An Evaluation of National Fire Danger Rating System Components for Use in Prescribed Fire Decisions

On the National Forests of Texas

Terry G. Harris Fuels Specialist

USDA Forest Service National Forests of Texas

415 South 1st Street Suite 110

Lufkin, Texas 75901

T e c h nical Fire Management - 12Washington Institute

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JASPER

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NEW T O N

PANOLA

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WALKER

TRINITY

ANGELINA

JEFFERSON

SABINE

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MONTGOMERY

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Table of Contents Preface Page ii Executive Summary Page iii Introduction Page 1 Background Page 2 Scope Page 6 Problem Statement Page 6 Goal Statement Page 7 Objectives Page 7 Methods Page 7 Methods for Statistical Analysis Page 10 Assumptions Page 11 Discussion & Recommendation Page 21 References Page 22 List of Figures Figure 1. Fuel Characteristics Page 9 List of Tables Table 1. Prescribed Fire Parameters Page 4 Table 2. NFDRS Inputs Page 5 Table 3. NFDRS Outputs Page 6 Table 4. Statistical Results Page 12 Table 5. Statistical Results Page 13 Table 6. Statistical Results Page 14 Table 7. Statistical Results Page 15 Table 8. Statistical Results Page 16 Table 9. Statistical Results Page 17 Table 10. Statistical Results Page 18 Table 11. Statistical Results Page 19 Table 12. Statistical Results Page 20

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Preface I am the Fuels Specialist for the National Forest & Grasslands in Texas (NFGT). I

was an employee on the Angelina Ranger District, NFGT for 25 years and in my

current position for the last four years. The majority of my time spent on the

Angelina Ranger District I worked in the fire shop. The last fifteen years I worked as

the district’s Fire Management Officer. I graduated from Zavalla High School in

Zavalla, Texas.

I would like to thank my supervisor, Ron Haugen, NFGT FMO for advice and time

to complete the project; to Larry Ford, retired NFGT FMO for his support through all

phases of this project; and Bob Loveless for all his technical advice and

encouragement.

Terry G. Harris March 4, 2006

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EXECUTIVE SUMMARY

The National Forest and Grasslands in Texas (NFGT) have several prescribed fire

parameters used in the decision-making process (GO/NO-GO) prior to

implementation of any prescribed fires. This project analyzes statistically National

Fire Danger Rating System (NFDRS) indices to determine if they could also be used

in our decision-making process. The goal of this project is to provide management

with the most effective and efficient information available for use in the prescribed

fire decision-making process. My objective for this project is to tests the null

hypothesis of no difference with prescribed fire results and NFDRS indices to

determine if a significant relationship exist. The first statistical method used was a

one-way analysis of variance to determine if a significant difference exists between

prescribed fire results and NFDRS indices. A pair-wise comparison was performed

in step two of our statistical process to identify where the differences occurred

between prescribed fire results. The findings of this analysis did indicate several of

the NFDRS indices to be useful in distinguishing between different prescribed fire

results. Additional analysis will be required before recommending any indices to

management as a parameter for our decision-making process.

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INTRODUCTION This project tests the null hypothesis of no difference to determine if a significant

relationship can be determined between prescribed fire results and National Fire

Danger Rating System indices. The results of our analysis could be used to establish

new parameters or change existing ones currently used in the decision-making

process for prescribed fires on the National Forest and Grasslands in Texas. A

change with existing parameters or the establishment of new parameters could

increase the number of days we are allowed to implement prescribed burning.

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BACKGROUND The National Forest and Grasslands in Texas (NFGT) have several prescribed fire

parameters used in the decision-making process (GO/NO-GO) prior to

implementation of any prescribed fires. The NFGT prescribed fire parameters are

summarized in Table 1.

