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, knowing 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. Our sensitivity results show similarities, as well as differences, between fuel models. For example, the sensitivities of the input parameters, i.e., fuel depth, low heat content, and wind, are large in all fuel models and as high as 85% of the total model variance in the fuel model light logging slash. On the other hand, the fuel depth explains around 40% of the total variance in the fuel model light logging slash but only 12% of the total variance in the fuel model short grass. The quantification of the importance of parameters across fuel models helps identify the parameters for which additional resources should be used to lower their uncertainty, leading to effective fire management.
Global sensitivity analysis for the Rothermel model based on high-dimensional model representation
