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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Soil moisture estimates from land surface models are important for forecasting floods, droughts, weather and climate trends. We show that by combining model estimates of soil moisture with measurements from large-scale, ground based sensors we can improve the performance of the land surface model in predicting soil moisture values.
Preprints
https://doi.org/10.5194/hess-2020-359
https://doi.org/10.5194/hess-2020-359

  06 Aug 2020

06 Aug 2020

Review status: a revised version of this preprint is currently under review for the journal HESS.

Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

Elizabeth Cooper1, Eleanor Blyth1, Hollie Cooper1, Rich Ellis1, Ewan Pinnington3, and Simon J. Dadson1,2 Elizabeth Cooper et al.
  • 1UK Centre for Ecology and Hydrology, Wallingford, UK
  • 2School of Geography and the Environment, South Parks Road, Oxford OX1 3QY
  • 3National Center for Earth Observation, Department of Meteorology, University of Reading, Reading, UK

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

Elizabeth Cooper et al.

 
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Elizabeth Cooper et al.

Elizabeth Cooper et al.

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Short summary
Soil moisture estimates from land surface models are important for forecasting floods, droughts, weather and climate trends. We show that by combining model estimates of soil moisture with measurements from large-scale, ground based sensors we can improve the performance of the land surface model in predicting soil moisture values.
Citation