13 citations as recorded by crossref.

1. Integrating land use scenarios with pluvial flood modelling to evaluate spatial development strategies for climate adaptation in the Stuttgart region J. McMillan et al. https://doi.org/10.1007/s10584-026-04267-5
2. Urban flood hazard in the context of rapid urban growth in the Kathmandu Metropolitan City, Nepal S. Banstola & S. Aldrich https://doi.org/10.1007/s44327-026-00219-x
3. Compounding hazards increase flood economic losses across Europe M. Ronco et al. https://doi.org/10.1038/s41467-026-73248-0
4. Flood Inundation Area Prediction Under Climate Change Scenarios by Integrating Hydrological and Hydraulic Models with a Hybrid Deep Learning Framework T. Nawasanchai et al. https://doi.org/10.3390/w18111360
5. Compound Spring Flood Hazards in Kazakhstan and Comparable Cold-Continental Regions: Mechanisms, Indicators, and Recovery Assessment S. Nurakynov et al. https://doi.org/10.3390/w18141717
6. Understanding the organizing scales of winter flood hydroclimatology and the associated drivers over the coterminous United States J. Hwang et al. https://doi.org/10.1016/j.hydroa.2025.100200
7. Partitioning uncertainties of extreme flood estimates using long continuous simulations E. Kritidou et al. https://doi.org/10.1016/j.jhydrol.2025.134804
8. Catchment hydrological response and transport are affected differently by precipitation intensity and antecedent wetness J. Knapp et al. https://doi.org/10.5194/hess-29-3673-2025
9. Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
10. Leveraging reforecasts for flood estimation with long continuous simulation: a proof-of-concept study D. Viviroli et al. https://doi.org/10.5194/nhess-26-1835-2026
11. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al. https://doi.org/10.5194/hess-30-2135-2026
12. From extreme days to event-scale persistence: Characterizing for persistent extreme precipitation across multisource datasets Z. Zhao et al. https://doi.org/10.1016/j.wace.2026.100905
13. Numerical Simulation of Rainfall-Induced Debris Flows Triggered by Cyclone Yaku 2023 in Chasquitambo, Peru H. Flores et al. https://doi.org/10.3390/hydrology13030083