Guide to Fire Behavior Fuel Models in Tropical Ecosystems€¦ · conservation. She then led a population reintroduction assessment on howler monkeys in Belize, Central America. After

Guide to Fire Behavior Fuel Models

in Tropical Ecosystems

Tricone F, Anderson TR Dos Fuegos Fire Management 2018

GUIDE TO FIRE BEHAVIOR FUEL MODELS IN TROPICAL ECOSYSTEMS

Photography credit Tricone F

Citation Tricone F and Anderson TR (2018) Guide to fire behavior fuel models in tropical ecosystems. Sarteneja, Belize, 28p.

Contact [email protected]

Website

dosfuegos.org

Authors’ bibliography Fanny Tricone is a French Fire Ecologist based in Belize. She is a graduate in Environmental Engineering from Agrocampus Ouest, France. During her graduate studies, she collaborated on field research in Borneo, Malaysia and became interested in tropical conservation. She then led a population reintroduction assessment on howler monkeys in Belize, Central America. After graduation, Fanny relocated to Belize and developed an interest in the impacts of fire in tropical ecosystems. She began her work with community fire

management through the Darwin Initiative project in 2017. After her work with the Darwin Project, Fanny attended Fire Modeling and Fire Behavior training at Tall Timbers Research Station, one of the world’s premier sites for wildland fire science. Since then, Fanny has collaborated on fire modeling and fire management projects in the Yucatan Peninsula of Mexico, Costa Rica and Belize. Her interest is in providing methods and techniques to assess fire risk and develop strategies to manage fire in tropical ecosystems. Fanny is the author of several guides and protocols for fuels assessment, fire ecology and fire effects monitoring.

Rick Anderson is an American Fire Ecologist. A descendent of Florida’s earliest pioneers, Rick learned the craft of fire from his ancestors. For over thirty years, he served as a fire manager and fire ecologist for the United States National Park Service in eight different National Park Units across the US in diverse locations, such as Yellowstone National Park, Saguaro National Park and the Everglades. Rick also worked as The Nature Conservancy’s Fire Manager for the Southeast US. During his career he achieved several qualifications from Incident Commander (Type III), to Division Supervisor and Prescribed Fire Burn Boss Type 1. Rick retired from the National Park Service in 2015 as Fire Management Officer at Everglades National Park. Now he works as an international fire consultant with fire management projects in Belize, Mexico, Costa Rica, Trinidad and Brazil. Rick Anderson is also Writer and Executive Producer with IntoNatureFilms.org a Florida documentary film company specializing in handcrafted stories focused on human partnerships with ecosystems.

http://www.intonaturefilms.org/


INTRODUCTION 1

BACKGROUND KNOWLEDGE 1

WHY DO FIRE MANAGERS NEED TO UNDERSTAND WILDLAND FUELS? 1 WHAT ARE THE BASIC CONCEPTS OF FIRE BEHAVIOR? 2 WHAT ARE FUELS? 3 WHAT IS THE INFLUENCE OF SURFACE FUELS ON FIRE BEHAVIOR? 4 WHAT IS LIVE FUEL MOISTURE CONTENT? 5 WHAT ARE DEAD FUELS? 6

What is dead fuel moisture content? 7 What is fuel moisture of extinction? 7 What is Equilibrium Moisture Content (EMC)? 7 What is the timelag principal? 8

FIRE BEHAVIOR FUEL MODELS 8

A BRIEF HISTORY OF FUEL MODELS 8 SELECTING A FUEL MODEL 11 CREATING FUEL MODEL MAPS 14 FIRE BEHAVIOR MODELLING SYSTEMS 15

Predictions for wildfires 16 Predictions for prescribed burning 16 Adaptive management 17

REFERENCES 18

APPENDICES 19

FIGURE 1: SMALL FUEL (LEFT) AND LARGE FUEL (RIGHT) 4 FIGURE 2: PATCHY GRASSES (LEFT) AND BURNING PALMETTO PATCH (RIGHT) 5 FIGURE 3: LEVEL OF CURING VERSUS LIVE HERBACEOUS MOISTURE CONTENT (SCOTT & BURGAN 2005) 11 FIGURE 4: UNBURNED FOREST DURING SAVANNA FIRE DUE TO HIGHER MOISTURE CONTENT 12 FIGURE 5: FIRE BEHAVIOR COMPARISON OF THE DIFFERENT GRASS FUEL MODELS (SCOTT & BURGAN 2005) 13 FIGURE 6: TABLE FROM MONZÓN-ALVARADO AND PADILLA PAZ (2015) OF THE SELECTION OF STANDARD FUEL MODELS THAT COULD BE

ADAPTED TO THE FUELS IN THE RESERVA DE BIOSFERA LOS PETENES, MEXICO 14 FIGURE 7: EXAMPLE OF THE MAPPING OF BURN AREAS TO UPDATE A FUEL MODEL MAP 15

TABLE 1: CLASSIFICATION OF THE 40 FUEL MODELS 10 TABLE 2: FUEL MODELS FOR DRY (IN RED) AND HUMID (IN BLUE) CLIMATES 12


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Introduction Some of the information included in this guide was compiled from World of Wildland Fire, Scott and

Burgan (2005) and the National Wildfire Coordinating Group.

