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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 2
Hydrol. Earth Syst. Sci., 13, 259–271, 2009
https://doi.org/10.5194/hess-13-259-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Remote sensing in hydrological sciences

Hydrol. Earth Syst. Sci., 13, 259–271, 2009
https://doi.org/10.5194/hess-13-259-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Feb 2009

23 Feb 2009

Matching ERS scatterometer based soil moisture patterns with simulations of a conceptual dual layer hydrologic model over Austria

J. Parajka1,3, V. Naeimi2, G. Blöschl1, and J. Komma1 J. Parajka et al.
  • 1Institute for Hydraulic and Water Resources Engineering, Vienna University of Technology, Austria
  • 2Institute of Photogrammetry and Remote Sensing, Vienna University of Technology, Austria
  • 3Institute of Hydrology, Slovak Academy of Sciences, Bratislava, Slovakia

Abstract. This study compares ERS scatterometer top soil moisture observations with simulations of a dual layer conceptual hydrologic model. The comparison is performed for 148 Austrian catchments in the period 1991–2000. On average, about 5 to 7 scatterometer images per month with a mean spatial coverage of about 37% are available. The results indicate that the agreement between the two top soil moisture estimates changes with the season and the weight given to the scatterometer in hydrologic model calibration. The hydrologic model generally simulates larger top soil moisture values than are observed by the scatterometer. The differences tend to be smaller for lower altitudes and the winter season. The average correlation between the two estimates is more than 0.5 in the period from July to October, and about 0.2 in the winter months, depending on the period and calibration setting. Using both ERS scatterometer based soil moisture and runoff for model calibration provides more robust model parameters than using either of these two sources of information.

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