Next Article in Journal
Use of Bacteria and Synthetic Zeolites in Remediation of Soil and Water Polluted with Superhigh-Organic-Sulfur Raša Coal (Raša Bay, North Adriatic, Croatia)
Previous Article in Journal
Scour around Spur Dike in Sand–Gravel Mixture Bed
Open AccessArticle

Comparison of Multivariate Spatial Dependence Structures of DPIL and Flowmeter Hydraulic Conductivity Data Sets at the MADE Site

Center for Applied Geoscience, University of Tübingen, 72074 Tübingen, Germany
Research Facility for Subsurface Remediation (VEGAS), University of Stuttgart, 70569 Stuttgart, Germany
Kansas Geological Survey, University of Kansas, Lawrence, KS 66047, USA
Author to whom correspondence should be addressed.
Water 2019, 11(7), 1420;
Received: 11 June 2019 / Revised: 4 July 2019 / Accepted: 5 July 2019 / Published: 10 July 2019
(This article belongs to the Section Hydrology)
We analyse two datasets of hydraulic conductivity (K) from the MAcroDispersion Experiment (MADE) site, one measured by direct-push injection logging (DPIL) and the other by flowmeter profiling. The analysis is performed using copula techniques which do not rely on the assumption of multivariate Gaussianity and provide a means to characterise differing degrees of spatial dependence in different quantiles of the K distribution. This characterisation provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and the traditional two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analysed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analysed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. We demonstrate clear indications of the existence of non-Gaussian spatial dependence structures of K at this site. We were able to improve the estimation of the K distribution by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that are dependent on K. View Full-Text
Keywords: hydrogeology; geostatistics; copula; censored data hydrogeology; geostatistics; copula; censored data
Show Figures

Figure 1

MDPI and ACS Style

Xiao, B.; Haslauer, C.; Bohling, G. Comparison of Multivariate Spatial Dependence Structures of DPIL and Flowmeter Hydraulic Conductivity Data Sets at the MADE Site. Water 2019, 11, 1420.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

Back to TopTop