In this study we investigate the ability of satellite visible and infrared data to produce reliable rainfall amount estimates that could be used by hydrological models to predict streamflow for large basins. Rainfall estimates are obtained by (1) classification of clouds to raining and nonraining clouds and (2) applying a multivariate statistical model between rainfall and indices derived from the satellite observations. Satellite data corresponding to 180 randomly selected days in the period May--September 1982--1988 are used in this study that focuses on the estimation of daily rainfall. The Des Moines River basin in the midwestern United States is the application area. The correlation coefficient between model-predicted and rain gauge-observed mean areal precipitation over areas of order 10,000 km2 is found to be about 0.85. In an example application the satellite rainfall estimates are used to force the operational National Weather Service hydrologic forecast model for a subbasin of the Des Moines River basin. The model has been calibrated with rain gauge data. The results show that differences between rain gauge and satellite rainfall input generate differences in flow forecasts and upper soil water model estimates, which are a function of the antecedent soil water conditions. A companion paper <Guetter et al., this issue> quantifies the effects that the differences between rain gauge and satellite rainfall estimates have on flow and upper soil water model predictions for various spatial scales and for hydrologic models calibrated with and without satellite data. ¿ American Geophysical Union 1996