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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-303
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-303
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Jul 2020

15 Jul 2020

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This preprint is currently under review for the journal HESS.

Improving Soil Moisture Prediction of a High–Resolution Land Surface Model by Parameterising Pedotransfer Functions through Assimilation of SMAP Satellite Data

Ewan Pinnington1, Javier Amezcua1, Elizabeth Cooper2, Simon Dadson2,3, Rich Ellis2, Jian Peng3, Emma Robinson2, and Tristan Quaife1 Ewan Pinnington et al.
  • 1National Center for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
  • 2UK Centre for Ecology and Hydrology, Wallingford, UK
  • 3University of Oxford, Oxford, UK

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear, because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure we find improved estimates of soil moisture for the JULES land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK). The spatial resolution of these COSMOS probes is much more representative of the 1 km model grid than traditional point based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain we find an average 22 % reduction in root-mean squared error, a 16 % reduction in unbiased root-mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Ewan Pinnington et al.

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Ewan Pinnington et al.

Ewan Pinnington et al.

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Latest update: 26 Oct 2020
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Short summary
Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth’s surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in-situ soil moisture sensors after combining model predictions with satellite observations.
Land surface models are important tools for translating meteorological forecasts and reanalyses...
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