Data-driven modelling of groundwater level time series: challenges posed by contrasting response dynamics

Groundwater systems exhibit substantially different response dynamics depending on site-specific and hydrogeological characteristics: While shallow aquifers often respond rapidly to meteorological forcing, deeper groundwater systems typically show delayed and strongly damped dynamics. In particular, monitoring wells with large depths to groundwater and long response times to external drivers remain challenging to model reliably using data-driven approaches. However, these systems are hydrogeologically highly relevant, as their substantial storage capacity and persistence across wet and dry periods strongly influence long-term water availability and the attenuation of climatic extremes.

To assess the potential of data-driven models for capturing contrasting groundwater dynamics, groundwater level time series from approximately 400 monitoring wells in the federal state of Brandenburg (Germany) are selected. All wells provide continuous observations since 1980, are distributed across three major aquifer complexes at different depths, and thus represent a wide spectrum of response behaviours. Recurrent neural networks (Gated Recurrent Units - GRU) are applied to predict groundwater levels based on meteorological inputs (precipitation and air temperature). Two key aspects are systematically investigated: (1) the length of the input sequence and (2) the optional integration of aggregated meteorological predictors. This design evaluates whether extended look-back periods or the incorporation of site-specific smoothed climate signals improves the predictability of damped groundwater systems.

The results indicate that input sequence lengths of two to three years substantially improve model performance for slow-responding groundwater systems, whereas shorter sequences are sufficient for more dynamic systems. Incorporating site-specific aggregated meteorological inputs further enhanced the representation of characteristic response times and led to a considerable increase in predictive skill for slow-responding aquifers. Although some strongly damped systems remain difficult to predict even with optimised model configurations, the overall results demonstrate a clear potential to better capture slow-responding groundwater dynamics and improve predictive performance.