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WILDFIRE RISK ASSESSMENT AND WILDFIRE SIMULATION IN

SOUTHEASTERN UNITED STATES MOUNTAINOUS AREAS:

GREAT SMOKY MOUNTAINS NATIONAL PARK

A THESIS PRESENTED TO

THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCE

IN CANDIDACY FOR THE DEGREE OF

MASTER OF SCIENCE

By

JONNATHAN B. OWENS

NORTHWEST MISSOURI STATE UNIVERSITY

MARYVILLE, MISSOURI

NOVEMBER, 2013

WILDFIRE RISK ASSESSMENT AND WILDFIRE SIMULATION

Wildfire Risk Assessment and Wildfire Simulation in Southeastern United States

Mountainous Areas: Great Smoky Mountains National Park

Jonnathan Owens

Northwest Missouri State University

THESIS APPROVED

________________________________________________________________________

Thesis Advisor, Dr. Yi-Hwa Wu Date

________________________________________________________________________

Dr. Ming-Chih Hung Date

________________________________________________________________________

Dr. Karen Schaffer Date

________________________________________________________________________

Dean of the Graduate School Date

iii

Wildfire Risk Assessment and Wildfire Simulation in Southeastern United States

Mountainous Areas: Great Smoky Mountains National Park

ABSTRACT

The Great Smoky Mountains National Park (GRSM) encompasses 520,191 acres

(210,521 ha) of protected forest located along the North Carolina – Tennessee border in

the southeastern United States. The Park is 95% forested and contains over 100 different

species of trees which constitute the most extensive collection of virgin hardwood forest

in the eastern United States. It is one of the most visited National Parks in the U.S. with

over 9 million visitors annually.

From 1942 to 2009 there were 795 unintentional, reported fires within the Park.

Even with the significant amount of wildfires in GRSM and the Southern Appalachian

Mountains, research concerning wildfire risk and behavior in these areas is limited. For

this thesis, a wildfire risk assessment was conducted for the Park and, for areas found to

be at the highest risk, potential wildfire behavior was modeled using the FARSITE fire

area simulator software.

Wildfire risk was assessed using spatial and statistical analysis of historic wildfire

locations relative to common variables generally found to be influential in wildfire

ignition: elevation, aspect, slope, vegetation type, and distance to human structures.

Wildfire risk was found to be highest in the northwestern and southwestern portions of

the Park with lower risk in the eastern portion due to higher elevations and their

associated vegetation types.

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Wildfire modeling showed that fires within the highest risk areas produced

relatively lower rates of fire spread (relative to fires in the Western U.S.) and that

vegetation type, wind speed, and wind direction appear to be the key factors influencing

fire spread. Wildfire simulations also revealed that many natural barriers located in the

Park may inhibit potential fire growth.

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Table of Contents

Abstract .............................................................................................................................. iii

Table of Contents ................................................................................................................v

List of Tables .................................................................................................................... vii

List of Figures .................................................................................................................... ix

Acknowledgements ............................................................................................................ xi

Chapter 1: Introduction ........................................................................................................1

1.1: Basic Fire Statistics for the U.S. and Southern U.S. ...............................................3

1.2: Fire Regime within the Great Smoky Mountains National Park ............................3

1.3: Use of GIS in Wildfire Studies ...............................................................................6

1.4: Research Objectives ................................................................................................7

1.5: Study Area ..............................................................................................................9

1.6: Data and Data Sources ..........................................................................................11

1.7: Additional Data Processing ..................................................................................12

Chapter 2: Literature Review .............................................................................................14

2.1: Wildfire Risk.........................................................................................................14

2.2: Distances to Anthropogenic Structures and Wildfire Risk ...................................16

2.3: Terrain and Wildfire Risk .....................................................................................17

2.4: Vegetation Type and Wildfire Risk ......................................................................18

2.5: Variables Excluded from the Risk Assessment ....................................................20

2.6: Wildfire Modeling and Simulation .......................................................................21

Chapter 3: Wildfire Risk Assessment Methods and Results .............................................23

3.1: Spatial Distribution of Historic GRSM Wildfires ................................................23

3.2: Variables used for Assessing Wildfire Risk for GRSM .......................................25

3.3: Fire Frequency and Distance to Structure .............................................................25

3.4: Reclassifying and Combining the Distance to Structure Grids ............................31

3.5: Determining the Relationship between Fire Frequency and Elevation ................33

3.6: Determining the Relationship between Fire Frequency and Slope.......................34

3.7: Determining Correlation between Fire Frequency and Aspect ............................37

3.8: Reclassifying and Combining Terrain Grids ........................................................40

3.9: Determining the Relationship between Fire Frequency and Fuel Type ...............43

3.10: Reclassifying and Combining Fuel Type Data ...................................................50

3.11: Combining Data for the Final Risk Assessment .................................................53

3.12: Final Risk Assessment Results ...........................................................................53

3.13: Risk by Zone .......................................................................................................56

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Chapter 4: FARSITE Fire Area Simulator.........................................................................58

4.1: FARSITE Verification ..........................................................................................59

4.2: Determining Potential Areas to Locate Ignition Points for FARSITE Modeling.63

4.3: Determining Exact Locations of Ignition Points ..................................................67

4.4: Determining Time of Year for Wildfire Simulation .............................................70

4.5: FARSITE Weather and Wind Input ......................................................................71

4.6: Developing Fuel Model for FARSITE Input ........................................................72

4.7: Elevation, Aspect, Slope, and Canopy Cover Data for FARSITE Input ..............76

4.8: Twenty Mile Trail – April Modeling Results .......................................................76

4.9: Twenty Mile Trail – November Modeling Results ...............................................77

4.10: Disparity between April and November BA for Twenty Mile Trail ..................79

4.11: The Flats – April Modeling Results ....................................................................80

4.12: The Flats – November Modeling Results ...........................................................82

4.13: Disparity between April and November BA for the Flats Models .....................83

Chapter 5: Conclusion and Discussion ..............................................................................87

5.1: Wildfire Risk Assessment Conclusions and Discussion.......................................87

5.2: FARSITE Modeling Conclusions and Discussion................................................88

5.3: Study Limitations and Further Research ..............................................................91

Appendix A: Wind Input Data for April 2012 FARSITE Modeling: ................................94

Appendix B: Wind Input Data for November 2012 FARSITE Modeling: ........................98

References: .......................................................................................................................102

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List of Tables

Table 1. Nearest neighbor analysis results of historic wildfires, Great Smoky

Mountains National Park, USA. .........................................................................24

Table 2. Distance from structures and wildfire frequency, Great Smoky

Mountains National Park, USA (only first 10 results displayed). ......................28

Table 3. Elevation and fire frequency, Great Smoky Mountains National Park,

USA.....................................................................................................................35

Table 4. Slope gradient and fire frequency, Great Smoky Mountains National

Park, USA. ..........................................................................................................36

Table 5. Aspect and fire frequency, Great Smoky Mountains National Park, USA. .........38

Table 6. Spearman’s R - aspect rank vs. fire frequency rank, Great Smoky


Table 7. Customized Classification of Vegetation for the Great Smoky Mountains

National Park, USA. ...........................................................................................45

Table 8. Analysis of overstory vegetation and fire frequency, Great Smoky


Table 9. Analysis of understory vegetation and fire frequency, Great Smoky


Table 10. Analysis of combined over/under vegetation and fire frequency, Great

Smoky Mountains National Park, USA. .............................................................48

Table 11. FARSITE input data for wildfire simulations, Great Smoky Mountains


Table 12. FARSITE output data for wildfire simulations, Great Smoky Mountains


Table 13. Estimated cloud cover percentages for FARSITE modeling, Great


Table 14. NCDC weather station # 720259 weather data for April 6 – 11, 2012..............73

Table 15. NCDC weather station # 720259 weather data for November 8 – 13,

2012.....................................................................................................................73

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Table 16. Rules for assigning fuel model classes for FARSITE modeling, Great


Table 17. Results comparison - April and November Twenty Mile Trail FARSITE

modeling, Great Smoky Mountains National Park, USA. ..................................80

Table 18. Results comparison - April and November Flats area FARSITE

modeling, Great Smoky Mountains National Park, USA. ..................................85

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List of Figures

Figure 1. Great Smoky Mountains National Park, USA. .....................................................4

Figure 2. Historic wildfire locations 1942 – 2009, Great Smoky Mountains


Figure 3. Euclidean distance to structure, Great Smoky Mountains National Park,

USA.....................................................................................................................27

Figure 4. Scatter plot of distance to structure vs. fire frequency(untransformed

data), Great Smoky Mountains National Park, USA. .........................................29

Figure 5. Scatter plot of distance to structure vs. fire frequency(data transformed

using log10), Great Smoky Mountains National Park, USA. ............................30

Figure 6. Wildfire risk according to distance to structure, Great Smoky Mountains


Figure 7. Scatter plot of elevation vs. wildfire frequency, Great Smoky Mountains


Figure 8. Ranking aspect classes for Spearman’s rank analysis – ranked according

to orientation to south cardinal direction. ...........................................................39

Figure 9. Wildfire risk according to terrain related variables, Great Smoky


Figure 10. Wildfire risk according to combined terrain variables, Great Smoky


Figure 11. Wildfire risk according to the vegetation types of each forest horizon,

Great Smoky Mountains National Park, USA. ...................................................51

Figure 12. Wildfire risk according to combined fuel type data, Great Smoky


Figure 13. Final wildfire risk grid, Great Smoky Mountains National Park, USA. ..........54

Figure 14. Risk by zone results, Great Smoky Mountains National Park, USA. ..............57

Figure 15. Actual Dalton wildfire vs. simulated Dalton wildfire, Great Smoky


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Figure 16. Leaf litter and woody debris in the Twenty Mile Trail area (March

2013), Great Smoky Mountains National Park, USA. ........................................65

Figure 17. Extinguished campfire in the Flats area (March 2013), Great Smoky


Figure 18. Little River Gorge limits human access to southern facing slopes – the

Sinks area (March 2013), Great Smoky Mountains National Park, USA. .........66

Figure 19. Twenty Mile Trail area ignition point, Great Smoky Mountains


Figure 20. The Flats area ignition point, Great Smoky Mountains National Park,

USA.....................................................................................................................69

Figure 21. Histogram - historic wildfire frequency by month, Great Smoky


Figure 22. Fuel model for FARSITE modeling, Great Smoky Mountains National

Park, USA. ..........................................................................................................75

Figure 23. Twenty Mile Trail FARSITE modeling results, Great Smoky


Figure 24. The Flats area FARSITE modeling results, Great Smoky Mountains


Figure 25. Position of the NE spreading fire front at the onset of the increased

winds – the Flats April model, Great Smoky Mountains National Park,

USA.....................................................................................................................86

Figure 26. Twenty Mile Trail April model without barriers, Great Smoky


xi

Acknowledgements

Foremost, I would like to express my sincere gratitude to my thesis committee,

Dr. Yi-Hwa Wu, Dr. Ming-Chih Hung, and Dr. Karen Schaffer for their continuous

support during my thesis writing and research. Their immense knowledge, patience, and

guidance have been invaluable throughout this long process.

My sincere thanks also goes to Dave Loveland and the Fire Management staff at

the Great Smoky Mountains National Park, for their kind help and for sharing their expert

knowledge of all things related to the Great Smoky Mountains.

Last but not the least, I would like to thank my wife Jenny, for her love, patience,

and understanding and for her willingness to “pick up the slack” as this thesis severely

diverted my attention away from my household and parenting duties.

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Chapter 1 Introduction

A wildfire is an uncontrolled fire burning in areas of combustible materials,

typically vegetation, and is generally located in uninhabited areas such as the

countryside or wilderness. The National Wildfire Coordinators Group (NWCG) (2007)

defines wildfire as any non-structural fire occurring in areas where human development

is essentially non-existent except for roads, railroads, utility lines, and similar

transportation facilities. Other common names associated with wildfire include wildland

fire, forest fire, and brush fire.

While wildfire is generally perceived as a hazard, it is one of the most important

naturally occurring processes offering many benefits to natural systems (Zimmerman,

2012). Wildfire helps in the cycling of soil nutrients and helps in the removal of excess

undergrowth which provides open areas for new vegetation growth and the foraging of

larger wildlife species (California Natural Resources Agency, 2009). Wildfire is often

responsible for the transfer of biomass to the detrital food chain eventually leading to a

release of nutrients for uptake by vegetation (Harmon, 1984). Mosaic patterns of burn

severity and extent can increase habitat heterogeneity and biodiversity in streams and

riparian forests (Reeves et al., 1995; Swanson and Lienkaemper, 1978). The absence of

wildfire may increase the chances of successful entrance of invasive species to forested

areas (Jenkins et al., 2011). In short, many ecosystems are dependent on the effects of

wildland fire for their establishment, development, and maintenance.

While wildfire offers many benefits to natural systems, it is not without its

negative consequences. Fire is a destructive force that can consume life, land, and

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property. The October 2007 California wildfires resulted in 9 fatalities, 85 injuries, the

destruction of over 1,500 homes and 500,000 acres (202,350 ha) of burned land. The

2011 Bastrop County fire complex, the worst wildland fire in Texas history, burned

34,068 acres (13,787 ha), destroyed 1,691 homes, and resulted in 2 fatalities. In the

summer of 2013, Colorado experienced several major wildfires which resulted in

approximately 40,000 acres of burned land, the destruction of 579 structures, and the

loss of two human lives.

Wildland fires can also have long term effects upon the ecological systems and

watersheds in which they occur. For instance, long term watershed responses such as

peak flows, runoff, and erosion typically increase with severity of wildland fire

(Robichaud et al., 2007). Research conducted by Neville et al. (2009) and Minshall

(2003) showed that, in catchments degraded by anthropogenic disturbance, fire effects

can be more severe and persistent because post-fire ecosystem processes may not

function properly.

Wildland fires in the Southern U.S. can be detrimental to local economies. More

than half the wood fiber produced in the nation comes from Southern forests (Andreu

and Hermansen-Baez, 2008). Wildfires may cause extensive damage to forest stands. In

individual trees, the damaged area can be an avenue for decay weakening the tree and

making it more susceptible to insect disease and infestation. These infestations can

ultimately kill the tree, leading to loss of lumber and wood fiber products used in

making wood fiberboard and paper.

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1.1 Basic Fire Statistics for the U.S. and Southern U.S.

In the U.S., between the years 1982 and 2012, there were an average of 76,000

wildfires per year and 4.5 million acres (1.8 million ha) burned per year (National

Interagency Coordination Center, 2013). In recent years, there has been a slight increase

in the number of large fires. From 2005 to 2012, there was an average of 12 fires per

year that had burned areas greater than 100,000 acres (40,470 ha). This number is up

slightly from the 1997 to 2004 average of 7 fires per year with burned areas greater than

100,000 acres (National Interagency Coordination Center, 2013).

The Southern U.S. typically leads the nation in the annual number of wildfires

with an average of 45,000 fires per year (Andreu and Hermansen-Baez, 2008). Southern

fires are typically smaller than fires occurring in the Western U.S.; however, the sheer

number of fires results in the number of acres burned per year to be higher in the South

than in other U.S. regions (Andreu and Hermansen-Baez, 2008). Similar to other U.S.

regions, Human Caused Wildfire (HCW) is the leading source of fire in the South

historically accounting for 93% of fire ignitions (Andreu and Hermansen-Baez, 2008).

1.2 Fire Regime within the Great Smoky Mountains National Park

This study investigates wildfire within the Great Smoky Mountains National

Park (GRSM). The GRSM is a 520,191 acres (210,521 ha) National Park located along

the North Carolina – Tennessee border in the southeastern United States (Figure 1). It

was established in 1934 in an attempt to stop damage to forests caused by fires and

erosion associated with the logging activities of the 1800s and early 1900s (Welch et al.,

2002).

