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Volume 20, issue 9 | Copyright

Special issue: Sub-seasonal to seasonal hydrological forecasting

Hydrol. Earth Syst. Sci., 20, 3601-3618, 2016
https://doi.org/10.5194/hess-20-3601-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Sep 2016

Research article | 06 Sep 2016

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

Louise Crochemore1,a, Maria-Helena Ramos1, and Florian Pappenberger2,3 Louise Crochemore et al.
  • 1Irstea, Hydrosystems and Bioprocesses Research Unit, 1 rue Pierre Gilles de Gennes, 92 761, Antony, France
  • 2ECMWF, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading, RG2 9AX, UK
  • 3School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK
  • anow at: Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden

Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias-corrected precipitation and streamflow ensemble forecasts in 16 catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e. ESP method) is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contributes mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

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This study investigates the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. Eight variants of bias correction approaches based on the linear scaling and the distribution mapping methods are applied to the precipitation forecasts prior to generating the streamflow forecasts. One of the main results of the study is that distribution mapping of daily values is successful in improving forecast reliability.
This study investigates the way bias correcting precipitation forecasts can improve the skill of...
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