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系統識別號 U0026-2807202016193500
論文名稱(中文) 台灣西部地區年流量指標之非定常性分析
論文名稱(英文) Nonstationary analysis of annual flow indices in western Taiwan
校院名稱 成功大學
系所名稱(中) 水利及海洋工程學系
系所名稱(英) Department of Hydraulics & Ocean Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 劉奕廷
研究生(英文) Yi-Ting Liu
學號 N86071015
學位類別 碩士
語文別 中文
論文頁數 89頁
口試委員 指導教授-蕭政宗
口試委員-張麗秋
口試委員-孫建平
口試委員-陳憲宗
中文關鍵字 非定常性  年流量指標  GAMLSS  水庫指數 
英文關鍵字 Nonstationarity  Annual flow indices  GAMLSS  Reservoir index 
學科別分類
中文摘要 流量指標在防洪減災以及水資源管理上均為不可或缺的依據,然而鑒於全球氣候異常致使水文特性改變情況下,過往的定常性流量分析可能難以適用,非定常性分析遂成為現今學者聚焦之分析方法。本文利用位置、尺度與形狀參數之廣義加成模式(generalized additive model for location, scale, and shape, GAMLSS)進行非定常性流量分析,研究台灣西部地區三大流域,包含北部淡水河流域、中部濁水溪流域,以及南部高屏溪流域共18個流量站之10項年流量指標,分別為年總逕流量、年最大1日流量、年最小1日、7日、30日流量、Q5、Q25、Q50、Q75,以及Q95,探討其分別以時間及水庫指數(reservoir index)為共變數之最佳機率模式,並將以時間為共變數之分析結果與前人趨勢研究進行比較。結果顯示在18個流量站之10個年流量指標共180個項目中,gamma分布最多(54項),log-normal次之(53項),Weibull再次之(41項),三種分布佔全部指標之82.2%;此外,高達145個指標(80.6%)呈非定常性,其中又以年最小1日流量為最多,所有流量站(18站)皆屬之,而年最大1日流量為最少,僅有一半的流量站(9站),顯示在較低流量具較強烈的非定常性變化。另外,以水庫指數為共變數分析結果顯示在秀朗站年最大1日流量及Q5指標中,相較以時間為共變數的模擬結果好,其他指標則仍是時間趨勢表現較好;霞雲站以及五堵站所有指標皆是以時間為共變數表現較佳。與前人趨勢研究比較結果顯示,以GAMLSS模式模擬成果能夠捕捉其趨勢性外,能夠詳細描述局部變化,提供更多資訊。
英文摘要 Flow indices are important and robust indicators to support the decisions for water resources management and flood control. Due to the climate change and rapid urbanization, the assumption of stationary condition used in water resources planning and hydraulic design may not be applicable. In this study, the generalized additive model for location, scale, and shape (GAMLSS) is used to model the nonstationarity of 10 annual flow indices of three main river basins in western Taiwan (Dan-Shui, Zhuo-Shui, Kao-Ping), which include total runoff, maximum 1-day streamflow, minimum 1-, 7-, 30-day streamflow, Q5, Q25, Q50, Q75, and Q95.The results indicate that (1) the best probability model for fitting flow indices (180 models in total) are the gamma distribution (54 models), log-normal distribution (53 models), and Weibull distribution (41 models). Among these models, 145 models show nonstationarity. (2) The minimum 1-day streamflow of all stations exhibits nonstationarity. On the other hand, only half stations show nonstationarity in the maximum 1-day streamflow. (3) The model incorporating the reservoir index as the covariate shows better performance than the model using the time as covariate for the maximum 1-day streamflow and Q5 indices. (4) The GAMLSS framework provides not only the trend of hydro-climate series, but also point out the information of distributional changes.
論文目次 摘要... I

Extended Abstract II

目錄... XI

表目錄... XIII

圖目錄... XIV

第一章 緒論... 1

1.1 研究目的與動機... 1

1.2 相關文獻回顧... 2

1.2.1 非定常性相關研究... 2

1.2.2 以GAMLSS模式分析非定常性相關研究... 3

1.3 論文架構... 5

第二章 研究方法... 7

2.1 位置、尺度和形狀參數的廣義加成模式(GAMLSS). 7

2.1.1 模式架構... 7

2.1.2 平滑加成項... 8

2.1.3 參數估計... 9

2.1.4 候選機率分布... 9

2.2 模式診斷-蠕蟲圖... 10

2.3 最佳模式選定-AIC訊息準則... 11

2.4 水庫指數... 11

2.5 年流量指標... 12

第三章 研究地區與資料... 14

3.1 研究區域介紹... 14

3.1.1 淡水河流域... 14

3.1.2 濁水溪流域... 14

3.1.3 高屏溪流域... 14

3.2 流量資料... 15

3.3 水庫堰壩資料... 22

第四章 結果與討論... 24

4.1 以GAMLSS模式模擬流量指標之成果... 24

4.1.1 年流量指標之最佳模式... 24

4.1.2 最佳模式診斷... 28

4.1.3 年流量指標之非定常性分析... 29

4.1.4 平均值及變異數之變化型態分類... 49

4.2 結合水庫指數之非定常性分析... 50

4.2.1 最佳模式與結果分析... 50

4.2.2 不同共變數之模式比較... 54

4.3 年流量指標分析成果與前人研究比較... 56

4.4 小結... 57

第五章 結論與建議... 58

5.1 結論... 58

5.2 建議... 59

參考文獻... 61

附錄A 以時間為共變數之模式蠕蟲圖... 66

附錄B 以水庫指數為共變數之模式蠕蟲圖... 76

附錄C 以時間為共變數之模式平均值及變異數隨時間變化... 78

附錄D 以水庫指數為共變數之模式平均值及變異數 隨時間變化圖 88
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