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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-629
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-629
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: technical note 20 Jan 2020

Submitted as: technical note | 20 Jan 2020

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This preprint is currently under review for the journal HESS.

Technical Note: Improved Sampling of Behavioral Subsurface Flow Model Parameters Using Active Subspaces

Daniel Erdal and Olaf A. Cirpka Daniel Erdal and Olaf A. Cirpka
  • University of Tübingen, Hölderlinstr. 12, 72074 Tübingen, Germany

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, which 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.

Daniel Erdal and Olaf A. Cirpka

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Daniel Erdal and Olaf A. Cirpka

Daniel Erdal and Olaf A. Cirpka

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Latest update: 18 Feb 2020
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Short summary
Assessing model sensitivities with ensemble based methods can be prohibitively expensive when large parts of the plausible parameter space results in model simulations with non-realistic results. In a previous work we used the method of active subspaces to create a proxy model with the purpose of filtering out such unrealistic runs at low cost. This work details a notable improvement in the efficiency of the original sampling scheme, without loss of accuracy.
Assessing model sensitivities with ensemble based methods can be prohibitively expensive when...
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