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研究生:李沅泓
研究生(外文):Yuan-Hung Li
論文名稱:結合經驗模態分解法與k-NN移動視窗法之新型態氣象繁衍模式與應用於新竹供水系統之氣候變遷風險評估
論文名稱(外文):Novel Weather Generator Using Empirical Mode Decomposition and K-NN Moving Window Method and Application to Climate Change Risk Assessment of Hsinchu Water Supply System
指導教授:童慶斌童慶斌引用關係
指導教授(外文):Ching-pin Tung
口試委員:林裕彬胡明哲李明旭
口試委員(外文):Yu-Pin LinMing-Che HuMing-Hsu Li
口試日期:2015-01-26
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物環境系統工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:128
中文關鍵詞:經驗模態分解法氣象繁衍模式k-NN移動視窗法氣候變遷水資源
外文關鍵詞:Empirical Mode DecompositionWeather Generationk-NNClimate ChangeWater Resource
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  氣候變遷受到許多關注,惟人為氣候變遷外,長期低頻率之自然變異也必須納入考量。氣候變遷風險評估所需要之未來氣象資料大都透過氣象繁衍模式產生,如何發展氣象繁衍模式,除了根據氣候情境產生氣象資料外,使其能重現低頻率之氣候循環特性就非常重要。本研究最主要目的在發展能重現低頻率氣候特性之新型氣象繁衍模式與應用於評估氣候變遷下新竹供水系統之風險。
本研究第一部份提出之新型氣象繁衍模式主要包括兩步驟,其一為利用經驗模態分解法EMD(Empirical Mode Decomposition) 分解出的不同的本質模態函數(Intrinsic Mode Functions),在繁衍資料過程,利用本質模態函數之包絡線與相位之關係,重新建構本質模態函數,以組成新的月雨量資料。其二為利用k-NN移動視窗法(k Nearest Neighbor - moving window),將已知的月雨量合理地降尺度至日雨量資料以及繁衍出需要的氣象參數,並進一步利用k-NN的特性建構區域內其他鄰近氣象站的氣象資料。研究方法應用於新竹地區,繁衍出的日、月氣象資料,必須能重現歷史資料的統計特性和時間序列上的特性,並且與Richardson-Type的氣象繁衍模式進行比較。確認具有表現歷史資料特性的能力後,再加入GCM氣候變遷情境,繁衍具有氣候變遷情境特性的氣象資料。
第二部分是評估氣候變遷之風險,以新型氣象繁衍模式產出的新竹地區氣象資料,套用GWLF中之水文模式,模擬新竹系統河川流量,並且進一步應用於新竹地區水資源的系統動力模式分析缺水之風險。水資源系統動力模式結合現有之水利設施,及可考量不同氣候、水文、社會經濟條件,本研究選擇不同評估指標,以及加入根據GCM模擬結果設計之氣候情境,分析未來水資源系統可能面臨的缺水風險。研究結果並且與Richardson-Type的氣象繁衍模式所呈現的新竹地區水資源系統動力模式結果進行比較,比較兩種繁衍模式在氣候風險評估結果之差異。


Climate change has drawn many attentions. However, not only human-induced climate change but also natural low frequency climate variability should be concerned. Weather generator is often used to produce weather data for climate change risk assessment. Thus, a novel weather generator needs to be developed to not only produce weather data based on climate scenarios but also reproduce the low frequency climate characteristics. The main purposes of this study is to develop a novel weather generation to preserve the low-frequency climate characteristics of the rainfall data and apply the generated data to evaluate the climate change risk of the Hsinchu water supply system.
The first part of this study is to develop a novel weather generator. The proposed weather generator includes two steps. The first step is to apply Empirical Mode Decomposition (EMD) to decompose the time series of historical monthly rainfall amount into Intrinsic Mode Functions (IMF) and trends. Each IMF represents a generally simple component of the rainfall time series. During generating process, the envelope curve and every single phases of each month derived from IMFs is used to form new ones. Assemble the new IMFs and the trend to generate monthly rainfall series. The second step is to appropriately downscale the generated monthly rainfall series into daily rainfall series. After the monthly rainfall amounts has been produced, the daily rainfall amounts could be allocated from the monthly rainfall by k-NN moving window method. Thus the newly simulated daily rainfall series with long-term and low frequency climate characteristics is produced. Furthermore, weather parameters of nearby stations can also be produced by k-NN method. The statistical characteristics of the generated monthly and daily weather data, such as mean, standard deviation and autoregressive coefficient, must be comparable to the historical ones. If their similarity is conformed, further use of adopting GCM scenarios can be applied by adding statistical properties on the historical data and redo all the steps to produce future weather data.
The second part of this study is to evaluate the climate change risk of a water supply system. The weather data generated by the novel weather generator are used as inputs for the GWLF model to simulate stream flows of the Hsinchu area. Then the simulated stream flows are further used in the water supply system dynamics model to analyze the risk of water deficits. The water supply system dynamics model considers current hydraulic facilities, different climate scenarios, hydrological conditions, and social and economic development. This study introduces several evaluation index and climate scenarios to assess future risks of the Hsinchu water supply system. Furthermore, the results are compared with the results using Richardson-Type generated weather data to identify the contribution of the proposed new weather generator.


