Quantifying parametric uncertainty in the Rothermel model
| Authors: | Scott Goodrick |
| Year: | 2008 |
| Type: | Scientific Journal |
| Station: | Southern Research Station |
| DOI: | https://doi.org/10.1071/wf07070 |
| 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 spreadmodel (implemented in software such as fire spread models in the United States. This model consists of a non-linear system of equations that relates environmentalvariables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. Thismodel predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effectivewind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploitssensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled withtraditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, whichimplies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficientmethod to quantify the impact of input uncertainties on the output parameters.