張斐章徐國麟Chang, Fi-JohnFi-JohnChangHsu, Kuo-LinKuo-LinHsu2010-01-132018-06-292010-01-132018-06-291991-12http://ntur.lib.ntu.edu.tw//handle/246246/176149水文序列常具有明顯的趨勢及序列相關性質，若忽略這些特性而以一獨立隨機序列加以模擬繁衍，將影響極端事件推估之結果。採用時間序列模擬繁衍資料，可兼顧考慮趨勢及序列相關性質，有助於吾人對極端事件之推估。本研究採用自迴歸理論序列及具序列相關性之河川年流量資料進行模擬分析，並應用於極端乾旱事件之推估，其結果顯示利用AR (1)模式所求得2年（或5年）移動平均之乾旱流量皆較隨機序列模式所得之乾旱流量為低。It is well known that hydrological time series contain nonconstant trends and autocorrelations. These properties are in conflict with the basic assumption of univariate probability distributions. Both properties can have a significant influence of estimates of the extreme values. By means of time series simulation, these effects can be considered concurrently in estimating the extreme values. This paper reports the results of such a simulation for determining the extreme values using actual annual streamflow data that contain autocorrelations. The results show that for 2(or5) years moving average drought streamflows, using AR(1) model will produce small values than using white noise model.en-US合成序列流量迴歸journal articlehttp://ntur.lib.ntu.edu.tw/bitstream/246246/176149/1/自迴歸合成流量序列之特徵及其應用.pdf