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000892347 0247_ $$2doi$$a10.5194/egusphere-egu21-590
000892347 0247_ $$2Handle$$a2128/30442
000892347 037__ $$aFZJ-2021-02015
000892347 1001_ $$0P:(DE-Juel1)176840$$aMa, Yueling$$b0$$eCorresponding author
000892347 1112_ $$aEGU General Assembly 2021$$cOnline$$d2021-04-19 - 2021-04-30$$wOnline
000892347 245__ $$aKnowledge transfer from simulation to reality via Long Short-Term Memory networks:  Estimating groundwater table depth anomalies over Europe
000892347 260__ $$c2021
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000892347 520__ $$aNear 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° (Furusho-Percot et al., 2019), to predict monthly wtda based on monthly precipitation anomalies (pra) and soil moisture anomalies (θa). Without additional training, we directly fed the networks with averaged monthly pra and θa 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. 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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000892347 536__ $$0G:(EU-Grant)689443$$aERA-PLANET - The European network for observing our changing planet (689443)$$c689443$$fH2020-SC5-2015-one-stage$$x1
000892347 588__ $$aDataset connected to CrossRef
000892347 7001_ $$0P:(DE-Juel1)129506$$aMontzka, Carsten$$b1
000892347 7001_ $$0P:(DE-Juel1)177038$$aBayat, Bagher$$b2
000892347 7001_ $$aKollet, Stefan$$b3
000892347 773__ $$a10.5194/egusphere-egu21-590
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000892347 9141_ $$y2021
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