Predictions of environmental models are affected by potentially large uncertainty due to a range of factors ranging from observation errors in the model forcingto uncertainty in model parameters and structure. Depending on the model application, such uncertainties can undermine the robustness and credibility of the modelling results up to the point that the usefulness of environmental models for supporting decision-making is called into question. Sensitivity Analysis (SA) is a valuable tool for both model developers and users to quantify the uncertainty in model outputs, estimate the relative contribution of the different input factors to such uncertainty, and thus prioritize efforts for its reduction. In the last few years, the development, application and comparison of SA techniques have been the subject of an increasing number of contributions in the literature. In this paper, we provide an overview of this literature and develop practical guidelines to help support researchers and practitioners in the application of SA in environmental modeling. By discussing the most critical and controversial steps in the application of SA methods, we also identify prioritiesfor further research in the field.
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