High-severity fire: Evaluating its key drivers and mapping its probability across western US forests

Abstract

Wildland fire is a critical process in forests of the western United States (US). Variation in fire behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire severity, however, is poorly understood. Here, we explore the drivers of high-severity fire for forested ecoregions in the western US over the period 2002–2015. Fire severity was quantified using a satellite-inferred index of severity, the relativized burn ratio. For each ecoregion, we used boosted regression trees to model high-severity fire as a function of live fuel, topography, climate, and fire weather. We found that live fuel, on average, was the most important factor driving high-severity fire among ecoregions (average relative influence = 53.1%) and was the most important factor in 14 of 19 ecoregions. Fire weather was the second most important factor among ecoregions (average relative influence = 22.9%) and was the most important factor in five ecoregions. Climate (13.7%) and topography (10.3%) were less influential. We also predicted the probability of high-severity fire, were a fire to occur, using recent (2016) satellite imagery to characterize live fuel for a subset of ecoregions in which the model skill was deemed acceptable (n=13). These ‘wall-to-wall’ gridded ecoregional maps provide relevant and up-to-date information for scientists and managers who are tasked with managing fuel and wildland fire. Lastly, we provide an example of the predicted likelihood of high-severity fire under moderate and extreme fire weather before and after fuel reduction treatments, thereby demonstrating how our framework and model predictions can potentially serve as a performance metric for land management agencies tasked with reducing hazardous fuel across large landscapes.

Full text

Environ. Res. Lett. 13 (2018) 044037 https://doi.org/10.1088/1748-9326/aab791
LETTER
High-severity fire: evaluating its key drivers and mapping
its probability across western US forests
Sean A Parks1,4, Lisa M Holsinger1, Matthew H Panunto2, W Matt Jolly2, Solomon Z Dobrowski3and
Gregory K Dillon2
1Aldo Leopold Wilderness Research Institute, Rocky Mountain Research Station, US Forest Service, 790 E. Beckwith Ave., Missoula, MT
59801, United States of America
2Missoula Fire Sciences Laboratory, Rocky Mountain Research Station, US Forest Service, 5775 Hwy 10 W, Missoula, MT 59808, United
States of America
3W.A. Franke College of Forestry and Conservation, Department of Forest Management, University of Montana, 32 Campus Dr.,
Missoula, MT 59812, United States of America
4Author to whom any correspondence should be addressed.
OPEN ACCESS
RECEIVED
8 December 2017
REVISED
7 March 2018
ACCEPTED FOR PUBLICATION
19 March 2018
PUBLISHED
18 April 2018
Original content from
this work may be used
under the terms of t he
Creative Commons
Attribution 3. 0 licence.
Any further distribution
of this work must
maintain attrib ution to
the author(s) and the
title of the work, journal
citation and DOI.
E-mail: sean_parks@fs.fed.us
Keywords: fire severity, wildland fire, burn severity, fuel, topography, climate, weather
Supplementary material for this article is available online
Abstract
Wildland fire is a critical process in forests of the western United States (US). Variation in fire
behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to
heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire
severity, however, is poorly understood. Here, we explore the drivers of high-severity fire for forested
ecoregions in the western US over the period 2002–2015. Fire severity was quantified using a
satellite-inferred index of severity, the relativized burn ratio. For each ecoregion, we used boosted
regression trees to model high-severity fire as a function of live fuel, topography, climate, and fire
weather. We found that live fuel, on average, was the most important factor driving high-severity fire
among ecoregions (average relative influence = 53.1%) and was the most important factor in 14 of 19
ecoregions. Fire weather was the second most important factor among ecoregions (average relative
influence =22.9%) and was the most important factor in five ecoregions. Climate (13.7%) and
topography (10.3%) were less influential. We also predicted the probability of high-severity fire, were
a fire to occur, using recent (2016) satellite imagery to characterize live fuel for a subset of ecoregions
in which the model skill was deemed acceptable (n=13).These‘wall-to-wall’gridded ecoregional
maps provide relevant and up-to-date information for scientists and managers who are tasked with
managing fuel and wildland fire. Lastly, we provide an example of the predicted likelihood of
high-severity fire under moderate and extreme fire weather before and after fuel reduction treatments,
thereby demonstrating how our framework and model predictions can potentially serve as a
performance metric for land management agencies tasked with reducing hazardous fuel across large
landscapes.
Introduction
Wildland fire is a critical natural disturbance and
ecological process in many ecosystems around the
globe, particularly in the forested regions of the west-
ern US (Agee 1993,Bondet al 2005). Fire affects a
wide range of ecosystem components and processes
such as post-fire successional trajectories, nutrient
cycling, hydrology, and carbon dynamics (Turner 2010,
McKenzie et al 2011,Larsonet al 2013). Wildland
fire often exhibits high inter-and intra-fire heterogene-
ity, generally burning with varying degrees of severity
(Lentile et al 2007) depending on fuel load, domi-
nant vegetation type, topography, climate, and weather
(Cansler and McKenzie 2014,Harveyet al 2016).
Fire severity is defined here as the amount of fire-
induced change to physical ecosystem components
such as vegetation and soil (Key and Benson 2006,
© 2018 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 13 (2018) 044037
Morgan et al 2014). The need to better understand
those factors controlling fire severity (e.g. Dillon
et al 2011) are invoked by concerns about public
safety, infrastructure, critical wildlife habitat, water-
shed health, and successional trajectories (e.g. Savage
and Mast 2005, Moody et al 2013,Calkinet al 2014).
Such concerns are heightened in forests with a legacy
of past logging and fire exclusion, where significant
shifts in ecosystem composition, structure, and func-
tion have triggered fuel conditions at greater risk for
high-severity fire (Mallek et al 2013, Hessburg et al
2015).
Over the last decade, our understanding of fac-
tors that influence fire severity has improved, but the
relative importance of these factors remains unclear.
Topography, for example, is clearly an influential fac-
tor (Holden et al 2009, Dillon et al 2011), as is the
amount and composition of live vegetation and dead
fuel (Fang et al 2015, Harris and Taylor 2015). A lim-
ited number of studies also indicate that long-term
climate (i.e. 30-year climate normals) is an impor-
tant factor driving fire severity (Parks et al 2014c,
Kane et al 2015a), although many suggest that cli-
mate likely has an indirect influence via its effect
on productivity and dominant vegetation type (e.g.
Miller and Urban 1999a, Pausas and Bradstock 2007,
Krawchuk et al 2009). Surprisingly, empirical evidence
is extremely varied pertaining to the importance of fire
weather as a driver of high-severity fire; some stud-
ies have shown that its influence is marginal (Fang
et al 2015,Birchet al 2015) whereas others have
concluded it is a highly influential factor (Keyser
and Westerling 2017,Lydersenet al 2017). Other
factors that influence fuel, such as vegetation man-
agement activities (Thompson et al 2007,Prichardand
Kennedy 2014) and the presence of previous wildland
fire (Parks et al 2014b,Stevens-Rumannet al 2016),
have also been shown to influence fire severity.
Research to date pertaining to the key drivers of
high-severity fire has been either comprehensive in
ecological scope but geographically limited, or geo-
graphically broad but lacking important environmental
components. Dillon et al (2011) conducted perhaps
the most comprehensive evaluation (in terms of geo-
graphic scope and number of fires) of the drivers of
high-severity fire using data from three ecoregions in
the northwestern US and three in the southwestern
US (∼1500 total fires). Dillon et al (2011), however,
did not evaluate some of the factors likely responsi-
ble for high-severity fire such as fuel, thereby making
it difficult to interpret their findings from an ecolog-
ical perspective. Keyser and Westerling (2017)also
conducted a comprehensive evaluation of fire sever-
ity in the western US, but their unit of analysis was
coarser—at the individual fire (i.e. fires were catego-
rized as either ‘high-severity’or ‘other’). Conversely,
most studies to date (and this study) evaluated pix-
els within individual fires as the unit of analysis,
thereby preserving and analyzing intra-fire variability.
Some studies have evaluated a more inclusive set of
environmental drivers but were often conducted at
disparate temporal and spatial scales, ranging from
those of individual fires (Thompson et al 2007,Har-
ris and Taylor 2015) to landscapes with ∼50–100 fires
(Fang et al 2015,Birchet al 2015), thereby making
broader-scale generalizations challenging. Differences
in methodology among these studies also compli-
cate interpretation. An evaluation using consistent
data and methods across the broad geographic range
of forested landscapes of the western US will allow
for an improved understanding of the most influ-
ential factors driving fire severity and will provide
forest managers with highly relevant information for
planning and mitigation purposes.
In this study, we assessed a comprehensive suite of
potential drivers of high-severity fire using a consistent,
repeatable approach that was not only geographically
extensive but also predictive in nature. We built a sta-
tistical model describing high-severity fire for each
ecoregion in the contiguous western United States
(hereafter western US) with the exception of those with
insufficient data (e.g. Sonoran Desert was excluded; see
Methods). We defined high-severity fire as those that
are stand-replacing as inferred by the relativized burn
ratio (RBR) (Parks et al 2014a), a gridded satellite-
based fire severity metric. Our evaluation included
explanatory variables representing live fuel, topog-
raphy, climate, and fire weather. The models we
developed have the potential to support fire and fuel
management (cf Hessburg et al 2007) because several
of the explanatory variables are dynamic (i.e. varying
on daily to annual time scales), such as those repre-
senting live fuel and daily fire weather. Consequently,
raster maps representing predictions of high-severity
fire (cf Holden et al 2009) can be updated annu-
ally and under different weather scenarios to assess,
for example, the potential for high-severity fire in
an upcoming fire season. Such products may facil-
itate the development of more adaptive strategies
for addressing the contemporary challenges of wild-
land fire management. Similarly, model predictions
have the potential to monitor and quantify potential
changes in the probability of high-severity fire result-
ing from management actions, such as fuel reduction
treatments.
Our overarching objectives were three-fold. First,
we aimed to identify the most influential factors driv-
ing high-severity fire for each ecoregion in the western
US. Second, we designed a quantitative framework
such that the model predictions for each ecore-
gion can be updated annually using recent (e.g.
2016) satellite imagery and implemented to evalu-
ate the probability of high-severity fire (were a fire
to occur) under a range of potential weather scenar-
ios. Third, we incorporated the capability for model
predictions to assess and monitor the effectiveness of
fuel treatments in changing the probability of high-
severity fire.
2

