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\def\citename	 {Williams} 		%"Author"
\def\citeemail	 {tmwllms@clemson.edu} 	%Use: {\href{mailto://\citeemail} {FirstName \citename}}
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\author{
     {\href{mailto:\citeemail}{Thomas M~\citename}}, %Change only 1st name of 1st author
		{\href{mailto:bwilli8@clemson.edu} {Brian J Williams}},
		{\href{mailto:bosong@clemson.edu} {Bo Song}}
}
\affiliation {
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} \\
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\small \it{\href{http://www.clemson.edu/cafls/departments/forestry/}{College of Agriculture, Forestry and Life Sciences, Clemson University, Clemson, SC, USA}} \\
}
\def\yourtitle
 {{
Modeling a historic forest fire using GIS and FARSITE
}} %need double {{for \\ e.g.: {{Title \\ Subtitle}}
\def\yourkwords
 {
19$^{th}$ century forest fire, Fire visualization, mega-fire, Hinckley MN.
}
\def\yourabstract
 {
Recent major wildfires may result from a combination of climate change and
fuel buildup due to fire exclusion policies of the last century. Are such
fires unique to the forests and climate of the 21st century or are they
similar to historic fires? Historic fires are recorded primarily by eye
witness accounts which seldom contain information needed to examine them
with modern fire management tools. The September 1, 1894 fire near Hinckley,
Minnesota has well documented accounts from many survivors that allow
position of the flame front to be established for a number of times
throughout the fire. These accounts allowed us to calibrate a FARSITE model
to represent the progression of that fire. FARSITE is a modern fire spread
rate simulation model. It requires spatial layers of elevation, slope,
aspect, timber type, derived layers of fuel type, canopy cover, stand
height, canopy base height, canopy bulk density, duff and coarse woody
debris. In this paper we will discuss how we were able to combine present
GIS data, historical map data, and present ecosystem properties to provide
data needed for these layers. GIS output of FARSITE spread predictions were
used to match flame front position to eyewitness accounts and model
parameters (primarily, wind speed and direction and fuel model spread rate
adjustments) were altered to produce a flame front location and time that
matched eyewitness location and timing.
} %----------------------------------------------------------------------
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%\\\\ {{\bf S\l {} owa kluczowe:} Polskie slowa kluczowe.}
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\section{Introduction }
Over the last two decades there has been a worldwide increase in number and
severity of wildland fires (Williams et al. 2010). Assessments of potential
fire risk in association with future climate modes (IPCC 2007) suggest that
forest fire risk will increase due to both more severe fire danger and a
lengthening of fire seasons (Flannigan et al. 2013, Liu et al. 2013). In
addition, across the US large regions have developed heavy fuel loads
through intensive fire protection as well as lowered rates of biomass
removal (Williams 2013). There is good reason to believe forest fires, that
are large, intense, and overwhelm our ability to control, may become more
prevalent in the US, and possibly worldwide, in the 21$^{st}$ century.
Failure of control in recent fires has renewed interest in understanding
``extreme fire behavior'' (Pyne 1986), that is fires that vary erratically
in spread rate, energy release, and atmospheric interaction (Werth et al.
2011). A number of terms used for such behavior, including `` blow up, fire
storm, mass fire'', have been used to describe sudden changes in fire
behavior that have lead to a loss of control and sometimes death of fire
fighters and civilians in major forest fires of the past.

Extreme fire behavior is not unique to recent major forest fires. Fires that
occurred in the Lake States between 1876 and 1918 were the most deadly
forest fires in US history. Over 2500 people lost their lives in three fires
in Pestigo, WS, 1876, Hinckley, MN, 1894, and Moose Lake- Cloquet, MN, 1918.
Unfortunately, descriptions of those fires were limited to eye witness
accounts that often described extreme fire behavior in anthropomorphic or
even religious terms. The scientific merit of information given in such
accounts has been discounted simply due to the language used. However, many
of these descriptions document incidents that are now classified as extreme
fire behavior (Werth et al. 2011).

