). All of 

the embers collected were cylindrical twigs, and the authors 

concluded that all needles were completely consumed (this 

differs from the SALTUS findings). Albini (1979) noted 

that embers may begin as twigs with leaves attached and 

end their life as a twig without leaves. Some of the needles 

may possibly have been consumed in flight, but this was not 

examined by Manzello et al. (2007a, 2007b).

The studies provide several observations of immediate 

value to fire management. For example, in the absence of 

wind, Douglas-fir with moisture content over 30 percent 

did not produce embers. The largest Douglas-fir embers 

came from the 5.2-m trees; maximum mass observed was 

3.7 to 3.9 g, maximum length was 200 mm, and maximum 

diameter was 10 mm. The 2.6-m Douglas-fir trees produced 

embers of similar diameters but lengths below 150 mm and 

maximum mass between 2.1 and 2.3 g. The average ember 

twig for all of the Douglas-fir trees was 3 mm in diameter 

and 40 mm long.

While these maximum dimensions are important, they 

do not reveal the full picture. Figure 7-2c reproduces the 

mass distribution Manzello et al. (2007a) found for embers 

from the 5.2-m Douglas-fir tests. This shows that 70 percent 

of the embers recovered were smaller than 0.3 g, with 10 

percent between 0.3 and 0.5 g. Larger size classes contained 

progressively smaller fractions of the total ember mass.

The Manzello et al. studies discussed some of the substantial difficulties involved in collecting data on embers. 

Laboratory facilities effectively limited the experiments to 

single trees, less than 5.2 m tall, in a windless environment. 

Furthermore, because the authors could not capture every 

ember produced, or every one that fell to the ground, their 

results are limited to expressing the mass distributions as 

percentages of the total collected. There is no information 

on the total number produced or landing on the ground.

Ember Combustion and Fall Speed

Where an ember ultimately lands depends on its fall speed 

relative to the air around it. That fall speed depends on the 

size, shape, and mass of the ember and so on how the ember 

burns. 

Tarifa et al. (1965, 1967) performed the first, and most 

extensive, tests of ember fall speed. They examined embers 

of various shapes, sizes, and wood species as well as charcoal embers and pine cones (table 7-1). Their wind tunnel 

measurements indicated that the embers lost mass quickly 

once they ignited, while size decreased slowly at first and 

then with increasing speed. Combined, these factors led to 

a rapid decrease in fall speed, followed by a period where 

the fall speed decreased more gradually. Figure 7-3 shows 

an example of the change in fall speed as the ember burns. 

Because denser wood also burned slower, they found that 

embers from denser tree species traveled both higher and 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 7-2—Mass distribution of collected firebrands for (a) 4.0-m Korean pine trees, (b) 2.6-m Douglas-fir 

trees, and (c) 5.2-m Douglas-fir trees. From Manzello et al. 2007b.
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Table 7-1—Sizes, shapes, and species of embers studied by Tarifa et al. (1967)

Shapes Sizes Species

Cylinder (Diameter x length) Aspen (Populus tremuloides)

6 × 18 mm Balsa (Ochroma lagopus)

8 × 24 mm Oak (Quercus rubra)

10 × 30 mm Pine (Pinus pinaster)

12 × 36 mm Spruce (Picea excelsa)

15 × 45 mm (Charcoal)

(Pine cones)

Square plate (Thickness x face length)

2 × 32 mm

4 × 32 mm

8 × 32 mm

16 × 32 mm

2.5 × 50 mm

5 × 50 mm

10 × 50 mm

25 × 50 mm

Sphere Diameter: 10, 15, 22 mm

Figure 7-3—Final velocities of fall. Cylindrical and spherical firebrands of spruce, aspen, and pine wood from Tarifa et al. (1967). 
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farther, even though the denser embers’ fall speeds were 

higher for a given size and shape. They concluded that 

charcoal embers, because of their slow burning rate, are 

among the most dangerous—they can travel much farther 

before they burn out completely.

In their model simulations of ember transport and 

combustion, Tse and Fernandez-Pello (1998) modeled ember 

combustion as two separate elements. Pyrolysis is the 

thermal decomposition expulsion of volatile gasses in the 

woody fuel; it reduces the mass but has very little effect on 

size. Combustion of the remaining carbon then leads to the 

ember shrinking. To determine the rates of these processes, 

they determined reaction coefficients from Tarifa et al.’s 

(1967) data, and the net result was similar mass and size 

changes. This two-process combustion model has since 

been used for numerous studies (e.g., Anthenien et al. 2006; 

Bhutia et al. 2010; Sardoy et al. 2007, 2008).

Ellis (2000) is the only study to examine the combustion and fall speed properties of eucalypt bark. Eucalypt 

bark fragments are considerably more difficult to characterize than cylinders, plates, or spheres, as they can be small 

plates with comparable length and width, long narrow 

strips, or curled tubes. The edges are often irregular. These 

shape variations produce two general categories of falling 

behavior, either “spin free” or fast-spinning. Furthermore, 

spinning behavior has two aspects, one where a long strip or 

tube rotates around its long axis, the other where the whole 

strip rotates around an axis perpendicular to its long axis. 

Ellis found that the spinning bark fell more slowly (ranging from 2.5 m/s to 6 m/s) than nonspinning bark (ranging 

from 3 m/s to 8/ m/s) when other properties were similar. 

Spinning or nonspinning, most bark samples had fall speeds 

between 2.5 m/s and 4 m/s, approximately one-half the initial fall speeds for the shapes tested by Tarifa et al. (1967).

The differences between solid wooden embers and 

bark embers, in terms of shape, density, falling behavior, 

and burning pattern identified by Ellis result in distinctly 

different spotting impacts. Any model derived for spotting 

distance or ember trajectories that depends on solid wooden 

ember behavior is inappropriate for application to eucalypt 

bark embers. The rates at which bark loses mass and at 

which its fall speed decrease are much slower than for solid 

wooden embers, resulting in greater spotting distances, on 

the order of twice as far.

Ellis (2000) also observed one characteristic of ember 

combustion not examined by others. Tarifa et al. (1965, 

1967), Muraszew (1974), and Muraszew et al. (1975, 1976) 

all mentioned that ignition time was important for them in 

getting consistent results for firebrand combustion, mass 

loss, and fall speed. These authors chose ignition times 

sufficiently long to ensure continued combustion throughout their experiments. Ellis looked at how ignition time 

influenced bark combustion, and by doing so identified two 

distinct types of burning behavior. Embers characterized by 

high combustion rates and maximum flaming duration were 

classified as “normal.” Other embers, generally subjected to 

ignition more briefly, burned slowly, remained flaming more 

briefly, and had a high tendency to reflame after a period of 

glowing combustion. Ellis classified these as “extreme,” and 

noted they were most likely to cause long-distance spotting. 

Ignition time was one factor distinguishing normal from 

extreme embers, but bark sample size and structure appeared to be important factors, as well.

Lofting and Transport of Embers

Fuels on the ground are relatively unlikely to get lofted by 

the fire’s plume–it is difficult for air currents to lift them. 

Because the fire’s updraft initially accelerates as it rises, it 

is more likely for embers to originate higher up in the fuel 

structure, and therefore for spotting to be more prolific in 

crown fires than in surface fires. Twigs, seeds, and bark 

in the canopy are the most likely source of embers in the 

updraft, and those likely to reach the establishment zone. 

None of the ember lofting studies have measured 

ember lofting in an actual wildland fire. All of the studies 

described here used some manner of model wind field to 

calculate ember lofting. The differences among the various 

models arise largely from the assumptions made by each set 

of authors, and there are no data available to compare them.

The winds, and specifically the updraft, near the 

flaming front are highly turbulent and difficult to measure. 

Acknowledging that “theoretical models calculated for 

thermal plumes do not apply” to convection columns over 

large wildland fires, Tarifa et al. (1965, 1967) nonetheless 
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relied on previous theoretical and laboratory studies of 

convection to model the updraft for lofting of embers. They 

tested two convection models; one was a purely vertical 

updraft with constant speed, the other a tilted convection column with constant speed in both the vertical and 

horizontal directions. Once the embers were lofted by either 

model, horizontal transport was based on a horizontal wind 

field constant in both space and time.

Gould et al. (2007) reported observations that illustrate 

the difficulties involved in modeling the wind field lofting 

embers. They saw the fire behavior alternate between updraft periods and downdraft periods. Strong updrafts pulled 

the flames inward to the base of the updraft and exhibited 

decreased rate of spread. Updrafts were also held responsible for producing spot fires tens to hundreds of meters 

from the fire front. Downdraft periods, the authors state, 

produced rapid fire progress as a downward rear inflow 

at the fire front drove the fire. Embers produced in these 

periods appeared in showers at distances of a few meters to 

a few tens of meters. The duration of these periods was on 

the order of 1 to 3 minutes, and not all parts of the flaming 

front exhibited the same behavior at any given time. Clearly, 

this behavior challenges any steady-state model.

Muraszew (1974) made assumptions similar to those 

in Tarifa et al. (1965, 1967) about the convective lofting of 

embers. He cited many of the theoretical models Tarifa et al. 

noted did not apply to wildland fire columns. Nonetheless, 

with no other data on convection over a wildland fire, there 

was little alternative. Muraszew (1974) therefore assumed 

a constant vertical velocity in the plume, although he 

hypothesized increasing velocity in the lowest 50 m or so, 

referring to this as the “high buoyancy zone.” One implicit 

assumption in the paper is that the atmosphere is neutrally 

stable. Because there is more potential for air rising in an 

unstable atmosphere to accelerate, the calculations may 

underestimate the lofting potential of the fire’s updraft. How 

much it may be underestimated is not clear and would vary 

widely based on case-by-case specifics.

Using these simplified convective models, Tarifa et al. 

(1965, 1967) and Muraszew (1974) reach similar conclusions. First, as shown in figure 7-4, for a fire updraft on the 

order of 30 m/s with 20 m/s horizontal winds, the modeled 

embers (cylinders 24 mm long and 8 mm in diameter) 

reached the ground 1300 m from the updraft (for the purely 

vertical convective column) or 5600 m from the fire front 

(for the tilted column). In the case of the tilted column, 

however, the embers had to originate 560 m behind the fire 

front and the 30 m/s updraft had to be present throughout 

this 560-m region—a physically unrealistic assumption. 

This does not account for the distance the fire would move 

while the ember was airborne, which could be substantial 

with a 20-m/s wind.

Aside from Muraszew’s inclusion of an accelerating 

updraft near the surface, these model winds were constant 

in both space and time. Increasing the realism of the plume 

model by adding spatial variability, Anthenien et al. (2006) 

and Sardoy et al. (2008) modeled ember transport using 

plume models that allow the winds to vary in space. The

details of the plume models differ, but both basically represent a stationary fire of fixed size with a rising plume that 

leans over with the crosswind and widens downstream from 

the heat source, with the speed of the updraft determined by 

the fire’s energy output rate. In Anthenien et al., the updraft 

ranged from about 10 m/s for a 10-MW fire to roughly 14 

m/s for a 40-MW fire. The embers and spotting produced 

by this model were very short range, with 2-mm embers in 

a 40-MW fire landing just 70 m downwind. Larger embers 

landed closer, while smaller embers burned up completely 

without reaching the ground. Considering the updrafts are 

much weaker and the embers are of comparable size, these 

results are not inconsistent with the results of Tarifa et al. 

(1965, 1967) and Muraszew (1974).

Sardoy et al. (2008) assumed that the plume updraft 

was 12 m/s at canopy top for fire energies between 10 and 

40 MW. Their study focus was not on ember size relationships to transport distance so the results do not directly 

compare with the previously mentioned studies. Generally, 

they found transport distance fell into short- and long-range 

categories, with high-density or large embers in the shortrange and less dense or smaller embers in the long-range 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 7-4—Flight paths of (a) cylindrical firebrands in a vertical convection column and (b) spherical firebrands in an inclined 

convection column. From Tarifa et al. 1967.
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group. Short-range spotting covered distances out to about 

400 m, while long-range spotting was concentrated between 

5000 and 9000 m. The embers in the short-range group 

were more numerous and landed in a flaming state, while 

the long-range embers, fewer in number, were not flaming. Furthermore, for a given fire intensity and horizontal 

windspeed, the distance groupings had a distinct separation 

between them that depended on the char content of the ember–higher char content decreased the separation distance. 

The general results of this study are not usable in the field, 

unfortunately, as they do not provide enough information on 

how the fire intensity, windspeed, and char content interact 

in determining spotting distance or long- vs. short-range 

separation. 

Albini (1983a) took a slightly different approach to 

modeling ember lofting. He assumed that embers are lofted 

by a line thermal, not in a steady-state wind field. Albini’s 

line thermal is a two-dimensional bubble of hot air, elliptical in shape (shorter in the vertical than in the along-wind 

direction) produced by a line of fire infinitely long in the 

across-wind horizontal direction. Starting with a thermal 

bubble at the ground and an ember at the top of that bubble, 

Albini derived a simple equation for the maximum height 

an ember could rise and still fall back to the ground to light 

a fire. After the assumptions Albini built into the model, 

this relationship depended only on the ambient atmospheric 

pressure and the energy (per-unit length in the across-wind 

direction) of the thermal bubble. He provides one example, 

wherein the heat released by burning 5 kg/m of fuel provides 108 J/m of energy and lofts the ember to 55 m above 

ground. Lofting the ember to 550 m would require 100 

times this energy, 1010 J/m, and 500 kg/m of fuel. Perhaps 

the greatest difficulty in using Albini’s equations lies in the 

question of how much fuel one assumes heats one individual 

thermal bubble—there is no guidance to suggest how long 

a thermal bubble remains near the ground to absorb energy 

from fuel burning at a certain rate.

All of these studies assume idealized, two-dimensional, 

near steady-state conditions for the lofting winds. In the 

last 5 years, there have been two papers that used coupled 

fire-atmosphere models to look at the influence of the short 

term, three-dimensional fluctuating winds at the fire front 

on lofting of embers. These models allow the combustion 

process to influence the winds, which in turn can influence 

the combustion process. This means, for example, that if the 

fire causes wind to accelerate, that accelerated wind will 

affect the rate of spread. 

Koo et al. (2007) used the Los Alamos FIRETEC 

model (Linn et al. 2002) to examine spotting. Their ember 

production was purely idealized (it did not depend on the 

burning fuels but rather on fuel temperature and the largest 

disk or cylindrical ember that model surface winds could 

loft) and they did not examine ignition directly. The question of lofting was not the focus of their work, and computational limits required them to model an area only 320-m 

square, but the results do show clearly the potential to use 

coupled models for spotting studies. Their simulations for 

full or patchy forest fires with a 6 m/s ambient wind yielded 

maximum ember landing distances near 190 m, with the 

average distance closer to 30 m. 

Bhutia et al. (2010) used the University of Utah’s Large 

Eddy Simulator (UU-LES, Sun et al. 2009) to perform 

similar simulations. The authors set out to examine the 

difference between a steady-state plume and a coupled 

fire-atmosphere model in terms of ember transport. The 

steady-state plume automatically produces the same ember 

lofting and transport for a specified size of ember. Combusting spheres 20 mm in diameter, for example, only rose to 

about 20 m before they burned out. Embers released at 50 m 

above ground traveled slightly less than 50 m before landing. When embers were released from 50 m above ground in 

the coupled model, however, they landed between roughly 

40 and 70 m downwind.

The authors state that the work is exploratory and recommend further exploration. This is reinforced by the fact that 

their model carries nonburning spheres much farther than initially identical burning spheres for both the steady-state and 

LES tests. Specifically, 10-mm diameter nonburning spheres 

released at 50 m in the steady-state case landed just short of 

100 m downwind of the release point while burning spheres 

landed approximately 70 m downwind. The authors claim 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

that this is due to the burning particles heating the air around 

them, thereby reducing its density and the drag exerted on 

the particle. In their model, this effect exceeds the effect of 

decreasing mass for the combusting particles so much that 

the net effect is that burning sphere trajectories are 30 percent 

shorter than nonburning spheres. No other study in spotting 

literature has compared burning and nonburning projectiles, 

so there is no other literature for direct comparison. However, 

the magnitude of the effect here is substantial, and until 

observations confirm this effect, one should exercise caution 

in applying any results from Bhutia et al. (2010).

Because there are documented spot fires at greater 

distances than 5 km, there must be some process besides 

“smooth” or steady-state convection involved. Candidates 

include more aerodynamic embers such as the eucalypt bark 

discussed in Ellis (2000), slower burning embers such as 

char discussed by Tariffa et al. (1967), or stronger lofting. 

Lee and Hellman (1969) proposed that the most likely candidate was fire whirls, citing Countryman (1964) in doing 

so. Countryman (1964) stated “...fire whirls and tornadoes 

contribute greatly to fire spread because they pick up large 

firebrands and scatter them over a wide area. Many wildfires seemingly controlled have been lost when a fire whirl 

scattered burning debris across the cleared fire lines.” Note 

that Countryman does not state that whirls throw firebrands 

farther than nonwhirl convection does, rather he states that 

whirls scatter embers over a wide area. A further curiosity 

arises in Byram’s (1959) discussion of spotting and fire 

whirls. In the original version of this text, “fire whirlwinds” 

appear as part of a section on spotting. In the revised 

version of the text, Brown and Davis (1973), fire whirlwinds are a separate section with essentially unchanged 

text—the formatting strongly suggests that the appearance 

as a subsection was a type setting error. It is possible that 

historically, the perception that fire whirls produce longer 

distance spotting is in part due to misinterpretation and a 

printing error.

These historical points notwithstanding, it is physically 

plausible that fire whirls could loft large embers high and 

produce long-distance spotting. Lee and Hellman (1969, 

1970) presented theoretical analyses of whirl lofting and 

transport. While these represented significant scientific 

progress, they could not be translated to the field. The 

models included too many variables that are unknown, 

perhaps unknowable, in the field before a whirl forms. The 

mathematical solution required replacing distances, speeds, 

and other quantities with unitless quantities, and without 

knowing such things such as the radius of the fire whirl, 

the upward speed of the air in the core of the whirl, and the 

rotational speed of the whirl at the bottom, one could not 

convert the unitless quantities to real, physical dimensions.

Muraszew et al. (1976) and Muraszew and Fedele 

(1976) simplified the work of Tarifa et al. (1967) and of Lee 

and Hellman (1969, 1970) to make it applicable in the field 

but did so with limited success. Specifically, Muraszew and 

Fedele (1976) stated they had to assume that “subsequent 

studies will provide for statistical determination of the 

strength and vertical extent of the ambient circulation and 

its probability of generating a fire whirl in terms of terrain, 

fuel and wind conditions.” No such information is available 

for wildland fire at this time, and so the computer model 

Muraszew and Fedele produced cannot yet be directly 

applied in the field. The tables Muraszew et al. (1976) 

produced using reasonable values for these unknowns, 

however, do provide useful information. At the least, they 

show that the updraft in a fire whirl can easily be 30 m/s, 

comparable to the nonwhirl updraft in Tarifa et al. (1967), 

and is more likely to be 100 m/s. Their highest value—

corresponding to the highest circulation, shortest whirl 

with the heaviest fuel loading—was approximately 170 m/s. 

Such an updraft, or even the 100 m/s updraft, could easily 

lift embers larger than any studied by Tarifa et al. (1967). 

Muraszew et al. (1976) illustrated this with an example 

involving a ponderosa pine bark plate 6.3 mm thick and 

100 mm square. They modeled a whirl lifting the plate to a 

height of 1200 m and found that landed approximately 3300 

m with a travel time of just over 6 minutes, still burning.

Berlad and Lee (1968) presented perhaps an alternative 

vortex model for long-range ember transport and spotting. 

The authors consider the possibility that vortices form at 

the fire, where they collect embers in their columns, and 
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that the vortices hold those embers aloft as they separate 

from the fire and travel downwind (see the discussion of 

vortex shedding in the chapter on vortices). As the vortices 

weaken over time, they propose, their ability to support 

the embers diminishes and eventually the embers fall out. 

That weakening can be the result of several factors, such as 

friction near the ground, slow mixing with air from outside 

the vortex, or moving upslope, which shortens the vortex 

and slows its spin.

Ignition

Spot fire ignition has three critical aspects. First is where 

the embers land, which depends on the transport issues 

already discussed. The probability of ignition, taken with the 

expected number of embers landing in recipient fuels, determines the overall likelihood of a spot fire developing. When 

an ember does cause ignition, it is not instantaneous and there 

is an ignition delay time. Both the probability of ignition and 

ignition delay time depend on many of the factors important 

to spotting, in general: ember size and mass, windspeed, and 

combustion state (flaming versus glowing). In addition, the 

type and state of the recipient fuels are important, as are the 

number and distance between multiple embers.

Probability of Ignition

Ignition probability depends in part on the ease of igniting 

the receptor fuels, their ignitibility. There are many studies 

of ignitibility, and White and Zipperer (2010) discussed these 

and the methods used in detail. However, the majority of such 

studies use radiative heating or ovens to test ignitability, rather 

than embers. The nature of the heat source used to evaluate 

ignitability exerts a strong influence on the results of any tests 

and should always be considered before using the results of 

any particular study or report in the context of spotting.

In unpublished work in 1969, Schroeder2 laid out a 

methodical description and analysis of how to determine 

probability of ignition from embers. His work is the precursor to the tables in the Fireline Handbook and the BEHAVE 

2 Schroeder, M.J. 1969. Ignition probability. Unpublished report. 

On file at: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Forest and Range Experiment Station, Fort Collins, CO.

tools. It considers the heat needed to ignite fuels based on 

their moisture content, the size distribution of fire brands 

and the heat they can provide, and the efficiency of heat 

transfer from the brands to the receptor fuels. The resulting 

tables are the probability of one or more ignitions by the 

total population of embers, not the probability that a single 

ember will result in an ignition. The latter probability is 

necessarily less than or equal to the former. (Note that 

Schroeder extensively cites an unpublished 1969 manuscript 

by Blackmarr, but the citation is incomplete. It appears to be 

the same as, or very similar to, Blackmarr 1972).

While earlier spotting studies noted the importance 

of ignition probability, none specifically addressed that 

probability. Similarly, earlier studies (such as Blackmarr 

1972) had addressed the general question of fuel ignitibility, 

but Bunting and Wright (1974) was the first to specifically 

examine ignition probability for spot fires. They studied 

ignition of partially decomposed (punky) wood and dried 

cow chips in Texas grasslands using nonflaming juniper 

embers. They found that ignition probability for either fuel 

increased most strongly with decreasing 10-h fuel moisture. The second most influential factor for cow chips was 

maximum windspeed, while for punky wood it was the time 

since last precipitation event.

The next experimental study of ignition probability 

appears to be Ellis (2000). This work involved laboratory 

observations of ignition when glowing or flaming fragments 

of stringybark landed on Monterey pine litter. With fine fuel 

moistures below 9 percent, flaming embers with mass between 0.7 and 1.8 g had a 100 percent probability of igniting 

the litter. Glowing embers had lower probabilities. Without 

any wind, the glowing embers (mass between 0.1 and 0.4 g 

did not produce ignition. With a light wind (1 m/s), the probability increased in a roughly linear manner from about 20 

percent at fine fuel moisture of 9 percent to approximately 65 

percent at a fine fuel moisture of 3.5 percent. Ellis did not test 

higher fuel moistures.

Manzello et al. (2006) studied ember ignition of pine 

needles, shredded paper (similar to house insulation), and 

cedar shingles in the laboratory. They did not express their 

results as a numerical probability, but noted whether embers 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

produced smoldering or flaming ignition. They found that 

flaming embers as small as 0.5 g could ignite pine needles 

with fuel moisture of 11 percent or less. Glowing embers 

did not ignite needles, even when four embers of 1.5 g each 

were placed within proximity (just how close was not specified, but the implication is a few centimeters.)

The most extensive study of ignition by embers is documented in Guijarro et al. (2002) and Ganteaume et al. (2009). 

These papers describe the results of dropping numerous types 

of vegetative embers into a variety of fuel bed types with a 

range of windspeeds (table 7-2). In one portion of their work, 

they used 2-g blocks of Scots pine as flaming embers to ignite 

the various fuel beds without wind. Their results produced an 

expression for ignition probability as a function of fuel bed 

bulk density (kg/m3) and fuel moisture (percentage), as shown 

in table 7-3. Higher bulk density or lower fuel moisture led to 

higher probability of ignition. Application of the equation or 

table 7-3 requires recognition that it came from measurements 

on specific Mediterranean fuel types, with bulk densities 

between 9 and 70 kg/m3 and fuel moisture between 1 and

11 percent.

When these authors looked at the various combinations 

of embers, fuel beds, flaming/glowing embers, and wind, 

they computed nine probability equations because the 

results varied widely. In summary, they found that some 

embers (Aleppo pine scales) had very high ignition success 

rates when flaming, but very low when glowing. Others 

(Aleppo pine bark) had low ignition rates when flaming but 

were among the highest when glowing and subject to wind. 

Similarly, among the fuel beds cured, grass ignited most 

often with flaming embers, but Stone pine litter ignited 

most readily when the ember was glowing and there was 

light wind.

Ignition Delay Time

Muraszew (1974) included discussion and analysis of 

ignition delay time based on laboratory measurements of 

ember burning time and a theoretical model analysis of how 

the burning ember interacts with unburning fuels around 

it. He studied both flaming and glowing embers, but the 

model analysis applied only to flaming embers and assumed 

that these embers would ignite fuels that were within the 

flame. He noted (but did not model or measure) that glowing 

Table 7-2—Ember types, fuel bed types, and windspeeds studied by 

Ganteaume et al. (2009)

Ember types Fuel bed types Windspeeds

Aleppo pine twig Aleppo pine needle bed None

Bark Stone pine needle bed 0.8 m/s

Cone Maritime pine needle bed 2.5 m/s

Cone scale Southern blue gum leaf bed 4.5 m/s

Stone pine twig Cured grass bed

Bark

Cone scale

Maritime pine bark

Cone scale

Monterey pine bark

Holm oak leaf

Acorn

Cork oak bark

Southern blue gum bark

Leaf
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GENERAL TECHNICAL REPORT PNW-GTR-891

Table 7-3—Probability of ignition for Mediterranean 

fuels, as a function of fuel bed bulk density and fuel 

moisturea

Fuel moisture

Bulk density 

(kg/m3) 1 3 5 7 9 11

Percent

10 79 74 69 62 56 49

20 84 80 75 69 63 57

30 88 85 81 76 70 64

40 91 88 85 81 77 71

50 93 91 89 86 82 77

60 95 93 92 89 86 82

70 97 95 94 92 89 87

a

 Based on the work of Ganteaume et al. 2009.

embers are cooler than flaming, and that therefore the ignition delay time for glowing embers would be longer. The 

equations Muraszew developed are useful for research but 

require a value for the total heat flux from the ember and so 

are not useful in a field or operational context. The results 

are still of use in the field, however, because they provide 

an estimate of ignition delay time and how it depends on 

recipient fuel size and moisture. Using fuel properties 

appropriate for chaparral, fuel moisture between 2 and 

10 percent, and fuel diameters between 0.2 and 4 cm, the 

model predicted ignition delay times from less than a second (fine, dry fuels) to near 50 s (coarse, moist fuels). The 

calculations also assumed no wind, which would increase 

the ignition delay time when combined with the other model 

assumptions.

Guijarro et al. (2002) and Ganteaume et al. (2009), 

previously mentioned in the discussion of ignition probability, also studied ignition delay time. They examined 

combinations of ember type, fuel bed type, flaming versus 

glowing embers, and glowing embers with or without wind. 

These experiments showed that ignition delay for flaming 

embers ranged from 1 s to about a minute. Glowing embers, 

as expected, produced longer ignition delay times. These 

ranged from 3 to 266 s with a wind of 0.8 m/s. Increasing 

wind to 2.5 m/s increased ignition delay time slightly, and 

further increasing wind to 4.5 m/s decreased ignition delay 

time. In all cases, however, glowing embers’ ignition delay 

times were on the order of a minute. Increasing fuel bed 

moisture from 0 to 4 percent increased ignition delay times 

by a factor of 1.2 to 1.5, but times were still on the order of 

a minute.

Alexander and Cruz (2006) illustrated the impact of 

ignition delay time on the potential for spot fires to establish 

beyond the overrun zone (fig. 7-5). The studies mentioned 

previously all produced ignition delay times of 4 min or 

less, so the lower two lines of the figure are most relevant. 

They show the minimum distance an ember must land 

ahead of the flaming front in order to establish as a new fire 

before that front overruns it. These distances are all in the 

realm of what the lofting literature would consider short, or 

nonvortex spotting distances.

Spot Fire Prediction Tools

Pastor et al. (2003) include spot fire models in their overview of all types of fire-spread models. Including all types 

of models, not just operationally usable tools, they identified 

eight spotting models. Of those eight, three are currently in 

operational use in the United States. The others are either 

more suited to research, or are not known to be in use at 

present.

The work of Tarifa et al. (1965, 1967), Muraszew 

(1974), and Muraszew et al. (1975, 1976) was funded by the 

U.S. Department of Agriculture Forest Service with the goal 

of creating a tool for spot fire prediction. Muraszew and 

Fedele (1976) outlined and described the culmination of that 

work—a system that would predict how many embers are 

produced, their size, and how far they would travel (both 

with a regular convection column and with vortex lofting) 

and the probability that one or more embers would ignite 

fuels or coalesce into a new fire. While some elements 

were not implementable (vortex lofting required detailed 

knowledge of airflow around the fire, for example), others 

appear to be. The system, however, is not directly part of 

any existing fire behavior tool.

Some elements of these studies went into three tools 

produced by Albini (1979, 1981, 1983b). These, in turn, are 

all incorporated into BehavePlus (Andrews 2007). Each of 

these tools predicts a maximum likely spotting distance for 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 7-5—Minimum distance at which a 

spot fire will not be overrun by the main fire. 

Distance depends on ignition delay and is 

shown for delays of 0.2 minutes (solid line), 1.5 

minutes (dotted line), 5 minutes (dashed line), 

and 10 minutes (short and long dash line). Following Alexander and Cruz (2006).

nonvortex lofted cylindrical embers, but the tools are for 

different types of fires. Albini (1979) is designed for embers 

lofting from 1 to 30 torching trees. This is the model used 

for spotting in the FARSITE fire spread model (Finney 

2004). The next tool, Albini (1981), modified the lofting calculations to allow for burning slash piles or fuel jackpots, a 

more sustained plume driver. Albini (1983b) added a linear 

grass fire as a possible heat source (Morris 1987 provided 

a simplification). This model only applies when there is no 

timber cover, and relies heavily on a theoretical model of 

surges in grass fire intensity. Albini explicitly stated “this 

hypothesis, crucial in the model’s development, is unlikely 

to ever be tested directly.” All of the Albini models assume

that wind direction is constant with height and that windspeed increases according to theoretical models of the 

atmospheric boundary layer in the absence of fire.

While the Albini tools are built into BehavePlus and 

FARSITE and are based on extensive laboratory studies 

cited earlier, they have not been methodically or rigorously 

tested in the field—indeed, it is practically impossible to do 

so, as one cannot know for certain that no embers landed 

beyond the calculated distance, unless the embers actually 

cause ignition. Even if a spot fire occurs beyond the modelpredicted distance, there is no way to know whether it was 

lofted by a vortex. Norum (1982) and Rothermel (1983) 

each used anecdotal spotting events to favorably evaluate 

the Albini models, but Ellis (2000) cited several instances 

where spot fires initiated far beyond where Albini’s model 

predicts they would. 

Bunting and Wright’s (1974) results provide limited 

guidance on spotting distance and fuel break widths for 

burning slash piles of juniper. Their general conclusions 

suggest 100-ft fuel breaks or spotting distances for air temperatures below 60 °F, but their equation for that distance 

depends strongly on temperature above 60 °F. The equation 

produces a distance of 48 ft at 60 °F; then, 205 ft at 70 °F, 

and 870 ft at 80 °F. This illustrates the potential problems 

of applying equations or models near or beyond the limits 

under which they were developed.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Wilson (1988) included two figures that provided 

guidance on fire break widths to prevent spotting (fig. 

7-6). These came out of field observations of grass fires in 

Australia’s Northern Territory and are valid for situations 

with no trees, or only scattered, sparse trees, comparable to 

fuel models 1 and 3 in Anderson (1982) or Scott and Burgan 

(2005). 

Figure 7-2, discussed earlier from Alexander and Cruz 

(2006) also qualifies as a tool. It serves for estimation of 

the depth of the overrun zone, which can be considered 

the region where spot fire suppression has limited effect. 

There is little advantage in trying to suppress fires that will 

be overrun before they establish (this calculation is also 

part of the Crown Fire Initiation and Spread [CFIS] System 

discussed in the crown fire chapter of this document and 

described in Alexander et al. 2006.)

