fire

Article

A Classification of US Wildland Firefighter

Entrapments Based on Coincident Fuels, Weather,

and Topography

Wesley G. Page *, Patrick H. Freeborn, Bret W. Butler and W. Matt Jolly

USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Lab, Missoula, MT 59808, USA;

patrick.h.freeborn@usda.gov (P.H.F.); bret.butler@usda.gov (B.W.B.); matt.jolly@usda.gov (W.M.J.)

* Correspondence: wesley.g.page@usda.gov; Tel.: +1-406-329-4821

Received: 12 September 2019; Accepted: 5 October 2019; Published: 9 October 2019





Abstract: Previous attempts to identify the environmental factors associated with firefighter

entrapments in the United States have suggested that there are several common denominators.

Despite the widespread acceptance of the assumed commonalities, few studies have quantified

how often entrapments actually meet these criteria. An analysis of the environmental conditions at

the times and locations of 166 firefighter entrapments involving 1202 people and 117 fatalities that

occurred between 1981 and 2017 in the conterminous United States revealed some surprising results.

Contrary to general assumptions, we found that at broad spatial scales firefighter entrapments happen

under a wide range of environmental conditions, including during low fire danger and on flat terrain.

A cluster-based analysis of the data suggested that entrapments group into four unique archetypes

that typify the common environmental conditions: (1) low fire danger, (2) high fire danger and steep

slopes, (3) high fire danger and low canopy cover, and (4) high fire danger and high canopy cover.

There are at least three important implications from the results of this study; one, fire environment

conditions do not need to be extreme or unusual for an entrapment to occur, two, the region and site

specific context is important, and, three, non-environmental factors such as human behavior remain a

critical but difficult to assess factor in wildland firefighter entrapment potential.

Keywords: burnover; fire behavior; fire environment; firefighter fatalities

1. Introduction

Wildland firefighters in the United States (US) are trained to identify and monitor particular

elements of the fire environment in order to assess possible dangers or potential suppression

opportunities. Much of the training curriculum that describes what specific aspects of the fire

environment to monitor was developed based on the findings from investigations conducted after

fatal firefighter entrapment incidents (e.g., [1–3]). Some of the findings from those investigations have

also been distilled into safety guidelines for the purpose of helping firefighters recognize dangerous

situations [4–7]. An understanding of the shared factors associated with firefighter entrapments has

been an important aspect of firefighter training at least since Wilson [5] presented a set of common

denominators based on an analysis of 67 fatal and 31 near-fatal fires that occurred in the US between

1926 and 1976 (see below). More recent analyses have acknowledged additional common denominators

related to US firefighter entrapments and wildland firefighter fatalities in general, which include

high fire danger, communication issues, and a failure to identify adequate escape routes and safety

zones [8,9].

Wilson [5] identified four major common denominators of fire behavior on fatal and near-fatal

fires:

Fire 2019, 2, 52; doi:10.3390/fire2040052 www.mdpi.com/journal/fire
Fire 2019, 2, 52 2 of 23

1. On relatively small fires or deceptively quiet sectors of large fires.

2. In relatively light fuels, such as grass, herbs, and light brush.

3. When there is an unexpected shift in wind direction or in wind speed.

4. When fire responds to topographic conditions and runs uphill.

Many of the safety guidelines currently used in the US for assessing entrapment potential

emphasize the monitoring of specific environmental variables before and during suppression

operations [6]. Table 1 displays example fuel, topographic, and weather factors with their respective

indicators that are used for determining possible fire behavior-related dangers [10]. Many of the

elements listed in Table 1 are associated with the potential for extreme fire behavior, including rapid

rates of spread and high fire intensities [11,12]. Extreme fire behavior, as well as what is often referred

to as ‘blow-up’ [13] or ‘eruptive’ [14] fire behavior, has been linked to firefighter fatalities both in the

US and other parts of the world [15–19]. It is thought that a sudden, unexpected increase in rate of

spread associated with eruptive fire behavior, particularly on steep slopes and in canyons, limits the

ability of firefighters to adapt or escape, thereby increasing the likelihood of entrapment [20–22].

Table 1. Fire environment factors and a partial list of their respective indicators used to assess

fire behavior-related dangers as identified in the Look Up, Look Down, Look Around training

curriculum [10] and outlined by National Wildfire Coordinating Group [6].

Factor Indicator

Fuel Characteristics

• Substantial amounts of cured or curing fine fuel/continuous

• Heavy dead and down

• Tight crown spacing (<20 ft [6 m])

• Unusual low live and dead fuel moisture values (locally defined)

Topography

• Steep slopes (>45% [24◦])

• Chutes/chimneys/passes/saddles

• Box and narrow canyons

Weather

• Wind (speeds above 10 mi h−1[16 km h−1], battling or shifting wind)

• Atmospheric instability (good visibility, inversion lifting)

• Temperature and relative humidity (above normal temperatures,

critically low humidity based on local thresholds)

Although the environmental elements related to firefighter entrapments appear to be well

characterized, few studies have examined if or how often entrapments conform to these criteria. Part of

the problem has been that, until recently, quantitative data related to firefighter entrapments have not

been widely available. Page et al. [21] have compiled a database of firefighter entrapments involving

burnovers that occurred within the conterminous US (CONUS) since 1979, which includes the location,

date, time, number of people involved, number of fatalities, and quality of the location information for

entrapments reported from all known sources. The date and location information contained within

this database allows entrapments to be linked to historical spatial layers of various environmental data

so that a comprehensive dataset of the fire environment during the day of the entrapment and at the

entrapment location can be compiled.

The primary purpose of this study is to assemble a dataset of environmental conditions at the

times and locations of all known firefighter entrapments involving a burnover that occurred within

CONUS and assess any commonalities. It is hypothesized that the majority of entrapments will have

fire environment conditions that are similar to those previously reported in other published work,

which are indicative of the potential for extreme fire behavior, including high fire danger, steep slopes

in narrow canyons, and in light fuel types (i.e., grass and shrub). It is hoped that such an assessment

will not only confirm or refute previously established guidelines but also establish a methodology for

conducting similar assessments in the future.
Fire 2019, 2, 52 3 of 23

2. Materials and Methods

2.1. Firefighter Entrapments

All known firefighter entrapments that occurred within CONUS from 1979 to 2017 were obtained

from the Fire Sciences Lab Merged Entrapment Database (https://www.wfas.net/entrap/, accessed

on 17 July 2019) [21]. Those entrapments listed as burnovers (excluding near-misses and close calls)

that had accurate location information were extracted from the full dataset and used for subsequent

analysis. The data fields of interest included the latitude and longitude of the entrapment location, the

incident name, the date and time that the entrapment occurred, the total number of people involved,

the number of fatalities, the incident phase (management activity) during the entrapment, and the type

of resource(s) involved. The incident phase was categorized as: (i) initial attack, the time when the

first resources arrived on the fire to begin operations, usually within the first 24 h after detection and

response, (ii) extended attack, if initial attack forces fail to contain or control the fire, it transitions to

extended operations that may last for several weeks, and (iii) prescribed fire, those incidents where the

fire was intentionally ignited to meet various objectives in accordance with all applicable laws and

regulations [23]. Note that if prescribed fires were declared escaped at the time of entrapment, then the

incident phase was categorized as initial or extended attack. The type of resource(s) involved were

defined as follows: (i) Dozer, tracked vehicles with a front mounted blade usually used to construct

mineral soil fireline, (ii) Engine, a vehicle with water, hose, a pump, and crewmembers, which is used

to suppress fires, (iii) Equipment, includes other miscellaneous vehicles used to construct fireline, such

as tractor plows, (iv) Hand Crew, a group of individuals who are organized and supervised as a team

for the purpose of conducting wildland fire operations, and (v) Overhead, includes personnel assigned

in supervisory positions [23]. Those instances where more than one resource type was involved in an

entrapment were categorized as involving multiple resources.