The Hinckley MN fire in particular has a wealth of eye witness information
collected into three contemporary books (Aldermark 1894; Brown 1895;
Wilkinson 1895), one book of survivor recollections (Anderson and
Anderson-McDerrmott 1954), and three scholarly/popular accounts (Brown 2007;
Swenson 1979; Larsen 1984). From these sources it is possible to extract a
series of positions of the fire front during the day of Sept. 1, 1894. These
observations allow a number of estimates of average spread rate for
differing portions of the fire. Alexander and Cruz (2006) surveyed spread
rate of recent severe crown fires across the US and Canada and found that
the maximum measured rates where slightly higher than 100 m/min. For a
period of five hours and over a distance of 34 km, the Hinckley Fire
observed average rate of spread was 114 m/minute (Williams et al. 2013).

The ability to predict fire spread for a set of climatic conditions and fuel
loads can be useful for both hazard reduction and active firefighting.
FARSITE (Finney 2004) is a spatially explicit model that implements the
Rothermel (1972, 1983) fire spread equations in a semi-empirical model of
forest fire behavior. Although FARSITE does not have theoretical support for
fire to fire or fire-atmospheric interactions of newer spread models (Coen
2011; Kochanski et al. 2013), it is widely regarded as the most reliable
model for fire suppression management as well as fire preparedness analysis.
In this paper we explore the use of FARSITE to examine the Hinckley Fire.
There were three questions that we wanted to answer:

\begin{enumerate}
\item Are there sufficient historical data to create the spatial files required to run the FARSITE model?
\item If so: can a model be created that mimics the observed positions of the fire recorded by eye witness accounts?
\item Are model modifications used to fit observed spread rates plausible?
\end{enumerate}

\section{Methods }
Throughout the paper units of distance and mass have been made consistent
with source materials which were primarily English units, while model
outputs and general comparisons use metric units to be more comparable to
general scientific literature. The FARSITE model can be run using either
system of units. More complete information on FARSITE can be found online at
\href{http://www.firelab.org/project/farsite}{http://www.firelab.org/project/farsite}.

FARSITE simulates spread of fire for a specific geographical area and for a
given set of fuel and climatic conditions. Data to run the model consists of
a set of spatial and non-spatial files that define the physical setting,
fuel availability and flammability, and climatic factors (Finney 2004). The
physical setting is defined by three spatial files of elevation, slope, and
aspect. Two spatial files of fuel availability; canopy cover and fuel model,
are required to model fires spreading in fuels along the ground. Fuel model
is a combination of values needed to solve the Rothermel (1972) basic fire
spread equations based on cover types. For this analysis, the fuel models
developed by Scott and Burgan (2005) were used. To model crown fires spatial
files of stand height, canopy base height, and canopy bulk density are also
needed. Finally, spatial files of coarse woody debris and duff layer are
needed to model burning after the fire front passes. In addition to the
spatial data, FARSITE also requires a weather file, wind file, and optional
fuel moisture adjustments and spread rate adjustments. The weather file
defines air temperatures, wind speeds, and relative humidity for a period
before and during the fire. The wind file allows greater definition of wind
speed and direction during the fire. Fuel moisture adjustment file allows
altering the initial fuel moisture of 1, 10, and 100 hour fuels. The spread
rate adjustment file can be used to alter the modeled spread rate of each
fuel model.

Applying the FARSITE model to the Hinckley Fire required finding and
converting historical information into the data files described above. The
most important source of spatial data was the online GIS data portal of the
Minnesota Department of Natural Resources (MNDNR 2014). From this source the
following data were obtained: digital elevation ( 30m DEM), locations of
state and county boundaries, county roads for Pine., Carleton, Aiken, and
Kanabec counties, state wide railroads, recreational trails (several
abandoned railroad lines are now bike trails), lakes and rivers, county
(same four) Public Land Survey township and range lines, and a witness tree
layer that included tree locations and attributes of species and size of
each tree. In addition, a map of original vegetation which had been
interpreted in 1935 (Marschner 1974) was also used.

