EXPOSURE INDEX FOR CARIBOO AND KAMLOOPS
Study Area
Study Area: Our group considered a variety of study areas while defining the scope of this project. The initial plan was to apply our analyses Canada-wide. Due to time constraints we decided to focus on two regions within British Columbia. British Columbia’s wildfire response and prevention activities are organized into six regions within the province. Out of the six, we selected the two regions with the highest incidence of wildfires, and greatest area of hectares burned; the Cariboo (273 fires per year, 216,000 hectares burned) and Kamloops (248 fires per year, 870,000 hectares burned) regions. These two regions account for approximately 40% of wildfires in British Columbia (14).
Highlighted regions are the two study areas. Cariboo to the north, Kamloops to the south
Cariboo Fire Serivce Area
Kamloops Fire Serivce Area
Exposure Index
Exposure represents resources that may be vulnerable to wild fire.
Population Density - Population data was joined to a map layer of the dissemination areas within both the Cariboo and Kamloops fire regions. This combined map layer was then joined with a 2.5 km squared grid covering each fire region area and clipped to cover only, each of the fire regions. Population densities were then normalized using the formula (Value-(min)/(Max)-(min)), and converted into raster map layers.
Example of population density processed, prior to rasterization
Building Density - Building data was clipped to the boundaries of each fire region, and spatially joined to a 2.5km squared grid. Grid cells with buildings present in them were scored 1, cells without were scored 0. Normalization was not necessary for this variable as it was dichotomous. The layer was then converted to a raster layer.
Road Density - A data set of British Columbia’s road network was spatially joined to a 2.5km squared grid for each fire region. Road density was derived by calculating the road length within each grid cell. This value was then normalized using the formula (Value-(min)/(Max)-(min)), and converted into raster map layers.
Protected Area Coverage - A data set of protected areas in Canada was spatially joined to a 2.5km squaredgrid and clipped for each fire region. This variable was treated as dichotomous. Grid cells with protected areas within them were scored 1, grids without were scored 0. Normalization was not necessary. The layer was then converted into a raster layer.
Vegetation coverage - A vegetation data set was spatially joined to a 2.5km squared grid for each fire region. This variable was treated as dichotomous; squares of the grid were scored 1 if vegetation fell within it, 0 if not. Normalization was not necessary. The layer was then converted into a raster layer.
Formula - Population density, protected area cover, road and building densities and vegetation and fuel cover were weighted, and added together using the raster calculator function to develop the exposure index.
EXPOSURE= [(population density*0.3) + (vegetation coverage*0.3) + (protected area coverage*0.2) + ((building density*0.5) + (road density*0.5))*0.2].
Example of road density processed, prior to rasterization
Sensitivity Index
Sensitivity modifies variables of exposure. While many exposure variables were based on dichotomous scores, sensitivity variables were scaled.
Senior’s populations - (65 and older) were present in the same data sets as the population densities. Seniors were represented as the proportion of seniors compared to the general population. The senior population proportions were normalized, spatially joined to a 2.5km2 grid for each fire region, and converted into a raster layer.
Fuel Sensitivity Level - A data set of vegetation types and cover was scored based on wildfire proneness (see below). Ranging from 0 (wetlands) to 10 (Shrub-grasslands). This scored data set was normalized, spatially joined to a 2.5km squared grid for each fire region, and converted into raster layers.
Protected Areas - Similar to fuel sensitivity, protected areas were scored based on their level of protection (see below). Ranging from 1 (recreational areas) to 4 (ecological reserves). This value was then normalized, spatially joined to a 2.5km squared grid for each fire region, and converted into raster layers.
Formula - Sensitivity = [(Senior population*0.4) + (Fuel sensitivity*0.4) + (Protected Areas*0.2)]
Example of Vegetation Index, prior to rasterization
Coping Capacity Index
Fire Surveillance Area - Point-data of fire weather stations in BC were fitted with a 15km, circular buffer to create a surveillance area. The surveillance areas were scored as 1, and spatially joined to a 2.5km2 grid for each fire region. Normalization was not necessary, as the presence of buffers were treated as dichotomous for each grid cell. The resulting layer was then converted into a raster layer.
Fire Station Service Area. Point data for fire stations in British Columbia were clipped to the individual fire areas, and joined to the BC road network map layer. Using the network analyst tool, a 100km service area for each fire station was drawn to approximate a coverage area that fire teams could reach within 1 hour of wildfire discovery. The service area layer was spatially joined to a 2.5km squared base grid, and within each cell the number of overlapping service areas were counted and standardized to a value between 0 and 1.
Formula - Coping Capacity = 1 - [(Fire Service Areas*0.8) + (Fire Surveillance Areas*0.2)]
Composite Map - The individual exposure, sensitivity and coping capacity indices were weighted together, and added using the raster calculator function in ArcGIS.
Formula - Vulnerability = [(exposure*0.5) + (sensitivity*0.3) + ((1-coping capacity)*0.2)]