Rascher (2003) provided suggested scales for fire 

breaks to prevent spotting in saltcedar and juniper/oak 

stands. As noted previously, saltcedar spotted more than 

juniper/oak in these burns. In both types of vegetation, however, the maximum spotting distance observed was about 

150 m. Accompanying winds were between 5 and 8 mi/h.

In terms of ignition probability, Muraszew and Fedele 

(1976) relied on the ignition probability table from the 

1974 National Fire Danger Rating System. Currently, the 

equivalent to that table is table 12 of the Fireline Handbook, 

Appendix B. Albini (1979) also included a table for spotting 

potential that gives qualitative rating of the ease of ignition.

Weir (2004) discussed spotting probability based on 

observations in a number of vegetation types in Oklahoma. 

Spotting probability in these observations is not ignition 

probability. The latter does not consider whether an ember 

lands to possibly cause ignition, it only looks at the probability of ignition assuming there is an ember. Weir looked 

at the frequency of actual spot fire development on 99 

Figure 7-6—Probability estimates for grass fires to breach firebreaks of a specified width. Probability depends on the presence 

or absence of trees in the grass stand, and on fireline intensity. 

Based on equations in Wilson (1988).
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

prescribed fires. He concluded that ignition occurred on all 

fires when relative humidity was below 25 percent, and very 

rarely (once) when relative humidity exceeded 40 percent. 

This guidance is perhaps valuable for prescribed fires, but 

not particularly limiting for wildland fire conditions, when 

relative humidity below 25 percent is common. 

In fuel types similar to those in Ganteaume (2009), the 

probability equations presented therein or table 7-3 may 

be suitable. Caution is necessary, however, because their 

equations were derived for specific fuels and have not been 

tested or applied to other fuels, even similar ones, nor have 

they been evaluated outside of the laboratory. 

Knowledge Gaps

Koo et al. (2010) identified several gaps in our understanding of spotting. Koo et al. focused on ember generation and 

on ignition potential for embers that land. They considered 

resolution of the airflow relatively well known and not as 

important for research at present. They also identified the 

need to understand the transition between smoldering and 

flaming combustion for embers as a lesser, but nonetheless 

important area for further research. Some of the work in 

Ellis (2000), not discussed here, confirms the complexity 

and need for this work.

From this review, four primary areas for further 

research are apparent. The first is the issue of ember generation identified by Koo et al. If spot fire tools are going to 

broaden their application beyond just maximum spotting 

distance to cover the numerous, nearby spot fires that can 

occur just beyond the overrun zone, then field observations 

of the number and size of embers produced by various fuel 

types are essential. Without these, there is no way to know 

how many embers could be carried downwind, or how far 

downwind they will go. These are important factors for 

determining whether spotting will affect the fire’s rate of 

spread and whether spot fires will be numerous enough to 

merge readily. Knowing how the number and size of embers 

vary by fuel type is important for understanding when 

spotting is of concern.

The second area needing more research is understanding the importance of the turbulent convective column and 

the true atmospheric conditions lofting and transporting 

the embers. Tarifa (1967) stated “Fire spread by fire brands 

depends essentially on the convective currents and wind 

conditions in forest fires. Therefore, an accurate knowledge 

of these phenomena is absolutely required in order to apply 

correctly the information obtained from the basic studies of 

firebrands.” Studies using coupled fire-atmosphere models, 

or at the least time-dependent, three-dimensional models 

are presently the best approach to understanding how the 

convective currents and the real atmosphere influence brand 

transport. Extremely long distance transport, long enough 

to reach the sailing/whirl zone in figure 7-1, involves lofting 

high enough that the directional changes in wind with 

height can lead to spotting to the sides of the surface winds, 

and should be at least tested for significance with some 

models. 

The other aspect of long distance transport that has 

not been examined in any way is the shed-vortex transport 

described by Berlad and Lee (1968). Documenting or 

measuring this process on real fires would be extremely 

challenging but must precede any attempt to model it 

credibly. If observations confirm this process is occurring 

in nature, then models may be developed. While this vortex 

transport concept is in some ways novel, it appears on the 

surface to accommodate many of the aspects of long range 

spotting that are more difficult to explain otherwise.

Third, Sardoy et al. (2008) illustrated that char content 

is an important factor in determining spotting distance and 

the degree of separation between short- and long-distance 

spotting. Extension of the research in this area, especially 

in combination with more information on the number and 

size of embers for various fuel types, could aid in development of tools to predict the distances of greatest concern 

for spot fire monitoring or control. Work needed in this area 

includes laboratory or field measurement of char content by 

species, as well as modeling study of the relationship among 

char content, fire intensity, environmental windspeed, and 

separation distance.

Ignition delay, the end of the spotting life cycle, is the 

fourth area where research is important. Connecting the research on realistic ember sizes, and the fuel conditions that 

produce those embers, with information on recipient fuels 

and ignition delays associated with them, would clarify the 
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GENERAL TECHNICAL REPORT PNW-GTR-891

depth of the overrun zone. Perhaps more importantly, it is 

important in determining the likelihood of multiple embers 

starting spot fires that could merge and establish a new, 

independent fire front.

Project Vesta (Gould et al. 2007) demonstrated that 

many poorly known properties of spotting can indeed be 

studied in the field. That project captured spatial distributions in several ways, including contour plots and empirical 

functions for the across-wind and along-wind variations in 

landing embers. Similar projects could clearly be executed 

in different spotting-prone ecosystems.

Finally, there is one opportunity that may have substantial potential for benefit in the field. Muraszew and Fedele 

(1976) outlined a model for prediction of spot fire number 

and distance, as well as the potential for ignition and 

merging of multiple spot fires. The report implies that the 

model was actually put into a computer model at that time, 

but it seems to have stopped there. It is probably well worth 

the time and effort to reexamine their model in light of the 

last 35 years of research and determine whether it could be 

translated into an operational tool. Some components may 

not be viable, but many others may well be.

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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Chapter 8: Vortices and Wildland Fire

Jason M. Forthofer1 and Scott L. Goodrick2

Introduction

Large fire whirls are often one of the more spectacular 

aspects of fire behavior. Flames flow across the ground 

like water feeding into the base of the vortex, the lowest 

thousand feet of which often takes on an orange glow from 

combusting gases rising within the vortex core. Burning debris lofted within the vortex can lead to a scattering of spot 

fires some distance from the main fire. With their sudden 

formation, erratic movement, and often sudden dissipation, 

fire whirls are a good example of extreme fire behavior. 

However, other forms of vortices are actually quite common 

on wildland fires and receive less attention despite their 

potential to dramatically alter fire behavior.

This chapter is designed to provide a better understanding of vortices associated with wildland fires, both 

fire whirls and horizontal roll vortices. A key point will be 

providing a basic understanding of what aspects of the fire 

environment contribute to the development and growth of 

these vortices. The next section of the chapter supplies a 

brief introduction to vorticity, a measure of the atmosphere’s 

tendency to spin or rotate about some axis. With this basic 

understanding of vorticity, we will examine the common 

vortex forms described in the fire behavior literature, fire 

whirls and horizontal roll vortices.

Vorticity Basics

Vorticity is the measure of spin about an axis. That axis can 

be vertical, as in the case of a fire whirl, or horizontal for 

a roll vortex, or somewhere in between. Mathematically, 

vorticity is a vector quantity (it has both magnitude and 

1 Jason M. Forthofer is a mechanical engineer, U.S. Department 

of Agriculture, Forest Service, Missoula Fire Sciences Laboratory, 

5775 US West Highway 10, Missoula, MT 59808.

2 Scott L. Goodrick is a research meteorologist, U.S. Department of 

Agriculture, Forest Service, Southern Research Station, 320 Green Street, 

Athens, GA 30602.

directional information) that is defined as the curl of the 

wind field.

(1)

or in component form.

(2)

As a simple hypothetical example, take a cross section 

through a fire with no ambient horizontal winds (fig. 8-1). 

The vertical winds near the ground can be characterized by 

a strong updraft over the fire and descending air outside of 

the fire area. The change in the vertical velocity along the 

x-axis imparts rotation to the flow field about the y-axis. 

Note that this does not describe how vorticity is generated, 

but rather just illustrates the definition. The complete vorticity equation can be derived by applying equation (1) to the 

Navier-Stokes equations that describe fluid motions to get

(3)

The first term on the left hand side of equation (3) is the 

time rate of change of vorticity at a point. All of the remaining terms in the equation describe processes by which the 

vorticity at a point is changed. The second term on the 

left hand side is the advection, or transport, of vorticity by 

the wind. Thus vorticity generated in one place can affect 

another location.

The right hand side of equation (3) begins with the tilting 

term, , that describes how velocity gradients

can transform horizontal vorticity into vertical vorticity

and vice versa. The second term on the right hand side, 

, describes how flow convergence (divergence) 

stretches (compresses) vortices and increases (decreases) 

the magnitude of the vorticity. Note that these tilting and 

ωωωω��⃗ = ∆ × VVVV�⃗
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GENERAL TECHNICAL REPORT PNW-GTR-891

stretching terms can only enhance the vorticity already 

present in the flow; they cannot generate new vorticity. 

The solenoidal or baroclinic term is the third term on 

the right hand side of equation (3), . This term 

generates vorticity in cases where the gradients in pressure and density are not parallel. In the case of a fire, rapid 

heating develops a horizontal temperature gradient that is 

not aligned with the vertical static pressure gradient. This 

misalignment of the vertical pressure gradient and horizontal 

thermal gradient leads to rotational motions to mix warm 

and cold fluid to restore balance.

The fourth term in equation (3), , provides 

for the generation of vorticity by viscous shear stress. Wind 

shear induced by surface drag is a source of vorticity; therefore, if the wind is blowing at the Earth’s surface, horizontal 

vorticity is being generated. The final term in equation (3),

 , represents changes in vorticity owing to body forces 

such as gravity acting on the fluid. 

In summary, the vorticity at any location changes as a 

result of the transport of vorticity from one place to another, 

the tilting of vorticity from one axis to another, the stretching and intensifying of vortices by convergence, or by the 

generation of vorticity through buoyancy or wind shear.

Figure 8-1—Cross section through idealized fire illustrating occurrence of vortices owing to horizontal gradient 

of vertical motion produced by buoyancy from the fire.

Fire Whirls

Fire whirls are vertically oriented, intensely rotating columns of gas found in or near fires. They have been observed 

in wildland, urban, and oil spill fires and volcanic eruptions. 

Dynamically they are closely related to other swirling 

atmospheric phenomena such as dust devils, waterspouts, 

and tornados (Emmons and Ying 1967). Fire whirls have 

also been called fire devils, fire tornados, and even firenados. They are usually visually observable because of the 

presence of flame, smoke, ash, or other debris. The definition of a fire whirl used here includes those whirls caused 

by the buoyancy of a fire but with no inner core of flame. 

Fire whirls range in size from less than 1 m in diameter and 

velocities less than 10 m/s up to possibly 3 km in diameter 

and winds greater than 50 m/s (Goens 1978). The smaller 

fire whirls are fairly common, while the larger whirls are 

less common. All fire whirls, especially the larger ones, represent a considerable safety hazard to firefighters through 

increased fire intensity, spotting, erratic spread rate and 

direction, and wind damage (Emori and Saito 1982, Moore 

2008, USDI BLM 2006).

Several extremely large fire whirls have been reported 

in urban fires that illustrate their potentially destructive 

ρ p

ρ2 ∇×∇ 1 .















  

ρ

τ

×∇ B
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

nature. In 1871, the Great Chicago Fire generated whirlwinds that lifted and transported burning planks 600 m 

ahead of the main fire, which contributed greatly to the 

spread and destruction of the fire (Musham 1941). On the 

same day, a fire in Peshtigo, Wisconsin, generated a whirl 

that was strong enough to lift a house off its foundations 

(Gess and Lutz 2002). Hissong (1926) also reported a whirl 

strong enough to move a house. This whirl was one of many 

that formed during a large oil storage facility fire. The whirl 

separated from the fire and moved 1000 m downwind, 

lifted a small house, and moved it 45 m killing the two 

residents inside. A much more devastating whirl formed 

in 1921 when a magnitude 7.9 earthquake hit the Tokyo, 

Japan, area causing a mass urban fire. This fire spawned an 

extremely large fire whirl that killed an estimated 38,000 

people in less than 15 minutes (Soma and Saito 1988). The 

victims had gathered in an area of sparse fuel 0.16 km2 in 

size and the whirl moved over the area. Last, the World 

War II city bombings of Hamburg, Dresden, and Hiroshima 

were reported to have caused very large and destructive fire 

whirls. The Hamburg whirl was estimated at 2.4 to 3 km in 

diameter and 5 km tall (Ebert 1963).

Large and intense fire whirls also occur on wildland 

fires. Graham (1952, 1955, 1957) described several large 

whirls that were able to lift large logs and other debris 

and break off large standing trees. He indicated that many 

form on lee slope locations. Pirsko et al. (1965) reported 

on a very intense fire whirl that moved out of the fire area 

in the downwind direction and destroyed two homes, a 

barn, three automobiles, toppled almost 100 avocado trees, 

and injured four people. They also believe that the terrain 

and lee slope location contributed to the formation of the 

whirl. Additionally, they cite moderate winds, an unstable 

atmosphere, and a large heat source as contributors. King 

(1964) analyzed video of a fire whirl and found that maximum vertical velocities in the whirl core were up to 91 m/s 

0.05 mi/s. Large fire whirls have also been documented on 

flat ground. Haines and Updike (1971) described several 

medium to large size fire whirls that occurred during 

prescribed fires on flat ground. They cited a super-adiabatic 

lapse rate in the lower atmosphere as an important factor. 

Umscheid et al. (2006) also reported on a large fire whirl 

that occurred on flat ground and gave convincing arguments 

that a major contributor to the whirl was vorticity associated 

with passage of a cold front. Billing and Rawson (1982) also 

reported on a large whirl that may have been influenced by 

a cold front passage. McRae and Flannigan (1990) described 

many large whirls that occurred on prescribed fires. One of 

the largest and most intense whirls was 400 m in diameter 

and ripped standing trees out of the ground and lifted 

them upwards. This whirl occurred on a cloudy day with a 

temperature lapse rate of 6 °C/1000 m in the first 1000 m 

above the ground. They concluded that the influence of the 

environmental lapse rate on fire whirl formation is unclear 

and that whirls can form under lapse rates other than dry or 

super adiabatic.

Fire whirls have severely injured firefighters in the past. 

Emori and Saito (1982) describe a wildland fire in Japan that 

may have spawned a fire whirl that injured firefighters. The 

2001 Fish Fire in Nevada generated a fire whirl that caused 

firefighters to deploy their fire shelters (USDI BLM 2001). 

Another whirl in 2006 in Nevada injured six firefighters 

(USDI BLM 2006). Finally, a very large whirl formed 

on the 2008 Indians Fire in California that injured four 

firefighters (Moore 2008).

Fire Whirl Physics

Over the past few decades, a significant body of information 

has accumulated on fire whirl structure and influencing 

factors. The different techniques used to investigate fire 

whirls include field- and laboratory-scale experiments, and 

analytical, physical, and numerical modeling. This work 

has revealed some of the main features of fire whirls. For 

example, it is commonly accepted that the formation of fire 

whirls requires a source of ambient vorticity and a concentrating mechanism (Emmons and Ying 1967, Goens 1978, 

Meroney 2003, Zhou and Wu 2007). Ambient vorticity in 

the atmosphere can be generated by the ground boundary 

layer of wind, by wind shear from nonuniform horizontal 

densities, and from the Earth’s rotation. The concentrating 

mechanisms in fires are produced by the buoyant flow. It 

reorients horizontal vorticity into the vertical direction and 

provides vortex stretching.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Whirl structure—

One of the first laboratory studies of fire whirls was that of 

Emmons and Ying (1967). They were able to generate a fire 

whirl with a combusting core by placing a liquid-fueled (acetone) pool fire in a cylindrical rotating screen. The rotation 

speed of the screen was varied and temperature, velocity, 

and burning rate were measured. Several important aspects of fire whirls were identified in this study. They found 

that a fire whirl develops an ascending and rotating core 

of fuel-rich gas. The core’s radial distribution of tangential 

velocity may be in more or less “solid body” rotation (also 

called a forced vortex), but Emmons and Ying were not 

able to take measurements to prove this. In a forced vortex, 

vorticity and angular frequency are constant and nonzero. 

Tangential velocity and circulation increase with radius. 

Outside of this core, Emmons and Ying described a fuellean area with tangential velocity that can be well described 

by a free or potential vortex plus small radial and vertical 

velocity components. Vorticity in a free vortex is zero, 

while angular velocity and frequency tend toward zero with 

distance from the axis. Snegirev et al. (2004) stated that the 

free and forced vortex system can be approximated with the 

Rankine vortex shown in figure 8-2, which is apparently 

common in other vortices in rotating fluids. Their numerical 

model, which used a k-epsilon turbulence closure modified 

for swirling flows, did indeed show that the radial profile of 

tangential velocity closely resembled the Rankine vortex. 

Chuah and Kushida (2007) and Chuah et al. (2009) used a 

Burger’s vortex for the core flow. A Burger’s vortex is an 

exact analytical solution to the Navier-Stokes equations that 

is sometimes used to describe vortex tubes. They also stated 

that the radial inflow velocity needed to maintain the vortex 

is a function of the core radius with a smaller core radius 

requiring more radial inflow to maintain the vortex. 

Akhmetov et al. (2007) used a particular image 

velocimetry method on a laboratory-generated fire whirl 

Figure 8-2—Tangential velocity struc-ture as a function of radius for a Rankine vortex.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

to provide currently the best measurements of the velocity 

structure in a fire whirl. They confirmed that a fire whirl 

generates a core region that rotates in approximately solid 

body rotation. The maximum vertical velocity in the core 

region was of the same order of magnitude as the maximum 

rotational component. Outside of the core, the rotational 

velocity component decreases with distance from the axis, 

and vertical velocities are much less. They also concluded 

that the basic features of fire whirl flows are the same as in 

other vertical tornado-like vortices such as dust devils and 

tornados.

Turbulent mixing—

Emmons and Ying (1967) also found that the rotational motion in and at the boundary of a fire whirl core causes an 

order of magnitude reduction in turbulent mixing motions. 

This is what gives a fire whirl its tall, slender appearance. 

They indicate that this turbulent mixing reduction is a very 

significant aspect of swirling flows and one of the main reasons fire whirls are able to achieve such strong intensities. 

Snegirev et al. (2004) expanded on the turbulent suppression idea explaining that, in the core, radial displacement 

of a fluid particle towards the axis is resisted by centrifugal acceleration, and displacement away from the axis is 

resisted by the radial pressure gradient. Because of the 

solid body rotation in the core, this radial pressure gradient increases with radius. The turbulence suppression in the 

core is analogous to that in a stable atmospheric boundary 

layer, but with different resisting forces. In the outer, free 

vortex area, fluctuations in the radial direction are destabilized, analogous to an unstable atmospheric boundary layer. 

Others (Beer et al. 1971, Chigier et al. 1970) have examined 

turbulence in laboratory fire whirls and jets in more detail 

and also showed a large reduction in turbulence in the whirl 

core. Some have proposed the use of a Richardson number 

for examining the stability of fire whirls (Beer et al. 1971, 

Snegirev et al. 2004). 

Emmons and Ying (1967) indicated that the stable 

core environment could lead to the existence of “surface 

waves” on the surface of the core, similar to water flowing in a river or stable atmospheric flow over a hill. In the 

case of the upward flow being faster than the surface wave 

speed (“shooting flow”), a hydraulic jump type situation is 

possible (Meroney 2003). This jump from so-called “shooting flow” to “tranquil flow” (flow speed slower than wave 

speed) would be accompanied by high turbulence, which 

could contribute to vortex breakdown, although this has 

never been confirmed. Emmons and Ying (1967) also mentioned that a hydraulic jump may be necessary if the whirl 

is to satisfy its ground-level and “high”-altitude boundary 

conditions for momentum and mass flow. This might be 

similar to water flowing down a dam spillway, where the 

initial velocity is high (supercritical flow) but it flows into 

an environment of much slower flow (subcritical flow) at the 

end of the spillway. The flow must form a hydraulic jump to 

satisfy these boundary conditions in a stable flow environment. Komurasaki et al. (1999) used numerical simulation

to investigate vortex breakdown in a thermal whirl and 

found that just as vortex breakdown begins, strong vorticity 

appears near the ground. This strong vorticity was attributed to strong jets of downward-moving air that impinge on 

the ground during the breakdown of the simulated whirl, 

which, if true, could have safety implications for nearby 

firefighters.

Note that several authors have found that, at very low 

rotation, a plume actually expands more than the nonrotating plume and reduces the flame height. As rotation is 

increased, turbulence is suppressed as discussed above 

and the plume expands less than the nonrotating plume 

giving a taller, more slender plume. Emmons and Ying 

(1967) showed this in their figure 7, but cannot explain this 

behavior. Battaglia et al. (2000) also showed this behavior in 

their numerical model. Zhou and Wu (2007) explained this 

by stating that it is due to the inflow boundary layer wind 

reducing the initial vertical velocity of gas and enhancing 

entrainment. 

The large reduction of turbulent mixing in the core of a 

whirl is one of the principle causes of the amazing velocities 

fire whirls can achieve. The low turbulence reduces transfer 

of momentum, mass (density), fuel, and oxygen to and from 

the core. In whirls with a combusting core, this causes 

a large increase in flame lengths because the flames are 

turbulent diffusion flames and mixing with oxygen outside 

the core is limited. Emmons and Ying (1967) reported flame 

length increases of up to seven times the nonwhirl lengths, 
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GENERAL TECHNICAL REPORT PNW-GTR-891

although the increased flame lengths included the combined 

effect of turbulent reduction in the whirl core and increased 

evaporation rates of the acetone pool. Chigier et al. (1970) 

used a metered methane burner to keep gas flow rates 

constant and showed that flame lengths doubled in their 

laboratory whirl produced using a rotating mesh cylinder. 

Because the fuel flow was held constant, this increase in 

flame lengths was due solely to the reduction in mixing. 

Even in whirls with noncombusting cores, this low turbulent 

mixing produced a tall column of lower density gas than the 

surroundings. Consequently, pressure at the ground level in 

the core may be very low (Emmons and Ying 1967) because 

of decreased hydrostatic pressure. This combines with the 

cyclostrophic flow effect discussed in the next section to 

produce extremely low pressures near the ground.

Cyclostrophic flow—

Contributing to a low pressure in the core is the roughly 

cyclostrophic flow (pressure gradient force balances the 

centrifugal force) (Jenkins et al. 2001). As the whirl spins 

faster, lower pressures occur in the core to balance the 

increased centrifugal force. Near the ground, this cyclostrophic balance is disrupted by drag forces, and the 

large radial pressure gradient produces flow toward the axis 

of rotation. Consequently, flow near the ground converges 

toward the center of the whirl and then is forced vertically. 

This draws air, rich in shear-produced horizontal vorticity, 

into the bottom of the whirl (Meroney 2003). Also, additional buoyant gases and fuel may be drawn into the core, aiding vortex stretching (Emmons and Ying 1967). Muraszew 

et al. (1979) stated that this effect, a result of the ground 

surface, is a requirement for the formation of a fire whirl.

Vortex stretching—

The primary vorticity-concentrating mechanism in fire 

whirls appears to be vortex stretching caused by vertically 

accelerating flow in the whirl core (Snegirev et al. 2004). 

This corresponds to the second term on the right-hand side 

of the vorticity transport equation (equation 3). The vertical 

acceleration is due to buoyant forces from hot gases in the 

core of the fire whirl. This acceleration causes a reduction in 

the diameter of a horizontal area enclosed by a chain of fluid 

particles (horizontal convergence), thereby increasing nonzero vorticity at any location on the horizontal area (Jenkins 

et al. 2001). This is analogous to a reduction in the moment 

of inertia of a rotating solid, causing increased rotation 

rate to conserve angular momentum. Snegirev et al. (2004) 

indicated that the whirl core radius is not dependent on the 

initial or imposed circulation, but that it is probably dependent on vortex stretching owing to vertical acceleration.

This same mechanism may also contribute to reduction in whirl vorticity (Snegirev et al. 2004) high up in the 

vortex where the vertical velocity decreases with height. 

This could occur when the core’s buoyancy is reduced 

from ambient air entrainment or encountering a stable 

atmospheric lapse rate aloft. The vertical deceleration would 

reduce the vorticity.

Increased combustion rates—

A number of researchers have noted significant increases in 

burning rates of laboratory fire whirls. In all of these studies, the burning rate is defined as the mass loss rate of the 

fuel source (solid or liquid). Byram and Martin (1962) found 

a three-fold increase in alcohol burning rate when a whirl 

formed. Emmons and Ying (1967) found that the burning rate 

of their acetone pool fires was a function of the externally 

imposed circulation, with increases of up to seven times the 

nonwhirl conditions. Martin et al. (1976) measured 1.4 to 4.2 

times faster burning rates in fires fueled by cross-piled wood 

sticks of varying sizes.

The increased burning rate is likely due to increased 

heat transfer and mixing near the solid or liquid fuel. The 

question of which mechanism of heat transfer (convection 

or radiation) is causing the increased burning rates has been 

examined by several researchers (Chigier et al. 1970, Chuah 

et al. 2009, Snegirev et al. 2004). Most have speculated 

that increased convective heat transfer owing to high levels 

of turbulence near the ground surface and fuel causes the 

increased burning rates (Chigier et al. (1970, Snegirev et 

al. 2004). Snegirev et al. (2004) used a computational fluid 

dynamics model that included a Monte Carlo radiation 

solver to show that radiation actually decreased when a 

whirl formed in their study. This was attributed to changes 

in flame shape, and suggests that radiative heat transfer is 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

not the cause of increased burning rates in fire whirls. These 

authors propose that the flow rotation intensifies the entrainment of air into the fuel-rich region near the ground and fuel 

surface, which causes increased mixing in this area resulting in higher gas temperatures and reaction rates. Chigier 

et al. (1970) used an isothermal laboratory experiment to 

show that increased mixing does occur. Their experiment 

used a suction tube to produce the needed vortex stretching. The isothermal nature of the experiment allowed easy 

measurement of velocity, including turbulence. They found 

that turbulence intensities over the first four diameters vertically were much higher than the nonrotating case. Above 

this height, turbulence intensities reduced to less than the 

nonrotating case as expected. Chuah et al. (2009) used a 

scaling analysis and measurements in small experimental 

fire whirls over pool fires to develop an analytical model of 

fire whirls, including a heat-feedback mechanism to the pool 

fire. They found that the average rate of heat transfer from 

the flame to the fuel surface was a function of the vortex 

core radius. A smaller radius provided more heat to the fuel 

surface.

Scaling fire whirls—

Much of what is known about fire whirls comes from smallscale laboratory experiments. Full-scale experiments are 

usually not practical because of safety concerns, economic 

aspects, and difficulties of controlling boundary conditions 

(Emori and Saito 1982). Because of this, scaling laws are 

very important to consider when attempting to apply information gained from small-scale experiments to full-scale 

fire whirls. Several authors have examined scaling related to 

fire whirls.

One of the first investigations of scaling laws related to 

fire whirls was Emmons and Ying (1967). They suggested 

that Froude and Rossby numbers were important parameters 

for understanding their laboratory whirls. Another investigation was that of Emori and Saito (1982). They used a scale 

model in a wind tunnel to recreate a firefighter entrapment 

that occurred. The scaling analysis concentrated on fluid 

flow and buoyancy from the fire, which was simulated in 

the scale model using electrically heated wires. A modified 

Froude number was considered important to proper scaling. 

In the experiment, they found that a fire whirl occurred 

on the lee side of a mountain at the location where the 

firefighters were injured. They also found that the whirl 

only formed when the ambient crossflow wind was within a 

certain speed range. Wind above or below this range did not 

produce a fire whirl.

Soma and Saito (1988, 1991) classified whirls into three 

different types according to causal factors and behavior 

and investigated each type using scaling analysis and 

experiments. Their scaling analysis determined that scaled 

experiments should be performed with wind velocities and 

heat generation rates proportional to the square root of the 

fire widths or one-fourth power of the fire area. They were 

able to produce fire whirls in their experiments that qualitatively matched the full-scale events. Length scale ratios 

between the experiments and full-scale whirls were 1/235, 

1/2500, and 1/4837. They also found that there was a range 

of crossflow windspeeds where whirls would form, but 

above or below this speed, whirls did not form.

Grishin et al. (2004) examined fire whirls using 

laboratory-generated whirls and found that Grashof and 

Froude numbers could be used for scaling. They concluded 

that fire whirl characteristics are determined by the heatflux density, lift force, and angular momentum of the external vortex flow. Grishin et al. (2005) used a Rossby number 

derived in a semiempirical way to determine the critical 

values under which a fire whirl would form in their laboratory experiments. They stated that the rotation velocity of 

a fire whirl decreases as its radius increases and increases 

as its height increases. Akhmetov et al. (2007) also used a 

Froude number for scaling and found that rotational velocity increased as whirl height increased. They mentioned 

that other similarity criteria based on Grashof number or 

Reynolds number will vary by many orders of magnitude 

for vortices of different scale.

To the authors’ knowledge, the only discussion of the 

horizontal movement of fire whirls is given by Grishin 

(2007) despite its importance to firefighter safety. The paper 

uses a theoretical basis to analyse how fire whirls move owing to interaction with other fire whirls. In particular, two 
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GENERAL TECHNICAL REPORT PNW-GTR-891

counter-rotating fire whirls with equal intensity in proximity are examined and found to move in the same direction 

with the same velocity. This could be important when 

counter-rotating vortices appear on the lee side of a fire 

plume (Church et al. 1980, Cunningham et al. 2005), which 

may be fairly common in wildland fires in a crossflow wind.

Kuwana et al. (2007, 2008) examined several experimental and full-scale whirls under crossflow conditions 

and concluded that a critical crossflow wind velocity exists 

where fire whirls are most likely to occur. This critical 

velocity was found to be proportional to the vertical buoyant 

velocity, which depends on the burning rate and length scale 

of the burning area.

Vorticity sources—

In the wildland fire context, it appears there are many possible sources of ambient vorticity that could contribute to 

fire whirls. Morton (1966) discussed some of these sources. 

One important source may be the shear layer that develops 

when ambient wind flows over the ground surface, producing horizontally oriented vorticity. This type of vorticity 

generation corresponds to the fourth term on the right-hand 

side of equation (3). As shown in figure 8-3, this horizontal 

vorticity can then be reoriented, or tilted, by the fire’s buoyant flow into the vertical (Church et al. 1980, Cunningham 

et al. 2005, Jenkins et al. 2001) and may be a major contributor to many fire whirls. Similarly, it is likely the indrafting 

to a buoyant plume develops a shear layer near the ground 

that also generates horizontally oriented vorticity that can 

also be tilted to the vertical. This source of vorticity could 

be present even in zero ambient wind situations. Complex 

terrain can also generate vorticity through channeling and 

shear of ambient and fire-induced winds (Pirsko et al. 1965). 

Turbulent wake regions behind terrain features such as hills 

and mountains are thought to produce favorable vorticity for 

fire whirls (Countryman 1964, 1971; Goens 1978; Graham 

1957). Another source of ambient vorticity for some whirls 

may be vorticity present along frontal boundaries (Billing 

and Rawson 1982, Umscheid et al. 2006). This may be similar to the meteorological setting for many nonmesocyclone 

tornadoes (Umscheid et al. 2006).

Figure 8-3—A schematic showing how shear generated horizontal vorticity present in the atmosphere near the ground can be reoriented to the vertical by a fire (from Church et al. 1980).
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Another possible source of vorticity in fire whirls is the 

baroclinic term in equation (3). At this time, it is unclear 

how important this source of vorticity is to fire whirls. 

McDonough and Loh (2003) provided an initial examination 

using numerical modeling. They mainly examined grid 

resolution requirements and are not able to make any strong 

conclusions about the significance of baroclinically generated vorticity, other than that it warrants further study.

Fire Whirls in the Real World: Common Features

Many factors influence the development of fire whirls on 

wildland fires. These factors interact in complex ways, and 

firefighters will likely not ever have very accurate predictive tools to foresee whirl formation, especially in a timely 

manner to make real time decisions. For now, the hope is to 

identify situations that are more likely to form whirls. The 

following are some likely scenarios where fire whirls have 

been known to form. It is probable that some of these types 

of fire whirl scenarios could be combined to possibly make 

whirl formation more likely or more intense.

