Articles | Volume 24, issue 6
https://doi.org/10.5194/hess-24-3189-2020
https://doi.org/10.5194/hess-24-3189-2020
Research article
 | 
19 Jun 2020
Research article |  | 19 Jun 2020

Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

Céline Monteil, Fabrice Zaoui, Nicolas Le Moine, and Frédéric Hendrickx

Related authors

Regional, multi-decadal analysis on the Loire River basin reveals that stream temperature increases faster than air temperature
Hanieh Seyedhashemi, Jean-Philippe Vidal, Jacob S. Diamond, Dominique Thiéry, Céline Monteil, Frédéric Hendrickx, Anthony Maire, and Florentina Moatar
Hydrol. Earth Syst. Sci., 26, 2583–2603, https://doi.org/10.5194/hess-26-2583-2022,https://doi.org/10.5194/hess-26-2583-2022, 2022
Short summary
Tailor-made spatial patterns for hydrological model parameters combining regionalisation methods
Laura Rouhier, Federico Garavaglia, Matthieu Le Lay, Timothée Michon, William Castaings, Nicolas Le Moine, Frédéric Hendrickx, Céline Monteil, and Pierre Ribstein
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-342,https://doi.org/10.5194/hess-2018-342, 2018
Manuscript not accepted for further review
Short summary

Related subject area

Subject: Water Resources Management | Techniques and Approaches: Modelling approaches
Assessment of upscaling methodologies for daily crop transpiration using sap flows and two-source energy balance models in almonds under different water statuses and production systems
Manuel Quintanilla-Albornoz, Xavier Miarnau, Ana Pelechá, Héctor Nieto, and Joaquim Bellvert
Hydrol. Earth Syst. Sci., 28, 4797–4818, https://doi.org/10.5194/hess-28-4797-2024,https://doi.org/10.5194/hess-28-4797-2024, 2024
Short summary
Making a case for power-sensitive water modelling: a literature review
Rozemarijn ter Horst, Rossella Alba, Jeroen Vos, Maria Rusca, Jonatan Godinez-Madrigal, Lucie V. Babel, Gert Jan Veldwisch, Jean-Philippe Venot, Bruno Bonté, David W. Walker, and Tobias Krueger
Hydrol. Earth Syst. Sci., 28, 4157–4186, https://doi.org/10.5194/hess-28-4157-2024,https://doi.org/10.5194/hess-28-4157-2024, 2024
Short summary
Developing water supply reservoir operating rules for large-scale hydrological modelling
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Chris Hutton, Michael Bliss Singer, Hilary McMillan, and Jim Freer
Hydrol. Earth Syst. Sci., 28, 4203–4218, https://doi.org/10.5194/hess-28-4203-2024,https://doi.org/10.5194/hess-28-4203-2024, 2024
Short summary
An investigation of anthropogenic influences on hydrologic connectivity using model stress tests
Amelie Herzog, Jost Hellwig, and Kerstin Stahl
Hydrol. Earth Syst. Sci., 28, 4065–4083, https://doi.org/10.5194/hess-28-4065-2024,https://doi.org/10.5194/hess-28-4065-2024, 2024
Short summary
The H2Ours game to explore water use, resources and sustainability: connecting issues in two landscapes in Indonesia
Lisa Tanika, Rika Ratna Sari, Arief Lukman Hakim, Meine van Noordwijk, Marielos Peña-Claros, Beria Leimona, Edi Purwanto, and Erika N. Speelman
Hydrol. Earth Syst. Sci., 28, 3807–3835, https://doi.org/10.5194/hess-28-3807-2024,https://doi.org/10.5194/hess-28-3807-2024, 2024
Short summary

Cited articles

Baluja, S. and Caruana, R.: Removing the genetics from the standard genetic algorithm, in: Machine Learning Proceedings 1995, Morgan Kaufmann, San Francisco, USA, 38–46, 1995. a
Campo, L., Caparrini, F., and Castelli, F.: Use of multi-platform, multi-temporal remote-sensing data for calibration of a distributed hydrological model: an application in the Arno basin, Italy, Hydrol. Process., 20, 2693–2712, 2006. a
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The Suite of Lumped GR Hydrological Models in an R package, Environ. Model. Softw., 94, 166–171, https://doi.org/10.1016/j.envsoft.2017.05.002, 2017. a, b, c
Coron, L., Delaigue, O., Thirel, G., Perrin, C., and Michel, C.: airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.3.2.23, available at: https://cran.r-project.org/package=airGR (last access: 15 June 2020), 2019. a, b, c
Deb, K., Pratap, A., Agarwal, S. Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182–197, 2002. a, b, c
Download
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.