Fire-model: converging research and education for the next-generation physical chemistry fire regime models

The National Science Foundation Office of Integrative Activities awarded the University of Missouri System $1,103,178 on October 1, 2026, under the Geosciences program (CFDA 47.050) to advance fire regime modeling through improved data integration, artificial intelligence and machine learning applications, and simulation techniques. The project, FIRE-MODEL: Converging Research and Education for the Next-Generation Physical Chemistry Fire Regime Models, addresses critical gaps in wildland fire prediction and management by developing improved fire regime models, increasing model accuracy at operational scales, and integrating research activities with educational opportunities through an innovative gameplay-based platform for fire regime training and decision-making. The work utilizes fire scar datasets and the Physical Chemistry Fire Frequency Model as primary sources for understanding fire dynamics across forests, landscapes, and regions. Enhanced fire regime modeling will improve fire projection, fire attribution, fire risk assessment, and prescribed fire usage by incorporating high-resolution data, uncertainty measures, and enhanced spatial resolution. The research responds to disrupted fire regimes in North America caused by population change, agricultural conversion, forest harvesting, and fire suppression, which currently hamper predictions and limit confidence in prioritizing areas for treatment of fuel surpluses. Performance occurs in Columbia, Missouri, with a period of performance from October 1, 2026, through September 30, 2030.

Abstract

This project advances fire regime modeling. Wildland fire regimes shape ecosystems, affect forest resource availability, and impact human safety, health, and property. Fire regimes in north america have been disrupted due to effects of population change, agricultural conversion, forest harvesting, and fire-suppression, among many other factors. Gaps in fire regime data, modeling, and education hamper predictions, reduce certainty, and limit confidence in prioritizing areas for treatments that are vulnerable to fuel surpluses. The targeted improvements to fire regime models benefit fire projection, fire attribution, fire risk assessment, and prescribed fire usage. Better-informed fire regime models will include improved data access, uncertainty measures, and enhanced spatial resolution. These improvements will contribute to a multitude of existing and future wildland fire challenges. Utilizing the fire scar datasets and the physical chemistry fire frequency model (PC2FM) as the primary sources for understanding the role of fire in a particular forest, landscape, and region, the project has three thrusts: 1) develop improved fire regime models, 2) increase fire regime model accuracy at operational scales, and 3) converge research activities, data access, and fire regime tools with educational opportunities. Advanced fire regime modeling, by integrating high-resolution data, AI/machine learning, and simulation techniques, significantly deepens our understanding of fire dynamics, enabling researchers to uncover complex patterns, predict future fire behavior, and inform more effective management and mitigation strategies. The research converges with education through a novel approach to informal learning, using a transformative platform for fire regime training and decision-making through gameplay. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are planned for this award.