4

The Park consists primarily of forested lands making it susceptible to the

occasional wildfire. An investigation of historic fire data obtained from the National

Park Service (NPS) shows that from the years 1942 to 2009, within the Park there were

795 unintentional (i.e., not prescribed), recorded fires with an average of 11.9 fires

annually. Average fire size for those years was 66 acres (27 ha) burned; however, a

standard deviation of 322 suggests high variability in fire sizes with a few large fires (up

to 6,000 acres or 2,428 ha) positively skewing the average size. According to the NPS

data, most Park fires, 82%, were human caused with a variety of specific causes ranging

from acts of arson to unattended campfires. The other sources of fires during the time

period were lightning strikes which accounted for 15% of fires and the remaining 3%

had unknown causes.

Figure 1. Great Smoky Mountains National Park, USA.

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Mean fire interval (number of years between fires that scarred one or more trees

within a given area ) for Park lands between 1856 and 1940 was 12.7 years with the

shortest interval in this era 2 years and the longest interval 49 years (Harmon, 1982).

Records showed that, in order to clear land for farming and settling, fires were set in the

Cades Cove area by early Euro-American settlers “as often as they would [burn]”

(Harmon, 1982). Post GRSM establishment, from 1940 to 1979, there were 2.5 lightning

fires every 10 years and 6.7 human caused fires every 10 years in the westernmost

portion of the park (Harmon, 1982). The contemporary fire cycle appears to be

significantly longer than in the past because of prevention and suppression practices

established by Park Fire Management Officers (Lafon et al., 2005). Lafon and Grassino-

Mayer (2007) calculated fire frequency over a cycle of 1,001 years, with human caused

fires making a larger contribution than natural fires to the total area burned.

Contemporary fire suppression techniques have raised concerns regarding the

buildup of potential wildfire fuels. Shang et al. (2004) found that fire suppression in

central hardwood forests has led to changes in the forest size, structure, and species

composition. They also found that suppression often results in the buildup of fuels and

increased fire risk with greater probability of large fire occurrence. Lafon et al. (2011),

found that the fire suppression practices and reduced fire activity in the GRSM forests

during the 20th

century has contributed to a large increase in the density of trees and

shrubs and has permitted the accumulation of fuel loads possibly increasing wildfire risk

within the Park.

6

1.3 Use of GIS in Wildfire Studies

Due to recent dry summers and devastating forest fires in U.S. national parks

(Yellowstone fires of 1988, Cedar Fire at Cleveland National Forest, CA 2003,

Yosemite fire 2013), there has been an increased interest in finding new tools for

wildfire control and management (Madden et al., 2004). GIS and remote sensing

techniques have been found to be effective tools in wildfire studies. Andreu and

Hermansen-Baez (2008) remarked that GIS and related technologies are “essential to

developing a meaningful preparedness plan,” and that “information on the uses and

benefits of this technology should be made available to policy makers and the public

along with details on current and anticipated wildland fire situations.” In research

regarding the use of spatial technologies in Wildland Fire Management Decision

Making, Zimmerman (2012) noted that recently developed applications in the area of

GIS and related information management systems “improve overall wildland fire

information management and decision making.”

Wildfires have an obvious spatial component and GIS offers an ideal platform to

study the spatial properties of these fires. Simulation of potential wildfire spread and

behavior can be achieved through combining temporal capabilities of GIS along with its

ability to simultaneously compute and analyze multiple layers of spatial data. Wildfire

risk can be assessed by overlaying, combining, and investigated the spatial properties of

certain remotely sensed data layers pertinent to wildfire risk (e.g. terrain, vegetation, and

climate data).

Many recent studies have successfully utilized GIS and geospatial tools to

produce useful data for wildfire management. Cova et al. (2005) used fire spread

7

modeling and GIS to demark evacuation trigger points for high fire risk areas in Coral

Canyon, California. Erdody and Moskal (2010) used a fusion of LiDAR and imagery for

estimating forest canopy fuels in Ahtanum State Forest, Washington State. Millington

(2005) used GIS in a wildfire risk assessment for a central Spain study area. Arca et al.

(2007) utilized the wildfire simulation software, FARSITE (Finney, 1998), to forecast

fire spread and behavior in shrubland areas across the Mediterranean basin.

1.4 Research Objectives

Wildfire risk assessment and modeling is well explored for fire prone areas in

Western and Midwestern portions of the United States (Arca et al., 2007; Cova et al.,

2005; Erdody and Moskal, 2010; Linn et al., 2002; Millington, 2005; Molina-Terrén et

al., 2006). Even with the significant amount of wildfires in the GRSM and the Southern

Appalachian Mountains, research concerning wildfires in these areas is limited. The

need to understand fire risk and potential behavior of fires in these areas is underscored

by the public misperception that ongoing fire management practices on public lands may

be more appropriate for the Western U.S. than for the Appalachian Mountains (Lafon et

al., 2011).

The purpose of this research is to assess risk for and model the potential behavior

of wildfire within GRSM. Specifically, this study aims to (1) use GIS to determine

wildfire risk in areas within GRSM, and (2) for areas determined to be at the highest

risk, use GIS to model potential wildfire behavior using ignition points located within

these areas.

8

To accomplish objective one, this study will examine historical Park fire data,

analyze current Park vegetation and terrain characteristics, compare historic fire data to

current conditions, and use spatial and geostatistical analyses to determine wildfire risk.

Common variables found to influence fire risk will be explored and qualitative measures

will be calculated to determine how each variable influences risk. A final synthetic risk

index ranging from low risk to high risk will be created which incorporates the findings

from the investigations into each variable.

The hypotheses for the risk assessment are that, spatially, historic fire locations

are more clustered than random and that previous fire locations, and thus future fire risk,

would be closely related to variables such as terrain, vegetation type, and human

activity.

To accomplish task two, this study will conduct wildfire simulations using the

fire area simulation software FARSITE. FARSITE is a spatially and temporally explicit

fire simulator that predicts fire perimeters and behavior over complex landscapes.

Although FARSITE has the capability of modeling crown fire (ignition of tree crowns

during a wildfire), only surface fires will be modeled for this study since, according to

the Park’s fire management officer, crown fires are very uncommon in the primarily

deciduous forests of GRSM (Loveland, personal communication, March 1, 2013).

FARSITE will be used only to gain a general understanding of how fire may

behave and spread within high risk areas. Outputs from fire behavior prediction tools

like FARSITE have been found to improve overall wildland fire information

management and decision making (Zimmerman, 2012) and the results from GRSM

9

FARSITE modeling could be used by Park personnel to assess the Park’s current fire

management plan.

The first hypothesis for the wildfire simulation phase is that surface fire will

have a relatively quick rate of spread (measure of how quickly the fire spreads) with the

higher values driven by the abundance of surface fuels in the form of leaf litter on the

Park’s forest floor. The second hypothesis is that Park’s many natural fire breaks (roads

and streams which stop the progress of fire) will significantly influence the final fire

size.

1.5 Study Area

The GRSM encompasses approximately 520,191 acres (210,521 ha) of protected

forest located along the North Carolina – Tennessee border in the southeastern United

States. The terrain is primarily mountainous with an average elevation of 3,294 ft (1,004

m), a maximum elevation of 6,643 ft (2,028 m) at Clingman’s Dome, and a minimum

elevation of 511 ft (156 m) at Chilhowee Lake.

The Park is located in the Caf/Daf, Humid Subtropical/Humid Continental

climate zone with the lower elevations experiencing a humid subtropical climate and the

higher elevations experiencing a humid continental climate. The Park has over 560 miles

(900 km) of streams and rivers which are replenished by over 80 inches (~200 cm) of

rainfall each year. Relatively high rates of evaporation and transpiration through leaves

of the Park’s vegetation produce a blue tinted haze from which the “Smoky” Mountains

gets its name (Welch et al., 2002).

10

The Park is 95% forested and contains over 100 different species of trees which

constitute the most extensive collection of virgin hardwood forest in the eastern United

States (Welch et al., 2002). Approximately 80% of the Park consists of deciduous

forests and specific locations of vegetation types vary with elevation. In the highest

elevations, 4,920 ft – 6,643 ft (1,500 m – 2,025 m), spruce-fir forests dominate with

common species including Fraser fir (Abies fraseri), red spruce (Picea rubens), yellow

birch (Betula alleghaniensis), beech gaps (Fagus sp.), hemlock (Tsuga canadensis),

minor species of Northern hardwood woodlands (Sorbus americana, Prunus

pensylcanica, Amelanchier laevis), northern red oak (Quercus rubra), white oak

(Quercus alba), mountain-ash (Sorbus americana), hobblebush (Viburnum lantanoides),

and blackberries (Rubus sp.). The low to middle and middle to high elevations consists

primarily of submesic to mesic forests. Middle to high elevations, 3,280 ft - 4,920 ft

(1,000 m – 1,500 m), are dominated by Northern hardwood forests similar to those

found in Northeast U.S. and southeast Canada. Common species in this elevation zone

include American beech (Fagus grandifolia), yellow birch (Betula alleghaniensis), red

spruce (P. rubens), red oak (Q. rubra), chestnut oak (Quercus montana), Eastern

hemlock (T. canadensis), grasslands (Poaceae sp., Cyperaceae sp., Juncaceae sp.),

skunk goldenrod (Solidago glomerata), Rugels ragwort (Rugelia nudicaulis), and

hydrangea (Hydrangea sp.). Cove hardwood forests dominate the low to mid elevations,

511 ft - 3,280 ft (156 m – 1,000 m). Common species in this zone include Carolina

silverbell (Halesia tetraptera), American basswood (Tilia americana), dogwood

(Cornus florida), various magnolia species (Magnolia sp.), yellow birch (Betula

alleghaniensis), yellow buckeye (Aesculus flava), tulip tree (Liriodendron tulipifera),

11

sugar maple (Acer saccharum), Carolina and shagbark hickory (Carya carolinae-

septentrionalis, Carya ovata), and Carolina hemlock (Tsuga caroliniana). Kalmia

woodlands (Kalmia sp.) and rhododendron (Rhododendron sp.) are found in understories

in all elevation zones and, according to field reconnaissance, appear especially heavy in

riparian areas.

The GRSM is considered ecologically diverse. More than 1,570 species of

flowering plants (10% of which are considered rare) and over 4,000 species of non-

flowering plants are found in the Park. Scientists estimate that only 10% of the species

documented to date are represented by the flora and fauna currently identified in the

Park (Welch et al., 2002).

GRSM contains roughly 550 miles (885 km) of roads and over 800 miles (1287.2

km) of hiking and walking trails. The Park has 9 campgrounds with its largest

campground, Elkmont, containing over 220 individual campsites. The Park is one of the

most visited Parks in the U.S. receiving roughly 9 million visitors every year.

1.6 Data and Data Sources

The primary dataset used for this study consists of all reported fires that occurred

within GRSM from 1942 to 2009, digital elevation model (DEM) data for GRSM, and

vegetation type data for Park forests. The reported fire data were obtained from the NPS.

“Reported” fires are all those that are either recorded on topographic maps housed at

park headquarters, or recorded on an official form used in fire reporting (Form DI 1202).

The vector dataset depicting fire locations consists of point and polygon data

representing small area fires, and large fires respectively. A visual inspection of the data

12

attributes suggests that, in general, all fires less than one acre (0.4 ha) were represented

by a point while those greater than one acre were represented by a polygon.

The DEM data were obtained from the United States Geological Survey (USGS)

and included elevation data with 32.8 ft (10 m) resolution. DEM data were used to

generate slope gradient and aspect datasets for the Park. The Park vegetation data were

produced by Madden et al. (2004) through the analyses of remotely sensed data and

aerial photography and were verified through ground truthing. The resulting vector

dataset was obtained from the GRSM and NPS and consisted of polygons representing

the dominant vegetation types found in the overstory and understory of Park forests.

Other data used in analyses include major roads and trails within the park, the

park boundary, and climate data. Roads, trails, and Park boundary data were obtained

from the NPS. Climate data included hourly weather and wind patterns and were

obtained from the National Climatic Data Center (NCDC). Climate data were recorded

at weather station # 720259 located in Macon County, North Carolina approximately 15

miles (24 km) south of the Park’s boundary at an elevation of 2,020 ft (615.7 m).

1.7 Additional Data Processing

Certain fires are ignited intentionally as part of a Park Resource Management

program. Purposes of intentional or “controlled” burns include: smoking out bees or

game, insect or snake control, repelling predators, and general prescribed burns to clear

excess undergrowth. Since the scope of this study is focused on the characteristics of

unintentional fires, all fires that were products of Resource Management burning were

excluded from the analyses.

13

Since all analyses conducted in this study required polygon overlay, fires

represented only by point data (roughly half the dataset) were converted to polygon data

by creating a buffer around each point that was equal in size to the acres reported for that

fire location.

14

Chapter 2 Literature Review

2.1 Wildfire Risk

The first objective of this study is the assessment of wildfire risk. The general

definition accepted by the wildfire community states fire risk as “the chance that a fire

might start, as affected by the nature and incidence of causative agents” (Hardy, 2005).

Understanding wildfire risk and where the highest risk areas are located helps fire

managers know where prescribed fire is most needed and where fire suppression efforts

will likely be required on a regular basis. Improvements in fire risk estimation are vital

to reduce the negative impacts of fire, either by lessening burn severity or intensity

through fuel management, or by aiding the natural vegetation recovery using post-fire

treatments (Chuvieco et al., 2010).

Understanding an area’s risk to wildfire and the potential behavior of an

occurring fire begins with an examination of the factors which influence wildfire.

Millington (2005) identified the following factors as important in determining wildfire

risk:

Location relative to human structures such as roads, trails, and campgrounds.

Topography characteristics including slope, aspect, illumination, and

elevation.

Land cover characteristics including vegetation type and density.

Firebreak locations (vegetation gaps which act as a barrier to slow or stop

fire spread).

Climate

Syphard et al. (2008) cited two sources of ignition, human caused and lightning,

and noted several important social and biophysical drivers that influence when and

where they occur. Important social variables in their study included distance to roads,

15

distance to trails, distance to development, and level of development. They found key

biophysical factors to be elevation, slope gradient, topographic aspect, and vegetation

type.

Hardy (2005) stated that to sustain fire requires fuel, heat, and oxygen. He

continued to argue that wildfire behavior is affected by fuel, weather, and topography

and an understanding of these three components is critical in predicting fire occurrence.

Keramitsogloua et al. (2008) argued that three of the most important factors in

determining fire risk and behavior include fuel type, fuel load, and fuel continuity. They

showed that different types of vegetation species produce different types of fuel. For

instance, it was shown that a certain species of pine, aleppo pine (Pinus halepensis), was

more flammable than others due to the species’ high content of flammable oils and

resins. Their study concluded that the location and distribution of potential fuels can

have a great influence on fire risk.

Using findings from the above studies and an initial investigation into historic

Park wildfire locations, the following variables were chosen to investigate as driving

factors influencing risk in the GRSM:

Distance to roads

Distance to trails

Elevation

Slope

Aspect

Overstory Vegetation Type

Understory Vegetation Type

Combined Overstory/Understory Vegetation Type

16

2.2 Distances to Anthropogenic Structures and Wildfire Risk

Important in assessing fire risk is an area’s location relative to anthropogenic

structures such as roads, buildings and recreational areas. In most countries human

activities are the main source of fire ignition (Chuvieco et al., 2010); therefore, an

understanding of where human activity takes place seems imperative for any wildfire

risk assessment. When researching human and biophysical factors that influence fire

disturbance in Northern Wisconsin forests, Sturtevant and Cleland (2007) found that the

likelihood of fire ignition is primarily influenced by human activity, whereas biophysical

factors determine whether those fire starts become large fires. Their observation of fire

scars on study area trees showed that fire occurrence was positively associated with

population, housing, road, and railroad density and negatively associated with distance

to railroads and roads. They concluded that the greatest risk of wildfire occurs “where

rural developments overlap with fire-prone ecosystems,” and that fire starts should be

associated with factors indicating human presence in the landscape, especially housing

units. Bar Massada et al. (2011) found that social related data (census data, e.g.) can be

combined with historical fire records to generate empirical ignition location models that

predict spatially explicit ignition probabilities. They found that up-to-date data about

fuel characteristics, coupled with the spatial location of human development and

activities may be able to predict areas of increased ignition probabilities due to

anthropogenic causes.

