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Water 2017, 9(2), 74; doi:10.3390/w9020074

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

1
College of Hydrology and Water Resources, Hohai University, No. 1 Xikang Road, Nanjing 210098, China
2
European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK
3
National Cooperative Innovation Center for Water Safety & Hydro-Science, Hohai University, No. 1 Xikang Road, Nanjing 210098, China
4
Bureau of Hydrology, Changjiang Water Resources Commission, No. 1863 Jiefang Avenue, Wuhan 430010, China
*
Author to whom correspondence should be addressed.
Academic Editor: Karl-Erich Lindenschmidt
Received: 14 November 2016 / Revised: 24 December 2016 / Accepted: 17 January 2017 / Published: 24 January 2017
View Full-Text   |   Download PDF [5735 KB, uploaded 24 January 2017]   |  

Abstract

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. View Full-Text
Keywords: multi-model grand ensemble forecasts; Bayesian Model Averaging; TIGGE; Xinanjiang model; Fu River basin multi-model grand ensemble forecasts; Bayesian Model Averaging; TIGGE; Xinanjiang model; Fu River basin
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Qu, B.; Zhang, X.; Pappenberger, F.; Zhang, T.; Fang, Y. Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging. Water 2017, 9, 74.

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