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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Oct 2020

12 Oct 2020

Review status
This preprint is currently under review for the journal HESS.

Satellite soil moisture data assimilation for improved operational continental water balance prediction

Siyuan Tian1, Luigi J. Renzullo1, Robert C. Pipunic2, Julien Lerat2, Wendy Sharples2, and Chantal Donnelly2 Siyuan Tian et al.
  • 1Fenner School of Environment & Society, Australian National University, Canberra, 2601, Australia
  • 2Bureau of Meteorology, Melbourne, 3000, Australia

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 states 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.

Siyuan Tian et al.

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Siyuan Tian et al.

Siyuan Tian et al.


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Latest update: 26 Oct 2020
Publications Copernicus
Short summary
Accurate continental water storage predictions is valuable for water management practitioners and policy makers in support of water resources assessment and agriculture planning. This study improves the accuracy and spatial representation of water storage predictions of an operational water balance modelling system through data assimilation. The integration of satellite soil moisture products can provide persistent constraints in model predictions for several weeks.  
Accurate continental water storage predictions is valuable for water management practitioners...