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A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits
Auteur(s)
Fandel, Chloé
Férré, Ty
Chen, Zhao
Goldscheider, Nico
Date de parution
2020-10
In
Hydrogeology Journal
No
29
De la page
229
A la page
248
Revu par les pairs
1
Résumé
Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in
extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often
unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble
method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and
to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study
site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of
three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by
the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through
each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of
the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations
and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters,
and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of
flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior.
This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate
between networks.
extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often
unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble
method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and
to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study
site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of
three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by
the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through
each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of
the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations
and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters,
and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of
flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior.
This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate
between networks.
Identifiants
Type de publication
journal article
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