Predicting Fire Season Severity in the
Pacific Northwest
Paul Werth1
Abstract—Projections of fire season severity that integrate historical weather and fire
information can be used by fire managers when making decisions about allocating and
prioritizing firefighting resources. They enable fire managers to anticipate fire activity
and pre-position resources to maximize public and firefighter safety, reduce environmental impacts, and lower firefighting costs. This research determines the potential
severity of fire seasons in the Pacific Northwest by using statistical techniques that
correlate weather data and annual-acreage-burned figures for five fire management
agencies in Washington and Oregon (U.S. Forest Service, Bureau of Land Management,
Bureau of Indian Affairs, Oregon Department of Forestry, and Washington Department
of Natural Resources). Weather and fire trends for the 1970 to 2004 time period were
calculated, and thresholds for above average, average, or below average fire seasons
were determined based upon annual acres burned. Eight weather parameters were
then correlated using scatter diagrams, contingency tables, and multivariate regression equations to predict above average, average, or below average fire seasons based
upon projected acres burned. Results show considerable variance in predictors by fire
agency with accuracy rates of 60 to 85% for predictions of above average fire seasons
and 85 to 90% for average and below average fire seasons.
Introduction
Several considerations affect fi re managers’ decisions regarding allocation of firefighting resources including: (1) public and firefighter safety (2) the
potential effect of fires on local environments, and (3) the increasing impact of
fi refighting costs on agency budgets. Over the past several years, the Northwest Interagency Coordination Center has demonstrated that pre-positioning
resources throughout Washington and Oregon in advance of fi re outbreaks,
improves their effectiveness in achieving all three of the above-listed goals.
The obvious question arises, “How do fi re managers determine the most
effective placement of resources prior to the fi re season?” One tool they use
is a pre-season assessment of historical weather and fi re information that
produces projections of expected fi re season severity for any given area in
the Pacific Northwest. This research takes that assessment to the next level
by applying statistical techniques to weather and annual acres-burned data
for five, fi re management agencies in Oregon and Washington, including the
U.S. Forest Service, Bureau of Land Management, Bureau of Indian Affairs,
Oregon Department of Forestry, and Washington Department of Natural
Resources.
USDA Forest Service Proceedings RMRS-P-41. 2006.
In: Andrews, Patricia L.; Butler, Bret W.,
comps. 2006. Fuels Management—How to
Measure Success: Conference Proceedings.
28-30 March 2006; Portland, OR.
Proceedings RMRS-P-41. Fort Collins,
CO: U.S. Department of Agriculture,
Forest Service, Rocky Mountain Research
Station.
1 Weather Research and Consulting
S er v ice s , L L C , B at t le G rou nd ,
Washington. pwerth@prodigy.net
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Predicting Fire Season Severity in the Pacific Northwest
Data
The assumption that wildland fi re severity is primarily driven by low fuel
moisture has historically directed research towards drought (Westerling and
others 2002; Hall and Brown 2003) as a pre-season, predictor of fi re season
severity. This research also uses drought, but expands the list of potential
predictors to include: seasonal precipitation, mountain snowpack, snowmelt
date, and the sea surface temperature of the Pacific Ocean.
Monthly precipitation figures for seven weather stations in Washington and
Oregon were used in this analysis. The seven stations used were Medford,
Portland, Redmond, Burns and Pendleton in Oregon, and Yakima and
Spokane in Washington (fig. 1) They were selected based on their location
near fi re-prone areas and completeness of record since 1970. Monthly precipitation data was divided into four groups: (1) winter (November-March), (2)
spring (April-May), (3) June, and (4) summer (July-August). June is a group
by itself because precipitation during the month of June can significantly
impact the duration of significant fi re danger.
Snow pack water equivalency (SWE) data for the Columbia River Basin of
Washington, Oregon, Idaho and portions of British Columbia, Montana, and
Wyoming was also used in this analysis. The April 1 SWE is of particular importance because the snowpack typically peaks around April 1st. SWE figures
for May 1st were used to determine the rate of spring snowmelt in the mountains. SWE data was used to track the annual snowmelt date at 39 Natural
Resources Conservation Service (NRCS) SNOTEL sites in Washington and
Oregon from 1986 to 2005 (fig. 2). These sites represent every major river
basin and different elevations within Washington and Oregon.
