Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/34975
標題: 土壤水力性質轉換函數之研究─以石門水庫集水區為例
A Study of Soil Hydraulic Properties Transform Function: A Case Study at Shihmen Reservoir Watershed Soils
作者: Wu, Cheng-Jhe
吳晟哲
關鍵字: Water retention curves;水分特性曲線;Pedotransfer functions(PTFs);van Genuchten Model;Multiple regression;Parameters α;Parameters n;土壤轉換模式(PTFs);van Genuchten Model;複迴歸分析;參數α;參數n
出版社: 水土保持學系所
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A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12 : 513-522. 19. Pachepsky, Y., Timlin, and G. Varallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci. Soc. Am. J. 60 : 727-733. 20. Rawls, W. J., and D. L. Brakensiek. 1982 . Estimating soil water retention from soil properties. J. Irrig. Drainage Div. ASCE 108 : 166-171. 21. Santra, P., and B. S. Das. 2008. Pedotransfer functions for soil hydraulic properties developed from a hilly watershed of Eastern India. Geoderma 146 : 439-448. 22. Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick. 1986. Estimating generalized soil-water characteristic from twxture. Soil Sci. Soc Am. J. 50 : 1031-1036. 23. Schaap, M.G., F.J. Leji, and M. Th. Van Genuchten. 1998. Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 62 : 847-855. 24. Scheinost, A.C.,W. Sinowski, and K. Auerswald. 1997. Regionalization of soil water retention curves in highly variable soilscape: I. Developing a new pedotransfer function. Geoderma 78 : 129-143. 25. Teepe, R., H. Dilling, and F. Beese. 2003. Estimating water retention curves of forest soils form soil texture and bulk density. J. Plant Nutr. Soil Sci., 166 : 111-119. 26. Topp, G.C., and E.E.Miller. 1966. Hystersis moisture characteristics and hydraulic conductivities for glass-head media. Soil Sci. Soc. Am. Proc. 30 : 156-162. 27. Tyler, S. W., and S.W. Wheatcraft. 1989. Application of fractal mathematics to soil water retention estimation. Soil Sci. Soc. Am. J. 53 : 987-996. 28. van den Berg, M. van den, E. Klamt, L.P. van Reeuwijk, and W.G. Sombroek. 1997. Pedotransfer functions for the estimation of moisture retention characteristics of Ferralsols and related soils. Geoderma 78 : 161-180. 29. van Genuchten, M. Th., 1980. 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摘要: 
模擬土壤剖面中水分與溶質流動情形時,土壤水力性質為不可或缺的輸入參數,其中水分特性曲線(SWCC)為相當重要的一環,每個土壤擁有其獨特的曲線形狀。近年來學者們提出許多類型之土壤轉換模式(PTFs),以間接方式推估水分特性,省略繁瑣費時的實驗過程,提供便利運用管道。
本研究採集台灣桃園縣石門水庫集水區50個點位之土壤樣本,而後進行室內實驗取得各土壤基本物理性質及水分特性曲線。隨機抽樣其中40個樣本為實驗組,以壓力勢能及其對應土壤水分含量代入van Genuchten Model擬合可得水分特性曲線之參數α、n值,爾後採用SPSS軟體,以原始與數值轉換後之土壤基本物理性質作為因變數進行複迴歸分析,歸納出實驗組土壤基本物理性質與參數α、n值之迴歸方程式。另以10個土樣作為驗證組,評估迴歸式之適用性與正確性。本研究所得之迴歸式如下所示:
ln(α) = -6.433 -0.177‧(OM^2)^-1-8642.344‧(f^2)^-1-21.376‧√D–187.755‧FC^-1–0.269‧D‧FC
ln(n) = 7.252 + 0.968‧D+0.063‧C + 0.026‧(OM^2)^-1- 109.785‧f^-1 – 0.453‧√C-3.73•(C^2)^-1 -1.118•ln(FC)+0.001•D•Sa+0.025•D•FC-0.04•D•f
研究結果顯示推估式之參數α、n判定係數分別為0.845與0.754,驗證組物理性質數據代入求得之參數α、n與實驗值比較,RMSE平均值分別為0.265及0.036,最後深入探討各土樣各壓力預測值之正確度,可得其RMSE為0.010 ~ 0.082,結果相當吻合,表示應用於預測集水區土壤之水力性質已足夠。

Soil hydraulic properties are essential as input data while simulating the transport of water and solutes in the soil profile. The soil water characteristic curves (SWCC) is one of the most important sections. The curve’s shape of SWCC is unique for each soil. In recent yeas the scholars perform many kinds of pedotransfer functions (PTFs) estimating the characteristic of soil water content. It abridges the procedure of laborious and time-consuming experiment and provides the convenient method to use.
This study collected 50 soil samples of Shihmen reservoir watershed in Taoyuan , Taiwan. Performing experiments to achieve every soil’s properties and retention curves in the laboratory. 40 samples were chosen randomly as training set and estimate the parameters α、n which use water content of these soils in specific pressure potential by curve fitting technique of the van Genuchten Model. Multiple regression equations were estimated using original and value-transforming soil physical properties and statistical SPSS software. This study also validated the accuracy of the equations by other 10 soil samples. These estimated regression equations were as follows:
ln(α) = -6.433 -0.177‧(OM^2)^-1-8642.344‧(f^2)^-1-21.376‧√D–187.755‧FC^-1–0.269‧D‧FC
ln(n) = 7.252 + 0.968‧D+0.063‧C + 0.026‧(OM^2)^-1- 109.785‧f^-1 – 0.453‧√C-3.73•(C^2)^-1 -1.118•ln(FC)+0.001•D•Sa+0.025•D•FC-0.04•D•f
The results perform the coefficient of determination (R2) of the parameter α、n of the estimating equations are 0.845 and 0.754. Compare the parameters α、n between training set and testing set , the root mean square (RMSE) are 0.265 and 0.036. This study finally discuss the accuracy of the predicting values of every soil. The range of RMSE is 0.010 ~ 0.082 and the results quite match. It shows the results is accurate enough for applications to predict the hydraulic properties of watershed soils.
URI: http://hdl.handle.net/11455/34975
其他識別: U0005-2807201018245800
Appears in Collections:水土保持學系

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