Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is the sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related

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... s and fluxes of the model in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) and evaluate its impact on the model's accuracy against in-situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in-situ observation increases from 0.54 (open-loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.