The document describes the development of a model to predict wildfire risk in California. It uses climatological data like temperature, precipitation, and humidity, as well as land cover data, as inputs to the model. Two models are created, one with equal weighting of all factors and one that weights land cover higher. The results of the second model better match actual 2014 wildfires in northern California but miss fires in southern California, suggesting room for improvement in the model formula. Overall, the model effectively predicts wildfires in western California but inaccuracies remain.

1. Nate Leibolt Fall 2015
Introduction / Background / Abstract
Wildfire activity has increased in western states in recent decades, due
in large part to an increase in spring and summer temperatures, a
decrease in precipitation, and reduced snowpack in mountains in the
region (Westerling 2006). A model that accurately predicts wildfires
would be especially valuable because of how difficult it can be to control
and stop wildfires after they begin. Variables that this model will include
are temperature, precipitation, wind potential, dewpoint temperature and
land cover.
A paper published as a paper by Haiganoush K Preisler and Anthony L
Westerling for the American Meteorological Society, is based on logistic
regression with explanatory variables including monthly average
temperature, and several other models I researched included wind,
dewpoint and precipitation in their analysis (Haiganoush and
Westerling). Because of the similarities that appear between these
models and what I am proposing, I am confident the relevance that these
variables have in modeling wildfires. However, I propose to differ from
some previous models in that I am not interested in modeling the spread
and behavior of wildfires after they have started, but rather forecasts the
potential for wildfires to occur in a given area.
California Wildfire Risk Assessment Model
Data
This model will be run to analyze the risk for wildfires for the area of the state of California. California was chosen as the study area because of the prevalence of wildfires that occur
within the state, which will allow for an accurate validation of the model. The climatological data (temperature, dewpoint and precipitation) that was fed into the model was gathered
from Oregon State University’s PRISM Climate Group, and the land use/land cover data comes from the U.S. Department of Agriculture Forest Service and the U.S. Department of the
Interior’s Landfire program (Anderson’s 13 fuel model). Below are the input rasters:
Land Cover Raster Precipitation Raster Data Dewpoint (Humidity) Raster Data Temperature Raster Data Wind Potential Raster Data
Objectives
•Develop a model that analyzes the risk for wildfires over a
specified area
•Compare the results of the model to observed wildfires in the area
to assess the model.
Results / Discussion / Conclusions
To validate the model, I gathered data for 2014
wildfires in the state of California. Below are the
actual fire perimeters overtop the second model’s
results:
The first model that had equally weighted factors
predicted a high number of fires in the southeastern
portion of the state, which is mostly desert, and thus,
inaccurate. This is due to the fact that temperature,
humidity and precipitation played equal role in
determining the site for fires, which is inaccuate in
this case. The second model was a bit better in
reflecting the fires that occur in the northern part of
the state, but left out the large expanse of fires that
occurred in the southwester part of the state near Los
Angeles. This may highlight the need for more
consideration of additional variables or a better
mathematical formula for the model, so future work
could be done in this area. Overall, this model
predicts wildfires for the wester part of the state, but
the innacuracy of the model suggests a better formula
is needed.
Methods
To develop the model, I reclassified the precipitation, temperature, land use/land cover,
wind and dewpoint temperature into 7 categories based on Jenks’ Natural Breaks method.
These categories reflect lowest to highest risk for fires (lower humidity and precipitation,
higher temperatures and wind potential, and ). These reclassifications were then fed into
two separate models, one which equally weighted all of the factors, and one which has
differing weights based on research into the most important wildfire factors. The first
mathematical model that was generated, where every variable had equal weighting, was in
the form: Result = Land Cover + Precipitation + Dewpoint + Temperature + Wind Potential.
The second mathmatical was based more on reality to hone the model. It was in the form:
Result = 5*Land Cover + 2*Precipitation + 2*Dewpoint + 2*Temperature + Wind Potential
First Model (Equal Weights) Second Model (Varying Coefficients)
Sources
Haiganoush K. Preisler and Anthony L. Westerling,
2007: Statistical Model for
Forecasting Monthly Large Wildfire Events in
Western United States. J. Appl.
Meteor. Climatol., 46, 1020–1030.
Westerling, A. G. "Warming and Earlier Spring
Increase Western U.S. Forest
Wildfire Activity." Science 313.5789 (2006): n. pag.
Web. 8 Oct. 2015.
<http://www.sciencemag.org/content/313/5789/940>.
National Renewable Energy Laboratory (NREL) Wind
Data: http://www.nrel.gov/gis/data_wind.html
NOAA’s Climate Prediction Center Precipitation,
Temperature and Dewpoint Data:
http://www.cpc.ncep.noaa.gov/products/GIS/GIS_DA
TA/
Oregon State University PRISM Land Cover data:
http://www.prism.oregonstate.edu