Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

Water

Tao Zhang¹  Bo Qu²  Xingnan Zhang²  Yuanhao Fang²  Florian Pappenberger³ 

[1] Bureau of Hydrology, Changjiang Water Resources Commission, No. 1863 Jiefang Avenue, Wuhan 430010, China;College of Hydrology and Water Resources, Hohai University, No. 1 Xikang Road, Nanjing 210098, China;European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK;

关键词: multi-model grand ensemble forecasts;    Bayesian Model Averaging;    TIGGE;    Xinanjiang model;    Fu River basin;   

DOI  :  10.3390/w9020074

来源: DOAJ

【 摘 要 】

Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members), using seven different “THORPEX Interactive Grand Global Ensemble” (TIGGE) weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs), over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.

【 授权许可】

Unknown