Predictions in Ungauged Basins: Promise and Progress (Proceedings of symposium S7 held during the Seventh IAHS Scientific Assembly at Foz do Iguaçu, Brazil, April 2005). IAHS Publ. 303, 2006, 226-234.

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Assimilation of remotely sensed soil saturation levels in conceptual rainfall–runoff models

PATRICK MATGEN¹, JEAN-BAPTISTE HENRY², LUCIEN HOFFMANN¹ & LAURENT PFISTER¹

1 Public Research Centre, Gabriel Lippmann, 41 rue du Brill, L-4422 Belvaux, Luxembourg

matgen@lippmann.lu

2 Service Régional de Traitement d’Image et de Télédétection, Bd. Sébastien Brant, BP 10413,
F-67412 Illkirch Cedex, France

Abstract Owing to the nonlinearity of the rainfall–infiltration–runoff relationship, soil water content in the river basin represents a key environmental variable to be monitored for flood management purposes. In this study an attempt was made to sequentially assimilate into a simple lumped conceptual rainfall–runoff model an estimate of the soil saturation level. The estimate was obtained from: (a) field measurements of water table depth; and (b) backscattering of the radar signal emitted by active microwave sensors on board ERS-1. The assimilation scheme is based on an extended Kalman filter as both simulated and observed soil saturation states are prone to errors. The magnitude of the internal state updating thus depends on the ratio of errors on the observations and the model. The analysis of a series of ERS-1 SAR images showed that hydrologically relevant information could be retrieved from radar imagery by averaging the backscattering coefficient over clusters of pixels for which the sensitivity towards changing moisture conditions is significant. The assimilation procedure is performed on the experimental Alzette River basin (1175 km²). Improvements of model performance through data assimilation demonstrate the usefulness of field measurements and remote sensing observations in flood forecasting applications.

Key words data assimilation; flood forecasting; Kalman filter; Synthetic Aperture Radar

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