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

Saman Razavi, Hoshin V. Gupta

Research output: Contribution to journalArticlepeer-review

58 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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