Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2621-2023
https://doi.org/10.5194/hess-27-2621-2023
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

Viewed

Total article views: 5,251 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,095 1,055 101 5,251 72 96
  • HTML: 4,095
  • PDF: 1,055
  • XML: 101
  • Total: 5,251
  • BibTeX: 72
  • EndNote: 96
Views and downloads (calculated since 05 Aug 2022)
Cumulative views and downloads (calculated since 05 Aug 2022)

Viewed (geographical distribution)

Total article views: 5,251 (including HTML, PDF, and XML) Thereof 5,037 with geography defined and 214 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Aug 2025
Download
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.
Share