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In presence of strong winds, wildfires feature nonlinear behavior, possiblyinducing fire-spotting. We present a global sensitivity analysis of a newsub-model for turbulence and fire-spotting included in a wildfire spread modelbased on a stochastic representation of the fireline. To limit the number ofmodel evaluations, fast surrogate models based on generalized Polynomial Chaos(gPC) and Gaussian Process are used to identify the key parameters affectingtopology and size of burnt area. This study investigates the application ofthese surrogates to compute Sobol' sensitivity indices in an idealized testcase. The wind is known to drive the fire propagation. The results show that itis a more general leading factor that governs the generation of secondaryfires. This study also compares the performance of the surrogates for varyingsize and type of training sets as well as for varying parameterization andchoice of algorithms. The best performance was achieved using a gPC strategybased on a sparse least-angle regression (LAR) and a low-discrepancy Halton'ssequence. Still, the LAR-based gPC surrogate tends to filter out theinformation coming from parameters with large length-scale, which is not thecase of the cleaning-based gPC surrogate. For both algorithms, sparsity ensuresa surrogate can be built using an affordable number of forward modelevaluations, while the model response is highly multi-scale and nonlinear.Using a sparse surrogate is thus a promising strategy to analyze new models andits dependency on input parameters in wildfire applications.

Surrogate-based global sensitivity analysis for turbulence and fire-spotting effects in regional-scale wildland fire modeling