Historic Palmer Drought Severity Index (PDSI) values for climate zones in
Washington and Oregon were collected from the National Climatic Data Center
(NCDC) database. Average March values for each state along with the number
of climate zones classified in moderate drought were used in this research.
Monitoring sea surface temperature anomalies in the central Pacific Ocean
is essential in determining the phases of the El Niño / Southern Oscillation
(ENSO). The warm phase, commonly called El Niño, is characterized by
abnormally warm sea surface temperatures in the central and eastern equatorial Pacific Ocean. The cool phase of this natural cycle is called La Niña.
El Niño often results in warm, dry winters and below normal snow packs in
the Pacific Northwest. La Niña has the opposite effect, producing cool, wet
winters and above average snow packs. Both phases appear to have minimal
effect on summer weather in the Pacific Northwest. The Multivariate ENSO
Index (MEI) combines six variables (sea-level pressure, zonal and meridianal
components of the surface wind, sea surface temperature, surface air temperature, and total cloudiness fraction of the sky) to monitor ENSO. Negative
values of the MEI represent the La Niña phase while positive values indicate
El Niño. Bi-monthly values of MEI were retrieved from the NOAA-CIRES
Climate Diagnostics Center in Boulder, Colorado.
The Eastern North Pacific (ENP) (fig. 3) sea surface temperature index is
a component of the Pacific (P) index (Castro, McKee, and Pielke 2001) that
combines tropical and North Pacific SSTs into one index. The P index has
been correlated with upper-level atmospheric circulation patterns over the
North Pacific Ocean and the Western and Central United States. It has also
been correlated to the onset of the Southwest Monsoon and precipitation
anomalies in the Great Plains states. Data to compute the ENP was downloaded from the Comprehensive Ocean Atmospheric Dataset (COADS).
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Predicting Fire Season Severity in the Pacific Northwest
Figure 1—Seasonal Precipitation Stations.
Werth
Figure 2—WA and OR SNOTEL Stations.
Figure 3—ENP and NINO 3.4 Pacific SST Regions.
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Area-burned figures (in acres) for federal- and state-protected land in
Washington and Oregon was obtained from the Oregon Department of
Forestry (ODF), Washington Department of Natural Resources (DNR),
and the Northwest Interagency Coordination Center annual summaries
dating back to 1970. The acres burned statistics include both lightning and
human-caused fi res.
Weather and Area Burned Trends
The fi rst step in determining the significance of seasonal precipitation on
fi re season severity in the Pacific Northwest is to determine whether there
are long-term trends in both weather and fi re data. This was accomplished
by constructing time lines for each dataset and then performing a regression analysis to determine whether there are identifiable trends in the data.
Linear regression equations were developed for each data set in the form of:
y = mx + b. The equation algebraically describes a straight line for a set of
data with (x) the independent variable, (y) the dependent variable, (m) the
slope of the line, and (b) the y-intercept. The sign (+ or –) and magnitude of
m signify whether the independent variable is increasing or decreasing and
at what rate.
Regression analysis indicates decreasing winter rainfall (November-March)
and Columbia River Basin April 1 SWE since 1970 (figs. 4 and 5). The decrease is more apparent in SWE, indicating warmer winter temperatures are
also a contributor in addition to decreased precipitation. However, the trend
in spring rainfall (April and May) is for wetter conditions (fig. 6). Rainfall
amounts for July and August also show a trend toward drier weather during
the summer in the Pacific Northwest (fig. 7).
Similar regression techniques were used to establish trends in acres burned
for federal and state land management agencies in Washington and Oregon.
All agencies trend toward more acres burned per year, especially since the
mid-1980s. This is most evident in the U.S. Forest Service data (fig. 8), which
shows the largest trend in acres burned of all the agencies.
Defining Fire Season Severity
Defi ning fi re season severity is a difficult question, one that may have many
answers. Some base it on the total number of fi res or the number of days
in high to extreme fi re danger; others use the number of large fi res during
the year. In order to predict fi re season severity, one must fi rst defi ne it. The
standard used in this research is the annual acres burned by fi re agency. The
dataset includes thirty-five years of annual acres burned by agency from 1970
to 2004. Data was sorted by agency and by year from the highest to the least
number of acres burned. Data was then divided into thirds, or terciles. Years
in the top tercile, (i.e., those with the largest number of acres burned,) were
classified as “Above Average” fi re seasons. Years in the middle tercile were classified as “Average” fi re seasons, and years in the bottom third were classified
as “Below Average” fi re seasons. This classification was performed for each
of the five federal and state fi re agencies. Threshold acres were identified for
each category as displayed in this graph for the Bureau of Land Management
(BLM) (fig. 9).
