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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

Quantifying parametric uncertainty in the Rothermel model

Edwin Jimenez A , M. Yousuff Hussaini A C and Scott Goodrick B
+ Author Affiliations
- Author Affiliations

A School of Computational Science, Florida State University, Dirac Science Library, Tallahassee, FL 32306, USA.

B USDA Forest Service Center for Forest Disturbance Science, 320 Green Street, Athens, GA 30602, USA.

C Corresponding author. Email: myh@scs.fsu.edu

International Journal of Wildland Fire 17(5) 638-649 https://doi.org/10.1071/WF07070
Submitted: 16 May 2007  Accepted: 6 February 2008   Published: 3 October 2008

Abstract

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used 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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