Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 22
Articles | Volume 29, issue 22
https://doi.org/10.5194/hess-29-6499-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-6499-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 22
Research article
 | 
19 Nov 2025
Research article |  | 19 Nov 2025

Data derived reservoir operations simulated in a global hydrologic model

Jennie C. Steyaert, Edwin H. Sutanudjaja, Marc Bierkens, and Niko Wanders

Viewed

Total article views: 6,714 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,516 1,031 167 6,714 177 224
  • HTML: 5,516
  • PDF: 1,031
  • XML: 167
  • Total: 6,714
  • BibTeX: 177
  • EndNote: 224
Views and downloads (calculated since 30 Jan 2025)
Cumulative views and downloads (calculated since 30 Jan 2025)

Viewed (geographical distribution)

Total article views: 6,714 (including HTML, PDF, and XML) Thereof 6,535 with geography defined and 179 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 01 Sep 2026
Download
Short summary
Using machine learning techniques and remotely sensed reservoir data, we develop a workflow to derive reservoir storage bounds. We put these bounds in a global hydrologic model, PCR-GLOBWB 2, and evaluate the difference between generalized operations (the schemes typically in global models) and this data derived method. We find that modelled storage is more accurate in the data derived operations. We also find that generalized operations over estimate storage and can underestimate water gaps.
Read more
Share