Department of Defense (DoD) fire managers are seeking guidance about the best ways to reduce risk of wildfires and minimize smoke impacts to communities near DoD installations. Prescribed fire is a tool that can improve ecosystem health and enhance a landscape’s resistance to wildfire. A key challenge to the successful implementation of prescribed fire results from gaps in data, incomplete understanding of the complexities involved, and limitations in current modeling capabilities. Addressing this challenge requires improved knowledge of the complex interactions between the physical processes of fire and factors such as live fuel moisture. This information can help managers anticipate effects of drought conditions on prescribed fire operations. New modeling capabilities and science integrating climate, vegetation, fuels, fire, and smoke can help bridge these knowledge gaps. The research team, consisting of scientists from the USDA USFS Rocky Mountain Research Station and partners from several institutions, is proposing to improve measurements and data collection, modeling capacity, and understanding of the interactions of various factors on fire and fuels. This research is designed with three broad objectives: 1) Integrate data sets characterizing fuels, fire, weather and smoke plume data on experimental fires. This will be done at multiple sites on both DoD and non‐DoD lands. Integrating data sets will uniquely complement other current Strategic Environmental Research and Development Program (SERDP) projects, 2) Expand modeling capabilities to close gaps between research models and management needs, making it easier to evaluate models and facilitate examination of live fuel moisture dynamics, and 3) Simulate modeling at multiple scales to close gaps in understanding of fuel/fire/plume interactions, test sensitivities, strengthen connections between scales and levels of detail, and characterize uncertainty. The results will pave the way for stronger, science‐based management on DoD lands.
Unmanned Aircraft Systems (UAS) will be used to collect integrated datasets capturing a high degree of detail and data to describe the critical dynamic interactions in time and space that drive fire intensity and plume characteristics in prescribed fires. This technology, using UAS imagery and three‐dimensional (3D) point clouds, can provide more spatially explicit field sampling to map fuels in 3D. Thermal time series imagery captures heat release and fire front geometry data over time and space. A separate UAS samples emissions, including O3 and PM, within the near‐field plume development zone. This new method for measuring plume dimensions and geometry over space and time meets a critical need for testing plume models.
The research project closes significant gaps between research models and management needs. This research will, improve a current 3D fuel and fire modeling platform called STANDFIRE, by
Incorporating multiple physics‐based fire models and building critical fuel and fire modeling capabilities;
Building capacity for model evaluation efforts, enabling explicit exploration of the effects of variability in different sorts of inputs (i.e., weather, fuels, ignition pattern, etc.) on fire behavior outcomes;
Leveraging a traceable approach known as the "burner method", the team will parameterize plume models from field experimental data and evaluate those models;
Closing a critical understanding gap by linking a process-based live fuel moisture dynamics model into STANDFIRE, strengthening understanding of drought stress and providing guidance for prescription windows for prescribed burns; and
Using the expanded modeling framework, the research team will carry out detailed simulations evaluating interactions of live fuel moisture dynamics, fire intensity, and plume dynamics in a range of conditions, spanning variability and characterizing associated uncertainty.
The project benefits communities, DoD fire managers, and researchers by advancing scientific capabilities relevant to fire and smoke management. This research will develop integrated datasets spanning fuels, fire, smoke and plume dynamics, substantially expand modeling capabilities, facilitate model evaluations with field data, and better characterize uncertainty. Finally, incorporating plant physiology‐based fuel moisture dynamics into the modeling will expand the solution space for prescribed burn planning. This is especially important in drought situations, which pose increasing constraints on fire management practices. The data collected and the modeling capabilities developed in this project, provide cutting edge tools and a sound scientific foundation for successfully planning and implementing prescribed burning to improve lives and landscapes.