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Figure 4—Winter Precipitation Trend.
Figure 5—April 1 Columbia Basin Snowpack Trend.
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Figure 6—Spring Rainfall Trend.
Figure 7—Summer Rainfall Trend.
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Figure 8—USFS Acres Burned Trend.
Figure 9—Sorted BLM Acres Burned and Severity Thresholds.
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Analysis Methods
Various statistical techniques were used to determine which variables would
be the best predictors of fi re season severity. Polynomial regression analysis
was used to create multivariate forecast equations. Graphical regression was
also used in conjunction with contingency tables. All analysis was performed
using Microsoft Excel.
Multivariate Equations
The fi rst step in this process was to identify which variables (seasonal precipitation, snow pack SWE, spring snowmelt date, March PDSI, and Pacific
SSTs) were the best predictors of acres burned for each agency. Each variable
was ranked from best to worst based on its correlation (R-squared value)
with acres burned. Table 1 displays the rankings of each variable by agency.
Overall, summer rainfall (July/August) was the best predictor, with March
PDSI, April 1 SWE, and May 1 SWEs a close second. There were considerable differences in the predictor rankings by agency.
However, even the best predictors did not do a good job of forecasting acres
burned alone. Much better results were achieved when all the variables were
used. This was accomplished by creating multivariate (multiple regression)
equations unique to each fi re agency using all the variables. Each variable
was “weighted” according to its correlation factor. The equation forecasting
acres burned took the form y=a1(m1x12 +n1x1)+...+an(mnx n2 +nnx n)+b, where
(y) is the dependent variable (acres burned), (x1) through (xn) the independent
variables, (a1) through (an) are variable weighting factors, (m1,n1) through
(mn,nn) are coefficients of each independent variable, and (b) a constant.
The resulting equation predicts acres burned by fi re agency using either
observed or forecasted values as input for each independent variable.
Scatter Diagrams and Contingency Tables
A second method of predicting acres burned is the utilization of scatter
diagrams and contingency tables. This technique plots one variable against
the other (i.e., April 1 SWE versus Spring Precipitation) on an x-y scatter
diagram, and then labels the intersection of those two variables as either
an “Above Average” fi re season or not. In this manner, threshold values for
each variable can be constructed, dividing the diagram into “YES - high
probability” or “NO - low probability” risk areas of fi re season severity (fig.
10). The results from multiple scatter diagrams, correlating a selection of
variables, are then input into a 2-way YES / NO contingency table (fig. 11)
that predicts the probability of an “Above Average” fi re season and the range
of acres burned in similar years dating back to 1970.
Table 1—Correlation Factor Ratings by Agency.
Parameters
Winter Rain
Apri1 1 Snowpack
Spring Rain
June Rain
Summer Rain
March PDSI
April ENP
Snowmelt Date
May 1 Snowpack
764
USFS
BLM
BIA
ODF
WDNR
Ave Rank
9
5
4
3
1
6
2
6
6
7
3
5
7
1
6
7
3
2
8
5
2
4
1
6
9
2
6
3
4
5
8
5
1
7
9
2
3
5
7
8
1
2
9
4
5
6.00
4.40
4.60
6.00
1.80
4.20
6.80
4.80
4.20
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Predicting Fire Season Severity in the Pacific Northwest
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Figure 10—WA DNR May SWE vs June Rain.
Figure 11—USFS YES/NO Contingency Table.
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Results
The combination of scatter diagrams, contingency tables, and multivariate
equations produces the following outputs used to predict the severity of fi re
seasons in the Pacific Northwest:
• a defi nition of fi re season severity (above average, average, below average)
based on acres burned by fi re agency,
• the projected acres burned for the coming fi re season, and
• the probability of an “Above Average” fi re season.
The program is based on thirty-five years of weather and fi re data (1970 to
2004). The relatively small number of data points is near the minimum needed
to draw confidence in the statistical analysis. However, significant changes
in fi refighting strategy, resource availability, and wildland fuel regimes over
the years produce additional uncertainty if data from years prior to 1970 is
included. Thus, the current evaluation of how well the program performs is
based upon “dependent” rather than ‘independent” data. Statistics in future
years will be able to provide more relevant verification.
