Hydrological models, serve as indispensable tools for assessing the availability, distribution, and quality of water resources. They enable informed decision-making for ecosystem preservation, water quality management, and the development of effective water policies. A pervasive challenge in hydrological modeling, impacting the accuracy and reliability of water resource assessments and predictions is the availability of adequate and accurate input data. On a global scale, majority of the watersheds are data-limited, where model input data are unavailable at required spatial and temporal resolutions. Thus, the hydrological simulations are significantly influenced due to poor representation and characterization of the system, which in turn introduces an uncertainty in the model output as a result of the propagation of uncertainty in the input through the modelling process. The main focus of the study is to improve the reliability of the hydrological simulations in data limited watersheds. Initially, the study investigates the variability in the datasets used in data intense and data limited scenarios in a pseudo-data limited watershed, St. Joseph River watershed. The limitations of the hydrological simulations in the watershed is demonstrated and quantified. The study proposed the use of data-assimilated remote sensing product to improve the reliability of the simulations. Multiple assimilation methods for correcting the Integrated Multi-satellite Retrievals for Global Precipitation Measurement product (IMERG GPM) are performed based on Correction Factor (CF) and Power Transformation Function methods (PF). The assimilated IMERG precipitation from the best-performing method is forced into the model, and the stream flow simulations were validated with the observed flow. From the study, it is observed that the assimilation methodologies performed with the monthly statistics performed better in both (CF and PF) methods. Ensemble simulations were performed with the correction parameters from the best-performing assimilation methods. The proposed methodology was validated in Bhima River watershed, and the results indicate the potential of using assimilated remote sensing data for hydrological modeling in the data-limited watershed with improved simulation accuracy and reliability. This seminar will provide the details of the analysis and specific conclusions.