{"id":4561,"identifier":"JUELICH-DATA/AMQ6NI","persistentUrl":"https://doi.org/10.26165/JUELICH-DATA/AMQ6NI","protocol":"doi","authority":"10.26165","publisher":"Jülich DATA","publicationDate":"2021-12-13","storageIdentifier":"s3://10.26165/JUELICH-DATA/AMQ6NI","datasetVersion":{"id":245,"datasetId":4561,"datasetPersistentId":"doi:10.26165/JUELICH-DATA/AMQ6NI","storageIdentifier":"s3://10.26165/JUELICH-DATA/AMQ6NI","versionNumber":1,"versionMinorNumber":1,"versionState":"RELEASED","lastUpdateTime":"2022-03-15T13:07:37Z","releaseTime":"2022-03-15T13:07:37Z","createTime":"2022-03-15T13:07:31Z","license":"CC0","termsOfUse":"CC0 Waiver","fileAccessRequest":false,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Ma, Yueling"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Forschungszentrum Jülich"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"https://orcid.org/ 0000-0002-1869-7702"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Montzka, Carsten"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Forschungszentrum Jülich"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Bayat, Bagher"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Forschungszentrum Jülich"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Kollet, Stefan"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Forschungszentrum Jülich"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Ma, Yueling"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"Forschungszentrum Jülich"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"y.ma@fz-juelich.de"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"This study introduced a number of hydrometeorological variables in addition to precipitation anomaly (pr_a) in the construction of Long Short-Term Memory (LSTM) networks to arrive at improved water table depth anomaly (wtd_a) at individual pixels over Europe in various experiments. The input variables in the experiments E1 and E2 are: E1.1: pr_a; E1.2: evapotranspiration anomaly (ET_a); E1.3: soil moisture anomaly (θ_a); E1.4: pr_a and ET_a; E1.5: pr_a and θ_a; E1.6: ET_a and θ_a; E1.7: pr_a, ET_a and θ_a; E2.1: pr_a, θ_a and scaled yearly averaged snow water equivalent (SWE_scaled); E2.2: pr_a, θ_a at the selected pixels and adjacent pixels; and E2.3: pr_a, θ_a at the selected pixels close to rivers and river stage anomaly (rs_a) at the adjacent pixels. The data files provide the TSMP-G2A ET_a, the TSMP-G2A θ_a, and the LSTM wtd_a data (obtained from E1.2 to E2.3) for the period 1996-2016, with a spatial resolution of 0.11 degrees. The TSMP-G2A yearly averaged snow water equivalent (SWE) data are also provided."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2021-12-13"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Earth and Environmental Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Groundwater"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Long Short-Term Memory (LSTM) networks"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Anomalies"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Europe"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Ma, Y., Montzka, C., Bayat, B. and Kollet, S.: An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis, Front. Water, 3, doi:10.3389/frwa.2021.723548, 2021."},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"https://doi.org/10.3389/frwa.2021.723548"}}]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Ma, Yueling"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2021-12-13"}]},"fzj":{"displayName":"FZJ Metadata","fields":[{"typeName":"institute","multiple":true,"typeClass":"controlledVocabulary","value":["IBG-3"]},{"typeName":"pof4","multiple":true,"typeClass":"controlledVocabulary","value":["Agro-biogeosystems: controls, feedbacks and impact (POF4-2173)"]}]}},"files":[{"description":"Monthly water table depth anomalies generated from the proposed LSTM networks of E1.2 (evapotranspiration 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