Articles | Volume 15, issue 7
Hydrol. Earth Syst. Sci., 15, 2349–2365, 2011

Special issue: Latest advances and developments in data assimilation for...

Hydrol. Earth Syst. Sci., 15, 2349–2365, 2011

Research article 21 Jul 2011

Research article | 21 Jul 2011

Assimilating SAR-derived water level data into a hydraulic model: a case study

L. Giustarini1, P. Matgen1,4, R. Hostache1, M. Montanari1, D. Plaza2, V. R. N. Pauwels2, G. J. M. De Lannoy2, R. De Keyser3, L. Pfister1, L. Hoffmann1, and H. H. G. Savenije4 L. Giustarini et al.
  • 1Centre de Recherche Public – Gabriel Lippmann, Département Environnement et Agro-biotechnologies, Belvaux, Luxembourg
  • 2Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
  • 3Department of Electrical Energy – Systems and Automation, Ghent University, Ghent, Belgium
  • 4Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, GA Delft, The Netherlands

Abstract. Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.