Articles | Volume 28, issue 12
https://doi.org/10.5194/hess-28-2705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2705-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jun 2024
Research article |
| 27 Jun 2024
| 27 Jun 2024
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Eduardo Acuña Espinoza
CORRESPONDING AUTHOR
Institute of Water and Environment, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Ralf Loritz
Institute of Water and Environment, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Manuel Álvarez Chaves
Stuttgart Center for Simulation Science, Statistical Model-Data Integration, University of Stuttgart, Stuttgart, Germany
Nicole Bäuerle
Institute of Stochastics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Uwe Ehret
Institute of Water and Environment, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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24 citations as recorded by crossref.
- RFM_Trans: Runoff forecasting model for catchment flood protection using strategies optimized Transformer N. Bao et al. https://doi.org/10.1016/j.eswa.2025.127228
- A review of how uncertainties are assessed in water quality modeling: A paradigm shift towards more complete practices X. Sun et al. https://doi.org/10.1016/j.envsoft.2026.107068
- H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation Z. Baghirov et al. https://doi.org/10.5194/gmd-18-2921-2025
- Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al. https://doi.org/10.3390/w16223335
- Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al. https://doi.org/10.5194/hess-29-5099-2025
- A physics-driven hybrid transformer model for hydrologic simulation under nonstationary environmental conditions H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135133
- Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
- Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble D. Spieler & N. Schütze https://doi.org/10.1029/2023WR036199
- A Robust Calibration and Evaluation Framework for Dynamic Catchment Characteristics in Hydrological Modeling T. Lan et al. https://doi.org/10.5194/hess-30-2455-2026
- Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
- Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv https://doi.org/10.1016/j.jhydrol.2025.133033
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
- How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al. https://doi.org/10.5194/hess-29-5005-2025
- Parametric 3D clothing generation: From sketch to dynamic fit Y. Zhou & B. Zhang https://doi.org/10.1177/15589250261441600
- CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. https://doi.org/10.5194/essd-16-5625-2024
- Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
- Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106648
- Can discharge be used to inversely correct precipitation? A. Manoj J et al. https://doi.org/10.5194/hess-29-6115-2025
- Improving evapotranspiration estimation by integrating process-based biophysical variables into a deep learning approach M. Xie et al. https://doi.org/10.1016/j.ejrh.2026.103114
- 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
- A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
- Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al. https://doi.org/10.1016/j.ejrh.2026.103158
- A two-phase physics-informed hybrid deep learning model for flood forecasting: improving process learning and interpretability C. Chen et al. https://doi.org/10.1016/j.advwatres.2026.105330
- Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
24 citations as recorded by crossref.
- RFM_Trans: Runoff forecasting model for catchment flood protection using strategies optimized Transformer N. Bao et al. https://doi.org/10.1016/j.eswa.2025.127228
- A review of how uncertainties are assessed in water quality modeling: A paradigm shift towards more complete practices X. Sun et al. https://doi.org/10.1016/j.envsoft.2026.107068
- H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation Z. Baghirov et al. https://doi.org/10.5194/gmd-18-2921-2025
- Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al. https://doi.org/10.3390/w16223335
- Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al. https://doi.org/10.5194/hess-29-5099-2025
- A physics-driven hybrid transformer model for hydrologic simulation under nonstationary environmental conditions H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135133
- Is smart sampling worth it? Impact of training data selection on the performance of LSTMs in streamflow prediction B. Heudorfer & R. Loritz https://doi.org/10.2166/nh.2026.119
- Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble D. Spieler & N. Schütze https://doi.org/10.1029/2023WR036199
- A Robust Calibration and Evaluation Framework for Dynamic Catchment Characteristics in Hydrological Modeling T. Lan et al. https://doi.org/10.5194/hess-30-2455-2026
- Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al. https://doi.org/10.5194/hess-29-1277-2025
- Understanding the inter-event variability of recession flow characteristics and its drivers O. Rashid & T. Apurv https://doi.org/10.1016/j.jhydrol.2025.133033
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. https://doi.org/10.5194/essd-17-1551-2025
- How well do process-based and data-driven hydrological models learn from limited discharge data? M. Staudinger et al. https://doi.org/10.5194/hess-29-5005-2025
- Parametric 3D clothing generation: From sketch to dynamic fit Y. Zhou & B. Zhang https://doi.org/10.1177/15589250261441600
- CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. https://doi.org/10.5194/essd-16-5625-2024
- Deep learning for the probabilistic prediction of semi-continuous hydrological variables – An application to streamflow prediction across CONUS J. Quilty & M. Jahangir https://doi.org/10.1016/j.jhydrol.2026.134986
- Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106648
- Can discharge be used to inversely correct precipitation? A. Manoj J et al. https://doi.org/10.5194/hess-29-6115-2025
- Improving evapotranspiration estimation by integrating process-based biophysical variables into a deep learning approach M. Xie et al. https://doi.org/10.1016/j.ejrh.2026.103114
- 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
- A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
- Deciphering the daily spatiotemporal dynamics and mechanisms of floods in the Tarim Basin desert region A. Tursun et al. https://doi.org/10.1016/j.ejrh.2026.103158
- A two-phase physics-informed hybrid deep learning model for flood forecasting: improving process learning and interpretability C. Chen et al. https://doi.org/10.1016/j.advwatres.2026.105330
- Unveiling the limits of deep learning models in hydrological extrapolation tasks S. Baste et al. https://doi.org/10.5194/hess-29-5871-2025
Saved (final revised paper)
Latest update: 19 Jul 2026
Short summary
Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Hydrological hybrid models promise to merge the performance of deep learning methods with the...
