4. Results

(4)

weather conditions for fuel moisture conditions and wind speed/direction, drawn from the

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

> PtSDIF= <sup>P</sup> ð Þ tSDIP ð Þ AF=AP

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

B5 Area within 5-km buffer internal to National Forest boundaries B2 Area within 2-km buffer internal to National Forest boundaries

possible pairwise comparisons (F\_P, B5\_P, B2\_P, B5\_F, B2\_F, and B2\_B5).

SDI\_ARF�<sup>P</sup> ¼

3.4. Quantify protection demand and protection priority

selected accounted for >99% of total POD area).

Analysis Unit Definition

P Area within entire POD network F Area within National Forest boundaries

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

Redfeather Remote Automated Weather Station.

52 Environmental Risks

### 4.1. POD network

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 PODs by incorporating them into adjacent PODs.

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

#### 4.2. tSDI results

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 areas with low road density (see Figure 2).

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

Figure 3. Derived POD network, overlaid with National Forest boundaries and Forests to Faucets catchment boundaries.

Figure 4. tSDI values mapped to the extent of the POD network (Figure 3).

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Figure 4. tSDI values mapped to the extent of the POD network (Figure 3).

Figure 3. Derived POD network, overlaid with National Forest boundaries and Forests to Faucets catchment boundaries.

54 Environmental Risks

order to avoid the spread of fire onto adjacent lands where fires could threaten the WUI and other infrastructure. The vast majority of cells on the landscape have low tSDI values: 46% are <0.1, 76% are <0.2, and 89% are <0.5. By contrast, only 4% have values >1.

structure density values ranged from 0 to 9.87, with a mean of 0.12 and a median of 0. A total of 5447 PODs contained zero structures, with a total of 62,359 structures contained within the remaining 3325 PODs. For F2F, the importance values differ sharply on the western and eastern sides of the Continental Divide, with the highest values generally located in the southeastern portion of the POD network. For dWUI, the highest values similarly occur in the eastern portions of the POD network, although generally further to the east than the highest

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Figure 6 displays a three-dimensional scatterplot of mSDI, F2F, and dWUI, along with twodimensional slices for pairwise comparisons (dWUI vs. mSDI; dWUI vs. F2F; F2F vs. mSDI). The preponderance of low mSDI values is evident in the 3D scatterplot, with a corresponding lack of points clustered in the high-mSDI, high-F2F, and high-dWUI space. In terms of pairwise comparisons, higher dWUI values tend to be align with lower mSDI values, which could in part reflect higher road densities and shallower slopes commonly associated with higher density human development. There is slight positive association with dWUI and F2F, particularly noticeable for the surface water importance scores of 85, which suggests opportunity to readily identify some PODs as very high protection demand. Referring back to Figure 5, some of these PODs are located near the community of Estes Park in the central-eastern

Figure 6. Three-dimensional scatterplot of mSDI, dWUI, and F2F, along with two-dimensional slices for pairwise com-

F2F values.

parisons.

Table 3 presents results for SDI\_AR indices across all six analysis unit pairwise comparisons. All indices relating to the analysis unit P are >1, which reflects the spatial distribution of low tSDI values outside of National Forest boundaries (Figure 4). Buffers B5 and B2 are effectively proportional, but all have indices >1 with respect to units F and P. Findings suggest therefore that suppression would be more challenging proximal to Forest boundaries, such that the probability of failing to prevent risk transmission could be substantial. Notably, these ratios would be even higher if we limited our buffers to the eastern and southern edges of the POD network.

#### 4.3. Protection demand and protection priority

Figure 5 displays POD-level summarization of F2F and dWUI protection demand. For F2F, importance scores ranged from 25 to 95, with a mean of 64.33 and a median of 65. For dWUI,


Table 3. Ratio-scale indices comparing area-adjusted tSDI values within and across various analysis units.

Figure 5. POD-level protection demand for F2F and dWUI.

structure density values ranged from 0 to 9.87, with a mean of 0.12 and a median of 0. A total of 5447 PODs contained zero structures, with a total of 62,359 structures contained within the remaining 3325 PODs. For F2F, the importance values differ sharply on the western and eastern sides of the Continental Divide, with the highest values generally located in the southeastern portion of the POD network. For dWUI, the highest values similarly occur in the eastern portions of the POD network, although generally further to the east than the highest F2F values.

order to avoid the spread of fire onto adjacent lands where fires could threaten the WUI and other infrastructure. The vast majority of cells on the landscape have low tSDI values: 46% are

Table 3 presents results for SDI\_AR indices across all six analysis unit pairwise comparisons. All indices relating to the analysis unit P are >1, which reflects the spatial distribution of low tSDI values outside of National Forest boundaries (Figure 4). Buffers B5 and B2 are effectively proportional, but all have indices >1 with respect to units F and P. Findings suggest therefore that suppression would be more challenging proximal to Forest boundaries, such that the probability of failing to prevent risk transmission could be substantial. Notably, these ratios would be even

Figure 5 displays POD-level summarization of F2F and dWUI protection demand. For F2F, importance scores ranged from 25 to 95, with a mean of 64.33 and a median of 65. For dWUI,

higher if we limited our buffers to the eastern and southern edges of the POD network.

