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    Author(s): Chad M. Hoffman; Carolyn H. Sieg; Rodman R. Linn; William MellRussell A. Parsons; Justin P. Ziegler; J. Kevin. Hiers
    Date: 2018
    Source: Fire. 1(2): 32.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (379.0 KB)


    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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    Hoffman, Chad M.; Sieg, Carolyn H.; Linn, Rodman R.; Mell, William; Parsons, Russell A.; Ziegler, Justin P.; Hiers, J. Kevin. 2018. Advancing the science of wildland fire dynamics using process-based models. Fire. 1(2): 32.


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    physics-based modeling, fire behavior, computational fluid dynamics, model validation

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