This guide is intended to provide the essential knowledge to understand and use surface fire behavior fuel models in tropical environments.

Background knowledge Many plant species in naturally fire affected environments require fire to germinate, establish and

reproduce. Many ecosystems, particularly prairie, savanna, and coniferous forests have evolved with fire as a contributor of ecosystem health, renewal and restoration.

It is important to recognize that the fire environment is very complex and variable, which makes it difficult to understand what kind of fire behavior could be expected under various environmental and fuel

conditions. We can reduce this complexity by using models which are simplified representations of the real world.

The fire triangle describes the factors necessary for combustion and therefore all the things in the environment that influence fire behavior. Fuels are living and dead plant matters like shrubs, trees, litter and woody debris. Oxygen comes from the atmosphere and heat comes from an ignition source such as lightning. If these three parameters are present, the fuel is ignited and it can become a source of heat for other fuels. It is called heat transfer and it is necessary for fire to spread.

Understanding the process of combustion and how heat is transferred is important in understanding fire behavior. It influences how fast fire spreads, how much heat it produces and how big the flames are.

Combustion formula: Sugars (C6H10O5) + O + ignition source = CO2 + H2O + heat

Sugars are formed by photosynthesis in plants. They are fuels.

Why do fire managers need to understand wildland fuels?

Fuels, weather and topography are the primary factors that influence how hot a fire gets and how fast it spreads. Fire itself can produce positive feedbacks that further influences fire behavior. The changing states of each element and how they interact with each other determine the behavior of a fire. For example, wind speed influences how fast fire will spread, and the amount of fuel available to burn influences how hot a fire will get.


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As fuel is an integral part of the fire environment triangle, understanding fuel and its properties is vital to understanding how a wildland fire might behave. This knowledge also informs strategies for safely manipulating fuels to influence potential fire behavior. In the context of fire management, there is nothing that can be done about the weather or topography. Fuel is the one part of the fire triangle that can be purposely managed to produce a change in fire behavior. Fuel management is simple in theory and complicated in practice and includes both modifying potential fire behavior and influencing fire effects. There are numerous methods used to modify fuel, including prescribed fire, managed wildfire, and mechanical removal or modification of fuel on sites. Understanding fire behavior helps managers make informed decisions to reduce risk. Expertise in fire behavior is essential to achieve land management and ecological objectives. Knowing how, when and where fire can burn is critical for safe and effective fire management.

What are the basic concepts of fire behavior?

The four basic concepts that describe fire behavior are:

- Rate of spread: rate of spread is the amount of time it takes for the leading edge of a fire to travel from point A to point B

- Fireline intensity: rate of energy release per unit length of the fire - Flame length - Flame height.

These four basic components are all influence by the components of the fire triangle.

The front (head), flank and rear of a fire will behave differently, and fire behavior can change quickly with a change in wind direction or slope. Most fire starts at a single point called origin and spread most rapidly in the direction of the wind or uphill, generally forming the shape of an ellipse. Fire behavior are:

- Head fire: The fastest spreading part of the fire is called the front or head, and it spreads with the wind or up-slope.

- Backing fire: The slowest spreading part of the fire, opposite the head, is the back or rear. Backing fire is normally moving against the wind and/or downslope.

- Flanking fire: The sides of the fire or flanks will spread at intermediate rates and burn outward at right angles to the wind or slope.


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Behavior of backing or flaking fires can change quickly with a wind shift, changing for example the slow rate of spread flanking fire into a fast rate of spread heading fire.

A spot fire is a new fire ignited outside the main fire perimeter.

The 3 types of fire are ground, surface and crown. Ground fire spread primarily by smoldering combustion through subsurface fuels including deep duff, roots, rotten buried logs and other organic material. Ground fire have slow rate of spread. Surface fires spread by flaming combustion through fuels at or near the surface, including needles, leaves, grass, woody debris, small plants and shrubs. Surface fires have a higher rate of spread than ground fires. Crown fires burn through shrub and tree crowns and canopies. They are fast spreading and release an enormous amount of energy over a short period of time.

What are fuels?

Fuels are organic materials that burn in a fire. They include living and dead material above mineral soil. Therefore this can include trees, down logs, shrubs, grasses, litter or duff. However, not all of this fuel is necessarily available for combustion. Some may be difficult to ignite under certain conditions. For example, needles may be too wet to ignite.

Fuels can be classified as:

- Aerial or crown: foliage, small branches in the canopy of trees and tall shrubs. - Surface (up to 6ft): litter, downed wood, herbaceous plants, and small trees and shrubs that are

on or near the surface of the ground. - Ground (below the surface): duff, buried logs, roots and other organic materials.

Surface fuels are important to understand because they are used in fire behavior models to describe potential surface fire behavior. Surface fuels and aerial fuels are used in fire behavior models to describe

(Monzón-Alvarado and Padilla Paz 2015)


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potential crown fire behavior. Some ground fuels are used in fire effects models to describe potential fire effects on soil, vegetation, and air quality.

The fuel models presented in this document are to simulate surface fire behavior at the flaming front. These fire behavior fuel models are applicable to fire behavior modelling systems that use Rothermel’s surface fire spread model.

What is the influence of surface fuels on fire behavior?

