The usefulness of CPTs for deterministic, spatially heterogeneous, large-scale aquitard parametrisation

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Aquitards can be effectively parametrised in a groundwater model by using standard cone penetration tests (CPTs). Several conceptual realizations of an aquitard hydraulic conductivity field based on the conventional methods of soil behaviour type classification, as well as recent literature-based and newly developed site-specific relations with hydraulic conductivity were tested. It is shown that use of most of these CPT-based hydraulic conductivity estimations in groundwater flow modelling effectively enhance model performance based on absolute head values, and head differences across the aquitard. Concepts that considered a spatially heterogeneous hydraulic conductivity for the aquitard performed better than the reference case with a uniform aquitard hydraulic conductivity. However, the hydraulic conductivity of thin heavy clay lenses, characteristic of the aquitard present in the study area, cannot be captured using these approaches. This leads to a bias in the direct hydraulic conductivity predictions and a need for inverse optimisation of the physically-based heterogeneous parameter fields. To address this issue, the concept of boundary energy associated with the CPT signal is also introduced for characterising the presence of heavy clay lenses. Overall, the CPT-based optimal concept provides a more accurate, robust, and high-resolution data-based parametrisation of the studied aquitard.


Original languageEnglish
Title of host publicationProceedings ModelCARE2011 held at Leipzig, Germany, in September 2011
Place of PublicationGermany
Publication statusPublished - Dec 2012
EventModelCare 2011 - Helmholtz Centre for Environmental Research – UFZ, Germany, University of Potsdam, Germany, Leipzig, Germany
Duration: 18 Sep 201122 Sep 2011

Publication series

NameIAHS red book


ConferenceModelCare 2011


  • groundwater modelling, hydraulic conductivity, soil behaviour types, cone resistance, friction ratio, geostatistics, inverse optimisation, upscaling, cone penetration tests, model performance

ID: 188927