Spatial Distribution of Wildfire Threat in the Far North: Exposure Assessment in Boreal Communities

doi:10.21203/rs.3.rs-2949561/v1

Increased wildfire activity has raised concerns among communities about how to assess and prepare for this threat. We modified an existing approach to assess decadal wildfire hazards based primarily on ember dispersal and wildfire proximity, referencing landscape changes from 1984 through 2014. The original method created multiple maps to capture ember dispersal and spread at different scales. Instead, we integrated this multi-scale information into a single exposure layer and utilized a straightforward flammability hazard classification scheme. Binomial exact and Kruskal–Wallis tested the relationship between exposure values, underlying flammability hazard classes, and wildfire scars, respectively, in three arctic communities (Anchorage and Fairbanks, Alaska and Whitehorse, Yukon) with a range of wildfire histories and amounts of urbanization. There was a significant difference in exposure values among burned and unburned locations (p < 0.001) and flammability hazard classes (p < 0.001). Areas with high exposure values are more prone to burn and thus desirable for mitigation actions. Wildfire fire hazards are extremely high within Whitehorse and Fairbanks, and lower in Anchorage. By working with wildfire practitioners, communities, and residents, we have created a tool that can rapidly assess wildfire hazards and be easily modified to help identify and prioritize areas for mitigation activities.

Alaska

Yukon

urban

hazard

mitigation

fire

Wildfire activity around the world has been increasing, and there appears to be no indication that feedback mechanisms will limit increases (Abatzoglou et al. 2021; Barlow et al. 2020; Chisholm et al. 2016; Grabinski and McFarland 2020; Kelly et al. 2020). Reasons for this increase include historic fire suppression (Doerr and Santin 2016; Parisien et al. 2020; Parks et al. 2015), climate change (Abatzoglou and Williams 2016; Mueller et al. 2020; Schoennagel et al. 2017; Scholze et al. 2006), land use (Silveira et al. 2020), and vegetation shifts (Hagmann et al. 2021; Schoennagel et al. 2017). This coupled with the rapid expansion of the transition zone between forested areas and human development, often called the wildland-urban-interface (WUI) (NWCG 2009; Radeloff et al. 2018) places thousands of communities at risk (NASF 2021). The US Forest Service has gone as far as to say the US is dealing with a wildfire crisis(USFS 2022). National efforts have been made to assess wildfire hazards and risks, but to do so they, do not incorporate localized datasets, lack regional factors (USFS 2020), and utilize a spatial resolution that is too coarse to be used effectively by communities (Ager et al. 2015). For example, one national effort only has two categories for wildfire risk in Anchorage, less and just above less, which is not overly helpful for making local decisions (USFS 2020). Furthermore, many of these efforts fail to include Alaska and the Arctic (Scott et al. 2020). Ager argues for a more localized approach that aligns with planning and actions. Our proposed method uses a finer spatial resolution (30m) that captures more subtleties than national efforts that is regionally adjusted for Alaska (Napoli et al. 2021; USFS 2020). There is a critical need to provide concrete information about the hazards and risks that these high-latitude communities face given they are faced with increased wildfire activity (Grabinski & McFarland, 2020; York et al. 2020), losses of homes and property due to wildfire (Bhatt et al. 2021; Rasker 2015), and a wildland-urban interface (WUI) on the precipice of potentially deadly events, given shrinking wildland fire response crews and capacity (Thompson et al. 2023). While it has been acknowledged that wildfire activity is increasing in the Arctic (Kelly et al. 2013; Masrur et al. 2018), very little work has been done to assess wildfire hazards and risk in northern boreal and tundra ecosystems (Wimberly et al. 2004; Zhang et al. 2021).

Currently, wildfire risk assessment processes use fire spread models to determine the manifest hazard (Parisien et al. 2019; Parisien et al. 2005; Scott 2013), estimating exposure based on the modeled fire intensity at each location. These fire models demand detailed descriptions of fuelbed characteristics, windspeed and direction, fuel moisture and flammability, and terrain characteristics to model fire spread and intensity. They combine a measure of burn frequency with the estimated intensity to produce the exposure estimate, subject, of course, to the accuracy of the critical assumptions required for all these listed factors. We use the wildfire exposure assessment method (Beverly et al. 2019, 2021) to determine the likelihood and significance of wildfire threats to the values at risk in communities of interest. This exposure method uses fewer subjective inputs and straightforward processes that provide communities with information they can use while still producing results comparable to with other more complex methods (Beverly et al. 2010, 2021; Beverly and McLoughlin 2019).

While the Beverly wildfire exposure method is rapid (Beverly et al. 2010, 2021), it uses various scales to assess wildfire hazards based on dispersal mechanism and wildfire proximity. This can be confusing when attempting to communicate science. Promoting wildfire resilient communities is not only about identifying hazards and risks, but also about effectively communicating these. The National Fire Plan strategy emphasizes information sharing and gathering feedback (USDA 2001). There is a critical need for wildfire risk mapping tools that can be easily used and understood by communities and land managers to promote wildfire-resilient communities (Cao et al. 2016; Meyer et al. 2015; Mozumder et al. 2009). Which product should be displayed to the public? Which classification should be used for fuel treatment planning? We modified the exposure method to provide a single integrated output that can be displayed and used by public and wildfire practitioners; it integrates wildfire exposure derived from differing hazards where appropriate. Overall, the integrated approach provides a clearer understanding of wildfire hazards without overstating the hazard or minimizing the threat from the landscape-level process.

This study is intended to demonstrate that the exposure assessment method employed here is simple and adaptable enough to be used as frequently as needed to update conditions based on changes in hazards after wildfires and specific fuel treatments intended to reduce hazards. The ability of this exposure method to effectively represent changes over time will be shown using four historic decadal (1984. 1994, 2004, and 2014) instances in three Arctic communities (Anchorage and Fairbanks, Alaska, USA and Whitehorse, Yukon, Canada). Each is in the boreal forest but reflects different ecoregions, wildfire histories, and social demographics. However, among them, the landscape characteristics are of paramount importance. There is a critical need to support easily applied methods that can assess wildfire hazards and the threats they pose, provide information that can be used for hazard fuel reduction actions like fuel treatments and other actions, and provide tools that can be easily used by land managers and wildfire practitioners.

3.1 Study areas

Our three study areas include two areas from Alaska, USA—Anchorage (2,704 km²) and Fairbanks (15,077 km²)—and one from the Yukon Territory, Canada —Whitehorse (12,305 km²; Fig. 1). They were selected because they contain a large WUI within the Arctic boreal forest region and communities expressed interest in assessing hazards. The Municipality of Anchorage (n = 291,247) encompasses the largest community in the Far North: Anchorage (n = 288,970) and several other smaller communities (US Census Bureau, 2020). This area is greatly influenced by a maritime environment with mild summers (July mean = 14.3°C) and a moderate amount of annual precipitation (mean = 839 mm). Fairbanks (n = 31,410) has hotter summers (July mean = 17.6°C) and minimal precipitation (mean = 375 mm) (Climate data, 2022). Our boundary aligns with portions of the Fairbanks North Star Borough and covers the area where most residents live within the borough (n = 95,655). Whitehorse is the capital of Yukon and the largest community (n = 28,201;(Statistics Canada, 2021). The study area boundaries were drawn to capture surrounding communities and wildfire activities that could encroach, especially from the south. The climate in Whitehorse comprises cooler summers (July mean = 13.1°C) and moderate precipitation (463 mm)(Climate data, 2022).

