Publication Date

4-30-2021

Document Type

Article

Publication Title

Environmental Modelling & Software

Volume

141

DOI

10.1016/j.envsoft.2021.105050

Abstract

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators.

Funding Number

ACI-1548562

Funding Sponsor

National Science Foundation

Keywords

Forest fire, Multifidelity Monte Carlo, Predictive science & engineering, Sensitivity analysis, Uncertainty quantification, FDS

Comments

This is the Version of Record and can also be read online here.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Department

Meteorology and Climate Science

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