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Global sensitivity analysis for uncertainty quantification in fire spread models

KC, U, Aryal, J ORCID: 0000-0002-4875-2127, Garg, S ORCID: 0000-0003-3510-2464 and Hilton, J 2021 , 'Global sensitivity analysis for uncertainty quantification in fire spread models' , Environmental Modelling and Software, vol. 143 , pp. 1-13 , doi: 10.1016/j.envsoft.2021.105110.

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Environmental models involve inherent uncertainties, the understanding of which is required for use by practitioners. One method of uncertainty quantification is global sensitivity analysis (GSA), which has been extensively used in environmental modeling. The suitability of GSA methods depends on the model, implementation, and computational complexity. Thus, we present a comparative analysis of different GSA methods (Morris, Sobol, FAST, and PAWN) applied to empirical fire spread models (Dry Eucalypt and Rothermel) and explain their implications. GSA methods such as PAWN, may not be able to explain all the interactions whereas methods such as Sobol can result in high computational costs for models with several parameters. We found that the Morris or the PAWN method should be prioritized over the Sobol and the FAST methods for a balanced trade-off between convergence and robustness under computational constraints. Additionally, the Sobol method should be chosen for more detailed sensitivity information.

Item Type: Article
Authors/Creators:KC, U and Aryal, J and Garg, S and Hilton, J
Keywords: fire behavior modeling, uncertainty quantification, global sensitivity analysis, wildfires
Journal or Publication Title: Environmental Modelling and Software
Publisher: Elsevier Sci Ltd
ISSN: 1364-8152
DOI / ID Number: 10.1016/j.envsoft.2021.105110
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© 2021 Elsevier Ltd. All rights reserved.

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