IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28770-2.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28770-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-28770-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. N. Maier & J. Dietrich, 2016. "Using SWAT for Strategic Planning of Basin Scale Irrigation Control Policies: a Case Study from a Humid Region in Northern Germany," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3285-3298, July.
    2. Mitter, Hermine & Schmid, Erwin, 2021. "Informing groundwater policies in semi-arid agricultural production regions under stochastic climate scenario impacts," Ecological Economics, Elsevier, vol. 180(C).
    3. Mohammad Naderianfar & Jamshid Piri & Ozgur Kisi, 2017. "Pre-processing data to predict groundwater levels using the fuzzy standardized evapotranspiration and precipitation index (SEPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4433-4448, November.
    4. Santiago Castaño & David Sanz & Juan Gómez-Alday, 2013. "Sensitivity of a Groundwater Flow Model to Both Climatic Variations and Management Scenarios in a Semi-arid Region of SE Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2089-2101, May.
    5. Dilip Kumar Roy & Sujit Kumar Biswas & Kowshik Kumar Saha & Khandakar Faisal Ibn Murad, 2021. "Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1653-1672, April.
    6. Christian Albert & Johannes Hermes & Felix Neuendorf & Christina Von Haaren & Michael Rode, 2016. "Assessing and Governing Ecosystem Services Trade-Offs in Agrarian Landscapes: The Case of Biogas," Land, MDPI, vol. 5(1), pages 1-17, January.
    7. Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
    8. Jielong Wang & Yi Chen, 2022. "The applicability of using NARX neural network to forecast GRACE terrestrial water storage anomalies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1997-2016, February.
    9. Qi Liu & Yi Liu & Jie Niu & Dongwei Gui & Bill X. Hu, 2022. "Prediction of the Irrigation Area Carrying Capacity in the Tarim River Basin under Climate Change," Agriculture, MDPI, vol. 12(5), pages 1-14, April.
    10. Georgios N. Kouziokas & Alexander Chatzigeorgiou & Konstantinos Perakis, 2018. "Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5041-5052, December.
    11. Schönhart, Martin & Trautvetter, Helene & Parajka, Juraj & Blaschke, Alfred Paul & Hepp, Gerold & Kirchner, Mathias & Mitter, Hermine & Schmid, Erwin & Strenn, Birgit & Zessner, Matthias, 2018. "Modelled impacts of policies and climate change on land use and water quality in Austria," Land Use Policy, Elsevier, vol. 76(C), pages 500-514.
    12. Melkonyan, Ani, 2015. "Climate change impact on water resources and crop production in Armenia," Agricultural Water Management, Elsevier, vol. 161(C), pages 86-101.
    13. I. Tsakmakis & N. Kokkos & V. Pisinaras & V. Papaevangelou & E. Hatzigiannakis & G. Arampatzis & G.D. Gikas & R. Linker & S. Zoras & V. Evagelopoulos & V.A. Tsihrintzis & A. Battilani & G. Sylaios, 2017. "Operational Precise Irrigation for Cotton Cultivation through the Coupling of Meteorological and Crop Growth Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 563-580, January.
    14. Lin She & Xue-yi You, 2019. "A Dynamic Flow Forecast Model for Urban Drainage Using the Coupled Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3143-3153, July.
    15. Mahadevan, Meera & Shenoy, Ajay, 2023. "The political consequences of resource scarcity: Targeted spending in a water-stressed democracy," Journal of Public Economics, Elsevier, vol. 220(C).
    16. Sangita Dey & Arabin Kumar Dey & Rajesh Kumar Mall, 2021. "Modeling Long-term Groundwater Levels By Exploring Deep Bidirectional Long Short-Term Memory using Hydro-climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3395-3410, August.
    17. Kristof Dorau & Tim Mansfeldt, 2023. "Vulnerability of diked marsh ecosystems under climate change," Climatic Change, Springer, vol. 176(3), pages 1-16, March.
    18. Kirchner, Mathias & Schmidt, Johannes & Kindermann, Georg & Kulmer, Veronika & Mitter, Hermine & Prettenthaler, Franz & Rüdisser, Johannes & Schauppenlehner, Thomas & Schönhart, Martin & Strauss, Fran, 2015. "Ecosystem services and economic development in Austrian agricultural landscapes — The impact of policy and climate change scenarios on trade-offs and synergies," Ecological Economics, Elsevier, vol. 109(C), pages 161-174.
    19. Nahid Sultana & S. M. Zakir Hossain & Salma Hamad Almuhaini & Dilek Düştegör, 2022. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand," Energies, MDPI, vol. 15(9), pages 1-26, May.
    20. Roland Barthel & Stefan Banzhaf, 2016. "Groundwater and Surface Water Interaction at the Regional-scale – A Review with Focus on Regional Integrated Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 1-32, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28770-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.