Wildland fuels have different properties that can influence fire behavior in different ways. These specific properties are often quantified so that we can have some understanding of potential fire behavior.

Quantity / loading: dry weight of fuel in a given area. The greater the fuel loading, the more heat will be released when the fuels are burning. However, greater fuel loading may also lead to slower rates of fire spread because there is less oxygen available for combustion. This is true in moist environments like the tropic where relatively high fuel loading may not generate as much heat because large fuels are wet and the surface to volume ration is decreased.

Size & shape: ratio of surface area to volume. Small fuels such as needle, litter, and grass blades have large surface area relative to their volume. Fuels with high surface area to volume ratio are relatively easy to ignite, thus can contribute to rapid fire spread.

Figure 1: Small fuel (left) and large fuel (right)


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Depth of the fuel bed: measured from the soil or duff surface to the top fuel layer. The deeper or taller the fuel, the higher the potential flame length.

Compactness: spacing between fuel particles. Compact fuel beds have little spacing between fuel particles. It is expressed as the packing ratio which is calculated from fuel bed depth, loading and size. Fuel beds with a high packing ratio are more compact. Compact fuels have little oxygen available for combustion, which makes them more difficult to ignite.

Arrangement: dominant orientation of the fuels. Fuel beds can be arrange either vertically or horizontally. Vertically oriented fuels will tend to have longer flame length compared to horizontally oriented fuels. Grasses and shrubs are vertically oriented while timber litter and logging debris are horizontally oriented.

Continuity: fuel beds can be continuous or patchy. Patchy fuel beds have fuels interspersed with areas of no fuel. Continuous fuel beds have few areas with no fuel. Fire is more likely to spread through continuous fuels.

Chemical content: some fuels have high amounts of volatile substances such as oil, resins, waxes, and pitch. Fuels with lots of volatile chemicals in the leaves can be easy to ignite even with high moisture content. Example: palmetto.

Moisture content: it is one of the most important fuel properties that influence potential fire behavior. Fuels with high moisture content are difficult to ignite.

What is live fuel moisture content?

Living fuels include both woody and herbaceous plant materials.

Figure 2: Patchy grasses (left) and burning palmetto patch (right)


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Live fuel moisture is the measure of the amount of water in living fuels and is a critical factor that

influences fire behavior and effects. It is expressed as a percentage of the dry weight of that fuel. It can range from 35-300%. Only brand new growth contains up to 300% moisture content.

% = 𝑤𝑤𝑤𝑤𝑤𝑤 − 𝑑𝑑𝑑𝑑𝑑𝑑 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ

𝑑𝑑𝑑𝑑𝑑𝑑 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ× 100%

The moisture content of live fuels is always changing and can change by plant type and growth stage. Most ecosystems hold a variety of perennial and annual vegetation and each species will have a different seasonal growth habit or time of the year when their fuel moisture content is highest.

Live fuel moisture affects the potential for live fuels to burn.

While curing, live herbaceous load shifts between live and dead depending on the specified live herbaceous moisture content. This is important as it makes the fuel models dynamic.

What are dead fuels?

Dead fuels include all dead parts such as litter, duff, fallen logs, cured plants (grasses), dead vegetation attached to living vegetation, and standing dead trees.


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What is dead fuel moisture content?

Fuel moisture content of dead vegetation is the measure of the amount of water in a piece of fuel and is expressed as a percentage.

% = 𝑤𝑤𝑤𝑤𝑤𝑤 − 𝑑𝑑𝑑𝑑𝑑𝑑 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ

𝑑𝑑𝑑𝑑𝑑𝑑 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ× 100%

Dead vegetation is critical in determining fire potential. Unlike live fuel, dead fuel responds solely to ambient environmental conditions and is highly susceptible to daily changes in moisture content levels. Dead fuel cells can absorb water vapor from the air and absorb liquid water in the form of dew or rain. Dead fuel loses water through evaporation when the atmosphere is drier than the fuel. Fine fuels such as grasses, needles and twigs can react daily. Yet larger fuels such as branches and logs will take longer to absorb and lose moisture, especially in the center of the fuel. For fire managers, using dead fuel moisture levels to determine the potential for fire to ignite and spread is critical on wildland and prescribed fires.

Fine dead fuel (i.e. the most flammable material: grasses, pine needles) moisture is an important factor in determining fire behavior including rate of spread, flame length and fire intensity. Generally, the drier the fuel, the faster and hotter the fire will spread. Prescribed fire managers use fuel moisture levels as a deciding factor of how best to meet burn objectives. If the moisture content is low, the fire can become intense and erratic and fire effects may be severe. If the moisture content is high, little fuel may be consumed.

The properties of the fuels directly impact fuel moisture. Compacted fuels such as a slash pile or dense pine needles will have slower evaporation rates than fuels that are more loosely arranged. This is because the more surface area exposed, the more oxygen available so more flammable.

What is fuel moisture of extinction?

At some moisture level, fuel is too wet for fire to spread. This is called moisture of extinction. This percentage will vary with fuel characteristics such as the size, amount and arrangement of the fuels on the landscape. In general, fine fuels, such as grasses, will have a lower moisture of extinction while for larger size fuels, the percentage will be higher. You can observe moisture of extinction during the evening or when fire reaches fuels adjacent to wetland areas. This is because the fuels have reached their moisture of extinction and are too wet to burn.

