Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-1987-2023
© Author(s) 2023. 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-27-1987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 May 2023
Research article |
| 25 May 2023
| 25 May 2023
Why do our rainfall–runoff models keep underestimating the peak flows?
András Bárdossy
Institute for Water and Environmental System Modeling, University of Stuttgart, 70569 Stuttgart, Germany
Faizan Anwar
CORRESPONDING AUTHOR
Institute for Water and Environmental System Modeling, University of Stuttgart, 70569 Stuttgart, Germany
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Cited
16 citations as recorded by crossref.
- Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al. 10.1016/j.jenvman.2023.118744
- Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models S. Eroshenko et al. 10.3390/inventions9050109
- Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna 10.1016/j.hydroa.2024.100198
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al. 10.1007/s11356-024-33594-2
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
- A unified runoff generation scheme for applicability across different hydrometeorological zones Q. Zhang et al. 10.1016/j.envsoft.2024.106138
- An Investigation into the Applicability of the SHUD Model for Streamflow Simulation Based on CMFD Meteorological Data in the Yellow River Source Region T. Bu et al. 10.3390/w16243583
- Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
- Flood projections for selected Costa Rican main basins using CMIP6 climate models downscaled output in the HBV hydrological model for scenario SSP5-8.5 H. Hidalgo et al. 10.3178/hrl.18.35
- Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. 10.1016/j.jhydrol.2024.131774
- An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy D. Xu et al. 10.1016/j.jhydrol.2023.130558
- Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models Y. Wei et al. 10.1007/s11269-024-03780-5
- A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins T. Nguyen et al. 10.1016/j.ejrh.2024.102095
- Streamflow forecasting in a climate change perspective using E-FUSE R. Vogeti et al. 10.2166/wcc.2022.251
- Interpolation of rainfall observations during extreme rainfall events in complex mountainous terrain T. Page et al. 10.1002/hyp.14758
14 citations as recorded by crossref.
- Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al. 10.1016/j.jenvman.2023.118744
- Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models S. Eroshenko et al. 10.3390/inventions9050109
- Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna 10.1016/j.hydroa.2024.100198
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al. 10.1016/j.jhydrol.2024.131438
- Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al. 10.1007/s11356-024-33594-2
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
- A unified runoff generation scheme for applicability across different hydrometeorological zones Q. Zhang et al. 10.1016/j.envsoft.2024.106138
- An Investigation into the Applicability of the SHUD Model for Streamflow Simulation Based on CMFD Meteorological Data in the Yellow River Source Region T. Bu et al. 10.3390/w16243583
- Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
- Flood projections for selected Costa Rican main basins using CMIP6 climate models downscaled output in the HBV hydrological model for scenario SSP5-8.5 H. Hidalgo et al. 10.3178/hrl.18.35
- Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al. 10.1016/j.jhydrol.2024.131774
- An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy D. Xu et al. 10.1016/j.jhydrol.2023.130558
- Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models Y. Wei et al. 10.1007/s11269-024-03780-5
- A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins T. Nguyen et al. 10.1016/j.ejrh.2024.102095
Latest update: 20 Jan 2025
Short summary
This study demonstrates the fact that the large river flows forecasted by the models show an underestimation that is inversely related to the number of locations where precipitation is recorded, which is independent of the model. The higher the number of points where the amount of precipitation is recorded, the better the estimate of the river flows.
This study demonstrates the fact that the large river flows forecasted by the models show an...