Environ. Res. Lett. 13 (2018) 044037
Figure 1. Ecoregions in the western US for which we built models describing the probability of high-severity fire.
Methods
This is an abridged version—see appendix A available at
stacks.iop.org/ERL/13/044037/mmedia for a detailed
description of the Methods.
Data
We built a statistical model describing high-severity
fire for each ecoregion in the western US (Olson and
Dinerstein 2002)(figure1). Fire severity was mea-
sured using the relativized burn ratio (RBR), a satellite
measure of fire severity (resolution = 30 m) that differ-
ences pre-and post-fire Landsat data. We classified the
RBR into binary categories representing high-severity
(RBR ≥298) and other severity (RBR <298) (Parks
et al 2014a). High-severity fire can be considered stand-
replacing fire in the context of this study.
We evaluated 16 explanatory variables in the model
for each ecoregion which can be categorized into four
groups representing live fuel, topography, climate, and
fire weather (table 1). The fuel group is comprised of
three satellite vegetation indices: NDVI, NDMI, and
EVI (table 1). These metrics implicitly incorporate
management activities and disturbances such as fuel
reduction treatments and wildland fire. Inclusion of
‘static’fuel metrics such as vegetation type or cover
(e.g. www.landfire.gov)(cfBirchet al 2015,Keyser
and Westerling 2017) was not considered since such
products are only updated periodically and are thus
not sensitive to annual dynamics. The variables rep-
resenting topography, climate, and fire weather are
summarized in table 1; see appendix A for further
details.
Sampling design and statistical models
We sampled individual 30 m pixels within fires that
occurred from 2002–2015. Each ecoregion was mod-
eled separately. We only sampled pixels identified as
forest (i.e. forest, woodland, and savanna). We removed
all pixels <100 m from the fire perimet er to reduce edge
effects common at fire boundaries (Stevens-Rumann
et al 2016). All analyses and predictions were con-
ducted using the native resolution of the response
variable (30 m). For each ecoregion, we used boosted
regression trees (BRT) using the ‘gbm’package in R to
model high-severity fire (binary response) as a func-
tion of live fuel, topography, climate, and fire weather
(table 1). A handful of ecoregions were not evaluated
because they contained a low proportion of forest or
did not have enough fire data (e.g. Sonoran Desert and
North Cascades ecoregion, respectively) (appendix A).
In an effort to reduce overfitting and build the most
parsimonious model for each ecoregion, we employed
a cross-validated stepwise procedure in which specific
variables were removed if they did not provide unique
information that improved model fit. Models for each
ecoregion were evaluated with five-fold cross validation
that was spatially and temporally structured such that
20% of fires (as opposed to pixels) within an ecore-
gion were held out in each iteration. Specifically, we
built a model for each ecoregion using the full suite
of variables (table 1) and evaluated it with the area
under curve (AUC) statistic derived from the receiver
operating characteristic curve as measured with the
’verification’package in R. We then built an addi-
tional set of models for each ecoregion in which each
explanatory variable was removed and calculated the
3