口試委員審定書 iii
謝誌 v
摘要 vii
Abstract ix
目錄 xi
圖目錄 xv
表目錄 xxi
第1章 緒論 23
1.1 研究緣起與目的 23
1.2 研究內容與架構 24
1.3 研究章節與流程 24
第2章 文獻回顧 27
2.1 氣候變遷對水資源的影響 27
2.2 氣象繁衍模式(weather generation) 28
2.2.1 參數型氣象繁衍模式WGEN 29
2.2.2 利用k-NN法之非參數型氣象繁衍模式 29
2.2.3 不同訊號分析法在氣象資料繁衍之應用 31
2.3 IPCC AR5之RCP介紹與風險評估 33
第3章 研究方法 39
3.1 EMD月雨量繁衍模式建立 39
3.1.1 本質模態函數 39
3.1.2 經驗模態分解法 39
3.1.3 本質模態函數的重建與組合 42
3.2 日氣象資料產生模式 45
3.2.1 日降雨事件產生 45
3.2.2 k-NN移動視窗法在日雨量分配之應用 46
3.2.3 鄰近氣象站的資料繁衍: 49
3.2.4 配合氣候變遷情境之修正 49
3.3 用以比較之氣象繁衍模式 49
3.4 GWLF水文模式 52
第4章 氣象繁衍模式在新竹系統之比較與討論 57
4.1 新竹月雨量 57
4.1.1 平均值、標準差 58
4.1.2 自相關函數之比較 60
4.1.3 EMD-BC殘留值之自相關係數 61
4.2 新竹日雨量、日溫度 62
4.2.1 日雨量 62
4.2.2 日均溫 63
4.2.3 極端降雨之日雨量比較 63
4.3 降雨事件 67
4.3.1 降雨機率 67
4.3.2 連續降雨日數與連續乾日數 69
4.3.3 極端降雨事件之觀察 72
4.4 鄰近氣象站氣象資料 74
4.4.1 梅花、新竹氣象站之雨量 76
4.4.2 梅花、太閣南降雨機率 78
4.4.3 梅花、太閣南極端降雨事件 79
4.5 使用GWLF模擬流量 84
4.6 加入GCM情境修正 86
4.6.1 降雨機率改變 86
4.6.2 月統計特性之比較 88
4.6.3 日平均特性變化之比較 89
4.7 不同GCM情境之模擬 92
4.8 小結 97
第5章 新竹系統承載力評估與風險討論 99
5.1 新竹系統介紹 99
5.1.1 流域概況 99
5.1.2 供水系統 99
5.1.3 需水概況 101
5.2 系統動力模式 101
5.2.1 新竹系統動力模式 102
5.3 承載力與評估指標 104
5.3.1 供水承載力介紹 104
5.3.2 水資系統源評估指標 104
5.4 未加入GCM情境之水資源評估指標 106
5.4.1 水資源指標數據 106
5.4.2 討論 109
5.5 加入GCM情境之水資源評估指標 111
5.5.1 水資源指標數據 111
5.5.2 水資源指標討論 114
5.6 風險討論 118
5.6.1 沿海低窪地區因為暴風雨、海平面上升而危及安全、健康、生計的風險: 119
5.6.2 內陸地區洪水影響城市人口的安危、生計: 119
5.6.3 由極端事件導致基礎設施網絡和關鍵服務崩潰的系統性風險 119
5.6.4 糧食系統之風險 120
第6章 結論與建議 121
6.1 結論 121
6.2 建議 123
參考文獻 125


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