EGU23-5492, updated on 31 Mar 2023
https://doi.org/10.5194/egusphere-egu23-5492
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimating the parameters of a flood forecasting model: with or without updating procedures?

Paul C. Astagneau, François Bourgin, Vazken Andréassian, and Charles Perrin
Paul C. Astagneau et al.
  • Université Paris-Saclay, INRAE, HYCAR Research Unit, Antony, France

When they are used for operational forecasting, hydrological models are almost always combined with some kind of updating procedures. Then a question arises: should the model parameters be calibrated with or without the updating procedures? Calibrating with the updating procedures often improves forecast efficiency, but it can also lead to parameter inconsistency and ultimately to a drop in performance in some cases.

In this study, we evaluate the pros and cons of making the parameters of a flood forecasting model vary with lead times. We investigate the dependencies of the model parameters to the lead times and determine where and when this procedure significantly improves forecast quality. A modified version of the GR5H hydrological model is used on 229 French catchments where 10,652 events were selected. The model is run at the hourly time step and combined with a simple updating procedure to produce forecasts at four lead times. The model parameters were estimated from a large screening of the parameter space (3 million runs for each catchment). Results show that the parameters related to fast catchment processes are the most dependant on lead times, indicating the need for more specific parameter estimation methods when modelling catchments prone to flash floods.

How to cite: Astagneau, P. C., Bourgin, F., Andréassian, V., and Perrin, C.: Estimating the parameters of a flood forecasting model: with or without updating procedures?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5492, https://doi.org/10.5194/egusphere-egu23-5492, 2023.