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

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. [ABSTRACT FROM AUTHOR]