The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method (PODEn4DVar) through R-localization. With R-localization, the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision. The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations (OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization. The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization, particularly under the imperfect model scenario. Moreover, the R-localization scheme is capable of outperforming the B-localization case to a certain extent. Further, the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.