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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林國峰(Gwo-Fong Lin) | |
dc.contributor.author | Jyue-Ting Wu | en |
dc.contributor.author | 吳爵廷 | zh_TW |
dc.date.accessioned | 2021-06-16T06:55:11Z | - |
dc.date.available | 2014-07-29 | |
dc.date.copyright | 2014-07-29 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-20 | |
dc.identifier.citation | 1. Ahmed, K.F., Wang, G., Silander, J., Wilson, A.M., Allen, J.M., Horton, R., Anyah, R. (2013). “Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast.” Global and Planetary Change 100: 320–332.
2. Ashrafi, K., Shafiepour, M., Ghasemi, L., Araabi, B.N. (2012). “Prediction of climate change induced temperature rise in regional scale using neural network.” International Journal of Environmental Research 6(3): 677-688. 3. Chang, F.J., Chang, L.C., Wang, Y.S. (2007). “Enforced self-organizing map neural networks for river flood forecasting.” Hydrological Processes 21(6): 741-749. 4. Chen, L.H., Hong, Y.T., Chen, C.T. (2012). “Study on drought characteristics of taiwan.” Journal of Chinese Soil and Water Conservation 43(1): 85-95. 5. Chen, C.J., Lu, M.M. (2007). “Detection of the climatic extreme rainfall events in Taiwan.” Atmospheric Sciences 35(2): 105-117. 6. Chen, H., Xiang, T.T., Zhou, X., Xu, C.Y. (2011). “Impacts of climate change on the Qingjiang Watershed's runoff change trend in China.” Stochastic Environmental Research and Risk Assessment 26(6): 847-858. 7. Chen, S.T., Yu, P.S., Tang, Y.H. (2010). “Statistical downscaling of daily precipitation using support vector machines and multivariate analysis.” Journal of Hydrology 385: 13-22. 8. Crane, R.G., Hewitson, B.C. (1998). “Doubled CO2 precipitation changes for the susquehanna basin: Down-scaling from the genesis general circulation model.” International Journal of Climatology 18(1): 65-76. 9. Cristianini, N., Shaw-Taylor, J. (2000). “An Introduction to Support vector machines and Other Kernel-Based Learning Methods.” Cambridge University Press, New York. 10. Fowler, H.J., Blenkinsop, S., Tebaldi, C. (2007). “Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling.” International Journal of Climatology 27(12): 1547-1578. 11. Fowler, H.J., K. C., Stunell, J. (2007). “Modelling the impacts of projected future climate change on water resources in northwest England.” Hydrology and Earth System Sciences 11(3): 1115-1126. 12. Fujihara, Y., Tanaka, K., Watanabe, T., Nagano, T., Kojiri, T. (2008). “Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: Use of dynamically downscaled data for hydrologic simulations.” Journal of Hydrology 353(1-2): 33-48. 13. Gong, Z.Q., Feng, G.L., Wan, S.Q., Li, J.P. (2006). “Analysis of features of climate change of Huabei area and the global climate change based on heuristic segmentation algorithm.” Acta Physica Sinica 55(1): 477-484. 14. Hewitson, B.C., Crane, R.G. (2002). “Self organizing maps: applications to synoptic climatology.” Climate Research 22(1): 13-26. 15. Hewitson, B.C., Crane, R.G. (2006). “Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa.” International Journal of Climatology 26(10): 1315–1337. 16. Hou, W., Zhang, D.Q., Zhou, Y., Yang, P. (2011). “Stochastially re-sorting detrended fluctuation analysis: a new method to define the threshod of extreme event.” Acta Physica Sinica 60(10): 109202.1-109202.15. 17. Hsu, K.L., Gupta, H.V., Gao, X., Sorooshian, S. (2002). “Self-organizing linear output map (solo): an artificial neural network suitable for hydrology modeling and analysis.” Water Resources Research 38(12): 38.1-38.17. 18. Huang, J., Zhang, J.C., Zhang, Z.X., Sun, S.L., Ya'o, J. (2012). “Simulation of extreme precipitation indices in the Yangtze River basin by using statistical downscaling method (SDSM).” Theoretical and Applied Climatology 180(3-4): 325-343. 