The National Forest of Texas is located in eastern part of the state. The forest is

comprised of four districts that total 600,000 acres together. Prescribed fire is an

essential tool in the management of the National Forests and Grasslands in Texas

(NFGT). A majority of the plant communities are fire-dependent southern pine

dominated forest types; which may include, intermingled hardwood tree species as

well as hardwood forest types. Most native flora and fauna, including rare and

endangered species, are dependent on frequent fire. The Forest Land and Resource

Management Plan sets a goal of treating approximately 100,000 acres annually with

prescribed fire. Over the last five years the forests have averaged 60,000-70,000

prescribed burned acres annually. Last year about 120,000 acres were burned.

Prescribed fires range greatly in size but average around 1,000 acres. Nearly all of

this is under-story burning which could be considered ecosystem maintenance or

restoration burning. To accomplish this program, prescribed burning must be done

on as many days as possible. The intent of this project is to determine by statistical

analysis if any of the National Fire Danger Rating System (NFDRS) indices could be

used as parameters in our decision-making process. There have been no studies done

to my knowledge to determine if this is a possibility. The findings of this project

could allow us to implement prescribed burning on more days. The NFDRS is the

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method currently used by the USDA Forest Service, and many other organizations to

predict into numerical indices the fire danger on a day-to-day basis. The National

Fire Danger System inputs are summarized in Table 2 and NFDRS outputs are

summarized in Table 3.

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TABLE 1. NFGT Prescribed Fire Parameters

Forest Service Manual National Forests & Grasslands In Texas

Chapter# - 5140 FIRE USE

PARAMETER SOURCE

STANDARD

NOTES 10 HR. FUEL MOISTURE

RO Minimum: 9% in open. 7% under canopy. Weather station is “open”.

RELATIVE HUMIDITY

RO >=25% unless approved by the Regional Fire Director.

Predicted RH between 25-29% requires FMSO or Forest FMO approval.

TEMPERATURE RO -------------- Forest.

Forest to develop. -------------------------------------- 95 Degrees F maximum except for site preparation burns. No maximum for site prep.

20 ft WIND (mph) RO ------------- RO/Forest

<=18 mph max. ------------------------------------- 6 mph minimum

NWS forecast of 15-20 MPH is accepted, includes gusts. ----------------------------- Texas State Air Quality Regulations.

TRANSPORT WIND SPEED (meters per second)

RO Sliding scale or State Regulations. ------------------------------------- 4 mps minimum

R8-5144 Exhibit 03. ------------------------------- Texas State Air Quality Regulations

MIXING HEIGHT (meters above ground level)

RO Sliding scale or State Regulations. ------------------------------------- 500 meters/agl minimum

R8-5144 Exhibit 03. ------------------------------- Texas State Air Quality Regulations.

SMOKE DISPERSION INDEX

RO >= 21 dispersion index or more restrictive State requirements

NWS does not provide Dispersion Index in Texas, State does not use. State regulations are more restrictive.

NFDRS: BURNING INDEX (BI)

RO ----------- Forest

90th percentile of Forest selected index, or indices. --------------------------------------- Forests: 65 BI Grasslands: 40 BI

---------------------------------------------- Exceptions must be approved by Regional Office.

PROBABILITY OF IGNITION. NFDRS (IC)

RO -------------- Forest

Forest to develop. ------------------------------------------ 50% maximum.

KBDI RO -------------- Forest

Forest to develop. ------------------------------------------ 550 maximum unless burn unit has received at least ¼ inch of rain within the previous 4 days.

---------------------------------------------- Exceptions must be approved by Fire Staff Office or Forest FMO.

DAYS SINCE RAIN

RO/Forest See KBDI above.

AMOUNT (inches) RO/Forest See KBDI above.

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TABLE 2. National Fire Danger Rating System inputs.

NFDRS Inputs Definition

1-hour fuel moisture The moisture content of fuels consisting of dead herbaceous plants and woody vegetation.

10-hour fuel moisture The moisture content of dead woody fuels consisting of one-fourth to one-inch in diameter.

100-hour fuel moisture The moisture content of dead roundwood one to three inches in diameter.

1000-hour fuel moisture The moisture content of dead roundwood three to eight inches in diameter

Herbaceous fuel moisture The content of water of a live herbaceous plant expressed in percent.

