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Advancing the Science of Wildland Fire Dynamics Using Process-Based Models

Department of Forest and Rangeland Resources, Colorado State University, Fort Collins, CO 80523, USA
USDA Forest Service Rocky Mountain Research Station, Flagstaff, AZ 86001, USA
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
USDA Forest Service Pacific Northwest Research Station, Seattle, WA 87545, USA
USDA Forest Service Missoula Fire Science Laboratory, Missoula, MT 59808, USA
Tall Timbers Research Station, Tallahassee, FL 32312, USA
Author to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 29 August 2018 / Accepted: 31 August 2018 / Published: 5 September 2018
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. View Full-Text
Keywords: physics-based modeling; fire behavior; computational fluid dynamics; model validation physics-based modeling; fire behavior; computational fluid dynamics; model validation
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Hoffman, C.M.; Sieg, C.H.; Linn, R.R.; Mell, W.; Parsons, R.A.; Ziegler, J.P.; Hiers, J.K. Advancing the Science of Wildland Fire Dynamics Using Process-Based Models. Fire 2018, 1, 32.

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