The outline of the entire area burned by the Hinckley Fire was obtained by a
map in Swenson (1979), which was drawn on a state map with county lines,
large lakes, rail lines as in 1894, and rivers, and location of several
small towns described in eyewitness accounts (Figure~1). Geography of most
eyewitness accounts is defined by location, a small lake, and eight small
towns; Quamba, Beroun, Brook Park, Mission Creek, Hinckley, Sandstone,
Askov, and Finlayson. Fires began in the early morning of September 1, 1894
near Quamba and Beroun. The Quamba fire destroyed Brook Park at 1400h (1400
hours 2:00 PM). The Beroun fire destroyed Mission Creek at 1430h. Both fires
combined and destroyed Hinckley at 1530h, Sandstone at 1735h, Askov and
Finlayson by 1900h. The fire overtook one of the trains carrying survivors
at a small lake called Skunk Lake at 1625h.

\begin{figure*}[htb!]\vspace{-.2in}
\centerline{\includegraphics[width=.85\textwidth]{GIS_for_fire_model1.eps}}
\caption{Location of the Hinckley Fire in east central Minnesota.
Inset of Pine, Kanabec, Carlton, and Aiken counties includes locations of
small towns listed in the text.}
\label{fig1}
\end{figure*}

%\textbf{Figure~1.  }

\begin{figure*}[htb!]
\centerline{\includegraphics[width=.75\textwidth]{GIS_for_fire_model2.eps}}\vspace{-.3in}
\caption{Spatial distributions of land and tree parameters used as input to the FARSITE model of the Hinckley Fire.}\vspace{.25in}
\label{fig2}
\end{figure*}

%\textbf{Figure~2. }


%\textbf{Table~1.

\begin{table*}[htb!]
\begin{center}
\caption{Vegetation types. Fuel models (Scott and Burgan 1972), and
multipliers used in the development of the Hinckley Fire FARSITE model.
Aspen-birch is a pioneer community that occurs on both hardwood and conifer
sites.}%\vspace{-3pt}
\begin{tabular}{llll} \hline\hline
\textbf{Vegetation Type}  & \textbf{Dominant Tree Species} & \textbf{Fuel Model}   & \textbf{Multi-plier } \\ \hline
\textbf{Aspen-birch} & {} & {} & {} \\
{}tending
conifer\textit{, } & Pt, Pgt, Bp\textit{, }Ab, Pg\textit{} & 162 & 15 \\
tending
hardwood  & Pgt, Pt, Bp\textit{, }As\textit{, }Ar, Ta & 186 & 18 \\
\textbf{Wet prairie}  &  & 109 & 16 \\
\textbf{White and red pine}  & Ps, Pr, Bp &  &  \\
uncut &  & 185 & 20 \\
cut  &  & 203 & 18 \\
\textbf{Mxd pine-hardwood} & Ps, Pr\textit{, }Qr, Ar & 189 & 18 \\
\textbf{White pine}  & Ps & 204 & 18 \\
\textbf{Hardwood } & Ta, As\textit{, }Fn,Ua & 189 & 18 \\
\textbf{Jack pine}  & Pb & 163 & 18 \\
\textbf{River bottom}  & Ar,\textit{ }Ua,\textit{ }As\textit{} & 189 & 18 \\
\textbf{Conifer bogs}\textit{} & Ll, Pm\textit{} & 164 & 20 \\
\textbf{Water} &  & 98 & 1 \\ \hline
\multicolumn{4}{p{5.2in}}{\footnotesize {Species abbreviations:\textit{ Abies balsamea-}Ab,\textit{ Acer saccharum}-As,\textit{ Acer rubrum} -- Ar, \textit{Betula papyrifiera}- Bp, \textit{Fraxinus nigra}-Fn,\textit{ Larix laricina,}-Ll\textit{ Picea gluaca}- Pg,\textit{ Picea mariana}- Pm, \textit{ Pinus banksiana- }Pb\textit{, Pinus strobus-} Ps,\textit{ Pinus resinosa}-Pr, \textit{Populus grandidentata}-Pgt,\textit{ Populus tremuloides}-Pt,\textit{ Quercus macrocarpa}-Qm,\textit{ Quercus rubra}-Qr,\textit{ Tillia Americana}-Ta,\textit{ Ulmus Americana}- Ua }}\\
\end{tabular}
\label{tab1}
\end{center}
\end{table*}

All of the small towns (except Mission Creek) are still present and can be
easily located on the four county maps. The outline of the burned area was
digitized from the Swenson (1979) map using county lines, rivers, railroads
and town locations as geographic reference (Figure~1). This outline was then
used as an area of interest for all spatial layers of the FARSITE model.

