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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 7
Hydrol. Earth Syst. Sci., 13, 1031–1043, 2009
https://doi.org/10.5194/hess-13-1031-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 13, 1031–1043, 2009
https://doi.org/10.5194/hess-13-1031-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Jul 2009

08 Jul 2009

Evaluation of a probabilistic hydrometeorological forecast system

S. Jaun1,2 and B. Ahrens3 S. Jaun and B. Ahrens
  • 1Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
  • 2Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
  • 3Institute for Atmosphere and Environment, Goethe-University Frankfurt a. M., Germany

Abstract. Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m.

Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with appropriate spread. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.

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