**1. Introduction**

Traditional sensitivity analysis (SA) methods are focused on model output [1]. SA is a computational procedure that divides and quantifies the uncertainty of input variables according to their influence on the uncertainty of the output of the mathematical model. Variance-based SA (generally called Sobol SA) introduces uncertainty as variance and decomposes the variance of the output of the model or system into portions that can be attributed to inputs or sets of inputs [2,3]. Sobol SA is very popular; the principles of the method are often mentioned [4–8] and many articles have applied Sobol SA in their research [9–13].

In a more general form, SA can be defined as the study of how the output of a system is related to, and is influenced by, its inputs. In practical applications, research does not usually end with obtaining the output as a random variable or histogram, but other specific point estimates, such as quantiles, are needed. However, what influences the variance may or may not have the same influence on the quantile.