Burnable areas within our study areas differ (Table 1) due to their maritime influence on climate and vegetation, topography, and population density. Burnable areas are defined as places not covered by ice, glacier, water, or barren (Appendix 1). Historically and recently, Fairbanks has had the most area burned, with 20% of the landscape burning in the last decade compared to less than 1% in the other two areas.

Table 1

Wildfire history within the landscape in the three AURA community study areas. Burnable areas are places not covered by ice, glacier, water, or barren.
		Fire Occurrence, Burned Area, and % of Burnable Area
	Burnable Area	1974–1983	1984–1993	1994–2003	2004–2013
Anchorage Study Area	1898 km²	171 fires 6.3 km² (0.3%)	88 fires 1.2 km² (0.1%)	81 fires 3.3.0 km² (0.4%)	138 fires 7.9 km² (0.2%)
Fairbanks Study Area	14,591 km²	686 fires 339.2 km² (2.3%)	1,083 fires 213.8 km² (1.5%)	835 fires 183.0 km² (1.3%)	723 fires 3,032.8 km² (20.8%)
Whitehorse Study Area	10,986 km²	237 fires 0.42 km² (0.0%)	371 fires 9.4 km² (0.09%)	283 fires 82.6 km² (0.75%)	122 fires 0.24 km² (0.0%)

3.2 Vegetation classification resource and area modifications

We used the Arctic Boreal Vulnerability Experiment (ABoVE) Landsat-derived annual dominant land cover classification, which provides a critical depiction of land cover that encompasses all three community areas (J. A. Wang et al. 2019). It provides 31 annual depictions (1984–2014) that capture changes due to disturbances and development that can inform projections of change in the future. No other dataset was found that could provide a combination of classification accuracy and precision, resolution of spatial distribution, or history of landscape changes that were critical to evaluating changes to hazard and risk across the three areas.

We used four years to represent decadal instances (1984, 1994, 2004, and 2014) to comprise the historic basis for this wildfire hazard and risk assessment. Of these, 2014 represents the baseline landscape characterization to which other instances are compared because it is the most current and most recognizable by residents of the decades. The vegetation classification includes 16 categories (Appendix 1) while providing important distinctions in non-forest and disturbed categories. It includes the unnamed NA class, which generally represents persistent ice and snow at high elevations. The ABoVE data have a single evergreen category, although those forests are dominated by different species combinations with different flammability and spotting fire behavior potentials. For each decade we distinguished four additional evergreen categories that were reclassified from the evergreen forest class (Appendix 2) based on the ecoregion and vegetation native to each study area (Wiken et al. 2011). This breakdown allows us to highlight important variations found in each of the three areas.

Within boreal Alaska, there are two dominant evergreen forests: black spruce (Picea mariana) and white spruce (Picea glauca). Black spruce prefers acidic, poorly drained soils; white spruce is more often found on well-drained, south-facing slopes (VanCleve and Viereck 1981; Viereck and Little 1972). In the Whitehorse area, the evergreen forest is primarily dominated by white spruce and/or lodgepole pine (Pinus contorta). We created a machine learning model using aspect, slope, elevation, climate factors, pH, and time since last fire (Calef et al. 2020) to differentiate evergreen forests dominated by black spruce and other spruce in Anchorage and Fairbanks, and in Whitehorse lodgepole pine was differentiated from other spruce. In Fairbanks, the other evergreen category represents white spruce. However, in Anchorage and Whitehorse there was a need to further reclassify the other evergreen category because of the diversity in the evergreen present.

In Anchorage, the other spruce was further classified as hemlock (Tsuga mertensiana). The flammability of hemlock was differentiated from spruce in an earlier vegetation classification used for wildfire hazard assessment (Goodrich et al. 2008). In each decadal vegetation classification, other evergreen forests were reclassified, and hemlock forests were identified in an earlier assessment. The remaining evergreen forests represent white spruce. In Whitehorse, we added subalpine fir (Abies lasiocarpa) forests as distinct and important evergreen forests. To identify them, we used a 5K vegetation inventory dataset that covered 63% of our study area (Government of Yukon 2012). In areas overlapping the other evergreen category in ABoVE and the subalpine fir in the 5K dataset, the other evergreen forest was reclassified as subalpine fir. For areas not covered by the 5K dataset, we first determined a lower elevation threshold for subalpine fir since it is known to grow at higher elevations (610 to 1,524 m) (Alexander et al. 1984; Government of Yukon 2022). Areas on the 5K map were reclassified as subalpine fir if they were other evergreens and above 1200 m. Like Fairbanks and Anchorage, the remaining other evergreen represents white spruce.

3.3 Boreal wildfire exposure assessment methods

The only assumptions imposed on exposure assessment are the flammability hazard rating based on vegetation type and the distance from which the hazard (embers transmission and surface intensity) can effectively be projected. With its use of circular neighborhood focal statistics of hazard ratings, exposure ranking makes no assertion about wind speed and direction, fuel moisture and drought, or the topography of the landscape (Beverly et al. 2010). Instead, our basic assertions of hazard potential are employed to estimate the relative likelihood of impact without an attempt to produce simulated probabilities. This approach shifts attention to distribution and relative flammability of vegetative cover in our study areas.

The original wildfire exposure assessment method identifies 500-meter, 100-meter, and 30-meter exposure neighborhoods. The 30-meter neighborhood was not practical with the available 30-meter vegetation data. Figure 2 outlines the exposure assessment method adapted from the published method. The initial two steps are the same: vegetation classification into a flammability hazard rating and the creation of exposure within defined surrounding spatial neighborhoods. The three differences are 1) changing from a dichotomous classification of the flammability hazard rating (see next section), 2) the use of the 30 m hazard classification to inform the flammability hazard rating at the 100 m and 500 m scales, and 3) the integration of 500 m and 100 m to create an integrated exposure map.

3.4 Flammability hazard rating

Hazard is any condition that can cause damage, loss, or harm to people, infrastructure, equipment, natural resources, or property (Scott 2013). In the case of a wildfire, anything that can burn during a fire (i.e., fuel) is a hazard (USDOI 2015). The first step is to reclassify vegetation into a flammability hazard, which is based on the potential for each vegetation class to burn with sufficient intensity to threaten nearby values. Vegetation classifications do not explicitly characterize fuel and flammability distinctions. The original exposure approach treated hazard potential at the most basic level, assigning ones or zeros (true or false). We have modified the approach to provide a more nuanced, scaled hazard rating from 0–100 (Table 2). The intermediate ratings give the hazard classification finer spatial resolution because they identify more of the variation in vegetation and flammability. Table 2 shows how the vegetation types produced for the three areas are reclassified into flammability hazard ratings and class.

The two neighborhood sizes considered, 500-meter and 100-meter circles, reflect two primary wildfire threats, long and short range spotting potential (Fig. 2). The larger search radius emphasizes the long-range spotting potential associated with evergreen and mixed forests; the smaller radius incorporates additional intermediate hazard emphasis for other vegetation types that can produce short-range spotting and sufficient surface intensity as threats (Appendix 1 and 2). Crown fire and the associated torching/spotting fire behavior potential provide the maximum hazard in the assessment process. A score of 1 (published method) or 100 (modified method) was reserved for the evergreen forest and woodland types, which are most likely to produce significant crown fire behavior or torching frequency and spotting distance (Table 2). Appendix 1 and 2 includes flammability information for each vegetation class, which was used to assign the values in Table 2. Ratings are higher for the 100 m than 500 m scale because it reflects more intense fire activity closer to threatened values. Based on local observations in Anchorage, the mixed forest category within the ABoVE data is largely dominated by deciduous, so it was given a lower hazard rating (50) than Fairbanks and Whitehorse (75) (Table 2). More detailed information about flammability classification is provided in Appendix 3.

