Neal McLoughlin, superintendent of predictive services at the B.C. Wildfire Service (BCWS), is striving to replicate the behavior of the Bald Range wildfire accurately in his computer models based on the events that unfolded in the Okanagan Valley on Aug. 7.
McLoughlin is adjusting various parameters on his computer, such as enhancing extreme fire behavior, substituting forest fuels with more combustible types, introducing stronger westerly winds, and altering local topography to a flat landscape to align his simulations with the real events that occurred that night in Summerland, leading to the evacuation of over 12,000 residents as flames rapidly approached the town.
According to McLoughlin, the observed fire behavior surpasses what their models would typically predict, indicating a significant discrepancy in the model’s estimations due to outdated data collected over 30 years ago when instances of extreme fire behavior, exacerbated by prolonged droughts linked to climate change, were less frequent.
The Southern Interior region of British Columbia, officially recognized as the driest area in Canada according to the Canadian Drought Monitor, faced a wildfire season that witnessed a lower-than-average burned area but resulted in substantial property destruction, highlighting the challenges posed by evolving fire conditions.
Although BCWS specialists heavily rely on predictive models akin to weather forecasts to strategize their response to wildfires, McLoughlin emphasizes the importance of incorporating lived experiences and skepticism towards model predictions, especially during extreme hot and dry conditions where the models may fall short in anticipating the severity of wildfires.
The Canadian Forest Fire Danger Rating System (CFFDRS) underpins the predictive software utilized by agencies nationwide to assess wildfire risks. While updates to the Fire Behavior Prediction System are anticipated by 2029, incorporating data from increasingly intense fires remains a costly and perilous endeavor, limiting the ability to refine the models effectively.
McLoughlin’s firsthand encounters with the Pear Lake Wildfire, British Columbia’s largest wildfire of the season, underscore the necessity for more precise modeling of fire behavior under extreme conditions fueled by persistent droughts, which can lead to column-driven fires characterized by towering pyrocumulonimbus clouds resembling thunderstorms.
The scarcity of data on extreme fire behavior poses a challenge in enhancing predictive modeling tools, with the last major update to the software dating back to 1992. Collaborative efforts with research entities like NASA and reliance on prescribed burns are avenues pursued to gather contemporary data and improve the accuracy of fire behavior predictions essential for effective wildfire management.