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A versatile data assimilation scheme for remote sensing snow cover products and meteorological data was developed, aimed at operational use for short-term runoff forecasting. Spatial and temporal homogenisation of the various input data sets is carried out, including meteorological point measurements from stations, numerical weather predictions, and snow maps from satellites. The meteorological data are downscaled to match the scale of the snow products, derived from optical satellite images of MODIS and from radar images of Envisat ASAR. Snow maps from SAR and optical imagery reveal systematic differences which need to be compensated for use in snowmelt models. We applied a semi-distributed model to demonstrate the use of satellite snow cover data for short-term runoff forecasting. During the snowmelt periods 2005 and 2006 daily runoff forecasts were made for the drainage basin Ötztal (Austrian Alps) for time lags up to 6 days. Because satellite images were obtained intermittently, prognostic equations were applied to predict the daily snow cover extent for model update. Runoff forecasting uncertainty is estimated by using not only deterministic meteorological predictions as input, but also 51 ensemble predictions of the EPS system of the European Centre for Medium Range Weather Forecast. This is particularly important for water management tasks, because meteorological forecasts are the main error source for runoff prediction, as confirmed by simulation studies with modified input data from the various sources. Evaluation of the runoff forecasts reveals good agreement with the measurements, confirming the usefulness of the selected data processing and assimilation scheme for operational use. [Copyright &y& Elsevier]