Table 2

Hazard rating assignments for cover types used in classifying the landscape in the three AURA study areas. The areas are Anchorage (A), Fairbanks (F), Whitehorse (WH), and all three (All). Published methods are based on previous work (Beverly and McLoughlin 2019) and modified as described in the methods section.
		Published Method Hazard Ratings					Hazard Class
Dom.	Veg Type	500m	100m	30m	500m-adj	100m-adj	Hazard Class
All	Evergreen Forest	1	1	1	100	100	Very High
All	Deciduous Forest	0	0	1	6	30	Low
F, Wh	Mixed Forest	1	1	1	75	75	High
A	Mixed Forest	1	1	1	50	75	Mod/ High
All	Woodland	1	1	1	100	100	Very High
All	Low Shrub	0	0	1	6	30	Low
All	Tall Shrub	0	0	1	6	30	Low
All	Open Shrub	0	1	1	20	50	Low/Mod
All	Herbaceous	0	0	1	6	30	Low
All	Tussock Tundra	0	1	1	20	50	Low/Mod
All	Sparsely Vegetated	0	0	0	0	0	Very Low
All	Fen	0	1	1	20	50	Low/Mod
All	Bog	0	0	0	0	0	Very Low
All	Shallows/Littoral	0	0	0	0	0	Very Low
All	Barren	0	0	0	0	0	Very Low
All	Water	0	0	0	0	0	Very Low
All	NA (Ice/Snow)	0	0	0	0	0	Very Low
Vegetation Classification Modifications
A, F	Black Spruce	1	1	1	100	100	Very High
A	Hemlock	1	1	1	20	50	Low/Mod
Wh	Lodgepole Pine	1	1	1	100	100	Very High
Wh	Sub-Alpine Fir	1	1	1	75	75	High

3.5 Wildfire exposure ranking

The second step integrates individual flammability hazard ratings into a composite ranking for the 500-meter (0.7 km²) and 100-meter (0.03 km²) circular neighborhoods surrounding individual locations (Fig. 2). With these rankings, the threat to values distributed across the study areas can be compared by referencing the wildfire exposure at each location. Exposure is the potential contact of an entity, asset, resource, system, or geographic area with a hazard (Thompson et al. 2016). Assumptions about the hazard (spotting distances, spotting potential, and surface fire intensity) are inferred in the neighborhood radius to represent comparative wildfire exposure rankings which range from 0 to 100. The flammability hazard ratings for the included cells are summed and divided by the total number of cells in each neighborhood to produce the exposure ranking for each individual location across the entire area. Exposure classes were created based on exposure values: very low (0–19), low (20–39), moderate (40–59), high (60–79), and extreme (80–100). To explore changes in exposure over time, we calculated the percentage of areas within each exposure class and the mean exposure value across decades and among the three study areas.

The focal statistic tool (ArcGIS Pro 2.9.1) is used to sum the hazard rating within the 500-meter and 100-meter circles surrounding each raster location. To produce a more easily understood exposure ranking, the result is divided by the number of cells within each circle (797 or 29) and rounded to an integer value for a result between 0 and 100. There is an edge effect for our study areas as the distance to the area boundary falls below 500 meters or 100 meters, depending on the radius used. But given the size of our study areas the edge effects are minimal.

3.6 Integrating exposure rankings

The 100- and 500-meter exposure assessments need to be applied across analysis areas where each is most appropriate and integrated into a single product that distributes exposure based on both assessments. These exposures are not fully independent because long-range spotting reflected in the 500 m radius can bring the threat within the 100 m radius, nor are they contingent because fires can threaten independently from distant and nearby sources. This integration needs to reflect the higher ignition frequency found concentrated in areas of human habitation and activity. And it needs to address the more local threat of fires within those same areas due to increased frequency of barriers to fire spread and the more effective suppression response to protect values there. The 100m analysis radius emphasizes the hazards close by and, in our study areas, reflects the increased frequency of wildfires by evaluating hazard ratings within the much smaller analysis area (0.03 sq km for 100m radius versus 0.72 sq km for 500m radius).

To emphasize these nearby ignitions and smaller fire sizes, structures have been identified as the locations where key values exist and where human ignitions are common and problematic. Represented with a 500m buffer around all structures in each study area, the area can be considered our delineation of the Wildland Urban Interface (WUI) extent. Structure information was obtained for each decade to the extent possible for each community. We started with the current buildings layer (i.e., near 2014) for each community and then worked backwards through the decades deleting buildings that were not observed. Supplementary information from Microsoft buildings was used for 2014 as needed (Microsoft 2018). As we went back to the 1980s coverage for the entire study areas was not available, so we used the 1994 and if there was no historic imagery for 1984 the buildings remained in place. The MOA in-house GIS department provided aerial imagery from 1990 (1m), 2004/2006 (1m), and 2015 (1m) that covered the entire area with limited details (MOA 2019). For 1984 we used Landsat (30m) (USGS 2023), but it only covered the immediate Anchorage area (outlying communities were excluded) and it was primarily used to identify larger areas that were not disturbed in 1984 compared with 1994. In Fairbanks, the FNSB provided pictometry data for 2014 and 2009 with a meter or less resolution (FNSB 2019). Spot 5 data from 2003 and 2004 (2.5m) and a combination of Quickbird (2.8 m, 2002–2003) and Digital Ortho Quad (1m, 2007) were used for 2004. Orth mosaiced images from 2006 created by the City of Whitehorse were used, but again we were limited to the Whitehorse city limits with our data(City of Whitehorse 2006). Alaska High Altitude Aerial Photography from 1985 was used (NASA 1986) provided excellent detail for buildings, but the extent was limited to the city of Fairbanks (outlying communities were excluded). Government of Yukon provided aerial imagery for 2014 (Yukon 2019) and we used background imagery in ArcGIS Pro. Orthomosaiced imagery from 2006 was obtained from the City of Whitehorse (City of Whitehorse, 2006). The National Air Photography Library of Canada from 1985 and 1995 was used which provided excellent detail for buildings, but the extent was limited to the city of Whitehorse (outlying communities were excluded) (NRC 2022a, 2022b).

Integration of the two assessments could simply assign the 100 m exposure ranking for areas within the 500m structure buffer and the 500 m exposure ranking elsewhere. However, while the 100 m exposure distributions generally produce higher exposure, there are situations where the exposure is directly from long-range spotting, especially when structures are themselves highly flammable and where the nearby cover produces low exposure estimates. Integration of these exposures should assume the maximum exposure from the two assessments within a neighboring area around the identified values and utilize the 500 m exposure elsewhere. Within this 500m structure buffer area used the higher of the two raster values, comparing the 100 m or 500 m exposure rankings at each location. This 500m structure buffer changed each decade based on the spatial distribution of structures. Not having an urban category in the ABoVE database surprisingly was a strength because it allowed us to capture vegetation nestled between structures in the WUI extent. A crosswalk between the recent Alaska Landfire (Landfire 2016) and ABoVE data indicate developed areas according to Landfire often contain some vegetation captured by the ABoVE data. Outside this area, we used the 500 m exposure value.

