Collaborative Research: FIRE-MODEL: Coupled Fire Spread and Firebrand Transport Modeling Using High Resolution Data

Firebrands, or airborne burning embers, are a major contributor to the complex and irregular spread of wildland fire, as they can ignite vegetation and structures ahead of and/or far away from the main fire front. Accurate modeling and real-time prediction of fire spread and firebrand transport are critical for resource allocation and decision-making of wildfire responders, especially in Wildland-Urban Interface (WUI) areas. Furthermore, advancing firebrand modeling and developing accurate prediction tools are crucial for improving safety during prescribed burns and reducing the risks of spot fires and fire escapes. The objective of this project is to develop coupled fire spread and firebrand transport modeling and real-time prediction to support operational wildland fire management with a more accurate and thorough representation of spotting fire behaviors. The proposed method will integrate inertial particle dynamics-based firebrand transport modeling, advanced fire spread modeling, and dynamic calibration and data assimilation using high-resolution active fire and wind data collected by unmanned aircraft systems (UAS). Project tasks are focused on: 1) coupled fire spread and firebrand transport modeling and real-time data assimilation for fire spread prediction; 2) firebrand transport modeling and dynamic calibration; and 3) high-spatiotemporal-resolution active fire and wind data sensing using multiple UAS equipped with stereo-vision cameras. A convergence approach is proposed for integration of key research components by engaging an interdisciplinary team of researchers and fire stakeholders from four universities, the US Forest Service, the Kansas Forest Service, Kansas Prescribed Burn Associations, and a Kansas local fire department. The project will organize yearly workshops focusing on themes such as high-resolution fire data collection using UAS and firebrand prediction and mitigation in prescribed woodland burns. The collected fire data, firebrand knowledge and model, and coupled fire spread and firebrand transport models will be evaluated from linked but different user perspectives, including training and planning for prescribed burns, real-time situational awareness for firefighting, and fire spread reconstruction and simulation to support the development of next-generation unmanned systems for fire sensing and firefighting. 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.