When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Hydrological Sciences Journal Année : 2021

When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias

Résumé

Identifying situations where a hydrological model yields poor performance is useful for improving its predictive capability. Here we applied an evaluation methodology to diagnose the weaknesses of a parsimonious rainfall-runoff model for flood simulation. The GR5H-I hourly lumped model was evaluated over a large set of 229 French catchments and 2990 flood events. Model bias was calculated considering different streamflow time windows, from calculations using all observations to analyses of individual flood events. We then analysed bias across seasons and against several flood characteristics. Our results show that although GR5H-I had good overall performance, most of the summer floods were underestimated. In summer and autumn, compensations between flood and recession periods were identified. The largest underestimations of flood volumes were identified when high-intensity precipitation events occurred, especially under low soil moisture conditions.

Domaines

Hydrologie
Fichier principal
Vignette du fichier
When does a parsimonious model fail to simulate floods Learning from the seasonality of model bias.pdf (11.67 Mo) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-03271359 , version 1 (01-08-2021)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Paul C. Astagneau, François Bourgin, Vazken Andréassian, Charles Perrin. When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias. Hydrological Sciences Journal, 2021, 66 (8), pp.1288-1305. ⟨10.1080/02626667.2021.1923720⟩. ⟨hal-03271359⟩
46 Consultations
16 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More