土壤導水度為影響溶質移動之重要因子，但其卻具有變異大、高度偏歪及具有極端值的特性，為了探討土壤導水度在空間上的變異，本研究利用地理統計中之一般克力金法(Ordinary kriging)及序列高斯模擬(Sequential Gaussian simulation)來推估土壤導水度之空間分佈。本研究區位在屏東縣新園鄉之一處農地，面積約為二公頃，農地內以30公尺為間距，利用葛洛夫通透儀(Guelph permeameter)現地測量田間飽和導水度。並於同樣位置規則採取上下二層土壤樣品(0-20cm、20-40cm)，測其土壤基本物理性質及土壤有機碳含量。經由相關係數的計算，顯示土壤飽和導水度與有機碳含量、總體密度、砂粒、砏粒、黏粒都有顯著的相關。空間結構分析顯示，土壤之總體密度、有機碳含量及土壤飽和導水度，都有良好的空間相依性，利用克力金法進行推估，可以得到良好的空間推估分佈圖。另外，本研究也使用了序列高斯模擬去進行空間推估，並探討克力金法和序列高斯模擬意義上之不同。序列高斯模擬之理論基礎為多重高斯分佈，本研究檢驗了雙點高斯分佈，顯示所有性質皆符合理論假設，故可使用序列高斯模擬來進行空間推估。結果顯示，序列高斯模擬可以改善克力金法在推估時所產生之平滑效應，且其可以得到每一個推估位置的機率分佈函數，而不只是單獨一個推估值。最後，並利用了克力金法、序列高斯模擬分別對土壤飽和導水度、總體密度及有機碳含量進行空間分佈推估，並將最後之結果，結合土壤溶質移動模式LEACHMP(Leaching Estimation And Chemistry Model)，來探討其對溶質移動的影響。結果顯示，由克力金法結合LEACHMP模式，可得到經過一定時間後，一定深度下每一個網格點位置農藥之濃度，其為一平滑的空間分佈等值圖，而由序列高斯模擬結合LEACHMP模式，可以得到經過一定時間後，一定深度下每一個網格點位置農藥濃度之機率分佈函數，由此分佈圖可看出其變異非常大，有許多極端值會產生，故面對此種高變異的性質，或許採用序列高斯模擬更能掌握極端值。

Soil saturated hydraulic conductivity is an important factor influencing the transport of solutes in soils. It usually exhibits great variation and high skewness. Therefore, spatial estimation of its distribution in soils is essential for prediction of solute transport. The objectives of this study are using ordinary kriging and sequential Gaussian simulation (SGS) methods to estimate the spatial distribution of soil saturated hydraulic conductivity and comparing the difference between these two methods. The study site is an area of 2ha farmland located at Pintun county. The saturated hydraulic conductivity (Kfs) was measured in situ by Guelph permeameter and topsoil (0-20cm) and subsoil (20-40cm) were sampled at an interval of 30 meter. Soil properties such as bulk density(Pb) , organic carbon content (O.C.%), sand %, silt %, clay % were also determined.The log transformed field saturated hydraulic conductivity (LogKfs) was highly correlated with Pb, O.C.%, sand %, silt % and clay %. The semivariograms of soil properties mentioned above showed that these properties were strongly spatial dependent. Therefore, the spatial distribution of LogKfs and these soil properties can be estimated using kriging.Since SGS is based on the multi-Gaussian model, it is necessary to check if these soil properties correspond to the biGaussian distribution. These soil properties are shown corresponding to the hypothesis of the biGaussian theory, so it is practicable to use SGS to get the spatial estimation. The comparison of spatial estimation obtaining using SGS and kriging shows that SGS can improve the smooth effect of kriging estimation, and in addition can obtain the frequency distribution function of these properties in every node of the grid in the study area.Finally, combining the kriging and SGS estimation of soil saturated hydraulic conductivity, bulk density and organic carbon content with the solute transport model─LEACHMP model to predict the transport of a pesticide-atrazine in study site. SGS estimation combined with LEACHMP model can predict the frequency distribution function of pesticide concentration at any depth after a certain time at each node. It can provide the uncertainty of pesticide concentration, which is not available by using kriging estimation combined with LEACHMP model. The comparison of pesticide concentration predicting using these two methods shows that SGS estimation combined with LEACHMP model can provide the extreme value, which may be smoothed in the kriging combined with LEACHMP model.

