Uncertainties in hydrological simulations can be quantified and reduced through data assimilation (DA). This study explores strategies for assimilating soil moisture (SM) data from Cosmic-Ray Neutron Sensors (CRNS) and groundwater level (GWL) data into the Terrestrial System Modeling Platform (TSMP), which integrates both land surface and subsurface processes. DA experiments incorporating both state and parameter estimation were performed using the localized Ensemble Kalman Filter (LEnKF) within a representative catchment in Germany over the period 2016 to 2018, with cross-validation conducted on non-overlapping years. Univariate assimilation of SM reduced the unbiased root mean square error (ubRMSE) by approximately 50 %, while univariate assimilation of GWL achieved up to a 70 % reduction in ubRMSE at assimilation sites. Improvements in GWL estimates extended up to 5 km from the assimilation points, with ubRMSE reductions ranging between 2 % and 50 %. However, assimilating GWL independently had a negative effect on SM representation, and similarly, assimilating SM alone degraded GWL predictions. To address these issues, a novel multivariate DA framework was developed, enabling SM and GWL to be assimilated independently through separate modules. Groundwater data were used to constrain the water table position, thereby improving the estimation of the boundary between unsaturated and saturated zones and allowing updates to hydraulic conditions within the saturated zone. Meanwhile, SM data improved the representation of hydrological processes in the unsaturated zone. The multivariate assimilation approach resulted in comparable improvements in GWL, SM, and evapotranspiration (ET) at the assimilation sites. Moreover, including parameter estimation alongside state updating further reduced the ubRMSE by up to 17 %.