鄭克聲2006-07-252018-06-292006-07-252018-06-2920002000-07-31http://ntur.lib.ntu.edu.tw//handle/246246/10690為符合頻率分析是針對年最大值序列計算的 特性，且希望反應出真實暴雨事件特性，在研究中 選取年最大值事件進行雨型設計。本研究擬以具自 我相似性之高斯馬可夫模式模擬暴雨事件之降雨歷 程，該模式為非定常性之一階馬可夫歷程。經由此 模式我們可推演出具有最大概似度之設計暴雨雨 型，並進而利用自我相似性，建立不同延時暴雨雨 型間之轉換模式。In this study we propose a simple-scaling, Gaussian- Markov model for rainfall process of storm events. We explain the simple-scaling characteristics in terms of the IDF curves. Rainfall depths of storm events were initially normalized with respect to storm duration and total depth. Our Gaussian- Markov model is a nonstationary first-order Markov process. We proved that, under simple-scaling assumption, the normalized rain rate (expressed in percentages ) process is an IID random process and thus normalized rainfall data of different storm durations can be combined together for parameter estimations. We showed that the maximum likelihood estimator of the dimensionless hyetograph is the average hyetograph.application/pdf60653 bytesapplication/pdfzh-TW國立臺灣大學生物環境系統工程學系暨研究所設計暴雨雨型自我相似高斯馬可夫歷程設計暴雨hyetographself-similarityGaussian-Markov processdesign stormreporthttp://ntur.lib.ntu.edu.tw/bitstream/246246/10690/1/892313B002038.pdf