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首页> 外文期刊>Arabian journal of geosciences >Probability distribution analysis of extreme rainfall events in a flood-prone region of Mumbai, India
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Probability distribution analysis of extreme rainfall events in a flood-prone region of Mumbai, India

机译：印度孟买洪水区极端降雨事件的概率分布分布

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Mumbai, being a coastal city surrounded by sea and creeks, is vulnerable to severe flooding almost every year owing to intense rainfall. Therefore, it is vital to quantify the magnitude of extreme rainfall events, which mainly depends upon the suitability of the derived distribution and the selection of a technique for processing and analysing data. The magnitude is often estimated using window-based approach of rainfall, which does not represent actual rainfall events since the duration of the rainfall is prefixed in this approach. Hence, the goal is to determine the best-fit probability distribution for extreme storms using the 'storm event analysis' method to overcome this limitation. The distribution is analysed with two-parameter [Lognormal (LN), Gamma (G), Gumbel (GUM) and Weibull (W)] and three-parameter [generalised extreme value (GEV), generalised Pareto (GP), Log Pearson 3 (LP3) and Frechet (F)] distribution functions on two sets [maximum extreme volume (MEV) and the maximum peak intensity (MPI)] of extreme events. Goodness-of-fit tests are applied to the distribution functions of the extreme storm series (2006-2016) of 26 meteorological stations. The best score results show that the GEV distribution fits best for 29% of the stations, and 27% and 22% of the stations showed the best fit for the F and GP distributions, respectively. On a basin scale, MEV events are best described by the GEV distribution while GP or F distributions show the best fits for MPI events. However, each station must be analysed individually as none of GEV, GP or F is always the best-fitting distribution function.
机译：孟买是海上沿海城市被海和小溪环绕，甚至每年都容易受到严重的洪水，因为剧烈的降雨。因此，量化极端降雨事件的大小至关重要，这主要取决于推导分布的适用性和选择和分析数据的技术的选择。通常使用基于窗口的降雨方法估计的幅度，这不代表由于降雨的持续时间以这种方法为前缀以来的实际降雨事件。因此，目标是使用“Storm事件分析”方法来确定极端风暴的最佳概率分布，以克服这种限制。用双参数[Lognormal（LN），γ（g），gummel（gum）和weibull（w）]分析分布，三参数[广义极值（gev），广义帕骑（gp），log pearson 3 （LP3）和Frechet（F）]分布函数在两组[最大极值（MEV）和最大峰值强度（MPI）]的极端事件。适用于26个气象站的极端风暴系列（2006-2016）的配电函数。最佳得分结果表明，GEV分布适合29％的车站，27％和22％的站点分别显示出F和GP分布的最佳拟合。在盆地刻度上，MEV事件最好通过GEV分布描述，而GP或F分布显示MPI事件的最佳适合。但是，必须单独分析每个站，因为GEV，GP或F始终是最佳的分布功能。

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