3.7 Validation of products

Flammability hazard ratings represent the source of the wildfire threat. To be effective, the hazard rating for each vegetation type needs to demonstrate its differential flammability in burned area frequency. Therefore, vegetation and flammability classes in the high to very high categories should occur more often than other classes within areas that have burned because their properties are more likely to produce threatening fire behavior. The Fairbanks study area experienced the greatest extent of fire disturbance among the three areas (Table 1). While Anchorage and Whitehorse have demonstrated very little fire disturbance in a four-decade period (approximately 0.2% of burnable area burned), the Fairbanks area has seen an average of nearly 7% of its burnable area burned per decade from 1974–2013. We used wildfire history to assess whether there were significant differences in the area burned among the hazard classes and the vegetative types included. To account for spatial autocorrelation, we created a 500-by-500 m grid and, at the centroid of each cell, recorded the 100 m and 500 m modified and integrated exposure values, flammability hazard class, and vegetation. Since an Anderson–Darling normality test (Thode 2002) indicated that the exposure values were non-normally distributed, we used non-parametric statistics (R package nortest; A = 1165, p-value < 0.001). To determine whether there was a significant difference in exposure values between burned (2014–2021) and unburned areas, including flammability hazard classes, we used the Wilcoxon rank-sum test. Burned areas are points within wildfire scars (2014–2021) and unburned outside. Spatial wildfire histories for Alaska do not differentiate unburned areas within fire scar perimeters. The Bonferroni correction was used for the Wilcoxon tests of burned versus unburned flammability hazard classes (Benjamini and Yekutieli, 2001). The Kruskal–Wallis test was used to assess whether exposure differed significantly among flammability hazard classes. An exact binomial test was used to assess whether the distributions of vegetation differed between burned and unburned areas (Clopper and Pearson 1934). This test was only done on vegetation types that were present more than 10 times within a category.

The integration of the modified 100 m and 500 m scales within a 500 m buffer has the potential to affect the distribution of exposure values within the WUI. To assess the influence of the two scales on exposure, we calculated the total area where the exposure value was greater within the 100 m, 500 m, or equal to the 500 m buffer of structures in our study areas.

4.1 Overview

The land composition of the three areas differed, with Anchorage containing the least amount of burnable land, due in part to the large urban footprint and mountainous regions (Table 3; Fig. 3). Nearly half of the vegetation in Fairbanks (46%) and Whitehorse (48%) has a very high flammability hazard rating, while only 10% of this class occurs in Anchorage. Even though Fairbanks and Whitehorse have comparable amounts of flammable vegetation, wildfire activity in Fairbanks is much greater than in Whitehorse (Table 1). This translated into a greater percentage of the 2014 exposure values occurring within the very high category in Fairbanks (32%) and Whitehorse (32%), with only 1% in this class for in Anchorage.

Figure 3. Results from the vegetation, hazard flammability rating (500 m), and integrated wildfire exposure ranking (500 m) among Anchorage (row 1), Fairbanks (row 2), and Whitehorse (row 3) study areas in 2014.

Table 3

Comparison of 2014 landcover distributions among three AURA areas in this assessment. Landcover based on ABoVE NASA data (see methods). Data are sorted from highest hazard class to lowest. Non-burnable areas are ice, snow, water, and barren.
Land Cover Class	Anchorage	Fairbanks	Whitehorse
Black spruce	0%	19%	NA
Lodgepole pine	NA	NA	13%
White spruce	5%	8%	26%
Woodland	4%	20%	8%
Mixed forest	13%	9%	2%
Subalpine fir	NA	NA	8%
Fen	1%	6%	3%
Hemlock	2%	NA	NA
Open shrubs	3%	0%	2%
Tussock tundra	0%	0%	0%
Deciduous forest	8%	9%	1%
Herbaceous	17%	5%	8%
Low shrub	0%	4%	3%
Tall shrub	9%	10%	8%
Barren	22%	2%	5%
Bog	0%	0%	0%
Ice/snow	6%	0%	0%
Shallows/littoral	0%	0%	0%
Sparsely vegetated	8%	7%	8%
Water	2%	1%	5%

4.2 Flammability hazard ratings

Figure 4 illustrates the use of the vegetation layer to create the original and our modified flammability hazard ratings. The use of a range (0-100) allows for more insight into the distribution of hazardous fuels than the original binary classification system.

Figure 5 shows that while there is a significant plurality (46%) in the very high flammability hazard class (rating of 100) in the Fairbanks Study Area, very high hazardous areas supported more than 80% of all burned areas. The distribution of flammability hazard classes between burned and unburned areas among vegetation types was significantly different (χ² = 33518, df = 16, p < 0.001). When the burned area in each hazard class is divided by the total land area in that hazard class, the percentages are distinctly highest (7%) in the very high class and decline (as intended), as shown in the inset table in Fig. 5.

Exposure ranking assessment

The box plot in Fig. 6 shows the utility of exposure rankings for distinguishing burned and unburned locations in each hazard class, with median values for burned areas distinctively higher. In fact, important thresholds in exposure rankings can be recognized, with levels of around 30 (lowest 25th percentile levels of burned exposure distributions) providing an important minimum for identifying any plausible threat and around 70 (low whisker bound for burned high and very high hazard classes) for elevated likelihood. Both metrics increase within burned areas and there were differences in exposure values between burned and unburned areas (W = 94098461, p < 0.001). Exposure values from 2014 in burned versus unburned areas were significantly different (Fig. 6; Wilcoxon rank sum W = 26109215, p < 0.001). It illustrates the difference between hazardous fuels and exposure with the outliers observed among the lowest and highest hazard classes. Since exposure is smoothed you can have interspersed highly flammable vegetation and unburned within each flammability hazard class (Kruskal–Wallis χ² = 31095, df = 4, p < 0.001).

4.3 Integrated exposure

Figure 7 shows the results from the modified (a) 500 m (b) and 100 m scales along with (c) their integration. The modified 500 m exposure ranking result shows broad gradients that range from extreme exposure outside developed areas to very low exposure in the heart of structure concentrations. In Fig. 8b, the 100 m exposure result exhibits both more extreme and very low exposure areas bounded by tight gradients between them. In multiple examples, extreme fire events, such as boreal crown fires, have proven the ability to penetrate deep into developed areas, suggesting that exposure is higher than estimated by the very low 100 m levels shown in this example. Capturing wildfire exposure rankings is important for communities that have interwoven vegetation and development such as our three communities: Anchorage (max: 28%, 100 m: 28%; 500 m 3%), Fairbanks (max: 53%, 100 m: 49%, 500 m: 36%), and Whitehorse (max: 62%, 100 m: 54%; 500 m 51%).

The maximum exposure assessment within 500 m of structures (Fig. 8c) derived from the 100 m and 500 m exposure results exhibits important similarities when compared to the 100 m assessment. Many of the extreme exposure features displayed in the 100 m assessment are retained. However, much of the very low hazard in the immediate vicinity of structures has been increased to reflect the slightly higher hazard from the 500 m assessment, which reinforces the wildfire hazard potential surrounding structure locations in the Whitehorse study area.

Exposure values were lowest among the Anchorage study area and the WUI (Table 4). Most of the study area in Anchorage was in very low exposure, but among where residents live, the most common classification was low. In the Fairbanks and Whitehorse study areas, both residents and the entire area were more often in very high exposure. Whitehorse has nearly a quarter of the study area within very low exposure, but this has declined over the decades as the presence of very high exposure has increased (Table 4).