The above mentioned studies found human activity and fire frequency to be

related. However, certain methods of these studies, using census data and housing and

population densities of census blocks, were not applied to the GRSM study due to the

17

fact that the area is sparsely populated. Census block data obtained from the U.S. Census

Bureau reveals that the population density for the Park is 11.8 people per square mile

with a housing unit density of only 10 units per square mile. On any given day, it is

highly probable that tourists (sight-seers, campers, and hikers) largely outnumber the

actual residents. Therefore, distance to structures including roads, trails (indicative of

human accessibility) was used as a surrogate for population density.

2.3 Terrain and Wildfire Risk

Observation of Park DEM reveals a relatively large range between high and low

elevations with a maximum of 6,653 ft (2028 m) and a minimum of 813 ft (248 m). It

was suspected certain elevation ranges may exhibit greater fire frequencies and previous

studies’ findings validated these suspicions. When studying the spatial patterns of

wildfire occurrence in the central Appalachian Mountains, Lafon and Grassino-Mayer

(2007) found elevation to be a determining factor for fire occurrence. Between 1986 and

2003, within their Virginia and Eastern West Virginia study areas, they found the

elevation zones with the greatest number of fires to be the 458-762 m zone with 511

fires, and the 763-1067 m zone, with 200 fire occurrences. They concluded that, in

general, fire density declined from lower to higher elevations since that increases in

elevation typically reflect a wetter climate, lower temperatures, and orographically

enhanced precipitation.

Slope gradient and aspect are frequently cited as being key factors in determining

where wildfire may likely occur (Bar Massada et al., 2011; Finney and Ryan, 1995;

Harmon, 1982; Heritage, 1939; Lafon and Grassino-Mayer, 2007; Lafon et al., 2011;

18

Millington, 2005). Slope gradient and aspect affect the amount of sunlight an area

receives therefore influencing the moisture content of potential fuels. Bar Massada et al.

(2011) found that slope gradient significantly influences the spatial predictions of fire

occurrence during both normal and extreme fire conditions. Finney and Ryan (1995)

found slope to be a common hazard element in identifying areas of severe fire potential.

Lafon et al. (2011) found that fires in Southern Appalachian Mountains tend to burn

more frequently on more illuminated, drier topographic situations such as south-facing

slopes. Lafon and Grassino-Mayer (2007) found that fire ignitions in the Central

Appalachians tended to peak on south-, east-, or west-facing slopes, and decline toward

north-, northeast-, or northwest-facing ones.

During this study, it was hypothesized that southern facing slopes in the GRSM

would have a higher number of wildfires due to the fact that they receive more sunlight

and tend to be drier than northern facing slopes.

2.4 Vegetation Type and Wildfire Risk

Vegetation or fuel type is critical in determining wildfire risk and potential

behavior since plants are the main ignition material in a forest fire (Chuvieco et al.,

2010). Sturtevant and Cleland (2007) found that vegetation type along with its related

moisture content, litter production, and relative flammability are key determinants in

successful fire start. Ryu et al. (2007) showed that spatial heterogeneity in vegetation

type is directly related to fire occurrence. In other words, large areas of like vegetation

considered to be flammable may increase fire risk. Their work strongly supported a

causal link between fuel loading heterogeneity and fire spread.

19

The fuels that burn during a wildfire are commonly classified into two classes,

live fuels and dead fuels. Live fuels consist of the leaves, stems, and flowers of growing

plants. Due to moisture content, most live fuels are difficult to ignite and do not burn

readily by themselves (Shang et al., 2004); however, if moisture content is reduced by

processes such as drought or dormancy, live fuels have better potential to burn,

sometimes very intensely (Finney, 1998). Dead fuels consist of dead woody materials,

leaf litter, dead twigs and branches, and standing or fallen dead trees. Fire ignites and

spreads more easily in these fuels (Shang et al., 2004) as long as no precipitation event

has increased their moisture content. In the western U.S., large woody fuels accumulate

over time, and dead fuel loading is a major driving force behind potential fire

occurrence. In contrast, fire in eastern hardwood forest such as GRSM is primarily

influenced by leaf litter (leaves accumulated on surface) and small woody fuels (Graham

and McCarthy, 2006; Loveland, personal communication, March 1, 2013).

The time interval since last fire is a common variable used in determining risk. It

is believed that post-fire effects, mainly in the form of vegetation or fuel reduction,

temporarily reduce the risk of subsequent fire (Millington, 2005). However, there was

insufficient evidence to support the belief that fire significantly reduces fuel loads in the

GRSM. In the eastern hardwoods such as those found in the study area, fires are

typically low in intensity and consume primarily leaf litter on the ground (Bando, 2009;

Graham and McCarthy, 2006; Loveland, personal communication, March 1, 2013).

Since crown fire is a rare occurrence within the Park (Loveland, personal

communication, March 1, 2013), live leaves typically remain in place to produce

20

subsequent leaf litter and replenish the forest floor with potential fuels presumably

making time since last fire less influential.

2.5 Variables Excluded from the Risk Assessment

Although cited by Syphard et al. (2008) as a key social factor, distance to and

level of development were not chosen due to the lack of major development within the

Park boundaries. The major commercial and developed areas, Gatlinburg, TN,

Townsend, TN, and Cherokee, NC, all lie outside the Park boundaries. Roads and trails

are considered the only major anthropogenic structures within Park boundaries,

therefore only distance to these types of structures was chosen.

Also excluded was precipitation. An obvious visible correlation can be seen

between precipitation and fire frequency with areas receiving less precipitation tending

to have more fires. Precipitation for the park is closely related to elevation with the

higher elevations receiving more rainfall than lower elevations. Since elevation was used

in the risk assessment, it is felt that variations in precipitation are accounted for by the

variations in elevation. Also, while there is a general trend of more rainfall in higher

Park elevations, precipitation varies throughout the year, therefore, unique temporal

aspects are present in the precipitation data that are not present in the other datasets; for

example, elevation is static throughout the year and distance to road does not change

throughout the year (unless, of course, a new road is constructed). During the risk

assessment, the aim of this research was to obtain a general understanding of fire risk

throughout the park at any given time of the year.

21

2.6 Wildfire Modeling and Simulation

Computational wildfire modeling and simulation are common methods used to

understand and predict wildfire behavior. Modeling has been found to be one of the

most cost effective tools for studying the relationship between fire, climate, and

vegetation (Ryu et al., 2007) and various computerized fire simulation models have been

described in scientific literature since the 1970’s (Finney and Ryan, 1995). Model

findings can be very valuable for fire management and forest agencies. Simulation

results can be incorporated into fuel management strategies in order to identify the

potential strength and weaknesses of current fire management plans.

There are several different types of wildfire models each customized to work

with specific geographies and their unique attributes. In the U.S., a commonly used

model is Rothermel’s (1972) fire model in conjunction with the National Fire Danger

Rating System (NFDRS). GIS based models such as FARSITE, developed by Finney

(1998), are also frequently used and offer the advantage of spatial display of model

outputs (rather than tables only).

Two basic approaches for wildfire modeling within GIS have emerged: cellular

and wave-type models (Finney and Ryan, 1995). Cellular or raster type models simulate

fire spread as a discrete process of fire ignition between adjacent cells on a regularly

spaced landscape grid. Typically, cellular type simulations use neighborhoods of 8

adjacent cells (Moore neighborhood) and simple transition rules to simulate fire spread

(Yassemi et al., 2008). It is often assumed that fire can spread to a non-burning cell only

when a neighboring cell is completely burning. Angle limitations inside cells are

commonly used during modeling to compensate for variables that influence fire spread

22

such as wind and slope characteristics. Wave or vector type models simulate fire growth

as a spreading wave front with the edge of fire defined by an expanding polygon

(Anderson et al., 1982). The fire polygon is defined by a series of two dimensional

vertices (points with x and y coordinates) with the number of vertices increasing over

time as the fire grows (Finney, 1998). Expansion is determined by calculating the rate

and direction of fire spread from each vertex and multiplying by a given time step (1

hour, for example).

Although effective, raster models have certain limitations. Finney (1998) argued

that cellular modeling has diminishing success in reproducing the expected two

dimensional shapes and growth patterns as environmental conditions become more

heterogeneous. Raster models are computationally demanding and the fire shapes are

often distorted by the gridded geometry of the calculations (Finney, 1998). Ryu et al.

(2007) showed that the vector functions such as those found in FARSITE offer two

advantages over cellular based models by (1) providing a more accurate representation

of two-dimensional fire growth patterns and (2) a better response to wind speed, shifts in

wind direction, and fuel moisture change.

23

Chapter 3 Wildfire Risk Assessment Methods and Results

3.1 Spatial Distribution of Historic GRSM Wildfires

The spatial location and distribution of historic wildfires may offer insights

during the assessment of an area’s risk to wildland fire (Sturtevant and Cleland, 2007).

Visual observation of historic fire point data for the Park suggests that the distribution of

wildfire locations may be more clustered than random (Figure 2). Nearest neighbor

(NN) analysis was performed to examine the spatial distribution of wildfires and

calculate the Nearest Neighbor Ratio (NNR) value. Since some fire point locations lay

just outside the park boundary, a minimum bounding polygon surrounding all points was

used as the boundary during analysis. The null and alternate hypotheses surrounding this

particular analysis were:

H0 : NNR = 1 (point pattern is random)

HA : NNR < 1 (point pattern is more clustered than random)

where NNR = observed mean distance between points/expected mean

distance between points

Results from the NN analysis are shown in Table 1. An NNR of 0.52 suggests

that fires are more clustered than random. The results also show that, on average, a

distance of 499 m separates a fire location from its nearest neighbor. A Z value of -26.13

shows a significant difference between the observed NNR and the corresponding NNR

value for a random spacing of points. Therefore, we should accept the alternate

hypothesis that the point pattern is clustered. The logical question to ask now is, “for

what reason(s) are points clustered?” and “could revealing the underlying forces

promoting fire location clustering help with assessing an area’s risk to wildfire?”.

24

Table 1. Nearest neighbor analysis results of historic wildfires, Great Smoky

Mountains National Park, USA.

Observed Mean Distance (m): 498.50

Expected Mean Distance (m): 967.02

NNR: 0.52

z-score: -26.13

p-value: 0.00

Figure 2. Historic wildfire locations 1942 – 2009, Great Smoky Mountains

National Park, USA.

25

3.2 Variables used for Assessing Wildfire Risk for GRSM

For the purposes of this study it was decided to study each independent variable

separately to see the effect of each on fire frequency. Variables studied included:

Distance to roads

Distance to trails

Elevation

Slope

Aspect

Overstory Vegetation Type

Understory Vegetation Type

Combined Overstory/Understory Vegetation Type

The above variables were classified into three separate categories. The first

category was Distance to Structures and included the distance to roads and distance to

trails variables. The second category was Terrain and included elevation, aspect, and

slope. The third category was Fuel Type and included overstory vegetation type,

understory vegetation type, and the combined overstory/understory vegetation type.

Once analyzed independently, the variables within each category were combined to

create a single dataset representing that category.

3.3 Fire Frequency and Distance to Structure

Visual observation of fire locations relative to locations of roads and trails

(herein referred to as “structures”) suggests that a negative correlation may exist

between the distance from structures and fire frequency. In other words, there appears to

be less fire occurrence as distance from structures increases. However, visual

observation is subjective, and only gives the observer a general impression of the

dispersion (McGrew and Monroe, 2000). In order to provide a quantitative means to

26

measure this apparent association, correlation and regression analyses, a common

approach to predict fire occurrence with respect to different geographic variables (Pew

and Larson, 2001; Vega-Garcia et al., 1995), were performed to determine if a

statistically significant relationship exists between the location of wildfires and their

proximity to structures.

For these analyses, only fires dated 1980 and onward were used since the exact

date that each road and trail was developed was not found in any readily available

dataset. However, traffic counts obtained from the Tennessee Department of

Transportation (TDOT) had traffic count data for all Tennessee roads within the Park

dating back to 1985 (no counts were recorded for prior years). Also, as a lifelong visitor

of the Park, the author’s intimate familiarity with the Park helped this study in

establishing what was felt to be a reasonably conservative 1980 cutoff point.

For correlation analyses, roads and trails were separated and analyses were

conducted for each structure type. Euclidean distances were calculated for each structure

type with 32.8 ft (10 m) resolution raster grid output with grid values equal to distance

from the nearest structure (Figure 3). Fire points were then overlaid the resulting

distance grids and the distance from the nearest road and distance from the nearest trail

was extracted from the grid for each fire point. Distances from structure were then

classified using an equal interval classification method with classes ranging from 656 ft

(200 m) to 29,528 ft (9,000 m) in intervals of 656 ft (200 m). The total number of fires

occurring within each distance class was calculated for each structure type; and the first

10 classes are displayed in Table 2.

27

(a) Distance to Road

(b) Distance to Trail

Figure 3. Euclidean distance to structure, Great Smoky Mountains National Park,

USA.

28

Table 2. Distance from structures and wildfire frequency, Great Smoky

Mountains National Park, USA. (only first 10 results displayed)

Distance to Road(m) No. of Fires Distance to Trail (m) No. of Fires

0 – 200 204 0 - 200 78

200 – 400 57 200 - 400 39

400 – 600 33 400 - 600 32

600 – 800 15 600 - 800 39

800 – 1000 22 800 - 1000 30

1000 – 1200 17 1000 - 1200 19

1200 – 1400 23 1200 - 1400 23

1400 – 1600 22 1400 - 1600 20

1600 – 1800 13 1600 - 1800 9

1800 – 2000 10 1800 - 2000 12

Figure 4 shows two scatter plots of distances from structures vs. frequency of

wildfire. Examination of the data reveals that a clear curvilinear relationship exists

between both distance to roads and distance to trails. Therefore, in order to perform a

linear correlation analysis, a common logarithm (log10) was used to transform the fire

frequency data into a linear form for both analyses. The results from the transformation

are shown in Figure 5.

Correlation and regression analyses show that a strong correlation exists between

fire frequency and the proximity to structures. For roads, R2 = 0.92 indicates that 92% of

the variability in fire frequency might be explained by the variability in distance from

roads. Likewise, for trails, R2 = 0.82 indicates that 82% of the variability in fire

frequency might be explained by the variability in distance to trails.

29

0

50

100

150

200

250

0 2000 4000 6000 8000 10000 12000 14000

Distance to Road (m)

No

. o

f F

ires

Frequency

(a) Distance to Road vs. Fire Frequency

0

10

20

30

40

50

60

70

80

90

0 2000 4000 6000 8000 10000 12000 14000

Distance to Trail (m)

No

. o

f F

ires

Frequency

(b) Distance to Trail vs. Fire Frequency

Figure 4. Scatter plot of distance to structure vs. fire frequency(untransformed

data), Great Smoky Mountains National Park, USA.

30

R2 = 0.9231

0

0.5

1

1.5

2

2.5

2 2.5 3 3.5 4 4.5

Distance to Road (log10)

No

. o

f F

ires (

log

10)

Frequency

Linear (Frequency)

(a) Distance to Road vs. Fire Frequency

R2 = 0.8179

0

0.5

1

1.5

2

2.5

2 2.5 3 3.5 4 4.5

Distance to Trail (log10)

No

. o

f F

ires (

log

10)

Frequency

Linear (Frequency)

(b) Distance to Trail vs. Fire Frequency

Figure 5. Scatter plot of distance to structure vs. fire frequency (data transformed

using log10), Great Smoky Mountains National Park, USA.

31

3.4 Reclassifying and Combining the Distance to Structure Grids

Analysis of the distance from structures produced two 32.8 ft (10 m) raster

datasets, distance to roads and distance to trails, with cell values equal to the distance in

meters from each respective structure. In order to develop a risk index, both datasets’

cell values were changed (reclassified) to reflect wildfire risk as determined by the

analyses. The risk index values ranged from 0 to 5 with 5 assigned to highest risk areas

and 0 assigned to the lowest risk areas. The following formula was used during the

reclassification process:

Rv = (pn/ph)*5

where:

Rv = reclassified value

pn = proportion of the total number of fires accounted for by n distance

range.

ph = proportion of the total number of fires accounted for by the distance

range with the highest number of fires

For example, for the distance to roads analysis, the 0 ft – 656 ft (0 m – 200 m) distance

range had the highest number of fires (204) and accounted for 43% (0.43) of the total

number of fires. The 656 ft – 1,312 ft (200 m – 400 m) distance range had the second

highest number of fires (57) accounting for 12% (0.12) of the total number of fires.

