Abstract. The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a fourday sliding window over the flood event. The background error covariances for water level and discharge were represented with anisotropic correlation functions where the correlation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improvement in the water level and discharge in both analysis and forecast modes.
Two strategies are proposed here to improve the water level-discharge relation in the model.
8At rst, a modeling strategy consists in improving the description of the river bed geometry 9 using topographic and bathymetric measurements. Secondly, an inverse modeling strategy 10 proposes to locally correct friction coe cients in the river bed and the ood plain through
The present study describes the assimilation of river water level observations and the resulting improvement of the river flood forecast. The BLUE algorithm was built on top of the one-dimensional hydraulics model MASCARET. The assimilation algorithm folds in two steps: the first one is based on the assumption that the upstream flow can be adjusted using a three-parameter correction, the second one consists in directly correcting the hydraulic state. This procedure is applied on a four-day sliding window over the whole flood event. The background error covariances for water level and discharge are represented with asymmetric correlation functions where the upstream correlation length is bigger than the downstream correlation length. This approach is motivated by the implementation of a Kalman Filter algorithm on top of an advection-diffusion toy model. The assimilation study with MASCARET is carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state along the flood event leads to a significant improvement of the water level and discharge in analysis and forecast modes
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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