Model errors are an inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. On the other hand, data assimilation aims to reduce the prediction error by updating the initial states through minimizing the error covariance matrix between model predictions and observations. In this talk, we compare the performance of multimodel combination and data assimilation in reducing the uncertainty in streamflow predictions at daily and monthly time scales. Further, the talk will also focus on reducing uncertainty in streamflow predictions at seasonal time scales by investigating strategies that effectively combine climate forecasts available from multiple GCMs with various hydrologic models.