Elevation, slope, and aspect were created from the downloaded DEM using the
fire outline as a feature mask (Figure~2). Marschner's (1974) original
vegetation polygons were then clipped with the fire outline. To these
polygons new features of canopy cover, fuel model, canopy height, canopy
base height, canopy bulk density, coarse woody debris, and duff were added.
All feature values were assigned based on vegetation types found in the area
today and fuel models were assigned as the most nearly matching standard
fuel model. Canopy bulk density was not assigned but is the default value in
FARSITE for each fuel model. Grid files were then created for fuel model,
canopy cover, tree height, canopy base height, canopy bulk density (Figure
2), coarse woody debris, and duff (Figure~3).

Non-spatial files of weather and wind were developed based on data found in
Haines and Sando (1969) and Haines et al. (1976). Air temperatures for the
week before the fire were found in those publications while wind speed and
humidity were assigned based on air temperature as that data was not
recorded. Likewise, the wind file was set at a steady 20  mph wind from the
southwest, known from surrounding stations (Haines et al. 1976). That file
was modified for calibration. Initial fuel moistures were also lowered to
reflect the extreme drought that preceded the fire (Haines and Sando 1969).
Finally, the spread rate adjustment file was modified in calibration. The
spread rate adjustment file allows multiplier factors from 0.001 to 20 to be
applied to each fuel model. These factors were also used to calibrate the
spread rates to match arrival times. Final values of spread rate adjustment
are shown in Table~1 and final values of wind speed and direction are shown
in Table~2.

\begin{figure*}[htb]
\centerline{\includegraphics[width=.75\textwidth]{GIS_for_fire_model3.eps}}
\caption{Spatial distributions of surface parameters used as input
to the FARSITE model of the Hinckley Fire.}
\label{fig3}
\end{figure*}

%\textbf{Figure~3. }


\section{Results }

The goal of this exercise was to produce a FARSITE model that mimicked the
observed spread of the 1894 Hinckley Fire. Alteration of files that defined
wind speed and direction and the file of multipliers were used to vary
spread rate among the various fuel models. Using the model parameters
described in Figures 2-3 and Table~1-2, FARSITE output produced flame front
perimeters as shown in Figure~4. Individual ignitions at Quamba and Beroun
at 0600h burned to the north and east until combining between 1300h and
1400h just to the southeast of Hinckley. The combined fire had two distinct
heads until roughly 1700h and burned rapidly during the rest of the day. The
model reproduced a flame front at Hinckley, Sandstone and Skunk Lake very
near the observed time and was slightly early at Mission Creek. The model
was considerably early at Brook Park and late at Askov and Finlayson (Table
3).

%\textbf{Table~2- }

\begin{table}[htb]
\begin{center}
\caption{Summary of wind data used in the final FARSITE model. Each
hour was divided into 15 minute intervals and wind speed and direction was
defined for that period. Summary shows maximum and minimum values used
during the hour. Order shows if speed was increasing or decreasing during
the hour.}\vspace{3pt}
\begin{tabular}{llll} \hline\hline
Time & Wing Speed  & Direction  & Direction \\
 & ( mph) & maximum & minimum \\ \hline
0600-0700 & 4-10 & 250 & 170 \\
0700-0800 & 10-15 & 225 & 160 \\
0800-0900 & 15-19 & 225 & 175 \\
0900-1000 & 19-28 & 255 & 175 \\
1000-1100 & 28-30 & 245 & 175 \\
1100-1200 & 28-30 & 245 & 175 \\
1200-1300 & 30-35 & 245 & 215 \\
1300-1400 & 35-40 & 255 & 185 \\
1400-1500 & 40 & 245 & 215 \\
1500-1600 & 40-45 & 245 & 215 \\
1600-1700 & 40-35 & 245 & 205 \\
1700-1800 & 40-30 & 245 & 225 \\
1800-1900 & 30-25 & 235 & 215 \\
1900-2000 & 20-28 & 245 & 225 \\
2000-2100 & 25-20 & 280 & 225 \\ \hline
\end{tabular}
\end{center}
\label{tab2}
\end{table}