19. Katz, R.W., Parlange, M.B., Naveau, P. (2002). “Statistics of extremes in hydrology.” Advances in Water Resources 25: 1287–1304. 20. Kozubowski, T.J., Panorska, A.K., Qeadan, F., Gershunov, A., Rominger, D. (2009). “Testing exponentiality versus Pareto distribution via likelihood ratio.“ Communications in Statistics-Simulation and Computation 38(1): 118–139. 21. Kurnaz, M.L. (2004). “Detrended fluctuation analysis as a statistical tool to monitor the climate” Journal of Statistical Mechanics: Theory and Experiment P07009. 22. Ku, C.Y., Chen, C.J., Chang, I.W., Hsu, S.M., Chen, N.C., Wen, H.Y. (2012). “Study on the assessment of regional rainfall-induced landslide hazards under extreme climate conditions.” Journal of Chinese Soil and Water Conservation 43(1): 75-84. 23. Lin, G.F., Chen, L.H. (2004). “A non-linear rainfall-runoff model using radial basis function network.” Journal of Hydrology 289(1-4): 1-8. 24. Lin, G.F., Chen, L.H. (2005). “Application of an artificial neural network to typhoon rainfall forecasting.” Hydrological Processes 19(9): 1825-1837. 25. Lin, G.F., Chen, L.H. (2006). “Identification of homogeneous regions for regional frequency analysis using the self-organizing map.” Journal of Hydrology 324(1-4): 1-9. 26. Lin, G.F., Chen, G.R., Huang, P.Y., Chou, Y.C. (2009). “Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods.” Journal of Hydrology 372(1-4): 17-29. 27. Lin, G.F., Wu, M.C. (2007). ”A SOM-based approach to estimating design hyetographs of ungauged sites.” Journal of Hydrology 339(3-4): 216-226. 28. Liu, B., Chen, J., Chen, X., Lian, Y., Wu, L. (2013). “Uncertainty in determining extreme precipitation thresholds.” Journal of Hydrology 503: 233-245. 29. Liu, C.M., Liu, W.B., Fu, G.B. (2012). “Ouyang R.L.A discussion of some aspects of statistical downscaling in climate impacts assessment.” Shuikexue Jinzhan/Advances in Water Science, 23(3): 427-437. 30. Murphy, J.M. (1999). “An evaluation of statistical and dynamical techniques for downscaling local climate. Journal of Climate.” 12(8): 2256–2284. 31. Neves, C., Alves, M.I.F. (2008). “Testing extreme value conditions-an overview and recent approaches.” Statistical Journal 6(1): 83–100. 32. Parajuli, K., Kang, K. (2014). “Application of statistical downscaling in GCMs at constructing the map of precipitation in the Mekong River basin” Russian Meteorology and Hydrology 39(4): 271-282. 33. Samadi, S., Carbone, G.J., Mahdavi, M., Sharifi, F., Bihamta, M.R. (2013). “Statistical Downscaling of River Runoff in a Semi Arid Catchment” Water Resources Management 27(1): 117-136. 34. Tokar, A.S., Johnson, P.A. (1999). “Rainfall-runoff modeling using artificial neural networks.” Journal of Hydrologic Engineering 4(3): 232-239. 35. Tolika, K., Anagnostopoulou, C., Maheras, P., Vafiadis, M. (2008). “Simulation of future changes in extreme rainfall and temperature conditions over the Greek area: A comparison of two statistical downscaling approaches.” Global and Planetary Change 63: 132-151. 36. Tomasz J.K., Anna, K.P., Fares, Q., Alexander, G., Debra, R. (2008). “Testing Exponentiality Versus Pareto Distribution via Likelihood Ratio” Communications in Statistics - Simulation and Computation 38(1): 118-139. 37. Tumbo, S.D., Mpeta, E., Tadross, M., Kahimba, F.C., Mbillinyi, B.P., Mahoo, H.F. (2010). “Application of self-organizing maps technique in downscaling GCMs climate change projections for Same, Tanzania.” Physics and Chemistry of The Earth 35(13-14): 608-617. 38. Vapnik, V. (1995). “The Nature of Statistical Learning Theory.” Springer, New York. 39. Von Storch, H., Zorita, E., Cuhasch, U. (1993). “Downscaling of global climate change estimates to regional scale: an application to Iberian rainfall in winter time.” Journal of Climate. 