Environ. Res. Lett. 13 (2018) 044037
Table 1. Variables used as predictors in modeling the probability of high-severity fire in forests of the western US.
Group Variable name Description Source
Live fuel NDVI Normalized differenced vegetation index. Calculated using pre-fire
imagery distributed by the Monitoring Trends in Burn Severity
(MTBS) program (Eidenshink et al 2007).
Pettorelli et al (2005)
NDMI Normalized differenced moisture index. Calculated using pre-fire
imagery distributed by MTBS (Eidenshink et al 2007).
McDonald et al (1998)
EVI Enhanced vegetation index. Calculated using pre-fire imagery
distributed by MTBS (Eidenshink et al 2007).
Huete (2002)
Topography DISS Dissection index with a 450 meter radius. DISS is a measure of
topographic complexity.
Evans (1972)
TPI Topographic position index with a 2000 meter radius. TPI is a
measure of valley bottom vs. ridge top.
NA
SRAD Solar radiation, as calculated using the SOLPET6 model. Flint et al (2013)
Slope Slope angle NA
Climate CMD Climatic moisture deficit (Wang et al 2016). Mean over the
1981–2010 time period.
Wang et al (2016),
https://adaptwest.databasin.org/
ET Evapotranspiration (i.e. Eref—CMD). Mean over the 1981–2010
time period.
Wang et al (2016),
https://adaptwest.databasin.org/
T.sm Average summer temperature. Mean over the 1981–2010 time
period.
Wang et al (2016),
https://adaptwest.databasin.org/
Fire weather BI.day Burning index; a measure of fire intensity. Raw value converted to
per-pixel percentile.
Preisler et al (2016)
Jolly and Freeborn (2017)
ERC.day Energy release component; an index describing the amount of heat
released per unit area at the flaming front of a fire. Raw value
converted to per-pixel percentile.
Preisler et al (2016)
Jolly and Freeborn (2017)
Tmax.day Maximum daily temperature. Raw value converted to per-pixel
percentile.
Abatzoglou (2013)
HM.ann Heat moisture for the year in which the fire occurred. HM is
calculated as follows: (annual temperature +10) / (annual
precipitation/1000). Raw value converted to per-pixel z-score.
Climate NA software package;
Wang et al (2016)
Temp.ann Mean annual temperature for the year in which the fire occurred.
Raw value converted to per-pixel z-score.
Climate NA software package;
Wang et al (2016)
CMD.ann Climatic moisture deficit for the year in which the fire occurred.
Raw value converted to per-pixel z-score.
Climate NA software package;
Wang et al (2016)
AUC as previously described. If the cross-validated
AUC increased when any given variable was removed
from the model, it indicates that the model is over-
fit and that the variable does not provide any unique
information. In these cases, the variable that resulted in
the largest increase in AUC was permanently removed
and the process was repeated until all variables resulted
in a decreased AUC when removed from the model.
As such, all variables in the final models provided
unique information and ensured that our models
were spatially and temporally transferable.
Once the final model for each ecoregion was iden-
tified,therelativeinfluenceofvariablegroupswas
calculated using the AUC of a five-fold cross valida-
tion using a process that excluded all variables from
a particular group. Specifically, we compared the five-
fold cross validated AUC of the full model to models
that iteratively excluded all variables representing live
fuel, topography, climate, and fire weather. The specific
equation was as follows:
Relative inf luence𝑖=
AUC.f ull−AUC.no.var𝑖
∑𝑖=4
𝑖=1(AUC.f ull−AUC.no.var𝑖)× 100
where AUC.full was the AUC of the full model,
AUC.no.var was the AUC of the model excluding any
particular variable group, and irepresented one of the
four variable groups.
Model implementation and map production
From the BRT models, we produced wall-to-wall
raster maps (objective 2) depicting the probability of
high-severity fire, if a fire were to occur, for each
ecoregion in which the cross-validated AUC ≥0.70.
For the fuel inputs (NDVI, NDMI, and EVI), satellite
imagery from 2016 spanning the entirety of each ecore-
gion was obtained using Google Earth Engine (GEE;
https://developers.google.com/earth-engine/). Conse-
quently, these raster predictionsrepresent fairly current
fuel conditions across each ecoregion. Predictions the-
oretically range from zero to one and depict the
probability of high-severity fire.
We aimed to produce these severity predictions rep-
resenting the average weather conditions under which
fires burn. This is somewhat challenging, however,
given that weather is spatially and temporally dynamic.
Consequently, we produced 100 initial predictions and
varied the weather for each of these predictions; all
other inputs across each ecoregion (fuel from 2016,
topography, and climate) were held static. To vary
the weather, we randomly selected 100 records from
our fire severity datasets. Each record represents one
burned pixel with a unique combination of observed
4

Environ. Res. Lett. 13 (2018) 044037
Table 2. Cross-validated AUC and the relative influence for each of the four groups of variables used to model the probability of high-severity
firein19ecoregionsinthewesternUS.
Relative influence
Region ID Ecoregion name Cross-validated AUC Live fuel Topography Climate Weather
1 Okanagan 0.66 40.5 14.4 29.9 15.2
2 Columbia Plateau 0.67 35.3 3.1 17.4 44.2
3 East Cascades 0.68 60.7 12.4 5.6 21.2
4 West Cascades 0.71 40.7 2.3 14.6 42.5
5 Klamath 0.68 38.8 25.0 0 36.2
6 Sierra Nevada 0.67 58.7 15.7 19.1 6.5
7 California North Coast 0.70 5.1 19.6 9.1 66.2
8 California Central Coast 0.73 64.6 22.3 13.1 0
9 California South Coast 0.72 40.6 9.4 0 50.1
10 Canadian Rockies 0.71 53.9 14.0 24.0 8.1
11 Northern Great Plains 0.69 43.4 9.7 29.3 17.6
12 Middle Rockies 0.72 46.2 14.8 34.9 4.1
13 Utah-Wyoming Rockies 0.75 99.0 1.0 0 0
14 Great Basin 0.76 63.6 7.0 17.6 11.8
15 Southern Rockies 0.72 57.4 12.5 22.9 7.3
16 Utah High Plateaus 0.76 93.2 5.1 0.2 1.5
17 Colorado Plateau 0.81 39.0 6.9 2.1 52.0
18 Arizona-New Mexico Mountains 0.79 75.0 0.2 9.7 15.0
19 Apache Highlands 0.75 53.3 1.0 9.9 35.9
AVERAGE 0.72 53.1 10.3 13.7 22.9
fire weather. We used the observed fire weather from
each random record for each of the 100 initial predic-
tions. We then averaged the 100 initial predictions over
each 30 m pixel, resulting in one raster map depict-
ing the probability of high-severity fire under average
weather conditions in which fires burn. An important
consideration here is that the severity predictions do
not represent ‘average weather conditions’,butthe
‘average weather conditions under which fires burn’.
That is, because fires often burn under more extreme
fire weather, our predictions implicitly incorporate
weather associated with high fire activity. This consid-
eration also pertains to our mapped predictions under
moderate and extreme fire weather, as described in the
next paragraph.
For those ecoregions in which the relative influ-
ence of fire weather ≥15%, we produced two additional
raster maps, one depicting the probability of high-
severity fire under conditions representing moderate
weather and the other under conditions representing
extreme weather. To do so, we calculated the 50th and
95th percentile for each pixel outofthe100previ-
ously described initial predictions. While these maps
represent the 50th and 95th percentile in predicted
outcomes for each pixel, we use them to represent
the outcomes of moderate and extreme fire weather,
respectively. Neither says anything specific about the
percentile of weather conditions under which they
occurred, but they can be interpreted as resulting
from moderate and extreme fire weather conditions.
To illustrate how our models can potentially be used
to monitor changes in the probability of high-severity
fire due to fuel treatments (objective 3), we made pre-
and post-treatment predictions using the BRT model
from the Arizona—New Mexico Mountains ecoregion.
We obtained imagery representing the live fuel vari-
ables using GEE for the years 2007 (pre-treatment)
and 2011 (post-treatment). Again, we produced two
sets of predictions for each time period (pre-and post-
treatment) representing moderate and extreme fire
weather, as previously described.
Results
We incorporated data from over 2000 fires across all
ecoregions to describe and explain the probability of
high-severity fire (appendix B). On average, the BRT
models performed moderately well for the 19 ecore-
gions for which we modeled (table 2). The average
spatially and temporally independent cross-validated
AUC statistic was 0.72 and ranged from 0.66 (Okana-
gan) to 0.81 (Colorado Plateau). Following Mason and
Graham (2002), all five cross-validated models were
statistically significant (p<0.01) for each of the 19
ecoregions.
Although there was substantial variation across
ecoregions (table 2), live fuel was the most impor-
tant variable group, with an average relative influence
of 53.1% among ecoregions; this ranged from 5.1%
(California North Coast) to 99.0% (Utah—Wyoming
Rockies). Fire weather was the second most influential
variable group (22.9% average), ranging from 0% (Cal-
ifornia Central Coast and Utah – Wyoming Rockies) to
66.2% (California North Coast). Climate was the third
most influential variable group (13.7% average) and
topography the least influential (10.3% average) (table
2). The cross-validated variable selection approach
reduced overfitting and produced parsimonious mod-
els (i.e. all variables provided unique information)
(table 3).
Raster maps depicting the probability of high-
severity fire were built for the 13 ecoregions in which
the cross-validated AUC ≥0.70 (figure 2; appendix C).
These gridded probabilities represent fuel conditions
(i.e. as measured with Landsat imagery) in 2016 and
5

