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The analysis of extreme rainfall events in Pekanbaru city using three-parameter generalized extreme value and generalized Pareto distribution

机译:基于三参数广义极值和广义帕累托分布的北干巴鲁市极端降雨事件分析

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Most extreme hydrological events cause severe human and material damage, such as floods and landslides. Extreme rainfall is usually defined as the maximum daily rainfall within each year. In this study, the annual maximum daily rainfalls from 1990 to 2007 are modeled for a station rainfal in Pekanbaru city. The three-parameter generalized extrem value (GEV) and generalizd Pareto (GP) distribution are considered to analized the extrem events. The paramters of these distributions are determined using L-moment method (LMOM). The goodness-of-fit (GOF) betwen empirical data and theorical distribution are then evaluated. The result shows that GEV provide best fit for station rainfall in Pekanbaru city. Based on the model that have been identified, the return levels of the GEV distribution for station rainfall and their 95% confidence interval are provided. In addition, the return period is also calculated based on the best model in this study, we can reasonably predict the risks associated the extreme event for various return periods.
机译:大多数极端的水文事件会造成严重的人身和财产损失,例如洪水和山体滑坡。通常将极端降雨定义为每年内的最大每日降雨量。在这项研究中,以北干巴鲁市的一个雨站为模型,模拟了1990年至2007年的年最大日降雨量。三参数广义极值(GEV)和广义帕累托(GP)分布被认为是对极端事件的分析。这些分布的参数是使用L矩法(LMOM)确定的。然后评估经验数据和理论分布之间的拟合优度(GOF)。结果表明,GEV最适合北干巴鲁市的车站降雨。根据已确定的模型,提供了站台降水的GEV分布的返回水平及其95%的置信区间。此外,还根据本研究中的最佳模型来计算回报期,我们可以合理地预测各种回报期与极端事件相关的风险。

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