Appendix D – Modeling Wildfires and Fire Severity
Appendix D – Modeling Large Stochastic Wildfires and Fire
Severity within the Range of the Northern Spotted Owl
Raymond Davis, Louisa Evers, Yanu Gallimore, Jena DeJuilio and C. Belongie
Introduction
Wildfire is a natural process within the identified range for the northern spotted owl (Strix occidentalis
caurina), especially in the southern and eastern portions of the range. While the bird has adapted to
wildfire and its effects in an intact landscape, human development and land use have reduced and
fragmented habitat and populations in large portions of the region (Davis and Lint 2005, Davis et al.
2011). One result has been to increase the potential for adverse effects of large, high severity wildfires on
remnant northern spotted owl habitats and populations. Over the past two decades, large wildfires have
accounted for the majority of northern spotted owl nesting and roosting habitat losses on federally
managed forests (Davis et al. 2011). In addition, fire suppression, inadequate levels of natural or
prescribed fire, and climate change are believed to have created conditions considered more favorable for
frequent, higher severity, and larger wildfires (Westerling et al. 2006, Littell et al. 2009, Dillion et al.
2011a, Miller et al. 2012).
In 2008, the Bureau of Land Management (BLM) attempted to revise six resource management plans in
western Oregon, although the decisions were subsequently withdrawn. A scientific review of that effort
noted that one significant weakness was the failure to account for the potential effects of high severity
wildfire on habitat for the northern spotted owl, a threatened species under the Endangered Species Act.
Specifically, the review stated that the models overestimated amounts of owl habitat and did not assume
that any would be lost to high severity wildfire during the projected modeling timeline (Drake et al.
2008). To address that weakness under the current planning effort, BLM assembled a team of northern
spotted owl experts, fire ecologists, silviculturists, and modelers to develop an approach to model the
occurrence of future wildfires and analyze their potential effects on northern spotted owl habitat and
populations.
This effort is also intended to support a U.S. Fish and Wildlife Service request that BLM evaluate
whether the resulting plan would provide sufficient habitat to assure persistence of the northern spotted
owl for the next 50 years. Estimating the quantity of habitat affected by fire over the next five decades is
needed to better inform the development of land management strategies for the BLM-managed lands in
western Oregon. This report describes the methods used to determine potential burned area and fire
severity and the results of that analysis. Subsequent modeling will evaluate the potential results on habitat
availability for the northern spotted owl. Since this analysis was conducted to directly support the analysis
of environmental effects in conjunction with an environmental impact statement, model parameters are
constrained by the “reasonably foreseeable” criteria in BLM’s planning regulations.
Study Area
The range of the northern spotted owl used in this analysis extends from the Canadian border through
northern California and from the west coast to the eastern foothills of the Cascade and Klamath Mountain
ranges. The BLM planning area for western Oregon comprises 19,647,000 acres, or approximately 34
percent, of the lands within this range and is located within the core of that range (Figure D-1), divided
among six Districts (Salem, Eugene, Roseburg, Coos Bay, Medford and Lakeview). The majority of
BLM-managed lands consist of so-called checkerboard (alternating square mile sections) largely
intermingled with privately owned industrial and non-industrial forests, along with state-owned lands, and
a limited amount of National Forest System lands and tribal lands. Large contiguous blocks of BLMmanaged lands are rare within the range of the northern spotted owl. The largest concentration of BLM1045 | P a g e
Appendix D – Modeling Wildfires and Fire Severity
managed lands in western Oregon occurs on Medford and Roseburg Districts in southwestern Oregon
(Figure D-1).
Figure D-1. Analysis area. We used the entire range of the northern spotted owl for the analysis area to
maintain consistency with the previous fire analyses conducted within the range of the northern spotted
owl.
Forest types within the study area range from dry mixed evergreen forests in California and southwestern
Oregon to temperate rainforests along the coast and in much of western Washington. The climate ranges
from maritime in western Washington, northwestern Oregon, and the coast to Mediterranean in
southwestern Oregon and northern California. Soils are highly variable in texture, depth, and other
characteristics and are largely derived from volcanic parent materials with ultramafic soils common in
southwestern Oregon.
Methods
We used the entire range of the northern spotted owl for our analytical framework to provide sufficient
data to capture the potential range of annual area burned and fire severity proportions to more accurately
reflect impacts to northern spotted owl habitat unaffected by arbitrary divisions along biologically
irrelevant lines, such as state, ownership, and administrative boundaries. The modeling regions used in
this analysis were similar to those used by the U.S. Fish and Wildlife Service (FWS) for the revised
Northern Spotted Owl Recovery Plan and designation of critical habitat (USDI FWS 2011 and 2012).
