Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-265-2022
© Author(s) 2022. 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-26-265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jan 2022
Research article |
| 18 Jan 2022
| 18 Jan 2022
Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning
Junjiang Liu
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Key Laboratory of Regional Climate-Environment for Temperate East
Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
100029, China
Junhan Zeng
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Yang Jiao
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Yong Li
Guangxi Meteorological Disaster Prevention Center, Nanning 530022,
China
Lihua Zhong
Guangxi Meteorological Disaster Prevention Center, Nanning 530022,
China
Ling Yao
Guangxi Guiguan Electric Power Co., Ltd., Nanning 530029, China
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- Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
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- Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
- A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al. 10.1007/s00477-022-02352-6
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Latest update: 18 Sep 2024
Short summary
Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological forecasts reduce both the probabilistic and deterministic forecast error compared with the ensemble streamflow prediction approach during the first week. The deterministic forecast error can be further reduced in the first 72 h when combined with the long short-term memory (LSTM) deep learning method. The forecast skill for LSTM using only historical observations drops sharply after the first 24 h.
Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological...
