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A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application
Author(s) -
Razavi Saman,
Gupta Hoshin V.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr017559
Subject(s) - sobol sequence , robustness (evolution) , sensitivity (control systems) , computer science , parameter space , variance (accounting) , range (aeronautics) , econometrics , data mining , statistics , mathematics , monte carlo method , engineering , biochemistry , chemistry , accounting , electronic engineering , aerospace engineering , business , gene
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.