Towards a Virtual Laboratory for Karst Groundwater Modelling

Groundwater modelling serves as an important tool for the study and management of karst water resources. In such context, and especially when we try to obtain karst system understanding through groundwater models, it is not only important to get the right answers (e.g., a well-fitted spring discharge simulation), but we have to get those answers for the right reasons (i.e., because our model realistically reflects the underlying system). This problem has been addressed before, e.g., via model evaluation upon multiple system signatures such as spring discharge and solute concentrations. However, because the conduit network and the geological structure of karst systems is usually largely unknown, lumped models are often employed, which simplify the karst system and simulate the processes in a spatially aggregated manner. Due to those simplifications, assessing model (structural and process related) realism beyond a model’s ability to simulate system output signals is often impossible. Yet, using models to better understand karst system functioning is vital for sustainable karst water resources management. This discrepancy necessitates the study of karst systems and corresponding (lumped or spatially aggregated) models on the basis of systems that are fully known. For this purpose, we develop a virtual laboratory for karst groundwater modelling. This virtual laboratory facilitates the generation and simulation of synthetic karst systems and system signatures such as spring discharge using state-of-the-art spatially distributed numerical modelling. The virtual laboratory makes the generation of synthetic systems more accessible to a wider hydro(geo)logical community, taking a step towards more realistic process representation in (lumped) karst models in the future. Building on our previous work, this combination allows for the stochastic generation of conduit networks using pyKasso [1] and their subsequent embedding and simulation with the spatially distributed discrete-continuum model MODFLOW CFPv2 [2] via the CFPy package for the Python programming language [3]. We demonstrate the capabilities of the virtual laboratory for a number of cases, show upcoming development goals, and discuss opportunities for future applications.

 

[1] Miville, F., Renard, P., Fandel, C., & Filipponi, M. (2025). pyKasso: An open-source three-dimensional discrete karst network generator. Environmental Modelling & Software, 186, 106362.

[2] Reimann, T., Giese, M., Geyer, T., Liedl, R., Maréchal, J. C., & Shoemaker, W. B. (2014). Representation of water abstraction from a karst conduit with numerical discrete-continuum models. Hydrology and Earth System Sciences, 18(1), 227-241.

[3] Reimann, T., Rudolph, M. G., Grabow, L., & Noffz, T. (2023). CFPy—A python package for pre‐and postprocessing of the conduit flow process of MODFLOW. Groundwater, 61(6), 887-894.