2. Case study location

problem coupled with improved technological capacity have spurred development of innovative mapping and assessment techniques to inform prioritization and mitigation efforts [8–10].

These efforts reflect broader trends of increasing sophistication of risk assessment and risk management within the global fire science and decision support communities [11–16]. One notable advancement relates to analysis of risk transmission (i.e., the analysis of ignition patterns, potential fire flow pathways, and simulated fire perimeters) in order to determine areas on the landscape that have a higher propensity to be a source of damaging fires [17–21]. Such analyses can inform demarcation of firesheds to support community wildfire planning, evaluation of comparative transmission rates across land designations and ownerships, and strategic identification of fuel treatment, ignition prevention, and suppression preparedness needs.

A related advancement is in the realm of pre-suppression response planning, particularly the assessment of opportunities to effectively manage fire. Proactive assessment and planning can reduce time pressures and uncertainties, thereby helping ensure that suppression response strategies and tactics are more likely to be safe and efficient [22, 23]. Information from risk assessment can play a critical role in planning efforts and development of suppression objectives, for instance identifying areas where aggressive suppression would be warranted due to a high likelihood of fire transmission to developed areas, or where less aggressive suppression might be appropriate to achieve resource objectives [24, 25]. The operational relevance of presuppression planning is enhanced though consideration of factors that influence suppression effectiveness, notably the identification of areas of high suppression difficulty as well as their converse, areas of high likelihood for effective fire control [26–29]. Developing maps with such information can help fire managers forecast likely suppression resource needs, develop strategic courses of action and plans for mobilization of suppression resources, and inform tactical

The research presented in this paper builds on the aforementioned body of work in the field of pre-suppression planning, with a focus on two key analytical products and planning tools: (1) the suppression difficulty index (SDI) [29]; and (2) a network of potential wildfire operational delineations (PODs) [24]. SDI, a raster layer, is a dimensionless metric that combines variables related to fire behavior (flame length, heat per unit area) with variables related to suppression operations (road and fuelbreak density, firefighter accessibility, fireline production, and cycle time for aerial resources). All else being equal, SDI increases with more extreme fire behavior. Similarly, SDI decreases as road density and firefighter accessibility increase, for example. For our purposes here, we use a modified version of SDI called the terrestrial SDI (tSDI) that excludes air support [27]. In the 2017 fire season in the USA, tSDI products were delivered to

PODs are polygons whose boundaries are relevant to fire control operations, such as roads, trails, ridgetops, drainages, and fuel transitions. In effect, PODs can be thought of as fire management units, within which risks and other fire-relevant information can be summarized. The process of POD delineation can range from full automation in a GIS environment to being handdrawn by local managers using expert judgment assisted by maps of tSDI and potential control locations [26, 27]. The POD concept has been used to support strategic response planning [24], large fire response optimization [25], and fuel treatment optimization [30]. As with tSDI, POD

fire managers for real-time decision support on more than a dozen large fires.

deployment decisions of where to send suppression resources.

46 Environmental Risks

We selected a case study landscape encompassing the Arapaho and Roosevelt National Forests, which is in north-central Colorado, USA. Figure 1 provides a map of the continental USA with the state of Colorado and the National Forests identified. Figure 2 further presents a topographic basemap and county boundary as reference for the study site. The study area includes the Front Range, which is located to the west of a heavily populated urban corridor stretching from Denver north to Fort Collins. The study area is 622,222 hectares in total, which includes 292,889 hectares from the Arapaho National Forest and 329,333 hectares from the Roosevelt National Forest [32]. Elevation of the study area ranges from mesas and high prairies at 1500 meters to mountain peaks exceeding 4250 m, with steep river canyons and dramatic changes in vegetation along the elevation gradient.

Figure 1. Location of the National Forests within Colorado, USA.

Addington et al. [33] summarize important characteristics of the Front Range that influence forest conditions and fire management concerns. A dry climate (average annual precipitation 25–50 cm) combined with shallow soils that have relatively low moisture-holding capacity leads to low site quality and slow accumulation of fuels relative to other more productive pine forests in the western USA. The highly dissected topography creates variability in productivity and fuel loadings, which tended to promote a mixed-severity fire regime. However, a legacy of fire exclusion in the Front Range has led to changes in forest composition and density, which has in turn shifted the low-mid elevation ponderosa pine and dry mixed-conifer forests from a relatively frequent, low- and mixedseverity regime to a higher-severity regime. Hence increased concern in the region over the potential for large damaging wildfires that are resistant to control. In particular, wildfire damages to structures in the wildland-urban interface (WUI) and municipal watersheds are key concerns [7, 10, 21, 31, 34, 35]. Notably, the 2012 High Park Fire in the study area resulted in 35,323 hectares burned, at least 259 homes destroyed, one casualty, and increased water treatment costs [5, 36]. Other high loss fire events from the region include the 2002 Hayman Fire (132 homes destroyed; 6 fatalities; human-caused), the 2010 Fourmile Canyon Fire (168 homes destroyed; human-caused), the 2012 Lower North Fork Fire (16 homes destroyed; 3 fatalities; human-caused), the 2012 Waldo Canyon Fire (346 homes destroyed; human-caused), and the 2013 Black Forest Fire (464 homes destroyed, 2 fatalities; human-caused) [21].

