This research aims to employ a novel methodology for modelling uncertainty in the GIS environment. The spatially explicit sensitivity and uncertainty analysis was applied on Multicriteria Decision Analysis (MCDA) for an economic vulnerability assessment within the Salzach Basin. The main objective of this research is to demonstrate how a unified approach of uncertainty and sensitivity analysis can be applied to minimize the associated uncertainty within an economic vulnerability assessment. In order to achieve this objective, the following methodology, composed four steps, was applied: (1) computation of criteria weights using Analytic Hierarchy Process (AHP); (2) Monte Carlo Simulation was applied for computing the uncertainties associated with AHP weights; (3) the Global Sensitivity Analysis (GSA) was employed in the form of the model-independent method of output variance decomposition, in which the variability of vulnerability maps is apportioned to every criterion weight, generating one first-order (S) and one total-effect (ST) sensitivity index map per criterion weight; and (4) an Ordered Weighted Averaging method was applied for producing vulnerability maps. The results of this research demonstrated the robustness of spatially explicit GSA for minimizing the uncertainty associated with GISMCDA models. According to the achieved results, we conclude that applying the variance based GSA leads to a spatial and statistical assessment of the importance of each input factor on the outcome of the GIS-MCDA method, which allows us to introduce and recommend GIS based GSA as a useful methodology for minimizing uncertainty of GISMCDA.