This study presents a land surface model designed to enhance the accuracy of streamflow predictions over Alberta, Canada. The model aims to assimilate potential observations from the Surface Water and Ocean Topography (SWOT) mission. As a result, it will yield an improved estimation of streamflow over the entire river network in the region. The study implements MESH (Modélisation Environmentale Communautaire—Surface and Hydrology) as the land surface model. MESH is a highly sophisticated package of modules that represent the dynamic processes of energy and water balance in vegetation, snow and soil from atmospheric forcings. An innovative aspect of this project lies in the incorporation of hydrological data assimilation techniques, facilitating the integration of SWOT-derived information. The model aims to assimilate high-resolution data into its framework, providing unprecedented insights into land surface dynamics. The aim of this project is to integrate modeling, in-situ observations, and SWOT-derived products into a data analysis process for potential operational use in Alberta. Utilizing established geo-fabric and a newly devised model-agnostic framework, we can create reliable and replicable workflows. This research contributes to advancing the field of hydrology by offering a robust framework that combines state-of-the-art land surface modeling with cutting-edge observational data. The proposed model not only refines streamflow predictions over Alberta, but also enhances our ability to comprehend and predict broader hydrological and meteorological patterns, ultimately supporting informed decision-making for water resource management and environmental planning in the region.