Whirl shedding on the lee side of a plume—

This type of whirl forms when a plume is subjected to a 

crossflow wind. The whirl forms on the lee wind side of 

the plume. It separates from the plume and advects in the 

downwind direction. Sometimes multiple whirls of opposite rotating direction shed periodically, similar in appearance to Von Karman vortex shedding behind an obstruction 

in a flow. Often, as the whirl moves away from the fire, it 

contains no flaming combustion. Wind in the whirl can be 

strong enough to cause damage to trees, structures, vehicles, etc., and the whirl may stay intact for several minutes 

and travel for distances of possibly 2 km. Its ability to stay 

intact even though most of its vortex stretching mechanism 

(buoyancy) is lost is probably due to the strong reduction 

in turbulent diffusion of the core. Examples of this type of 

whirl have been reported by many authors (Church et al. 

1980; Dessens 1962; Hissong 1926; Pirsko et al. 1965; Soma 

and Saito 1988, 1991) and video and images of others are 

on file at the U.S. Forest Service’s Missoula Fire Sciences 

Laboratory.

A critical crossflow wind velocity is likely very 

important to this type of fire whirl, as discussed in “Scaling 

fire whirls.” Cunningham et al. (2005) were able to simulate 

this type of whirl and hypothesized that the main source 

of vorticity comes from the tilting of horizontally oriented, 

shear-generated vorticity in the ambient crossflow. The significance of other sources of vorticity is currently unknown. 

Others (Fric and Roshko 1994, McMahon et al. 1971, 

Moussa et al. 1977) have shown that the same shedding 

whirls are present in an isothermal vertical jet in crossflow, 

although in these experiments, the whirl formation may also 

be influenced by the jet shear layer.

L-Shaped heat source in crossflow—

Soma and Saito (1988, 1991) first investigated this type of 

fire whirl as an explanation for a historical and catastrophic 

fire whirl that occurred in 1923 in Tokyo. Unlike the shedding whirl, this whirl seems to be mostly stationary. It occurs when a roughly L-shaped heat source is subjected to a 

crossflow wind as shown in figure 8-4. The whirl forms in 

the inside bend of the L-shaped heat source. As in the shedding whirl, a critical crossflow windspeed is thought to be 

important (Soma and Saito 1988, 1991). If the wind is above 

or below this speed, whirls are less likely to form. This type 

of whirl is probably very much related to the shedding whirl 

type, including the important vorticity source from the ambient shear flow.

Vorticity associated with cold fronts—

This type of whirl forms when ambient vertical vorticity 

from cold fronts interacts with a fire plume. Billing and 

Rawson (1982) and Umscheid et al. (2006) discussed cases 

where this type of whirl formed over flat terrain. The key 

feature of these two examples is that they occurred almost exactly when a cold front passed over the fire area. 

Umscheild et al. (2006) discussed the associated ambient 

vertical vorticity present along a cold front boundary and 

identified some similarities between this type of fire whirl 

and the formation mechanisms of nonmesocyclone tornadoes. It is currently unclear why fire whirls form under 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 8-4—Schematic of fire whirl formation for an L-shaped 

heat source in a crossflow wind. View is from above.

some cold front passage conditions, but not others. Perhaps 

non-mesocyclone tornado genesis research can help identify

why these whirls form.

Multiple interacting plumes—

This type of fire whirl occurs from the interaction of multiple 

plumes with no ambient crossflow wind. Entrainment into 

each plume is affected by the nearby plumes, and under the 

correct configuration and buoyant plume strengths, a whirl 

can form. Figure 8-5 shows a schematic of how five fires 

could be oriented to cause a fire whirl. Lee and Otto (1975) 

observed whirl formation owing to plume interaction in their 

experiment using two asymmetric-shaped burning wood 

piles. Zhou and Wu (2007) examined the multiple interacting plume whirl in more detail using experimental fires, 

Figure 8-5—Schematic showing one heat source configuration 

that can cause a multiple interacting plume type fire whirl (from 

Zhou and Wu 2007).

numerical simulation, and some scaling analysis. They discussed configurations under which whirls would and would 

not form. They also showed that whirls can form under randomly oriented plume locations (figure 8-6). This has implications to wildland fire under mass-ignition-type conditions. 

Occurrence of fire whirls under such conditions might be 

very likely, so long as the multiple plumes are drafting a significant amount of air and are properly spaced and organized.

Lee side of a hill or mountain—

These fire whirls occur when a fire plume exists on the 

lee side of a terrain obstruction such as a hill or mountain. 

The plume uses vorticity existing in the wake region of the 

obstruction to form the whirl. Countryman (1971) stated 

that this is the most favorable situation for generation of 

fire whirls. During investigations of full-scale mass fires, 

Countryman (1964) intentionally burned a fire on a lee 

slope under moderate wind to investigate this type of whirl. 
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Figure 8-6—Image of a computer simulation showing how 

randomly oriented heat sources could cause a fire whirl through 

plume interactions (from Zhou and Wu 2007).

shed from the plume in the downwind direction. The whirl 

caused significant wind damage to several houses, trees, 

and vehicles. Windspeed at the time was 9.4 m/s with gusts 

to 13 m/s. 

Horizontal Vortices

Horizontal vortices are quite common in the atmosphere 

and have been extensively studied (see Brown [1980] and 

Etling and Brown [1993] for reviews). In the absence of 

wind, when the ground is heated, the warm air near the 

ground will eventually begin to rise in circulation cells, a 

process known as Rayleigh-Bernard convection (Fernando 

and Smith 2001). In the presence of vertical wind shear, 

these cells begin to transition from disorganized and transient to an organized state of a hexagonal lattice of convective cells. Fair-weather cumulus clouds often mark the tops 

of updrafts of these cells. As the wind shear increases, the 

convective cells further organize into horizontal convective rolls that are perpendicular to the mean wind; further 

increases in the vertical wind shear change the balance 

between buoyancy-driven vorticity and shear-driven vorticity and leads to the convective rolls being oriented parallel 

to the mean wind (Küettner 1971). These longitudinal 

convective rolls are easily seen in satellite images as parallel bands of cumulus clouds known as cloud streets. Figure 

8-7 provides an illustration of the structure of these cloud 

streets. While such horizontal convective rolls are a common feature of the atmosphere in the planetary boundary 

layer, the presence of a fire adds a complicating factor in the 

form of a horizontal temperature gradient that can locally 

alter the convective organization of the boundary layer.

Horizontal vortices associated with wildland fires have 

received less attention than their vertical counterparts, 

fire whirls. Haines and Smith (1987) provided descriptions 

of three distinct types of horizontal vortices observed on 

wildland fires: the transverse vortex, which is perpendicular 

to the flow direction, a single longitudinal (flow parallel) 

vortex and a counter-rotating longitudinal vortex pair.

Figure 8-7—Illustration of the role of horizontal vortices in the 

development of cloud streets (based on Etling and Brown 1993).

Several whirls formed during the burn, with the largest occurring near the end. Pirsko et al. (1965) described a whirl 

that formed on the lee side of a terrain obstruction and then 
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Transverse Vortices

Transverse vortices are described in Haines and Smith 

(1987) as a series of vortices “climbing” the upstream side 

of the convective column under conditions of low ambient 

windspeeds and intense burning. The mechanism they 

proposed for the development of such vortices involves 

the development of buoyancy-forced ring vortices rising 

through the smoke column. They further hypothesized that 

only the upwind portion of the ring is clearly visible as turbulent mixing is thought to render the downwind section of 

the ring less distinct. While transverse vortices on wildland 

fires have received little attention, extensive literature is 

available on ring vortices associated with pool fires. 

Buoyancy-forced ring vortices are a common feature 

of fluid flows associated with heat sources ranging in 

scales from candles to pool fires up to large mass fires; 

however, they are most clearly visible under conditions 

of weak mean horizontal flow. For these ring vortices, the 

vorticity is generated through the baroclinic term from 

equation (3). Because the thickness of the density layer 

controls the magnitude of the baroclinically forced vorticity, the strongest vortices have scales similar to that of the 

flame surface (Cheung and Yeoh 2009). As buoyant forces 

cause these vortices to rise, a process often referred to as 

“amalgamation” takes place as the rising vortices merge and 

grow and manifest themselves in the oscillatory necking 

and bulging of the fire that results from the Rayleigh-Taylor 

instability. The same basic process can be observed at the 

scale of the smoke plume, leading to the development of the 

transverse vortices described by Haines and Smith (1987). 

The oscillatory nature of the development of these vortices 

has been extensively studied for pool fires (Cetegen and 

Ahmed 1993); however, little has been done at the scale of 

wildland fire events.

While descriptions of vortex rings are quite common in 

the literature, little is mentioned about transverse vortices 

outside of Haines and Smith (1987). These vortices manifest 

themselves on the upwind side of the plume and add a boiling appearance to the plume. While the vortices themselves 

are not a source of erratic fire behavior, their presence is an 

indicator of a potential increase in the rate of combustion 

and an associated change in fire behavior.

Longitudinal Vortices

Single longitudinal vortex—

Longitudinal vortices differ from their transverse counterparts in that their axis of rotation is oriented parallel to 

the mean flow. The first class of longitudinal vortices from 

Haines and Smith (1987) is the single longitudinal vortex, 

of which only one case is presented, the Dudley Lake Fire 

as described by Schaefer (1957). The vortex was oriented 

in the direction of the mean flow, which was quite strong 

that day as surface winds were between 16 and 22 m/s. The 

diameter of the vortex was estimated at 1800 m. Smoke 

entrained within the vortex delineated the corkscrew-like 

nature of the vortex and allowed the vortex to be observable 

500 km downwind. The scale of this vortex is similar to 

those of the convective boundary layer rolls responsible for 

cloud streets and shows strong similarities to roll vortices 

associated with other crown fires (Haines 1982) with the 

main exception being that this was only a single vortex.

A possible answer to the question of why only a single 

vortex was observed may be answered through the numerical modeling work of Heilman and Fast (1992). In this study, 

a computer model of the atmospheric boundary layer was 

initialized with multiple heat sources some distance apart 

to examine how circulations induced by each heat source 

interacted and how the collection of these flows responded 

to the introduction of a transverse wind component (wind 

blowing perpendicular to the axis of the roll vortices). The 

introduction of the transverse wind component tended to 

destabilize the longitudinal vortices, and, in some cases, 

eliminated the upwind vortex entirely. Haines and Smith 

(1992) similarly found in their wind tunnel studies that a 

slight transverse component to the flow destabilized the 

vortex pair, thereby causing the collapse of the downwind 

(relative to the transverse wind component) vortex, which 

on a wildland fire would cause the vortex to fall outward 

across the flank of the fire, providing an additional mechanism for lateral fire spread and a threat to firefighter safety. 
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On the Dudley Lake Fire, Schaefer (1957) observed at 

regular intervals, the outward/downward moving segments 

of the vortex would mark lateral surges in the fire growth, 

indicating the possible presence of some slight shifts in 

the wind that may have inhibited the presence of the other 

vortex. 

This vortex type differs from the other two types 

described in Haines and Smith (1987) in that the fire is not 

necessarily an integral forcing term in the development 

of the vortex. Conditions in the atmosphere may already 

favor the development of the convective rolls, and the fire 

may simply act to enhance the vortex through additional 

thermal instability. While the transverse vortices are most 

pronounced at low windspeeds, the Dudley Lake vortex 

was, accompanied by surface winds of 16 to 22 m/s (the 

mean windspeed for the 12 crown fire cases in Haines 1982 

was 5.5 m/s).

Counter-rotating, longitudinal vortex pair—

Of the three types of horizontal vortices described by 

Haines and Smith (1987), the counter-rotating, longitudinal 

vortex pair, is the best documented, although early work 

(Scorer 1968, Turner 1960) focused on vortex pairs associated with smokestack emissions rather than wildland fires. 

The key feature of this vortex type is the paired nature of 

the vortices rotating in opposite directions. These vortices often occur along the flanks of the fire and can also be 

observed in the main plume at the head of the fire—this is 

often referred to as a bifurcating smoke column. Figure 8-8 

shows a numerical simulation of a bifurcated smoke plume 

as viewed from behind the fire. Cunningham et al. (2005) 

showed that the degree to which the smoke plume splits is 

related to the depth of the surface shear layer.

The New Miner Fire in central Wisconsin in 1976 is one 

example of a bifurcated smoke column provided by Haines 

and Smith (1987). This fire burned under very low relative 

humidity conditions for the region (minimum of 23 percent) 

with light winds averaging around 2 m/s. The bifurcated 

column consisted of a pair of vortices approximately 30 

m in diameter, which rotated fairly slowly compared to 

other atmospheric whirls like tornadoes. These columns 

would intermittently collapse and spill over the fire’s flanks, 

bringing hot gases and embers into contact with unburned 

Figure 8-8—Bifurcated smoke plume from a computer simulation. 

View is from upwind of the fire (from Cunningham et al. 2005).

fuels and providing for rapid lateral spread. Obviously, 

such behavior is a threat to fire crews that often focus their 

suppression efforts along the flanks of the fire. A key difference between these vortex pairs and the single vortex is the 

scale: the bifurcated columns were approximately 30 m) in 

diameter while the vortex on the Dudley Lake Fire was over 

a kilometer.

As part of a 1979 study conducted at the Centre de 

Recherches Atmosphériques Henri Dessens in France, 

Church et al. (1980) studied the vortices produced by the 

Météotron, an array of 105 oil burners with a total heat 

output of 1,000 MW. Three types of vortices were observed: 

(1) a columnar vortex that had the entire smoke column 

rotating, (2) small dust-devil like vortices just downwind 

of the burner array, and (3) a large, counter-rotating vortex 

pair within the plume that started as vertical vortices at the 

burn site, but became horizontal and oriented parallel to the 

wind as the plume rose and moved downwind. The first two 
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GENERAL TECHNICAL REPORT PNW-GTR-891

vortex types are vertical vortices as described in the section 

on fire whirls. 

The third type resembles the bifurcating column

described for the New Miner Fire. At a height of 40 to 50 m, 

the smoke column of the Météotron experiment bifurcated 

into a pair of counter-rotating vortices with initial diameters 

of 30 to 60 m (Church et al. 1980). The dominant motion 

associated with these vortices was rotation about their axis 

with little noticeable motion along the axis, a stark contrast to 

the strong axial flow observed in many fire whirls.

The forcing of the counter-rotating vortex pair is 

complex and has parallels with the forcing of similar vortex 

pairs by nonbuoyant jets in a crossflow (see Margason 1993 

for a review). The split plume develops through the interaction of the ambient vorticity in the flow produced by vertical 

wind shear with the jet shear layer (or plume shear layer in 

the case of wildland fires). The presence of buoyancy adds 

complexity to the forcing of the split plume compared to 

the nonbuoyant jet. Church et al. (1980) put forth a pair of 

physical processes capable of describing the development

of the bifurcating smoke column. The first process focuses 

on the reorientation and stretching of the horizontal vorticity in the ambient flow. Initially, the ambient vorticity can 

be thought of as a collection of horizontal tubes oriented 

perpendicular to the wind with upward motion along the 

upwind side of the tube and downward motion along the 

downwind side. As these vortex tubes encounter the rising 

air at the fire, the portion of the tube over the fire is lifted, 

which acts to tilt the vortex tube at the edge of the fire 

into a vertical orientation, producing a hairpin-like shape. 

As the lifted portion of the vortex tube continues to rise 

in the plume, it encounters stronger horizontal winds that 

transport this portion of the tube downwind faster than the 

surface parts, stretching the arms of the hairpin vortex. 

Eventually the combined processes of the lifting and faster 

downwind transport leads to the majority of the hairpin 

vortex being oriented horizontal and parallel to the mean 

flow. This is illustrated in figure 8-3.

The second process proposed by Church et al. (1980) 

deals with the generation of vorticity through the combined 

effects of buoyancy and surface drag forces. This process is 

actually a variation on the buoyant rings discussed earlier. 

The variation is the impact of the crossflow on the rising 

ring vortex. On the upwind edge of the ring, the crossflow 

enhances entrainment of ambient air on that side of the 

plume, which decreases the vertical velocity of that part of 

the plume. This causes the downwind section of the ring to 

rise faster than the upwind side, tilting some of the vorticity into a vertical orientation. The downwind section also 

encounters the stronger winds aloft before the upwind side, 

which leads to a stretching/intensifying of the streamwise 

sections of the ring. Experiments by Tsang (1970, 1971) 

supported the viability of this method in generating the 

counter-rotating vortex pair.

While both physical processes are plausible explanations for the development of the counter-rotating vortex 

pair, both are not equally supported by the observations. 

Many of the observed fire plumes exhibited significant nearsurface vertical vorticity, which is best supported by the 

first process, which relies upon the reorientation of ambient 

vorticity (Cunningham et al. 2005). Wind tunnel studies 

of the longitudinal vortex pair offer further support for 

the ambient vorticity process as Smith et al. (1986) found 

the vorticity in the streamwise vortex pair to agree quite 

well with the vorticity of the ambient flow as it approached 

the heat source. This is not to suggest that the buoyancy 

generated from the fire has no impact, just that it is not the 

dominant forcing for the development of the vortex pair.

Numerical modeling studies of the longitudinal vortex 

pair have largely been two dimensional (Heilman and 

Fast 1992; Luti 1980, 1981; Porteire et al. 1999) or quasi 

three-dimensional (streamwise flow component assumed 

constant) where the governing equations are solved for a 

number of planes perpendicular to the streamwise flow 

(McGratten et al. 1996, Trelles et al. 1999, Zhang and 

Ghoniem 1993). Cunningham et al. (2005) was the first 

fully three-dimensional simulation of fire plumes to focus 

on the development of vortical structures. Their simulations 

revealed the relationship between the depth of the shear 

layer, fire intensity, and the behavior of the vortex pair. The 

basics of this relationship centered around how long it took 

a buoyant air parcel to traverse the shear layer. Keeping the 

mean crossflow constant, a deeper shear layer would lead 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

to a wider split of the smoke column. If the fire intensity 

is increased, the air parcels travel through the shear layer 

faster, which leads to a decrease in the width of the plume 

split. One interesting observation is that for a given fire 

intensity, the plume rise is not affected by the width of the 

smoke column’s bifurcation, although its horizontal spread 

and deviation from a Gaussian distribution are strongly 

affected.

Another aspect of the counter-rotating vortex pair 

described by the numerical simulations of Cunningham et 

al. (2005) is the potential for oscillations, with each branch 

periodically exhibiting dominance. These oscillations 

were linked with localized regions of vertical vorticity of 

alternating signs being shed from either side of the plume in 

a manner similar to wake vortices observed for fluid flowing 

around a cylinder. While these results were limited to a 

narrow range of flow parameters, these simulations indicate 

that the counter-rotating vortex pair are not necessarily 

stable. Wind tunnel studies using a heated wire to mimic the 

flank of a crown fire have shown that perturbations in the 

flow component perpendicular to the mean flow can cause 

the vortex pair to collapse (Haines and Smith 1992). These 

flow perturbations could be caused by upstream topographic 

features, possibly groups of trees, or even natural shifts in 

the ambient wind.

In the previous discussion, the wind profile reflected 

typical conditions where windspeed increased with height. 

Byram (1954) noted that a number of major fire runs 

occurred when the windspeed decreased with height near 

the surface, a conditions known as an adverse wind profile. 

Clark et al. (1996) examined the potential impact of an 

adverse wind profile on fire spread through the use of a 

three-dimensional coupled fire-atmosphere model. In their 

simulations, a counter-rotating vortex formed through the 

reorientation of the ambient boundary layer vorticity as 

described above; however, this time the rotation was in the 

opposite direction (see fig. 2 of Clark et al. 1996), which 

leads to narrow regions of hot, high-speed air shooting out 

of the fire front. This dynamic fingering occurred at scales 

of the order of tens of meters and has the potential to augment fire spread.

Tree Crown Streets

Some fires exhibit complex patterns of alternating strips of 

burned and unburned fuel, often referred to as tree crown 

streets. One possible explanation for the development of 

tree crown streets involves horizontal roll vortices (Haines 

1982). It is hypothesized that on one side of the vortex, 

descending air cools the fuels and causes surface winds to 

diverge, thus inhibiting crown fire spread. On the other side 

of the vortex, upward motion is enhancing the convective 

column owing to the associated surface wind convergence, 

which can, in turn, enhance a spreading crown fire. Tree 

crown streets are cited as evidence for the presence of 

horizontal roll vortices on the Mack Lake Fire (Simard et al. 

1983). Wade and Ward (1973) observed complex patterns of 

intermittent strips of unburned fuel in the Air Force Bomb 

Range Fire and suggested some potential hypotheses for 

these patterns including brief fluctuations of windspeed 

or direction, or pulsations of long-range spotting linked 

to an erratic convective column. While often considered a 

fingerprint for the presence of horizontal roll vortices, the 

exact cause of tree crown streets is not known.

Summary

Vorticity describes the degree of rotation in the atmosphere 

about some axis. Two factors that induce rotation in the 

atmosphere are wind shear and sharp horizontal gradients 

in temperature. Once one of these factors has generated 

vorticity, that vorticity can be transported by the mean wind 

to other locations, reoriented from one axis to another (a 

horizontal vortex can be tilted to become a vertical vortex), 

or enhanced by flow convergence, which stretches the vortex. The atmosphere is rarely completely devoid of vorticity. 

If the wind is blowing at all, vorticity is produced near the 

ground by surface drag. Terrain features provide flow obstacles whose drag produces wind shear and thus generates 

vorticity. Different ground surfaces heat at different rates, 

which also generates vorticity. Vortices are present across a 

broad spectrum of spatial scales, continuously transferring 

energy between scales, mostly from large scales to smaller 

scales. A fire not only interacts with and modifies this ambient vorticity but also generates additional vorticity. 
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Table 8-1—Summary of vortices, their causes and potential threats 

Phenomenon Causal factor(s) Potential danger

Fire whirl formation on the lee side of 

a plume

Shear-generated vorticity near the 

ground is concentrated and reoriented 

to the vertical on the lee side of the 

plume.

Increased energy release rate, spread rate, 

and spotting. The whirl could travel 

downwind from the fire and overtake 

firefighters.

Fire whirl formation near an L-shaped 

fire in a crossflow wind

Shear-generated vorticity near the 

ground is concentrated and reoriented 

to the vertical on the lee side of the L, 

as shown in figure 8-4.

Increased energy release rate, spread rate, 

and spotting. The whirl could suddenly 

form in the “interior” area of L.

Fire whirl formation near a cold front Vorticity along the frontal boundary is 

concentrated into a fire whirl.

Increased energy release rate, spread rate, 

and spotting.

Fire whirl formation due to multiple 

interacting fire plumes

The indrafting and blocking effects 

of multiple interacting fire plumes 

concentrate vorticity that was likely 

shear-generated near the ground.

Increased energy release rate, spread rate, 

and spotting. Whirl could build into a 

fire storm.

Fire whirl formation on the lee side of 

a hill/mountain

Vorticity associated with the wake 

region of a terrain obstruction such 

as a hill or mountain is concentrated 

into a fire whirl.

Increased energy release rate, spread rate, 

and spotting. The fire could quickly 

switch from a sheltered, backing fire 

with low fire behavior to more extreme 

fire behavior. The whirl could travel 

downwind from the fire and overtake 

firefighters.

Transverse vortex on the upwind side 

of a smoke column

Horizontal vorticity is produced 

through buoyancy.

Not a source of erratic fire behavior, 

but rather an indicator of a potential 

increase in the rate of combustion and 

an associated change in fire behavior.

Single longitudinal vortex Unstable atmosphere and strong winds 

generate horizontal vortices with axis 

parallel to the wind direction. Vortex 

formation is not tied to the fire.

Slight variations in wind direction can 

destabilize the vortex, causing the 

vortex to fall outward across the flank 

of the fire, providing a mechanism for 

lateral bursts in fire spread.

Counter-rotating longitudinal vortex 

pair

Transverse ambient vorticity from 

surface wind shear is altered by the 

fire as it is tilted into the vertical 

and reoriented to the longitudinal 

direction. Evident as a bifurcated 

smoke plume.

Can produce concentrated wind bursts at 

the head of the fire that lead to strong 

fingering of the fire front. The vortices 

are not always stable as variations in 

wind direction can cause one of the 

vortices to collapse and bring hot gases 

and fire brands into contact with the 

unburned fuel.
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For convenience, we split our discussion of wildland 

fire vortices into vertical and horizontal vortices. Vertical 

vortices, often referred to as fire whirls, are the most dramatic and frequently described type of vortex. Fire whirls, 

especially the larger ones, represent a considerable safety 

hazard to firefighters as these vortices can result in sudden 

increases in fire intensity, spotting, erratic spread rate and 

direction, and damaging winds. Most often, the source 

of vorticity for a fire whirl is not the fire itself; rather, the 

vorticity is present in the ambient atmosphere. This ambient 

vorticity may be generated by wind shear, vortex shedding 

in the wake of a plume or topographic obstruction, or an 

approaching cold front. The fire plays a much more important role in modifying the ambient vorticity field by tilting 

horizontal vortices toward the vertical, and increasing the 

vorticity magnitude through the stretching term as surface 

flow converges at the fire to feed the strong updraft. 

Similarly, two of the three horizontal vortex types 

described by Haines and Smith (1987) rely upon ambient 

vorticity. The counter-rotating vortex pair builds upon the 

tilting and stretching vortex modifications that enable a 

fire to transform horizontal vorticity generated by wind 

shear into a vertically oriented fire whirl. The key addition is stronger winds above the surface that sweep the 

upper part of the hairpin vortex described in figure 8-3 

downwind, bending the vortices back toward a horizontal 

orientation. For the single longitudinal vortex described for 

the Dudley Lake Fire, the fire is interacting with vorticity 

on a much larger scale, a boundary layer role whose depth 

can occupy the entire mixed layer. Again the fire’s role is 

one of modifying the vortex, which can in turn modify the 

fire environment by changing windflow patterns near the 

fire and creating a positive feedback loop leading to fire 

intensification.

Vortices are common features of the atmosphere 

occurring across a broad range of spatial scales. Our 

understanding of how wildland fires interact with this 

broad spectrum of atmospheric vortices is still very much 

in development. Table 8-1 summarizes the various vortices 

described in the text along with their causes and potential 

threats. While the occurrence of these vortices is currently impossible to predict with precision, having a basic 

understanding of the importance of ambient atmospheric 

vorticity for vortex development provides some guidance 

on situations that require awareness. Examine surrounding 

topography relative to the expected wind direction, noting 

features that may block or channel the flow. Wind profiles 

when available can provide information on wind shear as 

can watching direction/speed of cloud movements and their 

organization (are the clouds forming in lines?). Observe the 

behavior of the fire and smoke plume. Vortices are almost 

always present along the flaming front at some scale. Watch 

for vortices that grow or persist. Watch the smoke plume for 

signs of rotation or splitting. While this information is not 

sufficient for predicting the occurrence of intense vortices 

on wildland fires, it can help identify potentially hazardous 

conditions.

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Chapter 9: Crown Fire Dynamics in Conifer Forests

Martin E. Alexander1 and Miguel G. Cruz2

As for big fires in the early history of the Forest 

Service, a young ranger made himself famous by 

answering the big question on an exam, “What 

would you do to control a crown fire?” with the 

one-liner, “Get out of the way and pray like hell for 

rain.” – Norman Maclean (1992)

Introduction

Wildland fire behavior is broadly defined as the manner in 

which fuel ignites, flame develops, fire spreads and exhibits 

other related characteristics as determined by the interactions of fire with its environment (Merrill and Alexander 

1987). Not surprisingly, wildland fires have been referred 

to as highly volatile, multidimensional phenomena, not 

easily observed, monitored, documented, or necessarily 

explained. Many fire management decisions and actions 

require fire operations personnel to estimate, as quickly and 

accurately as possible, how a fire will behave under defined 

burning conditions. A prognosis of probable fire behavior 

provides for the safe and effective management or control 

of free-burning fires, whether of chance or planned origin 

(Countryman 1972). 

Barrows (1951) outlined the basic concepts of predicting or forecasting fire behavior over 65 years ago. The 

general problem or difficulty with predicting fire behavior 

simply boils down to the fact that there are numerous, 

interacting variables involved. Even predicting the most 

basic fire behavior characteristic—the type of fire (fig. 9-1)–

constitutes an immense challenge. As Rothermel (1991a) 

pointed out, for example, “The onset of crowning is 

exceedingly complex; wind, slope, humidity, fuel moisture, 

atmospheric stability, inversions, surface fire intensity, ladder fuels, time of year, amount of exposed fireline, and 

1 Martin E. Alexander, Department of Renewable Resources and 

Alberta School of Forest Science and Management, University of 

Alberta, Edmonton, Alberta, Canada.

2 Miguel G. Cruz, CSIRO Land and Water Flagship Ecosystem Sciences, 

Canberra, Australian Capital Territory, Australia.

frontal passage can all play a role.” Figure 9-2, while a gross 

oversimplification of the process, illustrates the information 

flow involved in predicting free-burning fire behavior, and 

the kinds of application of such knowledge, especially as it 

relates to crown fires.

In conifer-dominated forest fuel complexes (see

“Common and Scientific Names” section), three broad 

types of fire are commonly recognized on the basis of the 

fuel layer(s) controlling their propagation:

• Ground or subsurface fire,

• Surface fire, and

• Crown fire

Ground or subsurface fires spread very slowly (about 

3 cm/h), with no visible flame and sometimes with only 

the occasional wisp of smoke (Albini 1984, Frandsen 1991, 

Wein 1981). Heading surface fires can spread with the wind 

or upslope, and backing surface fires burn into the wind (fig. 

9-1A) or downslope. A crown fire is dependent on a surface 

fire for both its initial emergence and continued existence. 

Thus, a crown fire advances through both the surface and 

tree canopy fuel layers with the surface and crown fire 

phases more or less linked (figs. 9-1B and 9-1C). The term 

“crowning,” therefore, refers to both the ascension of fire 

into the crowns of trees and the spread of fire from crown to 

crown. As Van Wagner (1983) noted, “all large fires contain 

areas of low as well as high intensity, usually in a complex 

mosaic depending on vegetation type, topography, wind 

variations and time of day the fire passed a particular spot” 

(fig. 9-3).

From the perspective of controlling or managing 

wildfires or unplanned ignitions, the development and 

subsequent movement of a crown fire represents a highly 

significant event as a result of the sudden escalation in the 

rate of advance and the dramatic increase in flame size and 

thermal radiation as well as convective activity, including 

fire-induced vortices, and, in turn, both short- to long-range 
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Figure 9-1—Variations in fire behavior within the jack pine/black spruce 

fuel complex found at the International Crown Fire Modeling Experiment study area near Fort Providence, Northwest Territories, Canada: 

(A) surface fire, (B) passive crown fire, and (C) active crown fire. Photos 

by M.G. Cruz. For additional photography carried out on an experimental basis, see Alexander and De Groot (1988), Alexander and Lanoville 

(1989), Stocks and Hartley (1995), and Hirsch et al. (2000).
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Figure 9-2—Flow chart illustrating the linkages involved in the prediction of crown fire behavior and the application of 

such knowledge.
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Figure 9-3—Postfire mosaic associated with a portion of a highintensity wildfire that occurred near the community of Fort Ware 

in north-central British Columbia in late May 1995, illustrating an 

alternating pattern of surface and crown fire activity. The primary 

fuel types in the area comprise mixtures of white spruce, Engelmann spruce, and subalpine fir, changing to the latter at higher 

elevations, and lodgepole pine occurring at the lower elevations.

British Columbia Forest Service

Box 1: 

Account of the 1935 Big Scrub Fire, Ocala 

National Forest, North-Central Florida (From 

Kendrick and Walsh 2007: 422)

The following account of this recordbreaking 

forest fire is drawn from national forests in Florida

Historic File #5100, prepared by John W. Cooper, 

Ocala National Forest Ranger (1938–43).

“In the spring of 1935, the fastest spreading 

forest fire in the history of the U.S. Forest

Service, anywhere in the United States, 

burned 35,000 acres in four hours on the 

Ocala National Forest. The fire started from

Box 1: continued

a single set, a burning stump on muck land 

in the southwest corner of the forest, just 

north of Stark’s Ferry Bridge. Blowing and 

jumping ahead of a 60–mph wind, this fire

ran eighteen miles in a long narrow strip to 

the northeast, where it hit Lake George in 

just three hours. The wind switched suddenly to the northwest, and the fire then

advanced on an eighteen-mile front for 

one more hour, before a downpour of rain 

stopped it.

The Big Scrub Fire was definitely a “crown

fire”; it burned from one treetop to the next.

Burning embers blew more than a mile 

ahead of the main blaze setting spot fires

as they landed. At that time, there were 

300-foot wide firebreaks every three miles

across the Big Scrub. The firebreaks did not

even slow the fire up. It jumped six of them

in three hours. 

One of the firebreaks jumped was on the

Ocala-Daytona Beach Highway (SR 40) 

near Juniper Springs Recreation Area. Here, 

Forest Guard N.B. Owen and a crew of CCC 

enrollees attempted to make a stand by 

setting a backfire. When they realized their

efforts were fruitless and that they would 

be run over by the fire, they began running

to one side. In the black, smoke-caused 

darkness, fortunately they ran off the road 

and into a small pond. Here they lay on their 

backs with their noses above water, until the 

fire burned over them, only blistered noses

to account for their experience.

The average speed of the Big Scrub Fire 

was 6 mph; 8,750 acres burned per hour,

145.8 acres burned per minute, 2.4 acres 

burned per second. Needless to say, the 

300-foot firebreaks were abandoned, and

other methods of control were sought. The 

best fire control in the Big Scrub is and

always has been to never let a wildfire get in

the sand pine.”

spotting potential (Byram 1954, 1955, 1959b). Consequently, crown fires are dangerous and difficult for firefighters to control directly by conventional means (Alexander 

2000a). The major run associated with the Big Scrub Fire 

that occurred on the Ocala National Forest in north-central 

Florida on March 12, 1935, constitutes a good example of 

the perilous nature of crowning forest fires (box 1).
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Table 9-1—Percentage of fires by general character or type of fire behavior in Alaskan 

spruce fuel types at the time of initial attack in relation to final fire size over a 9-year 

period from 1950 through 1958

Fire size classa Smouldering Creeping Running Spotting Crowning

Percent

A 70 31 12 25 7

B 19 39 41 25 17

C 6 18 22 19 19

D 2 5 5 12 10

E 3 7 20 19 47

a Area burned: class A = less than 0.1 ha; class B = 0.1 to 4.0 ha; class C = 4.0 to 40.0 ha; class D = 40.0 to 121 ha; and 

class E = greater than 121 ha.

Source: Adapted from Hardy and Franks 1963.

There is, however, a window of opportunity to suppress 

crown fires during the initial stages of their development 

in some fuel types (Crosby et al. 1963, Hesterberg 1959, 

Johansen and Cooper 1965). Hardy and Franks (1963), for 

example, found from an analysis of individual fire report 

data in Alaska that 70 percent of class A size fires occurring in black spruce fuel types were of a “smoldering”

nature when first attacked but that 47 percent of class E

size fires were crowning when they were first initial attacked (table 9-1). 

Suppression actions and options at the head of highintensity crown fires tend to be severely restricted until 

there is a major change in the prevailing fuel, weather, or 

topographic conditions (e.g., a drop in windspeed, a major 

change in fuel type, a significant fuel discontinuity). Consequently, crown fires are capable of burning large tracts 

of forested landscape, thereby placing firefighters at risk, 

posing a threat to public safety and properties, potentially 

adversely impacting other values at risk, and increasing 

suppression expenditures (Alexander and Cruz 2011a, Alexander et al. 2012b). Suppression activities themselves can in 

fact sometimes contribute to the onset of crowning (box 2). 

Prolific crowning is an element or characteristic of 

extreme fire behavior in conifer-dominated forest cover 

types. This chapter constitutes a state-of-knowledge 

summary prepared for technical specialists in wildland fire 

behavior (e.g., a fire behavior analyst assigned to an incident 

management team or a fire behavior service center) in the 

United States concerning our current understanding of the 

characteristics and prediction of crown fire behavior in such 

fuel complexes—i.e., the so-called “timber crown fire” 

(Albini 1984). Other “fire behaviourists” (e.g., researchers, 

college and university professors) as well as fire weather 

meteorologists and students of wildland fire will find it of 

value. Information on crown fire phenomenology is drawn 

upon from a number of sources, including relevant observations and data from Canada and Australia. 

Crown fires can occur in fuel types other than conifer 

forests, for example, melaleuca stands in Florida (Wade 

1981) or gambel oak in Colorado when the leaf foliage has 

been frost killed or heat desiccated (Butler et al. 1998). The 

dynamics of crown fires in tall brushfields (e.g., chaparral) 

and other forest types such as eucalypt will not specifically 

be dealt with here per se, although some of the general 

principles may be valid. Crown fire dynamics in these fuel 

types are dealt with extensively by others (Cheney et al. 

2012; Cruz et al. 2012a, 2012b; Gould et al. 2007; Plucinski 

2003; Sullivan et al. 2012). In a number of cases, however, 

fire behavior in conifer-dominated forests versus other fuel 

complexes is compared and contrasted.

For present purposes, it is assumed that there is generally a distinct separation between the canopy fuel layer 

and the ground and surface fuels by an open trunk space 

in which ladder or bridge fuels may be present (fig. 9-4). 

Certain aspects of crown fire behavior are not addressed 

here but can be found in other chapters of this synthesis. 
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Box 2:

Aircraft Effects on Crown Fire Behavior

Based on his analysis of 67 fatal fires involving

222 wildland firefighter deaths in the United States

over a 61-year period (1926–1976), Wilson (1977) 

identified some common features connecting

these incidents. He found that there were common 

denominators of fire behavior associated with these

fatal fires:

1. Most of the incidents occurred on relatively 

small fires or isolated sectors of larger fires.

2.	 Most of the fires were innocent in appearance prior to the “flareups” or “blowups.” In

some cases, the fatalities occurred in the 

mopup stage.

3. Flareups occurred in deceptively light fuels.

4. Fires ran uphill in chimneys, gullies, or on 

steep slopes.

Wilson (1977) also suggested that aircraft 

working over wildland fires could have perilous

consequences: 

“Suppression tools, such as helicopters 

or air tankers, can adversely modify fire

behavior. (Helicopter and air tanker vortices 

have been known to cause flare-ups.)”

The effects of vortex turbulence on fire behavior-associated aircraft have been discussed at some 

length (Chandler et al. 1983b, Davis and Chandler 

1965, Haines 1989). Wilson (1977) pointed out that 

the four firefighter fatalities associated with the 1962

Timberlodge Fire on the Sierra National Forest in 

central California were likely related to aircraft activity over the fire:

“Loaded B-17 air tankers flew over fire.

Tornado-like action from air tanker vortices 

probably caused fire to blow-up and trapped

men.”

Countryman et al. (1969) described the circumstances that led to the deaths of a crew of eight 

firefighters resulting from a sudden upslope run on

the 1968 Canyon Fire that occurred near boundary of the Angeles National Forest in Los Angeles 

County in southern California:

“At about 1124 a patch of sumac and scrub 

oak ... crowned out suddenly. A fire whirl

quickly formed over the hot burning bush 

patch ... It appears likely that a firebrand

from the whirl moved down-slope and 

established fire well down into the ravine

and below the crew ... This fire crowned

immediately and ran up the ravine and over 

the crew ...”

It was surmised that the fire whirl that triggered

the flareup was caused by a sudden and localized

increase in airflow resulting from the sea-breeze

front reaching the fire area. Countryman et al.

(1969) suggested that “It also may have been 

caused by the turbulence created by the low-level 

passage of an air tanker through the area just 

before the fire flareup.”

Haines (1989) recounted an incident that occurred on the 1988 Stockyard Fire on the Hiawatha 

National Forest in Michigan’s Upper Peninsula as a 

result of a DC-4 having flown along the fire’s right

flank:

“Here three tractor-plow operators built line 

within a jack pine plantation. The trees were 

3 to 6 inches (8-15 cm) in diameter and 25 

to 30 feet (7.5-9 m) high. Compared with 

other sectors, this was a quiet area ...Winds 

were light and then became calm. The low 

flames suddenly began to “climb” up a few

trees into the crowns. Within a minute or 

two the flames became a high wall. The wall

changed into a crown fire, moving directly

toward the tractor crew ... Luckily no one 

was killed, although one of the tractor operators was badly injured and spent weeks 

in a medical burn center.”

The rotor downwash effects associated with 

helicopters can adversely affect fire behavior as well

(Chandler et al. 1983b, Shields 1969, Slijepcevic 

and Fogarty 1998).
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Figure 9-4—Cross section of a stylized, wind-driven surface head 

fire spreading behind a conifer forest canopy on level terrain.

These include, for example, horizontal roll vortices, plumeor convection-dominated crown fires, influences of atmospheric conditions aloft, and fire-atmosphere interactions.

This chapter expands upon the earlier treatment of the 

subject by Alexander and Cruz (2011b). The list of references has accordingly been expanded up to early 2015.

Types of Crown Fires

The term “crown fire” has appeared in the forestry and 

ecological literature for the last 125 years (e.g., Bell 2012) 

and is simply regarded as “a fire which runs in the tops of 

or which burns all or a large part of the upper branches 

and foliage of trees” (U.S. Forest Service 1930). Gradually, 

crown fires have come to be described by a whole host of 

adjectives. Harper (1944), for example, made mention of 

“mild,” “light,” “medium,” “heavy,” “severe,” and “very 

severe” crown fires in describing the immediate postburn 

visual evidence following crowning although he offered no 

specific descriptions. 

Beginning in the late 1930s, two basic types or classes 

of crown fire came to be recognized, namely the “running 

crown fire” and the “dependent crown fire” (cf. Kylie et al. 

1937), to distinguish the degree of independence from the 

supporting surface fire. These terms are commonly attributed to Brown and Davis (1973) by most modern day authors. 

A running crown fire is one that generates enough heat for 

crown-to-crown spread, whereas a dependent crown fire 

depends upon the heat generated by the surface fire for its 

spread (Woods 1944). 

For the next 40 years or so, the terms running crown 

fire and dependent crown fire would appear in most but 

not all (e.g., Barrows 1951) books, manuals, and glossaries dealing with forest fire protection (e.g., Brown and 

Folweiler 1953, Hawley and Stickel 1948, Luke 1961, U.S. 

Forest Service 1956). These terms still appear occasionally 

in the widland fire behavior literature (e.g., Norum 1982, 

Rothermel 1991b). The terms “intermittent crown fire” 

and “intermittent crowning” apparently followed later on 

(Douglas 1957, 1964) as did the term “fully developed 

crown fire” (Luke and McArthur 1978). 

Van Wagner (1977a) proposed that crown fires in 

conifer forests could be classified according to their degree 

of dependence on the surface fire phase, and the criteria 

could be described by several semimathematical statements 

(fig. 9-5). He recognized three types of crown fires: passive, 

active, and independent (box 3).

Figure 9-5—Type of fire classification scheme based on Van Wagner’s (1977a) theories regarding the start and spread of crown fires 

in conifer forests.
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Box 3:

Crown Fire Classification

Van Wagner (1977a) recognized three classes or 

types of crown fires:

Passive Crown Fire 

Passive or dependent crown fires can involve a

portion or all of the canopy fuel layer in combustion, 

but the overall rate of spread is largely determined 

by the surface phase. Passive crown fires cover

a range in fire behavior from moderately vigorous

surface fires with frequent crown ignition occurring

behind the surface flame front up to high-intensity

surface fires spreading with an almost solid flame

front occupying the canopy and subcanopy or trunk 

space that have nearly achieved the critical minimum spread rate for active crowning. Passive crown 

fires can occur under two broad situations. First, the

canopy base height and canopy bulk density (CBD) 

are considered optimum, but fuel moisture and wind 

conditions are not quite severe enough to induce 

full-fledged crowning (fig. 9-1B). Second, the canopy

base height (CBH) and CDB are, respectively, above 

and below the thresholds generally considered necessary for crowning (e.g., tall or open-forest stand 

types), so that even under severe burning conditions 

(i.e., critically dry fuels and strong surface winds), 

active crown fire spread is not possible, although

vigorous, high-intensity fire behavior can occur (e.g.,

National Fire Protection Association 1990).

Active Crown Fire

Active or running crown fires are characterized by

the steady advancement of a tall and deep coherent 

flame front extending from the ground surface to

above the top of the canopy fuel layer (fig. 9-1C). The

surface and crown phases are intimately linked, but 

fire propagation is largely determined by the crown

phase. The spread of active crown fires requires (1)

relatively dry and plentiful surface fuels that allow 

for the development of a substantial surface fire, (2)

low to moderately high CBH, and (3) a fairly continuous crown layer of moderate to high CBD (>0.1 kg/

m3) and low to normal foliar moisture content (e.g., 

National Fire Protection Association 1991). 

Independent Crown Fire

An independent crown fire no longer depends in

any way on the surface phase, spreading ahead of 

the surface phase in the crown fuel layer entirely on 

its own. Stand conditions favoring an independent 

crown fire are a continuous crown layer of low

to moderate CBD and an abnormally low foliar 

moisture content. For a truly independent crown fire

to develop on flat topography would require very

strong, sustained winds. In mountainous terrain, 

slope steepness would no doubt compensate for a 

lesser wind velocity.

Of Note

The vast majority of crowning forest fires spread either as passive or active crown fires, each controlled

by a different set of processes. Van Wagner (1993) 

acknowledged that the concept of a truly independent crown fire as a stable phenomenon on level

terrain is dubious but that it “may still have value in 

rough or steep terrain and as a short-term fluctuation

under the most extreme conditions.” Indeed, there

are reports of the flames in the crown extending 50

to 150 m ahead of the surface burning in momentary 

bursts and of crown fires spreading through closedcanopied forests up steep, partially snow-covered 

slopes in the spring (Mottus and Pengelly 2004). 

These incidents might possibly give the appearance of being evidence for independent crown fires.

However, there is no steady-state propagation as 

seen with passive and active crown fires.

Noteworthy is that the concept of passive 

crowning implies an element of forward movement 

or propagation of the flame front. The incidental

ignition of an isolated tree or clump of trees, with the 

flames spreading vertically from the ground surface

through the crown(s) without any form of forward 

spread following, does not constitute passive 

crowning. Flame defoliation of conifer trees by what 

amounts to stationary torching or “crowning out,”

especially common during the postfrontal combustion stage following passage of the surface fire,

generally does not generate any kind of horizontal 

spread.

Scott and Reinhardt (2001) claimed that the 

possibility exists for a stand to support an active 

crown fire that would otherwise not initiate a crown

fire. They referred to this situation as a “conditional

surface fire.” Later on, Scott (2006) termed this

a “conditional crown fire.” To our knowledge, no

empirical proof has been produced to date to substantiate the possible existence of such a situation, 

at least as a steady-state phenomenon. 
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According to Van Wagner (1977a), the class or type of 

crown fire to be expected in a conifer forest on any given 

day depends on three simple properties of the canopy fuel 

layer (box 4) and two basic fire behavior characteristics:

• Initial surface fire intensity

• Foliar moisture content

• Canopy base height

• Canopy bulk density

• Rate of fire spread after the onset of crown

combustion.

The first three quantities determine whether a surface 

fire will ignite coniferous foliage. The last two determine 

whether or not a continuous flame front can be sustained 

in the canopy fuel layer. A dichotomous key to a forest fire 

classification scheme incorporating Van Wagner’s (1977a) 

three classes of crown fire and the corresponding theory has 

been prepared by Alexander (1988). 

The initial surface fire intensity and rate of fire spread 

after the onset of crown combustion would, in turn, include 

the effects of windspeed, slope steepness, fuel dryness, air 

temperature, relative humidity, and fuel complex characteristics. As Van Wagner (1977a) noted, ladder or bridge 

fuels (e.g., loose bark and dead bole branches on tree boles, 

lichens, shrubs, and small conifers) in the space between the 

ground surface and the canopy “must presumably be present 

in sufficient quantity to intensify the surface fire appreciably as well as to extend the flame height.” 

Albini and Stocks (1986) considered the factors 

included in Van Wagner’s (1977a) proposed criteria for the 

start and spread of a crown fire as “heuristically valid.” 

Subsequent experience and analysis has shown both the 

strengths and limitations of his approach (Alexander and 

Cruz 2006; Cruz et al. 2003c, 2004, 2005, 2006a).

Unfortunately, our ability to assess ladder or bridge fuel 

effects on crown fire initiation remains largely qualitative 

in spite of its obvious importance (Kilgore and Sando 1975, 

LaMois 1958, Lawson 1972, McArthur 1965, Muraro 1971, 

Sackett 1975). Fahnestock (1970), for example, developed a 

dichotomous key on the basis of “observations and deductions by the author” that identifies the nature of ladder fuel 

and general tree crown characteristics that are conducive to 

Box 4:

Canopy Fuel Characteristics in Van Wagner’s 

(1977a) Crown Fire Initiation and Propagation 

Models

Canopy Base Height

Canopy base height (CBH) represents the mean 

height from the ground surface to the lower live 

crown base of the conifer trees in a forest stand 

(fig. 9-4). The CBH is dependent on the mean

tree height and live-stem density (fig. 9-6).

Canopy Bulk Density

Canopy bulk density (CBD) represents the 

amount of available crown fuel within a unit 

volume of the canopy. The CBD is computed by 

dividing the canopy fuel load (CFL) by the canopy 

depth (fig. 9-4), which represents the average

tree height of the stand minus the CBH. The CFL 

represents the quantity of crown fuel typically 

consumed in a crown fire, principally needle foliage. Both the CBD and CFL are in turn functions 

of stand structure characteristics (figs. 9-7 and

9-8).

Foliar Moisture Content 

Foliar moisture content (FMC) represents a 

weighted average or composite moisture content 

for the various needle ages found within the 

canopy fuel layer. Needles decrease in moisture 

content with age following their initial flushing

(Keyes 2006). Chandler et al. (1983a) considered 

that “A general rule of thumb with regard to living 

foliage moisture is that crown fire potential in

conifers is high whenever needle moisture drops 

below 100 percent of dry weight.”

Of Note

Some authors such as Scott and Reinhardt 

(2001) have applied different criteria to the CBH, 

CFL and CBD inputs in their use of Van Wagner’s 

(1977a) models. However, strictly speaking 

such ad hoc adjustments or modifications 

are not compatible with the use of these 

models (Cruz and Alexander 2010a, 2012). Still 

others have in some cases recommended or applied potentially unrealistically low values of FMC 

(Cruz and Alexander 2010a). 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Box 4 continued

Affleck et al. (2012) recently undertook a

state-of-knowledge review on the subject of 

crown fuel modeling in relation to fire behavior

modeling systems. They highlighted the primary 

limitations of current crown fuel models and 

suggested ways of incorporating work carried out 

on tree crown architecture in the other fields of

forestry research.

Reinhardt et al. (2006) questioned the validity 

of the regression equations for estimating CBH 

in coniferous forest fuel types developed by Cruz 

et al. (2003a) to produce logical results when 

applied to simulations involving low thinning. This 

turns out to be an error in interpretation on their 

part with regard to the stand height input parameter (Cruz et al. 2010b).

Repeated FMC sampling of coniferous tree 

foliage at several locations across Canada (e.g., 

Chrosciewicz 1986b, Van Wagner 1967c) has 

revealed a common pattern or cycle during the 

fire season, namely a period of relatively low

values in the spring and early summer before the 

emergence of new needles. This phenomenon 

is commonly referred to as the “spring dip” (Van

Wagner 1974). This seasonal pattern in FMC 

observed in Canada appears to occur in adjacent 

areas in jack pine and red pine in the U.S. Lake 

States region (Dieterich 1963; Johnson 1966), for

example, and for several Western U.S. conifers 

as well black spruce in Alaska (Alexander 2010a) 

and in sand pine in Florida (Hough 1973). Building on an earlier compilation of the literature on 

FMC by Alexander (1988), Keyes (2006) has prepared an excellent summary for North America.

crowning (table 9-2). The key ranks crowning potential by 

increasing numbers from 0 to 10. The ratings are arbitrary 

values and indicate the likely order of sustained crown fire 

development and are not to be construed as a proportionality or probability of occurrence (Rothermel 1983). Similarly, 

Menning and Stephens (2007) devised a semiqualitative, 

semiquantitative method of assessing four categories of 

ladder fuel hazard based on clumping of low aerial fuels, 

canopy base height, and maximum gaps in vertical fuel ladders. However, like the Fahnestock (1970) key, the approach 

has no physical basis. 

Examples of how canopy fuel properties differ in relation to specific tree and stand characteristics are presented 

in figures 9-6, 9-7, and 9-8 for four common forest fuel 

types found in the Western United States and Canada; 

tabulations can be found in Alexander and Cruz (2014). 

Similar models have been produced for the eastern half of 

North America as well (e.g., Agca et al. 2011, Bilgili and 

Methven 1994, Duveneck 2005, McAlpine and Hobbs 1994, 

Parresol 2007). An indication of the variation in canopy fuel 

load and bulk density with height above ground and within 

a given forest fuel type is presented in figure 9-9. Similar 

canopy fuel profiles have been constructed for other forest 

fuel types (e.g., Kilgore and Sando 1975, Sando and Wick 

1972, Scott and Reinhardt 2005).

Crown Fire Initiation 

In regards to predicting crown fire behavior, Rothermel and 

Andrews (1987) determined that the most significant issue 

was determining whether a surface fire will develop into a 

crown fire (i.e., identifying the conditions for the onset of 

crowning). For a crown fire to start, a surface fire is necessary (Molchanov 1957). The questions then become:

• How do we define fire intensity in terms that would 

be useful in predicting the onset of crowning?

• And, just how intense is intense enough with respect 

to the convective and radiative energy transferred 

upward to the canopy fuels (Byram 1957) that would 

be necessary to initiate crowning? 

Quintilio et al. (1977) offers an excellent description 

of the crowning phenomenon based on an experimental 

burning project in natural jack pine stands in northeastern 

Alberta, Canada (box 5); see Alexander and De Groot 

(1988) for color photographs of the experimental fires. Van 

Wagner (1964) observed that “a deep burning front seems 

necessary to initiate and sustain crowning.” 

The distance the canopy fuel layer (fig. 9-4) is from 

the heat source at the ground surface will dictate how much 

energy is dissipated before reaching the fuels at the base of 

the canopy. The higher the canopy base, the less chance of 

crowning (Malchanov 1957). Furthermore, if the moisture 

content of the canopy fuels is high, greater amounts of energy 

are required to raise the tree foliage to ignition temperature.
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Table 9-2—Fahnestock’s crowning potential key

Ladder fuel and general tree crown characteristics Rating

A. Foliage present, trees living or dead - B

B. Foliage living - C

C. Leaves deciduous or, if evergreen, usually soft, pliant, and moist; never oily, waxy, or resinous 0a

CC. Leaves evergreen, not as preceding - D

D. Foliage resinous, waxy or oily - B

E. Crowns dense - F

F. Ladder fuels plentiful - G

G. Canopy closure 75 percent 9

GG. Canopy closure less than 75 percent 7

FF. Ladder fuels sparse or absent - H

H. Canopy closure 75 percent 7

HH. Canopy closure less than 75 percent 5

EE. Crowns open - I

I. Ladder fuels plentiful 4

II. Ladder fuels sparse or absent 2

DD. Foliage not resinous, waxy or oily - J

J. Crowns dense - K

K. Ladder fuels plentiful - L

L. Canopy closure 75 percent 7

LL. Canopy closure less than 75 percent 7

KK. Ladder fuels sparse or absent - M

M. Canopy closure 75 percent 5

MM. Canopy closure less than 75 percent 3

JJ. Crowns open - N

N. Ladder fuels plentiful 3

NN. Ladder fuels sparse or absent 1

BB. Foliage dead - O

O. Crowns dense - P

P. Ladder fuels plentiful - Q

Q. Canopy closure 75 percent 10

QQ. Canopy closure less than 75 percent 9

PP. Ladder fuels sparse or absent - R

R. Canopy closure 75 percent 8

RR. Canopy closure less than 75 percent 4

OO. Crowns open - S

S. Ladder fuels plentiful 6

SS.  Ladder fuels sparse or absent 2

AA. Foliage absent, trees dead - T

T. Average distance between trees 10 m or less - U

U. Ladder fuels plentiful - V

V. Trees with shaggy bark and/or abundant tinder 10

VV. Trees without shaggy bark and/or abundant tinder 8

UU. Ladder fuels sparse or absent - W

W. Trees with shaggy bark and/or abundant tinder 10

WW.Trees without shaggy bark and/or abundant tinder 5

TT. Average distance between trees 10 m 2

a

 Rare instances of crowning have been reported, resulting from extreme drought conditions.

Source: Adapted from Fahnestock 1970.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-6—Canopy base height for four Western U.S. conifer forest fuel types as a function of average stand height and 

basal area according to Cruz et al. (2003a). The regression equations used to produce these graphs are not valid for tree 

heights of less than 1.0 m in the case of ponderosa pine, 2.0 m in the case of Douglas-fir, and 3.0 m in the case of lodgepole pine and mixed conifer (Alexander and Cruz 2010). 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 9-7—Canopy bulk density for four Western U.S. conifer forest fuel types as a function of stand density and basal area according 

to Cruz et al. (2003a). The regression equations used to produce these graphs do have upper limits in terms of both stand density and 

basal area (see Alexander and Cruz 2010).
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-8—Canopy fuel load for four Western U.S. conifer forest fuel types as a function of stand density and basal area according to 

Cruz et al. (2003a). The regression equations used to produce these graphs do have upper limits in terms of both stand density and basal 

area (see Alexander and Cruz 2010).
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 9-9—Vertical fuel profiles (with standard error bars) of the individual crown fuel components found in the 

jack pine – black spruce fuel complex associated with the International Crown Fire Modelling Experiment carried 

out in the Northwest Territories of Canada for (a) individual plots and (b) for the study area as a whole (adapted 

from Alexander et al. 2004). To view the variation with stand age in this fuel type, see Lavoie et al. (2010).
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GENERAL TECHNICAL REPORT PNW-GTR-891

Box 5:

Summary of Surface to Crown Fire Interaction 

Observed in Natural Jack Pine Stands During 

the Darwin Lake Experimental Burning 

Project in Northeastern Alberta, Canada (after 

Quintilio et al. 1977)

“On-site observations and examination of a series 

of 35-mm slides provided several interesting fire

behavior descriptions of surface to crown interaction. Crown involvement ranged from a silent 

flash in the beard lichens to a solid flame front

of greater intensity than the surface fire. Flame

height on Units 1 and 3 averaged less than 0.5 m 

yet the lichens carried flame into the tree crown

for very brief periods. Bark flakes on Units 4a

and 4b were burning the length of the trees and 

out into the branches, but even an intense core 

of fire surrounding the tree trunk for its full length

was not enough to torch out the average tree. Full 

crowning developed only when the surface fire

was intense and continuous enough to preheat the 

lower needle foliage and branchwood over a large 

area, a condition which occurred only on Unit 6. 

Flame heights of the initial surface fire, which occurred on Unit 6, were well into the canopy layer, 

resulting in simultaneous ignition of bark flakes

and needles. Although the fire front leaned slightly

downwind, the crown fire did not move independently of the surface fire.

Stand density and height of aerial fuels 

seemed to affect crown involvement significantly

during fires conducted under ‘moderate’ and ‘high’

fire danger conditions. Units 4a and 4b were of

mixed density, and height and fire behavior differences were noted at the fuel type boundaries. 

The west side of the plots was denser, and ladder 

fuels extended to within a meter of the ground. 

Fire spread was slower and more uniform in this 

area, with very little torching, presumably a result 

of higher moisture contents from shade effect. On 

the more open east side of the plots, spread was 

faster and the surface fuels more intense, which 

promoted torching even though ladder fuels were 

higher and less concentrated horizontally. At the 

“extreme” level of fire danger, on Unit 6, crowning

occurred throughout the plot regardless of density 

and crown height variations.”

To view the photographs associated with the 

Darwin Lake experimental fires, see Alexander

and De Groot (1988).

Byram (1959a) defined fireline intensity (I, kW/m) as 

the rate of heat released from a linear segment of the fire 

perimeter as calculated by the following equation:

I = H × w × r (1)

where, in compatible International System (SI) units, H is 

regarded as the net low heat of combustion (kJ/kg), w is the 

amount of fuel consumed in the active flaming front (kg/

m2), and r is the rate of fire spread (m/s) (Alexander 1982).3

Flame size is its main visual manifestation (Alexander and 

Cruz 2012a, 2012b). For photographic examples illustrating 

a wide range in I for a surface fire and crown fires in a given 

fuel type, see Alexander and De Groot (1988), Alexander 

and Lanoville (1989), Hirsch et al. (2000), and Stocks and 

Hartley (1995).

Byram (1959a) established an empirical relationship 

between IB and flame length for surface fires that is widely 

applied in wildland fire science and management (from 

Alexander 1982):

L = 0.0775 × I 0.46 (2)

where L is flame length (m) as illustrated in fig. 9-4.4 For 

photographic examples of surface flame lengths, see Brose 

(2009) and Wade et al. (1993).

If we assume H = 18 000 kJ/kg, then equation (1) can in 

turn be expressed as follows (Forestry Canada Fire Danger 

Group 1992):

I = 300 × w × ROS (3)

where ROS is the rate of fire spread given in m/min. A 

graphical representation of this relation showing the hyperbolic or inverse function between w and ROS is presented in 

fig. 9-10 in the form of a fire behavior characteristics chart 

3

 Note that several authors have failed to present the correct SI 

units for the input variables associated with equation (1) (e.g., 

Fryer and Johnson 1988, Johnson and Gutsell 1993, Johnson and 

Miyanishi 1995), the SI unit version of Byram’s (1959a) original 

L – I relation represented by equation (2) (e.g., van Wagtendonk 

2006), and the inverse of equation (2) (e.g., Chandler et al. 1983a, 

DeBano et al. 1998), namely I = 259.833 × L2.174.

4

 Note that the 0.46 exponent in Byram’s (1959a) flame length – fireline intensity equation presented in footnote a of table 8-1 of Alexander and Cruz 

(2011b) was improperly presented.
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Figure 9-10—Head fire rate of spread and fuel consumed in relation to the type of fire and six distinct levels of Byram’s (1959a) 

fireline intensity, assuming a net heat of combustion of 18 000 

kJ/kg, for the experimental surface and crown fires used in the 

development and testing of the onset of crowning and spread 

rate functions in the Crown Fire Initiation and Spread modeling 

system (Cruz et al. 2003b, 2004, 2005).

(Alexander et al. 1991) along with plotted experimental 

fire data. This chart is patterned after the original work 

of Rothermel and Anderson (1966) and later extended by 

Andrews and Rothermel (1982), Rothermel (1991a), and 

Andrews et al. (2011).

Wendel et al. (1962) concluded that the probability 

of blowup fires decreased rapidly when available fuel 

loads were less than 1.35 kg/m2, a value that is commonly 

exceeded in certain forest cover types such as Southwestern 

U.S. ponderosa pine and mixed-conifer stands for example 

(Sackett 1979). Violent physical behavior approaches certainty as fireline intensities begin to exceed 10,000 kW/m, 

in which case, suppression actions at the head and along the 

upper flanks must cease until burning conditions ameliorate 

(Sibley 1971).

Several empirical and semiphysical models have been 

developed over the past 20 years or so for predicting the 

initiation or onset of crowning (e.g., Alexander 1998; Cruz 

1999, 2004), including one (Xanthopoulos 1990, Xanthopoulos and Wakimoto 1991) intended for use with the BEHAVE System (Andrews 1986, Andrews and Chase 1989). 

However, the simplest explanation of the general processes 

involved is offered by Van Wagner (1977a). Using physical 

reasoning and empirical observation, Van Wagner (1977a) 

proposed that vertical fire spread could occur in a conifer 

forest stand when the surface fire intensity (SFI) attains or 

exceeds a certain critical surface intensity for combustion 

(SFIcritical, kW/m) as dictated by the foliar moisture content 

(FMC, %) and the canopy base height (CBH, m) according 

to the following equation (box 6), which is graphically 

presented in fig. 9-11:

SFIcritical = (0.01 × CBH × h)

1.5 (4)

where h is the heat of ignition (kJ/kg) and is turn determined by the following equation (Van Wagner 1989, 1993):

h = 460 + 25.9 × FMC (5)

Thus, according to Van Wagner’s (1977a) theory of 

crown fire initiation, if SFI > SFIcritical, some form of 

crowning is presumed to be possible, but if SFI < SFIcritical, 

a surface fire is expected to prevail (fig. 9-5). In applying 

the criterion represented by equation (4), it is assumed that 

(1) that a conifer forest stand possesses a minimum canopy 

bulk density that will allow flames to propagate vertically 

through the canopy fuel layer and (2) bridge or ladder fuels 

such as bark flakes on tree boles (Taylor et al. 2004), tree 

lichens (Agee et al. 2002), shrubs and understory trees 

(Reifsnyder 1961, Stocks 1989), dead bole branches

(Lawson 1972), and suspended needles (Burrows et al.

1988, McArthur 1965) exist in sufficient quantity to intensify the surface fire and extend the flame height

(Van Wagner 1977a).
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-11—Critical surface fire intensity for crown combustion 

in a conifer forest stand as a function of canopy base height and 

foliar moisture content according to Van Wagner (1977a).