Environ. Res. Lett. 13 (2018) 044037
Table 3. Final models for each ecoregion. Variables were selected through a cross-validated stepwise procedure to ensure that each variable
provides unique information and improves the cross-validated AUC (see Methods).
Region ID Ecoregion name Fuel Topography Climate Weather
1Okanagan EVI
NDMI
Slope ET Tmax.day
2ColumbiaPlateauNDVI
EVI
NDMI
DISS
TPI
SRAD
Slope
CMD
ET
T.sm
Tmax.day
ERC.day
HM.ann
Temp.ann
3 East Cascades NDVI
EVI
NDMI
DISS
TPI
SRAD
Slope
T.sm ERC.day
Tmax.day
HM.ann
4WestCascadesNDVI
EVI
NDMI
DISS
TPI
Slope
ET
T.sm
ERC.day
HM.ann
5Klamath EVI
NDMI
DISS
TPI
Slope
—BI.day
ERC.day
HM.ann
Temp.ann
6 Sierra Nevada NDMI DISS
SRAD
Slope
ET
T.sm
ERC.day
Tmax.day
HM.ann
CMD.ann
Temp.ann
7 California North Coast NDVI
EVI
NDMI
DISS
TPI
SRAD
T.sm Tmax.day
HM.ann
8 California Central
Coast
NDVI
EVI
NDMI
DISS
TPI
Slope
T.sm —
9 California South Coast NDVI
EVI
NDMI
DISS
Slope
—HM.ann
CMD.ann
Temp.ann
10 Canadian Rockies EVI
NDMI
DISS
SRAD
Slope
T.sm BI.day
Tmax.day
HM.ann
11 Northern Great Plains NDVI
NDMI
DISS
TPI
SRAD
Slope
CMD
ET
T.sm
BI.day
ERC.day
12 Middle Rockies NDVI
EVI
NDMI
DISS
SRAD
Slope
CMD
ET
T.sm
BI.day
ERC.tay
Temp.day
13 Utah-Wyoming
Rockies
NDVI
EVI
DISS — —
14 Great Basin NDVI
EVI
DISS
SRAD
Slope
CMD
T.sm
HM.ann
CMD.ann
Temp.ann
15 Southern Rockies NDVI
EVI
NDMI
DISS
SRAD
Slope
ET
T.sm
HM.ann
CMD.ann
Temp.ann
16 Utah High Plateaus NDVI
EVI
NDMI
DISS
TPI
SRAD
Slope
T.sm ERC.day
Tmax.day
17 Colorado Plateau NDVI
NDMI
DISS
SRAD
Slope
ET ERC.day
Tmax.day
HM.ann
Temp.ann
6

Environ. Res. Lett. 13 (2018) 044037
Table 3. Continued.
Region ID Ecoregion name Fuel Topography Climate Weather
18 Arizona-New Mexico
Mountains
EVI
NDMI
TPI CMD
ET
BI.day
Tmax.day
HM.ann
CMD.ann
19 Apache Highlands NDVI
EVI
TPI CMD Tmax.day
CMD.ann
Figure 2. Maps depict the probability of high -severity fire (were a fire to occur) in the Canadian Rockies (a)andSouthernRockies(b)
ecoregions. We used satellite imagery from 2016 to represent live fuel. See figure 1to reference ecoregion locations. Predictions for
other ecoregions available in appendix C.
average weather conditions under which fires burn
and show substantial spatial variability in the prob-
ability of high-severity fire. For ecoregions in which
the relative influence of weather ≥15% (n=6), we
produced two additional raster maps depicting the
probability of high-severity fire under conditions rep-
resenting moderate and extreme fire weather (figure 3;
appendix D).
Maps of pre-and post-treatment predictions pro-
vide an example of how our models and approach can
potentially be used to quantify and monitor changes
in the probability of high-severity fire due to fuel
treatments (figure 4). This example shows that, under
conditions representing both moderate and extreme
fire weather, there is an overall reduction in the proba-
bility of high-severity fire within treatment units.
Discussion
High-severity fire is often of high ecological and soci etal
consequence, thereby motivating increasing attention
and research towards better understanding its drivers
and distribution (Cansler and McKenzie 2014,Whit-
man et al 2015,Morganet al 2017, Reilly et al 2017).
Research to date has been either comprehensive in
ecological scope but geographically limited or geo-
graphically broad but capturing only a subset of the
key elements affecting fire severity. Our study expands
upon these previous investigations of fire severity by (a)
including a more complete suite of relevant explanatory
variables, (b) evaluating fires over a large geographic
extent (i.e. forests of the western contiguous US) at
fine spatial resolution (30 m), and (c) including a high
number of fires in our models (n= 2061 unique fires
among all ecoregions; appendix B). Our results show
that fuel is the most important driver of high-severity
fire in forested regions of the western US, followed by
fire weather, climate (i.e. 30 year normals), and topog-
raphy. Our results are supported by the findings of past
research but also contrast with several previous stud-
ies (see below) and provide important new insights
regarding the drivers of high-severity fire. Our study is
also a substantial step forward by providing a modellin g
framework that enables the prediction for high-severity
fire while incorporating fuel and fire weather inputs.
In particular, this framework involves the inclusion of
fuel and fire weather inputs as dynamic variables (i.e.
those that change over time) and gives us the ability
to produce maps depicting the probability of high-
severity fire, were a fire to occur, over entire ecoregions
(e.g. figures 2and 3). This framework also provides
the means to evaluate changes in the probability of
high-severity fire due to fuel treatments (e.g. figure 4).
Live fuel, as measured with Landsat vegetation
indices, was on average the most important group of
7

Environ. Res. Lett. 13 (2018) 044037
Figure 3. Maps depict the probability of high-severity fire (were a fire to occur) for the West Cascades ecoregion under weather
conditions representing moderate (i.e. 50th percentile prediction) (a) and extreme (i.e. 95th percentile prediction) (b). We used
satellite imagery from 2016 to represent live fuel. See figure 1to referen ce ecoregion location. Prediction s for other ecoregions available
in appendix D.
Figure 4. Example shows pre-and post-t reatment predictions (top and botto mrow, respecti vely) of the probability of high -severity fire
under moderate (50th percentile prediction) (a)and(b) and extreme (95th percentile prediction) (c)and(d) fire weather conditions
on the Apache-Sitgreaves National Forests, Arizona, USA. Treatment units are represented by the solid black outlines. All treatments
are commercial th inning that occurred in 2010 or 2011.
8

