Uncertainty quantification in Rothermel's model using an efficient sampling method
Document Type: Conference Proceedings
Author(s): Edwin Jimenez; M. Yousuff Hussaini; Scott L. Goodrick
Editor(s): Bret W. Butler; Wayne A. Cook
Publication Year: 2007

Cataloging Information

  • fire spread
  • Rothermel's fire behavior model
  • SDES - sensitivity derivative enhanced sampling
  • wildland fire management
Record Maintained By:
Record Last Modified: December 13, 2016
FRAMES Record Number: 7414


The purpose of the present work is to quantify parametric uncertainty in Rothermel's 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 nonlinear 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 (SDES), 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.

Online Link(s):
Jimenez, Edwin; Hussaini, M. Yousuff; Goodrick, Scott L. 2007. Uncertainty quantification in Rothermel's model using an efficient sampling method. Pages 111-121 In: Butler, Bret W.; Cook, Wayne (comps.). The fire environment-innovations, management, and policy; conference proceedings. 26-30 March 2007; Destin, FL. Proceedings RMRS-P-46CD. Fort Collins, CO: USDA Forest Service, Rocky Mountain Research Station.