000903941 001__ 903941
000903941 005__ 20220118142823.0
000903941 037__ $$aFZJ-2021-05549
000903941 1001_ $$0P:(DE-Juel1)176840$$aMa, Yueling$$b0$$eCorresponding author$$ufzj
000903941 1112_ $$aAGU Fall Meeting 2021$$cNew Orleans, LA and online$$d2021-12-13 - 2021-12-17$$wThe US and online
000903941 245__ $$aA Novel ML-Based Methodology for Estimating Water Table Depth Anomalies at the European Continent Scale
000903941 260__ $$c2021
000903941 3367_ $$033$$2EndNote$$aConference Paper
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000903941 520__ $$aEffective and efficient groundwater monitoring at the continental scale is still a challenge, mainly due to the scarcity of water table depth (wtd) observations. In this study, we proposed a novel methodology based on advanced machine learning techniques, Long Short-Term Memory (LSTM) networks and transfer learning, for estimating monthly wtd anomalies (wtda) over Europe with monthly precipitation and soil moisture anomalies (pra and θa) as input. In the methodology, the LSTM networks were trained on simulation results and then, without additional training, utilized to estimate wtda based on pra and θa from observational datasets, thereby transferring the simulated input-output relationship to the observation-based estimation. The obtained estimates were evaluated based on in-situ wtd measurements at 2,604 wells distributed over different European regions, achieving R from 0.39 to 0.79 and RMSE from 0.37 to 1.1 for regional averaged values. This constitutes of a >0.10 increase in R and a >0.13 decrease in RMSE compared to the simulation results used for training. In addition, compared with the LSTM networks directly trained on observations, the proposed methodology showed slightly worse test performance at the individual pixel level, lending confidence to applications in areas without wtd observations. The study provides a validated methodology for producing reliable wtda estimates over the European domain in the absence of observations, which can be used for data reconstruction and online groundwater monitoring useful in European groundwater management.
000903941 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000903941 536__ $$0G:(EU-Grant)689443$$aERA-PLANET - The European network for observing our changing planet (689443)$$c689443$$fH2020-SC5-2015-one-stage$$x1
000903941 7001_ $$0P:(DE-Juel1)129506$$aMontzka, Carsten$$b1$$ufzj
000903941 7001_ $$0P:(DE-Juel1)169794$$aNaz, Bibi$$b2$$ufzj
000903941 7001_ $$0P:(DE-Juel1)151405$$aKollet, Stefan$$b3$$ufzj
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000903941 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176840$$aForschungszentrum Jülich$$b0$$kFZJ
000903941 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129506$$aForschungszentrum Jülich$$b1$$kFZJ
000903941 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169794$$aForschungszentrum Jülich$$b2$$kFZJ
000903941 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151405$$aForschungszentrum Jülich$$b3$$kFZJ
000903941 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
000903941 9141_ $$y2021
000903941 920__ $$lyes
000903941 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
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000903941 980__ $$aI:(DE-Juel1)IBG-3-20101118
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