Preprints
https://doi.org/10.5194/egusphere-2024-3816
https://doi.org/10.5194/egusphere-2024-3816
03 Feb 2025
 | 03 Feb 2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung

Abstract. Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian Process learning (LSG) model, we developed a downscaling approach to accurately construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share

Journal article(s) based on this preprint

18 Aug 2025
An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025,https://doi.org/10.5194/hess-29-3833-2025, 2025
Short summary
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-3816', Anonymous Referee #1, 13 Feb 2025
    • AC1: 'Reply on RC1', Zeli Tan, 18 Apr 2025
  • RC2: 'Comment on egusphere-2024-3816', Anonymous Referee #2, 15 Feb 2025
    • AC2: 'Reply on RC2', Zeli Tan, 18 Apr 2025
  • RC3: 'Comment on egusphere-2024-3816', Anonymous Referee #3, 17 Feb 2025
    • AC3: 'Reply on RC3', Zeli Tan, 18 Apr 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-3816', Anonymous Referee #1, 13 Feb 2025
    • AC1: 'Reply on RC1', Zeli Tan, 18 Apr 2025
  • RC2: 'Comment on egusphere-2024-3816', Anonymous Referee #2, 15 Feb 2025
    • AC2: 'Reply on RC2', Zeli Tan, 18 Apr 2025
  • RC3: 'Comment on egusphere-2024-3816', Anonymous Referee #3, 17 Feb 2025
    • AC3: 'Reply on RC3', Zeli Tan, 18 Apr 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (30 Apr 2025) by Christa Kelleher
AR by Zeli Tan on behalf of the Authors (01 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (14 May 2025) by Christa Kelleher
RR by Anonymous Referee #3 (28 May 2025)
RR by Anonymous Referee #2 (28 May 2025)
ED: Publish as is (05 Jun 2025) by Christa Kelleher
AR by Zeli Tan on behalf of the Authors (05 Jun 2025)

Journal article(s) based on this preprint

18 Aug 2025
An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 29, 3833–3852, https://doi.org/10.5194/hess-29-3833-2025,https://doi.org/10.5194/hess-29-3833-2025, 2025
Short summary
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung
Zeli Tan, Donghui Xu, Sourav Taraphdar, Jiangqin Ma, Gautam Bisht, and L. Ruby Leung

Viewed

Total article views: 722 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
332 364 26 722 53 19 26
  • HTML: 332
  • PDF: 364
  • XML: 26
  • Total: 722
  • Supplement: 53
  • BibTeX: 19
  • EndNote: 26
Views and downloads (calculated since 03 Feb 2025)
Cumulative views and downloads (calculated since 03 Feb 2025)

Viewed (geographical distribution)

Total article views: 718 (including HTML, PDF, and XML) Thereof 718 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Aug 2025
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Flow depth and velocity determine many river functions, but their high-resolution simulations are expensive. Here, we developed a downscaling approach that can provide fast and accurate estimation of high-resolution river hydrodynamics. The 84-fold acceleration achieved by the method makes reliable flood risk analysis that needs hundreds or thousands of model runs feasible. More importantly, it provides an opportunity to couple large-scale hydrodynamics with local processes in river models.
Share