E3S Web Conf.
Volume 7, 20163rd European Conference on Flood Risk Management (FLOODrisk 2016)
|Number of page(s)||9|
|Section||Forecasting and warning|
|Published online||20 October 2016|
Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting
1 CECI, CERFACS – CNRS, 42 avenue Gaspard Coriolis, 31820 Toulouse, France
2 SCHAPI, 42 avenue Gaspard Coriolis, 31820 Toulouse, France
3 INPT, CNRS, IMFT, Toulouse, France
4 LNHE, EDF R&D, Chatou, France
5 LNSH, EDF R&D, Chatou, France
6 DREAL Champagne Ardenne, Châlons-en-Champagne, France
a Corresponding author: email@example.com
Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D), SCHAPI and CE-REMA resulted in the implementation of a Data Assimilation (DA) method on top of MASCARET in the framework of real-time forecasting. This prototype was based on a simplified Kalman filter where the description of the background error covariances is prescribed based on off-line climatology constant over time. This approach showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations. An ensemble-based DA algorithm has recently been implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. It was demonstrated that the flow dependent description of the background error covariances with the EnKF algorithm leads to a more realistic correction of the hydraulic state with significant impact of the hydraulic network characteristics
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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