Internally-consistent scenarios are increasingly used in social–ecological systems modelling
to explore how a complex system might be influenced by deeply uncertain future conditions …

Complex social-ecological systems models typically need to consider deeply uncertain long
run future conditions. The influence of this deep (ie incalculable, uncontrollable) uncertainty …

Dynamical earth and environmental systems models are typically computationally intensive
and highly parameterized with many uncertain parameters. Together, these characteristics …

Sensitivity analysis (SA) is a fundamental practice for analyzing model behavior under
different conditions of application. A number of SA techniques were proposed, ranging from …

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 …

This paper investigates the commonly overlooked “sensitivity” of sensitivity analysis (SA) to
what we refer to as parameter “perturbation scale”, which can be defined as a prescribed …

Complex environmental models typically require global sensitivity analysis (GSA) to account
for non-linearities and parametric interactions. However, variance-based GSA is highly …

Global sensitivity analysis (GSA) is a valuable tool for filtering out non-influential model
inputs. In combination with robustness, convergence and validation analyses, GSA can be …

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 …

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 …