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 (wtda) 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 (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda 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 wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 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 R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda 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 (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 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.

Highlights

  • Drought is one of the major natural disasters worldwide, considerably affecting environmental, human, and economic well-being

  • We designed experiments that introduced different input variable combinations into the Long Short-Term Memory (LSTM) networks proposed in Ma et al (2021), which utilized a supervised training algorithm with target data to guide the training process, and conducted wavelet coherence analysis to investigate the impact of the input variable combinations on the estimation of wtda over Europe in the time-frequency domain

  • Except for the original input variable pra, we introduced evapotranspiration anomaly (ETa), θa, SWEscaled, and anomalies at adjacent pixels as optional input variables to the LSTM networks

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Summary

Introduction

Drought is one of the major natural disasters worldwide, considerably affecting environmental, human, and economic well-being. Except for the last type, which reflects socio-economic situations, the severity of the others can be quantified by the following standardized hydrometeorological variables: (1) precipitation anomaly (pra) and evapotranspiration anomaly (ETa) for meteorological drought, e.g., the Standardized Precipitation Index (McKee et al, 1993) and the Standardized Precipitation Evapotranspiration Index (Vicente-Serrano et al, 2010); (2) river stage anomaly (rsa) and river discharge anomaly for hydrological drought, e.g., the Standardized Runoff Index (Shukla and Wood, 2008) and the Streamflow Drought Index (Nalbantis and Tsakiris, 2009); (3) soil moisture anomaly (θa) for agricultural drought, e.g., the Crop Moisture Index (Palmer, 1968); (4) water table depth anomaly (wtda) for groundwater drought, e.g., the Standardized Groundwater level Index (Bloomfield and Marchant, 2013) and the GRACE Groundwater Drought Index (Thomas et al, 2017) These examples are not exhaustive, providing some of the related drought indices that have been widely used for extreme event analyses

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