Accepted: Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces
I am very happy that our article Bayesian calibration and sensitivity analysis for a karst aquifer model using active subspaces was accepted in Water Resources Research (Wiley).
The article is a product of two collaborating teams at TUM within the UNMIX project. Daniel Bittner and Gabriele Chiogna from the Chair of Hydrology and River Basin Management and Steven Mattis, Barbara Wohlmuth, and I worked a lot on the revision and improved the quality of the manuscript due to very detailed reviews.
The paper is a description of our parameter calibration approach for a model of the Kerschbaum spring in Waidhofen a.d. Ybbs in Austria. We used active subspaces as a dimension reduction technique to infer parameters in lower dimensions while saving computational cost and to study parameter sensitivities in a Bayesian inverse problem.
I want to thank everyone who contributed to the article and made this a scientifically valuable contribution!
Journal link: doi:10.1029/2019WR024739
arXiv link: arXiv:1901.03283
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.