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.
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
27 citations as recorded by crossref.
- Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al.
- A Novel Monthly Runoff Prediction Model Based on KVMD and KTCN-LSTM-SA S. Zhang et al.
- Torrential rainfall in Valencia, Spain, recorded by personal weather stations preceding and during the 29 October 2024 floods N. Rombeek et al.
- Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al.
- Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al.
- Impact of climate change on the magnitude and extent of riverine floods in a Peruvian Andean–Amazonian basin D. Saavedra et al.
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al.
- A unified runoff generation scheme for applicability across different hydrometeorological zones Q. Zhang et al.
- Quantifying the Contribution of Global Precipitation Product Uncertainty to Ensemble Discharge Simulations and Projections: A Case Study in the Liujiang Catchment, Southwest China Y. Chang et al.
- 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.
- A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins T. Nguyen et al.
- Improving the accuracy of daily runoff prediction using informer with black kite algorithm, variational mode decomposition, and error correction strategy W. Wang et al.
- Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models S. Eroshenko et al.
- Synoptic mechanisms behind historical rainfall records in Australasia: Cyclone Jasper and Auckland low J. Callaghan & M. Osman
- 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.
- Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta
- Impact-based forecasting to cope with riverine floods in a Peruvian Andes-Amazon basin D. Saavedra et al.
- 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.
- Improving representation of hydrological process heterogeneity in grid-Xin’anjiang model through a stepwise approach Q. Zhang et al.
- Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
- Impact of precipitation station density and homogeneous regions on hydrological modeling performance J. Song & H. Kim
- A scalable framework for flash flood hazard assessment in data-scarce catchments using coupled modeling M. Khan et al.
- Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al.
- 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.
- Three-stage hybrid modeling for real-time streamflow prediction in data-scarce regions A. Ali et al.
- Impacts of spatial-temporal rainfall structures and antecedent wetness on flood variability at the catchment scale W. Yang et al.
27 citations as recorded by crossref.
- Developing a stochastic hydrological model for informing lake water level drawdown management X. He et al.
- A Novel Monthly Runoff Prediction Model Based on KVMD and KTCN-LSTM-SA S. Zhang et al.
- Torrential rainfall in Valencia, Spain, recorded by personal weather stations preceding and during the 29 October 2024 floods N. Rombeek et al.
- Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna
- Integrating Euclidean and non-Euclidean spatial information for deep learning-based spatiotemporal hydrological simulation L. Deng et al.
- Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers W. Wan et al.
- Impact of climate change on the magnitude and extent of riverine floods in a Peruvian Andean–Amazonian basin D. Saavedra et al.
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al.
- A unified runoff generation scheme for applicability across different hydrometeorological zones Q. Zhang et al.
- Quantifying the Contribution of Global Precipitation Product Uncertainty to Ensemble Discharge Simulations and Projections: A Case Study in the Liujiang Catchment, Southwest China Y. Chang et al.
- 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.
- A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins T. Nguyen et al.
- Improving the accuracy of daily runoff prediction using informer with black kite algorithm, variational mode decomposition, and error correction strategy W. Wang et al.
- Spring Runoff Simulation of Snow-Dominant Catchment in Steppe Regions: A Comparison Study of Lumped Conceptual Models S. Eroshenko et al.
- Synoptic mechanisms behind historical rainfall records in Australasia: Cyclone Jasper and Auckland low J. Callaghan & M. Osman
- 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.
- Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta
- Impact-based forecasting to cope with riverine floods in a Peruvian Andes-Amazon basin D. Saavedra et al.
- 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.
- Improving representation of hydrological process heterogeneity in grid-Xin’anjiang model through a stepwise approach Q. Zhang et al.
- Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
- Impact of precipitation station density and homogeneous regions on hydrological modeling performance J. Song & H. Kim
- A scalable framework for flash flood hazard assessment in data-scarce catchments using coupled modeling M. Khan et al.
- Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures A. Gupta et al.
- 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.
- Three-stage hybrid modeling for real-time streamflow prediction in data-scarce regions A. Ali et al.
- Impacts of spatial-temporal rainfall structures and antecedent wetness on flood variability at the catchment scale W. Yang et al.
Saved (final revised paper)
Latest update: 04 May 2026
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...