2.2. Environmental Variables

Several dynamic (varying over time) and static environmental datasets representing different fuel,

topographic, and fire danger conditions were collected for the day and location that each entrapment

occurred. Initially, a large set of environmental variables and transformations of those variables

were included in the analysis. These encompassed the various climatic factors available from the

Parameter-elevation Regressions on Independent Slopes Model (PRISM) [24], the topographic and fuel

information from the Landscape Fire and Resource Management Tools (LANDFIRE) [25], Normalized

Difference Vegetation Index (NDVI) data from the NOAA Center for Satellite Applications and

Research [26], and historical fire danger information from Jolly et al. [27]. Additional transformations

of those variables were also initially included in the analysis, such as ratios of slope steepness and

elevation within different sized buffers around the entrapment locations, relative greenness [28], and

variables for assessing the shape of the landscape, such as the Topographic Position Index [29]. Not

surprisingly, many of the environmental variables were highly correlated (e.g., relative greenness vs.

proportion of maximum NDVI, climate class vs. average annual precipitation, and slope steepness

vs. standard deviation of slope/elevation), thus only those variables that had Pearson’s correlation

coefficients of less than 0.7 were retained for further analysis (Table 2).

In this study, surrogates for the fire weather and fuel conditions at the times and locations of the

firefighter entrapments were utilized. Daily fire weather was summarized using fire danger indices

from the National Fire Danger Rating System (NFDRS) [30], including the Energy Release Component

(ERC) and the Burning Index (BI), rescaled to their local, historical percentile values (ERC’ and BI’,

respectively). The rescaled fire danger indices were utilized as opposed to the raw fire weather variables

(e.g., relative humidity) for several reasons. Normalized fire danger indices are comparable across

locations with different fuel types and climatologies, they do not require high temporal resolution (i.e.,

less than daily) of the underlying individual weather variables that are often not available for historical
Fire 2019, 2, 52 4 of 23

time periods, and wildland firefighters are familiar with their interpretation and already utilize them

to make fire management decisions.

Table 2. Environmental variables used to assess commonalties among US wildland firefighter

entrapments across the conterminous United States (CONUS). Dynamic variables vary either daily, i.e.,

the National Fire Danger Rating System (NFDRS) components [30], or weekly, i.e., the Normalized

Difference Vegetation Index (NDVI) proportion of maximum values. Some of the data were calculated

using a Geographic Information System (GIS).

Variable Description Source

Dynamic

Burning Index percentile

(BI’)

NFDRS output designed to represent the difficulty of controlling

a fire, numerically it is equivalent to potential flame length.

Daily, CONUS-wide, 4 km gridded values from a 39-yr

climatology (1979–2017) have been normalized and converted to

percentiles assuming a constant fuel model (G). Potential values

are in decimal form and range of 0 to 1 where 1 is the

100th percentile.

Jolly et al. [27]

Energy Release

Component percentile

(ERC’)

NFDRS output numerically equivalent to the potential available

energy released per unit area in the flaming zone. Daily,

CONUS-wide, 4 km gridded values from a 39-yr climatology

(1979–2017) have been normalized and converted to percentiles

assuming a constant fuel model (G). Potential values are in

decimal form and range from 0 to 1 where 1 is the

100th percentile.

Jolly et al. [27]

Proportion of maximum

NDVI (propMax)

Smoothed NDVI values from across CONUS (4 km grid spacing)

that are produced weekly based on data from the AVHRR 1

(1981–2012) and VIIRS 2(2013–2017) satellites. The gridded

weekly values are divided by the maximum value obtained over

the entire period (1981–2017) at each location to obtain the

proportion of maximum value. Potential values range from 0

to 1.

NOAA [26]

Static

Beer’s aspect (BA)

Aspect (in degrees), from LANDFIRE [25], transformed

following Beers et al. [31]. Values have 30 m resolution and are

symmetric about a SW to NE line where 0 represents a southwest

aspect (225◦) and 2 represents a northeast aspect (45◦).

GIS

Maximum NDVI

(maxNDVI)

The maximum observed NDVI value at each 4 km grid cell

across CONUS from 1981 to 2017 from NOAA [26]. GIS

slope Change in elevation over a specific area or the incline of a surface.

Values have 30 m resolution and are reported in degrees. LANDFIRE [25]

slope ratio

The ratio of the standard deviation of slope within a 500 m

radius to the standard deviation of slope within a 5 km radius

around each cell. Values have 30 m resolution.

GIS

Topographic Position

Index (TPI)

The difference between a cell elevation value, from

LANDFIRE [25], and the average elevation of the neighborhood

around that cell. Positive values indicate the cell is higher than

its surroundings and negative values indicate that it is lower. In

this case, the neighborhood is a 2 km square around each cell.

Values have 30 m resolution.

Jenness [29]

30-yr average annual

precipitation(precip)

Average annual precipitation (1981–2010) across CONUS (800 m

grid spacing). Values are reported in mm yr−1.

PRISM [24]

1 Advanced Very-High-Resolution Radiometer (AVHRR). 2 Visible Infrared Imaging Radiometer (VIIRS).

To represent vegetation conditions on a continuous scale, the 30-yr average annual precipitation

(precip) and the maximum NDVI value recorded over a 36-yr period (maxNDVI) were extracted

and calculated, respectively. Both annual precipitation and NDVI are known to be related to plant

productivity, canopy cover [32,33], and both have previously been used to classify vegetation types [34].

Additionally, relative greenness as represented by the ratio of the weekly smoothed NDVI value to the

maximum potential NDVI value (propMax) was used as a surrogate for the level of curing [35].
Fire 2019, 2, 52 5 of 23

2.3. Data Compilation

The relevant values for all of the environmental variables were extracted at the location of each

entrapment either on the day the entrapment occurred (i.e., the normalized NFDRS indices) or the

week prior to the entrapment (i.e., NDVI and propMax). The entrapments that occurred prior to

1981 did not have NDVI values available from the data source utilized, thus only 166 of the original

entrapments that happened between 1981 and 2017 were used in the analysis.

Additional data from the Fire Occurrence Database (FOD) [36] and the Monitoring Trends in Burn

Severity (MTBS) database [37] were retrieved and compiled for subsequent analysis. These databases

include the reported ignition location, discovery date, and containment date for all fires reported by US

wildland fire management agencies from 1992 to 2017 (FOD) and the final perimeters of all large fires

from 1984 to 2017 greater than 405 ha (1000 ac) in the western US and 202 ha (500 ac) in the eastern

US (MTBS). Applicable values for all environmental variables were assigned to each fire in the FOD

based on the date of discovery and the reported ignition location. The MTBS large fire perimeters were

used to clip geospatial layers of the environmental variables to each fire perimeter in a Geographic

Information System for each day from the discovery to containment date. Note that only the final

fire perimeter was available in the MTBS database, thus the final fire perimeter was used to clip all

geospatial layers for each day the fire was not contained.

2.4. Data Analysis

The compiled dataset was analyzed by viewing the distribution of values for each variable and

evaluating the results of a clustering-based algorithm. Specifically, the range of values associated

with each environmental variable were viewed by calculating the percentile rank of each observation

using a cumulative distribution function and plotting the results. Note that the sampling unit for

all analyses was an entrapment incident, regardless of the number of people or fatalities involved.

