EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Karst spring discharge modeling based on deep learning using spatially distributed input data

Andreas Wunsch1, Tanja Liesch1, Guillaume Cinkus2, Nataša Ravbar3, Zhao Chen4, Naomi Mazzillli5, Hervé Jourde2, and Nico Goldscheider1
Andreas Wunsch et al.
  • 1Institute of Applied Geosciences, Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 2HydroSciences Montpellier (HSM), Université de Montpellier, CNRS, IRD, 34090 Montpellier, France
  • 3ZRC SAZU, Karst Research Institute, Titov trg 2, 6230 Postojna, Slovenia
  • 4Environmental Resources Management, Siemensstr. 9, 63263 Neu-Isenburg, Germany
  • 5UMR 1114 EMMAH (AU-INRAE), Université d’Avignon, 84000 Avignon, France

Despite many existing approaches, modeling karst water resources remains challenging and often requires solid system knowledge. Artificial Neural Network approaches offer a convenient solution by establishing a simple input-output relationship on their own. However, in this context, temporal and especially spatial data availability is often an important constraint, as usually no or few climate stations within a karst spring catchment are available. Hence spatial coverage is often unsatisfying and can introduce severe uncertainties. To overcome these problems, we use 2D-Convolutional Neural Networks (CNN) to directly process gridded meteorological data followed by a 1D-CNN to perform karst spring discharge simulation. We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorologic-hydrological characteristics and hydrodynamic system properties. We compare our 2D-models both to existing modeling studies in these regions and to own 1D-models that are conventionally based on climate station input data. Our results show that our models are excellently suited to model karst spring discharge and rival the simulation results of existing approaches in the respective areas. The 2D-models show a better fit than the 1D-models in two of three cases, learn relevant parts of the input data themselves and by performing a spatial input sensitivity analysis we can further show their usefulness to localize the position of karst catchments.

How to cite: Wunsch, A., Liesch, T., Cinkus, G., Ravbar, N., Chen, Z., Mazzillli, N., Jourde, H., and Goldscheider, N.: Karst spring discharge modeling based on deep learning using spatially distributed input data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3825,, 2022.

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