What is Equilibrium Moisture Content (EMC)?

Dead fuel reaches its EMC when the fuel has reached equilibrium with its environment. The lower the EMC, the more fuel is readily available to burn because the fuel is dry and ready to ignite. However, the equilibrium is dynamic and changes with relative humidity, temperature and fuel type. For example, it increases with relative humidity increasing or temperature decreasing. The material or the size of the fuel is also a determining factor of the EMC of the fuel.


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What is the timelag principal?

A timelag is the amount of time it takes for a fuel to approach its EMC due to changes in the environment. When the environment changes, fuel requires time to wet or dry to approach EMC. Timelag is related to fuel size.

Smaller fuels have a greater surface area to volume ratio than larger fuels. Which means it takes less energy and time for smaller fuels to lose or gain moisture. The timelag of fine fuels is short, because they reach their EMC quickly.

Water near the surface of any fuel can be evaporated fairly easily but it requires more energy to evaporate water from the center of a fuel, especially in larger size class fuels. Heavy fuels have a much longer timelag and generally will never reach their EMC due to constantly changing conditions.

Fuels are heterogeneous across a landscape. The wide variety of fuel sizes and shapes, plus weather changes make it impossible for an entire fuel complex to be at EMC at the same time. When fuels get a day of precipitation, they will both gain and lose that moisture at different rates depending on their sizes.

Timelag is expressed as:

- 1-hour to EMC (1/4 in diameter) - 10-hours to EMC (1/4 - 1 in diameter) - 100-hours to EMC (1 - 3 in diameter) - 1000-hours to EMC (> 3 in diameter)

Topography, atmospheric conditions and fuel properties all affect fuel moisture levels. After drought or precipitation each fuel type will lose or gain moisture at different rates depending on sizes. When the environment is hot and dry, the EMC of all size classes of fuels will be lower, which means that the potential for a fire to ignite and spread is high.

Fire behavior fuel models Some of the fuel properties can be difficult to measure. To aid managers, several fuel models or

standardized description of fuels have been developed. These fuel models described typical values for a variety of fuel properties from different vegetation types. Rather than measuring fuel properties in a given area, these fuel models can be used to determine potential fire behavior under a variety of conditions.

A brief history of fuel models

In 1972, Rothermel developed a mathematical equation, known as Rothermel’s surface fire spread model, to predict the spread and intensity of a potential wildfire. This model does not require any information about a fire, only about the environment in which a fire occurs. Based on the principle of conservation of energy, it represents the rate of fire spread as a function of fuel density, particle size, bulk density, and rate of fuel consumption. In this model, the fuel complex is assumed to be continuous, uniform, and homogeneous.


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Rothermel defined a fuel model as the description of the fuel that is burning. Rothermel’s fuel models included:

- Fuel loading - Fuel bed depth - Fuel particle surface area to volume ratio - Dead fuel moisture of extinction - Heat content.

Rothermel developed a set of 11 fuel models to describe the surface fuels. These fuel models provided standardized information that could be used to calculate surface of spread and intensity of active fires at the peak of fire season. The associated dry conditions of the peak of fire season lead to a more uniform fuel complex. Fuels were classified into four groups: grass, brush, timber and slash.

In 1976, Albini expanded on Rothermel’s work by changing the moisture of extinction for several models. He also added two additional fuel models thus creating what are now known as the original 13. These models were developed to model fire behavior during the peak of the fire season when fuels are the driest and grasses have cured:

- Grasses and grass-dominated (3 models): 1 to 3 - Chaparral and shrub fields (4 models): 4 to 7 - Timber litter (3 models): 8 to 10 - Slash (3 models): 11 to 13

In 1982, Anderson compiled a report and included description of each fuel model as well as photographic examples to assist fire and fuel management specialists in selecting the most appropriate fuel model for a specific field situation (Anderson 1982).

Limitations of those models include poor representation of fuels that burn under high dead fuel moistures and always modelling herbaceous fuels as fully cured. Since their creation, fire management had changed dramatically and the models were insufficient for new uses such as modelling fire behavior in blowdown fuels, modelling the initiation of crown fire, simulating the effects of fuel treatments on fire behavior, and simulating changes in fuelbed characteristics over time. There was a need to take into account fires occurring in different seasons such as prescribed fire as well as seasonal changes in live fuel moisture. For example, grasses with 120% moisture will burn differently than grasses with only 60% moisture, which is important when planning for expected fire behavior.


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Scott & Burgan (2005) created 40 additional fire behavior fuel models. Also, a description of 5 non-burnable environment was included (NB1 (91), NB2 (92), NB3 (93), NB8 (98), NB9 (99)). The 40 fuel models can be used at any time of the year.

Table 1: Classification of the 40 fuel models

Grass Grass-Shrub

Shrub Timber-understory

Timber litter

Slash-blowdown

GR1 GS1 SH1 TU1 TL1 SB1




GR5 SH5 TU5 TL5

GR6 SH6 TL6

GR7 SH7 TL7

GR8 SH8 TL8

GR9 SH9 TL9

These additional 40 models were created to:

- Cover a wider range of seasons, other than the peak of fire season (prescribed fire, wild fire use). For example, grasses are not all cured the rest of the year.

