Global Sensitivity Study in Wildfire Modeling with FDS

Abstract

Wildfire spread predictions depend on numerous environmental and fuel parameters whose influence may vary among different modeling approaches. This study investigates whether sensitivities identified with the semi-empirical Rothermel-Albini fire spread model remain transferable among different modeling tools and how increasing physical coupling affects parameter importance. Global sensitivity analyses are performed using Borgonovo's Delta index and variance-based Sobol indices for the semi-empirical formulation. The resulting rankings are compared with Delta-based analyses of the Fire Dynamics Simulator (FDS) using two level-set formulations (LS1 and LS4) with different degrees of physical coupling.

The analysis considers the 1-hour dead fuel class and sample-size convergence assessments were performed to evaluate the stability of the dominant sensitivity rankings. FDS simulations are based on the CSIRO F19 grassland benchmark and evaluated using average spread speed and burned-area growth. In total, 17,280 simulations were prepared, of which 98.0% were successfully completed.

Both Delta and Sobol analyses identify midflame wind velocity U as the dominant parameter, followed by fuel bed depth delta and surface-area-to-volume ratio sigma. These dominant sensitivities remain largely transferable to FDS. Compared with the semi-empirical model, fuel bed depth gains importance, whereas surface-area-to-volume ratio decreases. Increasing physical coupling primarily modifies the relative importance of secondary parameters, while LS4 shows stronger wind influence, particularly for burned-area growth. In general, semi-empirical sensitivity analysis reliably identifies dominant wildfire spread parameters, while CFD refines their relative importance through additional physical interactions.