Keetch-Byram Drought Index A number that represents the effect of evaporation and precipitation in cumulative moisture to approximately eight inches into the duff layer and upper soil layers.

Relative humidity The ratio of the amount of water vapor in the air necessary to saturate expressed as a percentage.

Temperature Temperature of the air

Wind For NFDRS calculations wind is measured at 20 feet above the ground or the average height of the vegetative cover.

Woody fuels moisture The content of water of live woody vegetation

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TABLE 3. National Fire Danger Rating System outputs

NFDRS Outputs Definition

Burning Index A number related to the contribution of fire behavior to the effort of containing a fire. Scale is open-ended; thus it has no upper limit.

Ignition Component Rating of the probability that a firebrand will cause fire requiring suppression action. Scale of 0 to 100.

Energy Release Component A number related to the available energy (BTU) per unit area (square foot) within the flaming front at the head of a fire. Scale is open-ended; thus it has no upper limit

Spread Component A prediction of the rate of spread of a head fire. Scale is open-ended; thus it has no upper limit.

SCOPE

The scope of this analysis is limited to the relationships of NFDRS indices to

prescribed fire results on the National Forests in Texas. The findings may be

applicable to prescribed fires in similar vegetation types across the southeastern

coastal plains.

PROBLEM STATEMENT The National Fire Danger Rating System (NFDRS) is not designed to predict

behavior of an individual fire. However, inputs used to calculate its outputs are also

fire behavior factors, which suggest that NFDRS indices could be used as prescribed

fire parameters in the GO-NO/GO decision-making process for the NFGT. These

same inputs and outputs could be used to predict prescribed fire results. No other

studies of this have looked at using NFDRS indices as prescribed fire parameters on

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the NFGT. The findings of this project could provide the NFGT with better

information available to conduct our program of work in prescribed fire.

GOAL STATEMENT

The goal of this analysis is to provide management the most effective and efficient

information available for use in the prescribed fire GO/NO-GO decision-making

process.

OBJECTIVES This project tests the null hypothesis of no difference between prescribed fire results

and NFDRS indices to determine if a significant relationship exists between the two.

Prescribed fire results for our statistical analysis were classified into three classes:

burns deemed successful, which met management objectives, burns where the fire

intensity was too cool, and burns where the fire intensity was too hot. The fires

classified as too cool or too hot did not meet management objectives. The statistical

method we used to test the null hypothesis was a one-way analysis of variance. (A

one-way analysis of variance is considered the appropriate statistical method for this

data, Bob Loveless, personal communication, January, 2005).

METHODS

Initially 130 prescribed fires over a 16 year period were separated into three classes:

35 fires in class 1 in which fire intensity was too cool to meet management

objectives, 30 fires in class 2 in which fire intensity was to hot to meet management

objectives and 65 fires in class 3 (objectives met) where management objectives

were met. The classification of each fire was based on post-burn evaluations

conducted by the responsible Burn Boss for each prescribed fire unit. Actual records

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varied greatly. Most of the fires were several hundred acres or greater in size where

fuels, weather, time of day and other factors varied greatly over the burn area. The

13:00 hour fire weather observations for each day a prescribed fire occurred were

retrieved through the Weather Information Management System (WIMS) from the

following remote automated weather stations (RAWS) on the Forests. WIMS is a

web-based application used to collect, store, and manage current weather

information, as well as providing access to historical weather information. RAWS

are weather stations that tracks and stores weather observations. Our weather

observations were retrieved from the following RAWS: Conroe (415109), Sabine

South (413701), Lufkin (413509), Coldspring (414201), Sabine North (412901) and

Ratcliff (413302). The weather data from the nearest RAWS that existed at the time

was assigned to each prescribed fire. The following weather observations were

retrieved by WIMS for our analysis: temperature, relative humidity, wind, 1 – hour

fuel moisture, 10 – hour fuel moisture, 100 – fuel moisture, 1000 – fuel moisture,

herbaceous fuel moisture, woody fuel moisture, and Keetch-Byram Drought Index.

