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Predicting river flow dynamics using stable isotopes for better adaptation to climate and land-use changes

Nature Water (2024)Cite this article

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Abstract

Adapting to climate and land-use changes requires accurate prediction of river flow dynamics, particularly the seasonally varying water fraction with a rapid response to hydroclimate changes. By analysing stable isotopes in water molecules from precipitation and rivers, here we identified the young water fraction (<2–3 months) and introduced a dynamic water retention indicator to depict river flow dynamics. Examining 20,045 samples from 136 perennial rivers and 45 large catchments globally, we categorized dynamic water retention as high, moderate or low. Around 25% of rivers showed low dynamic water retention, indicating faster responses to hydroclimate events, whereas 50% exhibited high dynamic water retention, suggesting slower responses. Dynamic water retention and young water fraction correlated with changes in crop cover, forest cover, air temperature and precipitation, demonstrating temporal variations in three European rivers with decade-long isotope records. Isotope monitoring of rivers emerges as a cost-effective tool for understanding river flow dynamics and improving water resource management within ongoing hydroclimate and land-use changes.

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Fig. 1: Background information for study sites.
Fig. 2: Multi-way importance plot shows variable importance in relation to dynamic water retention.
Fig. 3: Biplots show results of dynamic water retention prediction and catchment responses.
Fig. 4: Times series of young water fraction and dynamic water retention.

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Data availability

The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information files. The isotope records for the different rivers are available open access at the GNIR (https://www.iaea.org/services/networks/gnir). The isotope records in precipitation are available open access at the GNIP (https://www.iaea.org/services/networks/gnip). The database for the Random Forest analysis is included in the Supplementary Information. Source data are provided with this paper.

Code availability

The R code for the Random Forest model is included in the Supplementary Information.

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Acknowledgements

The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. Isotope Hydrology Section, International Atomic Energy Agency, Vienna, Austria

    Yuliya Vystavna, Leo Chavanne, Astrid Harjung & Jodie Miller

  2. Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany

    David X. Soto

  3. School for Climate Studies, Stellenbosch University, Stellenbosch, South Africa

    Andrew Watson

  4. Federal Institute of Hydrology, Koblenz, Germany

    Johannes Cullmann

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Contributions

Y.V. designed the study, developed and ran the machine learning model simulations, did the analysis and wrote the paper. L.C. did the statistical analysis and prepared the databases. A.H. provided support on the data collection and R scripts development, reviewed the machine learning model and output analysis and helped in writing and revision of the paper. D.X.S. assisted in the precipitation and run-off isotope data modelling and validation and helped in writing and revision of the paper. A.W. assisted in the data analysis and interpretation, developed figures and reviewed the paper. J.M. advised on hypotheses, hydroclimate assessment and paper writing. J.C. coordinated the study design and reviewed figures and the paper.

Corresponding author

Correspondence to Yuliya Vystavna.

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The authors declare no competing interests.

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Nature Water thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Table 1.

Supplementary Code 1

The R code for the Random Forest model.

Source data

Source Data Fig. 2

Source data for the Random Forest model.

Source Data Fig. 3

Source data for the Random Forest model.

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Vystavna, Y., Chavanne, L., Harjung, A. et al. Predicting river flow dynamics using stable isotopes for better adaptation to climate and land-use changes. Nat Water (2024). https://doi.org/10.1038/s44221-024-00280-z

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