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 …

Abstract Karst regions represent 7–12% of the Earth's continental area, and about one
quarter of the global population is completely or partially dependent on drinking water from …

Accurate prediction of runoff is an important foundation for optimizing water resource
allocation and reservoir scheduling operations. However, due to its complex characteristics …

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

Abstract The Prediction in Ungauged Basins (PUB) initiative of the International Association
of Hydrological Sciences (IAHS), launched in 2003 and concluded by the PUB Symposium …

In order to use environmental models effectively for management and decision-making, it is
vital to establish an appropriate level of confidence in their performance. This paper reviews …

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

The quantification of uncertainty in the ensemble-based predictions of climate change and
the corresponding hydrological impact is necessary for the development of robust climate …

Predicting water runoff in ungauged water catchment areas is vital to practical applications
such as the design of drainage infrastructure and flooding defences, runoff forecasting, and …

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 …