Environ. Res. Lett. 13 (2018) 044037
variables driving high-severity fire and was the most
important group in 14 out of 19 ecoregions. This find-
ing provides valuable insight pertaining to the ongoing
debateastowhetherfuelorfireweatheraremore
important in driving fire severity (cf Thompson and
Spies 2009). Whereas some studies found fuel was
more important (Fang et al 2015,Birchet al 2015,
Harris and Taylor 2015), others concluded weather
was more important (Bessie and Johnson 1995,Brad-
stock et al 2010, Price and Bradstock 2010). We found
that live fuel was 2.3 times (on average) more influen-
tial than fire weather across the 19 ecoregions in the
western US we analyzed (but see ‘caveats’section).
This finding is not trivial in terms of management
efforts to reduce fire severity because land man-
agers can control fuel via fuel treatments, prescribed
fire, and managed wildland fire (formerly termed
wildland fire use) but cannot control fire weather.
Our study found that fire weather was, on average,
the second most important variable group driving high-
severity fire. Previous studies have reported somewhat
conflicting findings pertaining to the relative influence
of fire weather in driving fire severity in the western
US. Whereas some studies found weather to be mod-
erately to highly influential (e.g. Collins et al 2007,
Lydersen et al 2017), others found that the influence
of weather was marginal to negligible (e.g. Kane et al
2015a, Harris and Taylor 2015). We posit that the vari-
ability we observed pertaining to the influence of fire
weather among ecoregions could partly explain the
divergent findings of previous studies: the relative infl u-
ence of fire weather ranged from 0% to 66.2% and
was the most important variable group in five out of
the 19 ecoregions we analyzed. Although our results
show that weather was less influential in driving high-
severity fire than fuel, its influence was important in
most ecoregions and should not be discounted in terms
of managing fuel and fire. For example, the maps we
generated (e.g. figure 3) clearly show that the proba-
bility of high-severity fire is reduced under moderate
vs. extreme fire weather.
On average, climate ranked as the third most influ-
ential variable group. This contrasts with some previous
studies. For example, Kane et al (2015a)foundthat
climate was highly influential in driving fire severity
in the Sierra Nevada. However, we suspect that cli-
mate was less important in our study because, over
broad spatial and temporal extents, climate provides
an indirect measure of fuel associated with inherent
biophysical environments (Parks et al 2014c). More
specifically, biomass amount is known to vary along
climatic gradients (Meyn et al 2007,Krawchukand
Moritz 2011), which implies that climate can serve as
an indirect surrogate for biomass. However, satellite-
derived vegetation indices such as those used in this
study are a more direct measure of biomass (Zhao et al
2005). Consequently, when fuel and climate are both
included as variables (as was done in our study), cli-
mate is ranked as less important. This said, climate
was a non-negligible factor in most ecoregions. We
suggest, as do others (Miller and Urban 1999b), that
climate may indirectly measure factors that were not
well accounted for by our variables. We believe that cli-
mate may correspond to dominant vegetation type, in
that climate promotes particular physiognomic vegeta-
tion types and species that are more or less susceptible
to fire (Parks et al 2018). For example, cooler and wet-
ter climates are more likely to support species that are
more susceptible to fire-induced mortality (e.g. Engel-
mann spruce), whereas warmer and drier climates are
more likely to support species that can survive fire (e.g.
ponderosa pine) (Lutz et al 2010).
Nearly every fire severity study to date has found
that topography had a moderate to high influence on
fire severity (e.g. Holden et al 2009, Dillon et al 2011,
Fang et al 2015,Kaneet al 2015a,Birchet al 2015,
Estes et al 2017). Conversely, our study indicates that
topography is on average the least important variable
group. We posit that topography is an indirect mea-
sure of fuel, and that because we directly account for
fuel (using satellite-derived vegetation indices), topog-
raphy is deemed a relatively unimportant factor. It is
worth noting that many of these previously mentioned
studies do not incorporate any measure of fuel or vege-
tation into theiranalyses (Holden et al 2009, Dillon et al
2011,Kaneet al 2015b), and consequently, the influ-
ence of topography may be unintentionally elevated.
For example, even though Dillon et al (2011)found
topography to be the strongest driver of fire severity
across large regions of the western US, they clearly stated
that topography was serving as a proxy for variation in
fuel and bioclimatic variables (i.e. fuel moisture and
temperature) which were not accounted for in their
study. Since we capture such variability in live fuel
using satellite-derived vegetation indices, the influence
of topography on its own is greatly diminished.
Caveats
There are several difficulties associated with building
statistical models describing fire severity across broad
geographic regions. Our ability to characterize fuel, for
example, was limited to satellite indices that gener-
ally characterize overstory vegetation and have limited
capacity to measure live and dead surface and ladder
fuels known to drive fire behavior and effects (Rother-
mel et al 1972,ScottandBurgan2005). Our ability to
adequately characterize fire weather was also limited.
For example, we estimated the day at which any given
pixel burned using MODIS fire detection data. These
day-of-burning estimates are not without error (Parks
2014, Veraverbeke et al 2014); this error increases
uncertainty and likely diminishes our ability to char-
acterize the influence of weather. Also, the temporal
and spatial resolution of currently available gridded
weather datasets do not necessarily match the real-
ized spatial and temporal weather variability associated
with any given fire (Wagenbrenner et al 2016). Fires
have even been known to generate their own weather
9

Environ. Res. Lett. 13 (2018) 044037
(Potter 2012) and gridded weather datasets do not and
will not likely be able to account for such phenomenon
in the foreseeable future.
It is also worth noting that our data is potentially
biased due to undersampling of non-extreme weather
conditions, thereby limiting our ability to completely
capture the full range of weather conditions conducive
to fire and to completely characterize the influence
of fire weather. Specifically, fire suppression success
rates are higher under less-than-extreme weather con-
ditions (Arienti et al 2006, Fernandes et al 2016,
Beverly 2017), thereby reducing the amount of area
burned under more moderate weather; this biases the
weather associated with our fire severitydata. For exam-
ple, we found that the relative influence of weather was
zero in two ecoregions; this result is more likely an arti-
fact of biased data (and the other caveats mentioned
in this section) than an unconditional representation
of the relative influence of weather. Simply put, we
potentially mischaracterized the relative influence of
weather since we could not sample the full range of
weather under which fire can burn. We also suggest
that more data (i.e. fires) are needed in some ecore-
gions to better characterize the influence of fire weather
and other variables (e.g. the coastal ecoregions in
California; see appendix B).
Management implications
Managing for wildland fire has become incredibl y com-
plex as we face the nexus of increasingly large and
intense wildfires linked to a warming climate and more
frequent drought, landscapes with heavy fuel accumu-
lations due to prolonged fire exclusion, and a rapid
expansion of the wildland-urban interface (Littell et al
2016,Stephenset al 2016, Schoennagel et al 2017). Land
management agencies have a daunting challenge to
reduce risks from fire to communities and fire fighters
while simultaneously restoring forests to more resilient
conditions (www.forestsandrangelands.gov)(Barnett
et al 2016). In response, land management agencies
in the US established a long-term fuel reduction pro-
gram in which millions of hectares have been treated
since 2001 using a variety of methods such as mechan-
ical thinning and prescribed burning (US Congress
2003). Various efforts are underway to assess how
to best focus such fuel reduction activities given that
land management agencies have limited resources. In
particular, spatially explicit planning frameworks have
offered an effective means to strategize locating treat-
ments across landscapes (e.g. Ager et al 2016,Scott
et al 2016). These planning frameworks are often
built on spatial assessments of quantitative wildfire
risk that incorporate the probability of wildfire occur-
rence across a range of simulated fire intensities, and
the effects of fire on specific values at risk (e.g. nat-
ural resources, built assets) (Finney 2005,Scottet al
2013). We suggest that the modeling framework in
this study could complement these efforts and allow
predictions of high-severity fire to be integrated with
fire occurrence and behavior predictions to provide
managers with a more comprehensive set of risk-
analysis information to target locations in wildfire
mitigation planning.
We also suggest that our models and the result-
ing predictions of high-severity fire could potentially
serve as a performance metric for evaluating hazardous
fuel treatments (see figure 4). For example, the US
Forest Service often uses ‘acres treated’as a perfor-
mance measure, but this measure does not capture
anything about whether treatment objectives have been
met (USDA Forest Service 2016). Specifically, the pri-
mary objective of most hazardous fuel treatments is to
reduce the intensity and resulting severity of potential
wildland fires (Hudak et al 2011,USDAOIG2016).
Some treatments are quantitatively more effective at
achieving this objective than others (Wimberly et al
2009,Hudaket al 2011, Safford et al 2012). Further-
more, although detailed fuel treatment assessments
have been conducted at the stand (Johnson et al 2011,
Noonan-Wright et al 2014) and landscape scale (Vail-
lant et al 2009, Collins et al 2013), consistent and
long-term monitoring methods have yet to be realized.
The protocols developed in this study offer a means
to provide predictions that are objective, consistent,
updateable, spatially detailed (30 m resolution), and
spatially extensive as a measurable benchmark to char-
acterize changes in the potential for high-severity fire.
We acknowledge, however, that substantial financial
resources would be necessary to implement our frame-
work to monitor the potential for high-severity fire,
but such a tool is essential if not timely for filling a key
information gap in fire management on public lands
in the US and elsewhere.
Conclusions
Fuel is on average the most influential factor driving
high-severity fire in forests of the western US. Con-
sequently, efforts to reduce fuel will likely reduce the
potential for high-severity fire (Pollet and Omi 2002,
Stephens et al 2009,Arkleet al 2012). Our results also
indicate that fire weather has a substantial influence on
fire severity and highlight that the probability of high-
severity fire is reduced under conditions representing
moderate vs. extreme fire weather. This finding, when
considered with the fact that fire suppression is more
effective under less-than-extreme fire weather (Arienti
et al 2006, Beverly 2017), underscores that land man-
agement agencies may be paradoxically selecting for
high-severity fire by aggressively suppressing fire (cf
Calkin et al 2015). Simply put, aggressive fire sup-
pression reduces the occurrence of low severity fire,
thereby increasing fuel on the landscape and select-
ing for higher severity fire when the inevitable fire
occurs. This has substantial ecological and social con-
sequences, particularly for dry forests that historically
experienced low-and mixed-severity fire. For example,
10

