Quantifying parametric uncertainty in the Rothermel modelAuthor(s): S. Goodrick
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.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationJimenez, E., Hussaini, M.Y.; Goodrick, S. 2008. Quantifying parametric uncertainty in the Rothermel model. International Journal of Wildland Fire 17(5): 638-649.
- Uncertainty quantification in Rothermel's Model using an efficient sampling method
- A qualitative comparison of fire spread models incorporating wind and slope effects
- Assessing forest vegetation and fire simulation model performance after the Cold Springs wildfire, Washington USA
XML: View XML