Articles | Volume 26, issue 24
https://doi.org/10.5194/hess-26-6339-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-6339-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2022
Research article |
| 16 Dec 2022
| 16 Dec 2022
River flooding mechanisms and their changes in Europe revealed by explainable machine learning
Department of Computational Hydrosystems, Helmholtz Centre for
Environmental Research, 04318 Leipzig, Germany
Emanuele Bevacqua
Department of Computational Hydrosystems, Helmholtz Centre for
Environmental Research, 04318 Leipzig, Germany
Jakob Zscheischler
Department of Computational Hydrosystems, Helmholtz Centre for
Environmental Research, 04318 Leipzig, Germany
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Cited
20 citations as recorded by crossref.
- Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere M. Anand et al. 10.1088/1748-9326/ad627d
- Direct and lagged climate change effects intensified the 2022 European drought E. Bevacqua et al. 10.1038/s41561-024-01559-2
- Clustering of causal graphs to explore drivers of river discharge W. Günther et al. 10.1017/eds.2023.17
- Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. 10.1016/j.jhydrol.2024.131867
- Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. 10.1016/j.jenvman.2024.121295
- Feature Importance in Machine Learning with Explainable Artificial Intelligence (XAI) for Rainfall Prediction M. Patel et al. 10.1051/itmconf/20246503007
- Classification and mechanism of spring and summer floods in northern Xinjiang from 2006 to 2011 P. Chen et al. 10.1002/asl.1193
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
- Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
- Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin E. Rottler et al. 10.1002/hyp.14918
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen 10.1007/s11430-023-1201-y
- An increase in the spatial extent of European floods over the last 70 years B. Fang et al. 10.5194/hess-28-3755-2024
- Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess N. Venegas-Cordero et al. 10.1016/j.scitotenv.2023.164626
- A method of applying deep learning based optical flow algorithm to river flow discharge measurement J. Wang et al. 10.1088/1361-6501/ad3183
- Shifting cold regions streamflow regimes in North America affect flood frequency analysis D. Burn & P. Whitfield 10.1080/02626667.2024.2422531
- Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. 10.1111/gcb.16968
- Compounding effects in flood drivers challenge estimates of extreme river floods S. Jiang et al. 10.1126/sciadv.adl4005
- Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. 10.1016/j.ejrh.2024.101776
- Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future S. Qianjuan et al. 10.1016/j.ejrh.2024.101962
- Changes in Mediterranean flood processes and seasonality Y. Tramblay et al. 10.5194/hess-27-2973-2023
20 citations as recorded by crossref.
- Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere M. Anand et al. 10.1088/1748-9326/ad627d
- Direct and lagged climate change effects intensified the 2022 European drought E. Bevacqua et al. 10.1038/s41561-024-01559-2
- Clustering of causal graphs to explore drivers of river discharge W. Günther et al. 10.1017/eds.2023.17
- Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. 10.1016/j.jhydrol.2024.131867
- Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. 10.1016/j.jenvman.2024.121295
- Feature Importance in Machine Learning with Explainable Artificial Intelligence (XAI) for Rainfall Prediction M. Patel et al. 10.1051/itmconf/20246503007
- Classification and mechanism of spring and summer floods in northern Xinjiang from 2006 to 2011 P. Chen et al. 10.1002/asl.1193
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
- Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
- Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin E. Rottler et al. 10.1002/hyp.14918
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen 10.1007/s11430-023-1201-y
- An increase in the spatial extent of European floods over the last 70 years B. Fang et al. 10.5194/hess-28-3755-2024
- Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess N. Venegas-Cordero et al. 10.1016/j.scitotenv.2023.164626
- A method of applying deep learning based optical flow algorithm to river flow discharge measurement J. Wang et al. 10.1088/1361-6501/ad3183
- Shifting cold regions streamflow regimes in North America affect flood frequency analysis D. Burn & P. Whitfield 10.1080/02626667.2024.2422531
- Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. 10.1111/gcb.16968
- Compounding effects in flood drivers challenge estimates of extreme river floods S. Jiang et al. 10.1126/sciadv.adl4005
- Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. 10.1016/j.ejrh.2024.101776
- Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future S. Qianjuan et al. 10.1016/j.ejrh.2024.101962
- Changes in Mediterranean flood processes and seasonality Y. Tramblay et al. 10.5194/hess-27-2973-2023
Latest update: 13 Dec 2024
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.
Using a novel explainable machine learning approach, we investigated the contributions of...
