Abstract

The ability to predict forest fuel consumption is critical, namely in the frame of hazard-reduction burning treatments designed to achieve effective fuel reduction with minimal environmental impact. Litter and understory fine fuels (diameter <0.6cm) consumption by fire were measured at 90 experimental line-ignited surface fires (0.01–0.02ha) in maritime pine forests in northern Portugal. Our primary objective was to describe the variation and model plot-level fine fuel consumption and the corresponding CO2 emissions. Pre- and post-burn fuel sampling distinguished three fuel layers: L-layer litter, F-layer litter and understory vegetation. CO2 emissions were estimated from fuel consumption and reference emission factors. Fuel consumption (as depth of burn) did not differ between backward and forward fire spread. Surface (L-layer litter+understory) and total fuel consumption varied in the ranges of 59–100% and 24–94.8%, respectively. F-layer litter consumption was highly variable (0–96%), reflecting its slower drying rate after rainfall. The relative consumption of the L-layer litter was always the highest followed by the understory and F-layer litter. We modeled proportional fuel consumption through generalized linear modeling for the individual fuel layers, as well as for combinations of layers, using either fuel moisture content (of surface fuels or the F layer) or regional moisture codes of the FWI system as baseline independent variables. Additional variables did not improve the models. Goodness-of-fit was generally higher for models based on actual fuel moisture content, but models using the FFMC or DMC codes of the FWI system are still useful to assess regional trends in fuel availability. Using fuel loadings and either fuel moisture contents or the DMC we derived two equations to estimate CO2 emissions in pre-burn planning situations. Variation in CO2 emissions from prescribed fire in maritime pine stands is primarily a function of the F-layer litter availability to burn. The results allow improved support to prescribed fire management, with benefits that may extend to fire-modeling research applications and to more realistic estimates of carbon emissions at larger scales.

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