氣候變遷對水資源的衝擊議題，是現今全球許多學者及國家關注的焦點，研究過此類議題過程大都根據大氣環流模式(General Circulation Models, GCMs)進行模擬研究。然而，由於大氣環流模式解析度太粗糙，沒有辦法反應小尺度地區的資訊，故需要藉由降尺度(Downscaling)方法，推求小尺度的資訊。本文對台灣南部地區進行降尺度研究，利用奇異值分解(Singular Value Decomposition, SVD)降尺度方法來探討不同區域氣候場對降尺度結果分析結果，期望選擇一適合之區域氣候場做為降尺度分析，最後利用選擇的區域氣候場，推估台灣南部未來可能降雨趨勢。
本研究使用台灣南部區域歷史地面雨量站降水資料及IPCC第四次評估報告提供的七個GCM模式資料。選取七種不同範圍區域氣候場，再依照七個模式分別與台灣南部歷史降水資料，利用奇異值分解法建立七個統計降尺度關係式。由於得到的結果資訊太多，各GCM模式表現不一，故利用多模式系集平均(Multi-Model Ensemble, MME)來討論GCMs在不同區域氣候場之結果。利用MME之後的均方根誤差比較結果，不同範圍的區域氣候場對於降尺度結果沒有顯著性影響。但藉由比較不同模式個數的MME結果發現，愈多模式去進行MME，均方根誤差愈低。得到一個重要結論：不管如何選取區域氣候場，最後只要將各模式結果利用MME的方式呈現，其結果相差不大。本研究最後將選取出來的區域氣候場，對台灣南部進行未來的雨量推估，利用七個模式各別的結果與MME的結果呈現，發現未來GCM月平均雨量七個模式各別結果與MME結果大部分都很穩合。大致上枯水期雨量呈現減少趨勢，豐水期雨量呈現增加趨勢。

The impact of climate change on water resources study is very important in every county. In this study, a statistical downscaling model, which is based on the outputs of GCMs as predictors, is first developed to simulate the monthly rainfall over the southern Taiwan. The statistical method in downscaling model is Singular Value Decomposition(SVD). Moreover, select seven regional climate fields to discuss the impact of regional climate filed on downscaling. Finally, the variability of projected local rainfall based on predictors for different scenarios is investigated. Data of observed station rainfall over the southern Taiwan are utilized for downscaling and the regional climate field variables derived from outputs of GCMs.
In this study, using seven GCMs and seven regional climate fields to establish statistical downscaling model. However, GCMs show the different result lead to too much information, as a result, using Multi-Model Ensemble(MME) to discuss the GCMs in different regional climate fields. After using MME, all criterion shows there is no significant impact on downscaling result in different climate regional fields. Finally, the scenarios provided by the Intergovernmental Panel on Climate Change (IPCC) are used to simulate the future precipitation (2010‐2045) in southern Taiwan. The future precipitation shows an increasing trend during wet season (from May to October) and a decreasing trend during dry season (from November to April).

參考文獻[1-45]
1. 台灣大學生物環境系統工程學系：永續發展研究室, 第三章：氣候變遷預設情境與應用。. 國立台灣大學，台北市。.
2. 曾忠一, 氣象資料同化. 台北市：渤海堂文化公司。, 1997.
3. 魏綺瑪, 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 (Scenario Projection for Local Precipitation over Shimen Reservoir Catchment Using Statistical Downscaling Method). 國立成功大學水利及海洋工程研究所碩士論文，未出版，台南市。, 2009.
4. Anandhi, A., et al., Downscaling precipitation to river basin in India for IPCCSRES scenarios using support vector machine. International Journal of Climatology, 2008. 28(3): p. 401-420.
5. Benestad, R.E., A comparison between two empirical downscaling strategies. International Journal of Climatology, 2001. 21(13): p. 1645-1668.
6. Benestad, R.E., Tentative probabilistic temperature scenarios for northern Europe. Tellus Series a-Dynamic Meteorology and Oceanography, 2004. 56(2): p. 89-101.
7. Chen, H., et al., Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin. Advances in Atmospheric Sciences, 2010. 27(2): p. 274-284.
8. Chen, S.T., P.S. Yu, and Y.H. Tang, Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. Journal of Hydrology, 2010. 385(1-4): p. 13-22.
9. Chu, J.L., et al., Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling. Journal of Geophysical Research-Atmospheres, 2008. 113(D12): p. -.
10. Fowler, H.J., S. Blenkinsop, and C. Tebaldi, Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology, 2007. 27(12): p. 1547-1578.
11. Gerrity, J.P., A Note on Gandin and Murphy Equitable Skill Score. Monthly Weather Review, 1992. 120(11): p. 2709-2712.
12. Ghosh, S. and P.P. Mujumdar, Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Advances in Water Resources, 2008. 31(1): p. 132-146.
13. Harpham, C. and R.L. Wilby, Multi-site downscaling of heavy daily precipitation occurrence and amounts. Journal of Hydrology, 2005. 312(1-4): p. 235-255.
14. Haylock, M.R., et al., Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios. International Journal of Climatology, 2006. 26(10): p. 1397-1415.
15. IPCC, Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Integergovernmental Panel on Climate Change. 2001.
16. IPCC, Climate Change 2007: The physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Integergovernmental Panel on Climate Change. 2007.
