Sensitivity analysis of environmental models: A systematic review with practical workflow. (May 2016)

• Record Type: Journal Article
• Authors: Pianosi, Francesca; Beven, Keith; Freer, Jim; Hall, Jim W.; Rougier, Jonathan; Stephenson, David B.; Wagener, Thorsten
• Is Part Of: Environmental modelling & software. Volume 79(2016:May)
• Page Start: 214
• Page End: 232
• Publication Date: 2016-05
• Subjects: Sensitivity Analysis -- Uncertainty Analysis -- Calibration -- Evaluation -- Robust decision-making; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.02.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making. We provide a systematic review of existing approaches, which can support users in the choice of an SA method. We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices. We give best practice examples from the literature and highlight trends and gaps for future research.