Presented at the 2014 Aquarius/SAC-D Science Team MeetingThe impact on discharge simulation efficiency of satellite soil moisture data assimilation was assessed. Soil moisture estimation products of the instruments SMOS, Aquarius and AMSR2 were assimilated through Ensemble Kalman Filtering into two previously-calibrated conceptual hydrological models of the Gualeguay basin, located in the Argentinian temperate sub-humid flatlands. Data assimilation was used to correct the state variables of the models linearly related to the observed variable taking into account the ratio of their relative errors. The error of the observations was assumed equal to that informed by the data provider, while the error of the model estimates was estimated as the sampling variance of the Kalman ensemble. Variability in the ensemble was forced by adding zero-mean gaussian noise to the model inputs, with variances equal to their estimated observation errors. Each soil moisture product/model combination was assessed separately. Additionally, no-assimilation and discharge data assimilation runs were performed. In each case the model efficiency was computed as the sum of squared errors between observed and predicted discharge assuming different forecast lead times (0 to 3 days) for the period 7-2012 to 4-2014. Results range from minor decrease to no significant change in efficiency for soil moisture assimilation. This may be due to the impact of one or more of the many sources of uncertainty in the soil moisture retrieval techniques and/or a lack of linearity or even correlation between the observed and simulated variables. Further research steps are analyzed and future actions addressed.