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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 3 | Copyright

Special issue: Earth observation and water cycle science

Hydrol. Earth Syst. Sci., 15, 771-786, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Mar 2011

Research article | 04 Mar 2011

Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data

N. Ghilain, A. Arboleda, and F. Gellens-Meulenberghs N. Ghilain et al.
  • Royal Meteorological Institute, Brussels, Belgium

Abstract. We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Longwave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced from the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h−1 and the root-mean square error is between 0.06 and 0.10 mm h−1. Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.

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