Advancing AI-based pan-European groundwater monitoring

Abstract

The main challenge of pan-European groundwater (GW) monitoring is the sparsity of collated water table depth (wtd) observations. The wtd anomaly (wtda ) is a measure of the increased wtd due to droughts. Combining long short-term memory (LSTM) networks and transfer learning (TL), we propose an AI-based methodology LSTM-TL to produce reliable wtda estimates at the European scale in the absence of consistent wtd observational data sets. The core idea of LSTM-TL is to transfer the modeled relationship between wtda and input hydrometeorological forcings to the observation-based estimation, in order to provide reliable wtda estimates for regions with no or sparse wtd observations. With substantially reduced computational cost compared to physically-based numerical models, LSTM-TL obtained wtda estimates in good agreement with in-situ wtda measurements from 2569 European GW monitoring wells, showing r ⩾ 0.5, root-mean-square error ⩽1.0 and Kling-Gupta efficiency ⩾0.3 at about or more than half of the pixels. Based on the reconstructed long-term European monthly wtda data from the early 1980s to the near present, we provide the first estimate of seasonal wtda trends in different European regions, that is, significant drying trends in central and eastern Europe, which facilitates the understanding of historical GW dynamics in Europe. The success of LSTM-TL in estimating wtda also highlights the advantage of combining AI techniques with knowledge contained in physically-based numerical models in hydrological studies.