Analysis units tSDI ratio Area ratio SDI-AR F\_P 0.66 0.64 1.04 B5\_P 0.44 0.40 1.11 B2\_P 0.22 0.20 1.12 B5\_F 0.67 0.63 1.07 B2\_F 0.33 0.31 1.07 B2\_B5 0.50 0.50 1.00

Table 3. Ratio-scale indices comparing area-adjusted tSDI values within and across various analysis units.

<0.1, 76% are <0.2, and 89% are <0.5. By contrast, only 4% have values >1.

4.3. Protection demand and protection priority

56 Environmental Risks

Figure 5. POD-level protection demand for F2F and dWUI.

Figure 6 displays a three-dimensional scatterplot of mSDI, F2F, and dWUI, along with twodimensional slices for pairwise comparisons (dWUI vs. mSDI; dWUI vs. F2F; F2F vs. mSDI). The preponderance of low mSDI values is evident in the 3D scatterplot, with a corresponding lack of points clustered in the high-mSDI, high-F2F, and high-dWUI space. In terms of pairwise comparisons, higher dWUI values tend to be align with lower mSDI values, which could in part reflect higher road densities and shallower slopes commonly associated with higher density human development. There is slight positive association with dWUI and F2F, particularly noticeable for the surface water importance scores of 85, which suggests opportunity to readily identify some PODs as very high protection demand. Referring back to Figure 5, some of these PODs are located near the community of Estes Park in the central-eastern

Figure 6. Three-dimensional scatterplot of mSDI, dWUI, and F2F, along with two-dimensional slices for pairwise comparisons.

portion of the map. Lastly, F2F and mSDI values tend to show little relationship, apart from

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Figure 7 displays the PODs identified as "high" and "very high" protection priority. In total 1524 PODs are identified, 646 each that correspond to the top 10th percentile for either PP\_F2F or PP\_dWUI (Table 1, Eq. 5), and 232 of which correspond to PODs in the top 10th percentile for both. To reiterate, the latter category is what we deem to be "very high" protection priority. Not surprisingly given results in Figures 4 and 5, the greatest levels of protection priority run along the eastern flank of the POD network, many of which are within National Forest boundaries. The joint concentration of high suppression difficulty and high protection demand

Two primary innovations we introduce here are the summarization of tSDI within various analytical units to determine differences in area-adjusted suppression difficulty, and the summarization of tSDI within PODs to determine protection priorities. Notably, we attempted to expand the concept of risk transmission to include opportunities to safely and effectively restrict fire spread across ownership boundaries. The incorporation of suppression difficulty and control opportunities has, to date, been largely absent from the literature on wildfire risk transmission. What we presented here ideally informs decisions related to the need for suppression where protection demand is high, as well as decisions related to the need for suppres-

There are a number of foreseeable near- and long-term extensions to this work. Perhaps most immediate, the analysis could be extended across multiple ownerships to create a common operating picture for co-management of risk. Models of fire spread and containment could be updated to account for suppression difficulty, and could be used to game out various scenarios and alternative response strategies [42]. Similarly, models designed to optimize initial attack response could be updated to account for variable suppression resource needs as a function of tSDI [43]. Calculating tSDI values under different weather scenarios could be informative for gaming out how suppression opportunities change with conditions, and could further serve as the basis for prioritization of fuel treatment investments designed to enhance suppression effectiveness [44]. Analysis of tSDI values along POD boundaries could identify potential weakness in the POD network, which could also help inform prioritization of fuel treatments. Incorporating structure and watershed susceptibility to fire through more rigorous fire effects analysis, as well as incorporating fire likelihood, would likely allow for targeted identification of protection priority [41]. It is not necessarily the case that higher F2F importance weights imply higher potential for post-fire erosion, for example, or that higher mSDI values imply higher intensity fire leading to greater damage. Opting for more refined risk assessment of course comes with greater investment of time and resources, a tradeoff which must be evaluated in light of the marginal value that is added for decision processes [13]. This point encapsulates a common aspect of designing and delivering decision support, which is that modeling frameworks do not necessarily need to be complicated to demonstrate potential

lower mSDI values tending to align with lower F2F values.

highlight this area as for preventative risk management activities.

sion where the potential for risk transmission is high.

5. Discussion

Figure 7. POD-level protection priority for F2F, dWUI, and both.

portion of the map. Lastly, F2F and mSDI values tend to show little relationship, apart from lower mSDI values tending to align with lower F2F values.

Figure 7 displays the PODs identified as "high" and "very high" protection priority. In total 1524 PODs are identified, 646 each that correspond to the top 10th percentile for either PP\_F2F or PP\_dWUI (Table 1, Eq. 5), and 232 of which correspond to PODs in the top 10th percentile for both. To reiterate, the latter category is what we deem to be "very high" protection priority. Not surprisingly given results in Figures 4 and 5, the greatest levels of protection priority run along the eastern flank of the POD network, many of which are within National Forest boundaries. The joint concentration of high suppression difficulty and high protection demand highlight this area as for preventative risk management activities.
