Abstract
Abstract. In global sensitivity analysis and ensemble-based model calibration, it is essential to create a large enough sample of model simulations with different parameters that all yield plausible model results. This can be difficult if a priori plausible parameter combinations frequently yield non-behavioral model results. In a previous study (Erdal and Cirpka, 2019), we developed and tested a parameter-sampling scheme based on active-subspace decomposition. While in principle this scheme worked well, it still implied testing a substantial fraction of parameter combinations that ultimately had to be discarded because of implausible model results. This technical note presents an improved sampling scheme and illustrates its simplicity and efficiency by a small test case. The new sampling scheme can be tuned to either outperform the original implementation by improving the sampling efficiency while maintaining the accuracy of the result or by improving the accuracy of the result while maintaining the sampling efficiency.
Highlights
Global sensitivity analysis (e.g., Saltelli et al, 2004, 2008) is an established technique for quantifying the importance of uncertain parameters of a model
In a previous study (Erdal and Cirpka, 2019), we developed and tested a parametersampling scheme based on active-subspace decomposition. While in principle this scheme worked well, it still implied testing a substantial fraction of parameter combinations that had to be discarded because of implausible model results
A key issue when conducting a global sensitivity analysis is the requirement of a large enough sample of model simulations with parameters ranging over the full parameter space
Summary
Global sensitivity analysis (e.g., Saltelli et al, 2004, 2008) is an established technique for quantifying the importance of uncertain parameters of a model. A common sampling approach is to use a two-stage acceptance sampling scheme, in which a candidate parameter set is first tested with the surrogate model, and only if the surrogate model predicts the parameter set to be behavioral, it is applied in the full model. This idea has been applied to groundwater modeling by Cui et al (2011), Laloy et al (2013), and the authors of the current study (Erdal and Cirpka, 2019). The scope of the current technical note is to present
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