However, the cumulative distributions of the number of people and fatalities were also computed for

comparison. The data were also subjected to a clustering analysis to determine if patterns or clusters,

i.e., common characteristics, existed within the data. The raw environmental values were rescaled to

standard scores (Z-scores) and the nonparametric clustering techniques in the clues package [38] in

R [39] were used to determine the optimal number of clusters based on the Silhouette strength measure

index [40]. The similarity measures used to partition the data into clusters included the Euclidean,

centroid (k-means [41]), and medoids (Partition Around Medoids (PAM) [42]) methods. The method

that produced the highest average Silhouette value was used for further analysis.

Once the data were partitioned into clusters the nonparametric Kruskal–Wallis test [43] was

employed to determine if the values of the environmental variables for each cluster came from identical

populations. If sufficient evidence existed to suggest that at least one of the clusters had values that

came from a different population (i.e., α ≤ 0.05) then a post-hoc multiple comparison test (Dunn’s

test [44]) using the Benjamini–Hochberg adjustment [45] was employed to determine which clusters

differed. Statistical tests of independence and association in the vcd package in R [46] were also utilized

to assess the relationships between the cluster types and other categorical variables, including the

Geographic Area Coordination Center (GACC) in which the entrapment occurred, incident phase, and

type of resource(s) affected. The likelihood ratio test was applied to assess independence between the

categorical variables and Cramér’s V [47] was employed to determine the strength of the association.

To visualize the specific associations between categorical variables, association plots [48] were used,

which display standardized deviations of observed from expected values.

A Random Forests analysis [49] was also utilized to both assess the relative importance of each

environmental variable on predicting cluster type and to predict cluster type for all fires in the FOD

and within each large perimeter obtained from the MTBS database. The Gini splitting rule with at

least three variables per split and 1000 trees were used to create the Random Forest model using

the randomForestSRC package in R [50]. To aid in identifying important thresholds for classifying

cluster type, a decision tree using the rpart package in R [51] was also employed. Finally, to assess
Fire 2019, 2, 52 6 of 23

the frequency of the entrapment clusters through space and over time, the Random Forests model

was used to predict cluster type across CONUS for each day from 1981 to 2017 that had both the

appropriate NFDRS (i.e., BI’ and ERC’) and NDVI values available. The results of these predictions

were summarized by displaying the proportion of days in which both the dynamic variables (fire

danger and NDVI) and static variables (i.e., topography) were classified as the same cluster type.

3. Results

3.1. General Characteristics

The compiled dataset included a total of 166 separate entrapment incidents involving 1202 people

(injured and non-injured) and 117 fatalities. The number of incidents were nearly evenly distributed

between initial and extended attack fires, but the majority of people were affected during extended

attack fires (Table 3). Entrapments involving Engines occurred most frequently, but the bulk of people

involved in entrapments were working on Hand Crews. Entrapments with multiple resource types

tended to involve the most people and fatalities per incident, with an average of 27 people and

3 fatalities per incident.

Table 3. Summary of US wildland firefighter entrapments that occurred within CONUS (1981–2017) by

incident phase and type of resource involved.

Variable Number of Entrapments Number of People Number of Fatalities

Incident phase

Initial attack 84 310 62

Extended attack 73 863 52

Prescribed fire 9 29 3

Resource type

Dozer 11 19 4

Engine 62 322 22

Equipment 21 32 6

Hand Crew 49 560 56

Overhead 14 23 4

Multiple 9 246 25

Approximately 84% of entrapment incidents, 83% of people, and 73% of fatalities took place

between the hours of 1300 and 1600 local time, with about 52% of the incidents happening during the

months of July, August, and September (Figure 1).

Across CONUS, firefighter entrapments occurred under a wide range of environmental conditions

(Figure 2). Generally, the distribution of entrapment incidents, people, and fatalities were similar among

the environmental variables with the following exceptions. Firefighter fatalities tended to concentrate

at higher fire danger, more cured fuels, and on steeper slopes. For example, 75% of entrapment

incidents happened when ERC’ was greater than the 85th percentile, but the same proportion of

fatalities happened when ERC’ was greater than the 93rd percentile. Likewise, 75% of entrapments

incidents occurred on slopes steeper than about 2◦, but 75% of fatalities happened on slopes steeper

than 7◦.

The locations where entrapment incidents took place had both low (< 500 mm yr−1) and high

(> 1500 mm yr−1) annual precipitation (precip, Figure 2h), but the majority happened where precip was

less than about 800 mm yr−1. Similarly, the entrapment locations had maximum potential greenness

values (maxNDVI) that ranged from 0.27 to 0.67, which were mostly concentrated between 0.45 and

0.55 (Figure 2d). The topographic conditions at the entrapment locations also covered a substantial

range of values, with slopes ranging in steepness from 0 to 38◦(Figure 2e) and surrounding terrain

that was both substantially higher and lower than the entrapment location (Figure 2g). Somewhat
Fire 2019, 2, 52 7 of 23

unique amongst the entrapments was that they tended to occur when the ERC’ and BI’ values were

high, generally greater than the 90th percentile, although entrapments were still recorded at values as

low as the 6th percentile for BI’ and the 17th percentile for ERC’ (Figure 2a).

Fire 2019, 2, x FOR PEER REVIEW 7 of 24 

Figure 1. Distribution of 166 US wildland firefighter entrapments that occurred within CONUS (1981–

2017) by time of day (local time) and month of the year. 

Figure 2. Cumulative percentile distributions of entrapment incidents (solid line), fatalities (dashed 

line) and people (dotted line) for each of the environmental variables (a–h) at the times and locations 

of 166 wildland firefighter entrapments that occurred within CONUS (1981–2017). See Table 2 for 

variable definitions. 

Figure 1. Distribution of 166 US wildland firefighter entrapments that occurred within CONUS

(1981–2017) by time of day (local time) and month of the year.

Fire 2019, 2, x FOR PEER REVIEW 7 of 24 

Figure 1. Distribution of 166 US wildland firefighter entrapments that occurred within CONUS (1981–

2017) by time of day (local time) and month of the year. 

Figure 2. Cumulative percentile distributions of entrapment incidents (solid line), fatalities (dashed 

line) and people (dotted line) for each of the environmental variables (a–h) at the times and locations 

of 166 wildland firefighter entrapments that occurred within CONUS (1981–2017). See Table 2 for 

variable definitions. 

Figure 2. Cumulative percentile distributions of entrapment incidents (solid line), fatalities (dashed

line) and people (dotted line) for each of the environmental variables (a–h) at the times and locations

of 166 wildland firefighter entrapments that occurred within CONUS (1981–2017). See Table 2 for

variable definitions.
Fire 2019, 2, 52 8 of 23

3.2. Entrapments Cluster across Several Environmental Variables

The rescaled environmental data from the firefighter entrapments were optimally partitioned into

four clusters based on the Silhouette strength measure index. The average Silhouette value obtained

using the Euclidean clustering method was 0.12, while the average Silhouette values for the k-means

(centroids) and PAM (medoids) clustering methods were 0.15 and 0.10, respectively; thus, the k-means

clustering results were used for all further analyses.

The clustering results indicated that the environmental conditions at the times and locations of

the entrapments clustered in specific ways (Figure 3). The maxNDVI and precip variable values were

highest for entrapments within cluster 4 and lowest for entrapments within cluster 3. Additionally,

cluster 2 entrapments had the steepest slopes while cluster 1 entrapments had the lowest fire danger

values (see Figure A1 in Appendix A for cumulative percentile distributions by cluster type). Analysis

of the unscaled, raw environmental values confirmed that there were several significant differences

among the clusters (Table 4). Specifically, the distribution of values for all the environmental variables,

except BA, were significantly different among entrapment clusters. Furthermore, there is evidence

to suggest that the clusters represent entrapments with different potential impacts to firefighters as

cluster 2 entrapments affected more people per entrapment than cluster 4 entrapments.