17. Juneng, L., et al., Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables. Journal of Climate, 2010. 23(1): p. 17-27.
18. Kang, H., et al., Multimodel output statistical downscaling prediction of precipitation in the Philippines and Thailand. Geophysical Research Letters, 2007. 34(15): p. -.
19. Kaoru Takara, S.K., Yasuto Tachikawa, Eiichi Nakakita, Assessing Climate Change Impact on Water Resources in the Tone River Basin, Japan, Using Super-High-Resolution Atomospheric Model Output. Journal of Disaster Research, 2009. 4.
20. Krishnamurti, T.N., et al., Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 1999. 285(5433): p. 1548-1550.
21. Labraga, J.C., Statistical downscaling estimation of recent rainfall trends in the eastern slope of the Andes mountain range in Argentina. Theoretical and Applied Climatology, 2010. 99(3-4): p. 287-302.
22. Maurer, E.P. and H.G. Hidalgo, Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrology and Earth System Sciences, 2008. 12(2): p. 551-563.
23. Min, H.J. and J.G. Jhun, The change in the East Asian summer monsoon simulated by the MIROC3.2 high-resolution coupled model under global warming scenarios. Asia-Pacific Journal of Atmospheric Sciences, 2010. 46(1): p. 73-88.
24. Murphy, S.J., H.E. Hurlburt, and J.J. O'Brien, The connectivity of eddy variability in the Caribbean Sea, the Gulf of Mexico, and the Atlantic Ocean. Journal of Geophysical Research-Oceans, 1999. 104(C1): p. 1431-1453.
25. Nanjundiah, R.S., V. Vidyunmala, and J. Srinivasan, The impact of increase in CO2 on the simulation of tropical biennial oscillations (TBO) in 12 coupled general circulation models. Atmospheric Science Letters, 2005. 6(3): p. 183-191.
26. Paul, S., et al., Development of a statistical downscaling model for projecting monthly rainfall over East Asia from a general circulation model output. Journal of Geophysical Research-Atmospheres, 2008. 113(D15): p. -.
27. Penlap, E.K., et al., Downscaling of GCM scenarios to assess precipitation changes in the little rainy season (March-June) in Cameroon. Climate Research, 2004. 26(2): p. 85-96.
28. Perkins, S.E., A.J. Pitman, and S.A. Sisson, Smaller projected increases in 20-year temperature returns over Australia in skill-selected climate models. Geophysical Research Letters, 2009. 36: p. -.
29. Prudhomme, C. and H. Davies, Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate. Climatic Change, 2009. 93(1-2): p. 197-222.
30. Salathe, E.P., Downscaling simulations of future global climate with application to hydrologic modelling. International Journal of Climatology, 2005. 25(4): p. 419-436.
31. Schmidli, J., et al., Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps. Journal of Geophysical Research-Atmospheres, 2007. 112(D4): p. -.
32. Syu, H.H., J.D. Neelin, and D. Gutzler, Seasonal and Interannual Variability in a Hybrid Coupled Gcm. Journal of Climate, 1995. 8(9): p. 2121-2143.
33. Timbal, B., A. Dufour, and B. McAvaney, An estimate of future climate change for western France using a statistical downscaling technique. Climate Dynamics, 2003. 20(7-8): p. 807-823.
34. Tolika, K., et al., Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs). International Journal of Climatology, 2007. 27(7): p. 861-881.
35. Tripathi, S., V.V. Srinivas, and R.S. Nanjundiah, Dowinscaling of precipitation for climate change scenarios: A support vector machine approach. Journal of Hydrology, 2006. 330(3-4): p. 621-640.
36. Wang, H. and R. Fu, Cross-equatorial flow and seasonal cycle of precipitation over South America. Journal of Climate, 2002. 15(13): p. 1591-1608.
37. Wetterhall, F., S. Halldin, and C.Y. Xu, Statistical precipitation downscaling in central Sweden with the analogue method. Journal of Hydrology, 2005. 306(1-4): p. 174-190.
38. Wetterhall, F., S. Halldin, and C.Y. Xu, Seasonality properties of four statistical-downscaling methods in central Sweden. Theoretical and Applied Climatology, 2007. 87(1-4): p. 123-137.
39. Widmann, M. and C.S. Bretherton, Validation of mesoscale precipitation in the NCEP reanalysis using a new gridcell dataset for the northwestern United States. Journal of Climate, 2000. 13(11): p. 1936-1950.
40. Wilby, R.L., Non-stationarity in daily precipitation series: Implications for GCM down-scaling using atmospheric circulation indices. International Journal of Climatology, 1997. 17(4): p. 439-454.
41. Wilby, R.L., C.W. Dawson, and E.M. Barrow, SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 2002. 17(2): p. 147-159.
42. Wilby, R.L., et al., Hydrological responses to dynamically and statistically downscaled climate model output. Geophysical Research Letters, 2000. 27(8): p. 1199-1202.
43. Wilby, R.L., L.E. Hay, and G.H. Leavesley, A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. Journal of Hydrology, 1999. 225(1-2): p. 67-91.
44. Wilby, R.L. and T.M.L. Wigley, Precipitation predictors for downscaling: Observed and general circulation model relationships. International Journal of Climatology, 2000. 20(6): p. 641-661.
45. Yu, P.S., S.T. Chen, and I.F. Chang, Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 2006. 328(3-4): p. 704-716.