流量指標在防洪減災以及水資源管理上均為不可或缺的依據，然而鑒於全球氣候異常致使水文特性改變情況下，過往的定常性流量分析可能難以適用，非定常性分析遂成為現今學者聚焦之分析方法。本文利用位置、尺度與形狀參數之廣義加成模式(generalized additive model for location, scale, and shape, GAMLSS)進行非定常性流量分析，研究台灣西部地區三大流域，包含北部淡水河流域、中部濁水溪流域，以及南部高屏溪流域共18個流量站之10項年流量指標，分別為年總逕流量、年最大1日流量、年最小1日、7日、30日流量、Q5、Q25、Q50、Q75，以及Q95，探討其分別以時間及水庫指數(reservoir index)為共變數之最佳機率模式，並將以時間為共變數之分析結果與前人趨勢研究進行比較。結果顯示在18個流量站之10個年流量指標共180個項目中，gamma分布最多(54項)，log-normal次之(53項)，Weibull再次之(41項)，三種分布佔全部指標之82.2%；此外，高達145個指標(80.6%)呈非定常性，其中又以年最小1日流量為最多，所有流量站(18站)皆屬之，而年最大1日流量為最少，僅有一半的流量站(9站)，顯示在較低流量具較強烈的非定常性變化。另外，以水庫指數為共變數分析結果顯示在秀朗站年最大1日流量及Q5指標中，相較以時間為共變數的模擬結果好，其他指標則仍是時間趨勢表現較好；霞雲站以及五堵站所有指標皆是以時間為共變數表現較佳。與前人趨勢研究比較結果顯示，以GAMLSS模式模擬成果能夠捕捉其趨勢性外，能夠詳細描述局部變化，提供更多資訊。

Flow indices are important and robust indicators to support the decisions for water resources management and flood control. Due to the climate change and rapid urbanization, the assumption of stationary condition used in water resources planning and hydraulic design may not be applicable. In this study, the generalized additive model for location, scale, and shape (GAMLSS) is used to model the nonstationarity of 10 annual flow indices of three main river basins in western Taiwan (Dan-Shui, Zhuo-Shui, Kao-Ping), which include total runoff, maximum 1-day streamflow, minimum 1-, 7-, 30-day streamflow, Q5, Q25, Q50, Q75, and Q95.The results indicate that (1) the best probability model for fitting flow indices (180 models in total) are the gamma distribution (54 models), log-normal distribution (53 models), and Weibull distribution (41 models). Among these models, 145 models show nonstationarity. (2) The minimum 1-day streamflow of all stations exhibits nonstationarity. On the other hand, only half stations show nonstationarity in the maximum 1-day streamflow. (3) The model incorporating the reservoir index as the covariate shows better performance than the model using the time as covariate for the maximum 1-day streamflow and Q5 indices. (4) The GAMLSS framework provides not only the trend of hydro-climate series, but also point out the information of distributional changes.

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