Articles | Volume 23, issue 8
https://doi.org/10.5194/hess-23-3335-2019
© Author(s) 2019. 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-23-3335-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Aug 2019
Research article |
| 14 Aug 2019
| 14 Aug 2019
Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China
Li Liu
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Su Li Pan
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
Zhi Xu Bai
Institute of Hydrology and Water Resources, Civil Engineering and
Architecture, Zhejiang University, Hangzhou, 310058, China
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22 citations as recorded by crossref.
- A weights combined model for middle and long-term streamflow forecasts and its value to hydropower maximization Y. Guo et al. 10.1016/j.jhydrol.2021.126794
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- Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia M. Bari et al. 10.3390/w16101438
- A new fractal-theory-based criterion for hydrological model calibration Z. Bai et al. 10.5194/hess-25-3675-2021
- A preliminary investigation on the climate-discharge relationship in the upper region of the Yarlung Zangbo River basin S. Liu et al. 10.1016/j.jhydrol.2021.127066
- Seasonal streamflow forecasting by surrogate modeling in the Yarlung Zangbo River Basin, China H. Gu et al. 10.1016/j.ejrh.2024.101835
- Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China Z. Zhang et al. 10.3390/w15193337
- A surrogate model for the Variable Infiltration Capacity model using deep learning artificial neural network H. Gu et al. 10.1016/j.jhydrol.2020.125019
- Estimating the added value of GRACE total water storage and uncertainty quantification in seasonal streamflow forecasting L. Liu et al. 10.1080/02626667.2021.1998510
- Multi-century flow reconstruction of the Lhasa River, China J. Zeng et al. 10.1016/j.ejrh.2024.101795
- How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? L. Liu et al. 10.1002/joc.6867
- A surrogate model for the variable infiltration capacity model using physics-informed machine learning H. Gu et al. 10.2166/wcc.2025.767
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- High-Resolution Urban Flood Forecasting by Using a Coupled Atmospheric and Hydrodynamic Flood Models G. Chen et al. 10.3389/feart.2020.545612
- BHARAT: a MADM approach to prioritizing the best performing EPS in a semi-arid river basin R. Yadav & S. Yadav 10.1007/s11069-024-06566-5
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- A five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts Z. Huang et al. 10.1016/j.jhydrol.2021.126893
- Changes in and Modelling of Hydrological Process for a Semi-Arid Catchment in the Context of Human Disturbance Y. Liu et al. 10.3389/feart.2021.759534
- AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment Y. Guo et al. 10.5194/hess-25-5951-2021
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- Investigating climate change impacts on runoff and sediment transport processes in the midstream of the Yarlung Tsangpo river based on hydrological simulation L. Wang et al. 10.1016/j.catena.2025.108920
- A five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts Z. Huang et al. 10.1016/j.jhydrol.2021.126893
21 citations as recorded by crossref.
- A weights combined model for middle and long-term streamflow forecasts and its value to hydropower maximization Y. Guo et al. 10.1016/j.jhydrol.2021.126794
- A stepwise surrogate model for parameter calibration of the Variable Infiltration Capacity model: the case of the upper Brahmaputra, Tibet Plateau H. Gu et al. 10.2166/hydro.2020.010
- Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia M. Bari et al. 10.3390/w16101438
- A new fractal-theory-based criterion for hydrological model calibration Z. Bai et al. 10.5194/hess-25-3675-2021
- A preliminary investigation on the climate-discharge relationship in the upper region of the Yarlung Zangbo River basin S. Liu et al. 10.1016/j.jhydrol.2021.127066
- Seasonal streamflow forecasting by surrogate modeling in the Yarlung Zangbo River Basin, China H. Gu et al. 10.1016/j.ejrh.2024.101835
- Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China Z. Zhang et al. 10.3390/w15193337
- A surrogate model for the Variable Infiltration Capacity model using deep learning artificial neural network H. Gu et al. 10.1016/j.jhydrol.2020.125019
- Estimating the added value of GRACE total water storage and uncertainty quantification in seasonal streamflow forecasting L. Liu et al. 10.1080/02626667.2021.1998510
- Multi-century flow reconstruction of the Lhasa River, China J. Zeng et al. 10.1016/j.ejrh.2024.101795
- How well do the ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? L. Liu et al. 10.1002/joc.6867
- A surrogate model for the variable infiltration capacity model using physics-informed machine learning H. Gu et al. 10.2166/wcc.2025.767
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- High-Resolution Urban Flood Forecasting by Using a Coupled Atmospheric and Hydrodynamic Flood Models G. Chen et al. 10.3389/feart.2020.545612
- BHARAT: a MADM approach to prioritizing the best performing EPS in a semi-arid river basin R. Yadav & S. Yadav 10.1007/s11069-024-06566-5
- Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
- A five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts Z. Huang et al. 10.1016/j.jhydrol.2021.126893
- Changes in and Modelling of Hydrological Process for a Semi-Arid Catchment in the Context of Human Disturbance Y. Liu et al. 10.3389/feart.2021.759534
- AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment Y. Guo et al. 10.5194/hess-25-5951-2021
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- Investigating climate change impacts on runoff and sediment transport processes in the midstream of the Yarlung Tsangpo river based on hydrological simulation L. Wang et al. 10.1016/j.catena.2025.108920
1 citations as recorded by crossref.
Latest update: 14 May 2025
Short summary
The ensemble flood forecasting system can skillfully predict annual maximum floods with a lead time of more than 10 d and has skill in forecasting the snowmelt-related components about 7 d ahead. The accuracy of forecasts for the annual first floods is inferior, with a lead time of only 5 d. The snowmelt-induced surface runoff is the most poorly captured component by the system, and the well-predicted rainfall-related components are the major contributor to good performance.
The ensemble flood forecasting system can skillfully predict annual maximum floods with a lead...
