Homogeneous Regionalization via L-Moments for Mumbai City, India

Amit Sharad Parchure, S. Gedam
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引用次数: 2

Abstract

This study identified homogeneous rainfall regions using a combination of cluster analysis and the L-moments approach. The L-moments of heavy rainfall events of various durations (0.25, 1, 6, 12, 24, 48, 72, 96, and 120 h) were analysed using seasonal (June-September) rainfall measurements at 47 meteorological stations over the period 20062016. In the primary phase of this study, the homogeneity of Mumbai as a single region was examined by statistical testing (based on L-moment ratios and variations of the L-moments). The K-means clustering approach was applied to the site characteristics to identify candidate regions. Based on the most appropriate distribution, these regions were subsequently tested using at-site statistics to form the final homogeneous regions. For durations above 1h, the regionalisation procedure delineated six clusters of similarly behaved rain gauges, where each cluster represented one separate class of variables for the rain gauges. However, for durations below 1h, the regionalisation procedure was not efficient in the sense of identifying homogeneous regions for rainfall. Furthermore, the final clusters confirmed that the spatial variation of rainfall was related to the complex topography, which comprised flatlands (below or at mean sea level), urban areas with high rise buildings, and mountainous and hilly areas.
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基于L-Moments的印度孟买同质区域化
本研究采用聚类分析和l矩方法相结合的方法确定了均匀降雨区域。利用2006 - 2016年期间47个气象站的季节性(6 - 9月)降水资料,分析了不同持续时间(0.25、1、6、12、24、48、72、96和120 h)的强降雨事件的l矩。在本研究的初级阶段,通过统计检验(基于l矩比和l矩的变化)检验了孟买作为一个单一地区的同质性。将k均值聚类方法应用于站点特征来识别候选区域。根据最合适的分布,随后使用现场统计对这些区域进行测试,以形成最终的均匀区域。在持续时间超过1小时的情况下,分区程序划定了6组表现相似的雨量计,每组雨量计代表一个单独的变量类别。然而,对于低于1小时的持续时间,分区程序在确定降雨均匀区域方面效率不高。此外,最终的聚类还证实了降雨的空间变化与复杂的地形有关,包括平原(低于或平均海平面)、高层建筑的城市地区以及山地和丘陵地区。
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