Fire 2019, 2, x FOR PEER REVIEW 8 of 24 

3.2. Entrapments Cluster across Several Environmental Variables 

The rescaled environmental data from the firefighter entrapments were optimally partitioned 

into four clusters based on the Silhouette strength measure index. The average Silhouette value 

obtained using the Euclidean clustering method was 0.12, while the average Silhouette values for the 

k-means (centroids) and PAM (medoids) clustering methods were 0.15 and 0.10, respectively; thus, 

the k-means clustering results were used for all further analyses. 

The clustering results indicated that the environmental conditions at the times and locations of 

the entrapments clustered in specific ways (Figure 3). The maxNDVI and precip variable values were 

highest for entrapments within cluster 4 and lowest for entrapments within cluster 3. Additionally, 

cluster 2 entrapments had the steepest slopes while cluster 1 entrapments had the lowest fire danger 

values (see Figure A1 in Appendix A for cumulative percentile distributions by cluster type). 

Analysis of the unscaled, raw environmental values confirmed that there were several significant 

differences among the clusters (Table 4). Specifically, the distribution of values for all the 

environmental variables, except BA, were significantly different among entrapment clusters. 

Furthermore, there is evidence to suggest that the clusters represent entrapments with different 

potential impacts to firefighters as cluster 2 entrapments affected more people per entrapment than 

cluster 4 entrapments. 

Figure 3. Scaled values (Z-scores) of the nine environmental variables by cluster for each of the 166 

firefighter entrapments (1981–2017) used in the analysis. Dendrograms are shown in the margins for 

the entrapments and environmental variables. Differences in height of the branches in the 

dendrogram represent differences within and among clusters. Example entrapments (incident 

name—year of occurrence) for each cluster are also shown in the right margin. See Table 2 for variable 

definitions. 

Table 4. Descriptive information for each entrapment cluster along with group comparisons based on 

the Kruskal–Wallis test [43] followed by multiple comparisons using Dunn’s test [44]. Data shown for 

each cluster are mean ± standard deviation and the ranges (in round brackets). See Table 2 for 

definitions of variables, n is sample size. Clusters with common superscript letters are not 

significantly different by Dunn’s test at the 5% level of significance. 

Variable Cluster Χ2 p-Value 1 (n = 15) 2 (n = 37) 3 (n = 63) 4 (n = 51) 

Figure 3. Scaled values (Z-scores) of the nine environmental variables by cluster for each of the

166 firefighter entrapments (1981–2017) used in the analysis. Dendrograms are shown in the margins for

the entrapments and environmental variables. Differences in height of the branches in the dendrogram

represent differences within and among clusters. Example entrapments (incident name—year of

occurrence) for each cluster are also shown in the right margin. See Table 2 for variable definitions.

The Random Forests analysis indicated that a few key environmental variables were important for

classifying the entrapment data by cluster (Figure 4). The classification of cluster 1 was most strongly

affected by the values of BI’ and ERC’, cluster 2 was primarily related to slope steepness, and clusters

3 and 4 were predominantly influenced by the precip and maxNDVI values. Scatter plots of these

important variables grouped by fire environment type, i.e., fire danger (Figure 5a), fuels (Figure 5b), and

topography (Figure 5c) show how the entrapment data cluster in 2D space. Entrapments within cluster

1 tended to occur under relatively low fire danger conditions (i.e., low BI’ and ERC’ values) while

entrapments within clusters 2, 3, and 4 occurred under high fire danger conditions (i.e., high BI’ and

ERC’ values). Cluster 2 entrapments generally occurred on the steepest slopes. The primary difference

between entrapments within clusters 3 and 4 appears to be related to precip and maxNDVI where
Fire 2019, 2, 52 9 of 23

cluster 4 entrapments occurred under higher annual average precipitation and potential greenness

than cluster 3 entrapments.

Table 4. Descriptive information for each entrapment cluster along with group comparisons based on

the Kruskal–Wallis test [43] followed by multiple comparisons using Dunn’s test [44]. Data shown

for each cluster are mean ± standard deviation and the ranges (in round brackets). See Table 2 for

definitions of variables, n is sample size. Clusters with common superscript letters are not significantly

different by Dunn’s test at the 5% level of significance.

Variable

Cluster

X

2 p-Value

1 (n = 15) 2 (n = 37) 3 (n = 63) 4 (n = 51)

BI’ 0.42 ± 0.21A

(0.06–0.81)

0.85 ± 0.11B

(0.55–0.99)

0.88 ± 0.14BC

(0.44–1.0)

0.90 ± 0.12C

(0.47–1.0) 42.6 < 0.001

ERC’ 0.54 ± 0.21A

(0.17–0.79)

0.89 ± 0.11B

(0.54–0.99)

0.94 ± 0.06C

(0.75–1.0)

0.92 ± 0.08BC

(0.60–1.0) 42.1 < 0.001

propMax 0.59 ± 0.20A

(0.28–0.87)

0.69 ± 0.15AB

(0.36–0.94)

0.56 ± 0.15A

(0.09–0.90)

0.70 ± 0.16B

(0.29–0.98) 26.1 < 0.001

BA 1.2 ± 0.58

(0.05–1.99)

1.1 ± 0.62

(0.04–1.99)

0.9 ± 0.64

(0.0–1.98)

1.2 ± 0.68

(0.0–2.0) 4.9 0.182

maxNDVI 0.48 ± 0.07A

(0.32–0.59)

0.48 ± 0.05A

(0.37–0.61)

0.43 ± 0.07B

(0.27–0.55)

0.57 ± 0.06C

(0.44–0.67) 78.1 < 0.001

slope (◦)

3 ± 3.3A

(0–13)

18 ± 7.1B

(6–38)

5 ± 4.4A

(0–20)

6 ± 7.3A

(0–28) 66.7 < 0.001

slope ratio 0.68 ± 0.30AB

(0.10–1.27)

0.84 ± 0.25AB

(0.40–1.38)

0.63 ± 0.30A

(0.10–1.32)

0.86 ± 0.31B

(0.37–1.72) 17.8 < 0.001

TPI 5 ± 28AB

(−44–86)

42 ± 86A

(−199–199)

-20 ± 51B

(−192–99)

30 ± 63A

(−157–229) 27.6 < 0.001

precip (mm yr−1)

709 ± 389A

(311–1519)

602 ± 191A

(266–1098)

499 ± 168A

(171–1142)

1185 ± 339B

(619–2362) 90.1 < 0.001

People per

entrapment

6 ± 13AB

(1–50)

12 ± 18A

(1–89)

6 ± 7

AB

(1–41)

6 ± 13B

(1–68) 10.3 0.016

Fatalities per

entrapment

0.1 ± 0.35

(0–1)

0.8 ± 2.41

(0–14)

1.1 ± 4.0

(0–25)

0.3 ± 0.58

(0–2) 1.6 0.653

Fire 2019, 2, x FOR PEER REVIEW 9 of 24 

BI’ 0.42 ± 0.21A

(0.06–0.81) 

0.85 ± 0.11B

(0.55–0.99) 

0.88 ± 0.14BC

(0.44–1.0) 

0.90 ± 0.12C

(0.47–1.0) 42.6 < 0.001 

ERC’ 0.54 ± 0.21A

(0.17–0.79) 

0.89 ± 0.11B

(0.54–0.99) 

0.94 ± 0.06C

(0.75–1.0) 

