3. Materials and methods

#### 3.1. Analysis framework

Table 1 presents a generalized framework for the types of variables included our analysis. The tSDI variables are calculated for every raster cell on the landscape, whereas other variables are summarized at the polygon-level for each POD. We describe the primary analytical steps in the following sub-sections.

### 3.2. Generate POD network

We generated PODs within our study area using GIS data and analysis techniques described in [25]. The boundaries between adjacent PODs are either major roads, streams, or ridge tops. Our assumption is that fire management activities such as enhancing natural fire breaks, constructing fireline, or conducting burn out operations could be performed along these geographic features. The geospatial layers for stream and ridge (catchment boundary) locations are acquired from the U.S. EPA and U.S. Geological Survey's NHDPlusV2 dataset, and the road transportation layers are acquired locally from the US Forest Service. Rather than identifying PODs for every hectare on the landscape, we limited our consideration to areas within and proximal to National Forest boundaries, and attempted to maintain a fairly contiguous analysis landscape.


The desired size distribution of PODs will vary with the scale of the planning application [22]. The process we use intentionally creates small PODs, relative to PODs created elsewhere for

Table 1. Primary variables in analysis.

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 relatively homogenous with respect to suppression difficulty and protection demand.

#### 3.3. Calculate tSDI

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 landscape-

Table 1 presents a generalized framework for the types of variables included our analysis. The tSDI variables are calculated for every raster cell on the landscape, whereas other variables are summarized at the polygon-level for each POD. We describe the primary analytical steps in the

We generated PODs within our study area using GIS data and analysis techniques described in [25]. The boundaries between adjacent PODs are either major roads, streams, or ridge tops. Our assumption is that fire management activities such as enhancing natural fire breaks, constructing fireline, or conducting burn out operations could be performed along these geographic features. The geospatial layers for stream and ridge (catchment boundary) locations are acquired from the U.S. EPA and U.S. Geological Survey's NHDPlusV2 dataset, and the road transportation layers are acquired locally from the US Forest Service. Rather than identifying PODs for every hectare on the landscape, we limited our consideration to areas within and proximal to National Forest boundaries, and attempted to maintain a fairly contig-

The desired size distribution of PODs will vary with the scale of the planning application [22]. The process we use intentionally creates small PODs, relative to PODs created elsewhere for

SDI\_AR Ratio-scale index of relative tSDI and area ratios for predefined analysis units

F2F Forests to Faucets Surface Drinking Water Importance Score

scale prioritization and planning efforts incorporating tSDI and POD products.

3. Materials and methods

3.1. Analysis framework

50 Environmental Risks

following sub-sections.

3.2. Generate POD network

uous analysis landscape.

Raster-level summaries

POD-level summaries

Table 1. Primary variables in analysis.

Variable Definition

tSDI Terrestrial suppression difficulty index

mSDI Mean tSDI of all raster cells in POD

dWUI Areal density of structures in the WUI

PP\_F2F Protection priority for F2F PP\_dWUI Protection priority for dWUI 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 aerial sub-index excluded.

$$I\_{ct} = \left[\sum \left(\frac{2 \times FL\_i \times HUL\_i}{FL\_i + HUL\_i}\right) \times (A\_i/A\_t)\right] \tag{1}$$

$$SDI = \left[\frac{\sum \left(I\_{\text{ct}}\right)}{\sum \left(I\_d + I\_m + I\_p + I\_{ar} + I\_c\right)}\right] \tag{2}$$

$$tSDI = \left[\frac{\sum \left(I\_{ce}\right)}{\sum \left(I\_a + I\_m + I\_p + I\_c\right)}\right] \tag{3}$$

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 weather conditions for fuel moisture conditions and wind speed/direction, drawn from the Redfeather Remote Automated Weather Station.

Finally, we mapped tSDI at four spatial scales of decreasing size, hereafter analysis units (Table 2). The rationale for scaling down tSDI values to within National Forest boundaries is to focus on suppression difficulty in relation to risk transmission and prospects for preventing fire from spreading onto adjacent lands. To compare tSDI values across predefined analysis units it was necessary to adjust by the respective area of these units. We defined ratio-scale indices (SDI\_AR, Table 1) that relate ratios of total tSDI to ratios of total area across analysis units. Eq. 4 presents an example calculation for the F and P analysis units, where AF and AP are the area of each analysis unit. These ratios are always calculated using the larger analysis unit as the denominator. Where the index is equal to 1, it suggests that the difference in suppression difficulty across analysis units is directly proportional to the difference in area. Where the index is greater than 1, it suggests that suppression difficulty is disproportionately higher in the smaller analysis unit, and vice versa. Using these four analysis units, we arrive at six possible pairwise comparisons (F\_P, B5\_P, B2\_P, B5\_F, B2\_F, and B2\_B5).

