TY  - JOUR
A1  - Fandel, Chloé
A1  - Ferré, Ty
A1  - Chen, Zhao
A1  - Renard, Philippe
A1  - Goldscheider, Nico
T1  - A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits
Y1  - 2020-10-02
VL  - 29
IS  - 1
SP  - 229
EP  - 248
JF  - Hydrogeology Journal
DO  - 10.1007/s10040-020-02227-6
PB  - Springer Berlin Heidelberg
N2  - 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.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10679
ER  -