氣候繁衍模式常用來將月雨量進行時間降尺度到日雨量序列之重要工具之一。本文使用了三種不同的氣候繁衍模式分別為，Richardson - type、k-NN(all data base)本文稱為k鄰近法_全資料法以及k-NN(moving window)本文稱為k鄰近法_移動視窗法，針對日降雨量進行日雨量氣候繁衍，本研究選取了石門、曾文水庫兩個集水區，共15個雨量測站。各測站皆使用三種不同的繁衍模式繁衍30組與觀測年相同長度之資料。然後針對所有繁衍資料進行分析，在本文中共分成四大類分析，分別為降雨量分析、乾濕日分析、日暴雨分析以及降雨空間相關性分析。大致上k鄰近法_全資料和k鄰近法_移動視窗均較Richardson – type法為佳，尤其是在空間相關性分析上更能維持降雨之統計特性，而分析結果顯示k鄰近法_移動視窗較其他模式適合於本研究區域，因此最後選用k鄰近法_移動視窗法來做為日雨量氣候繁衍模式。
最後利用k鄰近法_移動視窗來進行情境模擬，透過IPCC所設定的兩種情境，透過繁衍結果以討論未來於研究地區降雨特性，在不同情境下可能出現不同的改變。結果顯示，k鄰近法_移動視窗繁衍之降雨量改變量受各月降雨量改變比值之影響大，而曾文水庫濕季降雨量改變比值大，因此一日暴雨量增加且濕季之降雨量有明顯的增加，石門水庫則改變不明顯，其中k鄰近法_移動視窗繁衍降雨日數之機制，受改變降雨量的影響小，因此兩地區之濕日數改變量皆不顯著。

Climate generators are the important tools for time downscaling generally. The approaches considered include Richardson – type, k-NN(all data base) and k-NN(moving window) and the study area are Shih – Men catchment area and Tseng – Weng catchment area, a total of 15 rainfall gauge stations. The three different models generate 30 sets of observed data for processing analysis. The analysis are divided into four different parts, which are rainfall analysis, dry and wet day analysis, daily storm events analysis and rainfall spatial correlation analysis. In general, k-NN(all data base) and k-NN(moving window) are better than Richardson – type, especially these two models can present great result in rainfall spatial correlation analysis. The analysis results showed that k-NN(moving window) is the most proper model for our study area.
By using two different sceniros by IPCC, the k-NN(moving window) generates 30 sets climate data for discussing future rainfall character in study area. Our results indicate that, monthly rainfall change ratio have great effect to generate rainfall amount. Because of the Tseng – Weng rainfall change ratio in wet season is greater than Shih – Men, Tseng – Weng catchment area get more daily storm events. However, through the k-NN(moving window), the total dry days or wet days in month have no obvious change by changing monthly rainfall change ratio.

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