SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

H. Lievens, S. K. Tomer, A. Al Bitar, G. J.M. De Lannoy, M. Drusch, G. Dumedah, H. J. Hendricks Franssen, Y. H. Kerr, B. Martens, M. Pan, J. K. Roundy, H. Vereecken, J. P. Walker, Eric F. Wood, N. E.C. Verhoest, V. R.N. Pauwels

Research output: Contribution to journalArticlepeer-review

185 Scopus citations


This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058m3/m3 to 0.046m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.

Original languageEnglish (US)
Pages (from-to)146-162
Number of pages17
JournalRemote Sensing of Environment
StatePublished - Oct 1 2015

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences


  • Data assimilation
  • Murray Darling Basin
  • SMOS
  • Soil moisture
  • Streamflow


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