000893348 001__ 893348
000893348 005__ 20220930130320.0
000893348 0247_ $$2doi$$a10.5194/hess-25-3555-2021
000893348 0247_ $$2ISSN$$a1027-5606
000893348 0247_ $$2ISSN$$a1607-7938
000893348 0247_ $$2Handle$$a2128/28111
000893348 0247_ $$2altmetric$$aaltmetric:108081031
000893348 0247_ $$2WOS$$aWOS:000667601500002
000893348 037__ $$aFZJ-2021-02700
000893348 041__ $$aEnglish
000893348 082__ $$a550
000893348 1001_ $$0P:(DE-Juel1)176840$$aMa, Yueling$$b0$$eCorresponding author
000893348 245__ $$aUsing Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe
000893348 260__ $$aKatlenburg-Lindau$$bEGU$$c2021
000893348 3367_ $$2DRIVER$$aarticle
000893348 3367_ $$2DataCite$$aOutput Types/Journal article
000893348 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1642505790_6598
000893348 3367_ $$2BibTeX$$aARTICLE
000893348 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000893348 3367_ $$00$$2EndNote$$aJournal Article
000893348 520__ $$aMany European countries rely on groundwater for public and industrial water supply. 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 for estimating 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 for producing monthly wtda using monthly precipitation anomalies (pra) as input. LSTM networks are a special category of artificial neural networks that are useful for detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pra and wtda. In the proposed methodology, spatiotemporally continuous data were obtained from daily terrestrial simulations of the Terrestrial Systems Modeling Platform (TSMP) over Europe (hereafter termed the TSMP-G2A data set), with a spatial resolution of 0.11°, ranging from the years 1996 to 2016. The data 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 on spatially distributed dry and wet events, with 2003 and 2015 constituting drought years over Europe. Moreover, we categorized the test performances of the networks based on intervals of yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (Sw), 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 the 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 for producing high-quality wtda based on other hydrometeorological data measured and predicted at large scales, such as pra. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe that is relevant to water management.
000893348 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000893348 536__ $$0G:(EU-Grant)689443$$aERA-PLANET - The European network for observing our changing planet (689443)$$c689443$$fH2020-SC5-2015-one-stage$$x1
000893348 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000893348 7001_ $$0P:(DE-Juel1)129506$$aMontzka, Carsten$$b1
000893348 7001_ $$0P:(DE-Juel1)177038$$aBayat, Bagher$$b2
000893348 7001_ $$0P:(DE-Juel1)151405$$aKollet, Stefan$$b3$$ufzj
000893348 773__ $$0PERI:(DE-600)2100610-6$$a10.5194/hess-25-3555-2021$$gVol. 25, no. 6, p. 3555 - 3575$$n6$$p3555 - 3575$$tHydrology and earth system sciences$$v25$$x1607-7938$$y2021
000893348 8564_ $$uhttps://juser.fz-juelich.de/record/893348/files/hess-25-3555-2021.pdf$$yOpenAccess
000893348 8767_ $$8101523$$92021-07-08$$d2021-08-10$$eAPC$$jZahlung erfolgt$$zBelegnr. 1200170536
000893348 909CO $$ooai:juser.fz-juelich.de:893348$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire$$pdnbdelivery
000893348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176840$$aForschungszentrum Jülich$$b0$$kFZJ
000893348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129506$$aForschungszentrum Jülich$$b1$$kFZJ
000893348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177038$$aForschungszentrum Jülich$$b2$$kFZJ
000893348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151405$$aForschungszentrum Jülich$$b3$$kFZJ
000893348 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$$x0
000893348 9141_ $$y2021
000893348 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-26
000893348 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000893348 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bHYDROL EARTH SYST SC : 2019$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000893348 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bHYDROL EARTH SYST SC : 2019$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-26
000893348 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-26
000893348 920__ $$lyes
000893348 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000893348 980__ $$ajournal
000893348 980__ $$aVDB
000893348 980__ $$aI:(DE-Juel1)IBG-3-20101118
000893348 980__ $$aAPC
000893348 980__ $$aUNRESTRICTED
000893348 9801_ $$aAPC
000893348 9801_ $$aFullTexts