Soil Moisture is a Stronger Predictor of Forest Fire Spread Potential Than Weather in the U.S. Northern Rocky Mountains

Accurate prediction of forest fire spread is a critical management and scientific challenge as the world adapts to rapidly changing fire regimes. We reconstructed 5,400 daily burned area progression maps for 196 U.S. Northern Rocky Mountain wildfires (2012–2021) and used machine learning to estimate daily fire growth given local weather, hydroclimate, fuels and topography. Optimized models explained 36% of the variation in daily fire growth, increasing to 56% when an index of fire activity the previous day wasincluded. Soil moisture and plant hydraulic stress were the dominant predictors of fire spread, increasing accuracy by 8%–9% over models with only fuel and weather. Wildfire danger forecasts and fire spread models in the U.S. use short‐ term weather indices and don't consider longer‐term drought. Our findings suggest that soil moisture and vegetation stress are critical indicators of fire spread potential in this region, with implications for fire modeling and prescribed burn planning. 

Plain Language Summary: Forest fires have been increasingly affecting the western United States and many other regions worldwide. To support wildfire planning, mitigation and response efforts, researchers have developed a range of physics‐based and data‐driven models to simulate fire propagation. The majority of these models rely on weather conditions and fire danger indices for their predictions, without directly considering soil moisture and its influence on live fuel moisture. We mapped daily fire perimeters for 196 large forest fires from 2012 to 2021 using VIIRS satellite fire detections and statistical interpolation and used boosted regression tree models to estimate the effects of weather, fire danger, soil moisture and fuels on daily fire growth. Our resultssuggest that soil moisture‐related variables strongly influence daily fire growth and potential for large fire growth days. Additionally, our models indicated that inclusion of previous day active pixel counts —that is, adding memory to the fire propagation model—can markedly enhance model performance. Our findings highlight the crucial role of soil moisture in influencing forest fire spread, with significant implications for future mitigation and response efforts