Bayesian Calibration and Sensitivity Analysis for a Karst Aquifer Model Using Active Subspaces. Issue 8 (22nd August 2019)

• Record Type: Journal Article
• Authors: Teixeira Parente, Mario; Bittner, Daniel; Mattis, Steven A.; Chiogna, Gabriele; Wohlmuth, Barbara
• Is Part Of: Water resources research. Volume 55:Issue 8(2019)
• Page Start: 7086
• Page End: 7107
• Publication Date: 2019-08-22
• Subjects: Bayesian inversion -- dimension reduction -- karst hydrology -- global sensitivity; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR024739
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: In this article, we perform a parameter study for a recently developed karst hydrological model. The study consists of a high‐dimensional Bayesian inverse problem and a global sensitivity analysis. For the first time in karst hydrology, we use the active subspace method to find directions in the parameter space that dominate the Bayesian update from the prior to the posterior distribution in order to effectively reduce the dimension of the problem and for computational efficiency. Additionally, the calculated active subspace can be exploited to construct sensitivity metrics on each of the individual parameters and be used to construct a natural model surrogate. The model consists of 21 parameters to reproduce the hydrological behavior of spring discharge in a karst aquifer located in the Kerschbaum spring recharge area at Waidhofen a.d. Ybbs in Austria. The experimental spatial and time series data for the inference process were collected by the water works in Waidhofen. We show that this case study has implicit low dimensionality, and we run an adjusted Markov chain Monte Carlo algorithm in a low‐dimensional subspace to construct samples of the posterior distribution. The results are visualized and verified by plots of the posterior's push‐forward distribution displaying the uncertainty in predicting discharge values due to the experimental noise in the data. Finally, a discussion provides hydrological interpretation of these results for the Kerschbaum area. Key Points: Quantification of uncertainties via Bayesian inference of a high‐dimensional karst aquifer model Dimension reduction and global sensitivity analysis with active subspaces Detection of parameter correlations and their hydrological interpretation