1046 | P a g e
Appendix D – Modeling Wildfires and Fire Severity
The median northern spotted owl territory size ranges from 1,300 to 11,800 acres, depending on
geographic location (Appendix B in Davis et al. 2011), thus it takes a rather large wildfire to have a
substantial effect on one owl territory (Davis and Lint 2005). We used the large wildfire suitability model
(LWSM) developed as part of the 15-year monitoring report for the Northwest Forest Plan (Chap. 4, in
Davis et al. 2011). This model was developed using large wildfires (• 1,000 acres) from 1970 through
2002, and validated against large wildfires that occurred from 2003 through 2009. The LWSM represents
a relative probability surface for large wildfire occurrence within the range of the northern spotted owl
that has continued to predict the locations of nearly all large wildfires that have occurred since 2009.
Using the regional wildfire history from 1970 through 2013 (4.4 decades), we modeled large wildfires
five decades into the future using a three-step process to determine; 1) number and location, 2) size
distribution, and 3) severity.
Step 1 - Estimating Number and Location of Future Large
Wildfires
Annual
200
Decade Total
183
160
120
95
83
80
39
40
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
0
1970
Number of Fires • 1,000 Acres
Large wildfire occurrence records from 1970 through 2013 showed a marked increased occurrence of
large wildfires in the last decade of this time record (Figure D-2). Whether this is a trend that will
continue to increase is uncertain, so for purposes of this analysis we used the decadal average of 100 to
generate 500 potential large wildfires over the next 5 decades. To do this, we used the “Generate Random
Points” tool in Geospatial Modeling Environment (GME version 0.7.2.1) software (Beyer 2012) to
produce five sets of randomly placed points (n=100) for each decade using the LWSM as a relative
probability surface for point placement. Points could occur anywhere, but were more likely to occur
where the probability of a large wildfire (a.k.a. wildfire suitability) was higher (Figure D-3).
Year
Figure D-2. Annual and decadal number of large wildfires (>1,000 acres) in the analysis area for 19702013. The numbers above the dark bars are decadal totals. While the decadal totals suggest the number of
large wildfires is increasing, the short period of record and the influence of the phase and annual sign of
the Pacific Decadal Oscillation are confounding factors in identifying a definite trend.
1047 | P a g e
Appendix D – Modeling Wildfires and Fire Severity
Figure D-3. Comparison of three decades of observed large wildfire history from MTBS fire occurrence
data (left) with the first three decades of randomly generated fire locations (right).
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Appendix D – Modeling Wildfires and Fire Severity
Reburning has been observed within the Northwest Forest Plan area on several occasions, for example,
the Biscuit Fire in 2002 reburned nearly all of the 1987 Silver Fire, and a portion of this area burned again
in 2013. Portions of the 1933 Tillamook Fire area reburned as much as five times before 1960. To account
for reburns we calibrated the model by comparing the area burned by projected large wildfires to the
actual area burned by past large wildfires. Initially, the model projected much more reburning than has
been observed. To correct for this over-prediction, we added a decadal constraint parameter on reburning
by preventing the placement of random points within 5 km of fire “perimeter” locations from the prior
decade. In subsequent decades, and consistent with historical observations, random points could occur
within or adjacent to previous modeled fire perimeters. Subsequent model runs produced similar levels of
acres burned and proportion of area reburned as the observed record.
Step 2 - Estimating Size of Future Large Wildfires
The majority of large wildfires within the study area burned less than 15,000 acres and only 1 percent of
them exceeded 100,000 acres in size (Figure D-4). Using this information, we created eight fire size bins.
We then calculated the median fire size for each bin and determined the appropriate radius to create a
circular fire perimeter of the median size (Table D-1). Because we were concerned more with the overall
potential loss of habitat than with accurately representing fire shapes, we simply represented wildfires as
circles associated with the median sizes. We then used the LWSM value for each random point to
determine fire size. The higher the underlying LWSM score, the more likely a random point would “burn”
more acres, although smaller fires could also occur in the higher probability areas.
Percentage of All Fires
45%
41%
30%
20%
15%
15%
9%
0%
1-2.5
2.5-5
5-10
7%
5%
10-15
15-25
25-50
Fire Size (u1,000 Acres)
2%
1%
50-100
>100
Figure D-4. Historical (1970-2013) large wildfire size distribution within the range of the northern
spotted owl. Most fires burn less than 2,500 acres while very few fires burn more than 100,000 acres.
