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Cite this article:王怀军,潘莹萍,冯如.基于空间贝叶斯层次模型的淮河流域气候极值特征分析[J].灌溉排水学报,0,():-.
WANG Huaijun,PAN Yingping,FENG Ru.基于空间贝叶斯层次模型的淮河流域气候极值特征分析[J].灌溉排水学报,0,():-.
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DOI:
Analysis of climate extremes by the spatial Bayesian hierarchical model in Huaihe River Basin, China
WANG Huaijun1, PAN Yingping2, FENG Ru1
1.School of Urban and Environmental Sciences,Huaiyin Normal University,Huai’an;2.Faculty of Geographical Science,Beijing Normal University
Abstract:
【Objective】The objective of this study is to verify the applicability of the spatial Bayesian hierarchical model for simulating the spatial and temporal changes of extreme climate events【Method】 In this paper, a hierarchical Bayesian spatial model, named HKEVP, is applied to capture the spatial variation of the extremes with longitude, latitude and altitude as the covariates. The generalized extreme value distribution (GEV) was used as its marginal distribution. The Markov Chain Monte Carlo method (MCMC) was employed to infer the parameters of the hierarchical Bayesian model. The model is applied to the 55-year monthly maximum 1-day precipitation (RX1day) and monthly maximum value of daily maximum temperature (TXx) in the Huaihe River Basin, China. The GEV parameters and return levels derived from hierarchical Bayesian model were compared with that by the maximum likelihood estimates. 【Result】We found that the hierarchical Bayesian model behaved well in estimated GEV parameters of the unobserved locations. The RX1day return level at different return period increased from northwest to southeast of the basin; and the return level of TXx displayed a typical longitudinal zonality, with low values in the eastern basin and high values in the western basin.【Conclusion】The spatial extreme model established in this paper can obtain the return level of climate extremes of ungauged station. This result expands the study of the spatial and temporal climate extremes in the Huaihe River Basin.
Key words:  Climate extremes; Bayesian hierarchical model; Spatial extremes; Huaihe River Basin