A comprehensive global sensitivity analysis using generic sampling designs by means of a combination of variance- and distribution-based approaches.

Global sensitivity analysis has been recognized as a fundamental tool to assess the input-output model response and evaluate the role of different sources of uncertainty. Among the different methods, variance- and distribution-based (or also called moment-independent) methods have mostly been applied. The first method relies on variance decomposition while the second method compares the entire distributions. The combination of both methods has also been recognized to provide possibly a better assessment. However, the methods rely on different assumptions and the comparison of indices is not straightforward. For these reasons, the methods are commonly not integrated or even considered as alternative solutions. 

In the present contribution, we show a new strategy to combine the two methods in an effective way to perform a comprehensive global sensitivity analysis based on a generic sampling design. The strategy is tested on three commonly-used analytic functions and one hydrological model. The strategy is compared to the state-of-the-art Jansen/Saltelli approach.

The results show that the new strategy quantifies main effect and interactions consistently. It also outperforms current best practices by converging with a lower number of model runs. For these reasons, the new strategy can be considered as a new and simple approach to perform global sensitivity analysis that can be easily integrated in any environmental models.