ABSTRACT Apparent diffusion constants in soil are generally estimated by dividing molecular diffusion coefficient for a solute with soil tortuosity (&tgr;) values. Several models have been proposed to estimate &tgr; from soil porosity (ϕ) alone, but most of these models fail when the variability in observed &tgr;-ϕ pairs increases. Pedotransfer functions can be used to predict &tgr; from easy-to-measure soil properties such soil texture, organic carbon contents, and ϕ, but such an approach requires more measurements to be performed than just measuring ϕ. Here, we show that &tgr; may be estimated from ϕ alone using the ensemble averaging approach. We examined seven different analytical expressions for &tgr;-ϕ and seven different ensemble-modeling approaches to estimate &tgr; for 100 pairs of &tgr;-ϕ collected from a wide geographical area. Modeling results showed that the Bayesian model averaging method was the best ensemble-modeling approach for estimating &tgr; from ϕ. Of 119 different combinations of &tgr; (ϕ) models, three models derived considering (1) packing of square-shaped particles, (2) fractal geometry with particles of different sizes, and (3) percolation theory were identified as the best individual models for ensemble modeling. The coefficient of determination (0.67), root-mean-squared error (0.23), and the Akaike information criterion (94.37) values for this ensemble model were better than those when a single model was used for prediction. Inclusion of these three models that are based on both fractal and regular geometrical shapes for particles of different sizes may be a reason for improved performance of the ensemble approach. These results suggest that &tgr; may be estimated from ϕ using the ensemble approach without the need for additional soil data, as is done in a pedotransfer function approach.

中文翻译：

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从孔隙度估计饱和土的扩散曲折度的集合建模方法

摘要 土壤中的表观扩散常数通常通过将溶质的分子扩散系数与土壤曲折度 (&tgr;) 值相除来估计。已经提出了几种模型来估计 &tgr; 仅来自土壤孔隙度 (ϕ)，但是当观察到的 &tgr;-ϕ 对的变异性增加时，这些模型中的大多数都失败了。Pedotransfer 函数可用于预测 &tgr; 易于测量的土壤特性，例如土壤质地、有机碳含量和 ϕ，但这种方法需要进行更多的测量，而不仅仅是测量 ϕ。在这里，我们证明 &tgr; 可以使用集成平均方法单独从 ϕ 估计。我们检查了 &tgr;-ϕ 的七种不同的分析表达式和七种不同的集成建模方法来估计 &tgr; 100双&tgr; -ϕ 从广泛的地理区域收集。建模结果表明，贝叶斯模型平均方法是估计 &tgr; 的最佳集成建模方法。从φ。119 种不同的 &tgr; 组合 (ϕ) 模型，考虑 (1) 方形颗粒的堆积，(2) 具有不同尺寸颗粒的分形几何和 (3) 渗流理论推导出的三个模型被确定为集成建模的最佳个体模型。该集成模型的决定系数 (0.67)、均方根误差 (0.23) 和 Akaike 信息准则 (94.37) 值优于使用单个模型进行预测时的值。包含这三个基于分形和规则几何形状的不同尺寸粒子的模型可能是提高集成方法性能的一个原因。这些结果表明 &tgr; 可以使用集成方法从 ϕ 估计，而不需要额外的土壤数据，就像在 pedotransfer function 方法中所做的那样。