Destruction of homes and businesses from Wildland Urban Interface (WUI) fires has been steadily escalating as have the fire suppression costs associated with them. Since 2000, in the United States, over 3,000 homes per year are lost to WUI fires. This is compared to about 900 homes in the 1990s, and 400 homes in the 1970s. In 2011, in Texas alone, over 2,000 homes were destroyed during WUI fires. The WUI fire problem affects both existing communities and new construction. In the U.S, the problem is most acute in the western and southern states; however, WUI fires have also recently destroyed homes in the Mid-Atlantic States and the Pacific Northwest.

As scientists and managers seek to understand fire behavior in conditions that extend beyond the limits of our current empirical models and prior experiences, they will need new tools that foster a more mechanistic understanding of the processes driving fire dynamics and effects. Here we suggest that process-based models are powerful research tools that are useful for investigating a large number of emerging questions in wildland fire sciences. These models can play a particularly important role in advancing our understanding, in part, because they allow their users to evaluate the potential mechanisms and interactions driving fire dynamics and effects from a unique perspective not often available through experimentation alone. For example, process-based models can be used to conduct experiments that would be impossible, too risky, or costly to do in the physical world. They can also contribute to the discovery process by inspiring new experiments, informing measurement strategies, and assisting in the interpretation of physical observations. Ultimately, a synergistic approach where simulations are continuously compared to experimental data, and where experiments are guided by the simulations will profoundly impact the quality and rate of progress towards solving emerging problems in wildland fire sciences.

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& Key message We describe a modeling system that enables detailed, 3D fire simulations in forest fuels. Using data from three sites, we analyze thinning fuel treatments on fire behavior and fire effects and compare outputs with a more commonly used model. & Context Thinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts. & Aims The study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models. & Methods The modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs. & Results The study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management. & Conclusion This system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling.

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