One of the appealing aspects of equation (4) is its 

simplicity, but with this comes a major underlying assumption. According to Van Wagner (1977a), the 0.01 value given 

in equation (4) is “best regarded as an empirical constant of

complex dimensions whose value is to be found from field

observations.” He derived this value from an outdoor experimental fire in a red pine plantation stand with CBH of 

6.0 m and a FMC of 100 percent5 and the SFI was about 

2,500 kW/m just prior to the onset of crowning (box 6) (Van 

Wagner 1968). This widely used relation represented by 

equation (4) therefore incorporates a fixed set of burning 

conditions, fuel characteristics, and surface fire behavior 

(e.g., in-stand windspeed, ladder fuels, fuel consumed, 

flame depth, and spread rate). Subsequent research has 

shown this empirical constant to be a variable quantity 

dependent on these factors (Alexander 1998, Cruz et al. 

5

 This is incidentally the general default value as suggested for 

FMC by Scott and Reinhardt (2001) and Agee et al. (2002). Keyes 

(2006) on the other hand recommended 90 or 100 percent as a 

“prudently conservative” default value.

Box 6:

The Origins of Van Wagner’s (1977a) 

Equation for Determining the Initiation of 

Crowning

Van Wagner (1999) readily acknowledged that 

the previous works of Thomas (1963, 1967) 

and Thomas et al. (1964) were critical to the 

development of his equations for judging the 

requirements needed for crown fire initiation and

continuous crown fire spread. Van Wagner’s

(1977a) theory for the initiation of crowning is 

founded on the combining of two basic equations. 

The first equation concerns the temperature rise

above ambient air conditions (ΔT) reached at 

height z above a line fire of intensity I burning on 

the surface (after Thomas 1963):

ΔT ∝ I 2/3 z

The second equation involves the heat energy required to raise the crown foliage to ignition 

temperature (after Van Wagner 1968):

h = 460 + 25.9 × m

where m is the moisture content of the canopy 

foliage (percentage oven-dry weight basis) and h

is the heat of ignition (kJ/kg).

Assuming that the ΔT required for crown ignition at an arbitrary value of h, which we will call 

ho, and that the actual ΔT at z or the canopy base 

height varies with the ratio of h/ho. The left-hand 

side of Thomas’ (1963) relation given above then 

becomes ΔT . h/ho. We thus have:

ΔT (h/ho) ∝ I 2/3/z

Replacing ΔT/ho with an empirical quantity C, 

termed the criterion for initial crown combustion, 

ultimately yields the following equation:

Io = (C × z × h)

3/2

where I

o is now the critical surface fire intensity

needed to initiate crowning. The value of 0.010 

derived for C by Van Wagner (1977a) in turn relied upon a rearrangement of the above equation:

C = Io

2/3 /(z × h)

This equation was inadvertently presented in 

error in Alexander (1998: 32) and Alexander 

(2006: 190).
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 9-12—Critical surface fire flame length for crown combustion in a conifer forest stand as a function of canopy base height according to the flame length-fireline intensity models of (A) Byram (1959a) and (B) Thomas (1963) based on Van Wagner’s (1977a) crown fire 

initiation model for various foliar moisture contents. The dashed line represents the boundary of exact agreement between flame length 

or height and canopy base height.

2006a). Catchpole (1987), for example, derived an empirical 

constant of 0.0513 in lieu of 0.01 for a heathland fuel complex in New South Wales, Australia, in a manner similar to 

Van Wagner (1977a).

Figure 9-11 clearly shows that the higher the CBH or 

FMC, the more intense a surface fire must be to cause crowning. Note that the flames of a surface fire do not necessarily 

have to reach or extend into the lower tree crowns to initiate 

crowning (Alexander 1988, Alexander and Cruz 2012a) (fig. 

9-12). The experimental fire used by Van Wagner (1977a) to 

parameterize his crown fire initiation model represented by 

equation (4) would, for example, have had just prior to the 

onset of crowning, a flame length of around 2.8 m according 

to Byram’s (1959a) formula linking flame length to fireline 

intensity represented by equation (2) (Alexander 1988). 

Rossotti (1993) indicated that “leaves from the forest canopy 

may ignite if they are no more distance than one and a half 

times the flame height,” although she offered no basis for this 

pronouncement. 

Equation (5) defines the heat energy required to drive 

off the moisture content in the crown foliage and raise it 

to its ignition temperature. Van Wagner (1977a) assumed 

that all of the moisture in the fuel must be driven off before 

ignition can occur. Pickett et al. (2010) has recently shown 

in the laboratory with green leaf samples of various shrub 

species that some moisture can remain in the leaf after 

ignition occurs. 

The effect of FMC on the onset of crowning represented by equation (5) has not been corroborated by field 

data. This is not to say that the derivation of equation (5), as 

originally described by Van Wagner (1967a, 1967c, 1968), is 

not technically accurate. However, while the effect of FMC 

on foliar ignition (Xanthopoulos 1990, Xanthopoulos and 

Wakimoto 1993) and vertical fire propagation in individual 

trees (Van Wagner 1967b) has been demonstrated in the 

laboratory, a statistical analysis of an experimental dataset 

comprising surface and crown fires in coniferous forests 
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GENERAL TECHNICAL REPORT PNW-GTR-891

failed to find a significant effect of FMC on the likelihood 

of crown fire occurrence (Cruz et al. 2004). Sensitivity 

analysis of the physics-based crown fuel ignition model 

developed by Cruz et al. (2006a) identified a weak effect 

of FMC on the ignition of canopy fuels by a surface fire in 

the range of 80 to 160 percent. A possible explanation of 

this weak effect could be that the change in energy required 

to ignite canopy fuels owing to the increase in FMC is 

relatively small when compared to the cumulative energy 

flux absorbed by crown fuels above a high-intensity surface 

fire (Cruz et al. 2006b). 

Ideal evidence to substantiate an FMC effect on crown 

fire initiation would be a set of experimental surface and 

crown fires carried out under nearly identical burning 

conditions, with FMC thus being the only variable. Such an 

experiment would be difficult to arrange under field conditions (Van Wagner 1971, 1979a).

Norum (1975) offered the following observation regarding a crowning tendency in relation to FMC in connection 

with a study of 20 understory fires carried out in western 

larch–Douglas-fir stands in western Montana:

One ... phenomenon which was obvious to all members of the research team was the greater prevalence 

of crown fires in the late spring and early summer 

fires. Douglas-fir of all sizes were prone to support 

fire in the crowns during this season. Conversely, 

high intensity fires in late summer and early fall presented almost no such problems. Many cases were 

observed where complete scorching of crowns and 

total bole cambium kill occurred without ignition of 

the crowns in the late summer and early fall fires. 

No explanation of this phenomenon can be offered 

now, and more study is needed, but it was obviously 

true in this set of fires.

Norum (1975) made no mention of the study by Philpot 

and Mutch (1971), which indicated that the moisture content 

of 1- and 2-year-old needles of Douglas-fir increased from 

late spring to early fall. It was only later on that he attributed the crowning tendency he had observed to FMC (Norum 

and Miller 1984).

Given the empirical nature of Van Wagner’s (1977a) 

crown fire initiation model with respect to the FMC, applying it and any of the fire modeling systems that utilize it to 

insect- and disease-killed stands (Alexander and Stam 2003, 

Klutsch et al. 2011, Kuljian and Varner 2010, Schoennagel

et al. 2012, Simard et al. 2011) is highly inappropriate (Page 

et al. 2014); in fact, crowning is but one factor to consider in 

such fuel complexes (Page et al. 2013a). What is needed is 

an empirical constant for use in equation (4) that is based on 

lower FMC values than is presently the case (Jenkins et al. 

2012; Kuljian 2010; Page et al. 2014). The reduction in FMC 

in mountain pine beetle (Dendroctonus ponderosae)—attacked lodgepole pine stands has recently been documented 

by Jolly et al. (2012) and Page et al. (2012). Clearly, FMC 

values of “red and dead” needles on mountain pine beetleattacked lodgepole pine trees can, depending on weather 

conditions, approach levels as low (say 5 to 10 percent) as 

those found in exposed surface needle litter (Pook and Gill 

1993), although Page et al. (2013b) found that there was little 

diurnal variation in FMC.

Crown Fire Propagation

Assuming a surface fire is intense enough to initiate and 

sustain crown combustion from below, the question now 

becomes, Can a solid flame front develop and maintain 

itself within the canopy fuel layer in order for horizontal 

crown fire spread to occur? Van Wagner (1977a) theorized 

that a minimum flow of fuel into the flaming zone of a 

crown fire is required for combustion of the canopy fuel 

layer to continue. In this conceptual formulation, the flame 

front is viewed as stationary with the fuel moving into it 

(Agee 1996). 

Building on the previous work of Thomas et al. (1964) 

and Thomas (1967), Van Wagner (1977a) proposed that a 

critical minimum spread rate needed to preserve continuous 

crowning (ROScritical, m/min) could be estimated on the 

basis of a stand’s canopy bulk density (CBD, kg/m3) using 

the following simplistic equation:

ROScritical = 3.0 ÷ CBD (6)

CBD is in turn calculated as follows:

CBD = CFL ÷ CD (7)
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Figure 9-13—Critical minimum spread rate for active crowning 

in a conifer forest stand as a function of canopy bulk density according to Van Wagner (1977a) in relation to plotted experimental 

crown fire data (after Cruz and Alexander 2010a). 

where CFL is available canopy fuel load (kg/m2) and CD 

is the crown depth (m) (i.e., the stand height less the CBH). 

Dickinson et al. (2009) claimed to have recalibrated the 

Van Wagner (1977a) model represented by equation (6) on 

the basis of CFL as opposed to CBD. Cruz and Alexander 

(2010a) have clearly shown that this modification is unreliable as a means of distinguishing active crown fires from 

passive crown fires.

According to equation (6), ROScritical increases as the 

CBD decreases (fig. 9-13). High CBD levels are associated 

with dense stands and low values with open stands (fig. 

9-8). Active crowning is presumably not possible if a fire 

does not spread rapidly enough following initial crown 

combustion. Albini (1993) viewed this criterion for active 

crowning as a “lean flammability limit.” Thus, if a fire’s 

actual ROS after the initial onset of crowning, which is in 

turn a function largely of the prevailing windspeed or slope, 

is less than ROScritical, a passive crown fire is expected to 

occur (fig. 9-5). Some authors have misinterpreted ROScritical to mean the surface as opposed to crown fire spread rate 

(e.g., Keyes 1996).

The 3.0 empirical constant given in equation (6) was 

derived largely on the basis of a single experimental crown 

fire in a red pine plantation stand exhibiting a CBD of 0.23 

kg/m3 (Van Wagner 1964). In contrast, Thomas (1967) 

determined, on the basis of experimental fires conducted in 

the laboratory in wooden crib fuel beds, that the empirical 

constant in equation (6) varied from 3.6 to 4.8 as opposed

to the value of 3.0 derived by Van Wagner (1977a). The 

robustness of the value derived by Van Wagner (1977a)

for conifer canopies has since been confirmed on the

basis of an analysis of a relatively large data set of experimental crown fires (fig. 9-13) in several different conifer

forest fuel complexes (Cruz et al. 2005) and a detailed 

wildfire behavior case study (Alexander 1998). Subsequent analyses have shown that CBD levels of around 

0.05 and 0.1 kg/m3, corresponding to Rcritical values of 60 

and 30 m/min, respectively, constitute critical thresholds 

for passive and active crown fire development (Agee 1996, 

Cruz et al. 2005, Cruz and Alexander 2010).

In Van Wagner’s (1977a) crown fire theories, both passive and active crown fires are dependent on surface fire for 

their continued existence. Furthermore, it is assumed that 

the canopy fuel layer is horizontally uniform and continuous for efficient crown-to-crown heat transfer. Admittedly, 

it does not explicitly account for the spacing between the 

tree crowns. The extent to which the intercrown distance 

directly affects active crown fire propagation is presently 

unknown as this fuel complex characteristic is embedded in 

the CBD. However, from observational evidence obtained 

through experimental burning, it appears that active crown 

fires are able to breach gaps in the forest canopy of 10 to 20 

m with ease (Alexander et al. 1991).

Van Wagner’s (1977a) models of crown fire initiation 

and propagation reveal that some conifer fuel complexes are 

far more prone to or have a greater propensity for crowning 

than others simply because of their intrinsic fuel properties. 

For example, many of the black spruce forest types found

in Alaska and the Lake States as well as Canada are notoriously flammable (Archibald et al. 1994, Hardy and Franks 

1963, Hirsch et al. 2000, Johnson 1964, Norum 1982), 

making fire suppression difficult (table 9-1). This occurs as 
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a result of a combination of low CBH, which typifies this 

tree species over much of its range, the abundance of ladder 

or bridge fuels (i.e., bark flakes, lichens, and dead branches 

on the lower tree boles), low FMC levels, and moderately 

high CBD values (Barney et al. 1978, Chrosciewicz 1986b, 

Cronan and Jandt 2008, Dyrness and Norum 1983, Norum 

and Miller 1984) and perhaps other fuel properties (e.g., 

cones as firebrand source material, high live-to-dead ratios 

of available fuel within the tree crowns).

Crown Fire Rate of Spread

Surface fires spreading beneath conifer forest canopies 

seldom exceed 5 to 10 m/min (Dryness and Norum 1983, 

Kiil 1976, Norum 1982, Quintilio et al. 1977) without the 

onset of crowning in some form or another (fig. 9-10). The 

exceptions would involve open stands with a low-canopy 

bulk density (say less than 0.05 kg/m3) or closed-canopied 

stands exhibiting a very high canopy base height (perhaps 

12 to 15 m or greater), in which case, spread rates might 

reach as high as 25 m/min with associated fireline intensities of 10,000 kW/m (Graham et al. 2012, National Fire 

Protection Association 1990). Van Wagner (1977a), for 

example, conducted one experimental fire in a jack pine 

stand exhibiting a CBH of 6 m and a CBD of 0.04 kg/m3 in 

which the spread rate and fireline intensity reached 15 m/

min and 15,800 kW/m, respectively. While the requirement 

for the initial crown combustion was met (SFIcritical = 2,490 

kW/m), environmental conditions were such that an

ROScritical of 75 m/min could not be attained, the result 

being a high-intensity, passive crown fire.

Countryman (1964) described the situation between a 

“closed’ versus “open” fire environment when it comes to a 

free-burning fire in a conifer forest stand:

A fire burning under a dense timber stand is burning 

in an environment quite different than that above or 

outside the stand. Fuel moisture is frequently much 

higher and wind movement is greatly slowed within 

the stand. If the fire builds in intensity and breaks 

out through the crowns of the trees and becomes a 

Figure 9-14—Example of rate of fire spread as a function of 

windspeed for three broad fuel complexes according to the models 

of Cheney et al. (1998), Rothermel (1972), and Cruz et al. (2004, 

2005) for grassland, shrubland, and conifer forest, respectively. 

More specifically, the Cheney et al. (1998) natural pasture grass 

fuel type assuming a 100 percent degree of curing and Fire Behavior Fuel Model 4–Chaparral as described by Anderson (1982) with 

a wind-reduction factor of 0.6 (Albini and Baughman 1979) were 

used with the Rothermel (1972) model for the shrubland fuel complex. The following fuel complex characteristics were employed in 

the Cruz et al. (2004, 2005) models for the conifer forest: available 

surface fuel load, 1.3 kg/m2

; canopy base height, 6.0 m; canopy 

bulk density, 0.23 kg/m3; and stand height, 14 m. The following 

environmental conditions were held constant: slope steepness, 

0 percent; ambient air temperature, 30 °C; relative humidity, 20 

percent; fine dead fuel moisture, 4.8 percent for grassland and 6.0 

percent for conifer forest and shrubland; foliar moisture content 

for conifer forest, 110 percent; and shrub live fuel moisture content, 75 percent. The “kink” in the conifer forest curve represents 

the point of surface-to-crown fire transition. The Beaufort scale 

for estimating 6.1-m open windspeeds is presented in table 9-3. 

A guide to estimating probable maximum 1-min and momentary 

gust speeds is given in table 9-4.
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Table 9-3—Beaufort scale for estimating 6.1-m open windspeeds

Wind class

Windspeed 

range Description Observed wind effects

(km/h)

1 < 5 Very light Smoke rises nearly vertically. Leaves of quaking aspen in constant motion; small branches of bushes sway; slender branchlets and twigs of 

trees move gently; tall grasses and weeds sway and bend with wind; 

wind vane barely moves.

2 6 to 11 Light Trees of pole size in the open sway gently; wind felt distinctly on face; 

loose scraps of paper move; wind flutters small flag.

3 12 to 19 Gentle breeze Trees of pole size in the open sway very noticeably; large branches of 

pole-size trees in the open toss; tops of trees in dense stands sway; wind 

extends small flag; a few crested waves form on lakes.

4 20 to 29 Moderate breeze Trees of pole size in the open sway violently; whole trees in dense stands 

sway noticeably; dust is raised in the road.

5 30 to 39 Fresh Branchlets are broken from trees; inconvenience is felt walking against 

wind.

6 40 to 50 Strong Tree damage increases with occasional breaking of exposed tops and 

branches; progress impeded when walking against wind; light structural 

damage to buildings.

7 51 to 61 Moderate gale Severe damage to tree tops; very difficult to walk into wind; significant 

structural damage occurs

8 >62 Fresh gale Surfaced strong Santa Ana; intense stress on all exposed objects, vegetation, buildings; canopy offers virtually no protection; windflow is 

systematic in disturbing everything in its path.

Source: Adapted from Rothermel 1983.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Table 9-4—Gust estimating table for windspeeds at the 6.1-m open 

height standard

Probable momentary gust speed Standard 10-min 

average speed

Probable maximum

1-min speed Average Maximum

mi/h km/h mi/h km/h mi/h km/h mi/h km/h

1 1.6 3 4.8 6 9.7 9 14.5

2 3.2 5 8.0 8 12.9 12 19.3

3 4.8 6 9.7 11 17.7 15 24.1

4 6.4 8 12.9 13 20.9 17 27.4

5 8.0 9 14.5 15 24.1 18 29.0

6 9.7 10 16.1 16 25.7 20 32.2

7 11.3 11 17.7 17 27.3 21 33.8

8 12.9 12 19.3 19 30.6 23 37.0

9 14.5 13 20.9 20 32.2 24 38.6

10 16.1 14 22.5 22 35.4 26 41.8

11 17.7 15 24.1 23 37.0 27 43.5

12 19.3 17 27.4 25 40.2 29 46.7

13 20.9 18 29.0 26 41.8 30 48.3

14 22.5 19 30.6 28 45.1 32 51.5

15 24.1 20 32.2 29 46.7 33 53.1

16 25.7 21 33.8 30 48.3 35 56.3

17 27.4 22 35.4 32 51.5 36 57.9

18 29.0 23 37.0 33 53.1 38 61.2

19 30.6 24 38.6 34 54.7 39 62.8

20 32.2 25 40.2 35 56.3 40 64.4

21 33.8 26 41.8 37 59.5 42 67.6

22 35.4 27 43.5 38 61.2 43 69.2

23 37.0 28 45.1 39 62.8 44 70.8

24 38.6 29 46.7 40 64.4 46 74.0

25 40.2 30 48.3 41 66.0 47 75.6

26 41.8 31 49.9 43 69.2 49 78.9

27 43.5 32 51.5 44 70.8 50 80.5

28 45.1 33 53.1 45 72.4 51 82.1

29 46.7 34 54.7 46 74.0 53 85.3

30 48.3 35 56.3 47 75.6 54 86.9

Source: Crosby and Chandler 2004.
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Figure 9-15—Convection column associated with 

the major run of the Tok River Fire in east-central 

Alaska on July 1, 1990. This photo was taken from 

the town of Tok, which was later threatened, 15 to 

30 minutes after the lightning strike that started the 

fire. The forests in the area are dominated by black 

spruce. The fire would continue to burn throughout 

the remainder of the summer, eventually covering 

some 400 000 ha. D. Blasor-Bernhardt.

crown fire, it then is burning in an open environment 

and comes under a different set of controls. Fire 

behavior and characteristics can change radically.

General observations of wildfires and documentation of 

experimental crown fires indicate that an abrupt transition 

between surface and crown fire spread regimes and vice 

versa is far more commonplace than a gradual transition 

(Alexander and Cruz 2011a, Burrows et al. 1988, Cohen 

et al. 2006, Fernandes et al. 2004, McArthur et al. 1966, 

Stocks et al. 2004b, Van Wagner 1964). With the onset of 

crowning, at a minimum, a fire typically doubles or even 

triples its spread rate in comparison to its previous state on 

the ground surface (McArthur 1965). This sudden jump in 

the fire’s rate of spread (fig. 9-14) occurs as a result of (1) 

the windspeeds just above the tree canopy are 2.5 to 6 times 

that of the winds experienced near ground level inside the 

stand, (2) increased efficiency of heat transfer into a tall and 

porous fuel layer, (3) the enhanced radiant heating owing 

to the taller and deeper flame fronts, and (4) an increase in 

spotting density and distance just beyond the fire’s leading 

edge (Anderson 1983, Countryman 1964, Taylor et al. 2004, 

Van Wagner 1968).

Once crowning has commenced, a fire’s forward rate of 

spread on level terrain is influenced largely by wind velocity 

(tables 9-3 and 9-4) and to a lesser extent by physical fuel 

properties and dryness (fig. 9-14).6 If ground and surface 

fuels are dry and plentiful, and ladder fuels or bridge fuels 

are abundant, crown fires can still propagate in closedcanopied forests, even if winds are not especially strong (fig. 

9-15), although spread rates may not be particularly high 

(e.g., Hirsch and Flannigan 1990). 

Some authors have reported cases in which strong winds 

have actually limited the degree of crowning rather

than increase it even though surface fire intensities were 

sufficient to induce crowning (e.g., Haftl 1998). For example, 

during the height of activity associated with the multiple 

fire situation around Spokane in northeastern Washington 

on October 16, 1991, with a fine dead fuel moisture of 10 

percent and sustained surface winds ranging from 61 to 72 

km/h (Alexander and Pearce 1992), the following observation 

was made regarding ponderosa pine fuel complexes (from 

National Fire Protection Association 1992): 

Typically stands of ponderosa pine contain dead 

branches extending to the ground. In some cases

6

 Note that the fire behavior simulation results presented in 

figures 9-14 and 9-19 (on p. 191) for grassland and conifer stand 

fuel complexes were derived from empirically based models that 

have been compared against wildfire observations and shown to 

be quite reliable. Rothermel’s (1972) semiphysical model has, on 

the basis of comparisons against the experimental fires carried by 

McCaw (1997), shown to produce creditable predictions for tall 

shrubland fuel complexes.
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GENERAL TECHNICAL REPORT PNW-GTR-891

these “ladder fuels” enabled the fire to reach the 

crowns of the 30- to 100-foot [9- to 30-metre] pine 

trees and would result in the fire spreading at 

extremely high rates. Unlike other severe wildland 

fires, however, this “crowning” was fairly limited. 

The high velocity of the winds did not allow the 

thermal columns from the fires to reach the crowns.

In addition to the strong winds, the lack of active 

crowning could have also been due to a combination of light 

fuel loads (including low available fuel loads resulting from 

high duff moisture contents), low canopy bulk densities, or 

high canopy base heights. 

Although surface fire rates of spread are greatly 

increased with increasing windspeed, flame heights are 

correspondingly reduced (McArthur 1967). This partly 

explains why some crown fires do not always occur when 

windspeeds and rates of spread are high (Luke and McArthur 1978). The Burnt Fire that occurred on the Coconino 

National Forest in northern Arizona as described by 

Dieterich (1979) constitutes a good case in point. The major 

run of this fire occurred on November 2, 1973, under the 

influence of strong surface winds (48 to 64 km/h) but cool 

air ambient temperatures (10 °C). Surface fuel consumption 

was estimated to have been low (0.56 kg/m2

). Spread rates 

ranged from 48 to 99 m/min with fireline intensities of 

2,325 and 5,250 kW/m (Alexander 1998). Dieterich (1979) 

summed up the impact on the overstory tree canopy resulting from the Burnt Fire as follows:

Damage from this fast-spreading fire was extremely 

variable ranging from complete destruction of crown 

material in patches of saplings and pole timber and 

an occasional mature tree, to large areas where 

the only evidence of fire was a blackened littler 

layer and slight scorch on the lowest portion of the 

crowns.

Dieterich (1979) noted that much of the ponderosa pine 

was open-grown with tree crowns extending within 1.2 to 

1.5 m of the ground surface. 

In addition to being a factor influencing the onset of 

crowning in conifer forest stands, Van Wagner (1974, 1989, 

1993, 1998) also believed that the natural variation in FMC 

would presumably have an effect on the rate of spread of a 

crown fire. Alexander and Cruz (2013c) recently reviewed 

the related literature on this topic. They concluded that 

while results from laboratory studies suggest that such an 

effect should exist, the available experimental field evidence 

does not support such a conclusion, at least within a normal, 

live fuel moisture range. None of the existing functions for 

adjusting crown fire rate of spread for the relative effect 

of FMC vary widely in their outcomes and relevancy, and 

none appear suitable for application to dead canopy foliage 

associated with attack by forest insects and pathogens.

Continuous active crowning generally takes place at 

spread rates between about 15 and 30 m/min (Van Wagner 

1980). Not surprisingly, many fire behavior researchers have 

over the years suggested a “mile an hour” (i.e., 1.6 km/h or 

27 m/min) as a nominal rate of spread for crown fires (e.g., 

Gisborne 1929, Van Wagner 1968). Alexander and Cruz 

(2006) from an extensive review of wildfire case studies 

found an average crown fire rate of spread of 2.3 km/h or

38 m/min.

Crowning wildfires have been known to make major 

runs of 30 to 65 km over flat and rolling to gently undulating ground during a single burning period or over multiple 

days (fig. 9-16), as was so vividly demonstrated, for 

example, on the Rodeo-Chediski Fire in northern Arizona 

in June 2002 (Paxton 2007) and the Wallow Fire in eastcentral Arizona in June 2011 (Keller 2011). A wildfire 

crowning through sand pine forests of the Ocala National 

Forest in north-central Florida on March 12, 1935 (box 1), 

initially travelled some 29 km in 3 hours (i.e., about 160 

m/min) before a change in wind direction (Custer and 

Thorsen 1996, Folweiler 1937, Kendrick and Walsh 2007). 

The Lesser Slave Fire in central Alberta advanced 64 km 

through a variety of boreal forest fuel types in a period of 

10 hours on May 23, 1968 (Alexander 1983, Kiil and Grigel 

1969) resulting in an average rate of spread of 107 m/min. 

During the major run of the Mack Lake Fire that occurred 

in the Huron National Forest in central Michigan on May 

5, 1980, the crown fire rate of spread in jack pine forests 

peaked, as graphically illustrated by Rothermel (1991a), at 

nearly 190 m/min during a 15-min interval (Simard et al. 

1983). Grass fires have been reported to spread at twice 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Florida (Doren et al. 1987, Hough 1973) for example. The 

same phenomenon has been observed in certain grassland 

and shrubland fuel complexes (Burrows et al. 1991, Cheney 

and Sullivan 2008, Clark 1983, Cruz and Gould 2010, Cruz 

et al. 2010a, Davis and Dieterich 1976, Lindenmuth and 

Davis 1973, Racher 2003).

Slope steepness dramatically increases the uphill rate of 

spread (fig. 9-17) and intensity of wildland fires by exposing 

the fuel ahead of the advancing flame front to additional 

convective and radiant heat as illustrated by Luke and 

McArthur (1978, p. 94). Fires advancing upslope are thus 

capable of making exceedingly fast runs compared to level 

Figure 9-16—Fire progress map for the 2002 Rodeo-Chediski Fire in northern Arizona illustrating the complex growth pattern associated with a long-duration fire. Adapted from http://www.floa.org/rodeo_chediski/rodeo628progress.jpg (31 May 2011).

these rates on level ground (fig. 9-14) and are thus capable of 

spreading the same distance of crowning forest fires in half 

the time (Cheney et al. 1998, Cheney and Sullivan 2008).

In some conifer forest fuel types exhibiting discontinuous or very low quantities of surface fuels, surface fire 

spread is nearly nonexistent even under moderately strong 

winds. However, once a certain windspeed threshold is 

reached with respect to a given level of fuel dryness, a 

dramatic change to crown fire spread suddenly occurs. This 

type of fire behavior has been observed in pinyon-juniper 

woodlands of the Western United States (Bruner and 

Klebenow 1979, Hester 1952) and in the sand pine forests of 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-18–Effect of slope steepness on 

degree of flame attachment and convective heat transfer for shallow (A) and

steep (B) sloping terrain (adapted from 

Rothermel 1985).

topography. For example, one would expect a crown fire 

burning on a 35 percent slope to spread about 2.5 times as 

fast as one on level terrain for the same fuel and weather 

conditions, according to Van Wagner (1977b). The rapid 

upslope runs that occurred on the 1979 Ship Island Fire in 

the Salmon National Forest of central Idaho as captured 

with time-lapse photography and shown in the video Look 

Up, Look Down, Look Around (National Wildfire Coordinating Group 1993), constitute a good example of such fire 

behavior.

As slope steepness increases, the flames tend to lean 

more and more toward the slope surface, gradually becoming attached, the result being a sheet of flame moving 

roughly parallel to the slope (fig. 9-18). Rothermel (1985) 

stated that although there is no definitive research on the 

subject of flame attachment, “it appears from lab work and 

discussions with users that the flames become attached 

near 50 percent slope with no prevailing wind.” The critical 

value will actually differ depending on the prevailing wind 

strength (Cheney and Sullivan 2008) as well as on the fuel 

type characteristics. The “power of the slope” in wildland 

fires was never more evident than with the Dodge escape 

fire associated with the 1949 Mann Gulch Fire on the

Helena National Forest in northwestern Montana (Alexander et al. 2009, Maclean 1992, Rothermel 1993). 

With the exception of very long slopes such as found, 

for example, in the Salmon River country of central Idaho, 

the rate of advance of wind-driven crown fires in mountainous terrain tends, over the duration of their run, to be well 

below what would be expected on flat ground, even under 

critical fire weather conditions (e.g., Goens 1998, Taylor and 

Williams 1967). As Chandler et al. (1963) noted:

There are strong indications, however, that for 

periods of 2 to 3 hours, rate of fire spread is greatest in mountainous or broken topography but, for 

periods of 12 to 24 hours, greatest on flat or gently 

Figure 9-17—The effect of slope steepness on uphill rate of spread 

of free-burning wildland fires in the absence of wind according to 

various sources. 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

rolling topography. The difference probably arises 

because mountainous country has more steep slopes 

which cause rapid fire spread, but also more breaks 

or barriers which retard spread for long periods.

This outcome also undoubtedly occurs as a result of the 

degree of terrain exposure to the prevailing winds, which 

limits the full effectiveness of windspeed on fire spread, 

as well as differences in fuel moisture owing to aspect or 

slope exposure (Schroeder and Buck 1970). However, when 

the advancing crown fire front encounters a situation where 

wind and topography are favourably aligned (Campbell 

2005), exceedingly rapid fire growth can occur. Spread 

rates of about 100 m/min are quite easily possible for a brief 

period over short distances with only moderately strong 

winds (e.g., Rothermel and Gorski 1987, Rothermel and 

Mutch 1986), such as occurred on the 1937 Blackwater Fire 

on the Shoshone National Forest in northwest Wyoming in 

which 15 firefighters perished (Brown 1937). Fire spread 

rates in grassland and shrubland fuel types at even twice 

this level can easily occur (Butler et al. 1998; Countryman 

et al. 1968, 1969; Fogarty 1996; Rothermel 1993; Wilson 

and Davis 1988). 

Crown fire runs in mountainous terrain are not limited 

to just upslope situations. Cases of crown fires burning 

downslope or cross-slope under the influence of strong 

winds have occurred in the past (Byram 1954, McAlpine 

et al. 1991). The major run of the Dude Fire on the Tonto 

National Forest in northern Arizona on June 26, 1990, that 

led to the deaths of six firefighters involved downslope and 

cross-slope spread as a result of the strong downdraft winds 

associated with the fire’s collapsing convection column 

(Goens and Andrews 1998).

Crown Fire Intensity and Flame Zone 

Characteristics

When a fire in a conifer forest stand crowns, additional fuel 

is consumed primarily in the form of needle foliage (Wendel 1960) but also mosses and lichens, bark flakes, and small 

woody twigs. Empirical data on the latter fuel component 

are limited to a single study (Stocks et al. 2004b). The additional fuel consumed by a crown fire owing to the canopy 

fuel involvement generally amounts to 0.5 to 2.0 kg/m2

depending on stand characteristics (i.e., an increase in fuel 

consumption with respect to fireline intensity of one-quarter 

to a doubling in the amount). Combined with the increase 

in rate of fire spread after crowning, fireline intensities can 

easily quadruple in value within a few seconds (e.g., from 

3,000 to 12,000 kW/m) and spotting activity very quickly 

increases in both density and distance (Albini et al. 2012, 

Byram 1959b). In such cases, there is little wonder why 

some fires just seem to literally “blow up” (Burrows 1984, 

Byram 1954, Gisborne 1929, McArthur et al. 1966).