6(6): 1161–1171. 40. Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P., Mearns, L.O. (2004). “Guidelines for use of climate scenarios developed from statistical downscaling methods, supporting material of the Intergovernmental Panel on Climate Change.” Available from the DDC of IPCC TGCIA. 27 41. Wilby, R.L., Dawson, C.W., Barrow, E.M. (2002). “SDSM decision support tool for the assessment of regional climate change impacts.” Environmental Modelling & Software 17: 147-159. 42. Wilby, R.L., Wigley, T.M.L. (1997). “Downscaling general circulation model output: a review of methods and limitations.” Progress in Physical Geography 21: 530-548. 43. Yang, P., Hou, W., Feng, G.L. (2008). “Determining the threshold of extreme event with detrended fluctuation analysis.” Acta Physica Sinica 57(8): 5334-5343. 44. Yang, T.C., Yu, P.S., Wei, C.M., Chen, S.T. (2011). “Projection of climate change for daily precipitation: a case study in Shih-Men reservoir catchment in Taiwan.” Hydrological Processes 25(8): 1342-1354. 45. Yin, C.H., Li, Y.P., Ye, W., Bornman, J.F., Yan, X.D. (2011). “Statistical downscaling of regional daily precipitation over southeast Australia based on self-organizing maps.” Theoretical and Applied Climatology 105(1-2): 11-26. 46. Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y., Kanani, R. (2011). “Impacts of climate change on runoffs in East Azerbaijan, Iran.” Global and Planetary Change 78(3-4): 137-146. 47. Zhang, Q., Xiao, M., Singh, V.P., Li, J. (2012). “Regionalization and spatial changing properties of droughts across the Pearl River basin, China.” Journal of Hydrology 472-473: 355-366. 48. 陳宜欣,林國峰,2011,發展以自組織映射圖網路為基礎之雨量繁衍模式於未來降雨推估,國立台灣大學土木工程學研究所碩士論文。 49. 張家銓,林國峰,2009,改良式自組織映射線性輸出模式於水庫入流量預報之研究,國立台灣大學土木工程學研究所碩士論文。 50. 廖信華,林國峰,2013,評估改良式統計降尺度模式應用於日雨量映射之研究,國立台灣大學土木工程學研究所碩士論文。 51. 鄭克聲,2003,降雨時空分布特性影響之探討,農業水利科技研究發展91年度成果發表討論會。 52. 譚克平,2008,極端值判斷方法簡介,台東大學教育學報,19(1),131-150。 53. 魏綺瑪,游保杉,2009,利用統計降尺度法推估石門水庫集水區未來情境降水研究,國立成功大學水利及海洋工程研究所碩士論文。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57634 | - |
dc.description.abstract | 氣候變遷議題日趨重要,研究中常使用大氣環流模式(General Circulation Models, GCMs)配合降尺度方法映射(Project)未來水文變化。降尺度方法主要分為兩種:動力降尺度和統計降尺度,其中被廣泛使用的統計降尺度法時常低估極端值。因此,本研究為了改善模式準確度,提出一個新的統計降尺度方法,並用此法映射台灣未來降雨趨勢。
本方法分成日雨量分類階段與日雨量推估階段。日雨量分類階段先以去勢波動分析(Detrended Fluctuation Analysis, DFA)定義極端雨量。接著建構極端雨量與濕雨量兩個分類模式。其使用日尺度的大氣因子資料,判斷該日的雨量型態為極端雨量、一般雨量或乾雨量(零雨量)。於日雨量推估階段,將日尺度大氣因子資料分別和極端雨量及一般雨量建立極端雨量與一般雨量兩個迴歸模式,以推估日雨量值。最後將極端雨量、一般雨量及乾雨量合成為日雨量序列。本研究引進兩種類神經網路,日雨量分類階段使用支援向量機(Support Vector Machine, SVM);日雨量推估階段使用改良式自組織線性輸出映射圖(Improved Self-organizing Linear Output Map, ISOLO)。模式建立使用到台灣12個雨量測站的日雨量資料與再分析資料(NCEP/NCAR reanalysis data)的大氣因子。 結果發現,本研究提出的基於雨量型態的統計降尺度方法,其推估結果十分接近歷史值,相關性高且誤差降低。於未來雨量映射時,使用IPCC (Intergovernmental Panel on Climate Change)第四次評估報告提出之CGCM3.1模式資料,選擇其A2與B1情境中期(2046-2065)與長期(2081-2100)資料映射各測站雨量變化情形。結果顯示,台灣各區域呈現不同趨勢。石門水庫地區中期與長期雨量皆減少。高山地區中期濕季雨量增加,長期濕季雨量顯著增加。平原地區中期雨量變化不大,長期濕季、乾季雨量皆增加。 總結而言,本研究提出的基於雨量型態的統計降尺度方法表現優異。日雨量分類階段,準確度高且穩定;日雨量推估階段,極端值明顯改善。證實本研究提出的統計降尺度方法解決低估問題,可配合GCM模式的未來情境映射其它區域水文變化。 | zh_TW |
dc.description.abstract | Statistical downscaling methods are widely used to downscale the outputs of General Circulation Models (GCMs). However, the statistical downscaling methods tend to underestimate, especially the extreme values. To solve this problem, a new statistical downscaling method based on rainfall patterns is proposed in this study. Daily rainfall data from 12 rainfall stations in Taiwan are used. In addition, the large-scale weather factors obtained from NCEP/NCAR reanalysis project and GCMs runs are employed. The proposed method is used to assess the impacts of specific climate change scenarios on future rainfall.