The 1300 weather observations were also used to calculate National Fire Danger

Rating System outputs: energy release component (ERC), ignition component (IC),

spread component (SC) and burning index (BI) by using Fire Family Plus. This is a

software application designed to perform fire danger analysis. Fuel model D was

used for these calculations since it adequately represents the represents the majority

of the land base on the NFGT. Figure 3 represents NFDRS fuel Model D

characteristics of the NFGT.

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Figure 1. NFDRS Fuel Model D Characteristics of the NFGT

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METHODS

Statistical Analysis

Step one our statistical analysis was performed using a one-way analysis of variance

(ANOVA). A one-way ANOVA was used to test the null hypothesis of no difference

for the following NFDRS outputs: energy release component, ignition component,

spread component and burning index. Step one our statistical analysis also included

performing a one-way analysis of variance for these NFDRS inputs: temperature,

relative humidity, wind, 1 – hour fuel moisture, 10 – hour fuel moisture, 100 – fuel

moisture, 1000 – fuel moisture, herbaceous fuel moisture, woody fuel moisture and

Keetch-Byram Drought Index. These indices (variables) were analyzed to determine

if a statistically significant difference exists in regards to prescribed fire results

(classes). The key indicator to determine if a significant difference exists in step one

is the P-value. P-value must be less than or equal to alpha, 0.05 to show a significant

relationship between our variables and prescribed fire results (classes). Any variable

with a P-value greater than alpha was analyzed no further. A pair-wise comparison

was used in step two of statistical analysis to identify where the differences occurred

between prescribed fire classes (Rx classes). The results of this analysis are

displayed in Tables 4-12.

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Assumptions The following assumptions have to be considered in this project: data used for the

analysis of variance analysis is normally distributed, National Fire Danger Rating

System process has not changed, and the weather conditions on the burn site did not

change from the weather observations collected at 1300 and the subjective method

used to classify prescribed fire results into different classes.

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Analysis Results

TABLE 4. ANOVA and pair-wise comparisons for energy release components (ERC). ANOVA Source P-value

Class of Fires 0.0184

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between energy release component and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

3 39.954 A

2 37.700 AB

1 32.571 B

ERC can be used to distinguish class 1 (fire intensity to cool) from class 2 (fire intensity to hot) and class 3 (objectives met).

.

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TABLE 5. ANOVA and pair-wise comparisons for ignition component (IC). ANOVA Source P-value

Class of Fires

0.0078

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between ignition component and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

2 21.833 A

3 21.077 A

1 14.000 B

IC can be used to distinguish class 1 (fire intensity to cool) from class 2 (fire intensity to hot) and class 3 (objectives met).

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TABLE 6. ANOVA and pair-wise comparisons for Keetch-Byram Drought Index (KBDI). ANOVA Source P-value

Class of Fires

0.0032

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between Keetch-Byram Drought Index and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

2 215.80 B

3 150.80 A

1 140.51 A

KBDI can be used to distinguish class 2 (fire intensity to hot) from class 1 (fire intensity to cool) from) and class 3 (objectives met).

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TABLE 7. ANOVA and pair-wise comparisons for 1-hour fuel moisture (1-FM). ANOVA Source P-value

Class of Fires

0.0043

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between 1-hour fuel moisture and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

1 9.4857 A

3 7.6462 B

2 7.3000 B

1-FM can be used to distinguish class 1 (fire intensity to cool) from class 2 (fire intensity to hot) and class 3 (objectives met).

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TABLE 8. ANOVA and pair-wise comparisons for 10-hour fuel moisture (10-FM). ANOVA

Source P-value

Class of Fires

0.0057

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between 10-hour fuel moisture and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

1 11.171 A

3 9.6000 B

2 7.3000 C

10-FM can be used to distinguish each class from one another.