Using the equation above, raster values lying in the 0–656 ft (0-200 m) range were

reclassified as 5 ((0.43/0.43)*5 = 5) and values lying in the 656–1,312 ft (200-400 m)

range were reclassified as 1.397 ((0.12/0.43)*5 = 1.397).

32

Once reclassified, the distance to road and distance to trail grids were overlaid

and grid values were combined to create a single distance to structure dataset. The

following weighted sum model (WSM) was used during this process:

DG = dr(0.625) + dt(0.375)

where:

DG = final distance to structure grid value,

dr = reclassified distance to road grid value,

and dt = reclassified distance to trail grid value

This resulted in a grid with highest risk areas (relative to distance to structures) valued at

5 and lowest risk areas valued at 0 (Figure 6).

Figure 6. Wildfire risk according to distance to structure, Great Smoky Mountains

National Park, USA.

33

There were several justifications as to why distance to road grid was given more

weight (0.625) than the distance to trail grid (0.375). First, correlation and regression

analysis produced a higher R value for distance to roads, 0.92 for roads compared to

0.82 for trails. Secondly, the high correlation to fire frequency found with both structure

types suggests that human activity could be a significant cause of Park wildfires.

Analysis of 2012 average daily traffic count data obtained from TDOT shows that

roughly 2.6 million vehicles traveled park roads in 2012. NPS hiking statistics for the

Park estimate roughly 400,000 people hike Park trails on an annual basis. Therefore,

given greater presence of human activity on Park roads and its higher correlation to fire

frequency, the distance to roads grid was given more weight during the final distance to

structure grid creation.

3.5 Determining the Relationship between Fire Frequency and Elevation

To examine fire frequency vs. elevation, polygons representing fire perimeters

were overlaid the study area DEM. Using each fire polygon as a unique “zone”, zonal

statistics GIS tools were used to determine the mean elevation for each fire. Put simply,

the zonal statistics tool worked by first determining all DEM grid cell values that

occurred within each zone (or fire). It then calculated the mean of those grid values and

transferred the calculated means to their respective fire polygons in the feature attribute

table.

Elevation ranges were classified using an equal interval classification method

with 20 classes ranging from 833 ft to 5,745 ft (254 m to 1,751 m) in intervals of 66 ft

34

(20 m). The number of fires occurring within each elevation class was then determined

and displayed in Table 3.

Examination of the results shows a clear pattern to wildfire occurrence with a

significant proportion (52%) occurring within the elevation range 1,572 ft – 2,306 ft

(479 m to 703 m) and roughly 25% of fires occurring in just the 1,572 ft – 1,814 ft (479

m to 553 m) range. These results appear to confirm the conclusions drawn by Lafon and

Grassino-Mayer (2007) and Heritage (1939) with marked decreases in fire occurrence in

both the higher and lower elevation ranges. These marked decreases are evident in the

scatter plot of elevation vs. fire frequency (Figure 7). A distinct curvilinear relationship

can be seen with peak fire frequency in the lower mid elevation ranges and lower fire

occurrence in the lowest and highest elevation ranges.

3.6 Determining the Relationship between Fire Frequency and Slope

An investigation into wildfire frequency with respect to slope characteristics was

conducted to see if Park fires tended to occur more often on terrain at a certain gradient.

Slope values (in degrees) for the study area were generated using the area DEM. As used

in the fire frequency vs. elevation investigation (Section 3.5), the zonal statistics tool

was used to determine the average slope of the burned area for each individual fire.

Slope values were then classified, using an equal interval classification approach, into 20

classes ranging from 0º to 49.2º in intervals of 2.5º. The total number of fires occurring

within each slope class was calculated and displayed in Table 4.

35

Table 3. Elevation and fire frequency, Great Smoky Mountains National

Park, USA.

Elevation (m) No. of Fires

254 – 329 28

329 – 404 45

404 – 479 76

479 – 553 192

553 – 628 121

628 – 703 102

703 – 778 57

778 – 853 43

853 – 928 21

928 – 1003 16

1003 – 1077 10

1077 – 1152 21

1152 – 1227 9

1227 – 1301 9

1301 – 1377 9

1377 – 1452 13

1452 – 1526 14

1526 – 1601 4

1601 – 1676 3

1676 – 1751 2

0

20

40

60

80

100

120

140

0 500 1000 1500 2000

Elevation (m)

No

. o

f F

ires

Frequency

Figure 7. Scatter plot of elevation vs. wildfire frequency, Great Smoky Mountains

National Park, USA.

36

Table 4. Slope gradient and fire frequency, Great Smoky Mountains National

Park, USA.

Slope (degrees) No. of Fires

0 - 2.5 32

2.5 - 4.9 31

4.9 - 7.4 30

7.4 - 9.8 27

9.8 - 12.3 35

12.3 - 14.8 39

14.8 - 17.2 46

17.2 - 19.7 69

19.7 - 22.2 85

22.2 - 24.6 114

24.6 - 27.1 104

27.1 - 29.5 78

29.5 – 32.0 42

32.0 - 34.5 23

34.5 - 36.9 12

36.9 - 39.4 14

39.4 - 41.9 8

41.9 - 44.3 3

44.3 - 46.8 2

46.8 - 49.2 1

According to the results, burned areas for historic GRSM fires tended to have

average slopes in the 20º to 30º range with approximately 49% of the fires falling in that

range. Interestingly, fire frequency significantly decreased in classes with slopes greater

than 34.5º and no fires exhibited an average slope greater than the range 46.8º - 49.2º.

Although steeper slopes (greater than 30º) have been found to have larger, more rapidly

spreading fires because of increased direct flame contact and forward heat transfer by

convection and radiation (Finney, 1998), they appear not to promote initial ignition of

wildfires within the GRSM.

37

3.7 Determining Correlation between Fire Frequency and Aspect

The DEM data was used to calculate aspect (horizontal direction to which a

mountain slope faces) for Park areas. It was hypothesized that southern facing slopes

would experience more fires due to greater sun exposure and drier fuels. To test this

hypothesis, Spearman’s rank correlation coefficient (rs), a method used to measure the

correlation between two ranked variables, was used to quantify the relationship between

slope aspect and fire frequency.

To conduct the analyses, fire perimeter polygons were first used to determine the

average aspect for each fire. The range of possible aspect values (0º-360º) was classified

using an equal interval approach so that each class (24 total classes in intervals of 15º)

had an equal sized range of values. For each aspect class, the number of fires that had an

average slope aspect that fell within its range of values was then calculated (Table 5).

For example, if the burned area of a fire had an average aspect of 34º, the fire was

assigned to the aspect class with values ranging 30º to 45º.

As mentioned, Spearman’s rank analysis quantifies the relationship between two

ranked variables. To establish the required rankings, aspect classes were first ranked

according to their orientation to the south cardinal direction with southern facing values

receiving the highest rank and northern facing values receiving the lowest rank. For

example, aspect classes with values ranging from 165º to 180º and 180º to 195º

(generally south facing) were combined and given a ranking of 1 while classes with

values ranging from 345º to 360º and to 0º to 15º (generally north facing) were

combined and given the lowest ranking of 12 (Figure 8). The combined aspect classes

were then ranked according to the number of fires that occurred within each (1 = highest

38

Table 5. Aspect and fire frequency, Great Smoky Mountains National Park, USA.

Aspect (degrees) No. of Fires

0 – 15 1

15 – 30 7

30 – 45 10

45 – 60 9

60 – 75 15

75 – 90 9

90 – 105 31

105 – 120 36

120 – 135 46

135 – 150 52

150 – 165 58

165 – 180 60

180 – 195 75

195 – 210 80

210 – 225 67

225 – 240 51

240 – 255 47

255 – 270 36

270 – 285 29

285 – 300 20

300 – 315 14

315 – 330 20

330 – 345 14

345 – 360 8

frequency, 12 = lowest frequency). Spearman’s rank coefficient was then calculated to

determine the relationship between the two ranked variables for each combined class

and displayed in Table 6.

The higher ranked southern facing slopes also ranked highest in fire occurrence.

Likewise, the lower ranked northern-facing slopes were ranked lowest in fire

occurrence. Fires most often occurred within regions with aspects ranging from 150º –

210º (generally south-facing). A Spearman’s rank coefficient of 0.986 between these

two ranked variables shows, with high statistical significance, that fires tend to occur on

southern facing slopes.

39

Table 6. Spearman’s R - aspect rank vs. fire frequency rank, Great Smoky


SPEARMAN'S R - ASPECT RANK VS. FIRE FREQUENCY RANK

ASPECT (in degrees)

RANK ACCORDING TO SOUTH (x)

Fire Frequency Rank (y) d (x - y) d^2

165 – 195 1 2 -1 1

150-165, 195-210 2 1 1 1

135-150, 210-225 3 3 0 0

120-135, 225-240 4 4 0 0

105-120, 240-255 5 5 0 0

90-105, 255-270 6 6 0 0

75-90, 270-285 7 7 0 0

60-75, 285-300 8 8 0 0

45-60, 300-315 9 10 -1 1

30-45, 315-330 10 9 1 1

15-30, 330-345 11 11 0 0

0 - 15, 345 - 360 12 12 0 0

Sum 4

Spearman's R (rs) 0.986

Z Score 3.27

Confidence 99.9%

Figure 8. Ranking aspect classes for Spearman’s rank analysis – ranked according

to orientation to south cardinal direction.

40

3.8 Reclassifying and Combining Terrain Grids

As in the distance to structure analyses, the three terrain related raster grid

datasets (elevation, slope, and aspect) were reclassified to show the highest risk areas

relative to each terrain property. As previously discussed, all terrain related datasets

were classified using an equal interval classification method and the fire frequency was

calculated for each class. Terrain raster datasets were reclassified according to the fire

frequency observed in each equal interval class. The following formula was used during

the reclassification process for each dataset:

Rv = (pn/ph)*5

where:


pn = proportion of the total number of fires accounted for by n class

ph = proportion of the total number of fires accounted for by the class

with the highest number of fires

For example, in the slope dataset, the slope class ranging from 22.2º to 24.6º accounted

for the highest proportion of the total number of fires (0.143) and the slope class ranging

from 24.6º - 27.1º accounted for the second highest proportion of the total number of

fires (0.131). During raster reclassification, slope grid values that fell in the range of

22.2º to 24.6º were reclassified as 5 ((0.143/0.143)*5=5), and grid values falling in the

24.6º - 27.1º range were reclassified as 4.56 ((0.131/0.143)*5= 4.56). Results from the

reclassification processes can be seen in Figure 9.

41

Figure 9. Wildfire risk according to terrain related variables, Great Smoky


42

Once reclassified, the elevation, slope, and aspect grids were overlaid and

spatially coincident grid values were combined using the following weighted sum model

(WSM):

TG = e(0.275) + s(0.275) + a(0.45)

where:

TG = final terrain grid value,

e = elevation grid value,

s = slope grid value,

and a = aspect grid value

The above formula produced a terrain grid with the highest risk areas, in respect to

terrain, valued at 5 and the lowest risk areas valued at 0 (Figure 10).

Figure 10. Wildfire risk according to combined terrain variables, Great Smoky


43

Weights used in the formula were determined based on several factors, one of

which was the correlation analyses for terrain vs. fire frequency. Spearman’s rank

correlation analysis for fire frequency vs. aspect produced and R value of 0.986. This is

significantly higher than R values found for elevation and slope which were 0.57 and

0.58, respectively. Expert opinion from a Park fire officer was also used to determine

weights. When examining the above assigned weights, the Park’s Fire Management

Officer confirmed that these were reasonable weights to assign to each terrain category

(Loveland, personal communication, April 15, 2013).

3.9 Determining the Relationship between Fire Frequency and Fuel Type

This study investigated fire occurrence within three different horizons of the

park’s forest cover: the overstory vegetation, the understory vegetation, and the

combined overstory and understory vegetation (herein referred to as “over/under”).

Unlike the analyses related to terrain characteristics, the vegetation type analyses did not

calculate an “average” vegetation type burned for each fire; rather, the total number of

acres burned in fires for each vegetation type was calculated for all three realms. To

accomplish this, fire perimeter polygons were used to clip vector datasets obtained from

NPS that represented the overstory and understory vegetation types. GIS statistics tools

were then used to calculate the total number of acres burned for each overstory and each

understory vegetation type. An over/under dataset was created by overlaying the

overstory and understory datasets and using a union tool to create a dataset to represent

the composite overstory and understory vegetation types found in any one area. Fire

44

polygons were then used to clip the composite dataset and the total number of acres

burned for each over/under vegetation combination was calculated.

The generalization of classification for the vegetation types was developed by

Madden et al. (2004) from the University Georgia and was customized specifically for

the GRSM. Vegetation classes varied in representative species and topographic zones in

which classes can be found. For example, the key difference between Northern

Hardwood and Appalachian Hardwood (Madden et al., 2004), are their representative

species and elevation. Northern Hardwood are those hardwood species occurring in the

sub-alpine and high elevation zones (3280 – 6643 feet) and are dominated by red spruce

(Picea rubens) yellow brich (Betula alleghaniensis), and northern red oak (Quercus

rubra). These forests are similar to those found in Northeastern US and Southeastern

Canada. Appalachian Hardwood are most common in the low to mid elevation (528 –

3280 feet and are dominated by Carolina hemlocks (Tsuga caroliniana), eastern

hemlock (Tsuga canadensis), and American basswood (Tilia americana). The

customized classification of vegetation for the GRSM is shown in Table 7.

It could be hypothesized that number of acres burned for any vegetation type

might simply be a reflection of how prominent that vegetation type is throughout park

forests. In other words, it was thought that the results would show a large proportion of

burned acres consisting of pine merely because there is a large proportion of the GRSM

forest that is dominated by pine trees. To test this hypothesis, a Chi-square test was

conducted to ensure that the amount of burned acres for each vegetation type was not

simply a reflection of the distribution of the vegetation throughout the park. The results

of the vegetation burned and chi-square analyses are shown in Tables 8-10.

45

Table 7: Customized classification of vegetation for the Great Smoky Mountains

National Park, USA

Dominant Vegetation Representative Species Topographical

Zone

OV

ERST

OR

Y

Submesic to Mesic Oak/Hardwoods

Northern red oak (Quercus rubra), scarlet oak (Quercus coccinea), chestnut oak (Quercus montana), white oak (Quercus alba), red maple (Acer rubrum var. rubrum), sourwood (Oxydendrum arboreum), tulip tree (Liriodendron tulipifera)

Low to mid elevations (511 – 3280 feet)

Mixed Pine Forest

Virginia Pine (Pinus virginiana), shortleaf pine (Pinus echinata), pitch pine (Pinus rigida), table mountain pine (Pinus pungens), northern red oak (Quercus rubra), black oak (Quercus velutina), eastern hemlock (Tsuga canadensis)


Appalachian Hardwoods

Carolina hemlock (Tsuga caroliniana), eastern hemlock (Tsuga canadensis), basswood (Tilia americana), yellow birch (Betula alleghaniensis), red maple (Acer rubrum var. rubrum)


Southern Appalachian Cove Hardwood

Carolina silverbell (Halesia tetraptera), northern red oak (Quercus rubra), tulip tree (Liriodendron tulipifera), various magnolia species (Magnolia sp.)


Pasture Grasslands (Poaceae sp., Cyperaceae sp., Juncaceae sp.)