Here we opt to focus on National Forest lands to simplify our illustration. This is not meant to diminish the importance of other federal, state, and local land and fire management agencies operating within the case study landscape (e.g., Rocky Mountain National Park). Rather, it

Figure 2. Study site including the Arapaho and Roosevelt National Forests.

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

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Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand http://dx.doi.org/10.5772/intechopen.76937 49

Figure 2. Study site including the Arapaho and Roosevelt National Forests.

Addington et al. [33] summarize important characteristics of the Front Range that influence forest conditions and fire management concerns. A dry climate (average annual precipitation 25–50 cm) combined with shallow soils that have relatively low moisture-holding capacity leads to low site quality and slow accumulation of fuels relative to other more productive pine forests in the western USA. The highly dissected topography creates variability in productivity and fuel loadings, which tended to promote a mixed-severity fire regime. However, a legacy of fire exclusion in the Front Range has led to changes in forest composition and density, which has in turn shifted the low-mid elevation ponderosa pine and dry mixed-conifer forests from a relatively frequent, low- and mixedseverity regime to a higher-severity regime. Hence increased concern in the region over the potential for large damaging wildfires that are resistant to control. In particular, wildfire damages to structures in the wildland-urban interface (WUI) and municipal watersheds are key concerns [7, 10, 21, 31, 34, 35]. Notably, the 2012 High Park Fire in the study area resulted in 35,323 hectares burned, at least 259 homes destroyed, one casualty, and increased water treatment costs [5, 36]. Other high loss fire events from the region include the 2002 Hayman Fire (132 homes destroyed; 6 fatalities; human-caused), the 2010 Fourmile Canyon Fire (168 homes destroyed; human-caused), the 2012 Lower North Fork Fire (16 homes destroyed; 3 fatalities; human-caused), the 2012 Waldo Canyon Fire (346 homes destroyed; human-caused), and the 2013 Black Forest Fire (464 homes

Here we opt to focus on National Forest lands to simplify our illustration. This is not meant to diminish the importance of other federal, state, and local land and fire management agencies operating within the case study landscape (e.g., Rocky Mountain National Park). Rather, it

destroyed, 2 fatalities; human-caused) [21].

Figure 1. Location of the National Forests within Colorado, USA.

48 Environmental Risks

allows us to focus particular attention on the problem of one-way risk transmission across ownership boundaries and its relevance to suppression difficulty and protection priority. Further, as of this writing, the Forests and local partners are actively involved in landscapescale prioritization and planning efforts incorporating tSDI and POD products.

the purposes of developing broad-scale strategic wildfire response zones [24]. This allows for identification of specific locations on the landscape that are high priority for protection, and facilitates targeted scheduling of activities like hazardous fuel reductions projects. The smallscale POD network could also facilitate optimal aggregation into larger PODs that are rela-

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We used an automated Python script to calculate the tSDI across the case study landscape. The script requires raster inputs including fuel model, flame length, heat per unit area, roads, trails, digital elevation model, slope, aspect. tSDI is calculated for each raster cell based on the equations originally developed by [29] and later modified by [27]. SDI is a sum of six sub-indices: the energy behavior sub-index (Ice, Eq. 1, a function of flame length (FL) and heat per unit area (HUA)), the accessibility sub-index (Ia), the mobility sub-index (Im), the penetrability sub-index (Ip), aerial resources sub-index (Iar) and fireline opening subindex (Ic) (Eq. 2). The first five sub-indices each has a value that ranges from 1 to 10. The last sub-index has a value between 1 and 20. Ai is the area of each fuel model and At is the size of total study area managed within each cell or pixel. In our case, tSDI values were calculated at a 30 � 30 m resolution, which is identical to the resolution of the fuel models, so in practice the area adjustment ratio in Eq. 1 is always equal to one. Further, since we did not consider aerial resources, the value of Iar is set to 0, and we quantify tSDI accordingly (Eq. 3). Based on these scaling coefficients, the final SDI values can range from 0 to 1.67 with all sub-indices included, and the final tSDI values can range from 0 to 2.5 with the

tively homogenous with respect to suppression difficulty and protection demand.

Ice <sup>¼</sup> <sup>X</sup> <sup>2</sup> � FLi � HUAi

P

P

SDI ¼

tSDI ¼

FLi þ HUAi � �

P

P

Two critical inputs for calculating tSDI are fuel models and modeled fire behavior metrics. Our primary source for data on fuel models was LANDFIRE 2014 [37, 38]. We then updated these data to account for treatments and other disturbances that occurred on the landscape in the intervening years up to 2016. We used two sets of treatment mosaics to reflect the changing of fuel treatments within the study area: treated areas identified through the Front Range Round Table 2016 Interagency Fuel Treatment Database, and the USDA Forest Service's Natural Resource Manager Forest Activity Tracking System. We then distinguished the effects of different fuel treatment types (e.g., mastication, surface fuel treatment, prescribed fire, thin from below) and used rulesets to update the fuel models after corresponding treatment. To model fire behavior we used the FlamMap fire modeling system [39], using 90th percentile

ð Þ Ice Ia þ Im þ Ip þ Iar þ Ic

> ð Þ Ice Ia þ Im þ Ip þ Ic

� ð Þ Ai=At � � (1)

� � " # (2)

� � " # (3)

3.3. Calculate tSDI

aerial sub-index excluded.
