Morris One-at-A-Time 293 ) are used to identify non-influential inputs that can be fixed at given values within their uncertainty ranges without significantly reducing the output variance. These methods are typically used in a qualitative manner (i.e., values of the sensitivity indices are only for comparative purposes), and smaller sample sizes are typically used to reduce model complexity by fixing non-influential inputs. In one study, for example, the Morris method was used to determine the most important individual and groups of input variables in estimating the yield of urban water supply systems. Unlike screening methods, variance-based methods (e.g., the Sobol method) decompose the variance in model output as the sum of effects associated with the model inputs. Due to their quantitative nature, variance-based methods are more computationally intensive than screening methods, especially when the quantification of interaction effects is desired. Thus, variance-based methods are often conducted for models with relatively small numbers of inputs or after fixing non-influential inputs. As an example, a variance-based sensitivity analysis was performed in one study to determine that 97% of the variance of a location's suitability for a hazardous waste landfill facility was jointly induced by only three variables, thus allowing the original model to be greatly simplified without compromising its accuracy. 285