Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model

TL;DR

This paper employs sparse polynomial chaos expansions to efficiently perform global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model, highlighting key parameters influencing uncertainty.

Contribution

It introduces a novel application of sparse polynomial chaos expansions for Sobol' indices computation in high-dimensional hydrogeological models, improving efficiency and insight.

Findings

Uncertainty in a few petrofacies layers explains most variability.

Sparse polynomial chaos expansions are highly efficient for high-dimensional sensitivity analysis.

Results guide future uncertainty modeling in subsurface groundwater studies.

Abstract

The study makes use of polynomial chaos expansions to compute Sobol' indices within the frame of a global sensitivity analysis of hydro-dispersive parameters in a simplified vertical cross-section of a segment of the subsurface of the Paris Basin. Applying conservative ranges, the uncertainty in 78 input variables is propagated upon the mean lifetime expectancy of water molecules departing from a specific location within a highly confining layer situated in the middle of the model domain. Lifetime expectancy is a hydrogeological performance measure pertinent to safety analysis with respect to subsurface contaminants, such as radionuclides. The sensitivity analysis indicates that the variability in the mean lifetime expectancy can be sufficiently explained by the uncertainty in the petrofacies, \ie the sets of porosity and hydraulic conductivity, of only a few layers of the model. The obtained results provide guidance regarding the uncertainty modeling in future investigations employing detailed numerical models of the subsurface of the Paris Basin. Moreover, the study demonstrates the high efficiency of sparse polynomial chaos expansions in computing Sobol' indices for high-dimensional models.