Mid elevations (1500 – 2000 feet)

Hemlock Eastern hemlock (Tsuga canadensis), Carolina hemlock (Tsuga caroliniana)


Montane Alluvial Forest

Sycamore (Platanus occidentalis), tulip tree (Liriodendron tulipifera), American hornbeam (Carpinus caroliniana), sweetgum (Liquidambar styraciflua)


Northern Hardwoods Red spruce (Picea rubens), yellow brich (Betula alleghaniensis), northern red oak (Quercus rubra)

Sub-apline and high elevations (3280 – 6643 feet)

Montane Forest Northern red oak (Quercus rubra), chestnut oak (Quercus montana), white oak (Quercus alba)

Sub-apline and high elevations (3280 – 6643 feet)

Kalmia Mountain laurel (Kalmia sp.) WR*

UN

DER

STO

RY

Herbaceous and Deciduous

Wild hydrangea (Hydrangea arborescens ssp. arborescens), various shrubs (Vaccinium sp.), summer grape (Vitis aestivalis var. bicolor) WR*

Pine with Kalmia Eastern white pine (Pinus strobus), yellow pine (Pinus sp.), mountain laurel (Kalmia sp.)


Hemlock with Rhododendron

Eastern hemlock (Tsuga canadensis), mixed rhododendron (Rhododendron spp.)


Graminoids Oak sedge (Carex pensylvanica), Grasslands (Poaceae sp., Cyperaceae sp., Juncaceae sp.),

Mid elevations (1500 – 2000 feet)

Kalmia - Light** Mountain laurel (Kalmia sp.) WR*

Pine with Rhododendron Eastern white pine (Pinus strobus), yellow pine (Pinus sp.), mountain laurel (Kalmia latifolia), mixed rhododendron (Rhododendron sp.)


Kalmia - Medium*** Mountain laurel (Kalmia sp.) WR*

Rhododendron - Light** Mixed rhododendron (Rhododendron sp.) WR*

Rhododendron - Medium*** Mixed rhododendron (Rhododendron sp.) WR*

* WR = Wide range of elevations ** Light indicates that greater than 50% of the ground surface was visible through the vegetation when analyzing remotely sensed data *** Medium indicates that 20-50% of the ground surface was visible through the vegetation when observing remotely sensed data

46

Table 8. Analysis of overstory vegetation and fire frequency, Great Smoky


(note: only first 10 vegetation types shown)

Overstory Dominant Vegetation

Observed Acres

Burned (o)

Proportion of Total Burned

Area (%)

Expected Acres

Burned (e)

Proportion of All

Vegetation (%)

Chi Square (o-e)

2/e

Submesic to Mesic Oak/Hardwoods

20,325.12 47.58

15,268.12 35.74

1,674.95

Mixed Pine Forest

8,122.18 19.01

3,805.77 8.91

4,895.58

Appalachian Hardwoods

4,894.95 11.46

6,083.74 14.24

232.29

Southern Appalachian Cove Hardwood

2,692.78 6.30

2,857.51 6.69

9.50

Pasture

2,656.56 6.22

174.71 0.41

35,254.90

Hemlock

1,064.25 2.49

1,242.55 2.91

25.59

Montane Alluvial Forest

877.13 2.05

519.20 1.22

246.76

Appalachian Northern Hardwoods

487.97 1.14

6,084.49 14.24

5,147.68

Montane Forest

447.47 1.05

1,652.97 3.87

879.16

Kalmia

306.76 0.72

102.37 0.24

408.11

----------------------- --------- --------- --------- --------- ---------

TOTAL* 42,722.18 100.00 42,722.18 100.00 53,676.48

Note: All values rounded to 2 decimal places *Total is for all classes. Only first 10 classes displayed. Total acres of overstory vegetation = 541,954

47

Table 9. Analysis of understory vegetation and fire frequency, Great Smoky



Understory Dominant Vegetation Observed

Acres Burned (o)


Area (%)

Expected Acres

Burned (e)

Proportion of All

Vegetation (%)

Chi Square (o-e)

2/e

Herbaceous and Deciduous 17,711.21 41.47

19,940.55 46.69

249.24

Pine with Kalmia 8,871.20 20.77

4,120.42 9.65

5,477.59

Hemlock with Rhododendron 4,666.97 10.93

5,432.27 12.72

107.82

Graminoids 2,829.72 6.63

180.08 0.42

38,986.05

Kalmia – Light 1,371.78 3.21

774.73 1.81

460.11

Pine with Rhododendron 1,055.40 2.47

524.68 1.23

536.82

Kalmia – Medium 908.95 2.13

682.80 1.60

74.90

Rhododendron Light 866.53 2.03

1,525.84 3.57

284.88

Rhododendron Medium 805.37 1.89

1,329.28 3.11

206.50

----------------------- --------- --------- --------- --------- ---------

TOTAL* 42,705.48 100.00 42,705.48 100.00 51,300.09

Note: All values rounded to 2 decimal places

*Total is for all classes. Only first 9 classes displayed.

Total acres of understory vegetation = 543,406

48

Table 10. Analysis of combined over/under vegetation and fire frequency, Great

Smoky Mountains National Park, USA.


Over/Under Dominant Vegetation Combination

Observed Acres

Burned (o)


Area (%)

Expected Acres

Burned (e)

Proportion of All

Vegetation (%)

Chi Square (o-e)

2/e

Submesic to Mesic Oak/Hardwoods/Herbaceous and Deciduous

10,489.88 24.60

8,744.20 20.51

348.50

Submesic to Mesic Oak/Hardwoods/Pine with Kalmia

3,861.82 9.06

1,977.41 4.64

1,795.77

Mixed Pine Forest/Pine with Kalmia

3,728.92 8.75

1,614.44 3.79

2,769.40

Pasture/Graminoids

2,586.56 6.07

158.99 0.37

37,065.50

Appalachian Hardwoods/Herbaceous and Deciduous

2,279.87 5.35

3,197.41 7.50

263.30

Mixed Pine Forest/Herbaceous and Deciduous

2,006.48 4.71

1,095.61 2.57

757.28

Southern Appalachian Cove Hardwood/Herbaceous and Deciduous

1,519.72 3.56

1,527.48 3.58

0.04

Submesic to Mesic Oak/Hardwoods/Hemlock with Rhododendron

1,459.26 3.42

1,079.77 2.53

133.38

Appalachian Hardwoods/Hemlock with Rhododendron

1,219.20 2.86

1,060.71 2.49

23.68

----------------------- --------- --------- --------- --------- ---------

TOTAL* 42,638.30 100.00 42,638.30 100.00 64,929.26

Note: All values rounded to 2 decimal places *Total is for all classes. Only first 9 classes displayed. Total acres of over/under vegetation = 533,602

49

Analysis of the total number of burned acres for each vegetation type shows

distinct patterns in all three realms. A significant number of burned areas (47.58%) had

overstories which were dominated by submesic to mesic oak/hardwoods. Nineteen and

one hundredth percent of burned area overstories dominated by mixed pine forest. The

understories of burned areas had distinct patterns with 41.47% dominated by herbaceous

and deciduous vegetation and 21.77% dominated by pine with Kalmia species. Not

surprisingly, for the composite over/under analysis, 24.60% of the burned area consisted

of an overstory/understory combination of submesic to mesic oak/hardwood -

herbaceous and deciduous with 9.06% dominated by a combination of submesic to

mesic oak/hardwoods - pine with Kalmia species.

The results of the Chi-square tests suggested that burned patterns within

vegetation types were not merely a reflection of the real world distribution of vegetation

types. When comparing the proportions of vegetation types within burned areas to the

proportions of vegetation types found throughout all areas (burned and unburned), clear

differences can be seen. For example, mixed pine forest dominated 19.01% of the total

burned area, yet dominated only 8.91% of all overstory vegetation. Chi-square values

for the overstory, understory, and under/over analysis are 53,676.48, 51,300.09, and

64,929.26, respectively. P-values for all three realms were <.01. These values indicate

that, for all realms, the distribution of vegetation types within burned area is

significantly different than the distribution of vegetation in all areas.

50

3.10 Reclassifying and Combining Fuel Type Data

Three fuel type raster grid datasets, one for each horizon (overstory, understory,

and over/under), were created when determining the relationship between fuel type and

fire frequency. Vegetation type data were reclassified to reflect the highest risk areas

relative to fuel type for all three horizons. The process for reclassifying values was very

similar to the process used in Sections 3.4 and 3.8 of this document and values were

determined using the formula below. Results for each horizon can be seen in Figure 11.

Rv = (pn/ph)*5

where:


pn = proportion of the total number of burned acres accounted for by n

vegetation type

ph = proportion of the total number of burned acres accounted for by the

vegetation type with the highest number of fires

Once reclassified, the three fuel type grids were combined to create a single risk

according to fuel type grid using:

FG = (o + u + c)/3

where:

FG = final fuel type grid value,

o = reclassified overstory grid value,

u = reclassified understory grid value,

and c = reclassified over/under grid value,

Since no research was conducted concerning which vegetation horizon had the strongest

influence on fire frequency, all vegetation horizons were given equal weight. The final

operation in the formula, dividing by 3, ensured the final fuel grid was consistent with

the final structures and terrain related grids in that the highest risk areas were

represented by 5 (Figure 12).

51

Figure 11. Wildfire risk according to the vegetation types of each forest horizon,

Great Smoky Mountains National Park, USA.

52

Figure 12. Wildfire risk according to combined fuel type data, Great Smoky


53

3.11 Combining Data for the Final Risk Assessment

The final grids representing distance to structure, terrain, and fuel type were

combined during the final risk assessment to determine final wildfire risk for all areas

within the GRSM. A final risk raster grid was created to reflect total wildfire risk

considering all the analyzed variables. Since all three variable categories (distance to

structure, terrain, and fuel type) were analyzed independently of one another, and since

all categories exhibited significant influence on wildfire occurrence, all three categories

were treated equally during the final risk grid creation and the following formula was

used:

FRG = DG + TG + FG

where:

FRG = final risk grid value

DG = final distance to structure grid value,

TG = final terrain grid value,

and FG = final fuel type grid value

3.12 Final Risk Assessment Results

The final risk grid dataset had risk index values ranging from 0.4 (lowest risk) to

15 (highest risk) (Figure 13). The highest valued cells were located in areas meeting the

following conditions:

Located within 200 meters of a road and/or trail

Southern facing slopes with slope gradients ranging from 22.2º to 27.1º

Located at an elevation between 479 m – 703 m

Overstory dominated by submesic to mesic oak hardwoods

Understory dominated by herbaceous and deciduous vegetation

54

Overall, the northwestern and southwestern portions of the Park were found to

have the highest risk for wildfire. Numerous high risk areas were found located near

Twenty Mile Trail, the Flats, and the Sinks. An analysis of the DEM data found that

average elevations in the western portion of the Park is 2,661 ft (811 m), just slightly

above the highest risk range. The area has several hiking trails and several major roads

pass through the area including the Foothills Parkway, US 129 and Cades Cove Loop.

Figure 13. Final wildfire risk grid, Great Smoky Mountains National Park, USA.

55

Another key component influencing high risk values in this area is fuel type. The

higher risk submesic to mesic oak/hardwoods vegetation type dominates 45% of the

forest overstories while herbaceous and deciduous dominates 53% of the forest

understories. Dividing the Park boundary into quadrates, the mean risk values for the

NW and SW sections are 6.3 and 6.0, respectively.

The north- and southeast sections of the Park were found to have relatively lower

risk with mean risk scores being 4.8 and 5.5, respectively. Higher elevations and the

dominant overstory vegetation types associated with higher elevation appear to be the

key factors in the lower risk scores. The average elevation in the eastern portion of the

Park is 3,747 ft (1,142 m), significantly higher than the high risk elevation range of

1,572 ft – 2,306 ft (479 m – 703 m). As would be expected, these higher elevations

influence the vegetation types found dominant in forest overstories. The higher risk

submesic to mesic oak hardwoods that dominated 45% of the western overstories was

found dominant in only 25% in the eastern overstories. The higher elevations in the east

exhibited large patches of areas dominated by Appalachian hardwoods, northern

hardwoods, and spruce forests. These fuel types only accounted for 11.4%, 1.1%, and

.05% of the total acres burned by previous fires, respectively. Also, as pointed out by

Lafon and Grassino-Mayer (2007), when compared to pine and oak forests, Appalachian

hardwood and non-pine, conifer forests are considered the least flammable vegetation

type.

56

3.13 Risk by Zone

The final risk raster grid (Figure 13) is a relatively high resolution dataset (10 m

resolution) with over 3 million individual raster grid cells. Easy interpretation of the

results may be difficult without data exploration and interaction using GIS software. In

order to generalize the data for what some may consider easier interpretation, square

zones were created using the extents of the Park boundary and the average risk for each

zone was calculated. The zones measured approximately 3.1 miles by 3.1 miles (5 km

by 5 km) and were designed to generalize the data enough for easy interpretation, yet not

so large that general variations in the final risk dataset were lost.

The average risk by zone results (Figure 14), mirrored the final risk grid results

with a large number of high risk zones located in the north- and southwestern sections of

the Park. Clusters of higher risk zones were found near Twenty Mile Trail, the Flats, and

the Sinks. Also highlighted by this more generalized data, is the low risk exhibited by

the Park’s central interior beginning in zone D5 and extending and fanning northeasterly

to the Park’s eastern edge. Low risk in this area is due to higher elevations and their

associated vegetation types. Also, few roads pass through this area, therefore, risk

relative to distance to structures is less influential on final risk values.

57

Figure 14. Risk by zone results, Great Smoky Mountains National Park, USA.

58

Chapter 4 FARSITE Fire Area Simulator

For this study, FARSITE was used for wildfire modeling. FARSITE was chosen

because it is based on the simulation mechanism most widely used in fire management

and research (Bar Massada et al., 2011). FARSITE is also recognized as the most

reliable of the currently available fire simulation models and is widely accepted among

wildfire management officers in the United States, Spain, Brazil, and Israel (Bar

Massada et al., 2011).

FARSITE is a fire behavior simulator for use on computers equipped with a

Windows operating system. Using the vector data model approach, FARSITE simulates

the fire area as a polygon with a series of two-dimensional vertices (point locations with

x, y coordinates). The number of vertices increases as the fire grows over time. The

expansion of the polygon is determined by computing the spread rate and direction from

each vertex and multiplying by the duration of the time-step. Environmental factors

(wind speed and direction, slope, aspect, fuel properties) at each vertex are used to

calculate the spread rate. The initial fire shape is assumed to be an ellipse but that shape

may change under varying environmental conditions (specifically strong winds or steep

slope conditions).

FARSITE modeling requires input of spatial and non-spatial data (Table 11).

Common non-spatial data include tabular data representing weather and wind elements.

Weather and wind data have to be supplied as temporal data streams for the time period

of the simulation. These data consist of minimum and maximum daily temperature and

59

relative humidity (including their respective time of day), daily precipitation, hourly

wind speed, and hourly wind direction.

Topographic spatial data include GIS raster layers representing elevation, slope,

aspect, and a fuel model (determined by the vegetation type). The ignition point and

duration and time period of the simulation is determined by the user. The model outputs

both vector and raster type data. Vector data output includes fire perimeters for every

time step of the model’s duration. For instance, if a time step of one hour is chosen, as

was for this study, the model will output a fire perimeter for every modeled hour. Raster

output includes fire arrival time, fireline intensity, flame length, rate of spread, reaction

intensity, and spread direction (Table 12).

Weather data are used to generalize a diurnal weather pattern so that dead woody

fuel moistures can be calculated. Topographic data are used to adjust temperature,

humidity, and fuel moisture across the simulation region (Finney, 1998). During

FARSITE modeling, the simulation often begins several days before the actual fire

ignition time. During this time, fuel moisture is automatically updated using the weather

and landscape data. For example, fuels on south-facing slopes typically become drier

than those on north-facing slopes, and adjustments are made for relative humidity and

precipitation.

4.1 FARSITE Verification

To verify the fuel model and the operation of FARSITE software, an initial test

model was generated using the ignition point from the historic “Dalton” fire that

occurred April 8, 1994. The historic Dalton fire burned for approximately 2 days

60

Table 11. FARSITE input data for wildfire simulations, Great Smoky Mountains

National Park, USA.

FARSITE INPUT DATA

NON-SPATIAL DATA Units

Weather data:

Total daily precipitation Inches

Maximum (max) daily temperature and time occurred ºF

Minimum (min) daily temperature and time occurred ºF

Max daily relative humidity Percent

Min daily relative humidity Percent

Cloud coverage and time occurred Percent

Wind data:

Wind speed and time occurred M.P.H.

