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

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  • Andreas Wunsch

    (Karlsruhe Institute of Technology)

  • Tanja Liesch

    (Karlsruhe Institute of Technology)

  • Stefan Broda

    (Federal Institute for Geosciences and Natural Resources)

Abstract

In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st 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.

Suggested Citation

  • Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Deep learning shows declining groundwater levels in Germany until 2100 due to climate change," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28770-2
    DOI: 10.1038/s41467-022-28770-2
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    References listed on IDEAS

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    1. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 39-54, January.
    2. Roland Barthel & Tim Reichenau & Tatjana Krimly & Stephan Dabbert & Karl Schneider & Wolfram Mauser, 2012. "Integrated Modeling of Global Change Impacts on Agriculture and Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1929-1951, May.
    3. Wen-Ying Wu & Min-Hui Lo & Yoshihide Wada & James S. Famiglietti & John T. Reager & Pat J.-F. Yeh & Agnès Ducharne & Zong-Liang Yang, 2020. "Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    4. Sandra M. Guzman & Joel O. Paz & Mary Love M. Tagert, 2017. "The Use of NARX Neural Networks to Forecast Daily Groundwater Levels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1591-1603, March.
    5. Peter Kreins & Martin Henseler & Jano Anter & Frank Herrmann & Frank Wendland, 2015. "Quantification of Climate Change Impact on Regional Agricultural Irrigation and Groundwater Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3585-3600, August.
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    Cited by:

    1. Stephen Afrifa & Tao Zhang & Peter Appiahene & Vijayakumar Varadarajan, 2022. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis," Future Internet, MDPI, vol. 14(9), pages 1-31, August.
    2. Timothy Neal, 2023. "The Importance of External Weather Effects in Projecting the Economic Impacts of Climate Change," Discussion Papers 2023-09, School of Economics, The University of New South Wales.

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