Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model
can be attributed to variations of its input factors. SA is increasingly being used in …

Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …

Abstract Global Sensitivity Analysis (GSA) is increasingly used in the development and
assessment of environmental models. Here we present a Matlab/Octave toolbox for the …

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance
and it plays important roles in model parameterization, calibration, optimization, and …

Variance-based approaches are widely used for Global Sensitivity Analysis (GSA) of
environmental models. However, methods that consider the entire Probability Density …

Complex environmental models are controlled by a high number of parameters. Accurately
estimating the values of all these parameters is almost impossible. Sensitivity analysis (SA) …

Uncertainty in the predictions of science when applied to the environment is an issue of
great current relevance in relation to the impacts of climate change, protecting against …

We address two critical choices in Global Sensitivity Analysis (GSA): the choice of the
sample size and of the threshold for the identification of insensitive input factors. Guidance to …

In catchment hydrology, it is in practice impossible to measure everything we would like to
know about the hydrological system, mainly due to high catchment heterogeneity and the …