Next Generation Fire Modeling with QUIC-Fire

Photo Credit

Michael Gallagher

Local managers respond to the Spring Hill fire in the Pinelands National Reserve of New Jersey in late March 2019.

As wildfires become more frequent and severe, and increasingly affect landscapes that have not experienced wildfire in the modern era, forest managers can use models to better understand how wildfires are likely to spread and how prescribed burns can be conducted safely, making future wildfires easier to control and less severe. New fire simulation models are emerging that leverage new knowledge learned from field and laboratory studies about fire behavior, allowing these models to better predict how fast, how intensely, and where a wildfire will move. These new models simulate the complex physical processes that drive fire intensity and spread and can better account for localized changes in weather and fuel of actual landscapes that drive fire behavior. 

These models can also now simulate complex prescribed burns to improve planning efforts, making communities safer and more resilient to wildfire. These models are simple enough to run on a laptop, making them ideal for land managers to use to plan prescribed burns and ideal for students and firefighters interested in learning more about fire behavior. 

Researchers at the Northern Research Station are partnering with collaborating agencies (e.g., US Department of Defense, Los Alamos National Laboratory, New Jersey Department of Environmental Protection, US Fish and Wildlife Service, Stonybrook University) to improve one of these next-generation fire behavior models: QUIC-Fire. QUIC-Fire combines an existing fire simulation model (FIRE-CA) and a wind movement model (QUIC-URB) to simulate the combustion of fine fuels in forests and grasslands. QUIC-Fire is well-suited for simulating prescribed burns and wildfires that spread in dead leaves, twigs, logs, shrubs, grasses, and the forest canopy without consuming entire trees or other heavy fuels. By using case-study examples to reproduce past fires in QUIC-Fire, researchers are evaluating the accuracy of this model and improving it based on manager feedback and preferences. 

Advances and refinements of QUIC-Fire are being made through science that closely integrates the fire management community in eastern landscapes with long histories of prescribed or traditional burning. Combining manager experience with modeler expertise results in improved models that are more useful for managers to planning prescribed burns and forest management practices. 

Key Findings

• QUIC-Fire was successfully used to simulate a wildfire that occurred in the Pineland National Reserves of New Jersey in 2019, near the Silas Little Experimental Forest. This fire occurred in an area that had not seen fire in many decades and was extremely intense and difficult to control.
• Additional modeling with QUIC-Fire enabled managers to see how different strategies of prescribed burning employed prior to the wildfire could have produced scenarios where fire growth rates would have been reduced up to as much by 40%.
• QUIC-Fire simulation outputs also include fire effects. Results of the Spring Hill Fire reconstruction produced “tree crown streets” observed in the field after the fire. This fire effect is believed to occur when wind driven fires produce a complex wind phenomenon called “horizontal role vortices” and are associated with extreme fire behavior. Further work with QUIC-Fire will enable exploration of this phenomenon that is exceedingly difficult to study in the field.

Approach

Researchers are using existing data from past fires to evaluate the accuracy and improve the usability of QUIC-Fire for predicting behavior of prescribed burns and wildfires. The primary case study for this work so far has been the Spring Hill fire in the New Jersey Pinelands National Reserve, which started in 2019 from an abandoned campfire. Due to its proximity to the Silas Little Experimental Forest, researchers can use weather data from towers installed by the station as well as fuel input data collected as part of the station’s research. 

Due to the researchers’ working relationships with local agency fire managers, they are also able to use fire progression data provided by local managers to refine and improve the QUIC-Fire model simulation. Researchers also collaborate with managers to define forest management scenarios to test in QUIC-Fire. 

This iterative collaboration is ongoing as modelers based at the Northern Research Station continue to refine the model with manager input, and managers test the model to see if it suits their needs.

Outcomes

The results of this research are responsive to the National Wildland Fire Crisis Strategy and the USDA Forest Service Climate Adaptation Plan by providing managers with planning tools to help them increase the pace and scale of prescribed burning for public safety and forest adaptation and resilience goals, among other important objectives. Northern Research Station QUIC-Fire research is improving understanding of factors that influence fire behavior, such as fuel loading (i.e., the amount of fuel in the forest) and fuel structure (i.e., the spatial arrangement of this fuel, which can be influenced by prescribed burning and historic wildfire), and improving managers’ ability to predict extreme fire behavior that can occur from feedbacks between combustion and local atmospheric conditions. By running different scenarios in the model, researchers and managers are also working together to determine the intensity, frequency, and locations of prescribed burning required to reduce the risk of uncontrollable wildfires, and to understand how weather and complex ignitions can be used to make prescribed fires safer and more effective at reaching those goals. This research also explores how fuel break size matters for firefighter safety and fire containment under managed and unmanaged fuel conditions. 

Photo Credit

Michael Gallagher

Aerial view of the acreage burned during the Spring Hill fire in the Pinelands National Reserve in New Jersey, March 2019

In addition to improving our understanding of fire behavior, this research is instrumental in producing a model that is easy for forest managers, students, and firefighters to use to anticipate the effects of prescribed burning and wildfire on different forests. QUIC-Fire has the potential to become a powerful planning tool in fire management by enabling managers to safely and quickly explore numerous complex fire scenarios and quickly develop data-based strategies for safe and effective management. 

Continued research with QUIC-Fire aims to do even more to help managers make predictions and incorporate data from their management practices into QUIC-Fire simulations. For instance, NRS researchers are working to integrate a new emissions and smoke dispersion model with QUIC-Fire that will ultimately help forest managers improve smoke management strategies. At the same time, Northern Research Station scientists are leaders in a national, interagency effort to develop easy-to-use fuels and fire monitoring approaches through terrestrial laser scanning, which can provide fuel input data for QUIC-Fire. 

Similarly, the USDA Forest Service and Department of Defense have partnered to pilot a new Innovation Landscape Program that will foster the testing and refinement of new models like QUIC-Fire. One of these pilot landscapes, the New Jersey Pine Barrens, leverages the Northern Research Station’s Silas Little Experimental Forest, significant amounts of field data for model testing and refinement, and close integration with local managers of state (New Jersey Forest Fire Service), Department of Defense, and US Fish and Wildlife lands in each phase of research and demonstration.