A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application

Saman Razavi, Hoshin Vijai Gupta

Research output: Contribution to journalArticle

46 Scopus citations

Abstract

Based on the theoretical framework for sensitivity analysis called "Variogram Analysis of Response Surfaces" (VARS), developed in the companion paper, we develop and implement a practical "star-based" sampling strategy (called STAR-VARS), for the application of VARS to real-world problems. We also develop a bootstrap approach to provide confidence level estimates for the VARS sensitivity metrics and to evaluate the reliability of inferred factor rankings. The effectiveness, efficiency, and robustness of STAR-VARS are demonstrated via two real-data hydrological case studies (a 5-parameter conceptual rainfall-runoff model and a 45-parameter land surface scheme hydrology model), and a comparison with the "derivative-based" Morris and "variance-based" Sobol approaches are provided. Our results show that STAR-VARS provides reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being 1-2 orders of magnitude more efficient than the Morris or Sobol approaches.

Original languageEnglish (US)
Pages (from-to)440-455
Number of pages16
JournalWater Resources Research
Volume52
Issue number1
DOIs
StatePublished - Jan 1 2016

Keywords

  • bootstrapping
  • computational efficiency
  • covariogram
  • dynamical models
  • model performance
  • morris
  • sampling
  • scale
  • sensitivity analysis
  • sobol
  • variogram

ASJC Scopus subject areas

  • Water Science and Technology

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