Table D-1. Parameters used to assign random points a wildfire size by buffering the point location.
Number of
Buffer Radius
Simulated Wildfire Size
Random Points
(Miles)
(Acres)
41
0.79
1,250
20
1.37
3,750
15
1.93
7,500
9
2.55
12,500
7
3.15
20,000
5
4.32
37,500
2
6.11
75,000
1
7.05
100,000
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Appendix D – Modeling Wildfires and Fire Severity
By decade, beginning with random points with the lowest wildfire suitability score, we assigned the
smallest radius to each point based on Table D-1 until the last random point with the highest wildfire
suitability score was assigned the largest radius. Each point was then buffered by their assigned radii to
form the hypothetical fire perimeters. These individual or overlapping circles represented that decade’s
“footprint” of large wildfires. We repeated this process for each decade.
Step 3 - Estimating Fire Severity of Future Large Wildfires
Once we determined potential future wildfire locations and sizes, we estimated fire severities within their
perimeters. We relied on data from the Severe Fire Potential Map (Dillon et al. 2011a and 2011b, Dillon
et al. 2012) portion of the Fire Severity Mapping Tools (FIRESEV) project (Keane et al. 2013) to assign
fire severities within each decadal wildfire footprint. FIRESEV data reflect spatial predictions of the
conditional likelihood of high severity fire. These projections were based on empirical models relating
topographic, vegetation, and fire weather variables to wildfire severity observed on wildfires from 1984 to
2007, as mapped by the Monitoring Trends in Burn Severity (MTBS) program (Eidenshink et al. 2007).
The model’s spatial predictions were based on 90th percentile fuel moisture conditions for dryness,
although actual fuel moistures often varied over the spatial and temporal extent of any given large fire
(Dillon et al. 2011a and 2011b, Miller et al. 2012).
Since FIRESEV only estimated the probability for high severity fire, we classified this probability into
three quantile classes. We assumed that lower severity fires would occur in areas modeled as having a
lower probability for high severity fire and that high severity fires would most likely occur in areas
modeled as high probability. These three quantile classes served as our low, moderate, and high severity
map classification for assignment of fire severity to the fire footprints created in step 2. To test this
assumption, we compared relative proportions of observed wildfire severity (based on MTBS severity
mapping from 1986–2011) to the classified FIRESEV model from the five decadal maps. We found
similar proportions between observed and modeled wildfire severities indicating that the assumption was
a valid one and would produce proportions of area burned by low to high severity that were similar to the
observed record (Figure D-5).
1986-2011
Modeled
100%
Area (%)
80%
60%
57.6%
40%
21.6%
20.8%
20%
0%
Low-Unburned
Moderate
High
Fire Severity Class
Figure D-5. Comparison of annual proportion of area burned by mapped fire severity from MTBS data
from 1986 to 2011 with modeled severity based on a three quantile classification of the FIRESEV map
for the five decadal models. Labels show modeled estimates of proportion of area burned by severity
class.
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Appendix D – Modeling Wildfires and Fire Severity
Finally, we also examined the MTBS data for any obvious temporal trends in wildfire severity, but did
not detect a strong signal (Figure D-6). Over the course of 25 years, there appears to be a slight increase
in the percentage of area burned by low and moderate severity wildfire, and a slight decrease in the
percent of area burned in high severity wildfire, although these trends are not statistically significant. We
also noted that the variability in the amount of area burned in the different severity classes has declined
since about 2002, but are not certain why this apparent smoothing has occurred. Given the nonsignificance of the observed trends and the uncertainty over whether these slight trends will continue into
the future, we did not attempt to model any fire severity trends in our framework.
Low Severity
Moderate Severity
High Severity
2 Periods Moving Average (Low)
2 Periods Moving Average (Moderate)
2 Periods Moving Average (High)
100%
Total Area Burned (%)
80%
60%
40%
20%
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0%
Year
Figure D-6. Trends in area burned by fire severity class. Interannual variability in the amount of area
burned in the different severity classes was much greater prior to 2003. The cause(s) for declining
variability are not known. Linear trend analysis indicates non-significant trends towards decreasing
amount of high severity fire and increasing amount of low and moderate severity fire, with much of the
change coming after variability declined.
