Key points are not available for this paper at this time.
Sensitivity analysis involves determining the contribution of individual input factors to uncertainty in model predictions. A number of techniques exist to carry out sensitivity analysis from a set of Monte Carlo simulations, some more efficient than others, depending on the approach used to sample the space of the uncertainties and on calculation methods. The most common approaches are summarised in this paper. In particular, the limitations of each in the context of a sensitivity analysis of a spatial model are critically examined. A novel approach for undertaking a spatial sensitivity analysis (based on the Sobol' method) is proposed and tested. This method is global, variance-based, and model-free, and enables the analysis of space-dependent uncertain inputs. The proposed approach is illustrated with a simple test model and a groundwater contaminant
Lilburne et al. (Sat,) studied this question.