The model also produced data that could be used to examine fire spread in a
geographic manner. Consistent estimates of spread rates and area burned can
be extracted from the model. Linear spread rate seems mostly related to wind
speed although there is a significant increase following the joining of the
fires (Figure~5). An interesting and perhaps most frightening aspect could
be extracted from the model burn perimeters. Each perimeter presented in
Figure~4 is a raster of the area burned during each hour of the fire. In the
GIS, these raster areas had 30x30 m pixels so that each pixel represented
900m$^{2}$ on the ground, allowing an estimate of area burned. In Figure~6
those estimates are expressed as a burn rate in hectares per second.

\begin{figure}[htb]
\centerline{\includegraphics[width=.55\textwidth]{GIS_for_fire_model5.eps}}
\caption{Estimated flame front locations from the final FARSITE model.}
\label{fig4}
\end{figure}

%\textbf{Figure~4.  }

\section{Discussion}
A FARSITE model of the 1894 fire in Hinckley could be developed with a
variety of historic sources and current data. In fact, most of the data
could be developed from online information from the Minnesota Department of
Natural Resources. The map of original vegetation was used to develop most
of the spatial layers needed to run the model. The original map was
developed from Public Land Survey witness trees, but from professional
judgment rather than modern analytic tools. This was quite evident in the
witness tree data downloaded for this paper. Although the map showed stand
types as white or red pine, often these trees were completely missing form
witness trees in that area. However, that is not hard to understand since
surveyors were required to use witness trees that were expected to remain
for long periods after the survey was completed. Since cruisers for timber
companies often accompanied those surveyors (Larson 2007) it would be
pointless to expect pines to remain long after the survey. The map was also
produced before the modern forest classification scheme (Erye 1990) making
correlation to current data compatible with FARSITE (Ryan and Opperman 2013)
difficult. Essentially the data used in this model is the product of
profession judgment of a Minnesota trained forester in 1935 and the author,
another Minnesota trained forester that lived in east central Minnesota for
the first 27 years of his life.

%\textbf{Table~3. }

\begin{table}[tb!]
\caption{Arrival times of the Hinckley Fire based on eye witness
accounts and derived from model perimeter files. All times refer to Sept. 1,
1894 unless noted.}\vspace{-9pt}
\begin{center}
\begin{tabular}{p{1in}p{.8in}p{.9in}} \hline\hline
Location  & Arrival Time \par Observed & Arrival Time \par Modeled \\ \hline
Brook Park & 1400 & 1000 \\
Mission Creek & 1430 & 1330 \\
Hinckley & 1500 & 1530 \\
Skunk Lake & 1615 & 1600 \\
Sandstone & 1725 & 1800 \\
Askov & 1900 & 0900 Sept.2 \\
Finlayson & 1900 & 0200 Sept. 2 \\ \hline
\end{tabular}
\label{tab3}
\end{center}
\end{table}

The FARSITE model was able to produce a set of fire perimeters that mimicked
the observed timing very well for the most intense and important period of
the fire. It was able to predict the timing of the town of Hinckley,
Sandstone and at Skunk Lake. With the exception of Brook Park, it also
predicted the fire well for the period in which human fatalities occurred.
The shape of the modeled fire also explains apparent very rapid spread from
Hinckley to Skunk Lake (175m/min- Williams et al. 2013). The model suggests
that Hinckley was burned as two heads of the combining fire burned to the
north and southeast of town. By the time the town burned the northern head
was already very near the track of the St. Paul to Duluth railroad on which
the escape train was overtaken at Skunk Lake. The model fire also shows the
Eastern Minnesota Railroad tracks between the two heads and burning late.
The train on this track was able to evacuate over 800 people from the fire.
The modeled fire reproduced a number of observations that were independent
of the calibration data used. FARSITE appears to be capable of producing a
retrospective model of a major forest fire.

