Articles | Volume 27, issue 14
Research article
19 Jul 2023
Research article |  | 19 Jul 2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-282', Anonymous Referee #1, 19 Sep 2022
    • AC1: 'Reply on RC1', Peishi Jiang, 28 Dec 2022
  • RC2: 'Comment on hess-2022-282', Anonymous Referee #2, 10 Oct 2022
    • AC2: 'Reply on RC2', Peishi Jiang, 28 Dec 2022
  • RC3: 'Comment on hess-2022-282', Anonymous Referee #3, 04 Dec 2022
    • AC3: 'Reply on RC3', Peishi Jiang, 28 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (06 Jan 2023) by Yue-Ping Xu
AR by Peishi Jiang on behalf of the Authors (08 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (24 Jan 2023) by Yue-Ping Xu
RR by Anonymous Referee #1 (20 Feb 2023)
RR by Anonymous Referee #3 (05 Jun 2023)
ED: Publish subject to technical corrections (06 Jun 2023) by Yue-Ping Xu
AR by Peishi Jiang on behalf of the Authors (08 Jun 2023)  Author's response   Manuscript 
Short summary
We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.