This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated.
The effects of the macro- and the microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully.
%0 Journal Article
%1 haslauer2017detecting
%A Haslauer, C. P.
%A Bardossy, Andras
%A Sudicky, E. A.
%D 2017
%I Elsevier
%J Advances in Water Resources
%K boundary_layer copula geostatistics myown
%P 439-450
%R 10.1016/j.advwatres.2017.05.007
%T Detecting and modelling structures on the micro and the macro scales:
Assessing their effects on solute transport behaviour
%U https://www.sciencedirect.com/science/article/pii/S0309170816307436
%V 107
%X This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated.
The effects of the macro- and the microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully.
@article{haslauer2017detecting,
abstract = {This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated.
The effects of the macro- and the microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully.},
added-at = {2019-07-12T14:16:19.000+0200},
affiliation = {Haslauer, CP (Reprint Author), Univ Tubingen, Ctr Appl Geosci, Holderlinstr 12, D-72076 Tubingen, Germany. Haslauer, C. P., Univ Tubingen, Ctr Appl Geosci, Holderlinstr 12, D-72076 Tubingen, Germany. Bardossy, A., Univ Stuttgart, Inst Modelling Hydraul \& Environm Syst, Dept Hydrol \& Geohydrol, Pfaffenwaldring 61, D-70569 Stuttgart, Germany. Sudicky, E. A., Univ Waterloo, Dept Earth \& Environm Sci, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada.},
author = {Haslauer, C. P. and Bardossy, Andras and Sudicky, E. A.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2db0c473d051bbe89f993b4a28da07407/claushaslauer},
da = {2018-04-12},
doi = {10.1016/j.advwatres.2017.05.007},
eissn = {1872-9657},
interhash = {7fd5ff651abe3ccd44a104563ee3b1e5},
intrahash = {db0c473d051bbe89f993b4a28da07407},
issn = {0309-1708},
journal = {Advances in Water Resources},
keywords = {boundary_layer copula geostatistics myown},
language = {English},
pages = {439-450},
publisher = {Elsevier},
research-areas = {Water Resources},
timestamp = {2019-07-12T12:16:19.000+0200},
title = {Detecting and modelling structures on the micro and the macro scales:
Assessing their effects on solute transport behaviour},
type = {Article},
unique-id = {ISI:000410674200032},
url = {https://www.sciencedirect.com/science/article/pii/S0309170816307436},
volume = 107,
year = 2017
}