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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Oct 2025
Research article |
| 01 Oct 2025
| 01 Oct 2025
Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration
Alonso Pizarro
CORRESPONDING AUTHOR
Escuela de Ingeniería en Obras Civiles, Universidad Diego Portales, Santiago, 8370109, Chile
Demetris Koutsoyiannis
Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Zographou, Athens, 15772, Greece
Alberto Montanari
Department DICAM, University of Bologna, Via del Risorgimento 2, Bologna, 40136, Italy
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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.
We introduce the ratio of uncertainty to mutual information (RUMI), a new metric to improve...
