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

Abstract

Abstract. Hydrological models are widely used to characterise, understand and manage hydrosystems. Data-driven models are of particular interest in karst environments given the complexity and heterogeneity of these systems. There is a multitude of data-driven modelling approaches, which can make it difficult for a manager or researcher to choose. We therefore conducted a comparison of two data-driven modelling approaches: artificial neural networks (ANN) 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 ANN 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 short calibration periods, and (iii) ANN models seem robust for reproducing high-flow conditions while reservoir models are superior for 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 shown to be useful to identify recharge areas and delineate catchment, 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.

Karst water resources in a changing world: Review of solute transport modelling approaches

“…Nonetheless, the characterization methods for the data acquisition and/or interpretation techniques in karst aquifers is rather appropriate for the continuum (Darcy) scale applications where the effective parameters can be readily used for the entire model domain (e.g., Faulkner et al, 2009;Huntoon, 1995;Kresic & Stevanovic, 2009;Zhang et al, 2021). For this reason, when the direct measurements of transport parameters (i.e., dispersivity, mass transfer coefficient) are not practical and/or their values might not be representative in resembling the process of interest, model parameters are frequently estimated manuscript submitted to Review of Geophysics via model calibration (e.g., Bittner et al, 2020;Cinkus et al, 2023;Dewaide et al, 2016;Sivelle et al, 2023;Çallı et al, 2023a).…”

Improvement of the KarstMod modeling platform for a better assessment of karst groundwater resources

“…KarstMod allows using either observation-based precipitation time series P [L.T -1 ] or estimated precipitation time series P sr [L.T -1 ] using a snow routine. The latter is similar to the one used by Chen et al (2018) -without the radiation components -which has been successfully used for improving the simulation of karst spring discharge in snow-covered karst systems (Chen et al, 2018;Cinkus et al, 2022b). It consists of a modified HBV-snow routine (Bergström, 1992) for simulating snow accumulation and melt over different sub-catchments based on altitude ranges (appendix A).…”