Abstract

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.

Highlights

  • Due to its all weather and day and night capability, Synthetic Aperture Radar (SAR) is regarded as the most promising technology to monitor floods from space

  • The hydrodynamic model, built from the 144 surveyed cross sections, was used to simulate water levels along the river reach, considering as inputs the ensemble of 64 hydrographs generated by the CLM 2.0

  • When a satellite observation becomes available, weights are computed for all the simulations at any cross section

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Summary

Introduction

Due to its all weather and day and night capability, Synthetic Aperture Radar (SAR) is regarded as the most promising technology to monitor floods from space. Remote sensing data have become more frequent and rapidly available and accuracies of SAR-derived flood detection have improved due to higher spatial resolutions and enhanced image processing algorithms. There is a growing pressure on the scientific community to find new ways to use the increased volume and accuracy of remote sensing data in order to improve near realtime flood monitoring and prediction applications (Di Baldassarre et al, 2009). Hostache et al, 2009; Raclot, 2006; Schumann et al, 2007) Direct measuring techniques such as those from the proposed swath altimetry “Surface Water and Ocean Topography” (SWOT) mission (Alsdorf et al, 2007) represent a potential enhancement of the indirect measuring techniques as they enable the systematic acquisition of elevation data

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