Quantifying parametric uncertainty in the Rothermel model

  • Authors: Goodrick, S.
  • Publication Year: 2008
  • Publication Series: Miscellaneous Publication
  • Source: International Journal of Wildland Fire 17(5): 638-649

Abstract

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.

  • Citation: Jimenez, E., Hussaini, M.Y.; Goodrick, S. 2008. Quantifying parametric uncertainty in the Rothermel model. International Journal of Wildland Fire 17(5): 638-649.
  • Posted Date: October 20, 2009
  • Modified Date: November 2, 2009
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