Wildfire exposure indicates the extent to which people, infrastructures and other tangible human assets, as well as ecosystems, could be affected by wildfires. Areas may be exposed directly through contact with the fire front or via flaming embers; or indirectly through the dispersion of smoke, or by fire-caused changes in hydrological cycles or soil erosion. The actual exposure to fire may change even in short periods of time as a result of weather patterns (e.g., heatwaves, changes in wind conditions that transport smoke in different directions) or population movements (summer holidays) [1].
As a component in wildfire risk assessment systems, exposure forms a link between danger and vulnerability. Hence, the assessment of exposure is a crucial step in establishing the location and characteristics of landscape entities that may be adversely affected by wildfires. Exposure does not account for the properties of the given value at risk (as these are part of its vulnerability characteristics; see next section), but reflects the potential of a given place to be affected by wildfires, or the extent to which it is exposed to it; studies on wildfire exposure are often based on the intersection of predictions of fire behaviour models, with available data on the presence of the elements at stake. For example, Bar-Massada et al. (2009)[2] overlaid the locations of built structures on top of the gridded predictions of burn probability derived from the FARSITE model [3] to estimate housing exposure to wildfires under both normal and extreme weather conditions in northern Wisconsin. Similarly, Alcasena et al. (2016)[4] estimated the exposure of highly-valued resources and assets in Navarra, Spain, based on the intersection of their locations and predictions of burn probability from the FlamMap model [5]. A simpler approach (without fire spread modelling) was used by Beverly et al. (2010)[6] to estimate the exposure of four communities in Alberta, Canada to wildfire based on the composition of hazardous fuels within multiple buffer distances around them, which reflect different mechanisms of fire spread (radiant heat, short-range and long-range spotting).
One critical aspect of fire exposure entails the wildland urban interface (WUI) mapping [7] [8]. WUI maps typically use a set of predefined rules and parameters to identify where human settlements adjoin or intermingle with flammable vegetation, to highlight locations of potential fire exposure across landscapes. To date, there are multiple WUI mapping approaches [9] [10], and these methods have been applied in many regions and at spatial scales ranging from local to global [11]. Information derived from these maps allowed policy makers and land managers to direct management efforts to reduce potential exposure to wildfires via the prioritization of fuel treatments around exposed areas.
Return to Conceptual Framework Diagram
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D., & Tatem, A.J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111, 15888-15893. ↩︎
Bar Massada, A., Radeloff, V.C., Stewart, S.I., & Hawbaker, T.J. (2009). Wildfire risk in the wildland–urban interface: A simulation study in northwestern Wisconsin. Forest Ecology and Management, 258, 1990-1999. ↩︎
Finney, M.A. (1998). FARSITE: Fire Area Simulator - Model development and evaluation. Missoula, MT: USDA Forest Service. RMRS-RP-4. ↩︎
Alcasena, F.J., Salis, M., & Vega-García, C. (2016). A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain. European Journal of Forest Research, 135, 87-107. ↩︎
Finney, M.A. (2006). An Overview of FlamMap Fire Modeling Capabilities. In P.L. Andrews, & B.W. Butler (Eds.), Fuels Management-How to Measure Success: Conference Proceedings RMRS-P-41 (pp. 213-220). Portland, OR: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research. ↩︎
Beverly, J., Bothwell, P., Conner, J., & Herd, E. (2010). Assessing the exposure of the built environment to potential ignition sources generated from vegetative fuel. International Journal of Wildland Fire, 19, 299-313. ↩︎
Argañaraz, J.P., Radeloff, V.C., Bar-Massada, A., Gavier-Pizarro, G.I., Scavuzzo, C.M., & Bellis, L.M. (2017). Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina. Journal of Environmental Management, 196, 499-510. ↩︎
Radeloff, V.C., Hammer, R.B., Stewart, S.I., Fried, J.S., Holcomb, S.S., & McKeefry, J.F. (2005). The wildland-urban interface in the United States. Ecological Applications, 15, 799-805. ↩︎
Bar-Massada, A., Stewart, S.I., Hammer, R.B., Mockrin, M.H., & Radeloff, V.C. (2013). Using structure locations as a basis for mapping the wildland urban interface. Journal of Environmental Management, 128, 540-547. ↩︎
Modugno, S., Balzter, H., Cole, B., & Borrelli, P. (2016). Mapping regional patterns of large forest fires in Wildland–Urban Interface areas in Europe. Journal of Environmental Management, 172, 112-126. ↩︎
Carlson, A.R., Helmers, D.P., Hawbaker, T.J., Mockrin, M.H., & Radeloff, V.C. (2022). The wildland–urban interface in the United States based on 125 million building locations. Ecological Applications, 32. ↩︎