Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung


  • Guillaume Cinkus - (Autor:in)
  • Andreas Wunsch - (Autor:in)
  • Naomi Mazzilli - (Autor:in)
  • Tanja Liesch - (Autor:in)
  • Zhao Chen - , Professur für Grundwassersysteme (Autor:in)
  • Nataša Ravbar - (Autor:in)
  • Joanna Doummar - (Autor:in)
  • Jaime Fernández-Ortega - (Autor:in)
  • Juan Antonio Barberá - (Autor:in)
  • Bartolomé Andreo - (Autor:in)
  • Nico Goldscheider - (Autor:in)
  • Hervé Jourde - (Autor:in)


Hydrological models are widely used to characterize, understand and manage hydrosystems. Lumped parameter models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of lumped parameter modelling approaches, which can make it difficult for a manager or researcher to choose. We therefore conducted a comparison of two lumped parameter modelling approaches: artificial neural networks (ANNs) and reservoir models. We investigate five karst systems in the Mediterranean and Alpine regions with different characteristics in terms of climatic conditions, hydrogeological properties and data availability. We compare the results of ANN and reservoir modelling approaches using several performance criteria over different hydrological periods. The results show that both ANNs and reservoir models can accurately simulate karst spring discharge but also that they have different advantages and drawbacks: (i) ANN models are very flexible regarding the format and amount of input data, (ii) reservoir models can provide good results even with a few years of relevant discharge in the calibration period and (iii) ANN models seem robust for reproducing high-flow conditions, while reservoir models are superior in reproducing low-flow conditions. However, both modelling approaches struggle to reproduce extreme events (droughts, floods), which is a known problem in hydrological modelling. For research purposes, ANN models have been shown to be useful for identifying recharge areas and delineating catchments, based on insights into the input data. Reservoir models are adapted to understand the hydrological functioning of a system by studying model structure and parameters.


Seiten (von - bis)1961–1985
FachzeitschriftHydrology and earth system sciences
PublikationsstatusVeröffentlicht - 23 Mai 2023

Externe IDs

Scopus 85160941827