Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model. (May 2017)

• Record Type: Journal Article
• Authors: Khorashadi Zadeh, Farkhondeh; Nossent, Jiri; Sarrazin, Fanny; Pianosi, Francesca; van Griensven, Ann; Wagener, Thorsten; Bauwens, Willy
• Is Part Of: Environmental modelling & software. Volume 91(2017)
• Page Start: 210
• Page End: 222
• Publication Date: 2017-05
• Subjects: Global sensitivity analysis -- Moment-independent method -- Variance-based method -- PAWN -- Sobol' -- SWAT; 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.2017.02.001
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Global Sensitivity Analysis (GSA) is an essential technique to support the calibration of environmental models by identifying the influential parameters (screening) and ranking them. In this paper, the widely-used variance-based method (Sobol') and the recently proposed moment-independent PAWN method for GSA are applied to the Soil and Water Assessment Tool (SWAT), and compared in terms of ranking and screening results of 26 SWAT parameters. In order to set a threshold for parameter screening, we propose the use of a "dummy parameter", which has no influence on the model output. The sensitivity index of the dummy parameter is calculated from sampled data, without changing the model equations. We find that Sobol' and PAWN identify the same 12 influential parameters but rank them differently, and discuss how this result may be related to the limitations of the Sobol' method when the output distribution is asymmetric. Highlights: We compare the results of Sobol' and PAWN methods for a complex environmental model. We introduce a "dummy parameter" to support parameter screening. Convergence rates and screening results are similar for Sobol' and PAWN. Differences among sensitivity indices of influential parameters are larger for PAWN.