乾旱是一個備受水資源規劃與管理關注的天然災害，因其會對社會和環境產生嚴重的影響，而近年來的氣候變遷可能加劇乾旱的嚴重性及發生頻率。因此，了解和研究乾旱的特性成為水資源管理重要的課題之一，本研究以歷史流量資料為基礎，並以標準化流量指數（Standardized Streamflow Index, SSI）定義水文乾旱，其次建立隱藏式馬可夫(hidden Markov model, HMM)乾旱模式，以進行水文乾旱序率模擬。流量資料本研究選用台灣北部區域蘭陽溪流域的蘭陽大橋測站1950到2021年的觀測月流量資料及台灣南部區域急水溪流域的新營測站1961到2021年的觀測月流量作為分析案例，先利用各月份流量資料擬合機率分佈，再計算一個月時間尺度的標準化流量指數，並通過隱藏的狀態轉換機制來模擬水文乾旱情況。使用標準化流量指數作為水文乾旱的代表指標，本研究所考慮的流量序列繁衍次數有5組、50組及300組，而繁衍乾旱資料以決定係（R^2）、平均百分相對誤差（MPE）和均方根誤差（RMSE）探討繁衍資料與觀測資料的統計特性差異。研究結果顯示，模擬次數越多，模擬性能越好，乾旱特性也會高於歷史流量資料SSI之乾旱特性。

Drought is a critical natural disaster affecting water resource planning and management due to its severe societal and environmental impacts Climate change potentially exacerbates frequency and severity of droughts. This study uses historical streamflow data and the Standardized Streamflow Index (SSI) to define hydrologic droughts. A Hidden Markov Model (HMM) is employed to stochastic simulate hydrologic drought series. Monthly streamflow data from the Lanyang Bridge station (1950-2021) in northern Taiwan and the Shinying station (1961-2021) in southern Taiwan were analyzed. Probability distributions were fitted to monthly streamflow data, and one-month SSI values were calculated. The HMM simulated drought series through hidden state transitions. Simulations were run with five, fifty, and three hundred streamflow sequences. Statistical characteristics of simulated and observed hydrologic drought data are compared in terms of R^2, MPE, and RMSE. The results indicate that increased simulations have consistent drought characteristics with historical data and have higher probabilities to generate extreme drought events, which are rarely observed in historical data.

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