Environ. Res. Lett. 13 (2018) 044037
fire-facilitated conversions from dry forest to non-
forest vegetation (shrubland and grassland) are now
evident, but it is important to note that such con-
versions appear to be triggered only by high-severity
fire (not low severity) (Savage and Mast 2005, Coop
et al 2016, Coppoletta et al 2016). Consequently, to
limit the probability of high-severity fire, fire-facilitated
conversions to non-forest, and altered successional tra-
jectories in dry forests (Johnstone et al 2016,Walker
et al 2018), land managers could consider, in addi-
tion to traditionalfuel reduction treatments, expanding
opportunities that allow wildland fires to burn under
less-than-extreme weather conditions.
Data accessibility statement
All ecoregional fire severity predictions shown in
appendices C and D are available for down-
load in a georeferenced raster format through the
Fire Research and Management Exchange System
(FRAMES; www.frames.gov/NextGen-FireSeverity).
Acknowledgments
We acknowledge funding from the Joint Fire Sci-
ence Program under project 15-1-3–20 and from
the National Fire Plan through the Rocky Moun-
tain Research Station. We thank two anonymous
reviewers whose feedback substantially improved this
manuscript. We also thank Diana Olson and Michael
Tjoelker (University of Idaho) for facilitating the dis-
tribution of our mapped severity predictions through
the Fire Research and Management Exchange System
(FRAMES; www.frames.gov/NextGen-FireSeverity).
ORCID iDs
Sean A Parks https://orcid.org/0000-0002-2982-
5255
W Matt Jolly https://orcid.org/0000-0002-0457-6563
References
Abatzoglou J T 2013 Development of gridded surface
meteorological data for ecological applications and modelling
Int. J. Climatol. 33 121–31
Agee J K 1993 Fire Ecology of Pacific Northwest Forests
(Washington, DC: Island Press)
Ager A A, Day M A, Short K C and Evers C R 2016 Assessing the
impacts of federal forest planning on wildfire risk mitigation in
the Pacific Northwest, USA Landscape Urban Plan. 147 1–17
Arienti M C, Cumming S G and Boutin S 2006 Empirical models
of forest fire initial attack success probabilities: the effects of
fuels, anthropogenic linear features, fire weather, and
management Can. J. Forest Res. 36 3155–66
Arkle R S, Pilliod D S and Welty J L 2012 Pattern and process of
prescribed fires influence effectiveness at reducing wildfire
severity in dry coniferous forests Forest Ecol. Manage. 276
174–84
Barnett K, Parks S A, Miller C and Naughton H T 2016 Beyond
fuel treatment effectiveness: characterizing interactions
between fire and treatments in the US Forests 7237
Bessie W C and Johnson E A 1995 The relative importance of fuels
and weather on fire behavior in subalpine forests Ecology 76
747–62
Beverly J L 2017 Time since prior wildfire affects subsequent fire
containment in black spruce Int. J. Wildland Fire 26 919–29
Birch D S, Morgan P, Kolden C A, Abatzoglou J T, Dillon G K,
Hudak A T and Smith A M S 2015 Vegetation, topography
and daily weather influenced burn severity in central Idaho
and western Montana forests Ecosphere 6art17
Bond W J, Woodward F I and Midgley G F 2005 The global
distribution of ecosystems in a world without fire New Phytol.
165 525–38
Bradstock R A, Hammill K A, Collins L and Price O 2010 Effects of
weather, fuel and terrain on fire severity in topographically
diverse landscapes of south-eastern Australia Landscape Ecol.
25 607–19
Calkin D E, Cohen J D, Finney M A and Thompson M P 2014
How risk management can prevent future wildfire disasters in
the wildland-urban interface Proc. Natl Acad. Sci. 111 746–51
Calkin D E, Thompson M P and Finney M A 2015 Negative
consequences of positive feedbacks in US wildfire
management Forest Ecosyst. 29
Cansler C A and McKenzie D 2014 Climate, fire size, and
biophysical setting control fire severity and spatial pattern in
the northern Cascade Range, USA Ecol. Appl. 24 1037–56
Collins B M, Kelly M, van Wagtendonk J W and Stephens S L 2007
Spatial patterns of large natural fires in Sierra Nevada
wilderness areas Landscape Ecol. 22 545–57
Collins B M, Kramer H A, Menning K, Dillingham C, Saah D, Stine
P A and Stephens S L 2013 Modeling hazardous fire potential
within a completed fuel treatment network in the northern
Sierra Nevada Forest Ecol. Manage. 310 156–66
Congress U S 2003 Healthy forests restoration act of 2003. HR1904
Coop J D, Parks S A, Mcclernan S R and Holsinger L M 2016
Influences of prior wildfires on vegetation response to
subsequent fire in a reburned southwestern landscape Ecol.
Appl. 26 346–54
Coppoletta M, Merriam K E and Collins B M 2016 Post-fire
vegetation and fuel development influences fire severity
patterns in reburns Ecol. Appl. 26 686–99
DillonGK,HoldenZA,MorganP,CrimminsMA,HeyerdahlEK
and Luce C H 2011 Both topography and climate affected
forest and woodland burn severity in two regions of the
western US, 1984–2006 Ecosphere 2art130
Eidenshink J C, Schwind B, Brewer K, Zhu Z-L, Quayle B and
Howard S M 2007 A project for monitoring trends in burn
severity Fire Ecol. 33–21
Estes B L, Knapp E E, Skinner C N, Miller J D and Preisler H K
2017 Factors influencing fire severity under moderate burning
conditions in the Klamath Mountains, northern California,
USA Ecosphere 8e01794
Evans I S 1972 General geomorphometry, derivatives of altitude,
and descriptive statistics Spatial Anal. Geomorphology 17–90
Fang L, Yang J, Zu J, Li G and Zhang J 2015 Quantifying influences
and relative importance of fire weather, topography, and
vegetation on fire size and fire severity in a Chinese boreal
forest landscape Forest Ecol. Manage. 356 2–12
Fernandes P M, Pacheco A P, Almeida R and Claro J 2016 The role
of fire-suppression force in limiting the spread of extremely
large forest fires in Portugal Eur. J. Forest Res. 135 253–62
Finney M A 2005 The challenge of quantitative risk analysis for
wildland fire Forest Ecol. Manage. 211 97–108
Flint A L, Flint L E, Hevesi J A and Blainey J B 2013 Fundamental
concepts of recharge in the desert southwest: A regional
modeling perspective Groundwater Recharge in a Desert
Environment: The Southwestern United States (Washington,
DC: American Geophysical Union) pp 159–84
Harris L and Taylor A H 2015 Topography, fuels, and fire
exclusion drive fire severity of the rim fire in an old-growth
mixed-conifer forest, Yosemite National Park, USA
Ecosystems 18 1192–208
11

