期刊
WATER RESOURCES RESEARCH
卷 52, 期 1, 页码 423-439出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/2015WR017558
关键词
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资金
- University of Saskatchewan's Global Institute for Water Security and Howard Wheater
- Canada Excellence Research Chair in Water Security
- Australian Research Council through the Centre of Excellence for Climate System Science [CE110001028]
- EU [294947]
Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to variogram analysis,'' that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.
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