Development and application of time correlated hourly rainfall generator for short-duration rainfall event
碩士 === 逢甲大學 === 水利工程與資源保育學系 === 107 === This study developed three time correlated hourly rainfall generators for short-duration rainfall event. The data of short-duration rainfall events were generated based on the rainfall characteristics of observed rainfall data from Taipei and Taichung meteorol...
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ndltd-TW-107FCU003980112019-07-16T03:45:10Z http://ndltd.ncl.edu.tw/handle/25gy5v Development and application of time correlated hourly rainfall generator for short-duration rainfall event 具時間相關性之短延時時雨量繁衍模式研發與應用 HSU,CHIH-YU 許智育 碩士 逢甲大學 水利工程與資源保育學系 107 This study developed three time correlated hourly rainfall generators for short-duration rainfall event. The data of short-duration rainfall events were generated based on the rainfall characteristics of observed rainfall data from Taipei and Taichung meteorological stations. Rainfall characteristics between observations and model generated data were compared. Comparison results showed that the model that uses Markov chain to account for rainfall correlation can produce generated rainfall with average, standard deviation, skewness and correlation coefficient closest to observations. In addition, this study used the rainfall generator and climate change scenarios to project short-duration rainfall data under climate change. The scenarios under RCP 2.6 and RCP 8.5 in Taipei and Taichung were adopted. Projection results indicate that short-duration rainfalls slightly decrease in January, but slightly increase in July under climate change scenarios. CHEN,SHIEN-TSUNG 陳憲宗 2019 學位論文 ; thesis 143 zh-TW |
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碩士 === 逢甲大學 === 水利工程與資源保育學系 === 107 === This study developed three time correlated hourly rainfall generators for short-duration rainfall event. The data of short-duration rainfall events were generated based on the rainfall characteristics of observed rainfall data from Taipei and Taichung meteorological stations. Rainfall characteristics between observations and model generated data were compared. Comparison results showed that the model that uses Markov chain to account for rainfall correlation can produce generated rainfall with average, standard deviation, skewness and correlation coefficient closest to observations. In addition, this study used the rainfall generator and climate change scenarios to project short-duration rainfall data under climate change. The scenarios under RCP 2.6 and RCP 8.5 in Taipei and Taichung were adopted. Projection results indicate that short-duration rainfalls slightly decrease in January, but slightly increase in July under climate change scenarios.
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CHEN,SHIEN-TSUNG |
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CHEN,SHIEN-TSUNG HSU,CHIH-YU 許智育 |
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HSU,CHIH-YU 許智育 |
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HSU,CHIH-YU 許智育 Development and application of time correlated hourly rainfall generator for short-duration rainfall event |
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HSU,CHIH-YU |
title |
Development and application of time correlated hourly rainfall generator for short-duration rainfall event |
title_short |
Development and application of time correlated hourly rainfall generator for short-duration rainfall event |
title_full |
Development and application of time correlated hourly rainfall generator for short-duration rainfall event |
title_fullStr |
Development and application of time correlated hourly rainfall generator for short-duration rainfall event |
title_full_unstemmed |
Development and application of time correlated hourly rainfall generator for short-duration rainfall event |
title_sort |
development and application of time correlated hourly rainfall generator for short-duration rainfall event |
publishDate |
2019 |
url |
http://ndltd.ncl.edu.tw/handle/25gy5v |
work_keys_str_mv |
AT hsuchihyu developmentandapplicationoftimecorrelatedhourlyrainfallgeneratorforshortdurationrainfallevent AT xǔzhìyù developmentandapplicationoftimecorrelatedhourlyrainfallgeneratorforshortdurationrainfallevent AT hsuchihyu jùshíjiānxiāngguānxìngzhīduǎnyánshíshíyǔliàngfányǎnmóshìyánfāyǔyīngyòng AT xǔzhìyù jùshíjiānxiāngguānxìngzhīduǎnyánshíshíyǔliàngfányǎnmóshìyánfāyǔyīngyòng |
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1719223736803000320 |