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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
dc.contributor.author | Chian-Fu Wang | en |
dc.contributor.author | 王千輔 | zh_TW |
dc.date.accessioned | 2021-06-15T11:27:03Z | - |
dc.date.available | 2021-08-30 | |
dc.date.copyright | 2016-08-30 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-08-17 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49402 | - |
dc.description.abstract | 空間及時間解析度精細的降雨資料,對氣候變遷在中、小流域的水文衝擊評估至關重要。然而,現存的模式較少關注跨日關係(inter-daily connection)及日夜循環(diurnal cycle)等,這兩個與水文循環緊密相關的統計數據。因此,本研究提出一個新的空間–時間統計降尺度模式,可以重現跨日關係、日夜循環,及日尺度和時尺度的統計特性,以此模式映射未來降雨,評估氣候變遷對降雨的衝擊。
本研究發展的模式包含空間降尺度階段及時間降尺度階段。空間降尺度階段使用K最近鄰居法(k-nearest neighbor method, KNN),建立大尺度大氣因子及雨量關係,獲得測站尺度日雨量序列。接著,時間降尺度階段使用KNN結合基因演算法(genetic algorithm, GA)的GAKNN模式,並同時考慮跨日關係及日夜循環,由測站尺度日雨量序列,降尺度獲得測站尺度時雨量序列。模式建立使用的大尺度大氣因子為NCEP/NCAR再分析資料(NCEP/NCAR reanalysis data, NNR),及測站尺度日與時雨量觀測資料。未來雨量映射使用CGCM3.1模式及BCM2.0模式,選用A2、A1B及B1三種情境之中期(2046-2065)和長期(2081-2100)資料,映射未來雨量變化情形。以實際案例評估本模式表現,顯示本模式降尺度結果可以保有觀測資料的統計特性。 總體而言,結果顯示本研究提出的模式,對於由大尺度大氣因子,降尺度產生測站尺度時雨量有著優異的表現。 | zh_TW |
dc.description.abstract | Finer spatiotemporal resolution rainfall generation is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing models have less attention on the inter-daily connection and the diurnal cycle which can strongly influence the hydrological cycle. To address this problem, a spatiotemporal downscaling model is presented which is capable of reproducing the inter-daily connection, the diurnal cycle, and the statistics on daily and hourly scales. The large-scale datasets, which are obtained from the NCEP/NCAR reanalysis data and the GCMs outputs, and the local rainfall data are analyzed to assess the impacts of climate change on rainfall.
The proposed 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 rainfall at station scale using the k-nearest neighbor method. Then, the hourly downscaling of daily rainfall 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. After the downscaling processes, the changes of rainfall statistics are analyzed for the periods 2046-2065 and 2081-2100 under the A2, A1B and B1 scenarios of CGCM3.1 and BCM2.0. An application to the Shihmen reservoir basin (Taiwan) has shown that the proposed model can accurately reproduce the local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal downscaling model is a powerful tool for generating hourly rainfall data from large-scale weather factors. The understanding of future changes of rainfall characteristics through this study are also expected to assist the planning and management of water resources systems. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T11:27:03Z (GMT). No. of bitstreams: 1 ntu-105-R03521305-1.pdf: 38896549 bytes, checksum: 694c61ebc53575891a03af967db9a413 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xv 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 5 第二章 研究區域與資料 6 2.1 研究區域概述 6 2.2 雨量資料 7 2.3 再分析資料 9 2.4 未來情境資料 11 第三章 研究方法 16 3.1 KNN 16 3.2 GAKNN 19 3.3 考慮跨日關係之修正 21 3.4 考慮日夜循環之修正 23 第四章 模式建立與應用 25 4.1 研究流程 25 4.2 空間降尺度階段 26 4.3 時間降尺度階段 26 4.4 評鑑指標 27 第五章 結果與討論 30 5.1 空間降尺度 30 5.1.1 因子篩選 30 5.1.2 移動視窗選擇 33 5.1.3 各雨量站降尺度結果 34 5.1.4 雨量站間相關性 43 5.2 時間降尺度 44 5.2.1 GAKNN 44 5.2.2 考慮跨日關係 53 5.2.3 考慮日夜循環 65 5.2.4 第一小時修正結果比較 75 5.3 未來情境映射 77 5.3.1 未來年雨量變化 77 5.3.2 未來日雨量變化 80 5.3.3 未來時雨量變化 90 第六章 結論與建議 98 6.1 結論 98 6.2 建議 99 參考文獻 100 附錄A 106 附錄B 122 | |
dc.language.iso | zh-TW | |
dc.title | 推求時雨量之空間–時間統計降尺度模式 | zh_TW |
dc.title | A novel spatio-temporal statistical downscaling model for hourly rainfall | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 賴進松(Jihn-Sung Lai),李方中(Fong?Chung Lee) | |
dc.subject.keyword | 氣候變遷,降雨,統計降尺度,K最近鄰居法,基因演算法, | zh_TW |
dc.subject.keyword | Climate change,Rainfall,Statistical downscaling,k-nearest neighbor method,Genetic algorithm, | en |
dc.relation.page | 137 | |
dc.identifier.doi | 10.6342/NTU201602826 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-08-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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