Leveraging a Physically-Based Dead Fuel Moisture Model to Inform Western United States Utility Wildfire Mitigation Operations

Wildfire spread characteristics are dependent, among other variables, on dead fuel moisture content and its variability both spatially and temporally across the terrain encountered by the wildfire. Over the past decade, western United States utilities have implemented various territory-wide wildfire potential forecast metrics to inform operational decisions with the goal of preventing wildfire ignitions and mitigating the risk of catastrophic wildfires. To support these utility wildfire mitigation efforts, we dynamically downscale an ensemble of operational NOAA and ECMWF large-scale weather forecasts and generate extreme weather and validated dead fuel moisture model forecast analytics. Our unique operational gridded weather and fuel moisture forecast framework allows for the generation of accurate and efficient wildfire fuel moisture forecasts at high spatial and temporal resolutions. The gridded dead fuel moistures available for each forecast are spun-up and carried forward for future forecasts allowing for larger diameter fuels to evolve. To complement these forecasts, we place each dead fuel moisture forecast into historical perspective using our 30 to 40-year dead fuel moisture data set forced from dynamically-downscaled historical reanalysis data. We will present the extensive validation results for both historical and operational dead fuel moisture and demonstrate how these analytics have been used to mitigate wildfires across the western US.