Articles | Volume 26, issue 24
https://doi.org/10.5194/hess-26-6339-2022
https://doi.org/10.5194/hess-26-6339-2022
Research article
 | 
16 Dec 2022
Research article |  | 16 Dec 2022

River flooding mechanisms and their changes in Europe revealed by explainable machine learning

Shijie Jiang, Emanuele Bevacqua, and Jakob Zscheischler

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Cited articles

Alfieri, L., Burek, P., Feyen, L., and Forzieri, G.: Global warming increases the frequency of river floods in Europe, Hydrol. Earth Syst. Sci., 19, 2247–2260, https://doi.org/10.5194/hess-19-2247-2015, 2015. 
Alfieri, L., Bisselink, B., Dottori, F., Naumann, G., de Roo, A., Salamon, P., Wyser, K., and Feyen, L.: Global projections of river flood risk in a warmer world, Earth's Future, 5, 171–182, https://doi.org/10.1002/2016ef000485, 2017. 
Barnes, E. A., Toms, B., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Indicator patterns of forced change learned by an artificial neural network, J. Adv. Model. Earth Syst., 12, e2020MS002195, https://doi.org/10.1029/2020ms002195, 2020. 
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Short summary
Using a novel explainable machine learning approach, we investigated the contributions of precipitation, temperature, and day length to different peak discharges, thereby uncovering three primary flooding mechanisms widespread in European catchments. The results indicate that flooding mechanisms have changed in numerous catchments over the past 70 years. The study highlights the potential of artificial intelligence in revealing complex changes in extreme events related to climate change.
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