Environ. Res. Lett. 13 (2018) 044037
Harvey B J, Donato D C and Turner M G 2016 Drivers and trends
in landscape patterns of stand-replacing fire in forests of the
US Northern Rocky Mountains (1984–2010) Landscape Ecol.
31 2367–83
Hessburg P F et al 2015 Restoring fire-prone Inland Pacific
landscapes: seven core principles Landscape Ecol. 30 1805–35
Hessburg P F, Reynolds K M, Keane R E, James K M and Salter R B
2007 Evaluating wildland fire danger and prioritizing
vegetation and fuels treatments Forest Ecol. Manage. 247 1–17
Holden Z A, Morgan P and Evans J S 2009 A predictive model of
burn severity based on 20 year satellite-inferred burn severity
data in a large southwestern US wilderness area Forest Ecol.
Manage. 258 2399–406
HudakAT,RickertI,MorganP,StrandE,LewisSA,RobichaudP,
Hoffman C and Holden Z A 2011 Review of fuel treatment
effectiveness in forests and rangelands and a case study from
the 2007 megafires in central Idaho USA Gen. Tech. Rep.
RMRS-GTR-252 (Fort Collins, CO: US Department of
Agriculture, Forest Service, Rocky Mountain Research
Station)
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G
2002 Overview of the radiometric and biophysical
performance of the MODIS vegetation indices Remote Sens.
Environ. 83 195–213
Johnson M C, Kennedy M C and Peterson D L 2011 Simulating
fuel treatment effects in dry forests of the western United
States: testing the principles of a fire-safe forest Can. J. Forest
Res. 41 1018–30
Johnstone J F et al 2016 Changing disturbance regimes, ecological
memory, and forest resilience Front. Ecol. Environ. 14 369–78
Jolly W M and Freeborn P H 2017 Towards improving wildland
firefighter situational awareness through daily fire behaviour
risk assessments in the US Northern Rockies and Northern
Great Basin Int. J. Wildland Fire 26 574–86
KaneVR,CanslerCA,PovakNA,KaneJT,McGaugheyRJ,Lutz
J A, Churchill D J and North M P 2015a Mixed severity fire
effects within the rim fire: relative importance of local climate,
fire weather, topography, and forest structure Forest Ecol.
Manage. 358 62–79
Kane V R, Lutz J A, Alina Cansler C, Povak N A, Churchill D J,
Smith D F, Kane J T and North M P 2015b Water balance and
topography predict fire and forest structure patterns Forest
Ecol. Manage. 338 1–13
Key C H and Benson N C 2006 Landscape assessment (LA).
FIREMON: Fire effects monitoring and inventory system Gen.
Tech. Rep. RMRS-GTR-164-CD Fort Collins, CO US
Department of Agriculture, Forest Service, Rocky Mountain
Research Station
Keyser A and Westerling A 2017 Climate drives inter-annual
variability in probability of high-severity fire occurrence in the
western United States Environ. Res. Lett. 12 065003
Krawchuk M A and Moritz M A 2011 Constraints on global fire
activity vary across a resource gradient Ecology 92 121–32
KrawchukMA,MoritzMA,ParisienMA,VanDornJandHayhoe
K 2009 Global pyrogeography: The current and future
distribution of wildfire PLoS ONE 45102
Larson A J, Travis Belote R, Alina Cansler C, Parks S A and Dietz M
S 2013 Latent resilience in ponderosa pine forest: effects of
resumed frequent fire Ecol. Appl. 23 1243–9
Lentile L B, Morgan P, Hudak A T, Bobbitt M J, Lewis S A, Smith A
and Robichaud P 2007 Post-fire burn severity and vegetation
response following eight large wildfires across the western
United States Fire Ecol. 391–108
Littell J S, Peterson D L, Riley K L, Liu Y and Luce C H 2016 A
review of the relationships between drought and forest fire in
the United States Glob. Change Biol. 22 2353–69
Lutz J A, van Wagtendonk J W and Franklin J F 2010 Climatic
water deficit, tree species ranges, and climate change in
Yosemite National Park J. Biogeogra. 37 936–50
LydersenJM,CollinsBM,BrooksML,MatchettJR,ShiveKL,
Povak N A, Kane V R and Smith D F 2017 Evidence of fuels
management and fire weather influencing fire severity in an
extreme fire event Ecol. Appl. 27 2013–30
Mallek C, Safford H, Viers J and Miller J 2013 Modern departures
in fire severity and area vary by forest type, Sierra Nevada and
southern Cascades, California, USA Ecosphere 4art153
Mason S J and Graham N E 2002 Areas beneath the relative
operating characteristics (ROC) and relative operating levels
(ROL) curves: statistical significance and interpretation Q. J.
R. Meteorol. Soc. 128 2145–66
McDonald A J, Gemmell F M and Lewis P E 1998 Investigation of
the utility of spectral vegetation indices for determining
information on coniferous forests Remote Sens. Environ. 66
250–72
McKenzie D, Miller C and Falk D A 2011 The Landscape Ecology of
Fire (Berlin: Springer)
Meyn A, White P S, Buhk C and Jentsch A 2007 Environmental
drivers of large, infrequent wildfires: the emerging conceptual
model Prog. Phys. Geog. 31 287–312
Miller C and Urban D L 1999a A model of surface fire, climate and
forest pattern in the Sierra Nevada, California Ecol. Model. 114
113–35
Miller C and Urban D L 1999b Forest pattern, fire, and climatic
change in the Sierra Nevada Ecosystems 276–87
Moody J A, Shakesby R A, Robichaud P R, Cannon S H and Martin
D A 2013 Current research issues related to post-wildfire
runoff and erosion processes Earth-Sci. Rev. 122 10–37
Morgan P, Hudak A T, Wells A, Parks S A, Baggett L S, Bright B C
and Green P 2017 Multidecadal trends in area burned with
high-severity in the selway-bitterroot wilderness area
1880–2012 Int. J. Wildland Fire 26 930–43
Morgan P, Keane R E, Dillon G K, Jain T B, Hudak A T, Karau E C,
Sikkink P G, Holden Z A and Strand E K 2014 Challenges of
assessing fire and burn severity using field measures, remote
sensing and modelling Int. J. Wildland Fire 23 1045–60
Noonan-Wright E K, Vaillant N M and Reiner A L 2014 The
effectiveness and limitations of fuel modeling using the fire
and fuels extension to the forest vegetation simulator Forest
Sci. 60 231–40
Olson D M and Dinerstein E 2002 The Global 200: priority
ecoregions for global conservation Ann. Mo. Bot. Gard. 89
199–224
Parks S A 2014 Mapping day-of-burning with coarse-resolution
satellite fire-detection data Int. J. Wildland Fire 23 215–23
Parks S A, Dillon G K and Miller C 2014a A new metric for
quantifying burn severity: the relativized burn ratio Remote
Sens. 61827–44
Parks S A, Holsinger L M, Miller C and Parisien M-A 2018
Analog-based fire regime and vegetation shifts in
mountainous regions of the western US Ecography in press
Parks S A, Miller C, Nelson C R and Holden Z A 2014b Previous
fires moderate burn severity of subsequent wildland fires in
two large western US wilderness areas Ecosystems 17
29–42
Parks S A, Parisien M A, Miller C and Dobrowski S Z 2014c Fire
activity and severity in the western US vary along proxy
gradients representing fuel amount and fuel moisture PLoS
ONE 9e99699
Pausas J G and Bradstock R A 2007 Fire persistence traits of plants
along a productivity and disturbance gradient in
mediterranean shrublands of south-east Australia Glob. Ecol
Biogeogr. 16 330–40
Pettorelli N, Vik J O, Mysterud A, Gaillard J-M, Tucker C J and
Stenseth N C 2005 Using the satellite-derived NDVI to assess
ecological responses to environmental change Trends Ecol.
Evol. 20 503–10
Pollet J and Omi P N 2002 Effect of thinning and prescribed
burning on crown fire severity in ponderosa pine forests Int. J.
Wildland Fire 11 1–10
Potter B E 2012 Atmospheric interactions with wildland fire
behaviour–II. Plume and vortex dynamics Int. J. Wildland Fire
21 802–17
Preisler H K, Riley K L, Stonesifer C S, Calkin D E and Jolly W M
2016 Near-term probabilistic forecast of significant wildfire
events for the Western United States Int. J. Wildland Fire 25
1169–80
12