Results
We produced five decadal maps of potential large wildfire “footprints” over the entire range of the
northern spotted owl along with potential wildfire severities. Given an average of 100 large wildfires per
decade, our model estimated that approximately 4.4 million acres would burn within the range of the
northern spotted owl over the next 50 years with 10 percent of the area burning twice and 0.2 percent
burning three times. On BLM-managed lands only, the model estimated that approximately 192,000 acres
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Appendix D – Modeling Wildfires and Fire Severity
would burn with 10 percent burning twice and no areas burning three times. In comparison,
approximately 4.4 million acres have burned within the range of the northern spotted owl over the past 44
years (1970-2013) with 16 percent of the area burning twice and 1.6 percent burning three times. In that
same time span, approximately 153,500 acres burned on BLM-managed lands with 16 percent burning
twice. Both spatially (Figure D-7) and from the burned area comparisons above, the model produced a
plausible scenario based on recent observed wildfire history for potential future large wildfires both
rangewide and on BLM lands in western Oregon over the next five decades.
Figure D-7. Comparison of actual area burned (black shading) by large wildfires between 1970 to 2013
(left) with modeled large wildfires over five decades (right).
Discussion
For the given analysis period (5 decades), our model projected relatively minor changes in potential
burned area within the range of the northern spotted owl generally and on Oregon BLM-managed lands
within that range. While the observed decadal trends suggest an increasing trend in the number of large
fires over time (Figure D-2), it is not clear that this trend will continue to increase. The observed large
wildfire history records contain a small number of anomalous years that may distort the data. Particular
stand-out years are 1987, 2002, and 2009. The 1987 fire season was particularly severe in southwest
Oregon and northwest California, while 2002 was particularly severe in southwest Oregon and 2009
particularly severe in northwest California. All three years were characterized by an unusually high
number of wildfire starts and an unusually high number of acres burned. Miller et al. (2012) did find an
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Appendix D – Modeling Wildfires and Fire Severity
trend of increasing numbers of large fires in the Klamath Mountains of northwestern California, but this
same trend is not apparent in the analysis area as a whole (Littell et al. 2009, supplemental information).
We note that this decade is no quite half over and there have already been a recorded 67 large wildfires as
of 2013. It is possible that future decades might incur more than the 100 large wildfires per decade used in
this analysis; however, selection of a higher number would be speculative and the decision was made to
base the analysis on what has been observed in recent decades.
While several studies have indicated that high severity fires are increasing across the western United
States (Westerling et al. 2006, Dillon et al. 2011a, Miller et al. 2012), no such trends were apparent in the
observed record within the range of the northern spotted owl (Figure D-6). The observed trends in
increasing fire severity in various studies appear to be scale-dependent in that these trends were typically
for the western United States as a whole. Much of the observed change is either occurring in other areas
besides that are encompassed by the range of the northern spotted owl or becomes apparent only when
analyzing a larger area that provides a much larger sample size. In such cases, many small changes that
are difficult to detect at finer scales can add up to larger, detectable changes for the aggregate area,
reflective of how the aggregate number of small emissions of greenhouse gases cumulatively are affecting
global climate. In part, trends in the amount of area burned as high severity is a function of total area
burned – the more area burned, the greater the amount of high severity fire (Dillon et al. 2011a, Miller et
al. 2012). In the absence of any clear trends, the 50-year projection could fall within a reasonable range of
what should be expected.
Given the uncertainty surrounding trends in frequency, size, and severity, our model results may be
proven, with time, to either underestimate or overestimate potential fire sizes and severity because of
several confounding factors we did not include in the model, such as extreme weather events and
interactions with insect outbreaks, management affects to vegetation composition and structure, and
climate change. Forest management in particular has potential to alter the outcomes of wildfires ((Pollet
and Omi 2002, Prichard et al. 2010, Kennedy and Johnson 2014, Wimberly and Liu (in press), StevensRumann et al. 2013), although it is less clear if forest management can effectively alter the size
distribution of large fires (Cochrane et al. 2012). Historically, extremely large wildfires have occurred
outside of the areas modeled as highly suitable for large wildfires, consistently associated with either
extreme weather events, such as the severe drought and east wind event that preceded the initial
Tillamook Burn in 1933, or with heavy, continuous, dry fuels, such as following an insect outbreak
(McClure 2005, Morris 1935). The large wildfires that do occur in areas of low suitability west of the
Cascade crest tend to be infrequent, but extremely large and severe and typically associated severe
drought and high winds (Agee 1993, Littell et al. 2009, Davis et al. 2011). The above management affects
to vegetation, stochastic disturbance, extreme environmental variables, and fire occurrence datasets
reflective of long fire return interval timelines were not included in our modeling.