Wind direction and time occurred Degrees

Other:

Model duration (start day and time, end day and time) -

SPATIAL DATA

Elevation Meters

Slope Percent

Aspect Degrees

Fuel Model -

Canopy Cover Percent

Ignition Point Coordinates

Table 12. FARSITE output data for wildfire simulations, Great Smoky Mountains

National Park, USA.

FARSITE OUTPUT DATA

Output Units

Vector Output

Fire Perimeters -

Raster Output

Time of Arrival Hours

Fireline Intensity English units: BTU per foot per second

Flame Lenth Feet

Rate of Spread Feet per minute

Reaction Intensity BTU per square foot per second

Spread Direction Degrees

61

(April 8 – 10, 1994). The historic climate data for those April days were obtained from

the NCDC and those data along with the approximate burn period were used during the

FARSITE modeling.

The results from the simulated Dalton fire results were compared against spread

patterns from the actual Dalton fire (Figure 15) to gauge the accuracy and

appropriateness of the FARSITE input (fuel model, terrain data, and selected

parameters) that would be used for the subsequent models.

Figure 15. Actual Dalton wildfire vs. simulated Dalton wildfire, Great Smoky


62

Observation of this initial test run found that the general features of the observed

spread patterns and fire behavior of the historic fire were in reasonable agreement with

the model projections. Both the actual and simulated fires expanded to the north in a

similar manner with a “mushroom cap” shaped fire edge that extended to the northwest

and northeast. The northern edges of the simulated and actual fire were separated by

only 500 ft (~150 meters).

There was a significant discrepancy in the burn patterns to the south of the

ignition point (the “stem” of the mushroom). It is unclear what factors drove this

discrepancy given that the only available data for the actual Dalton fire is the final,

estimated fire perimeter. It could have been that certain vegetation characteristics (e.g.,

an abundance of dead woody material on the forest floor) within the actual burned area

may have driven the actual Dalton fire to the south in the observed pattern. These

vegetation characteristics that may have existed in 1994 may not have been accounted

for in the 2004 Park vegetation data used to generate the FARSITE fuel model.

Despite the discrepancies in burn pattern shape, the total area of burned land

between the fires was in reasonable agreement with the actual Dalton fire producing 165

acres (67 ha) of burned area and the simulated Dalton fire producing 147 acres (59 ha).

GIS overlay found that 94 acres (38 ha) of burned area was shared by the actual fire and

simulated fire. The simulated fire burned 53 acres (21 ha) of land that was not burned in

the actual fire and the actual fire contained 71 acres (29 ha) of burned area that remained

unburned during the simulation.

The similarity between final burned area sizes (165 acres vs. 147 acres) is

indicative that factors that contribute ultimate fire size (rate of spread, flame length, and

63

fire intensity) are likely in general agreement. It was therefore felt that, given limitations

of available data on fuels and weather, FARSITE was adequate in producing results to

give the user a general understanding of how fire may spread within the GRSM.

4.2 Determining Potential Areas to Locate Ignition Points for FARSITE Modeling

The final results from the wildfire risk assessment were examined to determine

potential high risk areas for ignition locations. Visual observation of area risk shows that

higher risk areas tend to be located in the northwest and southwest sections of the Park.

As discussed in the wildfire risk assessment results, there are numerous high risk areas

near Twenty Mile Trail, the Flats, and the Sinks. Field work was conducted in late

March 2013 in these three areas to ground truth terrain and vegetation characteristics (to

validate GIS data used during the risk assessment) and to evaluate their potential as

suitable ignition point locations. Areas were evaluated as potential ignition locations

based on their location relative to structures, general terrain characteristics such as slope

and “lay of the land”, the presence of human activity (litter, campfires, disturbed ground,

houses, presence of humans, etc.), and presence of leaf litter and dead woody material.

Using the above criteria, field observation found the Twenty Mile Trail area to

be a fitting area for a potential ignition and wildfire modeling. During the field

observations hikers were observed using the trail (approximately 2 hikers every 30

minutes). The higher risk south-facing slopes were in the 20 degree to 30 degree range

which was consistent with the DEM generated data. GPS equipment recorded elevations

of around 1,378 ft (420 m) in and around the trail, also consistent with the DEM data.

No campfires were observed; however, this area might not be considered suitable for

64

camping. There were an abundance of leaf litter and dead woody debris found on the

forest floor (Figure 16). The observed vegetation types appeared to validate the vector

vegetation data with submesic to mesic oak hardwoods dominating the overstory and an

understory dominated by small herbaceous and deciduous trees mixed with Kalmia sp.

and pine.

The Flats area was also determined to be a fitting area for an ignition source.

According to Park wildfire data, this area is classified as being a part of the wildland

urban interface. Field observation confirmed this classification finding many residences

and park related buildings in the area (roughly one structure every 328 ft or 100 meters).

Terrain characteristics were consistent with the DEM generated data with south facing

slopes in the 15 to 20 degree range and GPS equipment recording elevations along Flats

Road to be between 2,067 ft to 2,133 ft (630 m to 650 m). Interestingly, there were

several extinguished campfires in the area (Figure 17). It was unclear the purpose of

these campfires (whether used by campers or area residents) but they appeared to have

been ignited and extinguished relatively recently. Litter in the form of cans and

packaging was observed scattered throughout the area close to Flats Road. Leaf litter

and dead woody material were in abundance on the forest floor and vegetation types

were consistent with the vector vegetation data with an overstory dominated by

submesic to mesic oak hardwoods/mixed pine and an understory dominated by small,

herbaceous and deciduous trees with mixed areas of pine, Rhododendron sp., and

Kalmia species.

The Sinks area was determined to not be fitting for a potential ignition source.

Field observation did confirm DEM generated data with south facing slopes ranging

65

from 20 to 30 degrees and elevations between 1,640 ft (500 m) and 1,969 ft (600 m).

Vegetation data was confirmed with an overstory dominated by submesic to mesic oak

hardwoods and mixed pine forests and an understory primarily of herbaceous and

deciduous vegetation with mixed Kalmia species.

While the Sinks area would be considered high risk in regards to terrain and

vegetation type, human activity characteristics prevented the area from being chosen for

FARSITE modeling. During field work, it was observed that this is a heavily trafficked

area for tourists with numerous road pull-offs for scenic observation. However a large

stream, Little River Gorge, lies between the road and the south facing slopes (Figure 18)

and limits human access to the south facing slopes minimizing human activity in the

higher risk areas. Since human activity was determined to be a significant driver for

wildfire occurrence, the limited human access to the higher risk areas prevented the

Sinks from being chosen as a modeling area.

Figure 16. Leaf litter and woody debris in the Twenty Mile Trail area (March

2013), Great Smoky Mountains National Park, USA.

66

Figure 17. Extinguished campfire in the Flats area (March 2013), Great Smoky


Figure 18. Little River Gorge limits human access to southern facing slopes – the

Sinks area (March 2013), Great Smoky Mountains National Park, USA.

67

4.3 Determining Exact Locations of Ignition Points

One of the key inputs for FARSITE modeling is the exact location of the ignition

point. The ignition locations of many fire simulations using FARSITE assume random

ignition locations (Bar Massada et al., 2011). When investigating non-random vs.

random placement of ignition points, Bar Massada et al. (2011) found that the locations

of ignitions used in fire simulations may substantially influence the spatial predictions of

fire spread patterns. Their results suggest that under extreme fire conditions (when

terrain and vegetation characteristics lend to high wildfire risk), random placement of

ignitions tended to produce smaller and more conservative fire sizes when compared to

non-random placement. Since the aim of this research was to model fire behavior in the

highest risk areas of GRSM (what could be considered extreme fire conditions), a non-

conservative, non-random approach was taken when locating ignition points. This non-

random approach would ensure that model output reflect the maximum fire size and

spread rate that could potentially result from a fire within the highest risk areas. Using

this logic, ignition point locations were located at the center most point within the high

risk areas that would still allow them to be relatively close, within 328 ft (100 m), to a

road or trail.

The Twenty Mile Trail area and the Flats area were chosen as location for

wildfire simulations. One point within each high risk area was chosen for modeling. The

latitude and longitude coordinates for the Twenty Mile Trail ignition point are 35° 28'

10.14" N, 83° 52' 31.83" W. This point is located in the south central section of a high

risk area and approximately 197 ft (60 m) from Twenty Mile Trail and approximately

1,312 ft (400 m) from NC State Highway 28 (Figure 19). As previously mentioned, field

68

observation confirmed that this area exhibited characteristics that would consider it high

risk according to the risk assessment results. The Flats area ignition point is located at

35° 38' 30.78" N, 83° 54' 53.37" W. The point is located in the general north central

section of a large high risk area and is approximately 253 ft (77 m) from Flats Road

(Figure 20).

Figure 19. Twenty Mile Trail area ignition point, Great Smoky Mountains

National Park, USA.

69

Figure 20. The Flats area ignition point, Great Smoky Mountains National Park,

USA.

70

4.4 Determining Time of Year for Wildfire Simulation

While temporal aspects of wildfire behavior were not included in the wildfire

risk assessment, they were critical in determining what time of year to run wildfire

simulations. The historical GRSM wildfire data were used to determine the highest risk

time of year for wildfire. The date of ignition for previous wildfires was used to generate

fire frequency by month. It was found that April had the highest fire frequency with 185

fires occurring within the month. March was second with 138 fires and November was

third with 135 fires (Figure 21).

Once the months with the highest fire frequency were determined, which days of

the highest ranking months had the highest number of wildfires was determined. For

April, the early to mid days of the month had a significantly higher number of fires with

106 fires occurring within the first 13 days of the month and 79 fires occurring within

the last 17 days. March was found to have a higher number of fires later in the month

with 84 fires occurring within March 18-31 and 54 fires occurring within March 1-17.

November had the highest fire frequency in the early to mid part of the month with 79

fires occurring with days 1-12 and 54 fires occurring within days 13-30.

0

20

40

60

80

100

120

140

160

180

200

1 2 3 4 5 6 7 8 9 10 11 12

Month

No

. o

f F

ires

Figure 21. Histogram - historic wildfire frequency by month, Great Smoky


71

Given the above results, the following dates were chosen for fire simulation:

April 06 – April 11, 2012

November 08 – November 13, 2012

For each selected area, one simulation was run for each selected time period for a total

of 4 wildfire simulations. It was felt that the above day combinations reflect the general

trend of the histograms and well represented the time of year that demonstrated the

highest fire frequency within the Park. Also, these dates coincide with findings by

Andreu and Hermansen-Baez (2008) who, when researching fire frequency in the South,

concluded that southern climatic conditions create two primary fire seasons per year,

one in the spring and one in the fall.

4.5 FARSITE Weather and Wind Input

Weather and wind data used during FARSITE simulations were obtained from

the NCDC. Data obtained included hourly observed surface weather data and included:

Month/Day/Hour of observation

Temperature (degrees Fahrenheit)

Wind speed (in MPH)

Wind Direction (in degrees)

Sky Cover (verbal description with values including clear, scattered, broken,

obscured, and overcast)

Relative humidity (as a percentage)

For operation, FARSITE requires cloud cover as a percentage value (equal to the

percentage of sky obstructed by cloud cover). Since cloud cover percentage data was not

supplied in the weather station data package, the Sky Cover verbal description was used

to estimate cloud cover percentages and the following values were assigned for each

description (Table 13).

72

Table 13. Estimated cloud cover percentages for FARSITE modeling, Great


Description Cloud Cover

Clear 0%

Scattered 30%

Broken 75%

Obscured 85%

Overcast 100%

The obtained NCDC weather and wind data were input FARSITE in tabular

format for the duration of the simulation time periods (April and November). Weather

data consisted of minimum and maximum daily temperature and relative humidity (and

their corresponding time of day), and daily precipitation (Tables 14 and 15). Wind data

consisted of hourly wind speed, hourly wind direction, and hourly cloud cover

(Appendices A and B).

4.6 Developing Fuel Model for FARSITE Input

Proper selection of a fuel model is a critical step in the simulation of potential

fire behavior (Anderson, 1982). The operation of FARSITE requires the input of a fuel

model which represents characteristics of the vegetation types that occur in the modeling

area. In essence, the fuel model provides FARSITE the physical description of the

surface fuel complex that is used to determine surface fire behavior (Finney, 1998). For

this study, the properties of the different fuel types in the modeled areas (i.e. live and

dead vegetation occurring within the model area) were represented by one of the 13

different Anderson fuel models (Anderson, 1982).

73

Table 14. NCDC weather station # 720259 weather data for April 6 – 11, 2012.

Month Day Precip.

(in)

Hour of Min.

Temp

Hour of Max. Temp

Min. Temp (ºF)

Max. Temp (ºF)

Max. Humidity

(%)

Min. Humidity

(%)

4 6 0 0955 2055 52 64 100 35

4 7 0 1055 1955 32 70 100 23

4 8 0 1155 1955 36 72 100 27

4 9 0 1155 1955 36 70 92 10

4 10 0 1055 1755 43 66 60 28

4 11 0 0855 0055 41 57 41 31

Table 15. NCDC weather station # 720259 weather data for November 8 – 13,

2012.

Month Day Precip.

(in)

Hour of Min.

Temp

Hour of Max. Temp

Min. Temp (ºF)

Max. Temp (ºF)

Max. Humidity

(%)

Min. Humidity

(%)

11 8 0 0655 1955 32 68 92 32

11 9 0 0555 1855 34 61 92 58

11 10 0.1 2255 0555 43 54 80 38

11 11 0 0955 1955 30 52 55 10

11 12 0 1055 1955 19 63 88 15

11 13 0 0955 1855 28 59 96 36

Anderson fuel model types are assigned based upon the type of live and dead

vegetation that occur within a study area (Anderson, 1982). The models are commonly

used for wildfire modeling (Finney, 1998), and incorporate factors such as fuel load, fuel

depth, and moisture content of dead fuels (Anderson, 1982). The 13 Anderson fuel

models were originally developed for wildfire behavior prediction as applied to

vegetation types occurring in the western United States (Madden, 2004). Madden et al.

74

(2004) worked closely with NPS fire managers to develop guidance for relating

overstory and understory vegetation classes within GRSM to the 13 Anderson fuel

model classes. The resulting guidance, as set forth in the Digital Vegetation Maps for the

Great Smoky Mountain National Park (Madden et al., 2004), was used in conjunction

with the NPS vegetation data to assign Anderson fuel models for the vegetation types

found in GRSM.

Table 16 shows the guidance used for determining fuel classes within GRSM.

The dominant overstory and dominant understory data were examined and an Anderson

Fuel Model class was assigned for each overstory/understory combination. Figure 22

shows the final GRSM fuel model for FARSITE modeling produced using said

guidance.

Due to the abundance of overstories dominated by hardwoods (Northern

hardwoods, Appalachian hardwoods, and submesic to mesic oak) and understories of

herbaceous and deciduous vegetation, a majority of the park is classified as Anderson

Fuel Model 9. Anderson Fuel Model 8 is also prevalent with many hardwood/pine

mixed areas throughout the Park. Both areas selected for FARSITE modeling, Twenty

Mile Trail and the Flats, are dominated by these two fuel models.

75

Table 16. Rules for assigning fuel model classes for FARSITE modeling, Great


General Overstory Vegetation General Understory Vegetation

Anderson Fuel

Model

Water, Montane Alluvial Forest, Road Water, Montane Alluvial Forest, Road 0

Pasture, Grasses, Human Influenced Pasture, Grasses, Human Influenced 1

Shrubs Shrubs 2

N/A* N/A* 3

Kalmia, Rhododendron Kalmia, Rhododendron 4

Successional Vegetation, Spruce Forest Successional Vegetation Spruce Forest, Other Evergreen Forest 5

Vines, Hardwood Slash Vines, Hardwood Slash 6

Montane Forest, Heath Balds Montane Forest Understory, Heath Balds, Vines 7

Submesic to Mesic Oak/Hardwoods with Pine mixed

Submesic to Mesic Oak/Hardwoods with Pine mixed, Vines 8

Appalachian Hardwoods, Submesic to Mesic Oak/Hardwoods, Northern Hardwoods

Appalachian Hardwoods, Submesic to Mesic Oak/Hardwoods, Northern Hardwoods, Herbaceous and Deciduous 9

Spruce Forests, Fir Forests Spruce Understory, Fir Understory 10

N/A* N/A* 11

Dead Vegetation Dead Vegetation 12

N/A* N/A* 13

* No corresponding GRSM vegetation for Anderson Fuel Model.

