Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 19
Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4913-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4913-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 19
Research article
 | 
01 Oct 2025
Research article |  | 01 Oct 2025

Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration

Alonso Pizarro, Demetris Koutsoyiannis, and Alberto Montanari

Viewed

Total article views: 1,305 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,172 105 28 1,305 31 49
  • HTML: 1,172
  • PDF: 105
  • XML: 28
  • Total: 1,305
  • BibTeX: 31
  • EndNote: 49
Views and downloads (calculated since 21 Jan 2025)
Cumulative views and downloads (calculated since 21 Jan 2025)

Viewed (geographical distribution)

Total article views: 1,305 (including HTML, PDF, and XML) Thereof 1,305 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Oct 2025
Download
Short summary
We introduce the ratio of uncertainty to mutual information (RUMI), a new metric to improve rainfall-runoff simulations. RUMI better captures the link between observed and simulated stream flows by considering uncertainty at a core computation step. Tested on 99 catchments and with the GR4J model, it outperforms traditional metrics by providing more reliable and consistent results. RUMI paves the way for more accurate hydrological predictions.
Read more
Share