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La Garonne après sa confluence avec le Lot a connu une crue majeure en février 2021, sa plus haute crue depuis décembre 1981. À cette occasion, de nombreuses données d’observations ont été acquises par le réseau Vigicrues : jaugeages sur des niveaux de crue parfois plus que décennaux, relevés de laisses de crues et photographies aériennes. Ces données ont permis d’actualiser la connaissance des débits, notamment la courbe de tarage de Tonneins, de produire et de valider les cartographies des zones inondées potentielles sur la plaine marmandaise, ainsi que de réévaluer les niveaux de vigilance crues de façon harmonisée entre la Garonne marmandaise et la Garonne girondine. Les travaux se poursuivront par la mise en place d’un modèle hydraulique 2D en mesure de représenter la nonlinéarité qui apparaît au-delà des 8,50 m à Tonneins et dont la prévisibilité conditionne la bonne prévision sur Marmande et La Réole lors des crues majeures.

Smurf is an open source modular system developed in Python for running and cycling data assimilation (DA) systems. It is organised around three super classes for numerical model management, assimilation schemes and observation instruments. Any new item can be easily plugged in by defining a child class that will override as many methods as necessary. Non intrusive, Smurf can be used in any applicative domain for numerical models written in any language.

A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filter (PKF). The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advection–diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advection–diffusion dynamics.

The SWOT (Surface Water and Ocean Topography) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in order to improve the dynamics of continental waters over the Garonne catchment, one of the major French catchments. The aim of this paper is to demonstrate that the sequential assimilation of SWOT-like river depths allows the correction of river bed roughness coefficients and thus simulated river depths. An extended Kalman filter is implemented and the data assimilation strategy was applied to four experiments of gradually increasing complexity regarding observation and model error over the 1995–2000 period. With respect to a “true” river state, assimilating river depths allows the proper retrieval of constant and spatially distributed roughness coefficients with a root mean square error of 1 m1/3 s−1, and the estimation of associated river depths. It was also shown that river depth differences can be assimilated, resulting in a higher root mean square error for roughness coefficients with respect to the true river state. Finally, the last experiment shows how one can take into account more realistic sources of SWOT error measurements, in particular the importance of the estimation of the tropospheric water content in the process.