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Conference Papers Year : 2023

Robust calibration of a hydrological model with stochastic surrogates


Misspecifying external forcings (such as rain) on a hydrological model can directly affect subsequent parameter calibrations. Indeed, by using classical calibration and problem inversion methods, the error in the external forcings is propagated to the model output, and then, if not treated correctly, this error is compensated by overcalibrating the model parameters. As a consequence, parameter values that were found optimal for one value of the external forcings, are not granted to be optimal for another one. Ideally however, estimated parameter values (that describe time-invariant soil properties) should be the same no matter the value of the external forcing.
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hal-03983100 , version 1 (10-02-2023)




  • HAL Id : hal-03983100 , version 1


Katarina Radišić, Claire Lauvernet, Arthur Vidard. Robust calibration of a hydrological model with stochastic surrogates. MASCOT-NUM 2023 - Workshop Méthodes d'Analyse Stochastique pour les Codes et Traitement NUMériques, Julien Bect (Université Paris-Saclay, CNRS, CentraleSupélec, L2S); Nicolas Bousquet (EDF R&D); Bertrand Iooss (EDF R&D) Anthony Nouy (Centrale Nantes, LMJL); Sidonie Lefebvre (ONERA, DOTA); Anthony Nouy (Centrale Nantes, LMJL); Emmanuel Vazquez (Université Paris-Saclay, CNRS, CentraleSupélec, L2S), Apr 2023, Le Croisic, France. pp.1-2. ⟨hal-03983100⟩
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