Figure 22. Fuel model for FARSITE modeling, Great Smoky Mountains National

Park, USA.

76

4.7 Elevation, Aspect, Slope, and Canopy Cover Data for FARSITE Input

Elevation data for FARSITE modeling were supplied by the study area DEM.

Slope (in degrees) and aspect (in degrees) were calculated from the DEM through GIS

tools. Canopy cover data were generated using a canopy cover dataset obtained from

NPS. The dataset consisted of polygons with canopy cover in percent for Park forests.

For FARSITE modeling, the vector canopy cover data were converted to raster grid

data. For computational efficiency, all terrain and canopy related datasets were clipped

using the extents of a 15 km buffer area around each ignition point prior to FARSITE

modeling.

4.8 Twenty Mile Trail – April Modeling Results

FARSITE modeling for the Twenty Mile Trail for April produced a total burned

area (BA) of 786 acres (318 ha) (Figure 23a). The fire perimeter was roughly oblong and

extended from the ignition point (IP) in a generally northern direction extending 6,234 ft

(1,900 m) north, 2,953 ft (900 m) east, and 2,297 ft (700 m) west of the IP. Due to the

ignition point’s position just 197ft (60 m) north of a barrier stream (Twenty Mile Creek),

the fire did not extend any significant distance to the south of the ignition point. The BA

was bounded by Judy Branch to the west and by an unnamed tributary to the east. The

fire extended fully to these barriers.

The fire had a relatively consistent rate of spread (ROS) spreading approximately

108 ft (33 m) per hour with a mean rate of spread of 1.8 ft (0.55 m) per minute (Figure

23c). The maximum rate of spread was in the 15 to 37 ft (4.6 – 11 m) per minute range

and was only exhibited in a 10 acre (4 ha) patch in the southwestern-most section of the

77

BA. Investigation of this area suggests that these increased ROS values are due to the

dominant grass and shrub vegetation types found in this area (resulting in a fuel model

classification of 1). The average flame length for the fire was 1.2 ft (0.4 m) with a

maximum flame length of 6.2 ft (1.9 m). There were numerous 1 to 2 acre (0.4 to 0.8 ha)

patches within the BA that exhibited flame lengths in the 1.5 to 3 ft (.7 to 1.0 m) range

(Figure 23e). Higher flame lengths tended to be located on peak areas with valley areas

producing lower flame lengths.

While the fire location is not within a populated area, a National Park office is

located within the burn area. Model results estimated that, under the model parameters,

the fire arrived at this location 40 hours from time of ignition.

4.9 Twenty Mile Trail – November Modeling Results

FARSITE modeling for the Twenty Mile Trail for November produced a smaller

fire relative to the April results with a total BA of 672 acres (272 ha) (Figure 23b). The

fire shape was very similar to the April fire extending in a generally northern direction

from the ignition point. The BA extended the same distance in the east and west

directions extending to the barrier streams, Judy Branch to the west and the unnamed

tributary to the east. However, the northern extent of the fire was slightly shorter than

the April results extending approximately 5,249 ft (1,600 m) from the ignition point.

The fire had consistent ROS values spreading approximately 98 ft (30 meters)

per hour with a mean ROS of 1.2 ft (0.4 m) per minute. As in the April model, the

highest ROS values, 29 to 44 ft (8.8 to 13.4 m) per minute, were exhibited in the shrub

and grass dominated southwestern section of the BA (Figure 23d).

78

(a) April Model Fire Perimeter (b) November Model Fire Perimeter

(c) April Model Rate of Spread (d) November Model Rate of Spread

(e) April Model Flame Length (f) November Model Flame Length

Figure 23. Twenty Mile Trail FARSITE modeling results, Great Smoky


79

The mean flame length for November was 1.1 ft (0.3 m) with a maximum flame

length of 3.4 ft (1.0 m), significantly shorter than the April maximum of 6.2 ft (1.9 m)

(Figure 23f). The highest flame lengths tended to be located on peak elevation areas

with valleys exhibiting shorter flame lengths.

4.10 Disparity between April and November BA for Twenty Mile Trail

There is a slight difference in BA values for the November model, 672 acres

(272 ha), when compared to the April model, 786 acres (318 ha). Model results were

investigated to examine any clues that could help explain this difference. Along with

maximum flame lengths, the largest disparity in modeling results between the two

periods occurred in the reaction intensity (RI) results. RI, a.k.a. combustion rate, is a

measure of heat release per unit area per unit time in the flame zone. It is measured in

British Thermal Units (BTU) per square foot per second and is indicative of the fires

intensity at the fires expanding perimeter (where the flames are presumably the most

intense). The RI values for the April fire averaged 321 BTU/ft2 sec with a maximum RI

of 1,085 BTU/ft2 sec. RI values for November averaged 310 BTU/ft

2 sec with a

maximum value of 549 BTU/ft2 sec (Table 17). While the mean values are relatively

close, there is a large disparity in maximum values and many of the highest April RI

values occurred in linear sections throughout the BA. The increased RI increased the

ROS thus increasing the April fire’s expansion allowing it to reach areas that remained

unburned in the November model.

80

Table 17. Results comparison - April and November Twenty Mile Trail FARSITE

modeling, Great Smoky Mountains National Park, USA.

TWENTY MILE TRAILHEAD MODEL RESULTS

April Result November Result

Parameter Units Mean Min Max ACRES Mean Min Max ACRES

Rate of Spread Feet per Minute 1.8 0.1 37.3

786.0

1.2 0.0 44.0

672.0 Flame Length Feet 1.2 0.3 6.2 1.1 0.2 3.4

Reaction Intensity

BTU per ft^2 per second 321.0 90.1 1085.2 310.0 42.0 549.0

The difference in RI values for the two periods may be contributed to the higher

April air temperatures. The average high during the April modeling period was 68º F

with an average low of 40º F compared to an average high of 61º F and an average low

of 32ºF during the November period. The higher April temperatures increase fuel

temperature and decrease the amount of time it takes for fuels to reach their ignitions

points. Warmer, more easily combustible fuel could have contributed to the high RI

values, longer maximum flame lengths, and the larger April BA.

As in the April results, the National Park office is located within the November

burn area. Model results estimated that, under the model parameters, the fire arrived at

this location 40 hours from time of ignition.

4.11 The Flats – April Modeling Results

April modeling for the Flats ignition point produced a BA of 400 acres (162 ha)

(Figure 24a). The fire was roughly triangular shaped fanning out from the ignition point

in southern, southwestern, and northeastern directions. From the ignition point, the BA

extended approximately 4,000 ft (1,219 m) to the northeast, 4,000 ft to the southwest,

and approximately 3,000 ft (914 m) south-southeast. The ignition point’s close

81

proximity to Flats Road prevented the fire from extending any significant distance to the

north.

Flats Road was the only significant barrier that impeded the fires progress;

however, there are two south-flowing streams (Kingfisher Creek and Buckshank

Branch) located just south of the ignition point that caused the fire to fork at their

locations producing linear sections of unburned areas.

The model results had a consistent spread rate with an average ROS value of 1.4

ft (0.4 m) per minute (Figure 24c). Maximum ROS values were in the 25 to 37 ft (7.6 to

11 m) per minute range and occurred in small 0.5 to 4 acre (0.2 to 1.6 ha) patches of the

BA where Rhododendron sp. shrubs are dominant. In general, the fire spread at a rate of

approximately 30 ft (9 m) per hour to the west and south of the ignition point and

approximately 60 to 80 ft (18 to 24 m) per hour to the northeast of the ignition point.

The higher spread rate to the northeast is likely due to winds that occurred during the

modeling period. The mode wind direction for the modeling period was 240 degrees

meaning most winds were coming from the southwest and blowing to the northeast. The

southwesterly winds likely increased northeastward ROS values.

The mean flame length for the model results was 1.2 ft (0.4 m) with a maximum

flame length of 19.1 ft (5.8 m). The higher flame lengths coincided with the maximum

ROS values and occurred in small patches throughout the BA where Rhododendron sp.

shrubs are dominant (Figure 24e).

As previously mentioned, the Flats ignition point is in what is considered the

wildland urban interface. The site visit and examination of the aerial data did, in fact,

reveal many structures in this area which included residences and park related structures.

82

Within the April BA, there are 13 structures with a majority of structures located in the

northeast section of the BA. The fire arrival times for the structure range from 37 hours

to 114 hour from time of ignition.

4.12 The Flats – November Modeling Results

November modeling for the Flats ignition point produced a BA of 264 acres (107

ha) (Figure 24b). The fire shape was roughly oval shaped with its semi major axis

running in a northeasterly/southwesterly direction. From the ignition point, the BA

extended approximately 2,000 ft (610 m) to the northeast, 4,000 ft (1,219 m) to the

southwest, and 2,400 ft (732 m) to the south. The Flats barrier inhibited fire from

spreading the north and Kingfisher Creek, located 1,000 ft (305 m) southwest of the

ignition point, caused the fire to fork creating a liner section of unburned area along the

creek.

The November modeling produced relatively consistent ROS values across the

BA with an average ROS of 1.2 ft (0.4 m) per minute (Figure 24d). Maximum ROS

values were in the 20 to 29 ft (6 to 9 m) per minute range, tended to spread in a

southwestern direction, and occurred in small, Rhododendron sp. dominated patches

located 1,500 ft (457 m) southwest of the ignition point. The elevated ROS values in this

section are likely due to vegetation type and winds that occurred during that point in the

modeling period. Winds directions were 80 degrees with wind speeds in the 6 to 14 mph

(10 – 23 kph) range and the northeasterly winds elevated ROS values for southwesterly

directed fire spread.

83

The mean flame length for the model was 1.2 ft (0.4 m) with a maximum flame

length of 23.8 ft (7 m). As demonstrated in the April model, the higher flame lengths

coincided with the Rhododendron sp. dominated patches (Figure 24f).

Concerning human structures within the BA, three structures were within the

November model. This is considerably less than the April model; however, 10 of the 13

structures within the April model were located in the northeast section of the BA which

remained unburned in the November model. For the three structures within the

November model, the estimated time of arrival, in hours since ignition, are 42, 56, and

72. A summary of the Flats April and November modeling results can be seen in Table

18.

4.13 Disparity between April and November BA for the Flats Models

There was a significant difference between BA acreage for the April and

November Flats models. In fact, the 264 acre (107 ha) BA for the November model was

45% smaller than the 400 acre (164 ha) BA for the April model. Similar disparities were

found between modeling periods during the Twenty Mile Trail modeling, but they

weren’t quite as significant with an April BA of 786 acres (318 ha) compared to a

November BA of 672 acres (272 ha). During the Twenty Mile Trail modeling, it was

suspected that temperature differences could explain differences between BA results for

the two modeling periods. While air temperatures were likely influential in the final BA

results for the Flats models, temperatures alone may not explain the greater disparities.

An investigation of slopes within the modeled areas reveals what may likely be driving

84

these differences. Specifically, observing where the fire perimeters were, geographically,

at the point when the strongest April winds occurred.

(a) April Model Fire Perimeter (b) November Model Fire Perimeter

(c) April Model Rate of Spread (d) November Model Rate of Spread

(e) April Model Flame Length (f) November Model Flame Length

Figure 24. area FARSITE modeling results, Great Smoky Mountains National Park,

USA.

85

Table 18. Results comparison - April and November Flats area FARSITE

modeling, Great Smoky Mountains National Park, USA.

FLATS MODEL RESULTS

April Result November Result

Parameter Units Mean Min Max ACRES Mean Min Max ACRES

Rate of Spread

Feet per Minute 1.4 0.1 36.7

400.0

1.2 0.1 29.3

264.0 Flame Length Feet 1.2 0.3 19.3 1.2 0.3 23.8

Reaction Intensity

BTU per ft^2 per second 408.9 128.8 2272.8 400.0 146.3 2437.6

The strongest April winds had a direction of 240 degrees, had wind speeds

between 8 mph (13 kph) and 17 mph (23 kph). The elevated wind speeds started on

4/9/2012 at 2000 hours and persisted until the models end time with brief, intermittent

periods of light to no winds. At that onset of the elevated southwesterly winds, the

section of fire spreading in a NE direction (i.e., the section of fire that would be the most

influenced by southwesterly winds in regards to increasing ROS) for the Flats model

was positioned at the lower section of a southwestern facing slope (the windward side of

the peak) spreading up the slope (Figure 25). To the southwest of this position, there

were no significant elevation peaks or topographical obstructions that would block the

southwesterly winds, reduce their speed and, thus, their influence. Therefore, the

unobstructed, strong winds “pushed” the fire up the slope significantly increasing the

final size of the BA.

In contrast, the northeasterly spreading fire perimeter for the Twenty Mile Trail

April model during the increased April winds was at the top of a mountain peak

beginning its decent down the northeast facing side of the mountain (the leeward side of

the peak). According to the basic concepts of the theories of airflow over mountains,

86

surface winds rise over mountains through the process of orographic lift (Corby, 1954).

As the air flows over the peak of the mountain, the moving air mass remains aloft for a

brief time and the immediate leeward side of the mountain, just below the peak,

experiences less wind than the windward side (Corby, 1954). Therefore, the increased

winds weren’t as influential in the Twenty Mile Model and didn’t push the fire spread as

it did in the Flats April model.

Figure 25. Position of the NE spreading fire front at the onset of the increased

winds – the Flats April model, Great Smoky Mountains National

Park, USA.

87

Chapter 5 Conclusion and Discussion

5.1 Wildfire Risk Assessment Conclusions and Discussion

The first objective of this research was to examine historical Park fire data,

analyze current Park vegetation and terrain characteristics, compare historic fire data to

current conditions, and use spatial and geostatistical analyses to determine wildfire risk.

It was hypothesized that historic fire locations are more clustered than random. Nearest

neighbor analyses confirmed this suspicion producing an NNR of 0.52. Using strictly

visual clues, it was suspected that there exists causal links which drive the clustering of

wildfire occurrence in the GRSM. For instance, observing point locations relative to

roads and trails indicated a possible relationship between distance to structure and fire

occurrence. Terrain related data such as aspect appeared to influence fire occurrence.

Therefore, it was hypothesized that wildfire risk would be closely related to variables

related to terrain, vegetation type, and human activity. Through statistical and spatial

analyses it was shown that fire location did, in fact, have a relationship with these

variables. Correlation and regression analysis showed a statistically significant

correlation between fire frequency and distance to roads and distance to trails with R

values of 0.92 and 0.82, respectively. Spearman’s rank coefficient analysis showed that

fires tend to occur more often on southern facing slopes than on northern facing slopes

(Spearman’s R = 0.986). A majority of the historic park fires occurred in the elevation

range 1,572 ft to 2,306 ft (479 m to 703 m) at slope gradients between 22.2º and 27.1º

and it was also shown that previously burned areas within GRSM had over- and

understories dominated by one vegetation type. Therefore, using the results from this

88

research, one might conclude that, in general, areas in close proximity to structures

(within 656 ft or 200 meters), that are at an elevation range of approximately 1,475 ft to

2,300 ft (450 m to 700 m), that have southern facing slopes with slope gradients between

20º and 30º and that have overstories dominated by submesic to mesic oak/hardwoods

and understories dominated by herbaceous and deciduous vegetation are at the greatest

risk to wildland fire.

Locations that exhibited characteristics that most closely met the above criteria

tended to be located in the northwestern and southwestern portions of the park with

many high risk areas located near Twenty Mile Trail, the Flats, and the Sinks. The

eastern portion of the Park exhibited lower wildfire risk scores and it was discovered

that higher elevations and “lower risk” vegetation types were the key factors influencing

the lower scores. According to this study’s results, it would be reasonable for Park

officials to appropriate more resources for fire management efforts in the north- and

southwestern sections. These efforts could be in the form of prescribed burns to reduce

fuel load, increased monitoring of the western lands for wildfire, western biased

locations to house wildfire response personnel and equipment, and increased

development of man-made fire breaks.

