Comparison of Precipitation and Streamflow Correcting for Ensemble Streamflow Forecasts

Yilu Li,Hui Lu,Hao Duan,Xiaohui Lei,Yunzhong Jiang,Fuqiang Tian

doi:10.3390/w10020177

Abstract

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.

Highlights

Streamflow forecasts play a significant role in the management of water resources [1,2,3,4].Forecasts at different time scales can provide valuable information for decision-making in water regulation
The quantile mapping method could effectively remove bias when the bias was the main deficiency demonstrated that the quantile mapping method could effectively remove bias when the bias was of the raw forecasts, e.g., European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 precipitation forecasts, Predictive Ocean–Atmosphere the main deficiency of the raw forecasts, e.g., ECMWF System 4 precipitation forecasts, Predictive
This study investigated the benefits of bias-correcting ECMWF System 4 precipitation forecasts

Summary

Introduction

Streamflow forecasts play a significant role in the management of water resources [1,2,3,4]. Forecasts at different time scales can provide valuable information for decision-making in water regulation. Seasonal streamflow forecasts contribute to a series of water resource management activities including flood preparation [5], reservoir operation [6] and drought management [7]. Two approaches are often used in seasonal streamflow forecasting, namely, statistical methods and dynamic methods [8]. Mixed methods have been applied to seasonal streamflow forecasts, owing to the advances in seasonal predictability of general circulation models (GCMs) and the use of large-scale climate features. The hydrological ensemble prediction system (HEPS) approach

Objectives

Methods

Results

Discussion

Conclusion