臺灣博碩士論文加值系統

本研究首先針對歷史紀錄之乾溼日移轉機率進行統計檢定，發現乾溼日移轉機率確實會隨著時間而變化，因此嘗試探討乾溼日移轉機率在氣候變遷下對水文特性之影響，並建立「可反應氣候變遷條件之修正型氣象繁衍模式」(以下簡稱修正型氣象繁衍模式)做為分析工具。本研究採用隨機森林、區別分析與支撐向量機分別建立乾溼日分類模式與雨量回歸模式並比較其優劣，且由最佳分類模式推算未來氣候變遷情境的乾溼日移轉機率，並由最佳回歸模式推算未來氣候變遷情境之平均月雨量以供修正型氣象繁衍模式使用。為探討乾溼日移轉機率在氣候變遷下對水文特性之影響，本研究將修正型與傳統型氣象繁衍模式之未來繁衍結果相互比較，並將繁衍結果做為降雨-逕流模式之輸入，比較修正型與傳統型氣象繁衍模式對未來水庫入流量模擬結果之差異。
研究結果發現：乾溼日分類與雨量回歸模式中均以隨機森林模擬的結果最為理想，能適切反應實際之降雨情況。不論是乾溼日移轉機率或是平均月雨量之分析結果均顯示：未來相較於基期而言豐枯差異更加明顯。且由未來繁衍結果可以發現：修正型與傳統型氣象繁衍模式在低雨量之模擬結果差異不大，但在極端雨量部份則視不同GCM而有不同之結果。在未來水庫入流量的部份，不論修正型與傳統型氣象繁衍模式之模擬結果，其豐枯差異情形均比基期更為懸殊，其中修正型會比傳統型氣象繁衍模式之模擬結果更加劇烈。

This study first find the dry-wet transition probabilities (DWTPs) in our study area have increasing/decreasing trends with time based on the statistical tests during baseline period. That may imply it is necessary to consider the future DWTPs into the weather generator (WGs) when applying WGs for projecting future rainfalls. Therefore, this study try to develop a WG with the ability of considering DWTPs under climate change conditions (called“the modified WG”hereafter). Modified WG is further used to assess the effects of dry-wet transition probabilities on hydrological characteristics under climate change. Three methods, including random forests, discriminate analysis and support vector machine are applied to develop dry/wet-day classification models. The best one among the three classification models is chosen to derive the future DWTPs. Besides, the mean monthly rainfall is required to be the input of WG for rainfall projection. Therefore, the rainfall regression models were built by the methods of random forests, linear regression and support vector regression. The best regression result is to be the input of WG for rainfall projection. To investigating the effects of DWTPs on hydrological characteristics under climate change, the differences of future rainfall projection and inflow were compared between the modified and conventional WGs.
The analysis results show that the random forests has the best performances in classification and daily rainfall regression. Based on the analysis of DWTPs and monthly mean rainfall, the difference of rainfall between the dry and wet seasons during the future period could be more significant than during the baseline period. Future rainfall projection with low rainfall intensity has no difference between the modified and conventional WGs, but the difference of extreme rainfall between two WGs is significant and dependent on model of GCM. The future reservoir inflow between dry and wet seasons could be more significant than those of baseline period. The modified WG show a larger difference between the dry and wet seasons than conventional WG.

摘要 I
Abstract II
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1-1前言 1
1-2研究動機與目的 2
1-3本文組織與架構 3
第二章 文獻回顧 6
第三章 研究區域資料處理與分析 14
3-1研究區域概述 14
3-2資料說明與處理 17
3-2-1水文資料概述 17
3-2-2情境格網資料 18
3-3歷史乾溼日移轉機率之趨勢分析 20
第四章 研究方法 27
4-1可反應氣候變遷條件之修正型氣象繁衍模式 27
4-2隨機森林 29
4-3區別分析 32
4-4支撐向量機 32
4-5降雨-逕流模式 34
第五章 水文特性之分析 41
5-1乾溼日分類模式之建立與比較 42
5-2雨量回歸模式之建立 65
5-3乾溼日移轉機率變化對水文特性之影響 77
第六章 結論與建議 85
6-1結論 85
6-2建議 87
參考文獻 88
附錄A 乾溼日期距機率分布結果 93

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