- Increase the number of fuel models applicable in high-humidity areas - Fill gaps between predicted fire behavior in each fuel type - Add dynamic fuel moisture - Better simulate fuel treatments - Better drive crown fire initiation models - Reduce need for custom fuel models

In this new set, all fuel models with an herbaceous component are dynamic. In a dynamic fuel model, live herbaceous load shifts between live and dead depending on the specified live herbaceous moisture content. The dynamic fuel model process is described by Burgan (1979):

- If live herbaceous moisture content is 120 percent or higher, the herbaceous fuels are green, and all herbaceous load stays in the live category at the given moisture content.

- If live herbaceous moisture content is 30 percent or lower, the herbaceous fuels are considered fully cured, and all herbaceous load is transferred to dead herbaceous.


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- If live herbaceous moisture content is between 30 and 120 percent, then part of the herb load is transferred to dead. For example, if live herb moisture content is 75 percent (halfway between 30 and 120 percent), then half of the herbaceous load is transferred to dead herbaceous, the remainder stays in the live herbaceous class.

Load transferred to dead is not simply placed in the dead 1-hr timelag class. Instead a new dead herbaceous class is created so that the surface-area-to-volume ratio of the live herbaceous component is preserved. However, for simplicity, the moisture content of the new dead herbaceous category is set to the same as that for the dead 1-hr timelag class.

Today, the 40 standard fuel models are preferably used. If you are still working with the 13, you can use a provided crosswalk to find the equivalent fuel models from the 40 (Scott and Burgan 2005, p12). Comparisons between the new and the original 13 fuel models can only be made if the live herbaceous moisture content is 30 percent (fully cured) or lower.

The 40 and the 13 fuel models are applicable to fire behavior modelling systems that use Rothermel’s surface fire spread model. Therefore, they have its limitations:

- Fuels are uniform and continuous - Fire is free burning and no longer affected by the source of ignition - Severe wildland fire behavior is not predicted by the model - Describes behavior at the head of the fire where fine fuels carry the fire - Residual combustion that takes place after the flaming front has passed is not simulated.

Selecting a fuel model

Fire behavior fuel models are generalizations of conditions in the field and the selection of the wrong fuel model can result in very inaccurate fire behavior predictions. It is possible to have more than one fire behavior fuel model present at a site, depending on the time of year. You should be selecting a model that best matches your expected fire behavior for the area at that time of year, and not just the vegetation type. It is important to become a skilled observer of fire behavior.

Figure 3: Level of curing versus live herbaceous moisture content (Scott & Burgan 2005)


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Figure 4: Unburned forest during savanna fire due to higher moisture content

In tropical ecosystems:

- Fine fuels are where most fires occur.

- Organic soil moisture is important. When organic soils are burned, roots are killed and trees may die. Organic soil can burn for high moisture contents (up to 84%) in tropical environments.

- Time since last fire occurrence is related to increased fuel loads.

- Fuel load increases faster after fire than in temperate ecosystems.

- High fuel loads do not translate to high flammability. Areas of high fuel loads are often the least flammable. For example, wet, dense forests may have high fuel loads, but remain wet and less flammable for a longer period of time than savannas which burn quickly with lower fuel loads.

The standard fire behavior fuel models were created for temperate climates. There is a need to adapt and approximate the existing models to use in tropical environments. Therefore, of the existing fuel models for grass, grass-shrub, shrub and timber-understory, only models created for humid climates should be considered in your initial selection process.

Table 2: fuel models for dry (in red) and humid (in blue) climates

Grass Grass-Shrub Shrub Timber-understory

GR1 GS1 SH1 TU1

GR2 GS2 SH2 TU2

GR3 GS3 SH3 TU3

GR4 GS4 SH4 TU4

GR5 SH5 TU5

GR6 SH6

GR7 SH7

GR8 SH8


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GR9 SH9

To determine fuel models in your area, field trips are necessary. In the field, the general methodology is:

- Determine the general fire-carrying fuel type. Ex: grasses only. - Find the humid climate models corresponding. Ex: GR3, GR5, GR6, GR8 and GR9. - Note general depth, load, compactness and size of fuel and the relative amount of live vegetation

to select what seems to be the closest fuel models. Ex: low load, moderate dead fuel moisture, fuel bed depth of 2 feet may be GR3 or GR5.

- Compare the rate of spread and flame length from the models you selected with which fire behavior you expect or observe in the area. Do not restrict your selection by the type of fuel or the name of the fuel model. It should fit the predicted fire behavior. Ex: I know that for wind speed ≈ 6 mi/h and moderate dead fuel moisture, I have flame length ≥ 10 feet and rate of spread ≈ 80 ch/h it is more likely to be GR6 instead.

Figure 5: Fire behavior comparison of the different Grass fuel models (Scott & Burgan 2005)

An attempt of adaptation of the standard fire behavior fuel models to tropical ecosystems was made in the Reserva de Biosfera Los Petenes (Monzón-Alvarado and Padilla Paz 2015, Figure 6).


