Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 2
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.
https://doi.org/10.5194/hess-26-265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 2
Research article
 | 
18 Jan 2022
Research article |  | 18 Jan 2022

Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning

Junjiang Liu, Xing Yuan, Junhan Zeng, Yang Jiao, Yong Li, Lihua Zhong, and Ling Yao

Viewed

Total article views: 7,394 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,774 1,508 112 7,394 91 125
  • HTML: 5,774
  • PDF: 1,508
  • XML: 112
  • Total: 7,394
  • BibTeX: 91
  • EndNote: 125
Views and downloads (calculated since 30 Jul 2021)
Cumulative views and downloads (calculated since 30 Jul 2021)

Viewed (geographical distribution)

Total article views: 7,394 (including HTML, PDF, and XML) Thereof 7,010 with geography defined and 384 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Nov 2025
Download
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.
Read more
Share