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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-2019-163
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-163
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Apr 2019

Research article | 24 Apr 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Global Sensitivity Analysis and Adaptive Stochastic Sampling of a Subsurface-Flow Model Using Active Subspaces

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

Abstract. Integrated hydrological modelling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity which of these parameters really influence system behavior, and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building-up the ensemble, we use the already existing ensemble members to construct low-order meta-models based on the first two active subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior, so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface flow model including uncertain hydraulic parameter, uncertain boundary conditions, and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningful be done with second-order polynomials.

Daniel Erdal and Olaf A. Cirpka
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Status: open (until 19 Jun 2019)
Status: open (until 19 Jun 2019)
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Daniel Erdal and Olaf A. Cirpka
Daniel Erdal and Olaf A. Cirpka
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Latest update: 22 May 2019
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Short summary
Assessing how sensitive uncertain model parameters are to observed data can be done by analyzing an ensemble of model simulations in which the parameters are varied. In subsurface modelling, this involves running heavy model. To reduce time wasted simulating models which anyway show poor behavior, we use a fast polynomial model based on a simple parameter decomposition to approximate the behavior prior to full model simulation. This largely reduces the cost for the global sensitivity analysis.
Assessing how sensitive uncertain model parameters are to observed data can be done by analyzing...
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