18 Dec 2023
 | 18 Dec 2023
Status: this preprint is currently under review for the journal HESS.

Towards understanding the influence of seasons on low groundwater periods based on explainable machine learning

Andreas Wunsch, Tanja Liesch, and Nico Goldscheider

Abstract. Seasons are known to have a major influence on groundwater recharge and therefore groundwater levels, however, underlying relationships are complex and partly unknown. The goal of this study is to investigate the influence of the seasons on groundwater levels (GWL), especially on low-water periods. For this purpose, we train artificial neural networks and apply layer-wise relevance propagation to understand what relationships are learned by the models to simulate GWLs. We find that the learned relationships are plausible and thus consistent with our understanding of the major physical processes. Our results show that the models learn summer as the key season for periods of low GWL in fall, a connection to the preceding winter is usually only subordinate. Specifically, dry summers show strong influence on low-water periods and generate a water deficit, that (preceding) wet winters cannot compensate. Temperature is, thus an important proxy for evapotranspiration in summer and overall identified as the more important variable, but only on average. Single precipitation events show by far the highest influences on GWL and summer precipitation seems to mainly control the severeness of low GWL periods in fall, while higher summer temperatures do not systematically cause more severe low-water periods.

Andreas Wunsch, Tanja Liesch, and Nico Goldscheider

Status: open (until 30 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-284', Jacopo Boaga, 22 Dec 2023 reply
    • AC1: 'Reply on RC1', Andreas Wunsch, 03 Jan 2024 reply
Andreas Wunsch, Tanja Liesch, and Nico Goldscheider

Data sets

Groundwater Data Andreas Wunsch, Tanja Liesch, and Stefan Broda

Supplement Andreas Wunsch

Model code and software

Model Code Andreas Wunsch

trained Models Andreas Wunsch

Andreas Wunsch, Tanja Liesch, and Nico Goldscheider


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Short summary
Seasons have a strong influence on groundwater levels, but relationships are complex and partly unknown. Using data from wells in Germany and an explainable machine learning approach, we showed that summer precipitation is the key factor that controls the severeness of a low-water period in fall, high summer temperatures do not per-se cause stronger decreases. Preceding winters have only minor influence on such low-water periods, in general.