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Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Nature communications, 2022-03, Vol.13 (1), p.1221-1221, Article 1221 [Periódico revisado por pares]

England: Nature Publishing Group

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  • Título:
    Deep learning shows declining groundwater levels in Germany until 2100 due to climate change
  • Autor: Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan
  • Assuntos: Anthropogenic factors ; Artificial neural networks ; Climate Change ; Deep Learning ; Germany ; Groundwater ; Groundwater levels ; Human influences ; Machine learning ; Mitigation ; Neural networks ; Trends ; Water resources
  • É parte de: Nature communications, 2022-03, Vol.13 (1), p.1221-1221, Article 1221
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
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  • Descrição: In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21 century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under different RCP scenarios (2.6, 4.5, 8.5). We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. While less pronounced and fewer significant trends can be found under RCP2.6 and RCP4.5, we detect significantly declining trends of groundwater levels for most of the sites under RCP8.5, revealing a spatial pattern of stronger decreases, especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.
  • Editor: England: Nature Publishing Group
  • Idioma: Inglês
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