It was far less clear how to incorporate projected climate changes into our model. We can estimate how
the large wildfire suitability area may change as climate changes (Figure D-8) since LWSMs include
climate parameters. But to what extent these changes may influence the frequency of large wildfires is
uncertain. Additionally, large wildfires, particularly in moist forests, in the Pacific Northwest are at least
modestly associated with the phase of the Pacific Decadal Oscillation (PDO) and not associated with the
phase of El Niño-Southern Oscillation (ENSO) (Hessl et al. 2004, Gedalof et al. 2005). Interannual
variability within a given PDO phase appears to have a stronger influence than the interdecadal
variability, as well (Gedalof et al. 2005). Hessl et al. (2004) also found about a 5-year lag between PDO
and regional fire years in eastern Washington. The period of record used for this large wildfire analysis
does include the latter stages of a cool phase PDO that ended in about 1977 and a warm phase that began
in 1977 and appears to have ended around 2005 with considerable interannual variability between circa
1998 and 2005 (Gedalof et al. 2005, http://www.jisao.washington.edu/pdo/). Given that we have
apparently entered a cool phase PDO, we should expect to see a reduction in the number of fires and acres
1053 | P a g e
Appendix D – Modeling Wildfires and Fire Severity
burned for one or two decades unless the current climate forcing from greenhouse gas emissions “overrides” the PDO signal. The apparent increase in number of fires and acres burned may be more a
reflection of the combined influences of increasing fuel loadings due to land use changes, the PDO phase,
and the sign of PDO in a given year than of a trend useful for predicting future losses of northern spotted
owl habitat from wildfire.
Figure D-8. Comparison of a large wildfire suitability model based on the current climate normal (left)
with same model based on projected climate normal changes in temperature and precipitation by 2060
(right) (from Yang et al. in prep.).
Most climate change projections that discuss wildfire indicate that fires are expected to get larger and
more severe. Several studies have found that as the climate warms in forested ecosystems, burned area
increases (Westerling et al. 2006, Halofsky et al. 2011, Loudermilk et al. 2013) with large increases
projected by mid-century within the range of the northern spotted owl (McKenzie et al. 2004, Spracklen
et al. 2009, Littell et al. 2009 and 2010, Rogers et al. 2011). Many of these same studies indicate an
increase in overall fire severity as well. However, projections in burned area do not tell us how to adjust
the potential number of fires, the relative distribution of the size classes, or the proportion burned in the
different severity classes over time. If these projections are accurate, our model results could
underestimate the potential to adversely affect northern spotted owl habitat, particularly towards the end
of the analysis period.
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Appendix D – Modeling Wildfires and Fire Severity
Lastly, there is the problem that climate change is not linear. Natural variability in the climate system is
still an important factor. Thus, overall changes in burned area until mid-century would also not be linear.
We expect to continue to experience considerable variability in fire season severity (number of fires, acres
burned, and extent of high severity fire). Despite our inability to include these confounding factors, our
model successfully predicted the locations of many of the large wildfires that occurred in 2013 and which
were ultimately included in the final analysis.
Conclusions
Over the next 50 years, large wildfires will continue to affect suitable northern spotted owl
nesting/roosting habitat on BLM-managed lands. Wildfires do not always remove nesting/roosting
habitats; often low to moderate severity wildfire alters habitat such that it may still be suitable for nesting
and roosting. Some spotted owl studies show that low to moderate severity wildfire may actually benefit
the owl, perhaps due to changes in prey species habitat (Bond et al. 2002, Ganey et al. 2014). However,
extensive high severity wildfire usually removes nesting/roosting habitats, decreasing survival and
occupancy rates related to loss and fragmentation of suitable nesting and roosting habitat (Clark et al.
2011 and 2013, Tempel et al. in press).
Although the relationship between large wildfire frequency and severity on owl demography is not fully
understood, habitat loss was the primary reason for the bird’s decline and subsequent listing as
“threatened” under the Endangered Species Act (USDI FWS 1990). The map products produced from this
modeling effort will be input into the vegetation modeling process being used to inform the BLM on
habitat changes due disturbance and recruitment over the next five decades. The results of that modeling
in turn will be used in their northern spotted owl population modeling effort. The results of all these
modeling efforts will be used to make more informed management decisions on lands administered by the
Bureau of Land Management in western Oregon.
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