Table 4

Percentage of each integrated exposure score and mean value for the decades examined among our study areas. The magenta color breaks are < 10%, 10–25%, 26–50%, and > 50%.
	Exposure class	Wildland-urban interface				Study area
Area	Exposure class	1984	1994	2004	2014	1984	1994	2004	2014
Anchorage	Very low	18%	17%	18%	16%	57%	56%	57%	57%
	Low	31%	31%	31%	31%	21%	21%	22%	22%
	Moderate	23%	23%	23%	25%	14%	14%	14%	14%
	High	22%	23%	21%	21%	7%	7%	7%	6%
	Extreme	7%	7%	6%	6%	1%	1%	1%	1%
Fairbanks	Very low	7%	6%	6%	6%	11%	10%	10%	18%
	Low	27%	25%	24%	21%	14%	13%	13%	17%
	Moderate	19%	20%	19%	20%	16%	16%	16%	16%
	High	19%	20%	21%	22%	20%	21%	20%	18%
	Extreme	28%	29%	30%	31%	39%	40%	40%	31%
Whitehorse	Very low	14%	12%	11%	9%	28%	26%	25%	24%
	Low	18%	16%	15%	15%	10%	10%	10%	10%
	Moderate	13%	14%	14%	15%	11%	11%	11%	11%
	High	16%	18%	18%	19%	16%	16%	16%	16%
	Extreme	38%	41%	42%	43%	35%	38%	39%	39%
Anchorage	Mean exp.	44	45	43	44	22	23	22	22
Fairbanks	Mean exp.	58	60	60	56	64	65	65	56
Whitehorse	Mean exp.	61	64	66	58	55	57	58	58

5.1 Overview

In the case of the boreal communities studied here, the significant and growing threat comes primarily from large fire growth arising from crown fire potential of evergreen trees and flammable shrubs that can advance fires past barriers and less flammable vegetation (Westhaver 2017). Only once fires reach the immediate vicinity of values in these communities is the potential from surface fuels and intermediate fire intensities significant. The exposure assessment method described here provides a rapid tool that communities can use for assessment and to make informed mitigation and planning decisions. The increase in wildfire activity (Abatzoglou et al. 2021; Bhatt et al. 2021; Kelly et al. 2020), highly altered landscape, and increase in the WUI (Radeloff et al. 2018) over the last decade (Bento-Gonçalves and Vieira 2020; Calkin et al. 2014) has made clear that science-based methods to assess wildfire hazards are needed (Ager et al. 2015; Cao et al. 2016; Meyer et al. 2015; Mozumder et al. 2009). The methods and tools developed here help address the need to create science-based and understandable results on how to perceive hazards and risks. While Quantitative Wildfire Risk Assessment is a common approach (Allaire et al. 2005), many people struggle with how to interpret this information (Visschers et al. 2009). Additionally, feedback from communities and agencies and hazard change is generated much more quickly and easily than can be accommodated by typical wildfire spread and growth models, such as Flammap, FSIM, Burn-P3, etc.(Finney 2006; Finney et al. 2011; Parisien et al. 2005), improving the opportunity to co-produce a product, which is needed between researchers and practitioners (Adams et al. 2017). Co-production helps increase the acceptance of science, trust in results, and use among decision-makers and wildfire practitioners (Glenn et al. 2022). We have modified previous efforts to provide an integrated wildfire exposure map that captures multiple spatial scales with an understandable five-category classification approach.

5.2 Flammability hazard ratings

The results indicate that flammability hazard classes relate closely to burned area frequency (Fig. 5). This approach adequately identifies areas with increased potential for wildfire activity; 80% of the areas that subsequently burned were within the very high flammability class from the assessment of the 2014 landscape. This speaks to the utility of both the coarse yet high resolution ABoVE vegetation classification effort (Wang et al. 2019) and the flammability hazard ratings assigned to each vegetation class. Hazard ratings were based primarily on the torching and spotting potential in the boreal biome. Even though flammability hazard rating maps are intermediate products, they have the potential to be very useful to identify areas for vegetation removal or thinning. These maps can be used to identify small concentrations of high and very high flammability hazards in areas of otherwise moderate and lower hazard that can ignite, but are less likely to spread.

5.3 Exposure ranking assessment

Given the utility of hazard classification in identifying the wildfire threat spatially, risk assessments have historically used it for that purpose. However, wildfire exposure assessment captures the importance of hazard juxtaposition for increasing fire spread potential and the resulting potential likelihood of wildfire impact by averaging the hazard level of all area within the analysis circle around the location. Due to the use of focal statistics, wildfire exposure represents the potential for a flammability hazard to reach and impact specific locations, even if the hazard at the destination may be lower. In the boreal landscapes considered here, exposure is enhanced dramatically by crown fire that commonly casts embers from the source location up to 500 meters from the source location onto the vicinity of structures, igniting them and threatening the habitations they represent (Beverly et al. 2010; Bierwagen 2005; Page et al. 2019). By examining and averaging flammability hazards from the area surrounding individual locations, exposure highlights how likely it is that hazard may reach that location and, once there, impact it as smoke, heat, and fire. Recent lessons from wildfires have shown us that embers are what ignite most structures (Cohen 2000; Westhaver 2017).

5.4 Integrated exposure

As stated in published evaluations of the exposure assessment method (Beverly et al. 2010, 2021; Beverly and McLoughlin, 2019), the initial greatest hazard from boreal wildfire potential is long-range spotting from extreme events that can breach significant barriers and vegetation types with lower flammability hazards. Once a wildfire enters a community, smaller-scale factors become important, such as the flammability of the buildings themselves (Knapp et al. 2021; Syphard et al. 2017). One advantage of using the maximum of the 100 m and 500 m scales is that 100 m alone fails to adequately recognize the potential for long range spotting and buildings as fuel; with remote sensed vegetation data, these pixels are often classified as barren. By incorporating the 500 m when it is greater, the result maintains at least some exposure value when there is sufficient long range threat. This minimizes the risk of underestimating wildfire hazards and risks to communities, which can provide a false sense of confidence (Beverly et al. 2021). Natural causes, such as lightning, produce fewer fires overall (Brey et al. 2018; Grabinski and McFarland, 2020) and are more randomly distributed across a landscape. The threat from these fires is associated with the distribution of hazards over the larger area, as reflected in the 500 m assessment. Thus, the 500 m scale is most appropriate outside of our 500 m structure buffer. The 100 m maps depict the different local information about exposure to nearby ignitions (Fig. 8b), they underestimate exposure from large tracts of forested lands, such as wildlands outside the WUI, as well as parks and refuges within. The 500 m captures this danger, so the use of the maximum of either scale (i.e., 100 m or 500 m) around structures better captures wildfire potential and exposure.

5.5 Applications

One tool used to address wildfire hazards and risks is community wildfire protection plans (CWPPs) (Jakes et al. 2011). Some of the goals of CWPPs are to communicate risks to the public and to identify areas that may benefit from hazard-reduction activities. Our integrated exposure approach provides a rapid tool where there is no need to run specialized wildfire simulation models; rather, it can be utilized by Geographic Information Systems (GIS) and fire departments within communities. The model can be re-run quickly to refine results and incorporate feedback from residents or peers such as incorporating potential fuel treatments, different vegetation categories, vegetation changes due to infestation mortality and development. Reaching out to the public, incorporating feedback, and treating the creation of hazard and exposure maps as a process align with the best practices for risk and crisis communication and the acceptance of risk reduction actions (Steelman and McCaffrey 2013). Our hazard maps were presented to local government structures with repeated feedback and subsequent improvements, which is possible due to the less data-demanding and time-consuming approach than found in other stochastic wildfire models. We have also developed an ArcGIS Pro tool that allows users to choose their input vegetation, create a reclassification table, and a buildings layer to create their own flammability hazard and exposure layers. Providing categories that are clear and understandable is key to hazard and risk communication; previous research on flood risk mapping found five categories to be optimal (Fuchs et al. 2009). Providing effective communication about hazards improves resilience and, in the case of wildfires, increases social acceptance of more flexible wildfire management options (McCaffrey and Olsen 2012; Steelman and McCaffrey 2013).

