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

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.

Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

“…GCM-based forecasts are, however, sharper than ESP forecasts, which combined with the high biases lead to a poorer accuracy than ESP forecasts. Our results are directly comparable to those of Crochemore et al, (2016) as they also deal with seasonal streamflow forecasting 10 with ECMWF System 4 for a region located not too far from our study area. They also found GCM-based forecast to be sharper than those of the ESP.…”

“…This rather mild enhancement was also found by Crochemore et al, (2016). Moreover, postprocessing alone does a better job in removing biases in the mean, that in turn help to ameliorate issues with the statistical consistency.…”

“…A couple of studies have quantified the effects on streamflow skill by preprocessing either seasonal (Crochemore et al, 2016) or medium range forecasts (Verkade et al, 2013) forecasts. Other studies have assessed the efficiency of postprocessing streamflow forecasts only (Bogner et al, 2016;Madadgar et al, 2014;Ye et al, 2015;Zhao et al, 15 2011;Wood and Schaake, 2008).…”

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

“…Only forecasts with lead times up to 90-days were considered. In a previous study, several bias corrections were applied to System 4 precipitation forecasts and compared based on their impacts on seasonal streamflow forecasts (Crochemore et al, 2016). The study showed that the empirical distribution mapping of daily values improved the reliability of both precipitation and streamflow forecasts.…”

A coupled human–natural system to assess the operational value of weather and climate services for agriculture

“…2) aims at post-processing the forecast products. Depending on the characteristic of the forecast, we perform bias correction by means of the change factor approach (Crochemore et al, 2016) or the quantile mapping technique (Déqué, 2007). Given the strong intra-annual seasonal cycle of our study site, the bias correction was applied on a monthly basis.…”