Distribution-based sensitivity analysis from a generic input-output sample

Francesca Pianosi*, Thorsten Wagener

*Corresponding author for this work

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

33 Citations (Scopus)
235 Downloads (Pure)


In a previous paper we introduced a distribution-based method for Global Sensitivity Analysis (GSA), called PAWN, which uses cumulative distribution functions of model outputs to assess their sensitivity to the model's uncertain input factors. Over the last three years, PAWN has been employed in the environmental modelling field as a useful alternative or complement to more established variance-based methods. However, a major limitation of PAWN up to now was the need for a tailored sampling strategy to approximate the sensitivity indices. Furthermore, this strategy required three tuning parameters whose optimal choice was rather unclear. In this paper, we present an alternative approximation procedure that tackles both issues and makes PAWN applicable to a generic sample of inputs and outputs while requiring only one tuning parameter. The new implementation therefore allows the user to estimate PAWN indices as complementary metrics in multi-method GSA applications without additional computational cost.

Original languageEnglish
Pages (from-to)197-207
Number of pages11
JournalEnvironmental Modelling and Software
Early online date3 Aug 2018
Publication statusPublished - Oct 2018


  • Distribution-based methods
  • Global sensitivity analysis
  • Moment-independent methods
  • Multi-method GSA


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