Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background

Varouchakis Emmanouil, Theodoridou Panagiota, Karatzas Georgios

Full record


URI: http://purl.tuc.gr/dl/dias/F10C6D75-C53C-4009-95C9-5F434ECFCF7D
Year 2019
Type of Item Peer-Reviewed Journal Publication
License
Details
Bibliographic Citation E.A. Varouchakis, P.G. Theodoridou and G.P. Karatzas, "Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background," J. Hydrol., vol. 575, pp. 487-498, Aug. 2019. doi: 10.1016/j.jhydrol.2019.05.055 https://doi.org/10.1016/j.jhydrol.2019.05.055
Appears in Collections

Summary

The joint spatiotemporal modeling of aquifer level fluctuations provides a significant tool in the prediction of groundwater levels at unvisited locations. Two types of variogram functions, appropriately modified to incorporate physical information, are presented and compared under Space-time Residual kriging methodology using the Bayesian Bootstrap idea to quantify uncertainty. The spatiotemporal trend is approximated using a component of physical meaning. A recently developed Spartan type non-separable variogram function employs tools of physical meaning to enhance the efficiency and reliability of spatiotemporal geostatistical modeling in groundwater applications. In addition, the product-sum space-time variogram is applied and involves in the separable variogram functions a scale factor of hydraulic conductivity directional ratio. The proposed variogram functions involve a non-Euclidean distance metric and are mathematically valid (i.e., constitutes permissible models). Herein, the efficiency of the proposed tools is tested using groundwater level data from an alluvial unconfined aquifer. Both functions provide improved results compared to those of the Space-time Ordinary kriging. Between the two, the non-separable one has the most efficient performance.

Services

Statistics