Comprehensive Fuel and Emissions Measurements Highlight Uncertainties in Smoke Production Using Predictive Modeling Tools

Predictive
modeling tools, such as the First Order Fire Effects
Model (FOFEM), are used to generate estimates of the effects from
wildland fires, including fuel consumption and smoke emissions. Given
the use of such models in planning and forecasting for wildland fires,
coupled with the adverse health and climate impacts of smoke, there
is a need to understand the sensitivity to model inputs and processes,
evaluate smoke production, and identify critical uncertainties. In
this work, FOFEM was applied to a series of prescribed burns at the
Blodgett Forest Research Station (BFRS), a western mixed coniferous
forest in northern California, adapted to a frequent low-severity
fire regime. We evaluated the sensitivity of predicted smoke emissions
to parametric uncertainty in model inputs, including fuel characteristics
(composition, loading, and moisture) and emission factors (EFs), and
structural uncertainty in the consumption model. The results of the
modeling simulations and comparison with a unique and comprehensive
suite of fuel and emissions measurements suggest that in this application
of FOFEM, fuel loadings based on land cover maps had the highest uncertainty
and resulted in the largest sensitivity in predicted smoke emissions.
The use of land-cover-based fuel loading values significantly underpredicted
gas and particle emissions from the prescribed burns by up to ∼80%
for carbon monoxide (CO) and carbon dioxide (CO2) and by
up to ∼85% for fine particulate matter (PM2.5).
Improvement in the predicted smoke emissions could specifically be
achieved by more accurate fuel loading data, particularly for duff
and coarse wood, the consumption of which generated the majority of
gas (∼50–70%) and particle (∼65%) emissions.
For individual gaseous nonmethane organic compounds (NMOCs), predicted
emissions were additionally sensitive to uncertainty in EFs, demonstrating
that the accurate prediction of these NMOCs requires accurate representation
of fuel consumption as well as representative EFs.