Representing uncertainty with diverse model ensembles: A test case in an alpine karst system

Chloé Fandel, Ty Ferré, Zhao Chen1, & Nico Goldscheider1

Department of Hydrology and Atmospheric Sciences

The University of Arizona

Karst aquifers are difficult to model because the discrete, heterogeneous nature of groundwater flow through conduits, rather than through distributed pore spaces, leads to high structural uncertainty. Existing models rely either on a detailed conduit map, or on effective flow parameters approximating a porous medium. Neither approach is adequate for most karst systems, where conduits are unmapped, yet flow patterns are fundamentally different from those in porous media. Our approach links three components: 3D geologic modeling with GemPy, an open-source Python package; conduit network generation with the Stochastic Karst Simulator (SKS), a pseudo-genetic structural model; and pipe flow modeling with the EPA Storm Water Management Model (SWMM). We use pre-existing data from a long-term research site, the Gottesacker karst system in the German/Austrian Alps. First, several geologic models are built in GemPy.  Each geologic model is fed to SKS, which generates many proposed conduit network maps. For each network, hydraulic parameters are estimated, and the flow behavior is modeled with SWMM. This yields an ensemble of competing models, organized into a model tree recording the geologic structure, conduit network map, and hydraulic parameters for each model. Themodels in the ensemble will then be ranked based on the fit of model-predicted spring discharge timeseries to observed data. The models that best reproduce discharge behavior can then be compared to the known conduit network, to assess the effectiveness of this approach.

1Institute of Applied Geosciences, Karlsruhe Institute of Technology, Karlsruhe, Germany

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