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Background Existing fire spread models focus exclusively on wildland or urban fire simulation. Aims This study aims at an offline coupling of two fire spread models to enable a continuous simulation of a wildfire incident transitioning from wildland into wildland–urban interface (WUI) communities, evaluate the effects of wind input on simulation results and study the influence of building types on fire spread patterns. Methods The selected models are WRF-Fire, a wildland fire behaviour simulation platform, and SWUIFT, a model for fire spread inside the WUI. The 2021 Marshall Fire serves as the case study. A map of the fire’s timeline and location is generated using public information. Three simulation scenarios are analysed to study the effects of wind input resolution and building type on the predicted fire spread and damage. Key results The most accurate results are obtained using a high-resolution wind input and when incorporating different building types. Conclusions The offline coupling of models provides a reliable solution for fire spread simulation. Fire-resistant buildings likely helped limit community fire spread during the Marshall Fire. Implications The research is a first step toward developing simulation capabilities to predict the spread of wildfires within the wildland, WUI and urban environments. 

Abstract. Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The results are in good agreement with observations recorded during the event. Extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making and emergency response management during wildfire events. 

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed.