A modest increase in fire weather overcomes resistance to fire spread in recently burned boreal forests.

Recently burned boreal forests have lower aboveground fuel loads, generating a negative feedback to subsequent wildfires. Despite this feedback, short-interval reburns (≤20 years between fires) are possible under extreme weather conditions. Reburns have consequences for ecosystem recovery, leading to enduring vegetation change. In this study, we characterize the strength of the fire-fuel feedback in recently burned Canadian boreal forests and the weather conditions that overwhelm resistance to fire spread in recently burned areas. We used a dataset of daily fire spread for thousands of large boreal fires, interpolated from remotely sensed thermal anomalies to which we associated local weather from ERA5-Land for each day of a fire's duration. We classified days with >3 ha of fire growth as spread days and defined burned pixels overlapping a fire perimeter ≤20 years old as short-interval reburns. Results of a logistic regression showed that the odds of fire spread in recently burned areas were ~50% lower than in long-interval fires; however, all Canadian boreal ecozones experienced short-interval reburning (1981-2021), with over 100,000 ha reburning annually. As fire weather conditions intensify, the resistance to fire spread declines, allowing fire to spread in recently burned areas. The weather associated with short-interval fire spread days was more extreme than the conditions during long-interval spread, but overall differences were modest (e.g. relative humidity 2.6% lower). The frequency of fire weather conducive to short-interval fire spread has significantly increased in the western boreal forest due to climate warming and drying (1981-2021). Our results suggest an ongoing degradation of fire-fuel feedbacks, which is likely to continue with climatic warming and drying.

Fire suppression makes wildfires more severe and accentuates impacts of climate change and fuel accumulation.

Fire suppression is the primary management response to wildfires in many areas globally. By removing less-extreme wildfires, this approach ensures that remaining wildfires burn under more extreme conditions. Here, we term this the "suppression bias" and use a simulation model to highlight how this bias fundamentally impacts wildfire activity, independent of fuel accumulation and climate change. We illustrate how attempting to suppress all wildfires necessarily means that fires will burn with more severe and less diverse ecological impacts, with burned area increasing at faster rates than expected from fuel accumulation or climate change. Over a human lifespan, the modeled impacts of the suppression bias exceed those from fuel accumulation or climate change alone, suggesting that suppression may exert a significant and underappreciated influence on patterns of fire globally. Managing wildfires to safely burn under low and moderate conditions is thus a critical tool to address the growing wildfire crisis.

The Canadian Fire Spread Dataset.

Satellite data are effective for mapping wildfires, particularly in remote locations where monitoring is rare. Geolocated fire detections can be used for enhanced fire management and fire modelling through daily fire progression mapping. Here we present the Canadian Fire Spread Dataset (CFSDS), encompassing interpolated progressions for fires >1,000 ha in Canada from 2002-2021, representing the day-of-burning and 50 environmental covariates for every pixel. Day-of-burning was calculated by ordinary kriging of active fire detections from the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite, enabling a substantial improvement in coverage and resolution over existing datasets. Day of burning at each pixel was used to identify environmental conditions of burning such as daily weather, derived weather metrics, topography, and forest fuels characteristics. This dataset can be used in a broad range of research and management applications, such as retrospective analysis of fire spread, as a benchmark dataset for validating statistical or machine-learning models, and for forecasting the effects of climate change on fire activity.