  Global sensitivity analysis (GSA) can provide rich information for
controlling output uncertainty. In practical applications, segmented models are
commonly used to describe an abrupt model change. For segmented models, the
complicated uncertainty propagation during the transition region may lead to
different importance rankings of different GSA methods. If an unsuitable GSA
method is applied, misleading results will be obtained, resulting in suboptimal
or even wrong decisions. In this paper, four GSA indices, i.e., Sobol index,
mutual information, delta index and PAWN index, are applied for a segmented
fire spread model (Dry Eucalypt). The results show that four GSA indices give
different importance rankings during the transition region since segmented
characteristics affect different GSA indices in different ways. We suggest that
analysts should rely on the results of different GSA indices according to their
practical purpose, especially when making decisions for segmented models during
the transition region.
