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    Author(s): S. Goodrick
    Date: 2008
    Source: International Journal of Wildland Fire 17(5): 638-649
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (355.14 KB)


    The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread
    model (implemented in software such as
    fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental
    variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This
    model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective
    wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits
    sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with
    traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which
    implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient
    method to quantify the impact of input uncertainties on the output parameters.

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    Jimenez, E., Hussaini, M.Y.; Goodrick, S. 2008. Quantifying parametric uncertainty in the Rothermel model. International Journal of Wildland Fire 17(5): 638-649.

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