Using the concept that exposure projects the flammability hazard beyond its source to locations where values exist, our exposure maps can be combined with value measures to produce relative risk ratings that identify where values need protection. Exposure, as a likelihood measure is often based on either historic frequencies. More recently, stochastic simulation fire occurrence and spread has been used to represent likelihood as probabilities. But risk is not able to predict the future. It is a measure of uncertainty in evaluating potential losses. Wildfire Exposure Ranking provides a scaled likelihood estimate for the risk assessment process by cumulating surrounding hazards and reflecting changes in landscape hazard over time. Risk rating is the logical next step, but to ensure that results are accurate and useful, communication and partnership with communities are needed to assess the values distribution.

Based on our four-decades analysis, the Whitehorse study area is of the most concern, with increases both within and outside the WUI (Table 4). This is largely due to the lack of wildfire activity. Table 1 supports the claim that Whitehorse is “at the edge of a blowtorch” (McKay 2018; Parisien et al. 2020). The Fairbanks study area has overall seen declines in wildfire exposure, largely due to increased wildfire activity outside the WUI (Table 1). However, Within the WUI, high and very high wildfire exposures have increased (Table 4). In our results, it is certain that climate change is one of the driving factors of wildfire activity and exposure (Abatzoglou and Williams, 2016; Littell et al. 2009; Wang et al. 2015). There is a need to incorporate climate change scenarios into wildfire risk models. However, incorporating and isolating the effects of climate can be challenging because vegetation changes from development. In WUIs, development and associated changes to land-use and vegetation are often drivers of wildfire risk (Bryant and Westerling 2014). Previous modeling efforts have incorporated climate change by altering the fire weather based on climate predictions, but use static vegetation and fuels layers to compare model outputs ignoring development (Riley and Loehman 2016). Future efforts will focus on incorporating climate and development of scenarios to assess future wildfire hazards and risks. Again, by minimizing other data requirements, others can use this approach to focus on the identification of development strategies that are more wildfire resilient.

Acknowledgments

Funding for this work was provided by the U.S. National Science Foundation, Award No. 1927563: NNA: Arctic Urban Risks and Adaptations (AURA): a co-production framework for addressing multiple changing environmental hazards and the Alaska EPSCoR and the state of Alaska provided funds for this project (NSF award #OIA-OIA-1757348)

