A simple and efficient method for global sensitivity analysis based on cumulative distribution functions

Francesca Pianosi*, Thorsten Wagener

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

173 Citations (Scopus)

Abstract

Variance-based approaches are widely used for Global Sensitivity Analysis (GSA) of environmental models. However, methods that consider the entire Probability Density Function (PDF) of the model output, rather than its variance only, are preferable in cases where variance is not an adequate proxy of uncertainty, e.g. when the output distribution is highly-skewed or when it is multi-modal. Still, the adoption of density-based methods has been limited so far, possibly because they are relatively more difficult to implement. Here we present a novel GSA method, called PAWN, to efficiently compute density-based sensitivity indices. The key idea is to characterise output distributions by their Cumulative Distribution Functions (CDF), which are easier to derive than PDFs. We discuss and demonstrate the advantages of PAWN through applications to numerical and environmental modelling examples. We expect PAWN to increase the application of density-based approaches and to be a complementary approach to variance-based GSA.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalEnvironmental Modelling and Software
Volume67
DOIs
Publication statusPublished - 1 May 2015

Keywords

  • Density-based sensitivity indices
  • Global sensitivity analysis
  • Uncertainty analysis
  • Variance-based sensitivity indices

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