Impact of Distribution Type in Bayes Probability Flood Forecasting

Wei Li (), Jianzhong Zhou (), Huaiwei Sun, Kuaile Feng, Hairong Zhang and Muhammad Tayyab
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Wei Li: Huazhong University of Science and Technology
Jianzhong Zhou: Huazhong University of Science and Technology
Huaiwei Sun: Huazhong University of Science and Technology
Kuaile Feng: Huazhong University of Science and Technology
Hairong Zhang: Huazhong University of Science and Technology
Muhammad Tayyab: Huazhong University of Science and Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 3, No 14, 977 pages

Abstract: Abstract Bayesian forecasting system (BFS) is widely applied to the hydrological forecast. Hydrological forecast processor (HUP), a key part of the Bayesian probability prediction, is conducted at the assumption that the rainfall is certain, which can simultaneously quantify the uncertainty of hydrological model and parameter. In the HUP, the runoff is usually assumed to obey Logweibull distribution or Normal distribution. However, Distribution type of the runoff is not certain at different areas, and there are few distribution types of HUP in existence. So common distribution types are needed to develop the HUP to provide an effective forecast result. In this paper, Nonparametric kernel density estimation, Pearson III and Empirical distribution were introduced as the prior distribution of HUP to eliminate the parameter uncertainty of probability density function. Also, the five distributions were compared in this study to get the diversity of distribution types and search the best appropriate distribution type. The 52 floods during 2004a-2014a of ZheXi basin are employed to calibrate and validate the different distribution types of BFS. The results show that the LogWeibull and Empirical Bayesian probabilistic model has the best performance on average results compared with the other four distribution models. Meanwhile the other distribution types proposed in this study have the similar ability on interval width and the containing rate of probability forecasting results. This demonstrates that more new distributions are required to make BFS more robust.

Keywords: The nonparametric kernel density estimation; Bayesian probability forecast; The normal quantile transform (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11269-016-1557-6

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