Global sensitivity analysis is a method used to better understand and estimate parameters in computational models. VarIance-based Sensitivity analysis using COpUlaS (VISCOUS) is a framework for this purpose. It estimates the sensitivity of model outcomes to uncertain model input factors by using the existing input and output data (e.g., water model parameters and responses). This work improved VISCOUS and tested it with various functions. Research found that VISCOUS is very good at estimating the importance of individual input factors, even with limited data (e.g., 200) and numerous input factors. It always correctly ranks input factor importance. When estimating the importance of input factors together, VISCOUS is recommended when the number of input factors is not very high (e.g., <20), as it is challenging to generate enough input and output data for estimating VISCOUS's parameters. To help people use VISCOUS, we provide examples and an open-source Python code, pyVISCOUS.