A fire’s flame zone characteristics (i.e., depth, angle, 

height, and length) are a reflection of its heat or energy-release rate. As the fireline intensity or rate of energy released 

per unit area of the flame front increases (fig. 9-19A) because of a faster rate of spread and a larger quantity of fuel 

being volatilized in the flaming front, flame size or volume 

increases (Albini 1981a, Nelson 1980, Thomas 1963). Fireline intensities of wind-driven crown fires can easily reach 

30,000 kW/m (DeCoste et al. 1968, Dieterich 1976, Simard 

et al. 1983, Wade and Ward 1973) and occasionally exceed 

100,000 kW/m for significant periods of time (Anderson 

1968, Kiil and Grigel 1969). 

The flame depth (D, m) of a spreading wildland fire (fig. 

9-4) is a product of its ROS and the flame front residence 

time (tr, min), which represents the duration that a moving 

band or zone of continuous flaming combustion persists at 

or resides over a given location (after Fons et al. 1963): 

D = ROS × tr (8)

Flame front residence times are dictated largely by 

the particle size(s) distribution, load, and compactness of 

the fuel bed (Burrows 2001, Cheney 1981, Nelson 2003b, 

Nelson and Adkins 1988).

Flame front residence times for conifer forest fuel types 

at the ground surface are commonly 30 sec to 1 min (McArthur and Cheney 1966, Taylor et al. 2004) compared to 5 to 

10 sec in fully cured grass fuels (Cheney and Sullivan 2008). 

Assuming t

r = 0.75 min (i.e., 45 sec), a surface fire in a 

conifer forest spreading at 4.0 m/min would thus have flame 

depth of around 3.0 m according to equation (8). Crown fires 

are capable of producing very deep flame fronts (fig. 9-19B). 

The depth of the burning zone in the surface fuels of a crown 

fire spreading at 60 m/min would, for example, be around 
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GENERAL TECHNICAL REPORT PNW-GTR-891

45 m. The flame depth of a grass fire advancing at this rate 

would in contrast be only about a tenth of this value (fig. 

9-19B). Residence times within the canopy fuel layer of a 

crown fire are about one-half to one-third those experienced 

at ground level (Anderson 1968, Despain et al. 1996, Taylor 

et al. 2004). This is reflected in the gradual convergence of 

the flaming zone depth with height ending in the flame tip 

above the tree crowns (fig. 9-20). 

The flame front of a crown fire on level ground appears 

to be nearly vertical. This appearance has led to the popular 

phrase “wall of flame” when it comes to describing crown 

fire behavior (fig. 9-20). Typically though, tilt angles are 5 to 

20 degrees from the vertical (Albini and Stocks 1986; Van 

Wagner 1968, 1977a). The fact that the flames of a crown 

fire stand so erect is a direct result of the powerful buoyancy associated with the large amount of energy released 

in the flame front (fig. 9-1C). Radiation from the crown fire 

wall of flame can produce painful burns on exposed skin 

at more than 100 m from the fire edge (Albini 1984, Butler 

and Cohen 1998). Such would have been the case during the 

major run of the 1985 Butte Fire in on the Salmon National 

Forest in central Idaho had firefighters not had protective 

fire shelters to avert thermal injuries (Butler and Cohen 

1998, Mutch and Rothermel 1986).

Figure 9-19—Examples of (A) fireline intensity and (B) flame depth as a function of windspeed for three broad fuel complexes according to the models of Cheney et al. (1998), Rothermel (1972), and Cruz et al. (2004, 2005) for grassland, shrubland, and conifer forest, 

respectively. More specifically, the Cheney et al. (1998) natural pasture grass fuel type assuming a fuel load of 0.35 kg/m2 and 100 

percent degree of curing, and for the shrubland fuel complex, Fire Behavior Fuel Model 4–Chaparral as described by Anderson (1982) 

with a wind reduction factor of 0.6 (Albini and Baughman 1979) was employed in Rothermel’s (1972) model. The following fuel complex 

characteristics were employed in the Cruz et al. (2004, 2005) models for the conifer forest: available surface fuel load, 1.3 kg/m2; canopy 

base height, 6.0 m; canopy fuel load, 1.0 kg/m2; canopy bulk density, 0.23 kg/m3; and stand height, 14 m. The following environmental conditions were held constant: slope steepness, 0 percent; ambient air temperature, 30 °C; relative humidity, 20 percent; fine dead 

fuel moisture, 4.8 percent for grassland and 6.0 percent for conifer forest and shrubland; foliar moisture content for conifer forest, 110 

percent; and shrub live fuel moisture content, 75 percent. The flame front residence times used for estimating flame depths for grassland, 

shrubland, and conifer forest were assumed to be 7.5, 60, and 45 sec, respectively. The “kink” in the curves associated with the conifer 

forest represent the point of surface-to-crown fire transition. The Beaufort scale for estimating 6.1-m open windspeeds is presented in 

table 9-3. A guide to estimating probable maximum 1-min and momentary gust speeds is given in table 9-4.
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Figure 9-20—Flame front of an active crown fire advancing through the jack pine-black spruce fuel 

type, International Crown Fire Modeling Experiment, Northwest Territories, Canada. 

M.G. Cruz

Given the difficulty of gauging the horizontal depth of 

the burning zone in a crown fire, flame height constitutes 

a more easily visualized dimension than flame length (fig. 

9-21). However, efforts to objectively estimate flame heights 

of crown fires are complicated by the fact that sudden 

ignition of unburned gases in the convection column can 

result in flame flashes that momentarily extend some 100 m 

or more into the convection column aloft; one such flame 

flash that extended almost 200 m above the ground was 

photographically documented by Sutton (1984). Such flashes 

can easily result in overestimates of average flame heights, 

which usually range from about 15 to 45 m on high-intensity crown fires (Byram 1959b). Average flame heights of 

crown fires are thus generally regarded as being about two 

(fig. 9-1C) to three times the stand height (Alexander 2006, 

Cruz and Alexander 2010b, Stocks et al. 2004b, Zárate 

et al. 2008) (fig. 9-20). This is in contrast to wind-driven 

grass fires where average flame heights seldom exceed 4 

m (Cheney and Sullivan 2008). High-intensity wildfires in 

mature chaparral in turn commonly exhibit flames of 15 m 

or more (Wilson and Davis 1988).

Byram (1959a) indicated that his flame length-fireline 

intensity relation represented by equation (2) would underpredict the flame length for “crown fires because much of 

the fuel is a considerable distance above the ground.” He 

suggested, on the basis of visual estimates, that “this can 

be corrected for by adding one-half of the mean canopy 

height” to the flame length value obtained by equation (2). 

Alexander (1998) concluded on the basis of some simple 

calculations using this approach coupled with a review of 

existing experimental crown fire observations that Byram’s 

(1959a) approach to estimating crown fire flame lengths 

would result in underestimates except perhaps at the lower 

fireline intensity levels (say less than 5,000 kW/m) in short 

to moderately tall stands.
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Crown Fire Area and Perimeter Growth 

For forest fires of today to become large, they typically have 

to involve some degree of crowning. A common axiom in 

wildland fire management is that about 95 percent of area 

burned is generally caused by less than 5 percent of the 

fires (Kasischke et al. 2006, Stocks et al. 2002, Strauss et 

al. 1989, USDA Forest Service 1966). When a forest fire at 

the very minimum doubles its spread rate after the onset of 

crowning, the area burned for a given period will be at least 

four times what would have been covered by a surface fire 

(table 9-5). In other words, the area burned is proportional 

to the rate of spread increase (following the transition to 

crowning) to a power of two (McArthur 1965). Thus, if 

a fire triples its rate of advance after crowning, the area 

burned will be nine times greater than had it remained as a 

surface fire (i.e., 32 = 9).

Other than dry and plentiful fuels, the principal ingredients for major crown fire runs in conifer forests are strong, 

sustained winds coupled with extended horizontal fuel continuity (Campbell 2005, Omi 2005, Rothermel 2000). The 

Hayman Fire that occurred along the Colorado Front Range, 

for example, burned close to 25 000 ha during its major 

run on June 9, 2002, and eventually grew to nearly 56 000 

ha towards the end of the month (Graham 2003a, 2003b). 

Much of the final area burned (about 45 000 ha) by the 1956 

Buckhead Fire that occurred on the Osceola National Forest 

in north Florida took place during the fire’s major run during 

a 10- to 12-h period on March 24-25 (Newcomb 1957, Storey 

and Merkel 1960). Under favorable conditions, crown fires 

on level to gently undulating terrain have been documented 

to cover in excess of 70 000 ha in a single, 10-hour burning 

period (Kiil and Grigel 1969) and up to a third that much in 

mountainous areas (Anderson 1968). 

Assuming continuous fuels, including no major barriers 

to fire spread, and no change in wind and fuel moisture 

conditions, the forward spread distance of a crown fire can 

be determined by multiplying its predicted rate of spread 

by a projected elapsed time (Rothermel 1991b). Provided 

the wind direction remains relatively constant and the fire 

environment is otherwise uniform, wind-driven surface and 

crown fires typically assume a roughly elliptical shape

(Alexander 1985, Anderson 1983, Van Wagner 1969) 

defined by its length-to-breadth ratio (L:B) (fig. 9-22), which 

in turn is a function of windspeed (fig. 9-23). The L:B 

associated with crown fires generally ranges from a little 

less than 2.0 (Brotak 1979, Kiil 1975) to around 6.0 (Sando 

and Haines 1972) and in exceptional cases to a maximum 

of about 8.0 (Folweiler 1937). Simple estimates of potential 

Figure 9-21—Schematic diagram illustrating the distinction between flame length (L) and flame height 

in a (A) surface fire and (B) crown fire (adapted from Alexander and Cruz 2012b).
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Figure 9-22—Schematic diagram of a simple elliptical fire growth 

model (after Van Wagner 1969). The point of ignition is at the 

junction of the four area growth zones. 

Figure 9-23—Length-to-breadth ratio of elliptical shaped fires on 

level terrain as a function of windspeed as used in the Canadian 

Forest Fire Behavior Prediction System (Forestry Canada Fire Danger Group 1992), Rothermel’s (1991a) guide to crown fire behavior, 

and McArthur’s (1966) model for grass fires in relation to the experimental fire and wildfire observations given in Alexander (1985) 

and Forestry Canada Fire Danger Group (1992) for various conifer 

forest fuel types. The Beaufort scale for estimating 6.1-m open 

windspeeds is presented in table 9-3. A guide to estimating probable 

maximum 1-min and momentary gust speeds is given in table 9-4.

Table 9-5—Semitheoretical comparison of fire behavior in pruned versus 

unpruned exotic pine plantations under high fire danger conditions as 

patterned after McArthur’s (1965) fire behavior analyses

Fire description and characteristics

Stand A

(pruned to 5 m)

Stand B 

(unpruned)

Type of fire Surface Crown

Head fire rate of spread (m/min) 5 10

Fuel consumed (kg/m2) 1.8 2.8

Fireline intensity (kW/m) 2700 8400

Flame height (m) 2 12

Elliptical fire area at 1 hr (ha) 4.86 19.4

Elliptical fire perimeter at 1 hr (km) 0.83a 1.65a

Spotting distance (m) < 200b Up to 2000 mb

a

 Theoretically, approximately 9 and 45 percent of the total fire perimeter would have fireline 

intensities exceeding 2000 kW/m (Catchpole et al. 1992), thereby precluding direct attack by 

conventional means (Alexander 2000a).

b

 After Douglas (1974).

Source: Adapted from Alexander 1992b.
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GENERAL TECHNICAL REPORT PNW-GTR-891

crown fire size in terms of area burned and perimeter length 

can be made on the basis of the forward spread distance and 

L:B (fig. 9-24).

This simplistic picture of fire growth as described here 

is applicable to cases involving a point source ignition (e.g., 

an escaped campfire or lightning-fire start), a prescribed fire 

holdover or escape, or perhaps a breach in an established 

control line on a wildfire, involving unidirectional winds 

and is generally limited to a 1- to 8-h projection period. 

This approach is thus not appropriate to estimating crown 

fire growth when the perimeter becomes highly irregular in 

shape with the passage of time as a result of changes in wind 

direction, fuel types, and terrain characteristics as shown in 

fig. 9-16 and in the progression maps compiled for the fires 

in the Greater Yellowstone Area during the 1988 fire season 

for example (Rothermel et al. 1994) as well as other incidents 

(e.g., Quintilio et al. 2001, Tymstra et al. 2005).7

7

 Information on similar large fire incidents in the United States 

is available from the Incident Information System (http://www.

inciweb.org/).

One particularly dangerous synoptic fire weather situation worth highlighting with respect to crown fire behavior 

is the case of the dry cold frontal passage (Krumm 1959, 

Schroeder and Buck 1970). In the Northern Hemisphere, 

as the cold front passes over an area, winds shift rapidly 

from southwesterly to the west, then northwest in direction. 

Windspeeds increase in strength as a front approaches 

and usually become quite strong and gusty when the front 

passes over an area. This can result in a long crown fire 

run in a north-northeast direction followed by a fire’s entire 

right flank crowning in an east-southeast direction at an

even greater rate of spread and intensity (DeCoste et al. 

1968, Simard et al. 1983, Wade and Ward 1973). In the 

Southern Hemisphere, the wind and fire spread patterns are 

similar, though the north and south components are reversed 

(Cruz et al. 2012b, Keeves and Douglas 1983). 

Oscillations in land-sea breezes can also lead to 

dramatic escalations in high-intensity crown fire behavior 

and sudden, radical changes in the direction of fire spread, 

Figure 9-24—Area burned (A) and perimeter length (B) of an elliptical shaped crown fire as a function of forward spread distance and 

windspeed on level terrain based on the length-to-breadth ratio embedded in the Canadian Forest Fire Behavior Prediction System (Forestry Canada Fire Danger Group 1992). The Beaufort scale for estimating 6.1-m open windspeeds is presented in table 9-3. A guide to 

estimating probable maximum 1-min and momentary gust speeds is given in table 9-4.
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which can in turn threaten the safety of firefighters (Williams 1969). Other mesoscale-related weather phenomena 

(i.e., weather observations resulting from causes too localized to be identifiable from the basic network observations, 

yet too widely separated to be reasonably deduced from 

observations at a single station) such as thunderstorm drafts 

(Goens and Krumm 2000, Haines 1988) are reviewed in 

Schroeder and Buck’s (1970) seminal work on fire weather 

meteorology. Chandler (1976) felt that more than half of 

the fire behavior-related wildland firefighter fatalities in 

the United States since 1957 were the result of mesoscale 

phenomena.

Crown Fire Spotting Activity 

Rothermel and Andrews (1987) have rightly stated that 

“Spotting is not restricted to crown fires, but is usually 

associated with severe fires of some type. It is one of the 

most intractable problems we face.” Spotting or mass ember 

transport can be an important mechanism determining a 

crown fire’s overall rate of spread under certain conditions 

(Kerr et al. 1971). Pioneer fire researcher Harry T. Gisborne8 gives a vivid account of observing an initiating spot 

fire that occurred on the Quartz Creek Fire (Gisborne 1927) 

in Kaniksu National Forest of northern Idaho in 1926:

In my experience on forest fires, I have actually seen 

only one case of a wind-blown ember falling and 

causing ignition. This was on the Quartz Creek fire 

on the Kaniksu in 1926, when a small twig, about 

one-eighth inch [~3 mm] diameter by one-half inch 

[~13 mm] long, fell from the smoke cloud above and 

came to rest on some rotten wood that I was looking 

at. As I examined the ember, without touching it, I 

saw that it was still glowing. During the four and a 

half minutes required to measure the temperature, 

humidity, and wind, which were 78o [F; 25.6oC], 

21% and 5.5 m.p.h. [8.9 km/h] respectively, this 

ember ignited the rotten wood, which ignited some 

8

 Gisborne, H.T. 1934. Unpublished memo dated September 11, 

1934. On file with: H.T. Gisborne Papers, Archives and Special 

Collections, Maureen and Mike Mansfield Library, University of 

Montana, Missoula, MT. 

dry grass and a minute or two later this spot fire was 

crowning in a small Douglas-fir. 

The general effect of spotting on crown fire rate 

of spread is determined by the density of ignitions and 

distances these ignitions occur ahead of the main fire 

(Alexander and Cruz 2006). These two characteristics are 

intimately linked, with density typically decreasing with 

distance from the main advancing flame front.

The effect of spotting on the overall spread and growth 

of a wildland fire is dependent on topography and fuel 

distribution. In certain fuel types, the propagation of active 

crown fires is linked to high-density, short-range spot fires 

occurring up to 50 m or so ahead of the main advancing 

flame front followed by their subsequent coalescence. 

Under such conditions, the overall fire spread is dictated by 

spotting as well as radiative and convective heat transfer 

mechanisms associated with the crowning phase (Taylor et 

al. 2004). In situations involving heterogeneous fuel type 

distributions and complex topography, spotting will allow 

the main advancing fire front to quickly bypass areas with 

low spread potential (e.g., downslope runs, pure hardwood 

stands in summer, discontinuous fuels) thereby effectively 

advancing the horizontal extent of the fire’s “head” (Sando 

and Haines 1972). Spotting from crown fires is also effective in breaching major barriers to fire spread, including 

large water bodies and other nonfuel areas (e.g., rock slides, 

barren ground). Thus, constructing fuelbreaks comprised 

of vegetation of low flammability can (Alexander 2010b), 

depending on their width, be an effective buffer against 

crown fires (Bickford 1972, Childs 1961, Green 1977) but 

only up to a point (Amiro et al. 2001).

When fire environment conditions are uniform and 

winds aloft are favorable, spotting can contribute to the 

overall spread and growth of crown fires provided the spot 

fires are able to burn independently of the main advancing fire front. In most high-intensity wildfires that involve 

crowning, spot fires originating out ahead of the advancing 

flame front are typically overrun and thus incorporated into 

the larger fire perimeter before they are able to develop and 

spread independently, or otherwise be influenced by the 

main fire (e.g., in-draft winds). For a crown fire spreading 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-25—Minimum separation distance required for a newly 

ignited spot fire to avoid being overrun by the main flame front of 

an advancing crown as a function of rate of spread and ignition 

delay (adapted from Alexander and Cruz 2006). Ignition delay 

represents the elapsed time between a firebrand alighting, subsequent ignition, and the onset of fire spread.

at a rate of 50 m/min or 3 km/h and burning under homogeneous fuel, weather, and topographic conditions, spotting 

distances would have to exceed, depending on the ignition 

delay, which can be as much as 10 minutes (Hirsch et al. 

1979), about 500 to 700 m (fig. 9-25) to have the potential to 

increase a fire’s overall rate of spread through a “leap frog” 

type of effect (Alexander and Cruz 2006). If there are sufficient spot fires at or just beyond this distance and that can 

rapidly coalesce, this “mass ignition” effect will temporarily lead to the formation of pseudo flame fronts with greatly 

increased flame heights (Wade and Ward 1973). 

Spotting distances of up to about 2 km are commonly 

observed on wind-driven crown fires in conifer forests (Kiil 

et al. 1977, Luke and McArthur 1978), but spotting distances close to 5 km have been documented as well (Haines and 

Smith 1987, Jemison 1932). Spot fire distances of 6 to 10 km

were reported to have occurred in the Northern Rocky 

Mountains during the 1910 and 1934 fire seasons (Gisborne 

1935, Koch 1942). This is similar to what has been reported 

in chaparral shrubfields of southern California

(Countryman 1974).

The occurrence of spotting distances greater than 5 

km requires a specific combination of convection column 

strength and vertical wind profile. For a viable firebrand 

to travel such distances, a large amount of energy needs to 

be released (associated with the postfrontal combustion of 

large fuels) to transport the firebrands at significant heights 

(Rothermel 1994). Spotting distances of this magnitude are 

likely to be associated with isolated peaks of fire intensity, 

such as those occurring in an upslope run, that will inject 

large quantities of firebrands in the plume. An atmospheric 

profile with very strong winds aloft is also necessary to 

considerably tilt the convection column and allow for significant drift of the firebrand after it leaves the plume (Kerr 

et al. 1971). Under exceptional circumstances, spotting 

distances greater than 10 km have been described. Especially noteworthy are the 16- to 19-km spot fire distances 

associated with the 1967 Sundance Fire in northern Idaho 

(Anderson 1968, Rothermel 1968), which were quite possibly caused by massive fire-induced vortices (Berlad and 

Lee 1968). Similar distances are reported to have occurred 

in Monterey or radiata pine plantations during the major 

run of the 1983 Mount Muirhead Fire in South Australia 

(Keeves and Douglas 1983, O’Connor and O’Connor 1993). 

However, this pales in comparison to the long-range spot 

fires of up to 30 km or more observed in the native eucalypt 

forests of Australia (Cruz et al. 2012b, McArthur 1967). 

Spotting can be one of the primary mechanisms 

contributing to the forward spread and growth of fires in 

certain conifer forest fuel types such as those found in the 

high-elevation forests of the Rocky Mountains and Pacific 

Northwest. When fire danger and fire weather conditions 

are not particularly conducive to active crowning until later 

in the fire season (Williams and Rothermel 1992), fires 

in these high-elevation fuel types tend to smolder in deep 

organic layers, moving very slowly until they encounter 

ladder fuels favorable for vertical fire development, thereby 

throwing numerous embers ahead of the main advancing 

fire front that lends to an outbreak of spot fires (Beighley 

and Bishop 1990). These newly developed spot fires 

continue to smolder until the process is repeated.
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Models, Systems, and Other Decision Aids 

for Predicting Crown Fire Behavior

Models for predicting various aspects of wildland fire 

behavior, including crowning, take various forms. The two 

primary types are empirical and theoretical or physical 

(Pastor et al. 2003; Sullivan 2009b, 2009c; Weber 1991). 

Hybrids involving each type also exist.

Fire behavior models and related decision support 

systems should be sensitive to those parameters known 

to affect fire behavior, such as variations in live and dead 

fuel moistures, windspeed, and slope steepness, amongst 

others. As Kessell et al. (1980) pointed out over 35 years 

ago, models “are only as good as the data, understanding, 

assumptions, and mathematics that go into their construction.” If the input data are not known accurately enough, 

model output may be significantly in error (Albini 1976b, 

Alexander and Cruz 2013b). Reeves et al. (2009) have, for 

example, recently shown the difficulty of obtaining accurate 

estimates of CBH and CBD with LANDFIRE for assessing 

and predicting crown fire behavior. Hopefully, advances in 

remote sensing technology will ultimately lead to improvements in this regard (Erdody and Moskal 2010, Kramer et 

al. 2011, Skowronski et al. 2011). 

Rothermel Guide to Predicting Size and Behavior 

of Crown Fires

Rothermel (1972) developed a model for predicting surface 

fire rate of spread and intensity that still forms the basis for 

the majority of guides and computerized decision support 

systems for predicting fire behavior in use today in the 

United States (Andrews and Queen 2001), including the 

National Fire-Danger Rating System (NFDRS) (Burgan 

1988; Deeming et al. 1972, 1977). In commenting on the 

1972 version of the NFDRS, McArthur (1977) had the 

following to say:

... it only considers an ‘initiating fire’. This is 

defined as a fire which is not behaving erratically 

and is spreading without spotting through fuels 

which are continuous with the ground (no crowning). ... the ‘state of the art’ cannot yet consider fires 

which exhibit erratic behaviour other than to show 

that extreme behaviour is correlated with increasing 

fire danger.

After forty years of research into fire weather and 

fire behaviour, it is a shocking admission of the inadequacy of the research program if we must eliminate 

that segment of the fire danger/fire behaviour spectrum which includes all major fires which probably 

account for around 90-95 per cent of the fire danger 

in a severe fire season. 

The initial field application of Rothermel’s (1972) 

model involved 13 stylized or static fuel models9 (Albini 

1976b, Anderson 1982), which was later followed up with 

custom fuel models (Burgan 1987, Burgan and Rothermel 

1984). There are now 40 standard fuel models (Scott 2007, 

Scott and Burgan 2005). Methods exist for adjusting the 

6.1-m open windspeeds (Crosby and Chandler 2004) to a 

midflame height as required by the Rothermel (1972) model 

(Albini and Baughman 1979, Andrews 2012). Procedures 

also exist for estimating dead and live fuel moistures for use 

with the model (Rothermel 1983, Rothermel et al. 1986).

While favorable evaluations of observed versus 

predicted rate of fire spread have been obtained with the

Rothermel (1972) model in some surface fuelbeds (e.g., 

Hough and Albini 1978, Norum 1982, Rothermel and 

Reinhardt 1983), he acknowledged early on that his model 

was not applicable to predicting the behavior of crown 

fires because the nature and mechanisms of heat transfer 

between the two spread regimes were quite different. In the 

mid to late 1970s, the general guidance to gauging whether 

crowning was possible or not was to use the predicted surface fireline intensity or flame length as shown in table 9-6 

for example and in other guides (Albini 1976b, Hough and 

Albini 1978). There was no method at that time for predicting the spread rate or forward spread distance of crown 

fires, but by the early 1980s, the suggestion was being made 

to assume that crown fire rate of spread would be two to

9

 A “fuel model” is a simulated fuel complex for which all 

fuel descriptors required for the solution of Rothermel’s (1972) 

mathematical rate of spread model have been specified (Deeming 

and Brown 1975).
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Table 9-6—Fire suppression interpretations of flame length and fireline intensity 

Flame lengtha Fireline intensity Fire suppression interpretations

m kW/m

< 1.2 < 346 Fire can generally be attacked at the head or flanks by persons using handtools. 

Handline should hold the fire.

1.2 to 2.4 346 to 1,730 Fires are too intense for direct attack on the head by persons using handtools.

Handline cannot be relied on to hold fire. Equipment such as plows, dozers,

pumpers, and retardant aircraft can be effective.

2.4 to 3.4 1,730 to 3,459 Fires may present serious control problems—torching out, crowning, and spotting. Control efforts at the fire head will probably be ineffective. 

> 3.4 > 3,459 Crowning, spotting, and major fire runs are probable. Control efforts at head of 

fire are ineffective.

a Based on equation (2). 

Source: Adapted from Burgan 1979.

four times that of the predicted surface fire rate of spread of 

Anderson’s (1982) Fire Behavior Fuel Model 10 (Rothermel 

1983, Rothermel and Mutch 1986). 

Rothermel (1983) did include a graph similar to figure 

9-11 in his “how to” manual that was prepared much 

earlier and eventually published separately by Alexander 

(1988), which also included tables for estimating the critical 

surface fire intensity required for the onset of crowning as 

a function of CBH and FMC. These tables were eventually 

included in a fire behavior field reference (National Wildfire 

Coordinating Group 1992).

The 1988 fires in the Great Yellowstone Area are 

generally regarded as the impetus for developing a more 

robust method of predicting crown fire behavior in conifer 

forests (Alexander 2009a, Rothermel 1991c), although such 

a general need had been recognized for many years (e.g., 

Alexander and Andrews 1989, Buck 1941, USDA FS 1980). 

Rothermel (1991a) produced such a guide for the northern 

Rocky Mountains or mountainous areas with similar fuels 

and climate using currently available information (box 7), 

including the method of estimating fine dead fuel moisture 

content (table 9-7) given in Rothermel (1983). The core component of his method or approach was a simple correlation 

(i.e., a 3.34 multiplier as opposed to 2.0 to 4.0 as suggested 

earlier) derived from eight wildfire observations of crown 

fire rate of spread and the corresponding predictions from 

his surface fire rate of spread model (fig. 9-26). Rothermel 

(1991a) also included an adjustment factor (1.7) for estimating the near-maximum crown fire rate of spread associated 

with upslope runs or sudden surges in crown fire activity 

but not as a general adjustment factor as Scott (2006) has 

suggested (Cruz and Alexander 2010). 

Rothermel (1991a) emphasized that his statistical 

model for predicting the spread rate of wind-driven crown 

fires was a first approximation and that more research was 

needed to strengthen the analysis. At the time, he did not 

explicitly include any specific criteria for determining the 

onset of crowning other than in the most general terms (e.g., 

examine the fire weather forecast). 

Rothermel (1991a) considered his predictive methods 

were not applicable to plume-dominated or convectiondominated crown fires (Byram 1959b) although Goens and 

Andrews (1998) applied Rothermel’s methods in their postfire analysis of the 1990 Dude Fire. However, he did end up 

incorporating Byram’s (1959b) ratio of the power of the fire 

versus power of the wind concepts (Nelson 1993a, 1993b) 

into his guide so as to distinguish the conditions favorable 

for plume-dominated crown fires as opposed to wind-driven 

crown fires (Rothermel 1991a). Neither Byram’s (1959b) criteria nor Rothermel’s (1991a, 1991c) adaptation of Byram’s 

criteria using surface winds alone have been evaluated for 

their robustness.
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Box 7:

Summary of Major Assumptions Associated 

With Rothermel’s (1991a) Guide to Predicting 

Crown Fire Behavior

• These methods are designed to provide a 

first approximation of the expected behavior

of a running crown fire.

• Applicable to the northern Rocky Mountains 

or mountainous areas with similar fuels and 

climate.

• The methods are designed to predict the rate 

of spread and other behavior features of a 

wind-driven crown fire and help identify the

onset of a plume-dominated fire.

• Rate of spread predictions were derived from 

a small number (8) of fires; prediction relies

on the correlation of these fires to predictions of rate of spread using the firespread

model of Rothermel (1972) and Fuel Model 

10 (Anderson 1982).

• The heat pulse associated with the development of the convection column can be interpreted from the short-term surge of energy 

predicted by Albini’s (1976a) burnout model.

•	 Thomas’ (1963) flame length model represents crown fire flames.

• The wind can be represented by using the 

upper end of the forecast windspeed at the 

6.1-m level.

• The moisture of fuels, live and dead, can be 

represented by five seasonal groups.

•	 The period of a crown fire run can be

estimated.

•	 The area and perimeter of a fire can be

represented by a simple ellipse.

•	 The effect of firebrands on spreading the fire

is accounted for in the correlation of spread 

to actual fires.

•	 The surging and stalling of a fire as it climbs

and descends slopes can be averaged by assuming zero slope.

• The maximum spread rate can be estimated 

by using the maximum slope and correlation 

to maximum observed spread rates during 

the run of actual fires.

•	 The range in fire behavior can be reasonably

represented by 75 percent confidence limits

about the average rate-of-spread estimate.

• Standard fuel models, with addition of large 

fuels in some cases, can adequately describe 

the energy release of the surface fuels.

• The energy available from the overstory can be 

estimated by the crown needle load.

• The effect of additional heat from an understory 

of reproduction can be assumed to be some 

fraction of the overstory.

• The burning of decayed logs will increase the 

heat per-unit area significantly, and this additional heat will have an upper limit approximated 

by Fuel Model 12.

Table 9-7—Predicted fine dead fuel moisture (FDFM)a

content as a function of ambient air temperature and 

relative humidity and assuming >50 percent shading at 

between 1200 to 1600 hours during May through July 

Relative 

humidity

Air temperature 

Degrees Celsius

0 to 9 10 to 20 21 to 31 32 to 42 > 43

Percent

0 to 4 4 4 4 4 4

5 to 9 5 5 4 4 4

10 to 14 5 5 5 5 5

15 to 19 6 6 5 5 5

20 to 24 7 7 6 6 6

25 to 29 8 8 7 7 7

30 to 34 8 8 8 7 7

35 to 39 9 9 8 8 8

40 to 44 10 9 9 9 9

45 to 49 10 10 10 10 10

50 to 54 10 10 10 10 10

55 to 59 11 11 11 11 11

60 to 64 12 11 11 11 11

65 to 69 12 12 11 11 11

70 to 74 13 12 12 12 12

75 to79 14 13 13 13 13

a The FDFM values are used in the Rothermel (1991a) crown fire rate of 

spread model and in the Cruz et al. (2004, 2005) models for predicting 

crown fire occurrence and crown fire rate of spread.

Source: Adapted from Rothermel 1983.
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Rothermel (1991a) suggested using the flame length–

fireline intensity relation of Thomas (1963) for predicting 

the flame lengths of crown fires (fig. 9-27). However, neither 

his suggestion nor the approach of others seems to work 

consistently well based on comparisons against data from 

experimental crown fires (Alexander 1998, 2006). Furthermore, his model for predicting the L:B of crown fires 

from windspeed, based on the work of Anderson (1983) and 

Andrews (1986), does not appear to produce realistic results 

in light of observational evidence (fig. 9-23). 

