Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting - IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (<b>anciennement Cemagref</b>)
Proceedings E3S Web of Conferences Year : 2016

Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting

Abstract

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.
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hal-04691417 , version 1 (08-09-2024)

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Sébastien Barthélémy, Sophie Ricci, Etienne Le Pape, Mélanie Rochoux, Olivier Thual, et al.. Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting. FLOODrisk 2016 - 3rd European Conference on Flood Risk Management, E3S Web of Conferences, 7, pp.18022, 2016, ⟨10.1051/e3sconf/20160718022⟩. ⟨hal-04691417⟩
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