|SMITH, JOSEPH - University Of Montana|
|ALLRED, BRADY - University Of Montana|
|JONES, MATTHEW - University Of Montana|
|KLEINHESSELINK, ANDREW - University Of Montana|
|NAUGLE, DAVID - University Of Montana|
Submitted to: Rangeland Ecology and Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2022
Publication Date: N/A
Interpretive Summary: At present, tools for predicting wildfire likelihood and distribution are extremely limited for sagebrush rangeland; extant tools are largely developed for forested ecosystems and are of limited utility to sagebrush managers. Burns, Oregon ARS worked with the Rangeland Analysis Platform team at the University of Montana to create a spatially-explicit model that predicts probability of large wildfires (> 1000 acres) in the Great Basin region as a function of antecedent fuel (primarily perennial grass) accumulation and precipitation. The wildfire probability model explains 70% of the variation in yearly acres burned in the Great Basin from 1988 to present and model predictions are available on April 1 of each year, allowing sufficient time for local and regional fire managers to conduct fuels treatments and pre-position suppression equipment prior to the onset of the fire season. This is the first fuel-driven, spatially explicit, and pre-emptive (i.e. available before the fire season) model to be accurate enough for broad management utility in predicting wildfire occurrence in sagebrush vegetation.
Technical Abstract: Wildfires are a growing management concern in western US rangelands, where invasive annual grasses have altered fire regimes and contributed to an increased incidence of catastrophic large wildfires. Fire activity in arid, non-forested regions is thought to be largely controlled by interannual variation in fuel amount, which in turn is controlled by antecedent weather. Thus, long-range forecasting of fire activity in rangelands should be feasible given annual estimates of fuel quantity. Using a 32 yr time series of spatial data, we employ machine learning algorithms to predict the relative probability of large (>400 ha) wildfire in the Great Basin based on fine-scale annual and 16-day estimates of cover and production of vegetation functional groups, weather, and multitemporal scale drought indices. We evaluate the predictive utility of these models with a leave-one-year-out cross-validation, building spatial forecasts of fire probability for each year that we compare against actual maps of large wildfires. Herbaceous vegetation aboveground biomass production, bare ground cover, and long-term drought indices were the most important predictors of fire probability. Across 32 fire seasons, >80% of the area burned in large wildfires coincided with predicted fire probabilities =0.5. At the scale of the Great Basin, several metrics of fire season severity were moderately to strongly correlated with average fire probability, including total area burned in large wildfires, number of large wildfires, and average and maximum fire size. Our findings show that recent years of exceptional fire activity in the Great Basin were predictable based on antecedent weather and biomass of fine fuels, and reveal a significant increasing trend in fire probability over the last three decades driven by widespread changes in fine fuel characteristics.