Understanding the seasonal storage of water in snow and glaciers -- and how and when it contributes to streamflow -- is essential for predicting water availability and ensuring its sustainable use in mountain regions. This project aims to improve streamflow simulations by combining remote sensing observations with modelling techniques. The ultimate goal is to provide insights that would enhance the efficiency and planning of hydropower production, while also benefiting other water-dependent sectors. Different Synthetic Aperture Radar (SAR) satellite observation types, including C- and X-band backscatter and polarimetry and L-band interferometry, show promise for retrieving snow mass. The first objective is to compare these different approaches and assess their synergistic value. The second objective is to explore whether these SAR observations can be leveraged to assess glacier mass balance by analysing time-series signatures of winter snow accumulation and end-of-season melt. The combination of SAR with state-of-the-art mass balance estimates from optical remote sensing techniques based on proxies such as the equilibrium line altitude and snow-covered area fraction will also be investigated for its ability to improve sub-seasonal mass balance estimates. The final goal is to assess how these innovative satellite observations of snow and glacier properties can benefit streamflow simulations through data assimilation into a coupled snow-glacier and hydrological model.