Abatzoglou JT, Battisti DS, Williams AP, Hansen WD, Harvey BJ, Kolden CA (2021) Projected increases in western US forest fire despite growing fuel constraints. Commun Earth Environ, 2(1):8 https://doi.org/10.1038/s43247-021-00299-0
Abatzoglou JT, Williams AP (2016) Impact of anthropogenic climate change on wildfire across western US forests. Proc Natl Acad Sci U S A 113(42):11770-11775 https://doi.org/10.1073/pnas.1607171113
Adams T, Butler B W, Brown S, Wright V, Black A (2017) Bridging the divide between fire safety research and fighting fire safely: how do we convey research innovation to contribute more effectively to wildland firefighter safety? Int J Wildland Fire 26(2):107-112 https://doi.org/10.1071/WF16147
Ager AA, Kline JD, Fischer AP (2015) Coupling the Biophysical and Social Dimensions of Wildfire Risk to Improve Wildfire Mitigation Planning. Risk Anal 35(8):1393-1406 https://doi.org/10.1111/risa.12373
Alexander R R, Shearer RC, Shepperd WD (1984) Silvical characteristics of subalpine fir. Fort Collins, CO.
Allaire F, Filippi JB, Mallet V (2018) Generation and evaluation of ensemble simulations of wildfire spread for probabilistic forecast. Nov 09-16, Paper presented at the 8th International Conference on Forest Fire Research, Coimbra, Portugal.
Barlow J, Berenguer E, Carmenta R, Franca F (2020) Clarifying Amazonia's burning crisis. Glob Chang Biol 26(2):319-321 https://doi.org/10.1111/gcb.14872
Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29(4):1165-1188 https://doi.org/10.1214/aos/1013699998
Bento-Gonçalves A, Vieira A (2020) Wildfires in the wildland-urban interface: Key concepts and evaluation methodologies. Sci Total Environ 707, 135592. https://doi.org/10.1016/j.scitotenv.2019.135592
Beverly J L, Bothwell P, Conner JCR, Herd EPK (2010) Assessing the exposure of the built environment to potential ignition sources generated from vegetative fuel. Int J Wildland Fire 19(3), 299-313. https://doi.org/10.1071/WF09071
Beverly J L, McLoughlin N (2019) Burn probability simulation, and subsequent wildland fire activity in Alberta, Canada - Implications, for risk assessment and strategic planning. For Ecol Manage 451 https://doi.org/10.1016/j.foreco.2019.117490
Beverly, J. L., McLoughlin, N., Chapman, E. (2021) A simple metric of landscape fire exposure. Landsc Ecol 17 https://doi.org/10.1007/s10980-020-01173-8
Bhatt US, Lader RT, Walsh JE, Bieniek PA, Thoman R, Berman M, et al. (2021) Emerging Anthropogenic Influences on the Southcentral Alaska Temperature and Precipitation Extremes and Related Fires in 2019. Land 10(1):82 https://doi.org/10.3390/land10010082
Bierwagen BG (2005) Predicting ecological connectivity in urbanizing landscapes. Environ Plann B Plann Des 32(5):763-776 https://doi.org/10.1068/b3113
Brey SJ, Barnes EA, Pierce JR, Wiedinmyer C, Fischer EV (2018) Environmental Conditions, Ignition Type, and Air Quality Impacts of Wildfires in the Southeastern and Western United States. Earths Future, 6(10):1442-1456 ttps://doi.org/10.1029/2018EF000972
Bryant BP, Westerling AL (2014) Scenarios for future wildfire risk in California: links between changing demography, land use, climate, and wildfire. Environmetrics, 25(6):454-471 https://doi.org/10.1002/env.2280
Calef M, Schmidt JI, Varvak A, Ziel RH (2020) Updating ABoVE vegetation data for wildfire hazard modeling. Retrieved from Anchorage, AK: https://iseralaska.org/wp-content/uploads/2022/02/Vegetation%20mapping%20update%20to%20black%20spruce.pdf Accessed May 2023
Calkin DE, Cohen JD, Finney MA, Thompson MP (2014) How risk management can prevent future wildfire disasters in the wildland-urban interface. Proc Natl Acad Sci U S A 111(2):746-751 https://doi.org/10.1073/pnas.1315088111
Cao YH, Boruff BJ, McNeill I (2016) Is a picture worth a thousand words? Evaluating the effectiveness of maps for delivering wildfire warning information. Int J Disaster Risk Reduct 19:179-196 https://doi.org/10.1016/j.ijdrr.2016.08.012
Chisholm RA, Wijedasa LS, Swinfield T. (2016) The need for long-term remedies for Indonesia's forest fires. Conserv Biol 30(1):5-6 https://doi.org/10.1111/cobi.12662
City of Whitehorse (2006) Othoimagery. https://uwaterloo.ca/library/geospatial/collections/canadian-geospatial-data-resources/other-provinces-territories/city-whitehorse-2006-orthoimagery Accessed May 2023
Climate data (2022) Climate data for cities worldwide. https://en.climate-data.org/
Clopper CJ, Pearson ES (1934) The Use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial. Biometrika, 26(4), 404-413. http://doi.org/10.2307/2331986
Cohen JD (2000) Preventing disaster - Home ignitability in the wildland-urban interface. J For, 98(3), 15-21. https://doi.org/10.1093/jof/98.3.15
Doerr SH, Santin C (2016) Global trends in wildfire and its impacts: perceptions versus realities in a changing world. Philos Trans R Soc Lond B Biol Sci 371:20150345 https://doi.org/10.1098/rstb.2015.0345
Fairbrother A, Turnley JG (2005) Predicting risks of uncharacteristic wildfires: Application of the risk assessment process. For Ecol Manage 211(1-2):28-35 https://doi.org/10.1016/j.foreco.2005.01.026
Finney, M. A. (2006) An overview of FlamMap fire modeling capabilities. March 28–30, Paper presented at the Fuels management-how to measure success: conference proceedings, Portland, OR.
Finney MA, McHugh CW, Grenfell IC, Riley KL, Short KC (2011) A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25(7):973-1000 https://doi.org/10.1007/s00477-011-0462-z
FNSB (2019) Get FNSB GIS data. https://www.fnsb.gov/438/Get-FNSB-GIS-Data Accessed May 2023
Fuchs S, Spachinger K, Dorner W, Rochman J, Serrhini K (2009) Evaluating cartographic design in flood risk mapping. Environ Haz 8(1):52-70 https://doi.org/10.3763/ehaz.2009.0007
Glenn E, Yung L, Wyborn C, Williams DR (2022) Organisational influence on the co-production of fire science: overcoming challenges and realising opportunities. Int J Wildland Fire 31(4):435-448 https://doi.org/10.1071/WF21079
Goodrich CP, Rodman SU, Stam J (2008) Municipality of Anchorage Community Wildfire Protection Plan, Anchorage, AK. https://www.muni.org/departments/fire/wildfire/documents/cwpp_lowres_jan8-08.pdf Accessed May 2023
Government of Yukon (2012) Vegetation Inventory - 5k - Land Cover. https://hub.arcgis.com/datasets/yukon::vegetation-inventory-5k-land-cover/about Accessed May 2023
Government of Yukon (2022) Sub-alpine fir. https://yukon.ca/en/sub-alpine-fir Accessed May 2023
Grabinski Z, McFarland HR (2020) Alaska’s changing wildfire environment [outreach booklet]. Retrieved from Fairbanks, AK, USA.
Hagmann RK, Hessburg PF, Prichard SJ, Povak NA, Brown PM, Fule PZ, et al. (2021) Evidence for widespread changes in the structure, composition, and fire regimes of western North American forests. Ecol Appl 31(8):34 https://doi.org/10.1002/eap.2431
Jakes PJ, Nelson KC, Enzler SA, Burns S, Cheng AS, Sturtevant V, et al. (2011) Community wildfire protection planning: is the Healthy Forests Restoration Act's vagueness genius? Int J Wildland Fire 20(3):350-363 https://doi.org/10.1071/WF10038
Kelly LT, Giljohann KM, Duane A, Aquilue N, Archibald S, Batllori E, et al. (2020) Fire and biodiversity in the Anthropocene. Science 370(6519):eabb0355 https://doi.org/10.1126/science.abb0355
Kelly R, Chipman ML, Higuera PE, Stefanova I, Brubaker LB, Hu FS (2013) Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proc Natl Acad Sci U S A 110(32):13055-13060 https://doi.org/10.1073/pnas.1305069110
Knapp EE, Valachovic YS, Quarles SL, Johnson NG (2021) Housing arrangement and vegetation factors associated with single-family home survival in the 2018 Camp Fire, California. Fire Ecol 17(25) https://doi.org/10.1186/s42408-021-00117-0
Landfire (2016) Existing Vegetation Type Layer, Landfire 2.0.0. https://landfire.gov/lf_maps.php Accessed May 2023
Littell JS, McKenzie D, Peterson DL, Westerling AL (2009) Climate and wildfire area burned in western U. S. ecoprovinces, 1916-2003. Ecol Appl 19(4):1003-1021 https://doi.org/10.1890/07-1183.1
Masrur A, Petrov AN, DeGroote J (2018) Circumpolar spatio-temporal patterns and contributing climatic factors of wildfire activity in the Arctic tundra from 2001-2015. Environ Res Lett 13(1):11 https://www.doi.org/10.1088/1748-9326/aa9a76
McCaffrey SM, Olsen CC (2012) Research perspectives on the public and fire management: a synthesis of current social science on eight essential questions. Newtown Square, PA https://doi.org/10.2737/NRS-GTR-104
McKay J (2018) Whitehorse 'at the edge of a blowtorch', group says urging more action to prevent wildfire. November 20, https://www.cbc.ca/news/canada/north/wild-fire-whitehorse-prevention-1.4913890 Accessed May 2023
