Reconstructing Groundwater Levels with Pastas
Many organizations managing groundwater monitoring networks face a common challenge: gaps in time series data. These gaps can significantly limit the usefulness of the data for analysis and modeling. The Office for Water and Waste (AWA) of the Canton of Bern oversees an extensive groundwater monitoring network and is planning to develop a numerical groundwater model to inform decision-making in the Aaretal region between Thun and Bern, Switzerland.
As part of this initiative, the canton reactivated several abandoned monitoring wells and resumed measurements. To prepare the groundwater level data for model calibration, Hydroconsult was commissioned to analyze and reconstruct missing data using Pastas.
How Pastas Helped
Pastas was used to simulate groundwater levels over a 30-year period, including periods without measurements. For each well, a Pastas model was developed, incorporating key hydrological stresses such as precipitation, potential evaporation, and the water levels of the Aare River and Lake Thun. The results for a single time series (Figure 1) demonstrate how the model effectively fills a 20-year data gap.
Figure 1. Example results from the gap-filling exercise using Pastas.
Key Findings
The models performed very well across the study area (Figure 2). Notably, fluctuations in the Aare River’s water levels were found to explain much of the variability in groundwater levels. This insight enabled accurate reconstruction of groundwater levels during periods without measurements.
During calibration, the models were tested using only historical data from before the measurements resumed. This approach revealed unexplained changes and a structural shift in groundwater levels during the gap period in some wells. Further investigation by the Cantonal Office identified a likely cause: the development of beaver dams, which influenced local groundwater dynamics.
Figure 2. Overview map of the model performance measured as R squared.
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