Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts

Meteorological centers constantly make efforts to provide more skillful seasonal climate forecast, which has the potential to improve streamflow forecasts. A common approach is to bias-correct the general circulation model (GCM) forecasts prior to generating the streamflow forecasts. Less attention has been paid to the issue of bias-corrected streamflow forecasts that were generated by GCM forecasts. This study compares the effect of bias-corrected GCM forecasts and bias-corrected streamflow outputs on the improvement of streamflow forecast efficiency. Based on the Upper Hanjiang River Basin (UHRB), the authors compare three forecasting scenarios: original forecasts, bias-corrected precipitation forecasts and bias-corrected streamflow forecasts. We apply the quantile mapping method to bias-correct precipitation forecasts and the linear scaling method to bias-correct the original streamflow forecasts. A semi-distributed hydrological model, namely the Tsinghua Representative Elementary Watershed (THREW) model, is employed to transform precipitation into streamflow. The effects of bias-corrected precipitation and bias-corrected streamflow are assessed in terms of accuracy, reliability, sharpness and overall performance. The results show that both bias-corrected precipitation and bias-corrected streamflow can considerably increase the overall forecast skill in comparison to the original streamflow forecasts. Bias-corrected precipitation contributes mainly to improving the forecast reliability and sharpness, while bias-corrected streamflow is successful in increasing the forecast accuracy and overall performance of the ensemble forecasts