中文摘要 I英文摘要III目錄V表次VI圖次VII第一章 前言1第二章 原理6第一節 區域化變數理論6第二節 克力金法與序列高斯模擬12第三章 材料與方法22第一節 試驗之土壤22第二節 資料分析與研究30第三節 將二種不同推估方式結合LEACHMP模式，以探討其對溶質移動之影 響 34第四章 結果與討論41第一節 研究資料的結構與特性41第二節 比較二種推估方式去推估土壤性質空間分佈之差異50第三節 比較原採樣點觀測值直接輸入LEACHMP模式與二種不同推估方式結 合LEACHMP模式對溶質移動之影響 76第五章 結論82第六章 參考文獻:83附錄91

1.李達源、莊愷瑋.1997.地理統計應用於重金屬污染土壤的調查與界定. 第五屆土壤污染防治研討會論文集 p. 169-198. 台北.2.孫定國、廖龍盛. 1970. 實用農藥. 台灣省農林廳出版.3.莊愷瑋. 1995. 地理統計預測污染土壤中重金屬的空間分佈。碩士論 文。 國立臺灣大農業化學研究所。4.莊愷瑋、李達源、陳尊賢.1996a.地理統計預測污染土壤中重金屬的 空間分佈：I.極端值與半變異圖模式的影響.中國農業化學會誌 34 (5):560-574.5.莊愷瑋、李達源、陳尊賢.199 6b.地理統計預測污染土壤中重金屬的空 間分佈：II.採樣方式的探討.中國農業化學會誌 34(6):683-694.6.張明暉、李達源、陳尊賢. 1992. 鎘、鉛在砂質與黏質污染土壤中移動 之初步評估。中國農業化學會誌30(2)：204-215。7.張尊國、王允義、林裕彬.1996.利用地理統計方法鑑識土壤重金屬污染 之空間分佈. 第九屆環境規劃與管理研討會論文集 p. 388-395. 國立 中央大學.8.鄭森源、萬鑫森.1994.地理統計學在土壤污染方面之應用.中國農業化 學會誌 32(4):406-429。9.簡宜如. 1995. 應用一般與協同克力金法預測土壤性質空間變異之研 究。碩士論文。國立臺灣大農業化學研究所。10.闕培德、駱尚廉.1996.土壤污染評估決策支援系統之敏感度分析. 第 九屆環境規劃與管理研討會論文集 p. 169-176. 台北.
