Fire frequency and intensity has led to a focus on proactive wildland fire management to mitigate risks and maintain military test and training lands. Current operational models lack the complex interactions typical of prescribed fire planning, highlighting the utility of next-generation coupled fire-atmosphere modeling tools to enhance safety, predict smoke behavior, and align management goals. To address this need, this work will capture the influence of local weather and vegetation and fuel structure and condition on fire behavior to inform multi-scale, predictive modeling of fire behavior and effects, as well as smoke dispersion and lofting.

The project team will collect real-time data on micrometeorology, vegetation, and fuel conditions before, during, and after prescribed burn research campaigns facilitated by SERDP and ESTCP. The data will be used for multiscale characterization of vegetation structure and fuels, fine scale micrometeorological conditions, and patterns of fire energy release.

Data collection will be achieved by using a combination of ground sampling and sensors deployed on unmanned aerial systems and crewed aircraft. These inputs will be used to improve models to better predict fire behavior and effects including smoke lofting, trajectory, and dispersion. The technical questions to be addressed focus on fuels characterization, interactions and feedbacks of fire through time, data integration for fire impacts, and enhanced smoke tracking. Specific questions are outlined here:

• Fuels characterization: How can we integrate fuel characterization from ground measurement, terrestrial scanning, airborne scanning, and earth orbit sampling to update fire models and inform prescribe burn planning and active wildland fire management and resource allocation through space and time? What is the capacity for capturing fuel structure, geometry, and moisture through space and time?
• Interactions and feedbacks in real time: How do weather, fuels, and topography interact at the flame, stand, and landscape scale to create hazard conditions for wildland fire management and what is the importance of scale and measurement frequency across these factors?
• Data integration for fire impacts: How can improved data integration and scaling from point measurements to landscapes and across data types (moisture, terrestrial laser scanning, airborne, and satellite) capture heterogenous impacts of fire to vegetation and soils?
• Enhanced smoke tracking: How can high–resolution coupled models aid in burn ignition planning and smoke forecasting? How will variation in fuel moisture and type alter consumption and emissions for prescribed versus wildland fire?

Managing prescribed burns is critical for infrastructure protection and fuels reduction on Department of War and Federal lands. To ensure effective planning and management, collecting real-time data on weather, vegetation, and fuel conditions before, during, and after burns is essential. These inputs will be used to validate and improve models to better predict fire behavior and effects including smoke lofting, trajectory, and dispersion, thereby protecting critical infrastructure and personnel.

A continuing challenge for advancing wildfire science to operations involves lines of communication, conflicting authority for national, state, local coordination, as well as data coordination and sharing. Streamlined, coordinated distribution methods, along with standardized data formats, consistent definitions, and rigorous quality assurance, are essential to support transition and adoption by local, state, and federal fire managers. Increasing the capacity for data and model fusion allows decision makers to synthesize information in an integrated manner to promote faster decision making and integration of new technology and datasets into their day-to-day routines. Collected data will be standardized and archived in a publicly accessible data repository. Data will be formatted for inclusion into the publicly accessible National Aeronautics and Space Administration FireSense data repository. Data curation and archiving will be coordinated with partners to ensure efficient use of resources and effective availability of the data. (Anticipated Project Completion - 2026)