The proposed method is composed of two steps. The first step is daily rainfall classification step in which two classification models, extreme rainfall and wet rainfall, are established. These two models are used to identify the rainfall patterns (extreme rainfall, normal rainfall and dry rainfall) of the daily weather factors. The Detrended Fluctuation Analysis (DFA) is used to define the extreme rainfall. The classification models establish the relationship between NCEP/NCAR weather factors and rainfall patterns using Support Vector Machine (SVM). The second step is daily rainfall estimation step. Improved Self-Organizing Linear Output Map (ISOLO) is used to estimate the rainfall amounts for three different rainfall patterns. The changes of downscaled rainfall based on the outputs of 20C3M and the projections of GCMs for A2 and B1 scenarios over the study area are investigated. The results demonstrate that the proposed method provides reliable and accurate rainfall-pattern classification. In addition, the improvement of the estimation of daily rainfall series is significant, especially for the extreme rainfall. In conclusion, the proposed method is practical and suitable for the future projections. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T06:55:11Z (GMT). No. of bitstreams: 1 ntu-103-R01521315-1.pdf: 2431424 bytes, checksum: a80cdddd9d0d9e40e2a69818b8b68f2f (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員審定書 I
誌謝 II 中文摘要 III Abstract V 目錄 VII 圖目錄 X 表目錄 XIII 第1章 緒論 1 1.1 前言與目的 1 1.2 文獻回顧 3 1.3 論文架構 5 第2章 研究方法 6 2.1 支援向量機 6 2.2 改良式自組織線性輸出映射圖 11 2.3 去勢波動分析 17 2.3.1 理論介紹 17 2.3.2 DFA流程圖 19 2.3.3 DFA與傳統方法之比較 26 第3章 研究區域與資料 28 3.1 雨量站資料 28 3.2 NCEP/NCAR再分析資料 31 3.3 大氣環流模式資料 (GCM) 33 第4章 方法建立與應用 37 4.1 研究流程 37 4.2 日雨量分類階段 39 4.2.1 極端雨量分類模式 39 4.2.2 濕雨量分類模式 41 4.2.3 雨量分類結果後處理 43 4.3 日雨量推估階段 44 4.4 傳統統計降尺度方法 46 4.5 評鑑指標 47 第5章 結果與討論 48 5.1 去勢波動分析 (DFA) 49 5.2 分類模式 54 5.2.1 極端雨量分類模式準確率 54 5.2.2 濕雨量分類模式準確率 55 5.2.3 綜合討論 56 5.3 降尺度結果與比較 58 5.3.1 各測站降尺度結果 58 5.3.2 各測站月雨量平均結果 62 5.4 未來情境映射 72 5.4.1 各站未來中期雨量變化 76 5.4.2 各站未來長期雨量變化 86 5.4.3 小結 88 第6章 結論與建議 97 6.1 結論 97 6.2 建議 99 參考文獻 100 | |
dc.language.iso | zh-TW | |
dc.title | 基於雨量型態的統計降尺度方法研究 | zh_TW |
dc.title | A statistical downscaling method based on rainfall patterns | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李方中(Fang-Zhong Li),林文欽(Wen-Ging Lin) | |
dc.subject.keyword | 氣候變遷,降尺度,去勢波動分析法,支援向量機,改良式自組織線性輸出映射圖, | zh_TW |
dc.subject.keyword | Climate Change,Downscaling,Detrended Fluctuation Analysis,Support Vector Machine,Improved Self-organizing Linear Output Map, | en |
dc.relation.page | 105 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-07-21 | |
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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