Conditional stochastic analysis of solute transport in heterogeneous, variably saturated soils
Conditional stochastic analysis of solute transport in heterogeneous, variably saturated soils
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DOI：
10.1029/96wr00503
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发表时间：
1996-06
影响因子：
5.4
通讯作者：
T. Harter;T. Yeh
中科院分区：
文献类型：
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作者：
T. Harter;T. Yeh
本文提出了一种有条件（Monte Carlo）模拟饱和多孔介质中点源稳态流动和瞬态输运的方法。它结合了地质统计学方法、土壤水张力扰动解的线性化近似和有限元数值模型。该方法被用来调查的有用性条件模拟预测溶质运移下的各种采样网络设计应用到一些假设的土壤。饱和导水率数据产生的条件不确定性的最大减少在相对潮湿的土壤中，具有轻度的异质性。在高度非均质土壤或干燥条件下，土壤水张力数据本身，在一个到两个相关尺度的采样密度沿着预期的平均行程路径，可以大大降低预测的不确定性溶质浓度。独立随机变量的统计特性的参数不确定性变得不那么重要的条件数据的数量增加。然而，即使有非常多的采样数据，预测浓度水平的不确定性仍然很大。
A method is developed for the conditional (Monte Carlo) simulation of steady stateflow and transient transport from point sources in variably saturated porous media. It combines the geostatistical method, a linearized approximation of the soil water tension perturbation solution, and afinite element numerical model. The method is used to investigate the usefulness of conditional simulation for predicting solute transport under a variety of sampling network designs applied to a number of hypothetical soils. Saturated hydraulic conductivity data yield the largest reduction of conditional uncertainty in relatively wet soils with mild heterogeneities. In highly heterogeneous soils or under dry conditions, soil water tension data by themselves, taken at a sampling density of one to two correlation scales along the expected mean travel path, can greatly reduce prediction uncertainty about solute concentration. Parameter uncertainty about statistical properties of the independent random variables becomes less important as the number of conditioning data increases. However, even with a very high number of sampling data, uncertainty of predicted concentration levels remains significant.