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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 environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research.
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Francesca Pianosi
University of Bristol
Keith Beven
University of Bern
Jim Freer
Veterinary Medicines Directorate
Environmental Modelling & Software
University of Oxford
University of Bristol
University of Exeter
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Pianosi et al. (Thu,) studied this question.
synapsesocial.com/papers/69db1ce6387cf70698688229 — DOI: https://doi.org/10.1016/j.envsoft.2016.02.008