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Figure 6: Table from Monzón-Alvarado and Padilla Paz (2015) of the selection of standard fuel models that could be adapted

to the fuels in the Reserva de Biosfera Los Petenes, Mexico

The fuel models selection was made by using similar vegetation types and fuel loads. Nevertheless, fuel models must be selected based on similar fire behavior as it is critical to obtain appropriate predictions. Indeed, similar fuel loads between ecosystems in the tropics and the ones in the United States do not necessarily transcript into similar fire behavior. The most important parameter to take into account when selecting a fuel model from the existing fuel models is fire behavior. For example, a grassland with shrubs may be better modeled by a Timber-understory fuel model than by a Grass-shrub fuel model.

As the 53 fuel models were created for temperate ecosystems and particularities of tropical ecosystems can have an important influence on fire behavior, you may not find a fuel model that fits your observations. Therefore, some fuel models may need to be customized in the modelling systems.

Creating fuel model maps

Fuel model maps are useful tools to provide inputs to models and to know where there is risk for future wildfires. It also can assist in prioritizing fuels management projects such as black-lining placement of fuel breaks and areas to prescribed burn or in conducting public education of risks.

After each burn, the maps need to be updated to follow post burn changes. The burned area may have


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changed fuel models due to fuel load reduction and change in vegetation composition. Permanent fuel monitoring plots can be set up in the managed area to assess fire effects on fuel load and vegetation (See Tricone & Anderson 2018).

You may have different fuel models in the same area depending on the time of year, meaning that you will have different fuel maps for different time of year. For example, some grassland areas are flooded during wet season and dry during dry season. Therefore, during wet season these areas may be non-burnable. The fuel model associated to these areas varies throughout the year. Moreover, at the driest of the dry season, herbs can shifts load from live to dead depending on their level of curing. Knowing the moisture content of your live fuel throughout the year is essential to determine fuel models (Annex A).

Collecting knowledge on fire behavior (i.e. flame length, rate of spread) is essential to determine the closest fuel models for your area (Annex B). During each fire, it is therefore essential to collect data on fire behavior and weather conditions. Monitoring plots (20x50m, Tricone & Anderson 2018) can be installed to conduct experiments on how it burns under different conditions (i.e. dry/wet seasons, fuel moisture contents, fuel loads) and assess fuel load.

Measuring fuel load may be needed in the different fuelbed types and through the different seasons and burned areas to determine the appropriate fuel models. Photo guides are a common reference tool for informing forest management decisions. They are often used to estimate fuel loading and to predict potential fire behavior. Guidelines are provided in Appendix C to start creating a fuel photo guide of your area.

Fire behavior modelling systems

Fuel models are used as the inputs to a variety of fire behavior modelling systems (i.e. BehavePlus, FARSITE, NEXUS, FlamMap, FuelCalc). Fire behavior modelling systems are used to predict the spread and intensity of a potential wildfire. Today there is 53 fire behavior fuel models to choose from. Any fire modelling systems that relies on Rothermel’s equation can use both the 13 and the 40.

Instead of just being a tool to inform fire suppression efforts, fire behavior fuel models now assist analysts with expected fire behavior predictions which in turn assist fire managers with a variety of fire management decisions. Learning how to us fuel models is a skill that takes time and effort and comes with

Figure 7: Example of the mapping of burn areas to update a fuel model map


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a big responsibility. It is also important to understand that these are only one of many tools that should be considered when planning and preparing for expected fire behavior.

Predictions for wildfires

In the case of a wildfire, fire behavior modelling systems can help you make quick predictions on the fire behavior to better respond and control a fire. To do so, it is essential to establish an accurate fuel model map of your working area.

Predictions for prescribed burning

Prescribed burning is the process of planning and applying fire to a predetermined area, under specific environmental conditions, to achieve a desired outcome.

Prescribed burning can help maintain the health of ecosystems, reduce the risk of destructive wildfires by decreasing dense understory shrubs and accumulated dead fuels, perpetuate fire-dependent plants and animal, and their habitats, control tree diseases and insects, perpetuate fire-dependent ecosystems, and maintain successful forest regeneration.

In the case of prescribed burning, using fire behavior modelling systems can help you find the different conditions (weather, fuel model) wanted for a burn, and therefore plan the burn. It can help you determine fire behavior such as rate of spread and flame length, and lightning patterns that will provide the burn needed to meet the management objectives. Before running predictions, it is essential to go to the field to determine or verify the fuel model(s) present in the area where the burn is planned.

To implement prescribed burning, it is essential to identify management objectives to determine what type of fire is wanted or not in the area. Based on these objectives, running predictions with different scenarios (i.e. different fuel moisture contents, weather conditions, fuel loads) will assist in planning the burns.


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Adaptive management

During and after each fire (prescribed or wild), it is essential to collect and analyze burn data (i.e.

weather, burned area, fire behavior, rate of spread, flame length) and to evaluate if the predictions matched the observations. If the predictions were far off the observations, it is important to determine why, as it may be necessary to readjust parameters in the modelling systems or fuel model maps. For example, one of the selected fuel model may need to be changed for one that would better match the fire behavior observations. Using this process of adaptive management is critical to improve knowledge on fuel models and fire behavior in the working area and improve future predictions so they closely match reality and properly assist fire managers.


