  Models with high-dimensional parameter spaces are common in many
applications. Global sensitivity analyses can provide insights on how uncertain
inputs and interactions influence the outputs. Many sensitivity analysis
methods face nontrivial challenges for computationally demanding models. Common
approaches to tackle these challenges are to (i) use a computationally
efficient emulator and (ii) sample adaptively. However, these approaches still
involve potentially large computational costs and approximation errors. Here we
compare the results and computational costs of four existing global sensitivity
analysis methods applied to a test problem. We sample different model
evaluation time and numbers of model parameters. We find that the emulation and
adaptive sampling approaches are faster than Sobol' method for slow models. The
Bayesian adaptive spline surface method is the fastest for most slow and
high-dimensional models. Our results can guide the choice of a sensitivity
analysis method under computational resources constraints.