Accuracy rates indicate the program will produce correct forecasts of fire season
severity in Washington and Oregon in 70 to 85% of the years on which the data
was based. A forecast of an “Above Average” fire season should verify correctly
60 to 85% of the time, and a forecast of “average” or “below average” 85 to 90%
of the time. There appear to be better accuracy rates in predicting acres burned
in timber fuels compared to grass / brush fuels, which isn’t surprising when
considering the sensitivity of fire spread rates in grass fuels.
2006 Northwest Fire Season
Early projections of 2006 fi re season severity in Washington and Oregon
are based on correlations with past fi re seasons and the following factors: weak
La Niña conditions, a wet winter, lack of drought, an above normal snowpack,
and projected late spring snowmelt dates. Additional assumptions are that
spring and summer will experience “near normal” or “typical” precipitation
patterns (i.e., periodic rains through June, followed by dry weather during
July and August) and there will be an average amount of lightning.
Considering the above factors, it is highly unlikely that Washington and
Oregon will experience a severe fi re season in 2006. However, the threat of
large fi res will vary considerably by fuel type. Forest fuels in the mid and
higher elevations of the Cascade and Blue Mountains will have the lowest
probability of sustaining large fi re growth. The threat of large fi res will be
the highest in grass fuels, primarily in the “High Desert” of central and
southeastern Oregon. Other locations that may experience a greater chance of
large fi res are the pine forests along the lower eastern slopes of the Cascades
and the lower slopes of the Blue Mountains, where grass is the primary carrier of fi re. Table 2 displays the severity forecast for each of the five federal
and state agencies, as well as the projected acres burned.
In general, western Washington and western Oregon, including the crest of
the Cascades, will likely see a Below Average fi re season. Eastern Washington
can expect an Average fi re season. Eastern Oregon may also see a Below
Average fi re season in the Klamath Basin and most of the Blue Mountains.
Central and southeastern Oregon are projected to experience an Average to
Above Average fi re season (fig. 11).
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Table 2—Projected 2006 Acres Burned by Agency.
Threshold acres
burned for an
above average
fire season
2006
Fire
season
Probability of an
above average
fire season
Projected
2006 acres
burned
USFS
Average to
below average
10%
25,000 to
50,000 acres
120,000 acres
BLM
Average to
above average
40%
50,000 to
90,000 acres
90,000 acres
BIA
Average to
above average
30%
10,000 to
20,000 acres
20,000 acres
ODF
Average to
below average
10%
5,000 to
9,000 acres
14,000 acres
WADNR
Average to
below average
10%
4,000 to
9,000 acres
10,500 acres
Agency
Figure 12—Northwest Fire Season Severity.
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Predicting Fire Season Severity in the Pacific Northwest
Summary and Conclusions
Potential fi re season severity in the Pacific Northwest is projected using
statistical techniques correlating weather data and annual-acreage-burned
figures for five fi re management agencies in Washington and Oregon. Weather
and fi re trends for the period 1970 to 2004 are calculated. Thresholds for
above average, average, or below average fi re seasons were determined based
on annual acres burned. Eight weather parameters were correlated using
scatter diagrams, contingency tables, and multivariate regression equations
to predict above average, average, or below average fi re seasons based on
projected acres burned. Future modifications to this research may include
replacing existing variables with new and better variables, and the development
of equations that predict fi refighting costs and resource needs. Although this
research is specific to the Pacific Northwest, the concept of using multiple
predictors to forecast fi re season severity is adaptable to other areas, nationally and internationally.
Acknowledgments
The author thanks Brian Potter, AirFIRE Team Seattle WA, USDA Forest Service; Tony Westerling, University of California, San Diego; and John
Werth, National Weather Service for their helpful comments and suggestions
in the review of this paper.
References
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American Monsoon to Tropical and North Pacific Sea Surface Temperatures as
Revealed by Observational Analysis. J. Climate. 14: 4449-4473.
Hall, B. L.; Brown, T. J. 2003. A Comparison of Precipitation and Drought Indices
Related to Fire activity and Potential in the U.S. AMS Fifth Symposium on Fire
and Forest Meteorology. J11.3.
Westerling, A.L.; Gershunov, A.; Cayan, D. R.; Barnett, T. 2002. Long Lead
Statistical Forecasts of Area Burned in Western U.S. Wildfi res by Ecosystem
Province. International Journal of Wildland Fire. 11: 257-266.
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