Articles | Volume 26, issue 2
Hydrol. Earth Syst. Sci., 26, 265–278, 2022
https://doi.org/10.5194/hess-26-265-2022
Hydrol. Earth Syst. Sci., 26, 265–278, 2022
https://doi.org/10.5194/hess-26-265-2022

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 et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-393', Anonymous Referee #1, 14 Aug 2021
  • RC2: 'Comment on hess-2021-393', Anonymous Referee #2, 17 Aug 2021
  • RC3: 'Comment on hess-2021-393', Anonymous Referee #3, 18 Aug 2021
  • RC4: 'Comment on hess-2021-393', Anonymous Referee #4, 25 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (11 Oct 2021) by Bob Su
AR by Xing Yuan on behalf of the Authors (12 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (14 Oct 2021) by Bob Su
RR by Anonymous Referee #1 (19 Oct 2021)
RR by Anonymous Referee #2 (24 Oct 2021)
RR by Anonymous Referee #4 (31 Oct 2021)
RR by Anonymous Referee #3 (14 Nov 2021)
ED: Publish subject to minor revisions (review by editor) (18 Nov 2021) by Bob Su
AR by Xing Yuan on behalf of the Authors (21 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 Dec 2021) by Bob Su
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.