Global sensitivity analysis for uncertainty quantification in fire spread models. (September 2021)

• Record Type: Journal Article
• Authors: KC, Ujjwal; Aryal, Jagannath; Garg, Saurabh; Hilton, James
• Is Part Of: Environmental modelling & software. Volume 143(2021)
• Publication Date: 2021-09
• Subjects: Forest fire -- Fire behavior modeling -- Uncertainty quantification -- Global sensitivity analysis -- Wildfires; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105110
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Performed comprehensive sensitivity analysis of input parameters in two widely used fire spread models. Conducted comparative analyses of four different sensitivity analysis methods applied to fire spread models. Presented the implications of using different sensitivity analysis methods and ways how such results can be interpreted.