\begin{figure}[htb]
%\hspace{.2in}
\centerline{\includegraphics[angle=-90,width=.49\textwidth]{Fig5_Sep30.eps}}
%GIS_for_fire_model6.eps}}
\caption{Model estimates of the rate of spread of the Hinckley
Fire. Rates are calculated at the most distant point of each hourly FARSITE
model perimeter.}
\label{fig5}
\end{figure}

%\textbf{Figure~5. }

\begin{figure}[htb]
\centerline{\includegraphics[angle=-90, width=.49\textwidth]{Fig6_Sep30.eps}}
%GIS_for_fire_model7.eps}}
\caption{Estimated rate of area burned during each hour of the
Hinckley Fire. Rates are estimated from the fire perimeters of the FARSITE
model.}
\label{fig6}
\end{figure}

%\textbf{Figure~6.

Winds up to 45  mph were required despite data that suggests wind speed of
only 20 mph in surrounding stations (Haines and Sando 1969). Also, the spread
rate adjustment file was used to multiply the spread rate of several fuel
types to the maximum 20 times normal. Were model modifications used to fit
observed spread rates plausible? Although the winds are completely
speculative, variations in direction of 90 degrees are not unlikely in early
morning and 45  mph is not completely unreasonable for maximum sustained
winds. Eyewitness accounts often described cyclone winds, which then could
refer to either hurricanes or tornados.

The FARSITE model we used did not include spread by spot fires, although
``firebrands falling like rain'' was a common comment by survivors. The
FARSITE method of solving spread equations of spot fires results in an
exponential increase in the number of numerical solutions required. With
spot fire enabled the model would require 5+ hours of computation for the
spread from 0945h to 1000h. Using a computer with twice the computing power
encountered the same problem at 1045h. For the period the spotting model
could produce perimeters, spread rate multipliers of 5-10 times normal
resulted in similar perimeters to the final version (Figure~4) which used
multipliers that were generally twice as large (Table~1). It seems that
increasing the rate of spread of fuel models has a roughly equivalent effect
as modeling spot fires.

The theoretical base of FARSITE is primarily spreading equations of
Rothermel (1972) and does not include atmospheric interaction beyond a
single estimate of wind speed. It cannot model fire induced convection nor
does it include atmospheric effects of two approaching fires. Newer models
include Rothernmel (1983) models of surface ignition and spread with
atmospheric models to account fire induced wind and fire-atmospheric
interactions that produce extreme fire behavior (Clark et al. 2004; Filippi
et al. 2009; Kochanski. et al. 2013). Application of these models to the
Hinckley Fire would also be an interesting exercise but would require even
more speculation for the various upper and lower atmospheric data needed to
parameterize these models.

\section{Conclusions}
We were able to obtain sufficient historical data to create a FARSITE input
dataset for the 1894 Hinckley Fire. When calibrated to known locations of
the fire front, FARSITE produced a model that fit the known locations of the
fire during the intense burning period. Model results also explained aspects
of the historical account that were not used in the calibration. Calibration
included adding speculative wind data and altering the spread rates normally
associated with fuel models (Scott and Burgan 2005) up to 20 times the
normal rate. It would seem the ability to increase the spread rate within
FARSITE allows it to be used for fires which include spot fires, atmospheric
interactions, and merging of fires that are not explicitly accounted for in
the theoretical derivation of the FARSITE model.

\section*{Acknowledgements }
This material is based upon work supported by NIFA/USDA, under project
number SC-1700405. The paper would not have been possible without the
outstanding GIS website of the Minnesota Department of Natural Resources. We
would also like to thank Sandy Hinds of the Hinckley Fire Museum for advice,
encouragement, and other sources of eyewitness accounts. The paper was
improved by comments of two anonymous reviewers.


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\end{document}