0.92 ± 0.08BC

(0.60–1.0) 42.1 < 0.001 

propMax 0.59 ± 0.20A

(0.28–0.87) 

0.69 ± 0.15AB

(0.36–0.94) 

0.56 ± 0.15A

(0.09–0.90) 

0.70 ± 0.16B

(0.29–0.98) 26.1 < 0.001 

BA 1.2 ± 0.58 

(0.05–1.99) 

1.1 ± 0.62 

(0.04–1.99) 

0.9 ± 0.64 

(0.0–1.98) 

1.2 ± 0.68 

(0.0–2.0) 4.9 0.182 

maxNDVI 0.48 ± 0.07A

(0.32–0.59) 

0.48 ± 0.05A

(0.37–0.61) 

0.43 ± 0.07B

(0.27–0.55) 

0.57 ± 0.06C

(0.44–0.67) 78.1 < 0.001 

slope (°) 3 ± 3.3A

(0–13) 

18 ± 7.1B

(6–38) 

5 ± 4.4A

(0–20) 

6 ± 7.3A

(0–28) 66.7 < 0.001 

slope ratio 0.68 ± 0.30AB

(0.10–1.27) 

0.84 ± 0.25AB

(0.40–1.38) 

0.63 ± 0.30A

(0.10–1.32) 

0.86 ± 0.31B

(0.37–1.72) 17.8 < 0.001 

TPI 5 ± 28AB

(-44–86) 

42 ± 86A

(-199–199) 

-20 ± 51B

(-192–99) 

30 ± 63A

(-157–229) 27.6 < 0.001 

precip 

(mm yr−1) 

709 ± 389A

(311–1519) 

602 ± 191A

(266–1098) 

499 ± 168A

(171–1142) 

1185 ± 339B

(619–2362) 90.1 < 0.001 

People per 

entrapment 

6 ± 13AB

(1–50) 

12 ± 18A

(1–89) 

6 ± 7AB

(1–41) 

6 ± 13B

(1–68) 10.3 0.016 

Fatalities per 

entrapment 

0.1 ± 0.35 

(0–1) 

0.8 ± 2.41 

(0–14) 

1.1 ± 4.0 

(0–25) 

0.3 ± 0.58 

(0–2) 1.6 0.653 

The Random Forests analysis indicated that a few key environmental variables were important 

for classifying the entrapment data by cluster (Figure 4). The classification of cluster 1 was most 

strongly affected by the values of BI’ and ERC’, cluster 2 was primarily related to slope steepness, 

and clusters 3 and 4 were predominantly influenced by the precip and maxNDVI values. Scatter plots 

of these important variables grouped by fire environment type, i.e., fire danger (Figure 5a), fuels 

(Figure 5b), and topography (Figure 5c) show how the entrapment data cluster in 2D space. 

Entrapments within cluster 1 tended to occur under relatively low fire danger conditions (i.e., low 

BI’ and ERC’ values) while entrapments within clusters 2, 3, and 4 occurred under high fire danger 

conditions (i.e., high BI’ and ERC’ values). Cluster 2 entrapments generally occurred on the steepest 

slopes. The primary difference between entrapments within clusters 3 and 4 appears to be related to 

precip and maxNDVI where cluster 4 entrapments occurred under higher annual average 

precipitation and potential greenness than cluster 3 entrapments. 

Figure 4. Relative importance of the various environmental variables used to classify firefighter

entrapments by cluster using Random Forests [49]. A negative value indicates that the variable had a

detrimental impact on the classification. See Table 2 for variable definitions.
Fire 2019, 2, 52 10 of 23 Fire 2019, 2, x FOR PEER REVIEW 11 of 24 

Figure 5. Scatter plots of environmental variables at the time and location of 166 firefighter 

entrapments (1981–2017). Plots representing the corresponding kernel densities by cluster are also 

shown for each axis. The variables are grouped by fire environment type: (a) fire danger, which 

includes BI’ and ERC’; (b) fuel related variables, including maxNDVI and precip; and (c) topography 

related variables, including TPI and slope steepness. See Table 2 for variable definitions. 

Figure 5. Scatter plots of environmental variables at the time and location of 166 firefighter entrapments

(1981–2017). Plots representing the corresponding kernel densities by cluster are also shown for each

axis. The variables are grouped by fire environment type: (a) fire danger, which includes BI’ and

ERC’; (b) fuel related variables, including maxNDVI and precip; and (c) topography related variables,

including TPI and slope steepness. See Table 2 for variable definitions.
Fire 2019, 2, 52 11 of 23

The above analysis suggests that it is reasonable to define four entrapment archetypes that typify

the environmental conditions associated with firefighter entrapments in the US. The archetypes include

(1) cluster 1, low fire danger entrapments (LFD), (2) cluster 2, high fire danger, steep slope entrapments

(HFD-SS), (3) cluster 3, high fire danger, low canopy cover entrapments (HFD-LC), and (4) cluster 4,

high fire danger, high canopy cover entrapments (HFD-HC). A decision tree developed to classify the

entrapments by cluster type using the environmental data helps define specific breakpoints (Figure 6).

At locations where precip exceeds 759 mm yr−1, LFD entrapments tended to occur when the ERC’

value was below the 71st percentile. Above the 71st percentile, HFD-SS entrapments occurred where

the slope exceeded 19◦[34%] and HFD-HC entrapments occurred where slopes were less than 19◦

[34%]. At locations where precip was lower than 759 mm yr−1, HFD-SS entrapments occurred on

slopes steeper than 10◦[18%], while, on flatter slopes, LFD entrapments occurred when ERC’ was less

than the 79th percentile. Entrapments within HFD-LC also occurred in drier climates on relatively flat

slopes when ERC’ was greater than the 79th percentile.

Fire 2019, 2, x FOR PEER REVIEW 12 of 24 

Figure 6. Decision tree used to classify US firefighter entrapments by cluster type. The values within 

each box, from top to bottom, include the cluster type with the most observations, the number of 

entrapments by cluster type (1 to 4 from left to right), and the percentage of observations that are 

defined by the particular set of conditions. See Table 2 for variable definitions. 

3.3. Additional Characteristics of Entrapment Clusters 

Several additional non-environmental characteristics associated with the entrapment clusters 

were identified. The GACC, type of firefighting resource involved, and incident phase were all 

strongly associated with cluster type (Table 5). Entrapments within LFD were positively associated, 

i.e., occurred more often than expected, with the Rocky Mountain GACC while entrapments within 

HFD-HC were positively related to the Southern and northern California (NorthOps) GACCs (Figure 

7). Entrapments within HFD-LC were negatively associated, i.e., occurred less than expected, with 

the Southern GACC. 

The type of resource involved in the entrapment as well as the incident phase during the time of 

the entrapment were both significantly associated with cluster type. Entrapments within HFD-LC 

were positively associated with Engines and negatively associated with Equipment. Conversely, 

entrapments within HFD-HC were negatively associated with Engines and positively associated with 

Equipment (Figure 8). Additionally, entrapments within LFD were positively associated with 

prescribed fires, and entrapments within HFD-SS were positively associated with extended attack 

d ilid ih iiil k (Fi9)

Figure 6. Decision tree used to classify US firefighter entrapments by cluster type. The values within

each box, from top to bottom, include the cluster type with the most observations, the number of

entrapments by cluster type (1 to 4 from left to right), and the percentage of observations that are

defined by the particular set of conditions. See Table 2 for variable definitions.

