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Characteristics of daily extreme wind gusts on the Qinghai-Tibet Plateau, China |
Zhengyi YAO1, Xiaoying LI1, Jianhua XIAO1,2,*() |
1 Key Laboratory of Desert and Desertification, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 2 Gansu Center for Sand Hazard Reduction Engineering and Technology, Lanzhou 730000, China |
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Abstract Severe wind is a major natural hazard and a main driver of desertification on the Qinghai-Tibet Plateau. Generally, studies of Qinghai-Tibet Plateau's wind climatology focus on mean wind speeds and its gust speeds have been seldom investigated. Here, we used observed daily maximum gust speeds from a 95-station network over a 5-year period (2008-2012) to analyze the characteristics of extreme wind speeds and directions by fitting Weibull and Gumbel distributions. The results indicated the spatial distribution of extreme wind speeds and their direction on the Qinghai-Tibet Plateau is highly variable, with its western portion prone to greater mean speeds of extreme wind gusts than its eastern portion. Maximum extreme wind speeds of 30.9, 33.0, and 32.2 m/s were recorded at three stations along the Qinghai Tibet Railway. Severe winds occurred mostly from November to April, caused primarily by the westerly jet stream. Terrain greatly enhances the wind speeds. Our spatial analysis of wind speed data showed that the wind speeds increased exponentially with an increasing altitude. We also assessed the local wind hazard by calculating the return periods of maximum wind gusts from the observational data based on the statistical extreme value distributions of these wind speeds. Further attention should be given to those stations where the yearly maximum daily extreme wind speed increased at a rate greater than that of mean value of daily extreme wind speeds. Severe extreme wind events in these regions of the plateau are likely to become more frequent. Consequently, building structural designers working in these areas should use updated extreme wind data rather than relying on past data alone.
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Received: 23 February 2017
Published: 10 October 2018
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