Preprints
https://doi.org/10.5194/hess-2020-382
https://doi.org/10.5194/hess-2020-382

  24 Aug 2020

24 Aug 2020

Review status: a revised version of this preprint is currently under review for the journal HESS.

Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe

Yueling Ma1,2, Carsten Montzka1, Bagher Bayat1, and Stefan Kollet1,2 Yueling Ma et al.
  • 1Institute of Bio and Geosciences (Agrosphere, IBG-3), Research Center Jülich, 52428 Jülich, Germany
  • 2Center for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, 52428 Jülich, Germany

Abstract. Many European countries mainly rely on groundwater for domestic water use. Due to a scarcity of near real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods to estimate wtd anomalies (wtda) using other hydrometeorological observations routinely available near real-time. In this work, we explore the potential of Long Short-Term Memory (LSTM) networks to produce monthly wtda, using monthly precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks, useful in detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. To set up the methodology, spatio-temporally continuous data were obtained from daily terrestrial simulations (hereafter termed the TSMP-G2A data set) with a spatial resolution of 0.11°, ranging from the year 1996 to 2016. They were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtda maps from LSTM networks agreed well with TSMP-G2A wtda maps in 2003 and 2015 constituting drought years over Europe. Moreover, we categorized test performances of the networks based on yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), and soil type (St) and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd < 3 m, ET > 200 mm, θ > 0.15 m3 m−3 and Sw < 10 mm, revealing a significant impact of the local factors on the ability of the networks to process information. Furthermore, results of cross-wavelet transform (XWT) showed a change in the temporal pattern between TSMP-G2A pra and wtda at some selected pixels, which can be a reason for undesired network behavior. Our results demonstrate that LSTM networks are useful to produce high-quality wtda based on other hydrometeorological data measured and predicted at large scales, such as pr. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe relevant to water management.

Yueling Ma et al.

 
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Yueling Ma et al.

Yueling Ma et al.

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Short summary
This study utilized spatio-temporally continuous precipitation anomaly (pra) and water table depth anomaly (wtda) from integrated hydrologic simulation results over Europe in combination with LSTM networks to capture the time-varying and time-lagged relationship between pra and wtda in order to obtain reliable models to estimate wtda at the individual pixel level.