{"id":4575,"identifier":"JUELICH-DATA/ZBLDIR","persistentUrl":"https://doi.org/10.26165/JUELICH-DATA/ZBLDIR","protocol":"doi","authority":"10.26165","publisher":"Jülich DATA","publicationDate":"2021-12-14","storageIdentifier":"s3://10.26165/JUELICH-DATA/ZBLDIR","datasetVersion":{"id":295,"datasetId":4575,"datasetPersistentId":"doi:10.26165/JUELICH-DATA/ZBLDIR","storageIdentifier":"s3://10.26165/JUELICH-DATA/ZBLDIR","versionNumber":2,"versionMinorNumber":0,"versionState":"RELEASED","lastUpdateTime":"2022-03-28T07:12:47Z","releaseTime":"2022-03-28T07:12:47Z","createTime":"2022-03-24T23:30:58Z","license":"CC0","termsOfUse":"CC0 Waiver","fileAccessRequest":false,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Advancing AI-based pan-European groundwater monitoring"},{"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":"Naz, Bibi"},"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 proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is named LSTM-TL. The data repository provides: i) data utilized for evaluating LSTM-TL performance, i.e., input averaged monthly precipitation and soil moisture anomalies from common observational data sets (pr_a,o and θ_a,o), wtd_a estimates obtained from LSTM-TL (wtd_a,lstm-tl), wtd_a estimates obtained from LSTM networks with modeling data as input (wtd_a,lstm(m)), and wtd_a estimates obtained from LSTM networks trained on observations (wtd_a,lstm(o)); ii) reconstructed European monthly wtd_a,lstm-tl data RD1-6 from the early 1980s to the near present and their input pr_a,o and θ_a,o data; and iii) a Jupyter Notebook showing an example about the implementation of LSTM-TL. All the data sets in the repository have a spatial resolution of 0.11 degrees (~12.5 km)."},"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":"Anomalies"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"LSTM-TL"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Long Short-Term Memory (LSTM) networks"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Transfer learning (TL)"}},{"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., Naz, B. and Kollet, S.: Advancing AI-based pan-European groundwater monitoring, in preparation."}}]},{"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 precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: \r\nBollmeyer, C., Keller, J. 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