Spatial pattern of high burn days and anomalies for 2023. Credit: Science (2025). DOI: 10.1126/science.ado1006
A team of forest management experts from institutions across Canada found that fuel dryness was the most influential factor in burn severity during wildfires in Canada over the past few many years.
In their article published in the diary Sciencethe group describes how they collected and analyzed 40 years of spatiotemporal wildfire data in Canada after which used these findings to construct a multinomial logistic regression model to raised understand the increase in the number and severity of wildfires in Canada over the past several many years.
Jianbang Gan from Texas A&M University published: Perspective an article in the same journal that described the work done by the team and others working to grasp the primary aspects causing fires in other parts of the world.
As the planet warms, wildfires have gotten more quite a few and more severe. They get more offended, grow larger and cause more damage. Such fires, called wildfires after they burn large swaths of forest, have develop into increasingly common in parts of Russia, North America and Australia over the past few many years. Studies of these fires have shown that in addition to the loss of trees and human structures, they cause more CO22 emissions into the atmosphere as carbon trapped in trees is released.
Such fires have also been found to be getting faster in places like the United States, where they’re releasing embers into the air ahead of rapidly advancing flames, igniting human structures before emergency services can intervene, all as a consequence of stronger winds. For this recent endeavor, a Canadian research team desired to learn more about the primary causes of the increase in the number and size of fires in their country over the past few many years.
To do that, they collected as much data as they might on wildfires which have occurred in Canada over the past 40 years. They then used this data to create what they describe as a multinomial logistic regression model – a sort of model that takes into consideration multiple categories of dependent or end result variables. It does this by making estimates amongst associations between sets of predictors and unstructured outcomes across multiple categories.
By analyzing the model results, scientists found that the most influential cause of burn severity is fuel dryness (tree dryness). They also found that summer wildfires are inclined to be more severe and that conditions have worsened over the past 20 years. Finally, the researchers note that driver differences led to different results in different parts of the country.
More information:
Weiwei Wang et al., in recent many years, Canadian forests have been more more likely to support high-severity fires, Science (2025). DOI: 10.1126/science.ado1006
Jianbang Gan, Unraveling the perpetrators of the fires, Science (2025). DOI: 10.1126/science.adu5463
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Quote: Fuel dryness emerges as dominant cause of severity of recent wildfires in Canada (2025, January 4), retrieved January 4, 2025, from https://phys.org/news/2025-01-fuel-aridity-emerges-dominant-driver .html
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