3.3. Additional Characteristics of Entrapment Clusters

Several additional non-environmental characteristics associated with the entrapment clusters

were identified. The GACC, type of firefighting resource involved, and incident phase were all strongly

associated with cluster type (Table 5). Entrapments within LFD were positively associated, i.e., occurred

more often than expected, with the Rocky Mountain GACC while entrapments within HFD-HC were

positively related to the Southern and northern California (NorthOps) GACCs (Figure 7). Entrapments

within HFD-LC were negatively associated, i.e., occurred less than expected, with the Southern GACC.
Fire 2019, 2, 52 12 of 23

Table 5. Additional non-environmental categorical variables associated with US wildland firefighter

entrapments (1981–2017). The results from a likelihood ratio test for independence between cluster

type and each categorical variable are shown with the associated chi-square statistic (X2), degrees of

freedom (df), and the strength of the association based on Cramer’s V [47].

Categorical Variable X

2 df p-Value Cramer’s V

Geographic Area Coordination Center 111.5 24 <0.001 0.45

Resource type 86.0 15 <0.001 0.40

Fire 2019, 2, x FOR PEER REVIEW 

Incident phase 22.2 6 0.001 0.27

13 of 24 

Figure 7. Locations of 166 US wildland firefighter entrapments (1981–2017) by cluster type and 

Geographic Area Coordination Center (GACC). The abbreviated GACCs include the Northwest 

(NW), North Ops (NOps), and South Ops (SOps). 

Figure 8. Association plot showing deviations from independence between the type of resource 

involved in the entrapment and cluster type. Light and dark blue shading corresponds to significant 

positive associations, α = 0.05 and α = 0.0001 respectively, while light red indicates a significant 

negative association (α = 0.05) and grey is non-significant. The height of each rectangle is proportional 

to the corresponding Pearson residual (legend values) while the width is proportional to the square 

root of the expected count (i.e., wider indicates a higher expected value). The type of resource includes 

Hand Crews (HC) and multiple types (ML). Note that Equipment comprises non-dozer related 

firefighting equipment including tractor plows

Figure 7. Locations of 166 US wildland firefighter entrapments (1981–2017) by cluster type and

Geographic Area Coordination Center (GACC). The abbreviated GACCs include the Northwest (NW),

North Ops (NOps), and South Ops (SOps).

The type of resource involved in the entrapment as well as the incident phase during the time of

the entrapment were both significantly associated with cluster type. Entrapments within HFD-LC

were positively associated with Engines and negatively associated with Equipment. Conversely,

entrapments within HFD-HC were negatively associated with Engines and positively associated

with Equipment (Figure 8). Additionally, entrapments within LFD were positively associated with

prescribed fires, and entrapments within HFD-SS were positively associated with extended attack and

negatively associated with initial attack (Figure 9).
Fire 2019, 2, 52 13 of 23 

Figure 7. Locations of 166 US wildland firefighter entrapments (1981–2017) by cluster type and 

Geographic Area Coordination Center (GACC). The abbreviated GACCs include the Northwest 

(NW), North Ops (NOps), and South Ops (SOps). 

Figure 8. Association plot showing deviations from independence between the type of resource 

involved in the entrapment and cluster type. Light and dark blue shading corresponds to significant 

positive associations, α = 0.05 and α = 0.0001 respectively, while light red indicates a significant 

negative association (α = 0.05) and grey is non-significant. The height of each rectangle is proportional 

to the corresponding Pearson residual (legend values) while the width is proportional to the square 

root of the expected count (i.e., wider indicates a higher expected value). The type of resource includes 

Hand Crews (HC) and multiple types (ML). Note that Equipment comprises non-dozer related 

firefighting equipment including tractor plows. 

Figure 8. Association plot showing deviations from independence between the type of resource

involved in the entrapment and cluster type. Light and dark blue shading corresponds to significant

positive associations, α = 0.05 and α = 0.0001 respectively, while light red indicates a significant negative

association (α = 0.05) and grey is non-significant. The height of each rectangle is proportional to the

corresponding Pearson residual (legend values) while the width is proportional to the square root of

the expected count (i.e., wider indicates a higher expected value). The type of resource includes Hand

Crews (HC) and multiple types (ML). Note that Equipment comprises non-dozer related firefighting

equipment including tractor plows.

Fire 2019, 2, x FOR PEER REVIEW 14 of 24 

Figure 9. Association plot showing deviations from independence between the incident phase 

(Management activity) during the time of the entrapment and the cluster type. Light blue shading 

corresponds to a significant positive association (α = 0.05), while light red indicates a significant 

negative association (α = 0.05) and grey is non-significant. The height of each rectangle is proportional 

to the corresponding Pearson residual (legend values) while the width is proportional to the square 

root of the expected count (i.e., wider indicates a higher expected value). 

3.4. Frequency Analysis 

3.4.1. Fire Occurrence Database and MTBS 

Reported wildfires in the FOD were primarily classified has having environmental conditions 

similar to those of HFD-HCSpecifically, of the total reported wildfires from 1992 to 2017 in CONUS, 

Figure 9. Association plot showing deviations from independence between the incident phase

(Management activity) during the time of the entrapment and the cluster type. Light blue shading

corresponds to a significant positive association (α = 0.05), while light red indicates a significant

negative association (α = 0.05) and grey is non-significant. The height of each rectangle is proportional

to the corresponding Pearson residual (legend values) while the width is proportional to the square

root of the expected count (i.e., wider indicates a higher expected value).
Fire 2019, 2, 52 14 of 23

3.4. Frequency Analysis

3.4.1. Fire Occurrence Database and MTBS

Reported wildfires in the FOD were primarily classified has having environmental conditions

similar to those of HFD-HC. Specifically, of the total reported wildfires from 1992 to 2017 in CONUS,

about 61% had environmental conditions, at the fire start location and during the day of discovery,

that were similar to firefighter entrapments within HFD-HC. Fires with environmental conditions

similar to entrapments within HFD-SS were the rarest with only about 6% of the total fires meeting

the pertinent criteria. The environmental conditions associated with entrapments within LFD and

HFD-LC occurred on approximately 16 and 17% of the fires, respectively. The Eastern, Southern, North

Ops, and Northwest GACCs had the majority of their fires classified as HFD-HC (Figure 10a). The

Southwest, Great Basin, southern California (South Ops), Rocky Mountain, and Northern Rockies

GACCs had the majority of fires similar to the HFD-LC entrapment environmental conditions.

Fire 2019, 2, x FOR PEER REVIEW 15 of 24 

Figure 10. Treemap showing the proportional area of the number of fires (a) and area burned (b), 

relative to the total number of fires (1992–2017) and total area burned (1984–2017), respectively, by 

cluster type for each Geographic Area Coordination Center (GACC). The value of the cluster with the 

highest proportion (%), relative to each GACC, is shown in the upper left-hand corner. 

3.4.2. CONUS 

At the scale of CONUS, the proportion of days and locations that had environmental conditions 

that were similar to previous wildland firefighter entrapments varied substantially by cluster type 

(Figure 11). Locations with similar fuel and topography to entrapments within the HFD-LC and HFDHC clusters were widespread, occupying approximately 35 and 56% of the area in CONUS, 

respectively (Figure 11c-d). However, the times when those locations had similar fire danger 

conditions to entrapments were much less common, generally occurring on less than 20% of the days 

between 1981 and 2017. Conversely, locations with similar fuel and topography to entrapments 

within the LFD and HFD-SS were quite rare, occupying approximately 3 and 6% of the area within 

CONUS, respectively (Figure 11a-b). The times when locations classified as LFD had fire danger 

Figure 10. Treemap showing the proportional area of the number of fires (a) and area burned (b),

relative to the total number of fires (1992–2017) and total area burned (1984–2017), respectively, by

cluster type for each Geographic Area Coordination Center (GACC). The value of the cluster with the

highest proportion (%), relative to each GACC, is shown in the upper left-hand corner.

