A number of factors such as ignition potential, fuel conditions and fuel type were used in order to determine the areas with high wildfire susceptibility (Figures 12, 13, 14). These layers were combined together to produce the final susceptibility layer (Figure 15).
The fuel conditions take into account the temperature, precipitation and biomass in the Southern Interior (Figure 12). These are significant in terms of susceptibility as increased biomass equals more fuel potentional, as well as lack of rain and increased temperature results in drier conditions making them prone to fire. These factors were weighted more heavily throughout the analysis, which is evident when compared to the susceptibility map (Figure 15).
Figure 12. Susceptibility of the Southern Interior to Wildfires Based on Fuel Conditions
Higher fuel type values represent species that are more susceptible to burning (Figure 13), which correlates highly with the susceptibility layer overall as a large number of these areas are found within areas of high susceptibility (Figure 15). There are also areas within the north-west that show higher values, but when compared to the susceptibility map, these areas are considered low risk (Figure 13). This is likely because fuel type was not ranked as heavily as the other factors in the susceptibility model, resulting in the stand type not greatly changing the overall results.
Figure 13. Susceptibility of the Southern Interior to Wildfires Based on Fuel Type
Different regions of the Southern Interior were susceptibile to fire ignitions to differing degrees (Figure 14). This ignition potential layer, which was determined from natural and anthropogenic ignition factors, takes into account the effects that varying elevations and the proximity to humans have on the frequency and location of wildfire events. When compared with the susceptibility output there are a number of areas that align in terms of high and low susceptibily which shows that the weighting of these factors were significant in terms of our analysis (Figure 14 and 15).
Figure 14. Ignition Potential of Vegetated Land in the Southern Interior
Final Susceptibility Layer
The Southern Interior of BC contained a number of areas that the susceptability model found to be of differing susceptibility to wildfires (Figure 15). These areas were mostly concentrated near water sources, which is noteworthy as it could aid in potentially helping fire managers when suppressing fires. There was very little area that was identified as low susceptibility (~4%), with a large portion of the model output showing high or moderately high susceptibility (~70%). This is an unexpected result of the model, and could have possibly been produced from the standardization methods used within the model, which could have right skewed the data and caused it to be given a high value on average.
Figure 15. Relative Wildfire Susceptibility of Vegetated Land in the Southern Interior
A number of factors such as ecological vulnerability, land value, and remoteness were used in order to determine the areas with high wildfire vulnerability (Figures 16, 17, 18). These layers were combined together to produce the final vulnerability layer (Figure 19).
Ecological vulnerability (Figure 16) showed a high degree of correlation with the final vulnerability output (Figure 19). The factors for species at risk and biodiversity contributed substantially to the final output of vulnerability.
Figure 16. Ecological vulnerability layer depicting regions of high and low ecological vulnerabilities.
The land value layer (Figure 17) displays three regions of value to human populations: low, medium and high. High vulnerability regions (Figure 19) were typically correlated with low land values due to the low overall weighting of land value and the higher weight assigned to ecological value (Figure 17). Where land value was at its lowest, ecological value was at its highest. This could have been caused by the fact that human activites in high land value areas (timber harvest and park land) produced a less suitable habitiat for species, which could have resulted in a less biodiversity as a result (Fisher and Wilkinson, 2005; Semlitsch et al., 2009).
Figure 17. Map of land value depicting low, medium and high regions of value.
The most remote areas were in the northern and southern regions of the study area (Figure 18). The rankings from the output of the remoteness map can be seen outlining disitnct regions in the final vulnerability map (Figure 19). These regions can help define vulnerable areas for human populations, hypothetically indicating the potential for loss of human life due to a lack of resources to escape prospective wildfires.
Figure 18. Map depicting regions of low, medium and high remoteness.
The final vulnerability output reveals high levels of vulnerability in the north and south regions of the study area (Figure 19). Regions of high vulnerability were typically associated with water resources. This could be due to the fact that these regions consisted of high ecological vulnerability (Figure 16) and remoteness (Figure 18) and the low values of land value (Figure 15). These low land values did not have a large affect on the overall output though, due to its low factor weighting. There was very little area that was determined to be of high (14.4%) and low (5.6%) vulnerability to wildfires, with almost half of the layer being distributed within the moderately high category (47.2%).
Figure 19. Vulnerability map depicting low to high regions of vulnerability.
The risk layer, which combined the factors analyzed in both vulnerbility and susceptibility, represents the areas that are of high risk to wildfires within B.C. (Figure 20). Comparing this final risk output to our susceptibility and vulnerbility output (Figure 15 & 19) we can see how the weighted factors affected the total area, and as a result it shows the areas of highest risk within the study area. The model once again resulted in an output that contained very little low risk areas (6.8%) and much more high and moderately high areas (36.7% and 30.9%). This affect was not as large as in the susceptibility model though, indicating a more even distribution of risk values than susceptibility values. This was most likely caused by the smaller area of high vulnerability that was identified in the vulnerability model output (Figure 19).
Figure 20. Overall Wildfire Risk of Areas in the Southern Interior