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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
vii
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
Mountains National Park, USA. .........................................................................39
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
Mountains National Park, USA. .........................................................................46
Table 9. Analysis of understory vegetation and fire frequency, Great Smoky
Mountains National Park, USA. .........................................................................47
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
National Park, USA. ...........................................................................................60
Table 12. FARSITE output data for wildfire simulations, Great Smoky Mountains
National Park, USA. ...........................................................................................60
Table 13. Estimated cloud cover percentages for FARSITE modeling, Great
Smoky Mountains National Park, USA. .............................................................72
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
viii
Table 16. Rules for assigning fuel model classes for FARSITE modeling, Great
Smoky Mountains National Park, USA. .............................................................75
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
National Park, USA. ...........................................................................................24
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
National Park, USA. ...........................................................................................32
Figure 7. Scatter plot of elevation vs. wildfire frequency, Great Smoky Mountains
National Park, USA. ...........................................................................................35
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
Mountains National Park, USA. .........................................................................41
Figure 10. Wildfire risk according to combined terrain variables, Great Smoky
Mountains National Park, USA. .........................................................................42
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
Mountains National Park, USA. .........................................................................52
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
Mountains National Park, USA. .........................................................................61
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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
Mountains National Park, USA. .........................................................................66
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
National Park, USA. ...........................................................................................68
Figure 20. The Flats area ignition point, Great Smoky Mountains National Park,
USA.....................................................................................................................69
Figure 21. Histogram - historic wildfire frequency by month, Great Smoky
Mountains National Park, USA. .........................................................................70
Figure 22. Fuel model for FARSITE modeling, Great Smoky Mountains National
Park, USA. ..........................................................................................................75
Figure 23. Twenty Mile Trail FARSITE modeling results, Great Smoky
Mountains National Park, USA. .........................................................................78
Figure 24. The Flats area FARSITE modeling results, Great Smoky Mountains
National Park, USA. ...........................................................................................84
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
Mountains National Park, USA. .........................................................................90
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
2
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).
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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.
5
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.
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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
Mountains National Park, USA.
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:
Rv = reclassified value
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
Mountains National Park, USA.
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
Mountains National Park, USA.
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)
Low to mid elevations (511 – 3280 feet)
Appalachian Hardwoods
Carolina hemlock (Tsuga caroliniana), eastern hemlock (Tsuga canadensis), basswood (Tilia americana), yellow birch (Betula alleghaniensis), red maple (Acer rubrum var. rubrum)
Low to mid elevations (511 – 3280 feet)
Southern Appalachian Cove Hardwood
Carolina silverbell (Halesia tetraptera), northern red oak (Quercus rubra), tulip tree (Liriodendron tulipifera), various magnolia species (Magnolia sp.)
Low to mid elevations (511 – 3280 feet)
Pasture Grasslands (Poaceae sp., Cyperaceae sp., Juncaceae sp.)
Mid elevations (1500 – 2000 feet)
Hemlock Eastern hemlock (Tsuga canadensis), Carolina hemlock (Tsuga caroliniana)
Low to mid elevations (511 – 3280 feet)
Montane Alluvial Forest
Sycamore (Platanus occidentalis), tulip tree (Liriodendron tulipifera), American hornbeam (Carpinus caroliniana), sweetgum (Liquidambar styraciflua)
Low to mid elevations (511 – 3280 feet)
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.)
Low to mid elevations (511 – 3280 feet)
Hemlock with Rhododendron
Eastern hemlock (Tsuga canadensis), mixed rhododendron (Rhododendron spp.)
Low to mid elevations (511 – 3280 feet)
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.)
Low to mid elevations (511 – 3280 feet)
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
Mountains National Park, USA.
(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
Mountains National Park, USA.
(note: only first 9 vegetation types shown)
Understory 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
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.
(note: only first 9 vegetation types shown)
Over/Under Dominant Vegetation Combination
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/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:
Rv = reclassified value
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
Mountains National Park, USA.
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
Mountains National Park, USA.
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
Mountains National Park, USA.
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
Mountains National Park, USA.
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
Smoky Mountains National Park, USA.
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
Smoky Mountains National Park, USA.
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
Mountains National Park, USA.
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.
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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
Mountains National Park, USA.
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
Month Day Time Speed (mph) Direction (degrees) Cloud Cover (%)
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
Month Day Time Speed (mph) Direction (degrees) Cloud Cover (%)
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
Month Day Time Speed (mph) Direction (degrees) Cloud Cover (%)
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
Month Day Time Speed (mph)
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
Month Day Time Speed (mph)
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
Month Day Time Speed (mph)
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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