Developing and Testing a Long Short-Term Memory Stream Temperature Model in Daily and Continental Scale

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.

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Knowledge transfer from simulation to reality via Long Short-Term Memory networks: Estimating groundwater table depth anomalies over Europe

10.5194/egusphere-egu21-590 ◽

2021 ◽

Author(s):

Yueling Ma ◽

Carsten Montzka ◽

Bagher Bayat ◽

Stefan Kollet

Keyword(s):

Short Term Memory ◽

Scientific Data ◽

Groundwater Table ◽

Short Term ◽

Term Memory ◽

Continental Scale ◽

Groundwater Table Depth ◽

Long Short Term Memory ◽

Simulation Results

Near real-time groundwater table depth measurements are scarce over Europe, leading to challenges in monitoring groundwater resources at the continental scale. In this study, we leveraged knowledge learned from simulation results by Long Short-Term Memory (LSTM) networks to estimate monthly groundwater table depth anomaly (wtda) data over Europe. The LSTM networks were trained, validated, and tested at individual pixels on anomaly data derived from daily integrated hydrologic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11&#176; (Furusho-Percot et al., 2019), to predict monthly wtda based on monthly precipitation anomalies (pra) and soil moisture anomalies (&#952;a). Without additional training, we directly fed the networks with averaged monthly pra and &#952;a data from 1996 to 2016 obtained from commonly available observational datasets and reanalysis products, and compared the network outputs with available borehole in situ measured wtda. The LSTM network estimates show good agreement with the in situ observations, resulting in Pearson correlation coefficients of regional averaged wtda data in seven PRUDENCE regions ranging from 42% to 76%, which are ~ 10% higher than the original simulation results except for the Iberian Peninsula. Our study demonstrates the potential of LSTM networks to transfer knowledge from simulation to reality for the estimation of wtda over Europe. The proposed method can be used to provide spatiotemporally continuous information at large spatial scales in case of sparse ground-based observations, which is common for groundwater table depth measurements. Moreover, the results highlight the advantage of combining physically-based models with machine learning techniques in data processing.&#160;Reference:Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S. (2019). Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific Data, 6(1).

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Modeling of groundwater table depth anomalies using Long Short-Term Memory networks over Europe

10.5194/egusphere-egu2020-5367 ◽

2020 ◽

Author(s):

Yueling Ma ◽

Carsten Montzka ◽

Bagher Bayat ◽

Stefan Kollet

Keyword(s):

Real Time ◽

Short Term Memory ◽

Groundwater Resources ◽

Scientific Data ◽

Great Promise ◽

Monthly Precipitation ◽

Short Term ◽

Term Memory ◽

Continental Scale ◽

Long Short Term Memory

Groundwater is the dominant source of fresh water in many European countries. However, due to a lack of near-real-time water table depth (wtd) observations, monitoring of groundwater resources is not feasible at the continental scale. Thus, an alternative approach is required to produce wtd data from other available observations near-real-time. In this study, we propose Long Short-Term Memory (LSTM) networks to model monthly wtd anomalies over Europe utilizing monthly precipitation anomalies as input. LSTM networks are a special type of artificial neural networks, showing great promise in exploiting long-term dependencies between time series, which is expected in the response of groundwater to precipitation. To establish the methodology, spatially and temporally continuous data from terrestrial simulations at the continental scale were applied with a spatial resolution of 0.11&#176;, ranging from the year 1996 to 2016 (Furusho-Percot et al., 2019). They were divided into a training set (1996 &#8211; 2012), a validation set (2012 &#8211; 2014) and a testing set (2015 -2016) to construct local models on selected pixels over eight PRUDENCE regions. The outputs of the LSTM networks showed good agreement with the simulation results in locations with a shallow wtd (~3m). It is important to note, the quality of the models was strongly affected by the amount of snow cover. Moreover, with the introduction of monthly evapotranspiration anomalies as additional input, pronounced improvements of the network performances were only obtained in more arid regions (i.e., Iberian Peninsula and Mediterranean). Our results demonstrate the potential of LSTM networks to produce high-quality wtd anomalies from hydrometeorological variables that are monitored at the large scale and part of operational forecasting systems potentially facilitating the implementation of an efficient groundwater monitoring system over Europe.Reference:Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S. (2019). Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific Data, 6(1).

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