U.S. Fire Modeling Systems

Since the late 1990s, a number of existing and newly 

developed decision support systems have either separately 

implemented or linked Rothermel’s (1972, 1991a) surface 

and crown fire rate of spread models with Van Wagner’s 

(1977a, 1989, 1993) crown fire transition and propagation 

criteria. These include both stand- and landscape-scale fire 

modeling systems:

Figure 9-26—Correlation between observed crown fire rate of 

spread associated with eight Western U.S. wildfires and the

Rothermel (1972) predicted surface fire rate of spread using

Anderson’s (1982) Fire Behavior Fuel Model 10–Timber (litter

and understory) (adapted from Rothermel 1991b). 

• BehavePlus (Andrews et al. 2008)

• FARSITE (Finney 2004)

• NEXUS (Scott 1999, Scott and Reinhardt 2001)

• Fire and Fuels Extension to the Forest Vegetation 

Simulator (FFE-FVS) (Reinhardt and Crookston 

2003)

• Fuel Management Analyst (FMA) Plus (Carlton 

2005)

• FlamMap (Finney 2006)

Comparisons of these systems are covered in Andrews 

(2007) and McHugh (2006). To the above list, we can also 

add two new additional geographic information systembased decision support systems, namely ArcFuels (Ager

et al. 2011) and the Wildland Fire Decision Support

System (WFDSS) (Noonan-Wright et al. 2011, Pence and 

Zimmerman 2011).

These fire modeling systems are extensively used for 

fire operations, planning, and research (Andrews and Queen 

2001). McHugh (2006) and Andrews (2007, 2010) provided 

excellent overviews on the applications of most of these 

systems to wildland fire management and science. Stratton 

(2006) in turn provided additional guidance on their use. 

Each system outputs numerous predictions of fire behavior 

characteristics and fire effects, including those associated 

with crowning in conifer forests, although each system

has subtle differences in the manner in which they have 

linked Rothermel’s models with those of Van Wagner

(Scott 2006, Stratton 2009) as illustrated in figure 9-28 for 

the BehavePlus system. There has been no field validation 

of the crown fire component of these modeling systems. 

In spite of the popularity of these fire modeling systems 

over the years, there appear to be some user-oriented 

problems. Varner and Keyes (2009) have, for example, 

identified several commonly encountered errors in regards 

to modeling inputs involved in simulations of fire behavior 

potential for research purposes, that may also be applicable 

to fire operations and planning. These include live and dead 

fuel moisture estimation, wind adjustment factors, fuel

load estimates, fuel model selection, fuel decomposition 

rates, and fuel bed patchiness. Varner and Keyes (2009) 

suggested that the errors “can often be tied to unsupported 
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Figure 9-27—Predictions of crown fire flame lengths as a function of fireline intensity and stand height based on (A) Byram’s (1959a) 

model compared to (B) the model of Butler et al. (2004b). Predictions of surface fire flame length using Byram’s (1959a) model is shown 

for comparative purposes as well along with the model of Thomas (1963) based on Rothermel’s (1991a) suggestion. 
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GENERAL TECHNICAL REPORT PNW-GTR-891

•Fuel model

•Midflame wind

•Slope

•Fuel moisture

•Spread direction

•Foliar Moisture 

•Canopy base 

 height

•20-ft wind

•Surface fuel 

moisture

•Canopy bulk 

 density

Surface 

fireline intensity,

IB

Critical surface 

fireline intensity,

I0 

Crown fire

rate of spread,

RC 

Critical crown fire

rate of spread,

R0 

Transition ratio

TR = IB / I0 

Active ratio

AR = RC / R0 

Transition to crown?

=Yes if TR >=1

=No if TR < 1

Active crown fire?

=Yes if AR >=1

=No if AR < 1

Fire Type Active crown fire?

No Yes

Transition

to crown

fire?

No Surface Conditional 

Crown

Yes Torching Crowning

SURFACE CROWN

Figure 9-28—Information flow for the CROWN module in the BehavePlus fire behavior modeling system (from

Andrews 2007). 
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assumptions about actual conditions and over reliance on 

default values.”

A recent review of the use of many of the aforementioned fire modeling systems in several simulation studies 

examining fuel treatment effectiveness revealed that many 

users are unaware of a significant underprediction bias 

that exists within these systems when it comes to assessing 

potential crown fire behavior in conifer forests of western 

North America (Cruz and Alexander 2010a). The principal 

sources of this underprediction bias include (1) incompatible 

model linkages (fig. 9-29), (2) use of surface and crown fire 

rate of spread models that have inherent underprediction 

biases themselves (figs. 9-30, 9-31A, and fig. 9-31B), and (3) 

a reduction in crown fire rate of spread based on the use of 

unsubstantiated crown fraction burned (CFB) functions (fig. 

9-32).10 The CFB is a measure of the degree of crown fuel 

consumption expressed as a percentage of the total number 

of tree crowns and as such constitutes an indication of the 

probable type of fire activity to be expressed over a burned 

area for fuel types that are susceptible to crowning (Poulin 

et al. 1994). The use of uncalibrated custom fuel models to 

represent surface fuel beds (Cruz and Fernandes 2008) was 

also identified as a fourth potential source of bias. Ager et 

al. (2011) stated that such limitations “are well known by the 

user community” but offer no evidence for this claim. 

The underprediction tendency with the Rothermel 

(1991a) model was found to occur as well with the crown 

fire rate of spread model (Schaaf et al. 2007) of the Fuel 

Characteristic Classification System (FCCS). (Ottmar et al. 

2007). Parresol et al. (2012) have indicated that the reformulation of the Rothermel (1972) surface fire rate of spread 

model by Sandberg et al. (2007) as incorporated in the 

FCCS has overcome the underprediction tendency observed 

in model predictions. However, no empirical evidence has 

been offered to date to substantiate this claim.

10 Note that Van Wagner’s (1993) CFB function failed to produce 

values greater than 0.9 (i.e., threshold continuous crown fire 

development) for 8 of the 11 experimental fires that Stocks (1987b) 

identified as displaying active or continuous crowning “with 

flame heights reaching 20 m or twice the average stand height” as 

photographically illustrated in Stocks and Hartley (1995) for three 

of the crown fires. The mean and range in CFB values were 0.64 

and 0.30 to 0.96, respectively. For further information on the CFB 

concept, see Alexander and Cruz (2010a, pp. 387–389).

The Cruz and Alexander (2010a) review also highlighted some issues with the manner in which users have been 

handling certain inputs in their crown fire modeling (i.e., 

foliar moisture content, canopy base height, and canopy 

bulk density) and some perceived shortcomings of the two 

windspeed-based crown fire hazard indexes originally 

developed by Scott (1998a, 1998d) and expanded upon later 

by Scott and Reinhardt (2001). Many authors were found to 

be using the National Fire Danger Rating System (Deeming 

et al. 1977), which generated dead fuel moisture contents 

rather than using the procedures given in Rothermel (1983) 

as used by Rothermel (1991a) in the development of his 

crown fire rate of spread model. In spite of all the limitations identified by Cruz and Alexander (2010a), studies 

similar to those reviewed by Cruz and Alexander (2010a) 

continue to be published (e.g., Honig and Fulé 2012, 

Johnson et al. 2011, Stephens et al. 2012, Van de Water and 

North 2011). Furthermore, several authors have recently 

begun to apply the fire modeling systems to lodgepole pine 

stands attacked by the mountain pine beetle (Klutsch et al. 

2011, Schoennagel et al. 2012, Simard et al. 2011), a situation that they were clearly never designed for (Jenkins et al. 

2012; Page et al. 2014). 

Canadian Forest Fire Behavior Prediction System

Do alternative methods exist for predicting crown fire 

behavior in certain regions of the United States? The 

Canadian Forest Fire Behavior Prediction (FBP) System 

(Forestry Canada Fire Danger Group 1992, Taylor et al. 

1997, Wotton et al. 2009) constitutes one such possibility, at 

least for certain regions of the United States possessing fuel 

complexes structurally similar to those found in adjacent 

areas of Canada. The FBP System is a module of the larger 

Canadian Forest Fire Danger Rating System (CFFDRS) 

(Lawson and Armitage 2008, Taylor and Alexander 2006), 

which also includes the Canadian Forest Fire Weather Index 

(FWI) System (Van Wagner 1987). Work began on the 

development of the FBP System in the early 1980s (Lawson 

et al. 1985). Some states have adopted all or part of the 

CFFDRS such as Alaska (Alexander and Cole 1995, 2001; 

Cole and Alexander 1995a, 1995b), Minnesota, Michigan, 

and Wisconsin as well as some foreign countries such as 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-29—An example of the differences in the critical midflame windspeeds required for the onset of crowning resulting from the 

implementation of Van Wagner’s (1977a) crown fire initiation model in various U.S. fire behavior modeling systems for Fire Behaivor 

Fuel Models 2–Timber (grass and understory) and 10–Timber (litter and understory) as described by Anderson (1982) (adapted from 

Cruz and Alexander 2010a). The following environmental conditions were held constant: slope steepness, 0 percent; fine dead fuel moisture, 4 percent; 10-h and 100-h time lag dead fuel moisture contents, 5 and 6 percent, respectively; live woody fuel moisture content, 75 

percent; and live herbaceous fuel moisture content, 75 percent. The associated 6.1-m open winds would be a function of forest structure 

and can be approximated by multiplying the midflame windspeed by a factor ranging between 2.5 (open stand) and 6.0 (dense stand with 

high crown ratio) (Albini and Baughman 1979). 

Figure 9-30—Observed head fire rates of spread >1.0 m/min associated with prescribed burning experiments in ponderosa pine forests 

of Yosemite National Park, California, versus predictions based on 

the Rothermel (1972) surface fire rate of spread model for fuel model 

9—hardwood litter as described by Anderson (1982) (adapted from 

van Wagtendonk and Botti 1984). The dashed lines around the line 

of perfect agreement indicate the ±35 percent error interval. Similar 

undprediction trends were observed in mixed conifer–pine, mixed 

conifer–fir, and true fir forest fuel types.
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Figure 9-31—Observed rates of spread of experimental active crown fires and wildfires that exhibited extensive active 

crowning versus predictions based on (A and B) Rothermel’s (1991a) and (C and D) Cruz et al. (2005) crown fire rate 

of spread models (adapted from Cruz and Alexander 2010a). The dashed lines around the line of perfect agreement 

indicate the ± 35 percent error interval. 
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GENERAL TECHNICAL REPORT PNW-GTR-891

New Zealand (Alexander 2008, Pearce and Alexander 

1994). 

The FBP System provides estimates of head fire 

spread rate, fuel consumption, fireline intensity, type of 

fire description (table 9-8). With the aid of an elliptical fire 

growth model, it gives estimates of fire area, perimeter, and 

perimeter growth rate as well as flank and backfire behavior 

characteristics for 16 major fuel types (De Groot 1993), 11 

of which are subject to crowning (i.e., seven coniferous and 

four mixed-wood types). The type of fire classification in 

the FBP System is based on the CFB:

Type of fire CFB

Surface fire <0.1

Intermittent crown fire 0.1 to 0.89

Continuous crown fire >0.9

The continuous crown fire classification is analogous 

to Van Wagner’s (1977a) active crown fire type, but the intermittent crown fire type, representing a very broad range 

in the degree of vertical fire development and behavior, can 

only be considered loosely applicable to his passive crown 

fire criteria. 

The FBP System includes functions for the acceleration 

in rate of fire spread for a point source ignition to a quasisteady-state equilibrium (McAlpine and Wakimoto 1991), 

including a prediction of the elapsed time to crown fire initiation. Emphasis is placed on the influences of fire weather 

(i.e., fuel moisture and wind) on potential fire behavior for 

a given fuel type (fig. 9-33) and the mechanical effects of 

slope steepness (fig. 9-17). The effects of fuel characteristics 

can be seen in the differences in environmental conditions 

across fuel types required to yield the same level of fire 

behavior (table 9-8). 

The FBP System forms the basis for a major component 

of PROMETHEUS–the Canadian wildland fire growth 

simulation model (Tymstra et al. 2010), which is similar to 

FARSITE. Comparisons between hindsight reconstructed 

FBP System predictions and observed fire behavior derived 

from U.S. wildfire case studies have shown remarkably 

good agreement (e.g., Alexander 1991, 1992a, 2000b).

The FBP System is similar in many respects to predictive systems currently used in the United States. The 

Figure 9-32—Comparison of the effect of crown fraction burned 

(CFB) functions on rate of fire spread developed by Scott and 

Reinhardt (2001) and Finney (2004) as used in various U.S. fire 

behavior modeling systems (e.g., NEXUS, FFE-FVS, FARSITE, 

FlamMap) in relation to the Rothermel (1972, 1991a) surface and 

crown fire rate of spread models and Van Wagner’s (1977a) criteria 

for the critical minimum spread rates for crown fire initiation 

(ROSinitation) and active crowning (ROScritical) for the Anderson 

(1982) Fire Behavior Fuel Model 2–Timber (grass and understory) 

with a wind-reduction factor of 0.2 (Albini and Baughman 1979), 

a canopy bulk density of 0.1 kg/m3, canopy base height of 1.5 

m (adapted from Cruz and Alexander 2010a). A crown fraction 

burned function is not employed in BehavePlus. The following 

environmental conditions were held constant: slope steepness, 0 

percent; fine dead fuel moisture, 6 percent; 10-h and 100-h time 

lag dead fuel moisture contents, 7 and 8 percent, respectively;

live woody fuel moisture content, 75 percent; live herbaceous fuel

 moisture content, 75 percent; and foliar moisture content,140 

percent. The dashed portion of the Rothermel (1991a) curve 

represents output below the original dataset bounds for rate of 

spread. The Beaufort scale for estimating 6.1-m open windspeeds 

is presented in table 9-3. A guide to estimating probable maximum 

1-min and momentary gust speeds is given in table 9-4.
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 9-33—Rate of fire spread on level terrain to gently undulating terrain as a function of the Initial Spread Index component of the 

Canadian Forest Fire Weather Index System for three Canadian Forest Fire Behavior Prediction (FBP) System fuel types: boreal spruce 

(C-2); mature jack or lodgepole pine (C-3); and red and white pine 

(C-5). Typically, the lower section of the S-shaped curve represents 

surface fires, the upper flattening section represents continuous, active crowning, and the relatively steep intermediate section, a transition zone characterized by very high-intensity surface fires with 

significant torching and passive crown fire activity. Van Wagner’s 

(1977a) criiwb fire initiation model is incorporated into the BBP 

System by assigning nominal values for canopy base height to the 

coniferous and mixed-wood fuel types. Foliar moisture content can 

be estimated on a daily basis from calendar date, elevation, and geographical location (latitude/longitude) (Alexander 2010a, Forestry 

Canada Fire Danger Group 1992). 

Table 9-8—Type of fire as a function of the Initial Spread Index (ISI)a component of the 

Canadian Forest Fire Weather Index (FWI) System for the coniferous (C) and mixedwood (M) 

forest fuel types found in the Canadian Forest Fire Behavior Prediction (FBP) System 

Fuel type 

identifier Descriptive name

Surface 

fire

Intermittent 

crown fire

Continuous 

crown fire

ISI ISI ISI

C-1 Spruce-lichen woodland 3 to 8 9 to 15 ≥16

C-2 Boreal spruce ≤1 2 to 7 ≥8

C-3 Mature jack or lodgepole pine 2 to 9 10 to 15 ≥16

C-4 Immature jack or lodgepole pine ≤2 3 to 8 ≥9

C-5 Red and white pine 3 to 25 26 to 40 ≥41

C-6 Conifer plantation (7 m CBH) ≤8 9 to 17 ≥18

C-7 Ponderosa pine/Douglas-fir ≤15 16 to 30 ≥31

M-1 Boreal mixedwood–leafless 75C:25H ≤3 4 to 10 ≥11

M-2 Boreal mixedwood–green75C:25H ≤3 4 to 10 ≥11

M-3 Dead balsam fir mixedwood–leafless ≤1 2 to 3 ≥4

M-4 Dead balsam fir mixedwood-green ≤3 3 to 6 ≥7

a

 The ISI is a relative numerical rating that combines the effects of fine fuel moisture (based on past and current weather 

conditions) and wind speed on the expected rate of fire spread (Van Wagner 1987). In the above tabulation, level terrain, 

a foliar moisture content of 97 percent, and a Buildup Index (BUI) of 81 to 120 are assumed. The BUI component of the 

FWI System is a relative numerical rating of the fuel available for combustion based on fuel dryness as determined by past 

and current weather conditions (Van Wagner 1987). In addition, a canopy base height (CBH) of 7.0 m has been assigned to 

fuel type C-6, the M-1 and M-2 fuel types are assumed to comprise 75 percent conifer (C) and 25 percent hardwood (H), 

and the M-3 and M-4 fuel types are assumed to contain 100 percent dead fir.

Source: Adapted from Taylor et al. 1997.
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GENERAL TECHNICAL REPORT PNW-GTR-891

principal difference is in the technical basis (Stocks et al. 

2004a), reflecting on what Van Wagner (1971) called the 

“two solitudes” in wildland fire behavior research (box 8). 

The Rothermel (1972) surface fire model is based largely on 

laboratory fires and physical theory. The FBP System, on 

the other hand, is largely empirically based, representing 

the culmination of nearly 30 years of outdoor experimental 

burning (Alexander and Quintilio 1990) work in major 

Canadian fuel types (e.g., Alexander et al. 1991; Lawson 

1973; Quintilio et al. 1977; Stocks 1987a, 1987b, 1989; Van 

Wagner 1964, 1968, 1986; Weber et al. 1987) coupled with 

monitoring and documentation of numerous high-intensity 

wildfires (e.g., Alexander and Lanoville 1987, Kiil and 

Grigel 1969, Stocks and Flannigan 1987, Van Wagner 1965). 

A training manual on the FBP System has been prepared by 

Hirsch (1996) based on Forestry Canada Fire Danger Group 

(1992), and a variety of interpretive aids exist (e.g., Alexander and Cole 1995, Alexander and DeGroot 1988, Alexander 

and Lanoville 1989, Cole and Alexander 1995). 

Crown Fire Initiation and Spread (CFIS) System

The Crown Fire Initiation and Spread (CFIS) software 

system (Alexander et al. 2006) is a suite of empirically 

based models for predicting fire behavior (Alexander and 

Cruz 2009). These models are based largely on a reanalysis 

of the experimental fires carried out as part of developing 

the Canadian FBP System (Cruz 1999, Cruz et al. 2002). 

In this regard, the intention is to use the Cruz et al. (2005) 

active crown fire rate of spread model in the next generation 

of the CFFDRS (Wotton 2010).

The main outputs of CFIS are as follows (Alexander

et al. 2006):

• Likelihood of crown fire initiation or occurrence 

based on two distinct approaches, one of which relies 

on the CBH or certain components of the Canadian 

FWI System (Cruz et al. 2003b), whereas the other is 

determined by the fine dead fuel moisture (table 9-4), 

Box 8:

The Two Solitudes in Wildland Fire Research 

(from Van Wagner 1979b)

“... the researcher studying fire behaviour is

continually faced with the choice between the 

theoretical and empirical approaches. He cannot 

solve his problem by pure physics. ... if he tries 

miniaturized laboratory modeling, he is up against 

awesome difficulties in scaling all the dimensions

and energy transfer processes of a phenomenon 

that may be so much greater in size and intensity 

than anything he can amount in the laboratory. 

...taking a more empirical approach, he may 

seek to light experimental fires in the real forest.

He must sacrifice some control over burning

conditions, but his main problem is to sample 

the whole range of intensity. It is easy enough 

to accumulate plenty of data in the low intensity 

range, but the main interest is in what happens 

when the fire weather is at its most severe; these

moments come rather seldom and the practical 

difficulties of controlling the experiments are

obvious. However, much good information about 

fire behaviour in a particular fuel can be gained

from a very few successful experimental fires of

say 1⁄2 to 5 ha in extent. ... [the] final recourse

is to chase and observe accidental forest fires,

a most frustrating business as anyone who has 

tried it will tell you. Nevertheless, by being in the 

right place and the right time in a very few choice 

occasions, some valuable information obtainable 

in no other way can be gathered, including various bits of detective work that can be done after 

the fire has cooled down.”
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Table 9-9—Interpreting probabilities 

Probability Interpretation

<0.01 Extremely unlikely

0.01 to 0.10 Very unlikely or very improbable

0.10 to 0.33 Unlikely or improbable

0.33 to 0.66 Medium probability

0.66 to 0.90 Likely or probable

0.90 to 0.99 Very likely or very probable

>0.99 Virtual certainty

Source: Pollack 2003.

CBH, windspeed, and an estimate of surface fuel consumption (Cruz et al. 2004)

(fig. 9-34).11

• Type of crown fire (passive crown fire or active 

crown fire) and its associated rate of spread based on 

fine dead fuel moisture, CBD, and windspeed (Cruz 

et al. 2005) (figs. 9-35 and 9-36).

• Minimum spotting distance required to increase a 

crown fire’s overall forward rate of spread assuming 

a point ignition and subsequent fire acceleration to 

an equilibrium rate of spread based on the presumed crown fire rate of spread and ignition delay 

(Alexander and Cruz 2006) (fig. 9-25).

The primary models incorporated into CFIS have 

been evaluated against both outdoor experimental fires and 

wildfire observations as shown in figures 9-31C and 9-31D 

to be reasonably reliable (e.g., Alexander and Cruz 2006, 

Cronan and Jandt 2008, Stocks et al. 2004b). Output from 

CFIS is now being used as a proxy for reality by some users 

(e.g., Schreuder et al. 2010).

Scott (2006) claimed that the small size of the experimental fires used in the development of CFIS “may preclude 

direct application to real crown fires.” However, Alexander 

et al. (1991) had demonstrated earlier on that spread rate 

data collected from outdoor experimental fires does in fact 

mimic real-world situations.

The CFIS does allow one to evaluate the impacts of 

proposed fuel treatments on potential crown fire behavior 

based on the ability to manipulate three characteristics 

of a forest fuel complex (i.e., available surface fuel load, 

CBH and CBD) using silvicultural techniques (e.g., Agee 

and Skinner 2005; Graham et al. 1999, 2004; Johnson and 

Peterson 2005; Keyes and O’Hara 2002; Keyes and Varner 

2006; Scott 1998b, 1998c, 1998e). Whitehead et al. (2007), 

11 Model output is expressed as the probability (table 9-9) of 

initiating a crown fire between 0 and 100 percent for the stated 

environmental conditions as defined by the inputs. The probabilities are estimates of the likelihood that allow uncertainty to 

be quantified. A threshold probability of 50 percent is presently 

assumed to separate a surface fire (<50 percent) from a crown fire 

(>50 percent). The horizontal dashed line in each graph in figure 

9-34 represents this threshold value for the onset of crowning. Further experience with the CFIS may eventually suggest otherwise.

for example, examined the effect of reducing CBD by 

partial cutting on the threshold between passive and active 

crowning using CFIS. 

The CFIS is considered most applicable to free-burning 

fires that have reached a pseudo–steady state, burning in 

live, boreal, or boreal-like conifer forests found in western 

and northern North America (i.e., it is not directly applicable

to insect-killed or otherwise “dead” stands). The models 

that comprise CFIS are not applicable to prescribed fire or 

wildfire situations that involve strong convection activity as 

a result of the ignition pattern. Level terrain is assumed, as 

the CFIS does not presently consider the mechanical effects 

of slope steepness (Van Wagner 1977b) on crown fire behavior, although this is being planned for in a future version 

of the system. Furthermore, it is assumed that the heavy 

fuel moisture threshold (Lawson et al. 1994, Lawson and 

Dalrymple 1996) found in mature, high-elevation spruce-fir 

stands (Williams and Rothermel 1992) has been reached. 

Some Other Empirically Based Approaches

Some Australian eucalypt stand types are prone to crowning (e.g., McCaw et al. 1988). The basic index of the 

Australian Forest Fire Danger Meter (McArthur 1967) 

provides for the prediction of wildfire behavior characteristics in terms of forward rate of fire spread, flame height, 

and spotting distance on level to gently undulating terrain 

in a dry eucalypt forest with fine fuel quantities of 1.25 kg/

m2 (Luke and McArthur 1978). The meter also identifies the 

general conditions required for crown fire development in 

this fuel type (fig. 9-37). Prediction can be adjusted for the 

mechanical effects of slope steepness and varying quantities 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-34—The likelihood of crown fire occurrence as (A-B) a function of canopy base height and windspeed for two fine 

dead fuel moisture levels, assuming a surface fuel consumption of 1.0 to 2.0 kg/m2 and (C-D) as a function of surface fuel consumption and windspeed, assuming a fine dead fuel moisture of 4 percent, based on the Cruz et al. (2004) probability model. 

The horizontal dashed line in each graph represents the approximate threshold value for the onset of crowning (i.e., 0.5 probability of crown fire occurrence). The Beaufort scale for estimating 6.1-m open windspeeds is presented in table 9-3. A guide to 

estimating probable maximum 1-min and momentary gust speeds is given in table 9-4.
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Figure 9-35—Threshold conditions for passive versus active crown fire spread in terms of windspeed and fine dead fuel moisture for 

four canopy bulk density levels based on the Cruz et al. (2005) crown fire rate of spread models and Van Wagner’s (1977a) criteria for 

active crowning. The Beaufort scale for estimating 6.1-m open windspeeds is presented in table 9-3. A guide to estimating probable 

maximum 1-min and momentary gust speeds is given in table 9-4.
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GENERAL TECHNICAL REPORT PNW-GTR-891

Figure 9-36—Passive and active crown fire spread rates as a function of windspeed and fine dead fuel moisture for four canopy bulk 

density levels based on the Cruz et al. (2005) crown fire rate of spread models. The vertical “kinks” in the fine dead fuel moisture 

curves are considered to represent the windspeed thresholds between passive and active crowning. The Beaufort scale for estimating 

6.1-m open windspeeds is presented in table 9-3. A guide to estimating probable maximum 1-min and momentary gust speeds is given 

in table 9-4.
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Figure 9-37—Graphical representation of McArthur's (1967, 1973) guide to sustained crown fire propagation in Australian 

eucalypt forests based on (A) rate of spread and fuel load and (B) Forest Fire Danger Index and fuel load assuming that crowning 

occurs once flame heights exceed 14 m.

of fuel (Cheney 1968). Fire behavior characteristics in pine 

plantations, including those associated with crowning, have 

been related to index values (Douglas 1964, Luke 1962, 

McArthur 1965).

Roussopoulos (1978a, 1978b) used Rothermel’s (1972) 

surface fire and Van Wagner’s (1977a) crown fire models to 

determine crowning thresholds for broad fuel types found 

in northeastern Minnesota where an extensive forest-fire 

fuel inventory had been carried out in the Boundary Waters 

Canoe Area of the Superior National Forest. He chose to 

present the results in the form of graphical aids or “nomographs” similar to those constructed by Albini (1976b) for 

the Anderson (1982) fuel models.

Bruner and Klebenow (1979) developed the following simple formula for predicting whether a successful 

prescribed burn (i.e., what effectively amounts to a “controllable” crown fire) was possible in pinyon-juniper woodlands 

on the basis of 30 prescribed fires carried in Nevada on 

level terrain: score = maximum windspeed (mi/h) + air 

temperature (°F) + vegetative cover (percentage). Resultant 

scores are interpreted as follows: 

• Score <100: burning conditions are such that fires 

will not carry.

• Score of 110 to 125: fires will carry, but continual 

retorching will be necessary.

• Score of 125 to 130: burning conditions are optimal 

for a self-sustaining fire following ignition.

• Score >130: burning conditions are too hazardous 

for prescribed burning.

The authors acknowledged that there appeared to be 

a very narrow separation between conditions for successful prescribed burning and those that would result in an 

uncontrollable high-intensity wildfire that would escape the 

confines of the prescribed burn unit, a fact that is substantiated by general field observations (e.g., Hester 1952).
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Physics-Based Models

Physics-based models are formulated on the basis of the 

chemistry and physics of combustion and heat-transfer 

processes involved in a wildland fire (Grishin 1997, Morvan 

2011, Sullivan 2009b). They range in complexity from 

models for calculating rate of fire spread based solely on 

the radiation from the flaming front (e.g., Albini 1996) to 

three-dimensional models coupling fire and atmospheric 

processes. Examples of the latter include FIRETEC (Linn 

et al. 2002), FIRESTAR (Dupuy and Morvan 2005), and 

the Wildland-Urban Interface Fire Dynamics Simulator 

(WFDS) (Mell et al. 2007). 

Physically based models hold great promise in being 

able to advance our theoretical understanding of wildland 

fire dynamics and could possibly be used for operational 

prediction of wildand fire behavior in the future or at least 

lead to the improvement of existing operational models 

(Sullivan 2009b). Van Wagner (1985) emphatically noted 

that:

The fire world would beat a path to the door of the 

modeller who could account for vertical gradients 

and interruptions in moisture content and fuel 

density as well. Crowning fire is the most obvious 

application for such a comprehensive model. 

By their completeness, these models should be able 

to predict not only the development (Porterie et al. 2003) 

but the demise or cessation, spread rate, fuel consumption, 

intensity, and flame dimensions of crown fires in relation to 

any combination of fuel, weather, and topographic variables. In recent years, these models have been extensively 

used as research tools to evaluate the effects of fuel treatments (Contreras et al. 2012), canopy fuel structure (Linn 

et al. 2005; Parsons et al. 2010, 2011), and mountain pine 

beetle attack on crown fire dynamics (Hoffman et al. 2012, 

2013, Linn et al. 2013). Nonetheless, the capacity of these 

models to describe crown fire behavior for such applications 

is still open to question (Alexander and Cruz 2013a) given 

that the evaluation against any empirical crown fire data 

undertaken to date is limited to two fires (Linn et al. 2012).

Forty-two years ago, Lindenmuth and Davis (1973) 

suggested that “Quite likely the data from empirical study 

will be useful in making the inputs for theoretical models 

more accurate.” What appears to be to happening is the 

continuing emergence of empirical and physically based 

approaches (Cruz and Gould 2009, Sullivan 2009b, Van 

Wagner 1985). An example of such an approach is the semiphysically based crown fuel ignition model (CFIM) developed by Cruz (2004) to predict the onset of crowning based 

on fundamental heat transfer principles (Cruz et al. 2006b, 

2006c). A series of submodels that take into account surface 

fire characteristics along with canopy fuel properties is used 

to predict the ignition temperature of canopy fuels above 

a spreading surface fire (fig. 9-38). An evaluation of CFIM 

has been undertaken involving a sensitivity analysis of input 

Figure 9-38—Estimated crown fuel temperature for four canopy 

base heights above a surface fire based on the Canopy Fuel Ignition Model (CFIM) of Cruz et al. (2006b). The fuel particle for 

which the heat transfer calculation was done is located directly 

at the flame front edge (i.e., x = 0). The maritime pine stand with 

shruby understory fuel model of Cruz and Fernandes (2008) was 

used. A stand height of 12 m and an available surface fuel load of 

0.7 kg/m2 were assumed. The following environmental conditions were held constant: slope steepness, 0 percent; fine dead fuel 

moisture, 9 percent; and foliar moisture content, 160 percent. 
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parameters, comparison against other similar models under 

different burning conditions, and testing against outdoor 

experimental fires (Cruz et al. 2006a). Results have been 

favorable and provided new insights into the factors controlling the initiation of crown fires. 

Another example of the merging of empirical and 

physical modeling approaches was the International Crown 

Fire Modeling Experiment (ICFME) (Stocks et al. 2004a). 

One of the objectives of this experimental burning program 

carried out in the Northwest Territories of Canada from 

1995 to 2001 (Alexander 2005, Alexander et al. 2001) was 

to test a newly developed, deterministic physical model for 

predicting crown fire rate of spread (Albini 1996, Butler 

et al. 2004b, Call 1997), which included an empirically 

based model for predicting fuel consumption based on fuel 

particle diameter and moisture content (Call and Albini 

1997). Measurements of flames’ radiometric properties and 

temperatures (Butler et al. 2004a) allowed for the parameterizing of the heat-transfer components in Albini’s (1996) 

crown fire rate of spread model. Model evaluation indicated 

that the model predicted the relative response of fire spread 

rate to fuel and environmental variables, but it consistently 

overpredicted the magnitude of the spread rates observed on 

the ICFME crown fires. 

Not all physically based models for predicting wildland 

fire spread specifically take into account the effects of 

spotting in increasing a fire’s rate of spread. The effects of 

spotting on a fire’s overall rate of advance are implicitly 

accounted for in both the FBP System and the Rothermel 

(1991a) crown fire rate of spread model as a result of the empirical nature of their development (i.e., the use of wildfire 

observations as a data source). This assumes, however, that 

the fuels are continuous. Neither approach indicates how 

barriers to fire spread are to be handled. Short-range spotting from a crown fire is presumably able to easily breach 

fuel discontinuities of up to 100 m in width (Stocks et al. 

