Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF-Fire

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire-spread model WRF-Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re-gridding scheme, we compare WRF-Fire's heat output to geostationary satellite data at 1-hr temporal resolution. We also evaluate WRF-Fire's time-resolved burned area against high-resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF-Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5-day forecast. Prescribed diurnal cycles and those resolved by WRF-Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire-spread modeling has the potential to improve smoke emission forecasts. Predicting wildfire growth and smoke behavior is an important and historically difficult task. Here we compare a coupled fire-weather model to current practices used in air quality forecasting in their ability to predict daily wildfire spread and fire intensity for the 2019 Williams Flats Fire. We also evaluate the fire-weather model against high-resolution satellite and aircraft measurements in ways previously unexplored. We find that including fire-fighting efforts in our model makes the biggest improvements toward accurate wildfire forecasting. When we account for fire-fighting efforts, fuel moisture maps, and increased fuel loads, our coupled fire-weather model significantly outperforms simpler methods with reasonable computational time. Despite this, modeled fire intensity tends to lead observations by at least an hour. Optimal configuration options and avenues for forecasting improvements are discussed. Adding containment lines into our wildfire model made the biggest difference in improving accuracy of daily burned area predictionsAccounting for containment, fuel moisture content maps, and increased fuel density consistently increased prediction skill above persistenceModeled fire radiative power leads observations by at least an hour, but with similar accuracy to prescribed diurnal cycles