5.2 FARSITE Modeling Conclusions and Discussion

It was hypothesized that abundant surface fuels (leaf litter) found in the GRSM

fuel models would create relatively high ROS values. However, modeling results did not

support that hypothesis and GRSM ROS values were relatively small compared to

Western fires which often produce ROS values between 22 ft (7 m) and 88 ft (27 m) per

89

minute (Rothermel, 1991). According to model results, in areas not dominated by shrubs

and grasses, which constitutes a majority of the park, wind speed and wind direction

relative to slope seems to exhibit the strongest influence on ROS and final BA. This was

discovered by comparing the total BA for the different time periods. The Flats models

showed greater disparities between models than the Twenty Mile Trail models. These

disparities were explained by the fire perimeter position during wind events.

Specifically, if winds are blowing SE, for example, what is the position of fire edges that

are spreading in a SE direction? If the fire edges spreading in the same direction of the

wind (for example, fire edges spreading in a NW direction during southeasterly winds)

are in a position to experience unobstructed winds, such as on the windward side of a

slope, it could be expected that wind would “push” those edges increasing the ROS and

final BA.

Vegetation type was shown to have a significant impact on ROS as well as flame

length. As demonstrated in both model areas, areas dominated by Rhododendron sp. and

other shrub species, exhibited longer flame lengths and increased ROS. Maximum

values for flame length and ROS for all four models coincided with shrub dominated

areas.

There are many natural barriers evenly distributed across the modeled areas that

could be relied upon to inhibit fire spread. Within 1.9 miles (3 km) of the Twenty Mile

Trail ignition point, there are 19.1 miles (30.8 km) of natural barriers and 23.0 miles

(37.0 km) within 1.9 miles (3 km) of the Flats ignition point. As hypothesized, had the

barriers not been present to inhibit fire spread within the models, BA values would have

been significantly higher. A re-run of the Twenty Mile Trail April model without natural

90

fire barriers (except for Cheoah Lake which was large enough to be accounted for by the

fuel model) produced a BA of 2,570 acres (1,040 ha) which is substantially higher than

the 786 acres (318 ha) produced by the model with the barriers (Figure 26). The fire

reached areas to the east and west of the ignition point that were, otherwise,

unobtainable with the perennial streams. One of the current fire management tactics

used by Park officials is primary reliance on natural occurring fire breaks to inhibit fire

spread with the occasional creation of synthetic fire breaks in the form of linear gaps in

leaf litter (produced by using leaf blowers) (Loveland, personal communication, March

1, 2013). The Park’s strategy of primarily relying on natural barriers to inhibit fire

growth appears to be validated through the model results.

Figure 26. Twenty Mile Trail April model without barriers, Great Smoky


91

In general, for all GRSM modeled areas, fire spread at a rate of 1 to 2 ft (0.3 to

0.6 m) per minute and between 30 to 60 ft (9 to 18 m) per hour. The average burned

acreage per hour for the Twenty Mile Trail and the Flats models was 6.0 acres (2.4 ha)

and 2.8 acres (1.1 ha), respectively. The average response time for fire control personnel

(fire fighters, fire management officers, etc.) to arrive to a fire is approximately one hour

(Loveland, personal communication, March 1, 2013) and, for the modeled areas under

the modeling weather and wind conditions, fire managers might expect fires to burn

between 3 acres (1.2 ha) and 5 acres (2.0 ha) in that time.

5.3 Study Limitations and Further Research

Much of the wildfire risk analysis relied on the use of historic fire polygons to

determine how historic fires related to vegetation and terrain related characteristics.

These historic fire perimeter polygons were delineated by Park personnel using paper

maps and descriptions from completed fire forms. It is likely that many of these

polygons are approximations of the fire perimeter and some may be significantly

generalized. During this study, it was assumed that any overestimations in final fire sizes

delineations were equalized by underestimations.

As mentioned in Section 1.7, small fires only represented by point data were

converted to polygons by creating circular buffers equal to the acreage reported for each

fire. This, of course represents the fire’s perimeter as a perfect circle which is likely a

misrepresentation of the actual perimeter.

When performing the correlation analysis between distance to structures and fire

frequency, the year roads and trails were constructed was unknown. Using TDOT traffic

92

counts (available from 1985 onward) and the author’s intimate familiarity with the study

area, it was assumed that all major roads and trails were built prior to 1980. This

assumption has the potential of introducing error into the analysis if any of the roads or

trails were built subsequent to 1980.

Future research may include more investigation into how the vegetation horizons

and their dominant vegetation types influence fire occurrence. For this study, no

research was conducted concerning which vegetation horizon had the strongest influence

on fire frequency and all horizons were treated with equal weight when combining the

overstory, understory, and over/under datasets to determine risk according to fuel type.

However, further statistical testing might show that one horizon has a greater influence

on fire occurrence. Such findings could be used to refine the equation used to determine

the final risk according to final fuel type grid value (Section 3.10).

The weather and wind data used for FARSITE modeling were relatively mild

with no temperatures exceeding 72º F and no wind speeds exceeding 17 mph. Further

FARSITE modeling could be conducted to determine how more extreme conditions

(temperatures > 90º F and wind speeds > 25 mph, e.g.) effect fire spread in the high risk

areas.

Finally, further research may be needed to quantify the relationship between road

use and fire occurrence. When investigating road use frequency vs. fire frequency, no

correlation was found between traffic counts and fire occurrence. In other words, the

most heavily traveled roads did not necessarily have the highest fire frequencies within

their vicinities. For example, a heavily trafficked route entering the park, US 321, had

relatively high average traffic counts of approximately 39,000 vehicles per day from

93

1985 to 2012. During this same time, only sixteen fires were reported within 656 ft (200

m) of the roadway. In contrast, Cades Cove Loop, a much less traveled road with 4,000

vehicles per day from 1985 to 2012, had twenty-two reported fires within 656 ft of the

road.

One key difference between these two roadways could be the reason that

motorists are using them. Cades Cove Loop is a scenic area of the park with many

parking areas, a campground, walking trails, picnic areas and other areas for various

outdoor activities. While the US 321 route does provide scenic lookout areas, the road is

regarded as a thoroughfare between Pigeon Forge and Gatlinburg, TN. It is quite

possible that motorists using Cades Cove Loop intend to park their vehicle and engage

in one of the many available outdoor activities. Once outside their vehicles, motorists

may then partake in activities that could result in the ignition of a wildfire (cigarette

smoking, burning a campfire, etc). However, this is merely speculation and more

research would be required to determine the relationship between these dynamic

variables.

94

Appendix A

Wind Input Data for April 2012 FARSITE Modeling

Month Day Time Speed (mph) Direction (degrees) Cloud Cover (%)

4 6 55 3 130 75

4 6 155 3 150 100

4 6 255 3 100 100

4 6 355 0 0 100

4 6 455 0 0 100

4 6 555 0 0 100

4 6 655 0 0 100

4 6 755 0 0 100

4 6 855 0 0 75

4 6 955 0 0 100

4 6 1055 0 0 100

4 6 1155 0 0 100

4 6 1255 0 0 100

4 6 1355 3 240 100

4 6 1455 6 260 100

4 6 1555 7 120 100

4 6 1655 6 120 30

4 6 1755 6 120 30

4 6 1855 7 90 30

4 6 1955 7 120 0

4 6 2055 5 120 0

4 6 2155 0 0 0

4 6 2255 5 110 0

4 6 2355 6 110 0

4 7 55 0 0 0

4 7 155 3 250 0

4 7 255 3 240 0

4 7 355 0 0 0

4 7 455 0 0 0

4 7 555 0 0 0

4 7 655 0 0 0

4 7 755 0 0 0

4 7 855 0 0 0

4 7 955 0 0 0

4 7 1055 0 0 0

4 7 1155 0 0 100

4 7 1255 0 0 30

4 7 1355 0 0 0

4 7 1455 0 0 0

4 7 1555 0 0 0

4 7 1655 0 0 0

4 7 1755 0 0 0

4 7 1855 6 120 0

95


4 7 1955 8 110 0

4 7 2055 5 250 0

4 7 2155 6 240 0

4 7 2255 7 240 0

4 7 2355 8 240 0

4 8 55 5 240 0

4 8 155 0 0 0

4 8 255 0 0 0

4 8 355 3 240 0

4 8 455 0 0 0

4 8 555 0 0 0

4 8 655 0 0 0

4 8 755 0 0 0

4 8 855 0 0 0

4 8 955 0 0 0

4 8 1055 0 0 0

4 8 1155 0 0 0

4 8 1255 0 0 0

4 8 1355 0 0 0

4 8 1555 6 240 0

4 8 1655 6 250 0

4 8 1755 13 990 0

4 8 1855 11 990 0

4 8 1955 14 990 0

4 8 2055 11 990 0

4 8 2155 10 240 0

4 8 2255 6 240 0

4 8 2355 6 240 0

4 9 55 9 250 0

4 9 155 3 250 0

4 9 255 6 250 0

4 9 355 10 990 0

4 9 455 8 240 0

4 9 555 7 240 0

4 9 655 6 240 0

4 9 755 3 240 0

4 9 855 0 0 0

4 9 955 0 0 0

4 9 1055 0 0 0

4 9 1155 0 0 0

4 9 1255 0 0 0

4 9 1355 0 0 0

4 9 1455 3 110 0

4 9 1555 0 0 0

4 9 1655 5 120 0

4 9 1755 5 250 0

4 9 1855 7 240 0

96


4 9 1955 17 240 0

4 9 2055 10 240 0

4 9 2155 11 260 0

4 9 2255 13 260 0

4 9 2355 6 240 0

4 10 55 7 240 0

4 10 155 6 240 0

4 10 255 7 250 0

4 10 355 8 240 0

4 10 455 10 240 0

4 10 555 6 250 0

4 10 655 10 240 0

4 10 755 9 240 0

4 10 855 8 240 0

4 10 955 3 240 0

4 10 1055 5 120 0

4 10 1155 0 0 0

4 10 1255 0 0 0

4 10 1355 6 250 0

4 10 1455 5 190 30

4 10 1555 10 250 30

4 10 1655 14 250 75

4 10 1755 17 260 30

4 10 1855 14 260 75

4 10 1955 10 250 75

4 10 2055 14 260 75

4 10 2155 10 250 30

4 10 2255 10 260 0

4 10 2355 5 260 0

4 11 55 8 250 0

4 11 155 9 250 0

4 11 255 14 250 0

4 11 355 9 240 0

4 11 455 8 240 0

4 11 555 8 240 0

4 11 655 3 230 0

4 11 755 0 0 0

4 11 855 0 0 0

4 11 955 0 0 0

4 11 1055 5 250 0

4 11 1155 0 0 0

4 11 1255 10 240 0

4 11 1355 11 250 0

4 11 1455 11 240 0

4 11 1555 14 250 0

4 11 1655 13 240 0

4 11 1755 9 250 0

97


4 11 1855 7 250 0

4 11 1955 10 240 0

4 11 2055 9 990 0

4 11 2155 9 240 0

4 11 2255 11 250 0

4 11 2355 5 240 0

98

Appendix B

Wind Input Data for November 2012 FARSITE Modeling

Month Day Time Speed (mph)

Direction (degrees)

Cloud Cover (%)

11 8 55 0 0 0

11 8 155 0 0 0

11 8 255 0 0 0

11 8 355 0 0 0

11 8 455 0 0 0

11 8 555 0 0 75

11 8 655 0 0 85

11 8 755 0 0 100

11 8 855 0 0 100

11 8 955 0 0 100

11 8 1055 0 0 100

11 8 1155 0 0 100

11 8 1255 0 0 100

11 8 1355 0 0 100

11 8 1455 0 0 100

11 8 1555 0 0 0

11 8 1655 0 0 0

11 8 1755 0 0 0

11 8 1855 0 0 0

11 8 1955 0 0 0

11 8 2055 5 80 0

11 8 2155 6 80 0

11 8 2255 3 80 0

11 8 2355 0 0 0

11 9 55 0 0 0

11 9 155 0 0 0

11 9 255 0 0 0

11 9 355 0 0 0

11 9 455 0 0 0

11 9 555 0 0 30

11 9 655 0 0 30

11 9 755 0 0 100

11 9 855 0 0 100

11 9 955 0 0 100

11 9 1055 0 0 100

11 9 1155 0 0 100

11 9 1255 0 0 100

11 9 1355 0 0 100

11 9 1455 0 0 0

11 9 1555 3 80 0

11 9 1655 6 80 75

11 9 1755 0 0 100

99


Direction (degrees)

Cloud Cover (%)

11 9 1855 3 80 100

11 9 1955 3 80 100

11 9 2055 0 0 100

11 9 2155 0 0 100

11 9 2255 0 0 100

11 9 2355 0 0 100

11 10 55 0 0 100

11 10 155 0 0 100

11 10 255 0 0 100

11 10 355 0 0 100

11 10 455 0 0 100

11 10 555 5 80 100

11 10 655 6 80 75

11 10 755 5 80 30

11 10 855 6 80 75

11 10 955 3 80 100

11 10 1055 3 80 100

11 10 1155 11 80 100

11 10 1255 6 80 100

11 10 1355 7 80 30

11 10 1455 9 80 0

11 10 1555 13 80 0

11 10 1655 10 80 0

11 10 1755 14 80 0

11 10 1855 6 80 0

11 10 1955 6 80 0

11 10 2055 10 80 0

11 10 2155 8 80 0

11 10 2255 7 80 0

11 10 2355 11 80 0

11 11 55 9 80 0

11 11 155 6 80 30

11 11 255 7 80 0

11 11 355 10 80 0

11 11 455 9 80 0

11 11 555 6 80 0

11 11 655 0 0 0

11 11 755 0 0 0

11 11 855 0 0 0

11 11 955 0 0 0

11 11 1055 9 80 0

11 11 1155 0 0 0

11 11 1255 0 0 0

11 11 1355 6 80 0

11 11 1455 7 80 0

11 11 1555 0 0 0

11 11 1655 8 80 0

100


Direction (degrees)

Cloud Cover (%)

11 11 1755 6 80 0

11 11 1855 6 80 0

11 11 1955 7 80 0

11 11 2055 6 80 0

11 11 2155 0 0 0

11 11 2255 0 0 0

11 11 2355 0 0 0

11 12 55 0 0 0

11 12 155 0 0 0

11 12 255 0 0 0

11 12 355 0 0 0

11 12 455 0 0 0

11 12 555 0 0 0

11 12 655 0 0 0

11 12 755 0 0 0

11 12 855 0 0 0

11 12 955 0 0 0

11 12 1055 0 0 0

11 12 1155 0 0 0

11 12 1255 0 0 0

11 12 1355 0 0 0

11 12 1455 0 0 0

11 12 1555 0 0 0

11 12 1655 0 0 0

11 12 1755 3 80 0

11 12 1855 0 0 0

11 12 1955 3 80 0

11 12 2055 5 80 0

11 12 2155 3 80 0

11 12 2255 5 80 0

11 12 2355 0 0 0

11 13 55 0 0 0

11 13 155 0 0 0

11 13 255 0 0 0

11 13 355 0 0 0

11 13 455 0 0 0

11 13 555 0 0 0

11 13 655 0 0 0

11 13 755 0 0 0

11 13 855 0 0 0

11 13 955 0 0 0

11 13 1055 0 0 0

11 13 1155 0 0 0

11 13 1255 0 0 0

11 13 1355 0 0 30

11 13 1455 0 0 0

11 13 1555 0 0 30

101


Direction (degrees)

Cloud Cover (%)

11 13 1655 3 70 100

11 13 1755 3 80 0

11 13 1855 6 80 30

11 13 1955 0 0 30

11 13 2055 7 80 0

11 13 2155 5 80 0

11 13 2255 5 80 30

11 13 2355 5 80 100

102

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