本研究探討氣候變遷情境下河川入流量機率分布之變化特性，並以臺灣南部之南化水庫與甲仙攔河堰為研究對象，分析不同排放情境（SSP2-4.5與SSP5-8.5）及未來期程（近未來（2021–2040）與中未來（2041–2060））下流量特性之變動趨勢。採用郭又甄（2024）利用25組全球氣候模式（GCM）降尺度資料，並透過降雨–逕流模式推估之日入流量資料，進一步計算九項流量指數，以描述整體水量、極端高低流量及年內分布特性。在方法上，本研究結合機率分布比較、Kullback–Leibler散度（KLD）及統計量相對改變率（平均值與標準差）進行分析，並整合建立機率分布變化分類架構，以辨識流量分布在位置與形狀上的結構性轉變。KLD用以量化未來與基期流量機率分布之差異，並結合平均值與標準差分析流量變化方向及變異程度。研究結果顯示，氣候變遷對整體水量之影響相對有限，年總量與豐水期流量之整體變化幅度較小，但兩研究區呈現不同之變化方向。南化水庫年總量主要呈現平均值增加、標準差增加之型態，而甲仙攔河堰則以平均值減少、標準差增加之型態為主，顯示不同流域對氣候變遷之水文反應具有區域差異。相較之下，極端低流量指數為最敏感之流量指數，其KLD值與變異程度皆明顯高於其他指數，顯示低流量分布已顯著偏離基期。在測站比較方面，南化水庫與甲仙攔河堰之KLD變化範圍有所不同，顯示兩研究區流量分布變化程度及不確定性存在差異。此外，枯水期流量呈現分布相似但變異增加之特性，反映其潛在風險可能被低估。整體而言，氣候變遷對流量之影響並非單純增減，而是表現在機率分布結構與變異性之調整，且多數變化於近未來即已顯現，後續在中未來呈現調整趨勢。本研究建議未來水資源管理應優先關注低流量相關指數，並採用多時間尺度流量指數進行評估，以提升對氣候變遷影響之掌握與調適能力。

This study investigates changes in the probability distributions of streamflow characteristics under climate change scenarios, using Nanhua Reservoir and Jiaxian Weir located in southern Taiwan as case studies. Variations of streamflow characteristics were evaluated under two Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5 and SSP5-8.5) during two future periods: the near future (2021–2040) and the mid-future (2041–2060). Outputs from 25 Global Climate Models (GCMs) were downscaled and fed into a rainfall-runoff model, developed by Guo (2024), to project streamflow series. Based on these data, nine flow indices were calculated to characterize amounts, extreme high- and low-flow conditions, and streamflow unevenness. This study integrates probability distribution analysis, Kullback–Leibler divergence (KLD) and the relative change rates of statistical measures (mean and standard deviation). A probability distribution classification framework was further established to identify structural changes in streamflow distributions with respect to both their location and shape. KLD was employed to quantify the differences between future and baseline probability distributions, while changes in the mean and standard deviation were used to evaluate the direction and magnitude of streamflow variations. The results indicate that the impacts of climate change on amounts of streamflow are relatively limited. Changes in annual total flow and wet-season flow are generally small; however, the two study areas exhibit different response patterns. At Nanhua Reservoir, annual total flow is predominantly characterized by increases in both mean and standard deviation, whereas Jiaxian Weir is mainly characterized by a decrease in mean accompanied by an increase in the standard deviation. These findings suggest that hydrological responses to climate change vary among watersheds. In contrast, extreme low-flow indices are the most sensitive flow indicators, exhibiting substantially higher KLD values and greater variability than the other indices. This fact indicates that low-flow distributions have deviated considerably from the baseline conditions. Comparisons between the two study sites also reveal differences in the ranges of KLD values, suggesting different degrees of distributional change and uncertainty. In addition, dry-season flow exhibits a pattern of similar probability distributions but increased variability, implying that its potential risk may be underestimated. Overall, the impacts of climate change on streamflow characteristics are reflected not simply by increases or decreases in flow magnitude, but rather by structural changes in probability distributions and increased variability. Most of these changes emerge in the near future and subsequently exhibit adjustment trends during the mid-future period. This study suggests that future water resources management should prioritize low-flow-related indices and evaluate climate change impacts using flow indices across multiple temporal scales, thereby improving adaptive capacity.

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