11.Anderson, T. 1958. An introduction to multivariate statistical analysis. John Wiley &Sons, New York.12.Arrouays, D., M. Mench, V. Amans, and A. Gomez. 1996. Short- range variability of fallout Pb in a contaminated soil. Can. J. Soil Sci. 76:73-81.13.Biggar, J.W. and D.R. Nielson. 1976. Spatial variability of the leaching characteristics of a field soil. Water Resour. Res. 12:78-84.14.Bierkens, M. F. P. and P. A. Burrough. 1993a, The indicator approach to categorical soil data: I. Theory. J. Soil Sci., 44:361-368.15.Bierkens, M. F. P. and P. A. Burrough. 1993b, The indicator approach to categorical soil data: II. Application to mapping and land use suitability analysis. J. Soil Sci., 44:369-381.16.Boekhold, A.E. and S. E. A. T. M. Van der Zee. 1992. Significance of soil chemical heterogeneity for spatial behavior of cadmium in field soils. Soil Sci. Soc. Am. J. 56:747-754.17.Burgess, T.M. and R. Webster. 1980a. Optimal interpolation and isarithmic mapping of soil properties. I. The semi- variogram and punctual kriging. J. Soil Sci. 31:315-331.18.Burgess, T. M. and R. Webster. 1980b. Optimal interpolation and isarithmic mapping of soil properties. II. Block kriging. J. Soil Sci. 31:332-341.19.Burgess, T.M., R. Webster, and A.B. McBratney. 1981. Optimal interpolation and isarithmic mapping of soil properties. IV. Sampling strategy. J. Soil Sci. 32:643-659.20.Boyton, W. P.and W. H. Brattain. 1929. Interdiffusion of gases and vapors. Int. Critical Tables 5:62-63.21.Bruins, H. R. 1929. Coefficients of diffusion in liquids. Int. Critical Table 5:63-72.22.Campbell, G. 1974. A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci. 117:311-314.23.Carsel, R. F., R. S. Parrish, R. L. Jones, J. L. Hansen., and R.L. Lamb. 1988. Characterizing the uncertainty of pesticide leaching in agricultural soils. J. Contam. Hydrol. 2:111-124.24.Clark, I. 1979. Practical geostatistics. Applied Science Publishers. Essex, England.25.Cressie, N. 1990. The origins of kriging. Math. Geol. 22:239- 252.26.Cressie, N. 1991. Statistics for spatial data. John Wiley & Sons Inc., New York.27.Deutsh, C. V. and A. G. Journel. 1992. GSLIB: Geostatistical software library and user’s guide. Oxford University Press, New York.28.Di, H.J. and L.A.G. Aylmore. 1997. Modeling the probabilities of groundwater contamination by pesticides. Soil Sci. Soc. Am. J. 61:17-23.29.Di, H.J., R.S. Kookana, and L.A.G. Aylmore. 1995. Application of a simple model to assess the ground water contamination potential of pesticides. Aust. J. Soil Res. 33:1031-1040.30.Foussereau, X., A. G. Hornsby and R. B. Brown. 1993. Accounting for variability within map units when linking a pesticide fate model to soil survey. Geoderma. 60:257-276.31.Goovaerts, P. 1997. Geostatistics for natural resources evaluation . Oxford University Press, New York.32.Isaaks, E.H. and R.M. Srivastava. 1989. Applied geostatistics. Oxford Univ. Press, New York.33.Juang, K. W. and Lee, D. Y. 1998. A comparison of three kriging methods using auxiliary variables in heavy-metal contaminated soils. J. Environ. Qual. 27:355-36334.Journel, A. G.and Ch. J. Huijbregts. 1978. Mining geostatistics. Academic Press, New York.35.Journel, A. G. and D. Posa. 1990. Characteristic behavior and order relations for indicator variograms. Math. Geol. 22:1011-1025.36.Journel, A. G. 1989. Fundamentals of geostatistics in five lessons. Volume 8 short course in geology. American Geophysical Union, Washington, D. C.37.Jury, W.A., D.D. Focht, and W.J. Farmer. 1987. Evaluation of pesticide groundwater pollution potential from standard indices of soil-chemical adsorption and biodegradation. J. Environ. Qual. 16:422-428.38.Jury, W.A. and J. Gruber. 1989. A stochastic analysis of the influence of soil and climatic variability on the estimate of pesticide groundwater pollution potential. Water Resour. Res. 25:2465-2474.39.Jury, W. A., W. F. Spencer, and W. J. Farmer. 1983. Behavior assessment model for trace organics in soils: I. Model. Description. J. Environ. Qual. 12:558-564.40.Jury, W. A. ,W. F. Spencer, and W. J. Farmer. 1984. Behavior assessment model for trace organics in soils: III. Application of screening model. J. Environ. Qual. 13:573-579.
