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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 4
Hydrol. Earth Syst. Sci., 22, 2575-2588, 2018
https://doi.org/10.5194/hess-22-2575-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 22, 2575-2588, 2018
https://doi.org/10.5194/hess-22-2575-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Apr 2018

Research article | 26 Apr 2018

Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model

Ewan Pinnington1,2, Tristan Quaife1,2, and Emily Black1,3 Ewan Pinnington et al.
  • 1Department of Meteorology, University of Reading, Reading, UK
  • 2National Centre for Earth Observation, University of Reading, Reading, UK
  • 3National Centre for Atmospheric Science, University of Reading, Reading, UK

Abstract. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find an 18% reduction in unbiased root-mean-squared differences in the north of Ghana and a 21% reduction in the south of Ghana for a 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The use of an improved remotely sensed rainfall dataset contributes to 6% of this reduction in deviation for northern Ghana and 10% for southern Ghana. Improved rainfall data have the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying-down. In the north of Ghana we are able to recover improved estimates of soil texture after data assimilation. However, we are unable to do so for the south. The significant reduction in unbiased root-mean-squared difference we find after assimilating a single year of observations bodes well for the production of improved land surface model soil moisture estimates over sub-Saharan Africa.

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This paper combines satellite observations of precipitation and soil moisture to understand what key information they offer to improve land surface model estimates of soil moisture over Ghana. When both observations are combined with the chosen land surface model we reduce the unbiased root-mean-squared error in a 5-year model hindcast by 27 %; this bodes well for the production of improved soil moisture estimates over sub-Saharan Africa where subsistence farming remains prevalent.
This paper combines satellite observations of precipitation and soil moisture to understand what...
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