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TABLE 9. ANOVA and pair-wise comparisons for 100-hour fuel moisture (100-FM). ANOVA

Source P-value

Class of Fires

0.0026

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between 100-hour fuel moisture and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

1 18.457 A

3 17.431 B

2 17.033 B

100-FM can be used to distinguish class 1 (fire intensity to cool) from class 2 (fire intensity to hot) and class 3 (successful burns).

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TABLE 10. ANOVA and pair-wise comparisons for wind. ANOVA

Source P-value

Class of Fires

0.0261

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between wind and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

2 8.1667 A

1 6.6857 AB

3 6.4462 B

All-pairs comparison test reveals a significant difference between class 2 and class 3 prescribed fires. Wind can be used to distinguish class 2 (fire intensity to hot) from class 3 (objectives met).

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TABLE 11. ANOVA and pair-wise comparisons for relative humidity (RH). ANOVA

Source P-value

Class of Fires

0.0027

The results of our ANOVA show a p-value which less than alpha (0.05) therefore a significant relationship does exist between relative humidity and prescribed fire classes. Pair-wise comparison. Class Mean Homogenous

Groups

1 56.200 A

2 47.554 B

3 43.633 B

RH can be used to distinguish class 1 (fire intensity to cool) from class 2 (fire intensity to hot) and class 3 (successful burns).

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The results of our statistical analysis did not show a significant relationship for the variables summarized in Table 12. The P-value for these is equal to greater than alpha (0.05). TABLE 12. Variables with no significant relationship to prescribed fire results. Variable Source P-value

Spread Component

Class of Fires 0.3775

Burning Index Class of Fires 0.1428

Temperature Class of Fires 0.1302

1000-Fuel Moisture. Class of Fires 0.1907

Herbaceous Fuel Moisture Class of Fires 0.2037

Woody Fuel Moisture Class of Fires 0.2575

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Discussion

Based on this analysis the following indices could be useful in distinguishing

between prescribed fires where the fire intensity was too cool (class 1) too hot (class

2), or met management objectives(class 3): energy release component, ignition

component, Keetch-Byram Drought Index, 1-hour fuel moisture, 10-hour fuel

moisture, 100-fuel moisture, wind speed, and relative humidity. Several indices were

found not to be useful in distinguishing between any classes of our prescribed fires:

burning index, temperature, 1000-fuel moisture, herbaceous fuel moisture, and

woody fuel moisture. The only index that could be useful in distinguishing between

all three classes is 10-hour fuel moisture. Therefore, 10-hour fuel moisture is the

only indices that should be considered in the GO/NO-GO decision process to

distinguish between classes of prescribed fires. There are several limitations that

could have resulted in inaccuracies for our statistical analysis. The subjective method

used to classify our prescribed burns into different classes, changes in on-site

weather from the 1300 weather observations, data used for our analysis is normally

distributed and NFDRS process has not changed are all limitations for this project.

Based on this analysis I would recommend using 10-fuel moisture to distinguish

between the three classes of prescribed fires. Additional analysis would be necessary

before implementing this.

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References

NWCG. 2003. Gaining Intermediate National Fire Danger Rating System S-491 Student Workbook. Schlobohm, Paul and others. NWCG 1982. Aids to Determing Fuels Models For Estimating Fire Behavior. Anderson Hal E. USDA Forest Service. 2002. Fire Family Plus User Guide Version 3.0. Rocky Mountain Research Station, Fire Science Labs For Environmental Management. USDA Forest Service 1996. National Forests & Grasslands in Texas Land and Resource Management Plan. USDA Forest Service. 2005. Behave Plus Fire Modeling System Version 3.0 User Guide. Rocky Mountain Research Station. Andrews Patricia, Bevins Collin, Seli Robert. P 142 Statistix 8 Analytical Software User’s Manual. 2003. Berenson Mark and others. p 396 A Cartoon Guide To Statistics. 1993. Gonick Larry, Smith Woolcott. p 230. Modern Elementary Statistics. Ninth Edition. 1997 Freund John E. and Simon Gary A. P 588.

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