Abstract
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.
Highlights
Numerous activities with economic, environmental and political stakes benefit from knowing and anticipating future streamflow conditions at different lead times
The probability integral transform (PIT) diagram is the cumulative distribution of the PIT values, which are defined by the values of the predictive distribution function at the observations, computed at each time step
We assessed the quality of European Centre for Medium-range Weather Forecasts (ECMWF) System 4 precipitation forecasts for seasonal streamflow forecasting in 16 catchments in France
Summary
Environmental and political stakes benefit from knowing and anticipating future streamflow conditions at different lead times. Streamflow forecasting systems are frequently developed to take the latest useful information content into account (e.g. last observed discharges, soil moisture or snow cover) and to make use of numerical weather model outputs to extend the range of skilful predictions. Seasonal forecasts have shown to perfectly fall within a context of proactive risk management, for example, for drought management Extended-range forecasting systems can be valuable to help decision makers in planning long-term strategies for water storage (Crochemore et al, 2016) and to support adaptation to climate change (Winsemius et al, 2014). Several users still remain doubtful whether seasonal forecasts can be trustworthy or skilful enough to enhance decision making (Rayner et al, 2005). Several users still remain doubtful whether seasonal forecasts can be trustworthy or skilful enough to enhance decision making (Rayner et al, 2005). Lemos et al (2002) list the performance of seasonal forecasts, the misuse of seasonal forecasts by end-users and the lack of consideration of end-
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