臺灣博碩士論文加值系統

大氣環流模式配合降尺度獲得的高解析度雨量和溫度資料是評估氣候變遷對降雨逕流衝擊的重要一環，然而現存的溫度降尺度模式未考慮跨日關係與日夜循環，且降雨逕流模式鮮少評估小時尺度。因此，本研究提出同時考慮跨日關係及日夜循環的統計降尺度模式和降雨逕流模式，評估氣候變遷對小時逕流的衝擊。
首先本研究提出溫度降尺度模式可分為空間和時間降尺度階段。空間降尺度階段，使用K最近鄰居法(k-nearest neighbor method, KNN)建構大尺度大氣因子與溫度的關係，獲得測站尺度日溫度序列。時間降尺度階段使用KNN結合基因演算法(genetic algorithm, GA)，同時考慮跨日關係及日夜循環，由日溫度降尺度至小時溫度。進而將繁衍的溫度和雨量輸入HBV模式(Hydrologiska Byråns Vattenbalansavdelning)模擬日和小時的逕流量，最後評估氣候變遷對逕流的衝擊。本研究將提出之模式應用於臺灣石門水庫流域，模式建立使用大尺度大氣因子為NCEP/NCAR (NCEP/NCAR reanalysis data)再分析資料，及測站尺度日與時觀測資料。未來情境映射使用BCM2.0模式中的A2、A1B及B1情境之中期(2046-2065)和長期(2081-2100)資料。
結果顯示，本研究提出之統計降尺度模式能有效保有日和小時溫度的統計特性，並保持跨日關係與日夜循環，HBV模式可準確模擬小時的逕流。未來情境映射時，所有情境的溫度會增加，逕流會減少。本研究對溫度和降雨逕流評估結果，可幫助決策者進行水資源系統規劃及管理。

Downscaling is used to translate the general circulation model outputs to a finer spatiotemporal resolution rainfall and temperature data, which is essential for assessing hydrological impacts of climate change. However, existing downscaling models for hourly temperature are unable to consider the inter-daily connection and the diurnal cycle and consider the hourly scale. Besides, existing rainfall-runoff models have less attention on hourly streamflow. To address this problem, a spatiotemporal downscaling model is proposed which is capable of reproducing the inter-daily connection, the diurnal cycle, and the statistics on daily and hourly scales. Using the results of rainfall and temperature as input to the rainfall-runoff model, the hydrological impacts of climate change on hourly streamflow are assessd.
The proposed downscaling model consists of two steps, the spatial downscaling and temporal downscaling. The spatial downscaling is applied first to obtain the relationship between large-scale weather factors and daily temperature at station scale using the k-nearest neighbor method. Then, the hourly downscaling of daily temperature is conducted in the second step using the k-nearest neighbor method with the genetic algorithm and consideration of the inter-daily connection and the diurnal cycle. The large-scale datasets, which are obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data and the Bergen Climate Model version 2 (BCM2.0) outputs, and the local temperature data are analyzed to assess the impacts of climate change on temperature. After rainfall and temperature projections, the HBV model are applied to predict the future streamflow of Shihmen reservoir basin (Taiwan) under the A2, A1B and B1 scenarios of the BCM2.0 for the periods 2046-2065 and 2081-2100.
The results show that the proposed model can accurately reproduce the local temperature and its statistics on daily and hourly scales. The change trend of the future streamflow under the impacts of climate change is presented and discussed. To conclude, the results demonstrate that the proposed model is a powerful tool for predicting streamflow. The future changes of temperature and streamflow statistics are obtained which will help decision makers in planning and management of water resources systems.

口試委員會審定書 i
誌謝 ii
中文摘要 iv
Abstract v
目錄 vii
圖目錄 x
表目錄 xiii
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 論文架構 6
第二章 研究區域與資料 7
2.1 研究區域概述 7
2.2 研究區域資料 8
2.3 再分析資料 11
2.4 未來情境資料 13
第三章 研究方法 17
3.1 KNN 17
3.2 GAKNN 20
3.3 GAKNN-DH 22
3.3.1 考慮跨日關係之修正 22
3.3.2 考慮日夜循環之修正 23
3.4 HBV model 25
第四章 模式建立與應用 28
4.1 研究流程 28
4.2 統計降尺度階段 29
4.2.1 空間降尺度階段 29
4.2.2 時間降尺度階段 29
4.3 降雨逕流階段 30
4.3.1 日逕流階段 30
4.3.2 小時逕流階段 30
4.3.3 交替驗證 30
4.4 評鑑指標 32
第五章 結果與討論 35
5.1 空間降尺度 35
5.1.1 因子篩選 35
5.1.2 各雨量站降尺度結果 38
5.1.4 氣象站間相關性 41
5.2 時間降尺度 42
5.2.1 GAKNN 42
5.2.2 考慮跨日關係及日夜循環 46
5.3 降雨逕流 51
5.3.1 日逕流 51
5.3.2 小時逕流 54
5.3 未來情境映射 58
5.3.1 未來年變化 59
5.3.2 未來日變化 62
5.4.3 未來時變化 66
第六章 結論與建議 71
6.1 結論 71
6.2 建議 72
參考文獻 73
附錄A 83
附錄B 90
附錄C 93

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