The environmental conditions within the MTBS large wildfire perimeters were primarily classified

as similar to those of entrapments within HFD-LC (Figure 10b). The Rocky Mountain, Southwest,

Great Basin, and Northwest GACCs each had on average large wildfires with more than 50% of the

area within their perimeters classified as similar to HFD-LC. The environmental conditions similar to

entrapments classified as HFD-HC were the most common within large fire perimeters located in the

Eastern, Southern, and northern California (North Ops) GACCs.
Fire 2019, 2, 52 15 of 23

3.4.2. CONUS

At the scale of CONUS, the proportion of days and locations that had environmental conditions

that were similar to previous wildland firefighter entrapments varied substantially by cluster type

(Figure 11). Locations with similar fuel and topography to entrapments within the HFD-LC and

HFD-HC clusters were widespread, occupying approximately 35 and 56% of the area in CONUS,

respectively (Figure 11c,d). However, the times when those locations had similar fire danger conditions

to entrapments were much less common, generally occurring on less than 20% of the days between

1981 and 2017. Conversely, locations with similar fuel and topography to entrapments within the

LFD and HFD-SS were quite rare, occupying approximately 3 and 6% of the area within CONUS,

respectively (Figure 11a,b). The times when locations classified as LFD had fire danger conditions

similar to those during the entrapments were, however, quite common, generally occurring on about

50% or more of the days between 1981 and 2017. Locations classified as HFD-SS only had fire danger

conditions similar to those during the entrapments on less than 20% of the days between 1981 and 2017.

Fire 2019, 2, x FOR PEER REVIEW 16 of 24 

Figure 11. Proportion of days between 1981 and 2017 that were classified as having environmental 

conditions similar to firefighter entrapments within cluster 1 (LFD) (a), cluster 2 (HFD-SS) (b), cluster 

3 (HFD-LC) (c), and cluster 4 (HFD-HC) (d). 

4. Discussion 

The environmental conditions associated with US wildland firefighter entrapments varied more 

than originally anticipated. Although several of the previously published firefighter safety guidelines 

were corroborated by our results, particularly the existence of a critical period between 1400 and 1700 

local time and the importance of steep slopes [6], at the scale of CONUS, entrapments actually occur 

under a broad range of conditions. Additional statistical analysis indicated that unique sets of 

environmental characteristics related to firefighter entrapments can be identified, which define four 

archetypes that typify the times and locations where firefighter entrapments occur. Two of the 

archetypes are common geographically but rare temporally (HFD-LC and HFD-HC), while the other 

two archetypes are rare geographically and common (LFD) and rare (HFD-SS) temporally. 

4.1. Common Environmental Conditions 

Of the 166 firefighter entrapment incidents that occurred within CONUS between 1981 and 2017, 

15 (9%) were identified as belonging to the LFD group, which in total involved 97 (8%) firefighters 

and two (2%) fatalities. Example entrapments include the New Holland Rx in South Dakota (2006) 

[52] and Burn Unit 233 in Florida (2012) [53]The environmental conditions associated with these 

Figure 11. Proportion of days between 1981 and 2017 that were classified as having environmental

conditions similar to firefighter entrapments within cluster 1 (LFD) (a), cluster 2 (HFD-SS) (b), cluster 3

(HFD-LC) (c), and cluster 4 (HFD-HC) (d).

4. Discussion

The environmental conditions associated with US wildland firefighter entrapments varied more

than originally anticipated. Although several of the previously published firefighter safety guidelines

were corroborated by our results, particularly the existence of a critical period between 1400 and

1700 local time and the importance of steep slopes [6], at the scale of CONUS, entrapments actually

occur under a broad range of conditions. Additional statistical analysis indicated that unique sets

of environmental characteristics related to firefighter entrapments can be identified, which define

four archetypes that typify the times and locations where firefighter entrapments occur. Two of the

archetypes are common geographically but rare temporally (HFD-LC and HFD-HC), while the other

two archetypes are rare geographically and common (LFD) and rare (HFD-SS) temporally.
Fire 2019, 2, 52 16 of 23

4.1. Common Environmental Conditions

Of the 166 firefighter entrapment incidents that occurred within CONUS between 1981 and 2017,

15 (9%) were identified as belonging to the LFD group, which in total involved 97 (8%) firefighters and

two (2%) fatalities. Example entrapments include the New Holland Rx in South Dakota (2006) [52] and

Burn Unit 233 in Florida (2012) [53]. The environmental conditions associated with these entrapments

were primarily defined by the fire danger conditions on the day of the entrapment, generally occurring

when the ERC’ and BI’ values were less than the 70th percentile. Although the fuel and topographic

characteristics associated with these entrapments appear to be rare (Figure 11a), in reality, they

substantially overlapped with those of the other entrapment clusters (Table 4). These entrapments

represent an anomaly within our current understanding of the environmental conditions that are

considered dangerous to firefighters, as they occurred when fire danger was indicative of relatively

benign fire weather [54–56]. Their positive association with prescribed fires suggests that several

additional non-environmental characteristics may better define these types of entrapments. For

example, prescribed fires utilize complex firing patterns to actively manipulate fire behavior to achieve

different fire effects [57], which may have contributed to unexpected changes that surprised the affected

firefighters. Additionally, several human factors (e.g., sleep deprivation [58], leadership skills, and

situational awareness [59]) may have been more relevant to these entrapments than any specific

environmental condition.

Thirty-seven (22%) entrapment incidents accounting for a total of 439 (37%) firefighters and

31 (26%) fatalities were identified as belonging to the HFD-SS group. These entrapments occurred

during high fire danger and at locations with steep slopes, with about 75% having ERC’ values greater

than the 80th percentile and slopes greater than 12◦[21%] (max 38◦[78%]). Example entrapments

include the South Canyon Fire in Colorado (1994) [60] and the Cramer Fire in Idaho (2003) [61].

Additionally, these entrapments tended to involve the highest number of firefighters per incident

and were positively associated with extended attack fires. The environmental conditions that define

these entrapments were rare both geographically and temporally, mostly occurring within the western

GACCs. Although the times and locations where fires started within CONUS between 1992 and 2017

were seldom classified as having environmental conditions similar to those of HFD-SS entrapments, a

high proportion of the area within large fire perimeters in the western GACCs, especially in southern

California (South Ops), did have similar conditions. Southern California has an extensive history of

firefighter entrapments [1,62–65], with previous work showing a strong relationship between slope

steepness and both the likelihood of an entrapment and subsequent fatalities [66,67].

A total of 63 (38%) entrapment incidents that affected a total of 346 (29%) firefighters with 69 (59%)

fatalities were characterized as belonging to the HFD-LC group. Fire danger conditions on the day of

these entrapments were high, with 75% occurring when the ERC’ and BI’ values were greater than the

92nd and 81st percentiles, respectively. Additionally, these entrapments primarily took place within

the western GACCs, on relatively flat slopes (<8

◦

[14%]), where annual precipitation (precip) was less

than 759 mm yr−1, and were significantly associated with firefighters working on Engines. The static

environmental conditions (topography and fuels) associated with these entrapments are widespread

in the western US, but the dynamic conditions are rare. The precip and maxNDVI values linked to

these entrapments suggest that these locations most likely represent vegetation conditions typical of

grasslands [68], shrub lands, or dry conifer forest types [69]. Several well-known entrapments had

conditions that were typical of the cluster including, Oakland Hills (1991) [70], Esperanza (2006) [71],

and Twisp River (2015) [72].

