Articles | Volume 25, issue 11
Hydrol. Earth Syst. Sci., 25, 6041–6066, 2021
https://doi.org/10.5194/hess-25-6041-2021
Hydrol. Earth Syst. Sci., 25, 6041–6066, 2021
https://doi.org/10.5194/hess-25-6041-2021

Research article 25 Nov 2021

Research article | 25 Nov 2021

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Jiancong Chen et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (05 Dec 2020) by Carlo De Michele
AR by Jiancong Chen on behalf of the Authors (11 Jan 2021)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (19 Feb 2021) by Carlo De Michele
RR by Anonymous Referee #1 (19 Mar 2021)
RR by Anonymous Referee #3 (21 Mar 2021)
ED: Publish subject to revisions (further review by editor and referees) (12 Apr 2021) by Carlo De Michele
AR by Jiancong Chen on behalf of the Authors (21 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (26 May 2021) by Carlo De Michele
RR by Anonymous Referee #1 (28 May 2021)
RR by Anonymous Referee #4 (28 Sep 2021)
ED: Publish subject to minor revisions (review by editor) (04 Oct 2021) by Carlo De Michele
AR by Jiancong Chen on behalf of the Authors (14 Oct 2021)  Author's response    Manuscript
ED: Publish as is (29 Oct 2021) by Carlo De Michele
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Short summary
The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent neural network to estimate evapotranspiration (ET) and ecosystem respiration (Reco) with only meteorological and remote-sensing inputs. We developed four use cases to demonstrate the applicability of HPM. The results indicate HPM is capable of providing ET and Reco estimations in challenging mountainous systems and enhances our understanding of watershed dynamics at sparsely monitored watersheds.