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References Albini FA (1976) Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-30. Ogden, Utah:

Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 92 p.

Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. Gen. Tech. Rep. INT-122. Ogden, Utah: U.S.Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 22p.

Monzón-Alvarado CM and Padilla Paz SE (2015) Manejo de fuego en la Reserva de Biosfera Los Petenes: Diagnóstico inicial. PPY-CONANP.

Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Res. Pap. INT-115. Ogden, UT: U.S. Department of Agriculture, Intermountain Forest and Range Experiment Station. 40 p.

Scott JH and Burgan Robert E (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 72 p.

Tricone F and Anderson TR (2018) Fire effects monitoring for tropical forests, savannas and wetlands: a practical guide for managers to assess fire risk, fuels and vegetation. Sarteneja, Belize, 48p.


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Appendices

Appendix A – Live fuel moisture estimations using Growing Season Index

Appendix B - Collecting Fire Behavior and Weather Data

Appendix C – Datasheet – Fire behavior measurements

Appendix D – Datasheet – Fire & Weather

Appendix E - Guidelines to create a fuelbed photo guide to estimate surface fuel loads in tropical ecosystems

Appendix F – Fuelbed monitoring

Appendix A – Live fuel moisture estimations using Growing Season Index

A useful tool to estimate live fuel moistures is the Growing Season Index (Jolly et al. 2005). It is a simple metric of plant physiological limits to photosynthesis that allow to estimate live fuel moistures. This can be calculated for your area using the software Fire Family Plus and hourly weather data (Minimum temperature, vapor pressure deficit and photoperiod). Below are graphs obtained in South-Eastern Florida.

They can be used to estimate live herbaceous fuel moistures based on the time of year. Live woody fuel moistures can then be estimated using the following table provided by Scott & Burgan (2005):


Fire behavior can be estimated during wildfires and prescribed burns, and by observations in monitoring plots (20 by 50m plots, see Tricone & Anderson 2018).

The objective of monitoring fire characteristics in forest, brush or grassland types is to collect representative fire behavior measurements wherever possible. Measurements must be taken to be representative of the fire occurring.

Rate of Spread (ROS) Rate of spread describes the fire progression across a horizontal distance. It is measured as the time it

takes the leading edge of the flaming front to travel a given distance (from marker A to marker B).

Make your observations only after the flaming front has reached a steady state and is no longer influenced by adjacent ignitions. Use a stopwatch to measure the time elapsed during spread.

The selection of an appropriate marker, used to determine horizontal distance, is dependent on the expected ROS. Pin flags, rebar, trees, large shrubs, rocks, etc., can all be used as markers. Markers should be spaced perpendicular to the flame front. Five feet is a standard length for the distance between 2 markers; however, you may shorten or lengthen the distance to accommodate a slower or faster moving flame front.

Note: If you expect an irregular flaming front, set up another set of markers, perpendicular to the first set. That way you will be prepared to observe fire behavior from several directions. If the fire moves along either set, or diagonally, you can calculate ROS, because several intervals of known length are available. To distribute the markers, use a setup that you think makes sense for your situation. As the fire burns across each set of markers, record observations on the data sheet.

The time required for the fire to travel from one marker to the other divided by the distance is recorded as the observed rate of spread:

𝑅𝑅𝑅𝑅𝑤𝑤𝑤𝑤 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑑𝑑𝑤𝑤𝑅𝑅𝑑𝑑 =𝑇𝑇𝑤𝑤𝑇𝑇𝑤𝑤 𝑜𝑜𝑑𝑑𝑜𝑜𝑇𝑇 𝐴𝐴 𝑤𝑤𝑜𝑜 𝐵𝐵

𝐷𝐷𝑤𝑤𝑠𝑠𝑤𝑤𝑅𝑅𝐷𝐷𝐷𝐷𝑤𝑤 𝑏𝑏𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝐷𝐷 𝐴𝐴 𝑅𝑅𝐷𝐷𝑑𝑑 𝐵𝐵

Flame length


Flame length is the distance between the flame tip and the midpoint of the flame depth at the base of the flame—generally the ground surface, or the surface of the remaining fuel. Flame length is described as an average of this measurement as taken at several points.

Estimate flame length to the nearest inch if length is less than 1 ft, the nearest half foot if between 1 and 4 ft, the nearest foot if between 4 and 15 ft, and the nearest 5 ft if more than 15 ft long. Flame length can also be measured in meters.

During the fire, estimate flame length (FL) at 30-second intervals (or more frequently if the fire is moving rapidly), as the flaming front moves across the ROS observation interval. Use the data sheet to record data. If possible, make five to ten observations of FL per interval.

Note: Where close observations are not possible, use the height (for FL) of a known object between the observer and the fire behavior observation interval to estimate average flame length or flame depth.

𝐹𝐹𝐹𝐹𝑅𝑅𝑇𝑇𝑤𝑤 𝐹𝐹𝑤𝑤𝐷𝐷𝑤𝑤𝑤𝑤ℎ = ∑ 𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖=𝑛𝑛𝑖𝑖=1𝐷𝐷

With n being the total number of FL measurements

Fire weather observations Fire weather observations should be recorded at 30-minute intervals. Sample more frequently if you

detect a change in wind speed or direction, or if the air temperature or relative humidity seems to be changing significantly, or if directed to do so by the prescribed burn boss.

