Articles | Volume 28, issue 21
https://doi.org/10.5194/hess-28-4837-2024
© 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-28-4837-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Nov 2024
Research article |
| 07 Nov 2024
| 07 Nov 2024
On the use of streamflow transformations for hydrological model calibration
Guillaume Thirel
CORRESPONDING AUTHOR
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Léonard Santos
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Olivier Delaigue
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Charles Perrin
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
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Cited
17 citations as recorded by crossref.
- Rethinking future flood hazard: Hourly data challenge daily flood projections in Alpine catchments P. Astagneau et al. https://doi.org/10.1126/sciadv.aed6012
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- Flood Regulation Service Responses to Urban Green Space Change in a Plateau Valley City: A Case Study of Lhasa, China S. Zhao et al. https://doi.org/10.3390/rs18142331
- Hydrological climate change impact assessment in high latitudes: are bucket-type models up to the task? A. Todorović et al. https://doi.org/10.1016/j.envsoft.2026.106964
- Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests T. El Ouahabi et al. https://doi.org/10.5194/hess-30-3549-2026
- Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration A. Pizarro et al. https://doi.org/10.5194/hess-29-4913-2025
- How well do hydrological models simulate streamflow extremes and drought-to-flood transitions? E. Muñoz-Castro et al. https://doi.org/10.5194/hess-30-825-2026
- Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA C. Thébault et al. https://doi.org/10.5194/hess-30-3945-2026
- Hydrological variability of large rivers in West Africa: gap-filling with Earth observations and daily rainfall-runoff modelling P. Ndiaye et al. https://doi.org/10.1080/02626667.2025.2542477
- Variable transformations in consistent loss functions H. Tyralis & G. Papacharalampous https://doi.org/10.1016/j.knosys.2025.115202
- Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values? M. Saadi et al. https://doi.org/10.5194/hess-30-3623-2026
- Exploring future water resources and uses considering water demand scenarios and climate change for the French Sèvre Nantaise basin L. Santos et al. https://doi.org/10.5194/hess-30-1915-2026
- What can be expected from a semi-distributed multi-model approach for streamflow forecasting? Tailoring the structure and size of a super-ensemble on the Rhône basin C. Thébault et al. https://doi.org/10.1016/j.jhydrol.2025.133589
- Improving Distributed Hydrological Model Robustness Through Multi‐Variable Calibration M. Gibbs et al. https://doi.org/10.1002/hyp.70525
- Deep Learning-Based Monthly Runoff Simulation in Changing Environments: Enhancing Accuracy by Reducing Data Redundancy S. Liu et al. https://doi.org/10.1007/s11269-026-04680-6
- Estimation of lake storage volumes in the Lagan River catchment in southern Sweden using a modified version of the Australian Water Resources Assessment–Landscape model (AWRA-L) A. Bjerkén et al. https://doi.org/10.1016/j.envsoft.2026.107069
- Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity X. Pang et al. https://doi.org/10.1016/j.jhydrol.2026.135910
17 citations as recorded by crossref.
- Rethinking future flood hazard: Hourly data challenge daily flood projections in Alpine catchments P. Astagneau et al. https://doi.org/10.1126/sciadv.aed6012
- Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models J. Bohl et al. https://doi.org/10.5194/hess-30-4667-2026
- Flood Regulation Service Responses to Urban Green Space Change in a Plateau Valley City: A Case Study of Lhasa, China S. Zhao et al. https://doi.org/10.3390/rs18142331
- Hydrological climate change impact assessment in high latitudes: are bucket-type models up to the task? A. Todorović et al. https://doi.org/10.1016/j.envsoft.2026.106964
- Multi-site learning for hydrological uncertainty prediction: the case of quantile random forests T. El Ouahabi et al. https://doi.org/10.5194/hess-30-3549-2026
- Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration A. Pizarro et al. https://doi.org/10.5194/hess-29-4913-2025
- How well do hydrological models simulate streamflow extremes and drought-to-flood transitions? E. Muñoz-Castro et al. https://doi.org/10.5194/hess-30-825-2026
- Comparing multi-model mosaic and multi-model combination methods to simulate streamflow across the contiguous USA C. Thébault et al. https://doi.org/10.5194/hess-30-3945-2026
- Hydrological variability of large rivers in West Africa: gap-filling with Earth observations and daily rainfall-runoff modelling P. Ndiaye et al. https://doi.org/10.1080/02626667.2025.2542477
- Variable transformations in consistent loss functions H. Tyralis & G. Papacharalampous https://doi.org/10.1016/j.knosys.2025.115202
- Which strategy to improve the performances of an LSTM-based model for extreme stream temperature values? M. Saadi et al. https://doi.org/10.5194/hess-30-3623-2026
- Exploring future water resources and uses considering water demand scenarios and climate change for the French Sèvre Nantaise basin L. Santos et al. https://doi.org/10.5194/hess-30-1915-2026
- What can be expected from a semi-distributed multi-model approach for streamflow forecasting? Tailoring the structure and size of a super-ensemble on the Rhône basin C. Thébault et al. https://doi.org/10.1016/j.jhydrol.2025.133589
- Improving Distributed Hydrological Model Robustness Through Multi‐Variable Calibration M. Gibbs et al. https://doi.org/10.1002/hyp.70525
- Deep Learning-Based Monthly Runoff Simulation in Changing Environments: Enhancing Accuracy by Reducing Data Redundancy S. Liu et al. https://doi.org/10.1007/s11269-026-04680-6
- Estimation of lake storage volumes in the Lagan River catchment in southern Sweden using a modified version of the Australian Water Resources Assessment–Landscape model (AWRA-L) A. Bjerkén et al. https://doi.org/10.1016/j.envsoft.2026.107069
- Enhanced identification of watershed hydrological extremes using an improved drought index incorporating hydrological non-stationarity X. Pang et al. https://doi.org/10.1016/j.jhydrol.2026.135910
Saved (final revised paper)
Latest update: 06 Aug 2026
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
We discuss how mathematical transformations impact calibrated hydrological model simulations. We...
