Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 21
Articles | Volume 30, issue 21
https://doi.org/10.5194/hess-30-6737-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-6737-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 21
Technical note
 | 
02 Nov 2026
Technical note |  | 02 Nov 2026

Technical note: Benefits of Bayesian estimation of model parameters in a large hydrological model ensemble

Yohei Sawada and Shinichi Okugawa

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Short summary
We studied how best to handle uncertainty in hydrological models that simulate how rain becomes river flow. We tested a well-known Bayesian way to estimate uncertainty in model settings as well as in model design. This approach helps judge which models are better considering their uncertainty. However, considering uncertainty in model settings is not helpful to provide river flow forecasting, so costly Bayesian tuning is rarely justified for practical purposes.
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