Application of the flow dimension concept for numerical drawdown data analyses in mixed-flow karst systems
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A numerical discrete conduit-continuum model is employed to investigate large-scale groundwater abstraction in karst aquifers. The application of large-scale experiments is one approach to deal with the scale problem in hydraulic parameter assessment, caused by significant contrasts of hydraulic parameters in a karst aquifer. Here, conduit drawdown is evaluated by diagnostic plots and by considering the apparent flow dimension. These tools are frequently used for the interpretation of hydraulic borehole tests by analytical solutions. In contrast to existing analytical solutions, a numerical groundwater model allows the incorporation of the effect of complex parameter distributions. The objective is to demonstrate the application of diagnostic plots and flow dimension analysis for a systematic analysis of the effect of different boundary conditions as well as sink/source terms for idealized two-dimensional mixed karst aquifer systems, which ultimately extends existing analytical solutions and, therefore, contributes to the interpretation of measured field data. The analysis is focused on the apparent flow dimension and shows the extension of the cross-sectional flow area for selected models. The results are used to evaluate the large-scale pumping test of the karstified Cent Fonts catchment (Languedoc, France). The inverse calibration of two realistic, but still simplified, catchment models reveals that the apparent flow dimension supplies useful information about the general flow pattern during the Cent Fonts pumping test. The flow dimension after the end of the storage period can be explained by a large contribution of exchange flow resulting in a strong influence of radial flow on regional, i.e., kilometer scale.
Karst, Inverse Modeling, Pumping Test, Flow Dimension, France
Giese, M.; Reimann, T.; and Liedl, R., "Application of the flow dimension concept for numerical drawdown data analyses in mixed-flow karst systems" (2022). KIP Articles. 286.