Articles | Volume 24, issue 6
https://doi.org/10.5194/hess-24-3189-2020
© Author(s) 2020. 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-24-3189-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jun 2020
Research article |
| 19 Jun 2020
| 19 Jun 2020
Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm
EDF R&D LNHE – Laboratoire National d'Hydraulique et Environnement, Chatou, 78400, France
Fabrice Zaoui
EDF R&D LNHE – Laboratoire National d'Hydraulique et Environnement, Chatou, 78400, France
Nicolas Le Moine
UMR 7619 Metis (SU/CNRS/EPHE), Sorbonne Université, 4 Place Jussieu, Paris, 75005, France
Frédéric Hendrickx
EDF R&D LNHE – Laboratoire National d'Hydraulique et Environnement, Chatou, 78400, France
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- Investigating hydrological modeling uncertainties in the Mediterranean region by combining precipitation and soil moisture products V. Sivelle et al. https://doi.org/10.1016/j.ejrh.2025.103015
- Propagation of CMORPH rainfall errors to REW streamflow simulation mismatch in the upper Zambezi Basin W. Gumindoga et al. https://doi.org/10.1016/j.ejrh.2021.100966
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- The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model M. Kuban et al. https://doi.org/10.2478/johh-2022-0021
- Etude de sensibilité globale comparée des modèles hydrologiques GRP et MORDOR-TS A. Tiberi-Wadier et al. https://doi.org/10.1080/27678490.2021.1977014
- hydroMOPSO: A flexible and model-independent multi-objective optimisation R package for environmental and hydrological models R. Marinao et al. https://doi.org/10.1016/j.envsoft.2025.106851
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or a field measurement. Based on our experience in hydrological modelling, we propose combining two algorithms to obtain a fast and accurate way of calibrating complex models (many parameters and many objectives). We built an R package, caRamel, so that this multi-objective calibration algorithm can be easily implemented.
Environmental modelling is complex, and models often require the calibration of several...
