Although the uncertainty associated with hydrolometeorological prediction can be estimated via classical approach in a batch processing scheme, to take the best advantage of the temporal aggregation of information, sequential estimation is suggested where better compliance of model output with observations can be established. Emerging technologies in Bayesian estimation within the framework of sequential Monte Carlo provides a platform for improved estimation of hydrologic model components and uncertainty assessment by complete representation of forecast and analysis probability distributions. In this study we present the novel sequential Bayesian filtering approach using particle filter for uncertainty estimation of desired elements applicable in hydrologic and land surface models. Particle filters have originally been developed for estimating recursively the prognostic (state) variables posterior distribution while here we illustrate how we can extend the filter’s applicability to approximate the parameters posterior distribution. Unlike the recursive procedure rooted in Kalman filter, the particle filter is not limited to closure at the second order moment; therefore any expected value can be extracted from the conditional distribution. We demonstrate the capability and usefulness of the procedure by assimilating the streamflow observation into a parsimonious conceptual hydrologic model where prediction uncertainty as the final product of the methodology is obtained.