水力傳導係數顯著地影響地下水流動及污染傳輸，了解水力傳導係數之空間分布，有助於地下水流動狀態與污染物運移現象之解釋。本研究使用不同尺度之水力傳導係數資料，運用貝氏定理更新未知點之水力傳導係數平均值，再以地質統計方法進行資料之模擬，並利用軟性資料改善模擬之結果。由於貝氏定理具有處理不同尺度與種類資料之功能，經由同化不同尺度資料可以有效地降低水文傳導係數估測之不確定性。研究結果發現使用不同尺度資料，配合階段式資料同化技術可有效地增加估測結果之準度與精度；而全域之軟性資料資訊可大幅降低參數反推結果之不確定性。

The effect of the hydraulic conductivity on groundwater flow and pollution transport is significant. Understanding the spatial distribution of hydraulic conductivity is helpful for the explanation of groundwater flow movement and contaminant transport phenomena. In this study, hydraulic conductivity data with different scales are integrated using Bayesian geostatistical method. Soft data are used to improve the simulation results. The Bayes' theorem was the capability to deal with data of different scales and types. The assimilation of the different information can effectively reduce uncertainty the estimated hydrological conductivity. The result shows that integrating data of different scales can effectively increase the validity and reliability. The soft data providing whole field information can significantly reduce the uncertainty of the modeling result.

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