11 citations as recorded by crossref.

1. Attribution of flood impacts shows strong benefits of adaptation in Europe since 1950 D. Paprotny et al. https://doi.org/10.1126/sciadv.adt7068
2. Machine Learning for Flood Resiliency—Current Status and Unexplored Directions V. Uddameri & E. Hernandez https://doi.org/10.3390/environments12080259
3. Compounding hazards increase flood economic losses across Europe M. Ronco et al. https://doi.org/10.1038/s41467-026-73248-0
4. Transformed-stationary EVA 2.0: a generalized framework for non-stationary multivariate extremes analysis M. Bahmanpour et al. https://doi.org/10.5194/hess-30-2301-2026
5. Augmenting observation network design and assimilation frequency in distributed hydrological models: insights from the LISFLOOD-based hydrological data assimilation framework K. Kurugama et al. https://doi.org/10.1016/j.jhydrol.2025.134853
6. Prediction of Extreme Events in the Amazon under the Influence of Climate and LULC Change J. Silva Cruz et al. https://doi.org/10.1007/s40710-025-00805-y
7. Optimized afforestation reduces flood risk and limits water loss in Europe S. El Garroussi et al. https://doi.org/10.1038/s44458-026-00057-3
8. Method of Discharge Data Assimilation into the ECOMAG Model Based on the Inverse Stream Routing Approach A. Bugaets et al. https://doi.org/10.1134/S0097807824607088
9. Increasing frequency and intensity of single, multiple and concurrent meteorological hazards in Europe from a high-resolution dataset (1961–2020) A. Dosio et al. https://doi.org/10.1088/2752-5295/ae1ce0
10. More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps) M. Lemus-Canovas et al. https://doi.org/10.5194/hess-29-6781-2025
11. CLIMB: Framework for CLIMate data bias-adjustment and downscaling J. Śledziowski et al. https://doi.org/10.1016/j.softx.2025.102479