WHY IS THIS WORK IMPORTANT?
Fire managers need to be able to accurately predict potential smoke dispersion from low-intensity prescribed burning operations in order to manage the potential smoke impacts to air quality that affect human health and safety.
The BlueSky Modeling Framework links models and data sets together to make smoke dispersion predictions, and recent advancements have resulted in improved predictions for large wildfire events. However less attention had been paid to understanding the underlying capabilities of the Framework to predict smoke emissions, transport and surface concentrations from small low-intensity fires that often characterize prescribed burning operations.
This project was designed to collect information that could be used to develop new modeling pathways within the BlueSky Framework and improve smoke predictions for low intensity prescribed fires.
WHAT DID WE LEARN?
The best modeling pathway was identified within the BlueSky Framework to operationally predict smoke concentrations from low intensity burns.
Researchers found that emission and smoke dispersion predictions can be easily improved by updating emission factor algorithms used in the BlueSky Framework.
Bluesky modeling significantly under-predicted measured smoke concentrations no matter how fuels and consumption data were used to tune model results.
Using observed fuel loads would not improve predictions enough to warrant the expense and time to measure fuel loading.
A simple Gaussian line source model shows promise as a viable option for predicting smoke concentrations near the burn.
Fire managers could reduce the potential for unwanted smoke impacts by ending ignition earlier in the day. This would allow for more mixing and movement of smoke away from the forest floor and mitigate the amount of smoke trapped under the forest canopy.
BlueSky predictions for low intensity burns could be improved by incorporating planned start and end times for burns and flame length limitations.