Understanding the dynamics of terrestrial water storage (TWS), and its components such as surface soil moisture (SSM) and groundwater, is important for the Brahmaputra River Basin, where water resources are expected to experience increasing demand and are highly vulnerable to extreme hydrological events and climate change. However, water storage dynamics are complex and difficult to capture by state-of-the-art large-scale hydrological models. In this study, we implement a multi-variate daily TWS and daily SSM sequential Data Assimilation (DA) with the aim of improving model-derived water storage dynamics. In our methodology, we propose a model space covariance localization approach that is compared with three other approaches used in the previous literature. The results show that this new approach is the only one to effectively mitigate cross-variable influences along the vertical water storage profile, which have been reported as one of the main challenges of multi-variate land DA. A validation of the multi-variate DA estimates (for the period 2004–2015) indicates that more realistic decadal trend and inter-annual variability are introduced into the groundwater estimates, increasing the correlation coefficients with the Standardized Precipitation Evapotranspiration Index and observed groundwater levels by +0.24 to +0.54 correlation points. With respect to SSM, DA induces a general phase shift, especially around mountain areas. Improved land water storage estimates reveal a land water decline of 70.9 GT per decade for the period 2004–2015 in the Brahmaputra River basin, which constitutes approximately half of the TWS decline in that period, with the other half caused by glacier retreat (67.5 GT per decade).