Multi-scale analyses of wildland fire combustion processes: Large-scale field experiments – fire radiative power

The United States Department of Defense (DoD) Strategic Environmental Research and Development Program (SERDP) funded project: Multi-scale Analyses of Wildland Fire Combustion Processes in Open-canopied Forests using Coupled and Iteratively Informed Laboratory-, Field-, and Model-based Approaches (RC-2641) conducted a large-scale (management-scale) field experiment during an operational prescribed burn to quantify how atmospheric dynamics across a wide range of spatial and temporal scales affect fire propagation, energy exchange, and fuel consumption. This experiment also provided an opportunity to fully examine how combustion related processes transfer across scales of particles and simple fuel beds in the laboratory, wind tunnel, small-scale, and operational prescribed burns as instrumentation used in small-scale field experiments was embedded in this experiment. In addition, the large-scale experiment also provides data necessary for simulation and model testing of coupled atmosphere-fire behavior prediction systems (e.g., WRF-SFire, WFDS, QUIC-Fire, FIRETEC) and coupled atmosphere-canopy-smoke dispersion prediction systems (e.g., ARPS-Canopy/FLEXPART). The large-scale field experiment includes data from a heavily instrumented ~12.1 hectare (ha) management-scale fire conducted at the Silas Little Experimental Forest in the Pinelands National Reserve (PNR) on March 13, 2019. This data publication contains data collected in thirteen Krem Boxes (KB) that consisted of a dual band infrared (IR) radiometers and visible spectrum/long wave infrared camera pairs. The KB were scattered throughout the burn area at approximately 2 meters (m) above the ground pointed at the fuel beds. Data were collected at 1 hertz (Hz). The radiometric data were used to measure radiative heat fluxes, flame arrival times and persistence. In the same housing as the radiometer is a vertical flow instrument. This sensor did not produce any significant results, as the low-intensity fires in this experiment produced vertical flows lower than the threshold for detection. The visible spectrum cameras captured conventional RGB color-balanced images at 5m pixel resolution. The long wave infrared cameras captured 80 X 60 pixel images and was equipped with a neutral density filter to minimize saturation of this camera by the emission form the flames and hot background. The cameras are nearly spatially coincident and acquire images at the same time, and so can be aligned spatially and temporally.
Many DoD facilities utilize low intensity prescribed fire to manage hazardous fuels, restore ecological function and historic fire regimes, and encourage the recovery of threatened and endangered species in the forests they manage. Current predictive models used to simulate fire behavior during low-intensity prescribed fires (and wildfires) are empirically based, simplistic, and fail to adequately predict fire outcomes because they do not account for variability in fuel characteristics and interactions with important meteorological variables. This study used a suite of measurements at the fuel particle, fuel bed, field plot, and stand scales to quantify how variability in fuel characteristics and key meteorological factors interact to drive fire behavior during low intensity prescribed burns. These experiments were designed to inform the development and evaluation of mechanistic, physics-based models that explicitly account for combustion, turbulent transfer, and energy exchange by coupling and scaling individual component processes. These datasets provide measurements to improve the understanding of, and ability to accurately predict, fire behavior under a wide range of management scenarios.
A summary of the SERDP Project RC-2641 can be found at the RC-2641 Project Overview (serdp-estcp.org): https://www.serdp-estcp.org/projects/details/a4a4642d-f2be-4e52-b678-454fe06afbc2/rc-2641-project-overview. Please reference the burn layout and documentation data publication (Gallagher et al. 2023, https://doi.org/10.2737/RDS-2022-0089) as these data provide the sensor locations of each burn, a detailed description of data collected and a summary of the conditions during the burn periods.