Fire Models Stop Where Buildings Start

Wildfire behavior models (FARSITE, PHOENIX, FlamMap) predict fire spread through vegetation based on fuel type, terrain, weather, and moisture. Structural vulnerability models predict when buildings ignite based on construction materials, defensible space, and ember exposure. These two modeling communities work independently, yet the critical destruction pattern in wildland-urban interface (WUI) fires is the coupled interaction between fire and the built environment: structures ignite from ember showers, become fuel sources that generate their own firebrands, which ignite neighboring structures in cascading structure-to-structure fire spread that can outpace the wildland fire itself. No coupled model captures this feedback loop, meaning fire agencies cannot predict which communities will experience catastrophic structure-to-structure propagation versus survivable wildland fire exposure.

WUI fires caused $34.7 billion in insured losses in the US between 2017 and 2024 (Camp Fire, Tubbs Fire, Marshall Fire, Lahaina). The number of US homes in the WUI grew from 30.8 million in 1990 to over 44 million in 2020 and continues to increase. Post-fire investigations consistently find that structure-to-structure fire spread — not direct wildland flame contact — is the dominant destruction mechanism in WUI disasters, yet operational fire behavior predictions used for evacuation timing and resource deployment ignore this mechanism entirely. The 2023 Lahaina fire demonstrated that structure-to-structure spread can destroy an entire town in hours through a mechanism that wildland fire models do not represent.

NIST's WUI fire modeling research has developed physics-based structural ignition models for individual buildings but cannot simulate neighborhoods or communities at computational speeds needed for operational decision-making. The FDS (Fire Dynamics Simulator) can model fire behavior at building scale with high fidelity but a single building simulation takes hours, making community-scale simulation impractical. Empirical structure ignition vulnerability models (IBHS ratings) use scoring rubrics that predict relative vulnerability but not ignition timing or firebrand generation, preventing their use in spread models. Agent-based models that treat burning structures as ember sources are conceptually appealing but lack validated ember generation and transport data for different building types and construction materials. The fundamental barrier is that the relevant physics spans six orders of magnitude in spatial scale (millimeter-scale ember ignition to kilometer-scale community fire spread) and requires coupling atmospheric dynamics, combustion, heat transfer, and structural failure — each from different modeling communities with different tools and validation approaches.

A reduced-order model for structure-to-structure fire spread that captures the essential feedback (structure ignition → firebrand generation → downwind structure ignition) without requiring full physics simulation of each building. Validated empirical data on firebrand generation rates, sizes, and transport distances from burning structures of different construction types — data that can only come from controlled full-scale experiments or careful post-fire forensic analysis. Integration of high-resolution building and vegetation data (LiDAR, tax assessor records, aerial imagery) with fire behavior models to enable community-specific vulnerability assessment.

A student team could develop a simplified agent-based model where burning structures generate embers that ignite downwind structures based on distance, wind speed, and construction type, calibrating it against post-fire damage data from a well-documented WUI fire (Camp Fire, Marshall Fire) and comparing predictions to observed destruction patterns. Alternatively, a team could analyze high-resolution damage assessment data from a recent WUI fire to characterize the structure-to-structure propagation network, identifying which building attributes (roofing, siding, vegetation clearance) most strongly predict whether a building served as a firebrand source for its neighbors. Relevant disciplines include fire protection engineering, atmospheric science, data science, and urban planning.