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| DOI：10.13522/j.cnki.ggps.2019215 |
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| Using Spatial Bayesian Hierarchical Model to Analyze Extreme Climate Indexes in Huai River Basin |
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WANG Huaijun PAN Yingping, FENG Ru, XIAO Mingxian,WANG Huaijun PAN Yingping, FENG Ru, XIAO Mingxian,WANG Huaijun PAN Yingping, FENG Ru, XIAO Mingxian,WANG Huaijun PAN Yingping, FENG Ru, XIAO Mingxian
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1. School of Urban and Environmental Sciences, Huaiyin Normal University, Huaian 223300, China; 2. State Key Laboratory of Hydrology-water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; 3. Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China;4. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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| Abstract: |
| 【Objective】The objective of this paper is to test the feasibility of using spatial Bayesian hierarchical model to estimate the spatiotemporal change in extreme climate events in the Huai river Basin.【Method】The hierarchical Bayesian spatial model, HKEVP, was used to capture the spatial variation in extreme climates indexes, with the longitude and transverse coordinates and its associated latitude as independent variables and the generalized extreme value distribution (GEV) as the marginal distribution. We first calculated the parameters in the model using the Markov Chain Monte Carlo method (MCMC) and then applied the model to analyze the maximum daily precipitation (RX1day) and highest daily temperature (TXx) in each month from 1960—2015. The GEV parameters and the return level derived from the model were compared to those calculated from the maximum likelihood estimates.【Result】The hierarchical Bayesian model was adequate for estimating the GEV parameters for locations without observation, and the RX1day return level at different return periods increased from northwest to southeast in the basin. The return level of TXx was longitudinally zonal and its value was lower in the east than in the west.【Conclusion】The proposed spatiotemporal model for estimating extreme climate indexes is able to obtain the return levels of the extreme values at ungauged station, and the results improved our understanding of the spatiotemporal change in extreme climate indexes in Huai River Basin. |
| Key words: extreme climate indexes; Bayesian hierarchical model; spatial extremes; Huai river basin |
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