Fireflux experiments in the New Jersey Pine Barrens

We measured turbulence, energy, water vapor and carbon dioxide fluxes using eddy covariance during an operational prescribed fire conducted by the New Jersey Forest Fire Service in the Pine Barren of New Jersey in March 2008. Fuel consumption was quantified using pre- and post-burn sampling of the forest floor and understory (10 1 meter² plots at random locations in the burn), and LIDAR data were used to estimate canopy fuel consumption during the fire. PM 2.5 (particulate matter with diameters < 2.5 µm) was quantified using E-BAM beta particle attenuators at two sites, one located within the burn block and the second located in an adjacent clearing that did not burn. Turbulent transfer, as estimated using friction velocity (u*; m s^-1) measurements 4 meters above the canopy, was enhanced ca. 40% over ambient conditions during the prescribed burn. Energy flux, calculated as the sum of latent and sensible heat fluxes, was five times greater than available energy during the fire; half-hourly sensible heat flux peaked at 1700 W m^-2, and water vapor flux at 500 W m^-2. CO₂ release during the fire was equivalent to 107 g C m^-2, representing 84% of the annual C uptake averaged over the previous three years (2005-2007). Fuel combustion estimated from pre- and post sampling was 410 g C m^-2, indicating that flux measurement underestimated actual fuel consumption substantially. Possible reasons include that the flux measurements only sampled a small portion of the plume, whereas pre- and post measurements were distributed across the burn block. In addition, 10 hz data may underestimate instantaneous fluxes during extreme turbulence. PM 2.5 concentrations at 2 meter height peaked at 3823 µg m^-3 in the fire, but dropped to below 35 µg m^-3 within 8 hours. PM 2.5 concentrations in the clearing peaked at 880 µg m^-3 during the fire, and also decreased rapidly when the combustion plume dissipated.

Simultaneous quantification of fluxes and fuel consumption during fires are essential for evaluating predictive plume dispersion models such as CALPUFF and BlueSky, and fuel combustion models such as CONSUME3.