Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-2809-2024
© 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-28-2809-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jul 2024
Research article |
| 03 Jul 2024
| 03 Jul 2024
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Zhen Cui
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Hua Chen
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Dedi Liu
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Yanlai Zhou
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Chong-Yu Xu
Department of Geoscience, University of Oslo, Oslo, Norway
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Cited
14 citations as recorded by crossref.
- Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction D. De León Pérez et al. https://doi.org/10.3390/w17202932
- From precipitation forecasts to optimal reservoir operation: an integrated downscaling-forecasting-operation framework for reservoir floodwater utilization L. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.136371
- Improving Flood Control Optimal Operation of River-Type Cascade Reservoirs through Coupling with 1D Hydrodynamic Model L. Yao et al. https://doi.org/10.1007/s11269-025-04116-7
- A dual post-processing framework for probabilistic streamflow forecasting using deep learning ensembles: Integrating residual correction and Vine Copula-based BMA W. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.136437
- Demonstrating almost half of cotton fiber quality variation is attributed to climate change using a hybrid machine learning-enabled approach X. Li et al. https://doi.org/10.1016/j.eja.2024.127426
- Coupled dominant factors analysis, dual attention deep learning, and uncertainty quantification for long-term pan evaporation ensemble prediction in the Wuding River Basin, China Z. Cui et al. https://doi.org/10.1016/j.ejrh.2026.103323
- LSTM-based flood-stage forecasting under limited flood-event samples: Effects of forecasting scheme, spatial input configuration, and input-window length in the Pajiang River basin S. Li et al. https://doi.org/10.1016/j.ejrh.2026.103993
- Calibration of Ensemble Forecasts for Extreme Rainfall Using Bayesian Model Averaging: A Comparative Review of Gaussian and Gamma Distributions D. Faidah et al. https://doi.org/10.3390/su18126121
- Risk analysis of real-time reservoir scheduling decisions based on probabilistic inflow process forecasting Z. Cui et al. https://doi.org/10.1016/j.eswa.2025.130836
- Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting B. Jia & W. Fang https://doi.org/10.3390/rs17132314
- Propagation and future projection of dry-wet abrupt alternation through the hydrological cycle using a GCM-VIC modeling framework S. Liu et al. https://doi.org/10.1016/j.ejrh.2026.103631
- Probabilistic Prediction of Concrete Compressive Strength Using Copula Functions: A Novel Framework for Uncertainty Quantification C. Zhang et al. https://doi.org/10.3390/buildings16040754
- Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study B. Kuhaneswaran et al. https://doi.org/10.3390/w17152281
- Integration of deterministic initialization, real-time updating and probabilistic postprocessing in hydrological forecasting for enhancing flood risk reduction H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134725
14 citations as recorded by crossref.
- Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction D. De León Pérez et al. https://doi.org/10.3390/w17202932
- From precipitation forecasts to optimal reservoir operation: an integrated downscaling-forecasting-operation framework for reservoir floodwater utilization L. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.136371
- Improving Flood Control Optimal Operation of River-Type Cascade Reservoirs through Coupling with 1D Hydrodynamic Model L. Yao et al. https://doi.org/10.1007/s11269-025-04116-7
- A dual post-processing framework for probabilistic streamflow forecasting using deep learning ensembles: Integrating residual correction and Vine Copula-based BMA W. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.136437
- Demonstrating almost half of cotton fiber quality variation is attributed to climate change using a hybrid machine learning-enabled approach X. Li et al. https://doi.org/10.1016/j.eja.2024.127426
- Coupled dominant factors analysis, dual attention deep learning, and uncertainty quantification for long-term pan evaporation ensemble prediction in the Wuding River Basin, China Z. Cui et al. https://doi.org/10.1016/j.ejrh.2026.103323
- LSTM-based flood-stage forecasting under limited flood-event samples: Effects of forecasting scheme, spatial input configuration, and input-window length in the Pajiang River basin S. Li et al. https://doi.org/10.1016/j.ejrh.2026.103993
- Calibration of Ensemble Forecasts for Extreme Rainfall Using Bayesian Model Averaging: A Comparative Review of Gaussian and Gamma Distributions D. Faidah et al. https://doi.org/10.3390/su18126121
- Risk analysis of real-time reservoir scheduling decisions based on probabilistic inflow process forecasting Z. Cui et al. https://doi.org/10.1016/j.eswa.2025.130836
- Coupling Machine Learning and Physically Based Hydrological Models for Reservoir-Based Streamflow Forecasting B. Jia & W. Fang https://doi.org/10.3390/rs17132314
- Propagation and future projection of dry-wet abrupt alternation through the hydrological cycle using a GCM-VIC modeling framework S. Liu et al. https://doi.org/10.1016/j.ejrh.2026.103631
- Probabilistic Prediction of Concrete Compressive Strength Using Copula Functions: A Novel Framework for Uncertainty Quantification C. Zhang et al. https://doi.org/10.3390/buildings16040754
- Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study B. Kuhaneswaran et al. https://doi.org/10.3390/w17152281
- Integration of deterministic initialization, real-time updating and probabilistic postprocessing in hydrological forecasting for enhancing flood risk reduction H. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134725
Saved (final revised paper)
Latest update: 27 Sep 2026
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the...
