12 citations as recorded by crossref.

1. Clustering of causal graphs to explore drivers of river discharge W. Günther et al. 10.1017/eds.2023.17
2. Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. 10.1016/j.jenvman.2024.121295
3. Classification and mechanism of spring and summer floods in northern Xinjiang from 2006 to 2011 P. Chen et al. 10.1002/asl.1193
4. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. 10.1016/j.ejrh.2023.101584
5. 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
6. Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen 10.1007/s11430-023-1201-y
7. 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
8. A method of applying deep learning based optical flow algorithm to river flow discharge measurement J. Wang et al. 10.1088/1361-6501/ad3183
9. Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. 10.1111/gcb.16968
10. Compounding effects in flood drivers challenge estimates of extreme river floods S. Jiang et al. 10.1126/sciadv.adl4005
11. 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
12. Changes in Mediterranean flood processes and seasonality Y. Tramblay et al. 10.5194/hess-27-2973-2023