Rothermel's wildland surface fire spread model is widely used in North America. The model outputs depend on a number of input parameters, which can be broadly categorized as fuel model, fuel moisture, terrain and wind parameters. Due to the inevitable presence of uncertainty in the input parameters, the sensitivity of the model output to a given input parameter can be very useful for understanding and controlling the sources of parametric uncertainty. Instead of obtaining the local sensitivity indices, we perform a global sensitivity analysis that considers the synchronous changes of parameters in their respective ranges. The global sensitivity indices corresponding to different parameter groups are computed by constructing the truncated ANOVA-high dimensional model representation for the model outputs with a polynomial expansion approach. We apply global sensitivity analysis to six standard fuel models, namely, short grass, tall grass, chaparral, hardwood litter, timber and light logging slash. The sensitivity results are systematically compared.