Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-7047-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-7047-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
19 Nov 2021
Model description paper |
| 19 Nov 2021
| 19 Nov 2021
SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models
Marco Dal Molin
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dübendorf, Switzerland
Centre of Hydrogeology and Geothermics (CHYN), University of
Neuchâtel, Neuchâtel, Switzerland
Dmitri Kavetski
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dübendorf, Switzerland
School of Civil, Environmental and Mining Engineering, University
of Adelaide, Adelaide, SA, Australia
Civil, Surveying and Environmental Engineering, University of
Newcastle, Callaghan, NSW, Australia
Fabrizio Fenicia
CORRESPONDING AUTHOR
Eawag, Swiss Federal Institute of Aquatic Science and Technology,
Dübendorf, Switzerland
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Cited
16 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- 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
- 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
- A modular open-source suite of notebooks for multi-process hydrological analysis with GEOframe R. Busti & G. Formetta https://doi.org/10.1016/j.hydrch.2026.100024
- The matrix-vector differential-form Muskingum method for river network routing J. Zhao et al. https://doi.org/10.1016/j.advwatres.2026.105300
- 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
- Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation M. McCarthy et al. https://doi.org/10.1016/j.ejrh.2025.102518
- 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
- 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
- OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
- 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
- The impact of numerical error on hydrological model performance J. Zhao et al. https://doi.org/10.1016/j.jhydrol.2026.136510
- 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
16 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- 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
- 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
- A modular open-source suite of notebooks for multi-process hydrological analysis with GEOframe R. Busti & G. Formetta https://doi.org/10.1016/j.hydrch.2026.100024
- The matrix-vector differential-form Muskingum method for river network routing J. Zhao et al. https://doi.org/10.1016/j.advwatres.2026.105300
- 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
- Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation M. McCarthy et al. https://doi.org/10.1016/j.ejrh.2025.102518
- 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
- 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
- OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov https://doi.org/10.3390/geosciences12020067
- 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
- The impact of numerical error on hydrological model performance J. Zhao et al. https://doi.org/10.1016/j.jhydrol.2026.136510
- 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
Saved (final revised paper)
Latest update: 14 Oct 2026
Short summary
This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
This paper introduces SuperflexPy, an open-source Python framework for building flexible...
