近年來氣候變遷對水資源之衝擊成為許多學者關注之議題，多數學者根據大氣環流模式(General Circulation Model, GCM)模擬結果進行研究，但GCM屬於全球尺度的模擬推估，其模擬值往往忽略小尺度區域的變遷特性，故透過降尺度(Downscaling)過程推求能代表小尺度區域的變遷資訊。本文首先在石門水庫集水區建立適合之統計降尺度模式，並驗證其適用性，最後利用此模式推估石門水庫集水區未來可能降水趨勢。
本研究使用石門水庫集水區歷史降水資料、NCEP再分析資料及IPCC第四次評估報告提出之五個GCM模式資料。建立降尺度模式時，研究使用歷史降水資料與NCEP再分析資料建立模式，模式中包含分類與回歸兩程序，研究結果發現以多變量法建立分類模式、以支撐向量機法(Support Vector Machine, SVM)法建立回歸模式比較適合。研究結果發現，利用多變量法建立乾濕日分類模式，後利用SVM法建立雨量與預測因子之回歸模式之降尺度法較適合應用於石門水庫集水區之雨量模擬。利用GCM資料模擬降水發現，各GCM模式表現不一，故引入多模式系集平均(Multi-Model Ensemble, MME)來討論GCMs模擬結果。GCM模式控制模擬年模擬結果與觀測降水之統計分析十分接近，間接證實模式之適用性。最後，研究利用MME分析石門水庫集水區未來降雨趨勢，相較於GCM控制模擬年雨量，未來GCM月平均雨量、年雨量及雨量平均值都有減少趨勢。

In this study, a statistical downscaling model, which is based on the outputs of GCMs as predictors, is first developed to simulate the daily rainfall over the Shiman reservoir catchment. Moreover, the potential of this downscaling model in simulating local rainfall is evaluated. Finally, the variability of projected local rainfall based on predictors for different scenarios is investigated. Data of observed station rainfall over the Shiman reservoir catchment are utilized for downscaling. In addition, the large-scale variables derived from NCEP/NCAR Reanalysis project and outputs of GCMs taken from IPCC AR4 are also required.
The structure of this statistical downscaling model can be divided into two parts － classification and regression. Methods of multivariate and Support Vector Machine (SVM) are applied respectively to build up the classification and regression scheme. The results reveal that the statistical downscaling model based on two-step strategy performs better in simulating the daily rainfall over the Shiman reservoir catchment when compared with the downscaling model based on four-step strategy. Although the divergence exists in between the results of downscaling based on the outputs of GCMs, downscaling results based on MME strategy make reasonable and stable projections for daily rainfall. When compared with the downscaled rainfall based on outputs of 20C3M, a tendency of suppressive rainfall is found from the results downscaled by the projections of GCMs for A1B and B1 scenarios over the Shiman reservoir catchment.

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