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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-414
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-414
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 23 Aug 2019

Submitted as: research article | 23 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

Renaud Hostache1, Dominik Rains2,3, Kaniska Mallick1, Marco Chini1, Ramona Pelich1, Hans Lievens2,4, Fabrizio Fenicia5, Giovanni Corato1, Niko E. C. Verhoest2, and Patrick Matgen1 Renaud Hostache et al.
  • 1Luxembourg Institute of Science and Technology (LIST), Department Environmental Research and Innovation, Belvaux, Luxembourg
  • 2Ghent University, Department of environment, Ghent, Belgium
  • 3University of Leicester, Earth Observation Science, Department of Physics & Astronomy, Leicester, UK
  • 4KU Leuven, Department of Earth and Environmental Sciences, Heverlee, Belgium
  • 5Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Department Systems Analysis, Integrated Assessment and Modelling, Dübendorf, Switzerland

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

Renaud Hostache et al.
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Status: open (until 18 Oct 2019)
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Renaud Hostache et al.
Renaud Hostache et al.
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Short summary
The main objective of this study is to investigate how satellite microwave sensors, in particular SMOS, may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. We assimilated long time series of SMOS observations into a hydro-meteorological model and showed that this helps in significantly improving model predictions.
The main objective of this study is to investigate how satellite microwave sensors, in...
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