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WeightNameValue
1000 Titel
  • Disentangling coastal groundwater level dynamics in a global dataset
1000 Autor/in
  1. Nolte, Annika |
  2. Haaf, Ezra |
  3. Heudorfer, Benedikt |
  4. Bender, Steffen |
  5. Hartmann, Jens |
1000 Verlag
  • Copernicus Publications
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-03-14
1000 Erschienen in
1000 Quellenangabe
  • 28(5):1215-1249
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.5194/hess-28-1215-2024 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:p>Abstract. Groundwater level (GWL) dynamics result from a complex interplay between groundwater systems and the Earth system. This study aims to identify common hydrogeological patterns and to gain a deeper understanding of the underlying similarities and their link to physiographic, climatic, and anthropogenic controls of groundwater in coastal regions. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8000 groundwater hydrographs, pattern recognition using clustering algorithms, classification using random forest, and SHapley Additive exPlanations (SHAPs). Hydrogeological similarity was defined by four clusters representing distinct patterns of GWL dynamics. These clusters can be observed globally across different continents and climate zones but simultaneously vary regionally and locally, suggesting a complicated interplay of controlling factors. The main controls differentiating GWL dynamics were identified, but we also provide evidence for the currently limited ability to explain GWL dynamics on large spatial scales, which we attribute mainly to uncertainties in the explanatory data. Finally, this study provides guidance for systematic and holistic groundwater monitoring and modeling and motivates a consideration of the different aspects of GWL dynamics, for example, when predicting climate-induced GWL changes, and the use of explainable machine learning techniques to deal with GWL complexity – especially when information on potential controls is limited or needs to be verified. </jats:p>
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/Tm9sdGUsIEFubmlrYQ==|https://frl.publisso.de/adhoc/uri/SGFhZiwgRXpyYQ==|https://frl.publisso.de/adhoc/uri/SGV1ZG9yZmVyLCBCZW5lZGlrdA==|https://frl.publisso.de/adhoc/uri/QmVuZGVyLCBTdGVmZmVu|https://frl.publisso.de/adhoc/uri/SGFydG1hbm4sIEplbnM=
1000 Hinweis
  • DeepGreen-ID: 09dc07787222421d9308b988e27f9152 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Universität Hamburg |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
  1. Disentangling coastal groundwater level dynamics in a global dataset
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Universität Hamburg |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6481799.rdf
1000 Erstellt am 2024-05-23T23:57:03.557+0200
1000 Erstellt von 322
1000 beschreibt frl:6481799
1000 Zuletzt bearbeitet 2024-05-27T11:37:14.568+0200
1000 Objekt bearb. Mon May 27 11:37:14 CEST 2024
1000 Vgl. frl:6481799
1000 Oai Id
  1. oai:frl.publisso.de:frl:6481799 |
1000 Sichtbarkeit Metadaten public
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