Sensitivity analysis is used to qualitatively or quantitatively apportion the variation of model
output to different source of variation. It is a very useful tool in model parameterization and …

Environmental models have been regarded as a key method to study the complex
conditions of environmental systems. Sensitivity analysis (SA) is a vital step, which aims to …

Stochastic simulation models requiring many input parameters are widely used to inform the
management of ecological systems. The interpretation of complex models is aided by global …

Trying to cope with a growing human population and its demands on natural resources, we
aim to manage the use of our ecosystems by applying concepts of connectedness and …

Through several decades of development, global sensitivity analysis has been developed
as a very useful guide tool for assessing scientific models and has gained pronounced …

Sensitivity analysis is usually conducted independently for each parameter in turn. This can
give misleading results when parameter estimates are correlated. Such correlations can be …

Sensitivity analysis is an important step in understanding how uncertainty is propagated
through complex computer models. Unfortunately, the most reliable sensitivity analysis …

The analysis of models is a considerable problem when there is uncertainty in the model
parameters and when the model parameters interact and determine the model output in a …

I propose a methodology of sensitivity analysis applicable to complex process-based
models. In application and discussion of this methodology, a model is conceived as a …

The output of stochastic models is a distribution of values, rather than a single value such as
in deterministic models. Local sensitivity analyses of such models typically ignore the higher …