Skip to Main content Skip to Navigation
Journal articles

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

Abstract : 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.
Document type :
Journal articles
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03271359
Contributor : François Bourgin Connect in order to contact the contributor
Submitted on : Sunday, August 1, 2021 - 3:30:04 PM
Last modification on : Wednesday, September 8, 2021 - 3:33:06 AM
Long-term archiving on: : Tuesday, November 2, 2021 - 6:09:23 PM

File

When does a parsimonious model...
Publication funded by an institution

Licence


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

Identifiers

Collections

Citation

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, Taylor & Francis, 2021, 66 (8), pp.1288-1305. ⟨10.1080/02626667.2021.1923720⟩. ⟨hal-03271359⟩

Share

Metrics

Record views

52

Files downloads

38