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Water 2018, 10(2), 177;

Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts

State Key Laboratory of Hydro-science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Authors to whom correspondence should be addressed.
Received: 11 November 2017 / Revised: 25 January 2018 / Accepted: 6 February 2018 / Published: 9 February 2018
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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. View Full-Text
Keywords: bias-correcting; ECMWF System 4; quantile mapping; linear scaling; Upper Hanjiang River Basin bias-correcting; ECMWF System 4; quantile mapping; linear scaling; Upper Hanjiang River Basin

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Li, Y.; Jiang, Y.; Lei, X.; Tian, F.; Duan, H.; Lu, H. Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts. Water 2018, 10, 177.

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