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:	Refereed Article
Keywords:	fire behavior modeling, uncertainty quantification, global sensitivity analysis, wildfires
Research Division:	Environmental Sciences
Research Group:	Ecological applications
Research Field:	Fire ecology
Objective Division:	Environmental Policy, Climate Change and Natural Hazards
Objective Group:	Natural hazards
Objective Field:	Climatological hazards (e.g. extreme temperatures, drought and wildfires)
UTAS Author:	K C, U (Mr Ujjwal K C)
UTAS Author:	Aryal, J (Dr Jagannath Aryal)
UTAS Author:	Garg, S (Dr Saurabh Garg)
ID Code:	146351
Year Published:	2021
Web of Science® Times Cited:	4
Deposited By:	Information and Communication Technology
Deposited On:	2021-09-01
Last Modified:	2021-12-09
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