Meyer MD, Roberts SL, Wills R, Brooks M, Winford EM (2015) Principles of effective USA federal fire management plans. Fire Ecol 11(2):59-83 https://doi.org/10.4996/fireecology.1102059
Microsoft (2018) US building footprints. https://www.microsoft.com/en-us/maps/building-footprints Accessed May 2023
MOA (2019) MOA GIS. https://moa-muniorg.hub.arcgis.com/pages/maps
Mozumder P, Helton R, Berrens RP (2009) Provision of a Wildfire Risk Map: Informing Residents in the Wildland Urban Interface. Risk Anal 29(11):1588-1600 https://doi.org/10.1111/j.1539-6924.2009.01289.x
Mueller SE, Thode AE, Margolis EQ, Yocom LL, Young JD, Iniguez JM (2020) Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. For Ecol Manage 460:14 https://doi.org/10.1016/j.foreco.2019.117861
Napoli KC, Gilbertson-Day JW, Vogler KC, Scott JH (2021) Chugach all-lands wildfire risk assessment: method and results. http://pyrologix.com/downloads/ Accessed May 2023
NASA (1986) Alaska High Altitude Aerial Photography (AHAP) Program. https://cmr.earthdata.nasa.gov/search/concepts/C1214585044-SCIOPS Accessed May 2023
NASF (2021) Communities at Risk. National Association of Foresters https://www.stateforesters.org/wp-content/uploads/2022/06/NASF-2021-Communities-At-Risk-Report.pdf Accessed May 2023
NRC (2022a) National Air Photo Library. https://natural-resources.canada.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/air-photos/22030 Accessed May 2023
NRC (2022b) Open in Map Viewer. Orthomosaic of Whitehorse in 1985. https://www.arcgis.com/home/item.html?id=9aa66855cbd2451eb92db69204bcf958 Accessed May 2023
NWCG. (2009) Guidance for Implementation Federal Wildland Fire Management Policy and Fire Management Board Memorandum 19-004a. https://www.nwcg.gov/term/glossary/wildland-urban-interface-wui Accessed May 2023
Page WG, Wagenbrenner NS, Butler BW, Blunck DL (2019) An analysis of spotting distances during the 2017 fire season in the Northern Rockies, USA. Can J For Res 49(3):317-325 https://doi.org/10.1139/cjfr-2018-0094
Parisien MA, Barber QE, Hirsch KG, Stockdale CA, Erni S, Wang X, et al. (2020) Fire deficit increases wildfire risk for many communities in the Canadian boreal forest. Nat Commun 11(1) https://doi.org/10.1038/s41467-020-15961-y
Parisien MA, Dawe DA, Miller C, Stockdale CA, Armitage OB (2019) Applications of simulation-based burn probability modelling: a review. Int J Wildland Fire 28(12) https://doi.org/10.1016/j.ijdrr.2021.102189
Parisien MA, Kafka VG, Hirsch KG, Todd BM, Lavoie SG, Maczek PD (2005) Mapping fire susceptibility with the Burn-P3 simulation model. Edmonton, Alberta, Canada. https://cfs.nrcan.gc.ca/publications?id=25627 Accessed May 2023
Parks SA, Miller C, Parisien MA, Holsinger LM, Dobrowski SZ, Abatzoglou J (2015) Wildland fire deficit and surplus in the western United States, 1984-2012. Ecosphere, 6(12):13 https://doi.org/10.1890/ES15-00294.1
Radeloff VC, Helmers DP, Kramer HA, Mockrin MH, Alexandre PM, Bar-Massada A, et al. (2018) Rapid growth of the US wildland-urban interface raises wildfire risk. Proc Natl Acad Sci U S A 115(13):3314-3319 https://doi.org/10.1073/pnas.1718850115
Rasker R (2015) Resolving the increasing risk from wildfires in the American West. Solutions, 6(2):55-62
Riley KL, Loehman RA (2016) Mid-21st-century climate changes increase predicted fire occurrence and fire season length, Northern Rocky Mountains, United States. Ecosphere, 7(11) https://doi.org/10.1002/ecs2.1543
Schoennagel T, Balch JK, Brenkert-Smith H, Dennison PE, Harvey BJ, Krawchuk MA, et al. (2017) Adapt to more wildfire in western North American forests as climate changes. Proc Natl Acad Sci U S A 114(18):4582-4590 https://doi.org/10.1073/pnas.1617464114
Scholze M, Knorr W, Arnell NW, Prentice IC (2006) A climate-change risk analysis for world ecosystems. Proc Natl Acad Sci U S A 103(35):13116-13120 https://doi.org/10.1073/pnas.0601816103
Scott JH (2013) A wildfire risk assessment framework for land and resource management. https://www.fs.usda.gov/rm/pubs/rmrs_gtr315.pdf Accessed May 2023
Scott JH, Brough AM, Gilbertson-Day JW, Dillon GK, Moran C (2020) Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States. Fort Collins, CO. https://data.nal.usda.gov/dataset/wildfire-risk-communities-spatial-datasets-landscape-wide-wildfire-risk-components-united-states Accessed May 2023
Silveira MVF, Petri CA, Broggio IS, Chagas GO, Macul MS, Leite C, et al. (2020) Drivers of fire anomalies in the Brazilian Amazon: lessons learned from the 2019 fire crisis. Land 9(12) https://doi.org/10.3390/land9120516
Statistics Canada (2021) Census of Canada. https://www12.statcan.gc.ca/census-recensement/index-eng.cfm Accessed May 2023
Steelman TA, McCaffrey S (2013) Best practices in risk and crisis communication: Implications for natural hazards management. Nat Haz 65(1):683-705 https://doi.org/10.1007/s11069-012-0386-z
Syphard AD, Brennan TJ, Keeley JE (2017) The importance of building construction materials relative to other factors affecting structure survival during wildfire. Int J Disaster Risk Reduct 21:140-147 https://doi.org/10.1016/j.ijdrr.2016.11.011
Thode HC (2002) Testing for normality. Marcel Dekker, New York.
Thompson MP, Belval EJ, Bayham J, Calkin DE, Stonesifer CS, Flores D (2023) Wildfire Response: A System on the Brink? J For 121(2):121-124 https://doi.org/10.1093/jofore/fvac042
Thompson MP, Zimmerman T, Mindar D, Taber M(2016) Risk terminology primer: basic principles and a glossary for the wildland fire management community. Fort Collins, CO. https://www.fs.usda.gov/rm/pubs/rmrs_gtr349.pdf Accessed May 2023
US Census Bureau. (2020) U.S. Decadal Census. https://www.census.gov/data.html
USDA (2001) A Collaborative Approach for Reducing Wildland Fire Risks to Communities and the Environment: 10-Year Comprehensive Strategy. United States Department of Agriculture, Washington, DC.
USDOI (2015) Fuels Management. https://www.doi.gov/wildlandfire/fuels Accessed May 2023
USFS (2020) Wildfire risk to communities. https://wildfirerisk.org/ Accessed May 2023
USFS (2022) Wildfire crisis strategy. https://www.fs.usda.gov/sites/default/files/Confronting-Wildfire-Crisis.pdfAccessed May 2023
USGS (2023) Land Processes Distributed Active Archive Center. https://lpdaac.usgs.gov/ Accessed May 2023
VanCleve K, Viereck LA (1981) Forest Succession in Relation to Nutrient Cycling in the Boreal Forest of Alaska. In: West DC (ed) Forest Succession. Springer, New York, NY, pp. 185-212 https://doi.org/10.1007/978-1-4612-5950-3_13
Viereck LA, Little EL, Jr. (1972) Alaska trees and shrubs. Department of Agriculture, Forest ServiceWashington, DC, U.S.
Visschers VHM, Meertens RM, Passchier WWF, de Vries NNK (2009) Probability Information in Risk Communication: A Review of the Research Literature. Risk Anal, 29(2):267-287 https://doi.org/10.1111/j.1539-6924.2008.01137.x
Wang JA, Sulla-Menashe D, Woodcock CE, Sonnentag O, Keeling RF, Friedl MA (2019) ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014. In: ORNL Distributed Active Archive Center. https://daac.ornl.gov/ABOVE/guides/Annual_Landcover_ABoVE.html Accessed May 2023
Wang XL, Thompson DK, Marshall GA, Tymstra C, Carr R, Flannigan MD (2015) Increasing frequency of extreme fire weather in Canada with climate change. Clim Change 130(4): 573-586 https://doi.org/10.1007/s10584-015-1375-5
Westhaver A (2017) Why some homes survived: learning from the Fort McMurray wildland/urban interface fire disaster. Toronto, Ontario, Canada. https://issuu.com/iclr/docs/westhaver_fort_mcmurray_preliminary Accessed May 2023
Wiken E, Nava FJ, Griffith G. (2011) North American Terrestrial Ecoregions - Level III. Retrieved from Montreal, Canada. https://www.epa.gov/eco-research/ecoregions-north-america Accessed May 2023
Wimberly MC, Zhang YJ, Stanturf JA (2004) Digital forestry in the Wildland-Urban Interface. Paper presented at the 1st International Workshop on Digital Forestry, Jun 13-19, Beijing, Peoples Republic of China.
York A, Bhatt US, Gargulinski E, Grabinski Z, Jain P, Soja A, Thoman RL, Ziel R (2020) Arctic Report Card 2020: Wildland Fire in High Northern Latitudes. https://doi.org/10.25923/2gef-3964
Yukon GO (2019) Mapping. Retrieved from https://yukon.ca/en/statistics-and-data/mapping Accessed May 2023
Zhang Z Wang, L. L., Xue, N. T., Du, Z. H. (2021) Spatiotemporal Analysis of Active Fires in the Arctic Region during 2001-2019 and a Fire Risk Assessment Model. Fire-Switzerland, 4(3) https://doi.org/10.3390/fire4030057

Spatial Distribution of Wildfire Threat in the Far North: Exposure Assessment in Boreal Communities

Status:

Journal Publication

Version 1

Abstract

Figures

1 Introduction

2 Aims

3 Methods