41.Lin, M.L. 1991. Geostatistical analysis of soil chemical properties of large banana land area. Soil Sci. Soc. Am. J. 56:341-346.42.Litaor, M. I. 1995. Spatial analysis of plutonium-239+240 and americium-241 in soils around Rocky Flats, Colorado. J. Environ. Qual. 24:506-516.43.Loague, K. M., R. S. Yost, R. E. Green, and T. C. Liang. 1989. Uncertainty in a pesticide leaching assessment for Hawaii. J. Contam Hydro. 4:139-161.44.Loague, K. M., R. E. Green, T. W. Giambelluca, T. C. Liang, and R. S. Yost. 1990. Impact of uncertainty in soil, climate, and chemical information in a pesticide leaching assessment. J. Contaim. Hydrol. 5:171-194.45.Nelson, D. W., and L. E. Sommers. 1982. Total carbon, organic carbon, and organic matter. P.539-579. In A. L. Page et al. (ed..) Methods of soil analysis, part2. 2nd ed. 46.Agron. Monogr. 9.ASA and SSSA, Madison, WI.Nofziger, D.L. and A.G. Hornsby. 1986. A microcomputer-based management tool for chemical movement in soil. Appl. Agric. Res. 1:50- 56.47.Oliver, G.R. and D.K. Laskowski. 1986. Development of environmental scenarios for modeling the fate of agricultural chemicals in soil. Environ. Toxicol. Chem. 5:225-231.48.Olsen, S. R. and W. D. Kemper. 1968. A movement of nutrients to plant root. Adv. Agron. 20:91-151.49.Parker, R. D., H.P. Nelson, and R.D. Jones. 1996. Use of variable and uncertain data to quantify environmental pesticide risk. p. 131-142. In W.D. Nettleton, A.G. Hornsby,50.R.B. Brown, and T.L. Coleman (ed.) Data reliability and risk assessment in soil interpretations. SSSA Special Publication No. 47. Soil Science Society of America, Inc. Madison, Wisconsin, USA.51.Petach, M.C., R.J. Wagenet, and S.D. Degloria. 1991. Regional water flow and pesticide leaching using simulations with spatially distributed data. Geoderma 48:245-269.52.Rao, P.V., P. S. C. Rao, J. M. Davidson, and L. C. Hammond. 1979. Use of goodness-of-fit tests for characterizing the spatial variability of soil properties. Soil Sci. Soc. Am. J. 43:274-278.53.Reynolds, W. D. and D. E. Elrick. 1983. A reexamination of the constant head well permeameter method for measuring saturated hydraulic conductivity above the water table. Soil Sci. 136:250-269.54.Reynolds, W. D., and D. E. Elrick. 1985. In situ measurement of field-saturated hydraulic conductivity, sorptivity, and the -parameter using the Guelph permeameter. Soil Sci. 140:292-302.55.Reynolds, W. D., D. E. Elrick, and B. E. Clothier. 1985. The constant head well permeameter :effect of unsaturated flow. Soil Sci. 139: 172-180.56.Samra, J.S. and H.S. Gill. 1993. Modeling of variation in a sodium-contaminated soil and associated tree growth. Soil Sci. 155:148-153.57.Smith, J. L., J. J. Halvorson, and R. I. Papendick. 1993, Using multiple-variable indicator kriging for evaluating soil quality. Soil Sci. Soc. Am. J. 57:743-749.58.Srivastava, R. M. 1987. Minimum variance or maximum profitability. CIM Bulletin. 80:63-68.59.Trangmar, B. B., R. S. Yost, and G. Uehara. 1985. Application of geostatistics to spatial studies of soil properties. Adv. Agron.,38:45-94.60.Verly, G. 1986. MultiGaussian kriging─A complete case study. In R. V. Ramani,editor, Proceedings of the 19th International APCOM symposium, P. 283-298, Littleton, CO. Society of Mining Engineers.61.Webster, R., and M. A. Oliver. 1990. Statistical methods in soil and land resources surveys. Oxford University Press, New York.62.Xiao, H. 1985. A description of the behavior of indicator variograms for a bivariate normal distribution. Master’s thesis, Stanford University, CA.63.Yates, S.R. and A.W. Warrick. 1987. Estimating soil water content using cokriging. Soil Sci. Soc. Am. J. 51:23-30.