13 citations as recorded by crossref.

1. Probabilistic Downscaling for Flood Hazard Models S. Roth et al. https://doi.org/10.1007/s13253-026-00752-7
2. Elevation uncertainties in the Mekong Delta quantified using a transferable approach K. Seeger & P. Minderhoud https://doi.org/10.1038/s41598-026-38315-y
3. Monte Carlo-based sensitivity analysis of the RIM2D hydrodynamic model for the 2021 flood event in western Germany S. Khosh Bin Ghomash et al. https://doi.org/10.5194/nhess-25-975-2025
4. From pixels to policy: Multi-scale flood susceptibility mapping using interpretable machine learning for urban resilience S. Lee et al. https://doi.org/10.1016/j.landusepol.2026.107950
5. Unveiling hidden risks in healthcare from flood-induced transportation disruption in Germany J. Wassmer et al. https://doi.org/10.1038/s43247-025-02645-y
6. Local Scale Current and Projected Future Total Flood Hazard Mapping for Canada—Literature Review S. Simonovic & B. Perry https://doi.org/10.1111/jfr3.70091
7. Flood driving the migration of the prehistoric settlements in the Chengdu plain, southwest China D. Tang et al. https://doi.org/10.1016/j.quascirev.2026.110022
8. Evaluating the Impact of DEM Resolution on Landslide Hazard Assessment: A Comparative Study Using LiDAR and 1:5000 Topographic Map-Derived DEMs T. Kim et al. https://doi.org/10.3390/rs18172988
9. Drowning overconfidence with uncertainty: mitigating deep learning overconfidence in flood depth super-resolution through maximum entropy regularization M. El baida et al. https://doi.org/10.1007/s00477-025-02917-1
10. Multi-resolution downscaling of inundation extents using deep learning C. Yang & F. O’Loughlin https://doi.org/10.1016/j.jhydrol.2026.135932
11. Evaluating High-Resolution LiDAR DEMs for Flood Hazard Analysis: A Comparison with 1:5000 Topographic Maps T. Kim et al. https://doi.org/10.3390/app16021029
12. A hybrid machine learning and physics-based approach to model high-resolution inundation A. Peeples et al. https://doi.org/10.1016/j.jhydrol.2026.136246
13. Subgrid informed neural networks for high-resolution flood mapping H. Herath et al. https://doi.org/10.1016/j.jhydrol.2025.133329