2004b, Taylor et al. 2004). Nominal spotting from crown 

fires is undoubtedly capable of breaching even much wider 

barriers, perhaps up to 1000 m (Alexander et al. 2004). 

What is unknown, however, is how much of a reduction 

there will be in the head fire rate of spread as a result of the 

time delay involved (which might possibly be 30 to 60 min 

or longer) for the fire to resume its forward, equilibrium rate 

of advance.

Albini (1979) developed a physically based model for 

predicting the maximum spotting distance from single or 

group tree torching that covers the case of intermediaterange spotting of up to perhaps 1.5 to 3.0 km; he also developed similar models for burning piles of slash or “jackpots” 

of heavy fuels12 (Albini 1981a) and wind-aided surface 

fires in non-tree canopied fuel complexes such as grass, 

shrubs, and logging slash (Albini 1983a, 1983b). This model 

is included within the BehavePlus modeling system, and a 

manual procedure is given in Rothermel (1983). Rothermel 

(1991a) pointed out at the time he prepared his guide that 

no model existed for predicting the spotting distances 

for running or active crown fires. Venkatesh et al. (2000) 

subsequently extended Albini’s (1979) model to the case of 

wind-driven crown fires. The result was a 20- to 25-percent 

increase in spotting distance. However, no testing of this 

model has been undertaken to date to our knowledge. The 

Venkatesh et al. (2000) model, like the one developed 

by Albini (1979), provides a prediction of the maximum 

firebrand transport distance. Determining whether a given 

ember or firebrand will actually cause a spot fire must still 

be assessed based on its ignition probability (e.g., Beverly 

and Wotton 2007, Lawson 1973, Rothermel 1983). 

More recently, an alternative predictive system has 

been put forth for estimating the maximum spotting distance from active crown fires as a function of the firebrand 

particle diameter at alighting based on three inputs, namely, 

canopy top height, free flame height (i.e., flame distance 

above the canopy top height), and the windspeed at the 

height of the canopy (Albini et al. 2012). Although the 

system has not been specifically validated, the estimates 

produced by the system (fig. 9-39) appear realistic in light of 

existing documented observations.

12 Beginning in the late 1980s, some wildland fire behaviour analysts or 

specialists began applying the Albini (1981a) maximum spot fire distance 

model for burning piles to burning structures in the wildland-urban 

interface (Steele, J.K. 2011. Personal communication. Confederated Salish 

and Kootenai Tribes Division of Fire, PO Box 278, Pablo, MT 59855).
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Figure 9-39—Comparison of predictions for maximum potential 

spotting distance over level terrain as a function of windspeed for 

a specified set of burning conditions based on models developed 

by Frank A. Albini (adapted from Albini et al. 2012). A 6.1-m 

open windspeed of around 5 km/h is considered required for a 

consistently heading fire (Cheney et al. 1998). The Beaufort scale 

for estimating 6.1-m open windspeeds is presented in table 9-3. 

A guide to estimating probable maximum 1-min and momentary 

gust speeds is given in table 9-4.

Example of a Practical Application of Linking 

Empirical and Physically Based Models

Pine Plantation Pyrometrics (PPPY) is a new modeling 

system developed to predict fire behavior in industrial 

pine plantations in Australia over the full range of burning 

conditions in relation to proposed changes in fuel complex 

structure from fuel treatments (Cruz et al. 2008). The system comprises a series of submodels, including CFIM and 

elements of CFIS, that describe surface fire characteristics 

and crown fire potential in relation to the surface and crown 

fuel structures, fuel moisture contents, and windspeed 

(fig. 9-40). A case study application of the PPPY modeling 

system has highlighted the complex interactions associated 

with fuel treatments such as pruning and thinning have on 

surface and crown fire behavior potential (fig. 9-41). It is 

also noteworthy that no definite reduction or increase in rate 

of spread was identified. While a direct evaluation of the 

system’s overall performance has yet to be undertaken, its 

main components have been evaluated against independent 

datasets with encouraging results (Cruz et al. 2015). 

Implications for Fire and Fuel Management 

In the broadest sense, the general conditions favorable for 

the development of crowning in conifer forests have been 

known for some time now (e.g., Beale and Dieterich 1963, 

Byram 1954, Gisborne 1948, Rothermel 1995) and also 

apply to nonforested fuel types as well that exhibit high 

rates of fire spread and fireline intensities or very long flame 

lengths (e.g., Butler and Reynolds 1997). These include:

• Continuous fine fuels in sufficient quantity and arrangement, both vertically and horizontally.

• A dry spell of sufficient length to reduce the moisture content of dead fuels to a uniformly low, critical 

level coupled with high ambient air temperatures 

and low relative humidities.

• Strong prevailing winds or steep slopes.

In the past 25 years or so, these conditions have, in 

turn, been crudely codified in various forms suitable for use 

by field personnel. Other aspects of the fire environment 

such as low foliar moisture content (Reifsnyder 1961), high 

foliar heat content (Chrosciewicz 1986a), presence of flammable oils and resins in the needle foliage (Hough 1973, 

Philpot and Mutch 1971) or drought (Cohen 1989; Cohen et 

al. 1987, 1989, 1990) may lead to an increase in crown fire 

potential but by themselves have not been found as of yet to 

be a major predisposing factor. 

Assuming a threshold level in dryness has been reached 

in the forest floor layer, the potential for crown fire development and spread would generally follow the daily diurnal 

cycle in fire weather conditions, typically peaking in late 

afternoon (Beck et al. 2002). However, crown fire activity 

can extend late into the day if fire weather conditions are 

favorable for maintaining the moisture content of fine, dead 

surface fuels at low levels (Hartford and Rothermel 1991).

Rothermel (1991a) quite rightly pointed out that “Fires 

are seldom uniform and well behaved.” Given the chaotic 

nature of most extreme fire phenomena, can we expect the 
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Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists

Figure 9-40—Flow diagram of the Pine Plantation Pyrometrics (PPPY) modeling system for predicting fire behavior in exotic pine 

plantations (adapted from Cruz et al. 2008). CAC is the criteria for active crowning (Van Wagner (1977a), CFROS is the crown fire rate of 

spread, and SFROS is the surface fire rate of spread. 
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GENERAL TECHNICAL REPORT PNW-GTR-891

Box 9:

Underestimating Crown Fire Potential in 

Conifer Forests 

By linking Rothermel’s (1972, 1991a) models 

for predicting surface and crown fire rates of

spread with Van Wagner’s (1977a, 1993) crown 

fire transition and propagation models, Scott and

Reinhardt (2001) developed two crown fire hazard

indices–the Torching Index (TI) and the Crowning 

Index (CI). The TI and CI represent the threshold 

windspeeds required for the onset of crowning and 

active crown fire propagation in conifer forests, respectively. Each TI and CI value is tied to a unique 

set of surface fuelbed characteristics (expressed in 

terms of a stylized or custom fuel model), dead and 

live moisture contents of surface fuels, crown fuel 

properties (i.e., canopy base height and bulk density, foliar moisture content), and slope steepness. 

These two indexes have proven to be very popular 

amongst both researchers and fire managers alike.

Cruz and Alexander (2010a) found that many 

simulation studies that relied upon the TI and 

CI as a means of assessing crowning potential 

in relation to fuel treatment effectiveness, often 

produced unrealistic outcomes considering the 

associated environmental conditions and fuel 

characteristics. Quite often critically dry fuel 

moisture levels were specified (i.e., 1.5 to 3

percent) along with very low canopy base heights 

and relatively high canopy bulk densities and 

yet the simulations suggested that exceedingly 

strong winds were commonly required to initiate 

crowning and for fully developed or active crown 

fires to occur. In many cases, these simulation

studies have reported TI and CI values for gale 

force wind conditions (i.e., sustained winds 

greater than about 100 km/h). Such winds 

seldom occur inland, but, when they do, they 

generally result in trees and whole forest stands 

being blown down over large areas (table 9-6). 

Scott (2006) suggested that these very high wind 

velocities simply indicated “a very low potential 

for initiating a crown fire” and that windspeeds

at or in excess of 100 km/h “occur so rarely that 

crown fire can be considered nearly impossible

to initiate,” thereby implying there is no need for

any concern. 

Figure 9-41—Head fire rate of spread as a function of windspeed 

for 12-year-old thinned (50 percent basal area reduction treatment) 

and unthinned pine plantation stands based on the Pine Plantation 

Pyrometrics (PPPY) modeling system (adapted from Cruz et al. 

2008). The fuel complex characteristics for the thinned and unthinned stands were respectively: surface fuel available for combustion, 1.1 and 0.5 kg/m2

; canopy base height, 1.7 and 0.9 m; and 

canopy bulk density, 0.05 and 0.1 kg/m3. Given an air temperature 

of 40 oC and a relative humidity of 20 percent, the fine dead fuel 

moistures for the surface litter were, in turn, judged to be 5 and 

7 percent, respectively. Foliar moisture content was set at 100 

percent in both cases and level to gently undulating terrain was assumed. The Beaufort scale for estimating 6.1-m open windspeeds 

is presented in table 9-3. A guide to estimating probable maximum 

1-min and momentary gust speeds is given in table 9-4.
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behavior of crown fires to ever really be truly predictable? 

That depends on how accurate you expect the prediction to 

be. Certainly the minute-by-minute movement of a crown 

fire will probably never be predictable. 

We should also continue to expect “exceptions to the 

rule” (e.g., Alexander 2004). Byram (1954) summed up this 

sentiment very well:

As more case-history fires are studied, it is possible 

to assemble a collection of statements about these 

fires which could be called the facts of fire behavior, 

or perhaps better, the facts of extreme fire behavior. 

It seems permissible to call them facts, because 

more investigators would probably agree on their essential meaning even though different investigators 

might explain them differently. Possibly the simplest 

way to define and introduce the problem of extreme 

fire behavior is to list known conditions associated 

with blow-ups. 

Box 9 (continued)

It could be argued that the outcomes of these 

simulation studies are realistic in that they simply 

reflect the fact that both strong winds and dry

fuels are required to achieve any sort of torching 

or crowning activity. While this may be intuitively 

true for areas that have undergone some form 

of fuel treatment such as prescribed burning, for 

control or untreated areas the simulation results 

do not appear realistic based on general observation and experience. For example, Simard et 

al. (2011) indicated that TI levels above 700 km/h 

were necessary for passive crowning to occur in 

well-stocked, undisturbed lodgepole pine stands 

with an average CBH of 3.5 m. 

Clearly, burning conditions specified in these

simulation studies are not necessarily representative of those associated with large, highintensity wildfires that exhibit extensive crowning.

For example, the crown fire run in lodgepole pine

forests on the 1988 North Fork Fire that threatened the Old Faithful Inn complex in Yellowstone 

National Park occurred at a fine dead fuel

moisture content of 6 percent with windspeeds of 

around 25 km/h (Thomas 1991).

He went on to provide a list that “contains many 

seeming contradictions which any effective solution must 

resolve” (Byram 1954, pp. 3-4). That list is presented in 

chapter 1 of this volume.

Alexander and Cruz (2006) have shown that there is a 

certain degree of unexplained variation in crown fire rate of 

spread even in the most idealized field situations. Interestingly enough, the same applies to laboratory fires involving 

so called reproducible fuelbeds. However, in looking at 

crown fire propagation across longer timeframes (e.g., 30 

min to several hours), the available data have shown that 

some models and modeling systems are very capable of 

predicting fire spread within a margin of error that is useful 

to fire managers. Nevertheless, given the coarseness and 

uncertainty associated with the inputs in the crown fire 

initiation and propagation models, managers should be wary 

of their use for near-real-time predictions of fire behavior. 

Underestimating the potential for the onset of crowning 

under conditions that would sustain active crown fire propagation can, in turn, lead to substantial underpredictions 

in crown fire rate of spread and fireline intensity. Recent 

state-of-the-knowledge reviews have shown that there are 

limits to what can be expected from rate of fire spread 

models but that there are ways to deal with the uncertainty 

in model predictions (Alexander and Cruz 2013b, Cruz and 

Alexander 2013).

The value of utilizing a Monte Carlo-based ensemble 

method to predicting wildland fire behavior has recently 

been demonstrated by Cruz (2010). This approach provides 

for error bounds to be established and a probabilistic output 

of the uncertainties associated with model predictions, and 

allows one to capture the variability in bi-modal fire propagation systems, such as encountered when a fire transitions 

back and forth between surface and ladder fuels or surface/

understorey fuels and overstory crown fuels, such as occurs 

in situations involving intermittent crowning (Cruz and 

Alexander 2009).

Models or guides that have a good fundamental 

framework and a solid empirical basis presumably predict 

fire behavior well when used for conditions that are within 

the database used in their development (Sullivan 2009a). 

Overestimates of fire behavior can easily be readjusted 
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without serious consequences. However, underestimates can 

be potentially disastrous (Cheney 1981). The underprediction trends in predictions of crown fire behavior (box 9) 

mentioned earlier on should be of concern (Alexander and 

Cruz 2013a). If a system predicts or simulates that a fire will 

behave as a moderate-intensity surface fire under extreme 

fire weather conditions, why would it be necessary to undertake any form of fuel treatment or even be concerned about 

the general flammability of an area? Model underestimates 

of crowning potential can lead people to put themselves in 

grave danger. Considering that fire behavior prediction systems are used in gauging the need and timing of community 

evacuations associated with wildland-urban interface fires, 

underpredicting a fire’s forward rate of spread by a factor 

of three has some serious ramifications (Anguelova et al. 

2010). Creditable estimates of crown fire potential are also 

required in prescribed burning (Bryant et al. 1983, Custer 

and Thorsen 1996, Racher 2003, Woodard et al. 1983, 

Woodard and Van Nest 1990, Zimmerman 1990), including 

escape potential (Archibald et al. 1994).

It has been suggested that most wildland fire operations 

personnel base their expectations of how a fire will behave 

largely on experience and, to a lesser extent, on guides 

to forecasting fire behavior (Burrows 1984). Experienced 

judgement is needed in any assessment of wildland fire 

potential, but it does have some limitations (Gisborne 1948). 

The same can be said for mathematical models and computerized decision support systems. Given the present realities, 

practical knowledge and sound professional judgment 

coupled with experience are still needed and perhaps should 

take on an even more prominent role when it comes to 

adjusting, interpreting, and applying surface and crown fire 

behavior predictions (Andrews 1980, Beighley and Bishop 

1990, Rothermel 1991c). In this regard, the comments of 

Williams and Rothermel (1992) seem very apropos:

The best chance for success in fire behavior prediction requires a mix of fire experience with analytical 

modeling methods. But in situations where conditions are beyond the limits or outside the assumptions of the models, fire predictions must rely even 

more on intuitive judgements. Such judgements 

could be more easily made if managers know 

general patterns of fire behavior through a full range 

of burning conditions.

Predicting wildland fire behavior is, after all, both an 

art and a science (Alexander 2009c, Alexander and Thomas 

2004, Barrows 1951, Rothermel 1983, Scott et al. 2014).

Until recently, the “art” side of forecasting or predicting 

wildland fire behavior (e.g., Weick 2002) has been largely 

ignored as a field of endeavor in wildland fire research. In 

this respect, the “Learning From the Experts” videos created from the interviews associated with Fire Management 

Deep Smarts Project undertaken by former fire behavior 

analyst Dave Thomas with the support of the Wildland Fire 

Lessons Learned Center, the Aldo Leopold Wilderness 

Research Institute, and National Park Service–Aviation 

and Fire Management, (Thomas et al. 2012) should prove 

valuable (box 10). “Deep Smarts” represents experiencedbased wisdom amongst individuals who by virtue of their 

intuition, judgment, and knowledge are considered experts 

in their field (Leonard and Swap 2005). 

Wildland fire research has done much to contribute to 

our current understanding of crown fire behavior through 

laboratory experiments, outdoor experimental burning, 

numerical modeling, and wildfire case histories (box 10), 

as well as operational experiences (e.g., Rothermel 1991c, 

1998). See, for example, the selected case study summaries 

given in Pyne et al. (1996, pp. 82-89). Although operational 

fire behavior specialists have also made substantial case 

study contributions (e.g., Beighley and Bishop 1990, Miller 

et al. 2009, Murphy et al. 2007, Thomas 1991), valuable 

information and insights are not being captured in a 

systematic way. McAlpine and Wotton (2009) have outlined 

one possible way in the form of a “fire behavior knowledge 

base.”

Various attempts have been made over the years to 

monitor and document high-intensity fire behavior (Alexander and Taylor 2010, Vaillant and Fites-Kaufman 2009), but 

what is required is a permanently staffed, ongoing effort. 

Alexander (2002) has in fact suggested that there is a need 

to create operational fire behavior research units specifically 

for this purpose. The efforts made by fire researchers and 
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Box 10:

Useful Links to Further Information

U.S. Fire Modeling Systems

•	 http://firelab.org/applications

Canadian Forest Fire Danger Rating System

•	 https://www.frames.gov/cffdrs/applied-firebehavior/canadian-forest-fire-dangerrating-system/

•	 http://cwfis.cfs.nrcan.gc.ca/en_CA/background/summary/fdr

Crown Fire Initiation and Spread System

•	 http://www.frames.gov/cfis/

International Crown Fire Modeling Experiment

• https://www.frames.gov/icfme

• http://www.youtube.com/

watch?v=FYup7cYKE3w

•	 http://www.youtube.com/watch?v=zvPa_

yEEd4E

Joint Fire Science Program Crown Fire 

Behavior Synthesis Project

• http://www.fs.fed.us/wwetac/projects/

alexander.html

Fire Management Deep Smarts Project: 

Learning From the Experts

•	 http://wildlandfireleadership.blogspot.

ca/2012/01/leading-from-experts.html

Fire Management Today

•	 http://www.fs.fed.us/fire/fmt/fmt_pdfs/

FMT73-4.pdf

Canadian Fire Behaviour Knowledge Base

• http://fbkb.ca/

2002 Hayman Fire, Colorado

• http://www.fs.fed.us/rmrs/presentations/

hayman-fire-symposium/

Crown Fire Behavior Characteristics and 

Prediction in Conifer Forests: A State of 

Knowledge Synthesis

 • http://www.fs.fed.us/wwetac/projects/alexander.html

Box 10: continued

Applied Wildland Fire Behavior Research and 

Development

•	 https://www.frames.gov/applied_fire_

behavior

Fire on Earth: An Introduction

•	 www.wiley.com/go/scott/fireonearth

fire weather meteorologists of the country in the 1950s and 

1960s (DeCoste and Sackett 1966, Sackett and DeCoste 

1967, Schaefer 1961, Small 1957) were unfortunately not 

sustained beyond the early 1970s (Chandler 1976). However, 

recent advances in all aspects of the technology associated 

with monitoring and documenting high-intensity wildfires 

have gradually made the task easier (e.g., Gilles 2011).

The continuance of basic research into fire fundamentals is essential to gaining a complete understanding of the 

physical processes involved in crown fire dynamics (Clark 

et al. 1999, Coen et al. 2004, Cohen 2004, McRae and 

Ji-zhong 2004, Radke et al. 2000), but scientific knowledge 

alone will not be enough to develop a complete picture of 

crown fire dynamics. There is still an overriding need to 

bolster the efforts in observing crown fire behavior and 

completing the necessary case study documentation (Alexander and Thomas 2003a, 2003b) in order to evaluate new 

and existing predictive models of crown fire behavior (box 

11). Such a program should be regarded as a shared responsibility between wildland fire research and fire management 

and be considered part and parcel of adaptive management 

(Alexander and Taylor 2010). 

Case study knowledge will prove a useful complement 

to fire behavior modeling and experienced judgment when it 

comes to appraising potential crown fire behavior (Alexander 2007b, 2009a; Alexander and Thomas 2003a, 2003b) as 

illustrated, for example, by Rothermel (2000). However, one 

still needs to be wary of properly interpreting observations 

of extreme fire behavior (Alexander 2009b).
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Box 11: 

On Validating Fire Models (From Watts 1987)

“To many, computer models are the proverbial 

“black box” – we put something in and we get

something else back out. Our confidence in the

output may be solely a function of the reputation 

of the modeler. We may recognize the need to 

understand the important and fundamental principles involved, but there may not be time to work 

through all the aspects of the model. Validation 

should be rigorously pursued despite time and 

economic constraints, however, because, it is a 

vital link between science and its application.

Yet if validation is a process for determining 

that the outputs of a model conform to reality, no 

model can be validated in an absolute sense; i.e.,

a model can never be proved correct, it can only 

be proved wrong. Acceptance of a model does 

not imply certainty, but rather a sufficient degree

of belief to justify further action. Thus, in practice, 

validating a fire model is really a problem of

invalidation. The more difficult it is to invalidate

the model, the more confidence we have in

it. To increase our confidence we can subject

the model to tests and comparisons designed 

to reveal where it fails. One approach used to 

validate models ... is to compare the results to 

those of another model in which one already has 

great confidence.

Correct “invalidation” of a fire model is also

difficult. The fire modeler is working in an area in

which relations among important variables are not 

precisely known. To build a model, many aspects 

of the real world must be aggregated or simplified. Simplifications are introduced for analytical

or computational convenience or sometimes as 

a compromise to the cost of gathering data. 

Documentation should clearly state what has 

been assumed and what sort of uncertainty or 

bias the assumption is likely to introduce in the 

model output. It should also be made clear how 

the aggregations and simplifications restrict the

types of predictions the model can and cannot 

make.”

Watts (1987) comments echo an earlier 

statement by Box (1979) who stated that “All 

models are wrong, some are useful.”

Future Outlook 

In discussing his dichotomous key for appraising crowning 

potential (table 9-2), Fahnestock (1970) indicated that “No 

technique is available for calculating the mathematical 

probability that a fire will crown under given conditions.” 

In turn, Kerr et al. (1971) considered that “In the foreseeable 

future, there is little prospect of predicting the behavior 

of a fast-spreading crown fire in timber over any extended 

period of time.” More recently, Agee (1993) stated, “The 

chances of firebrand spotting and crown fires can be 

estimated, but the behavior of crown fire is still relatively 

unpredictable.” In light of these comments, obviously much 

has been accomplished and experienced in the past 20 to 

40 years when viewed from the point of our current understanding and predictive capability with respect to crown 

fires (box 12). 

Presumably, the future holds the same promise as the 

recent past provided we are willing to readily admit what 

we know and more importantly what we presumably still do 

not know about crown fires with respect to their environment, characteristics, and prediction. Several knowledge 

gaps have been alluded to throughout this summary. Furthermore, many basic wildland fire behavior research needs 

identified some 30 years ago, some of which are relevant 

to crown fires, have yet to be addressed (e.g., Albini 1984, 

1997; Alexander and Andrews 1989; Van Wagner 1985). 

As Cohen (1990) has pointed out, research must be directed 

at both the operational products desired by fire and fuel 

managers, and the fundamental understanding that forms 

the basis for such end-user tools (box 13). Research into the 

prediction of crown fire behavior with respect to the safety 

of the general public and firefighters might be regarded 

as the very “raison d’être” (i.e., reason or justification for 

existence) for wildland fire research (Alexander 2007a). 

Further discoveries and advancements in understanding of crown fire dynamics in conifer forests will require 

a dedication in time, money, and staff (Blatchford 1972). 

While basic or fundamental fire research should continue to 

be pursued (Finney et al. 2013), we also need to “promote 

studies that critically analyze and synthesize our existing 

knowledge” (Trevitt 1989). In actual fact, a comprehensive synthesis on crown fire behavior has been underway 
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Box 12:

Crown Fire Dynamics in Conifer Forests–A 

Summary of the Salient Points

Types of Crown Fires 

Three kinds or classes of crown fire are recognized according to their degree of dependence 

on the surface phase of fire spread (i.e., passive,

active, and independent, although the latter is 

generally regarded as a rare and short-lived 

occurrence).

Crown Fire Initiation 

The amount of heat energy required in the form 

of convection and radiation to induce the onset of 

crowning is dictated by the canopy base height 

and foliar moisture content as manifested in the 

surface fire’s intensity. A rather abrupt increase in

fire activity should normally be expected as a fire

transitions from the surface to crown fire phase.

Crown Fire Propagation 

Whether a passive or active crown fire develops

following the onset of crowning depends on the 

spread rate after initial crown combustion and is 

in turn related to canopy bulk density. A minimum 

value of about 0.1 kg/m3 appears to represent a 

critical threshold for active crowning.

Crown Fire Rate of Spread 

At a minimum, a doubling or tripling in a fire’s

rate of advance follows the onset of crowning. 

Wind-driven crown fires have been documented

to spread at up to 100 m/min for several hours 

and in excess of 200 m/min for up to an hour. 

Although the mechanical effect of slope steepness on increasing a fire’s rate of spread is well

known, fires in mountainous terrain generally do

not spread nearly as far for a given period of time 

compared to those on flat topography.

Crown Fire Intensity and Flame Zone 

Characteristics 

As a result of the increase in spread rate and 

fuel available for combustion, a fire can easily

quadruple its intensity in a matter of seconds when 

crowning takes place (e.g., from 3,000 to 12,000 

kW/m). The resulting wall of flame,

standing nearly erect, is on average up to two to 

three times the tree height and emits fierce levels of

radiation. Flame fronts commonly exceed 30 to 45 

m in depth.

Crown Fire Area and Perimeter Growth 

The area burned by a crown fire is at least four to

nine times greater that of a surface fire for the same

period of time. Assuming unlimited horizontal fuel 

continuity, crown fires are capable of burning an

area upwards of 70 000 ha with a perimeter length 

of 160 km in a single burning period and have done 

so in the past.

Crown Fire Spotting Activity 

Crown fires commonly display high-density, shortrange spotting (<50 m). Spotting distances of up to 

about 2.0 km, although less common, are frequently 

seen on crown fires, resulting in normal barriers

to fire spread being breached. Many spot fires are

simply overrun by the main advancing flame front of

a crown fire before they effectively contribute to an

increase in the fire’s overall rate of advance. Cases

of long-distance spotting in excess of 10 km have 

been reported. 

Models, Systems, and Other Decision Aids for 

Predicting Crown Fire Behavior 

The current set of guides and decision support system for assessing potential crown fire behavior used

in the United States do require considerable adjustment on the part of trained and informed users (e.g., 

fire behavior analysts, long-term fire analysts) for

proper application. Alternative models and systems 

that have undergone far more extensive testing and 

requiring a minimum of inputs are available. 

Implications for Fire and Fuel Management 

Operational fire management personnel can readily

help themselves when it comes to being able to assess crown fire behavior by increasing the amount of

wildfire monitoring and case study documentation.
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(Alexander 2011, 2014; Alexander et al. 2012a, 2013), new 

research into the complexities of crown fire phenomenology 

has already been initiated (Cohen et al. 2006, Cruz and Alexander 2009), and allied studies completed (e.g., Cruz and 

Alexander 2013, Cruz et al. 2014). We should not lose sight 

of the fact that “The prediction of surface fire behavior is, in 

fact, probably more difficult than the prediction of crowning 

potential, because of the multiplicity of possible forest floor 

and understory fuel complexes” (Van Wagner 1979a).

In the long run, scientific investigations into crown 

fire behavior might be best accomplished in the form of a 

collaborative, international or global research, development, 

and application effort (Christensen et al. 2007, McCaw 

and Alexander 1994, Weber 1995). Networked, multidisciplinary teams that can build on extant understanding while 

creating new knowledge regarding the mechanisms associated with crown fire initiation and spread may provide the 

necessary platform.

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Box 13:

The Wildland Fire Behavior Prediction 

Paradox

Rothermel (1987) has articulated the perpetual 

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“... experience shows that predicting 

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Acknowledgments

“This chapter is a contribution of Joint Fire Science

Program (JFSP) Projects 09-2-01-11 and 09-S-03-1. 

Dave Thomas (U.S. Forest Service, retired), Phil Omi 

(Professor Emeritus, Colorado State University), Mike 

Jenkins and Wesley Page (Utah State University), Chad 

Hoffman (University of Idaho), John Kern (Florida Division 

of Forestry), David Weise (U.S. Forest Service), Nicole Vaillant (U.S. Forest Service), Bill Bereska (Alberta Sustainable 

Resource Development, retired), Terry Van Nest (Alberta 

Sustainable Resource Development, retired), Bill Droog 

(Ontario Ministry of Natural Resources), Steve Jakala (U.S. 

Fish and Wildlife Service), Brad Armitage (Ember Research 

Services Ltd.), Charley Martin (U.S. Geological Survey), 

Sonny Stiger (FireSafe Montana), Mark Heathcott (formerly 

Parks Canada), Ross Wilmore (U.S. Forest Service), and 

Jason Loomis (Bureau of Land Management). The support 

of Mike Hilbruner (U.S. Forest Service, retired) is sincerely 

appreciated.” 

Common and Scientific Namesa

Common name Scientific name

Allepo pine Pinus halepensis Mill.Show AllShow Tabs

Black spruce Picea mariana (Mill.) BSP

Douglas-fir Pseudotsuga menziesii (Mirb.) Franco

Engelmann spruce Picea engelmannii Parry ex Engelm.

Jack pine Pinus banksiana Lamb.

Korean pine Pinus koraiensis

Lodgepole pine Pinus contorta Douglas ex. Louden

Maritime pine Pinus pinaster Ait.

Monterey pine Pinus radiata D. Don

Oak Quercus spp.

Ponderosa pine Pinus ponderosa C. Lawson

Red pine Pinus resinosa Aiton

Sand pine Pinus clausa (Chapm. ex Engelm.) Vasey ex Sarg

Scots pine Pinus sylvestris L.

Singleleaf pinyon Pinus monophylla Torr. & Frém.

Stone pine Linus pinea L.

Subalpine fir Abies lasiocarpa (Hook) Nutt.

Utah juniper Juniperus osteosperma (Torr.) Little

Western larch Larix occidentalis Nutt.

White pine Pinus strobus L.

White spruce Picea glauca (Moench) Voss

Spruce Picea spp.

a

 Source: USDA NRCS 2010. 
258

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International System (SI)-to-US Customary Unit Conversion Factors

SI Unit Conversion US Customary Unit

Revers 

Conversion

Centimeters (cm) ×0.394 Inches (in) ×2.54

Degree Celsius (°C) (9/5×°C)+32 Degree Fahrenheit (°F) 5/9×(°F-32)

Gram (g) ×0.0352 Ounce (oz) ×28.4

HectoPascal (hPa) ×0.030 Inches of mercury (in Hg) ×33.8

HectoPascal (hPa) ×1 Millibar (mb) ×1

Hectare (ha) ×2.47 Acre (ac) ×0.405

Joule (J) ×0.000948 British thermal unit (Btu) ×1050

Joule per kilogram-degree C

(J kg-1 °C-1)

×0.000239 Btu per pound-degree Fahrenheit

(Btu lb-1 °F-1)

×4180

Joules per meter (J m-1) ×0.000251 Btu per foot (Btu ft-1) ×3990

Kelvins (K) [9/5×(K-273)]+32 Degree Fahrenheit (°F) 5/9×(°F-32)+273

Kilogram (kg) ×2.2 Pound (lb) ×0.455

Kilogram per cubic meter (kg m-3) ×0.0623 Pound per cubic foot (lb ft-3) ×16.0

Kilogram per square meter (kg m-2) ×0.204 Pound per square foot (lb ft-2) ×4.89

Kilogram per square meter (kg m-2) ×4.46 Tons per acre (t ac-1) ×0.224

Kilojoule per kilogram (kJ kg-1) ×0.431 Btu per pound (Btu lb-1) ×2.32

Kilometer ×0.621 Mile (mi) ×1.61

Kilometer per hour (km h-1) ×0.621 Mile per hour (mi h-1) ×1.6

Kilowatt per meter (kW m-1) ×0.290 Btu per second-foot (Btu s-1 ft-1) ×3.45

Megawatt (MW) ×948 Btu per second (Btu s-1) ×0.00105

Megawatt per meter (MW m-1) ×289 Btu per second-foot (Btu s-1 ft-1) ×0.003

Meter (m) ×3.28 Foot (ft) ×0.305

Meter per minute (m min-1) ×3.28 Foot per minute (ft min-1) ×0.305

Meter per minute (m min-1) ×2.98 Chain per hour (ch h-1) ×0.335

Meter per second (m s-1) ×3.28 Feet per second (ft s-1) ×0.305

Number per hectare (no. ha-1) ×0.405 Number per acre (no. ac-1) ×2.47

Square meter (m2) ×10.76 Square foot (ft2) ×0.093

Square meter per hectare (m2 ha-1) ×4.36 Square foot per acre (ft2 ac-1) ×0.23
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