Entrapments classified as belonging to the HFD-HC group represented 51 (31%) incidents,

320 (27%) firefighters, and 15 (13%) fatalities. Example entrapments include the Salt Fire in Idaho

(2011) [73] and the Crane Island Fire in Florida (2017) [74]. Much like the entrapments associated with

the HFD-LC group, the fire danger conditions on the day of the entrapment were high with 75% of the

incidents having ERC’ and BI’ values above the 89th and 88th percentiles, respectively, and slopes less

than 10◦[18%]. Contrary to the HFD-LC entrapments, however, these incidents occurred at locations
Fire 2019, 2, 52 17 of 23

with relatively high precip and maxNDVI values. These high values combined with their positive

association with firefighters working on Equipment (i.e., tractor plows) suggests that they are typical

of the conditions associated with entrapments in timber-dominated fuel types, primarily in the Eastern

and Southern GACCs, but also wetter areas, e.g., coastal forests or high-elevation areas, within the

western GACCs.

4.2. Practical Implications

Given the findings of this study and previously published firefighter safety guidelines, we have

identified a few key practical implications for wildland firefighters: (1) the fire environment conditions

or subsequent fire behavior, particularly rate of spread, at the time of the entrapment does not need to

be extreme or unusual for an entrapment to occur; it only needs to be unexpected in the sense that the

firefighters involved did not anticipate or could not adapt to the observed fire behavior in enough

time to reach an adequate safety zone [75]; (2) the site and regional-specific environmental conditions

at the time and location of the entrapment are important; in other words, the set of environmental

conditions common to firefighter entrapments in one region do not necessarily translate to other

locations; (3) as noted by several authors [5,59,76,77], human factors or human behavior are a critical

component of firefighter entrapments, so much so that while an analysis of the common environmental

conditions associated with entrapments will yield a better understanding of the conditions that increase

the likelihood of an entrapment, it will not produce models or define characteristics that predict where

and when entrapments are likely to occur.

At first glance, many of the current firefighter safety guidelines related to entrapments, including

those from Wilson [5] and the Look Up, Down, And Around training [10], seem to focus on identifying

the environmental characteristics associated with extreme fire behavior [11]. However, upon further

examination, it is evident that substantial emphasis is also placed on those characteristics that promote

a rapid change in fire behavior. Changes in wind speed or direction, slope reversals, and the ability of

fires burning in light, grassy fuels to rapidly increase in spread rate have been noted as contributing

factors in previous entrapments [5,14,21,22,78,79]. It is both the unexpected nature of the change in fire

behavior coupled with the inability of firefighters to adapt to the observed fire behavior given their

current situation, as, for example, during equipment malfunctions or breakdowns, that ultimately

leads to entrapment [23,80]. In this study, we were not able to temporally resolve rapid changes in

wind speed or wind direction, but our findings indicate that their influence on entrapment potential

is stronger when fire danger is high relative to historical conditions. Based on these findings, we

propose that the current suite of firefighter safety guidelines related to entrapment potential are still

largely valid, however, we caution against an over-emphasis on those guidelines that focus only on

the potential for extreme fire behavior and argue for increased recognition that the environmental

conditions capable of resulting in an entrapment are much broader than previously acknowledged.

Although it is apparent at the scale of CONUS that entrapments are possible under a wide

range of environmental conditions, at smaller spatial scales with more site-specific context, there are

several characteristics that define entrapment archetypes or the typical conditions associated with

entrapments. We identified four entrapment archetypes that were best described by the fire danger

conditions during the day of the entrapment, slope steepness, annual precipitation, and maximum

potential greenness. The finding that there is site and regional-specific variation among the common

environmental conditions associated with entrapments suggests that it may be unwise to assume that a

fixed set of common denominators are applicable at all locations and all times. This is true both across

CONUS and other parts of the world, as it is expected that site and regional-specific variability in the

environmental conditions that influence fire occurrence, fire behavior, and fire suppression operations

will affect what defines dangerous conditions for firefighters. Thus, we do not anticipate that the specific

findings presented in this study will be broadly applicable to areas outside of CONUS, although we do

expect general commonalities related to the importance of hot, dry, and windy conditions [18,19,81,82].
Fire 2019, 2, 52 18 of 23

The one characteristic that was common for the majority of entrapments (~91%) was high fire

danger. As a general guideline, regardless of location, the data suggest that entrapment potential is

highest when the fire danger indices (ERC’ and BI’) are both above their historical 80th percentile.

Until recently, spatially-explicit information on fire danger has not been widely available as most

firefighters have relied on fire danger information available at specific weather stations, which are

often summarized into Pocket Cards [83]. Fortunately, fire danger forecasts across CONUS are now

available in a mobile-friendly format (see https://m.wfas.net) that can be displayed spatially for each of

the fire danger indices separately or combined into a Severe Fire Danger Index [27].

Several non-environmental characteristics that were not evaluated in this study, including those

related to human behavior, likely have significant impacts on entrapment potential [59] that may

override any specific set of environmental conditions. The human aspect of wildland firefighting

includes a broad range of factors that have been studied in a variety of disciplines, including psychology,

management, and communications [84]. Understanding the role of these non-environmental factors on

firefighter entrapments to improve organizational learning has been difficult for a variety of reasons,

but continued work in this field is encouraging [85,86]. It is hoped that future work related to human

factors employs rigorous, quantitatively-based assessments that allow for the identification of specific

and objective criteria that influence entrapment potential.

5. Conclusions

The times and locations where wildland firefighter entrapments occur in the US cover a wide

range of conditions. Current firefighter safety guidelines seem to emphasize only a subset of the

possible conditions due to a focus on the factors that maximize the potential for extreme fire behavior.

While many of these safety guidelines are still intuitively valid (e.g., Wilson [5]), caution should be

exercised during relevant firefighter training so as to not ignore or undermine the fact that entrapments

and fatalities are possible under a much wider range of conditions. Despite the wide range of

environmental conditions associated with entrapments, we have shown that it is possible to identify

unique combinations of environmental variables to define similarities among groups of entrapments,

but these will necessarily be context and site specific. For most entrapments, the only common

environmental condition was high fire danger, as represented by fire danger indices that have been

normalized to represent the historical percentile at a particular location. As such, at large spatial

scales, fire danger and its association with fire weather should continue to be monitored and reported

to firefighters using both traditional methods (i.e., morning fire weather forecasts) and also newer

methods that take advantage of advancements in mobile technology.

Author Contributions: Conceptualization, W.G.P., P.H.F., B.W.B., and W.M.J.; Methodology, W.G.P. and P.H.F.;

formal analysis, W.G.P. and P.H.F.; writing—original draft preparation, W.G.P.; writing—review and editing,

P.H.F., B.W.B., and W.M.J.; funding acquisition, B.W.B. and W.M.J.

Funding: This research was funded by the USDA Forest Service National Fire Plan through the Washington Office

of the Forest Service Deputy Chief for Research and the Joint Fire Science Program, Project 18-S-01-1.

Acknowledgments: We gratefully acknowledge review of the manuscript by M. E. Alexander and three

anonymous reviewers.

Conflicts of Interest: The authors declare no conflict of interest.
Fire 2019, 2, 52 19 of 23

Appendix A

Fire 2019, 2, x FOR PEER REVIEW 20 of 24 

Appendix A 

Figure A1. Cumulative percentile distributions by cluster type; cluster 1 (solid line), cluster 2 (dashed 

line), cluster 3 (dotted line), and cluster 4 (dot-dash line). Data represent distributions for each of the 

environmental variables (a–h) at the times and locations of 166 wildland firefighter entrapments that 

occurred within CONUS (1981—2017). See Table 2 for variable definitions. 

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