Environ. Res. Lett. 13 (2018) 044037
Price O F and Bradstock R A 2010 The effect of fuel age on the
spread of fire in sclerophyll forest in the Sydney region of
Australia Int. J. Wildland Fire 19 35
Prichard S J and Kennedy M C 2014 Fuel treatments and landform
modifylandscapepatternsofburnseverityinanextremefire
event Ecol. Appl. 24 571–90
Reilly M J, Dunn C J, Meigs G W, Spies T A, Kennedy R E, Bailey J
D and Briggs K 2017 Contemporary patterns of fire extent
and severity in forests of the Pacific Northwest, USA
(1985–2010) Ecosphere 8e01695
Rothermel R C et al 1972 A mathematical model for predicting fire
spread in wildland fuels
Safford H D, Stevens J T, Merriam K, Meyer M D and Latimer A M
2012 Fuel treatment effectiveness in California yellow pine
and mixed conifer forests Forest Ecol. Manage. 274
17–28
Savage M and Mast J N 2005 How resilient are southwestern
ponderosa pine forests after crown fires? Can. J. Forest Res. 35
967–77
Schoennagel T et al 2017 Adapt to more wildfire in western North
AmericanforestsasclimatechangesProc. Natl Acad. Sci. 114
4582–90
Scott J H and Burgan R E 2005 Standard fire behavior fuel models:
a comprehensive set for use with Rothermel’ssurfacefire
spread model. USDA Forest Service, Rocky Mountain
Research Station, General Technical Report RMRS-GTR-153
Scott J H, Thompson M P and Calkin D E 2013 A wildfire risk
assessment framework for land and resource
management. General Technical Report RMRS GTR 315 US
Department of Agriculture Forest Service Rocky Mountain
Research Station
Scott J H, Thompson M P and Gilbertson-Day J W 2016 Examining
alternative fuel management strategies and the relative
contribution of National Forest System land to wildfire risk to
adjacent homes–A pilot assessment on the Sierra National
Forest, California, USA Forest Ecol. Manage. 362 29–37
Stephens S L, Collins B M, Biber E and Ful´
e P Z 2016 US federal
fire and forest policy: emphasizing resilience in dry forests
Ecosphere 7e01584
Stephens S L et al 2009 Fire treatment effects on vegetation
structure, fuels, and potential fire severity in western US
forests Ecol. Appl. 19 305–20
Stevens-Rumann C, Prichard S, Strand E and Morgan P 2016 Prior
wildfires influence burn severity of subsequent large fires Can.
J. Forest Res. 46 1375–85
Thompson J R and Spies T A 2009 Vegetation and weather explain
variation in crown damage within a large mixed-severity
wildfire Forest Ecol. Manage. 258 1684–94
Thompson J R, Spies T A and Ganio L M 2007 Reburn severity in
managed and unmanaged vegetation in a large wildfire Proc.
Natl Acad. Sci. USA 104 10743–8
Turner M G 2010 Disturbance and landscape dynamics in a
changing world Ecology 91 2833–49
USDA Forest Service 2016 Fiscal year 2017 budget justification
USDA OIG 2016 Forest service wildland fire activities––hazardous
fuels reduction Report 08601-0004041
Vaillant N M, Fites-Kaufman J A and Stephens S L 2009
Effectiveness of prescribed fire as a fuel treatment in
Californian coniferous forests Int. J. Wildland Fire 18 165–75
Veraverbeke S, Sedano F, Hook S J, Randerson J T, Jin Y and Rogers
B M 2014 Mapping the daily progression of large wildland
fires using MODIS active fire data Int. J. Wildland Fire 23
655–67
Wagenbrenner N S, Forthofer J M, Lamb B K, Shannon K S and
Butler B W 2016 Downscaling surface wind predictions from
numerical weather prediction models in complex terrain with
WindNinja Atmos. Chem. Phys. 16 5229–41
Walker R B, Coop J D, Parks S A and Trader L 2018 Fire regimes
approaching historic norms reduce wildfire-facilitated
conversion from forest to non-forest Ecosphere in press
Wang T, Hamann A, Spittlehouse D and Carroll C 2016 Locally
downscaled and spatially customizable climate data for
historical and future periods for North America PLoS ONE 11
e0156720
Whitman E, Batllori E, Parisien M-A, Miller C, Coop J D,
Krawchuk M A, Chong G W and Haire S L 2015 The climate
space of fire regimes in north-western North America J.
Biogeogra. 42 1736–49
Wimberly M C, Cochrane M A, Baer A D and Pabst K 2009
Assessing fuel treatment effectiveness using satellite imagery
and spatial statistics Ecol. Appl. 19 1377–84
Zhao M, Heinsch F A, Nemani R R and Running S W 2005
Improvements of the MODIS terrestrial gross and net primary
production global data set Remote Sens. Environ. 95 164–76
13