Observations will include:

- Temperature - Relative humidity - Cloud cover - Wind speed - Wind direction

Appendix C – Datasheet – Fire behavior measurements

Appendix D – Datasheet – Fire & Weather


Photo guides are a common reference tool for informing forest management decisions. They are often used to estimate fuel loading and to predict potential fire behavior. Forest managers can use the photos and accompanying data to compare with observed conditions. This data can be used to support management decision-making regarding prescribed fire or fuels treatment implementation, timing, and consequences for potential wildfire activity.

In tropical fire ecosystems fine fuels like savannas support frequent fire. Forested areas have larger quantities of fuels but are less likely to burn because large diameter fuels remain moist. Only in the driest conditions do the large diameters fuels burn. In the most dry conditions organic topical soils can burn and cause significant ecological damage. To save time and resources, large fuel loads are not monitored in the proposed protocols. It is recommended to only measure fuel loads where fire is known to spread.

The proposed protocols to elaborate a fuelbed photo guide are designed to help users estimate loadings of the following surface fuel components:

- Fine dead woody debris o 1h o 10h

- Ground fuels o Litter o Duff

- Live fuels o Herbs o Shrubs

The techniques in this document are derived from available research and field tests to provide accurate data. The methods were developed to enable protected area managers a low cost program that is effective for good fire management.

Sampling sites selection Visually divide the study area into areas where there are obvious differences in fuels. Sampling sites

are selected to represent the most common fuelbeds and fuel loading conditions observed in the study area. They are stratified by dominant vegetation and condition categories. For example: forests, shrublands, grasslands.

At each fuelbed, a description form must be filled to aid in the vegetation and fuels description provided for each photograph (Appendix F).

Sampling sites visits Sites must be visited twice during each season for a given year with no disturbance.

In case of a fire occurring in the sampling sites, sites must be visited once a month for 6 months to assess changes in fuel loading and fuelbed composition.

Data collection Photographs


At each sampling site visit, install a 1m² frame in a representative area. Take a photograph of the

fuelbed looking straight down (including the 1m² frame) and a photograph at eyelevel facing the site. Each photograph must include a sign showing the site name and the date.

Fuel characteristics

Collect the following data and record on the site datasheet:

- Determine the dominant fuel components and species if known in the area - Measure fuelbed height for shrubs and herbs - Estimate percent cover of herbs and shrubs in the 1m² frames in 10 percent classes - Estimate the live versus dead ratio for herbs in the 1m² frame

Fuel load estimation

Microplots

After taking photographs, clip and collect all of the fuels within three 1m² frames outside of the visual field of the site photo for live fuels and fine dead woody debris. Ground fuels will be collected only in a 30x30cm portion of each frame.

Sort the fuels into the different fuels categories (1h, 10h, herbs, shrubs, duff, and litter), place them in airtight plastic bags, and label the bags according to fuel type, date and site number.

Samples are then taken to the laboratory to:

- Be oven-dried at 85°C until the weight becomes constant - Be weighed when dry

This will allow to determine actual loadings (mass per unit area) for each fuel component.

Planar intercept transects

Loading estimates can also be obtained using fuel transects (Brown 1974, Tricone & Anderson 2018) if permanent monitoring plots and/or transects have been installed in the different fuelbeds of your area. This will give you estimates for fine dead woody debris, live fuels and ground fuels.

Fire behavior estimates It is recommended to determine fire behavior (flame length and rate of spread) for each fuelbed type

and loading conditions. This will assist fire managers to accurately assess fire conditions and better guide decisions on fire management. Methods to determine observed fire behavior is provided in Appendix B.


Surface fuel models can be used to determine expected fire behavior if available and accurate for your area.

Guide design Based on the most abundant fuel components at each sites, sites will be grouped as: forests,

shrublands, grasslands. The fuelbed photo guide will be categorized by these sampling site groups.

Each site will present photos along with load measurement and fire behaviors.

How do you use the fuelbed photo guide? After collecting the proposed data for each of the fuelbeds present in your area, fire managers can use

the guide to assess fire conditions in the future. The user undertakes a visual inventory of the site of interest by observing fuel characteristics and then comparing them with the photographs and data in the photo guide.

To go further The photos collected of the 1m² frames can also be used for training on and calibration of load

estimates provided by the Photoload sampling protocol that was designed in the United States (Keane and Dickinson 2007).

The photoload sampling protocol is a fuel sampling technique used to estimate the loading of surface fuels for a number of fire management objectives but primarily for the prediction of fire effects. This technique uses a series of downward- or sideward-looking photographs of synthetic fuelbeds of gradually increasing fuel loadings as reference for visually estimating fuel loadings in the field. The protocol is to simply match the fuel loading conditions observed on the ground with one of the photoload pictures in the set for that fuel component. It is a quick, cost-effective, and easy method for estimating loading, and it also appears to offer the same level of accuracy as other intensive sampling techniques (Sikkink and Keane 2008).

Appendix F – Fuelbed monitoring

Documents

Guide to Fire Behavior Fuel Models in Tropical Ecosystems€¦ · conservation. She then led a population reintroduction assessment on howler monkeys in Belize, Central America. After