AbstractModeling is crucial to understand the behavior of environmental systems. A deeper comprehension of a model can be aided by global sensitivity analysis (GSA). Variability ascribed to model variables could have a stochastic (i.e., lack of knowledge) or an operational (i.e., possible design values) origin. Despite the possible different nature in the variability, current GSA strategies do not distinguish the latter in their formal derivations/developments. We propose to disentangle the variability in the operational and stochastic variables while assessing the model output sensitivity with respect to the former. Two operational sensitivity indices are introduced that serve to characterize the sensitivity of a model output of interest with respect to an operational variable in terms of (a) its average (with respect to the stochastic variables) intensity and (b) its degree of fluctuation (across the set of possible realizations of the stochastic variables), respectively. We exemplify our developments considering two scenarios. Results highlight the relevance of employing an operational GSA when the focus is on the influence of operational variables on model output.