This study introduces a novel multivariate data assimilation framework for snow-dominant basins to enhance hydrologic prediction capabilities. Applied to 94 snow-dominant watersheds from the CAMELS dataset over a 30-year period, our approach first implements a multivariate calibration strategy that optimizes Snow-17 model parameters using observed snow water equivalent (SWE) and SACSMA model parameters using observed streamflow. This calibration strategy improved SWE prediction while maintaining streamflow performance. Following this calibration, we implemented a multivariate data assimilation using the Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) algorithm. This system simultaneously integrates both SWE and streamflow observations with the models, enhancing prediction accuracy according to multiple performance measure. The framework demonstrates exceptional seasonal performance, with peak SWE prediction skill in winter and improved accuracy in predicting peak flow during spring snowmelt. Over 90% of the basins showed improved peak flow prediction, critical for water resources planning and management. A comprehensive assessment across diverse geographic regions, hydroclimatic conditions, and multiple metrics confirms the framework’s robustness and versatility….Read more