$$SDI\\_AR\_{F-P} = \frac{\left(\sum tSDI\_F / \sum tSDI\_P\right)}{\left(A\_F / A\_P\right)}\tag{4}$$

To quantify protection priority, we merged tSDI results with protection demand layers. We created individual priority indices for F2F and dWUI, which are calculated simply as the product of each POD's mSDI and its protection demand level. Eq. 5 presents an example of

To identify "high" priority PODs, we sorted PODs on the basis of protection priority and selected only those with protection priority levels in the top 10th percentile for F2F and dWUI. We further identified which PODs, if any, were included in the top 10th percentile for both F2F

Figure 3 displays the POD network in relation to boundaries of the National Forests as well as the Forest to Faucets catchments. In total we identified 8772 PODs for further analysis, which ranged in size from <1 to 2532 hectares. The mean POD size was 125 hectares, and the median POD size was 63 hectares. For operational purposes users would likely make post-hoc adjustments to the POD network created through automation, for example eliminating very small

There are a few noteworthy observations. First, POD boundaries rarely align with Forest boundaries, but often align with catchment boundaries. Second, PODs are much smaller than catchments, such that each catchment may contain multiple PODs. This reflects our analytical process that uses smaller catchment boundaries as well as the presence of roads on the landscape. The full extent of this POD network corresponds to analysis unit P (Table 2), and

Figure 4 presents tSDI values mapped to the full extent of the POD network as depicted in Figure 3. Some patterns are immediately evident. High elevation areas along the Continental Divide have zero or near-zero tSDI values due to lack of burnable vegetation. The scar from the High Park Fire in the northeast portion of the landscape similarly has very low tSDI values. This appears as a break between an otherwise largely uninterrupted corridor of high tSDI values running along the eastern edge of the analysis area. Isolated patches of high tSDI values elsewhere appear at least partially driven by steep slopes, sometimes in remote or wilderness

The most notable aspect of Figure 4 is the concentration of high tSDI values located on the eastern side of the analysis area. This simultaneously highlights the importance and challenge of fire management in these areas, where aggressive suppression would likely be warranted in

PP\_F2F ¼ mSDI∗F2F (5)

Analyzing Wildfire Suppression Difficulty in Relation to Protection Demand

http://dx.doi.org/10.5772/intechopen.76937

53

protection priority calculated for F2F.

4. Results

4.1. POD network

4.2. tSDI results

and dWUI. These PODs we identify as "very high" priority.

PODs by incorporating them into adjacent PODs.

areas with low road density (see Figure 2).

is used for summarizing results in the remainder of this paper.

#### 3.4. Quantify protection demand and protection priority

To quantify protection demand we use variables related to the WUI and to municipal watersheds (Table 1). For the WUI layer, we used high resolution built structure data derived from [10]. We summarized total count of structures by POD, and then divided by total POD area to derive an areal density measure for each POD (dWUI, Table 1). For the watersheds layer, we used data obtained from the Forest Services's Forests to Faucets (F2F, Table 1) project [40]. Specifically we used a layer that assigns surface drinking water relative importance scores (0–100) to each 12-digit hydrologic unit code catchment. More information on this layer and its use in risk assessments can be found in [40, 41]. The data layer we use assigns each catchment a score on a range, and we use the midpoint of that range to assign each POD a unique surface drinking water importance score (e.g., 75 from 70 to 80). In cases where a POD overlapped multiple catchments, we used the importance score from the catchment that comprised the majority of POD area (across the case study landscape, on average the majority catchment selected accounted for >99% of total POD area).


Table 2. Analysis units for analyzing tSDI ratios, sorted in order of decreasing size.

To quantify protection priority, we merged tSDI results with protection demand layers. We created individual priority indices for F2F and dWUI, which are calculated simply as the product of each POD's mSDI and its protection demand level. Eq. 5 presents an example of protection priority calculated for F2F.

$$P P\_{\\_F 2F} = m SDI \* F 2F \tag{5}$$

To identify "high" priority PODs, we sorted PODs on the basis of protection priority and selected only those with protection priority levels in the top 10th percentile for F2F and dWUI. We further identified which PODs, if any, were included in the top 10th percentile for both F2F and dWUI. These PODs we identify as "very high" priority.
