14 citations as recorded by crossref.

1. When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
2. Improving structure identifiability of hydrological processes by temporal sensitivity with a flexible modeling framework L. Zhou et al. https://doi.org/10.1016/j.jhydrol.2022.128843
3. Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin M. Morlot et al. https://doi.org/10.1016/j.jhydrol.2023.130587
4. RoGeR v3.0.5 – a process-based hydrological toolbox model in Python R. Schwemmle et al. https://doi.org/10.5194/gmd-17-5249-2024
5. Why do we have so many different hydrological models? A review based on the case of Switzerland P. Horton et al. https://doi.org/10.1002/wat2.1574
6. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. https://doi.org/10.5194/hess-28-2705-2024
7. The matrix-vector differential-form Muskingum method for river network routing J. Zhao et al. https://doi.org/10.1016/j.advwatres.2026.105300
8. Enhancing runoff simulation by combining superflex with deep learning methods in China's Qinghai Lake Basin, Northeast Tibetan Plateau K. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102331
9. Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation M. McCarthy et al. https://doi.org/10.1016/j.ejrh.2025.102518
10. RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling R. Chlumsky et al. https://doi.org/10.5194/gmd-15-7017-2022
11. Prediction of Qinghai Lake's level under future climate change: A hybrid modeling approach based on hydrological model and deep learning method K. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102283
12. OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
13. A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106802
14. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. https://doi.org/10.5194/hess-26-4773-2022