水文資料是水利工程規劃及設計的重要參考依據，尤其是長期且完整無缺失的資料。台灣多數河川有完整日流量觀測值，而輸砂量觀測值個數僅佔一年總天數的2%~8%，傳統使用輸砂量率定曲線以流量推估輸砂量，推估之輸砂量與觀測流量為固定關係，無法描述觀測流量及輸砂量間的散佈特性。因此本文以聯結函數建立日流量及日懸浮載輸砂量之雙變數機率模式，使用條件於特定觀測流量之輸砂量機率分布進行機率式推估，並以四種單一值輸砂量推估方法及率定曲線推估單一輸砂量值，使用五種誤差指標分別為RMSE(均方根誤差)、MAPE(平均絕對值百分比誤差)、NSE(Nash-Sutcliffe效率)、MNSE(修正Nash-Sutcliffe效率)及KGE(Kling-Gupta效率)評估不同方法之輸砂量推估值與輸砂量觀測值間的差異。本研究以花蓮溪仁壽橋測站1960–2019年的觀測資料為例說明，結果顯示方法3(以條件輸砂量機率密度函數之眾數為推估值)在MAPE及MNSE誤差指標表現最佳，而率定曲線在RMSE及NSE有最佳誤差指標，方法4(條件輸砂量機率密度函數配合蒙地卡羅法)在KGE有最佳值。若以機率分布型態來評估，方法1至4均能產生低輸砂量高發生頻率、高輸砂量低發生頻率，類似於實際觀測輸砂量的特性，而率定曲線推估值無法有此特性。本研究建議於補遺輸砂量的方法各有優缺點，無任一方法在所有評估指標均有最佳表現，未來宜增加不同變數以增加模式補遺資料的準確性。

Hydrological data, especially long and complete data is an important factor for water resources engineering planning and design. Most rivers in Taiwan have complete daily streamflow discharge, but the number of suspended sediment load observations only account for 2% to 8% of the total streamflow observations. Traditionally, suspended sediment load rating curves are used to estimate suspended sediment load by observed discharge. The relationship between suspended sediment load and the observed streamflow discharge of the rating curve is fixed, and it cannot describe scattered characteristics between the observed streamflow discharge and the suspended sediment load. Therefore, this study establishes a bivariate probability model of daily streamflow discharge and daily suspended sediment load using copula to infill sediment in probabilistic manners. In addition, four single-value estimations to impute suspended sediment load are also used in this study for practical applications. The obtained outcomes of these methods associated with the results of the traditional sediment rating curve are compared with recorded data and evaluated in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), modified Nash-Sutcliffe efficiency (MNSE), and Kling-Gupta efficiency (KGE). The proposed approach is applied to Janshou station located in eastern Taiwan with recorded daily data for the period of 1960–2019. The results indicate that the infilled sediments by the sediment rating curve exhibit better performance in RMSE and NSE, while the copula-based methods outperform in MAPE and MNSE.

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