An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

Abstract

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies ( wtd a ) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies ( pr a ), which is the most common proxy of wtd a , for the networks to arrive at improved wtd a estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtd a estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pr a and soil moisture anomaly (θ a ) as input, which achieved the optimal network performance. The regional medians of test R 2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R 2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pr a as input. Our results show that introducing θ a significantly improved the performance of the trained networks to predict wtd a , indicating the substantial contribution of θ a to explain groundwater anomalies. Also, the European wtd a map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events ( wtd a ≥ 1.5) and ~29% of extreme drought events ( wtd a ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

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