Abstract Larger pores allowing macropore flow can rapidly transport contaminants as well as nutrients from the soil surface to the subsoil and groundwater. Modeling of the involved macropore flow is complex due to large number of required parameters. One way to deal with this difficulty is the precise evaluation of the soil hydraulic conductivity function using robust pedotransfer functions for the near-saturated part. Aiming to develop pedotransfer functions to estimate saturated and unsaturated hydraulic conductivities [Ks and K(h)] as well as water contents at specific pressure heads, we used Gaussian Process Regression (GPR), a non-parametric machine learning model, to obtain van Genuchten (1980) retention and conductivity parameters for Danish soils based on soil texture and organic matter. We defined K10 as K(h) at − 10 cm pressure head. The difference between the logarithm of Ks and K10 [log(Ks) − log(K10)] was denominated log(Kjump) and was assumed to convey the potential degree of macropore flow considering only soil hydraulic properties. Macropore flow in Denmark was evaluated through the developed PTFs for log(Ks) and log(K10) with an average RMSE of 0.635 and 0.594 (for K in cm d−1) under a confidence interval obtained by bootstrapping. Dynamics of pressure head in an initially saturated soil column with a zero-flux top boundary condition and a 2 m deep groundwater level were simulated using the Hydrus-1D model. The result of this modeling showed that soils in the eastern part of Denmark had a higher likelihood to experience macropore flow. Finally, using the spatial distribution of meteorological data, a macropore flow map of Denmark was produced. Precipitation was a dominating factor on macropore flow in sandy clay loam and sandy loam soils when compared to sandy and loamy sand soils. Finally, maps of relative risk classes of macropore flow qualitatively differentiated land areas in Denmark for vulnerability of nutrient loss and groundwater contamination.

中文翻译：

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使用近饱和水力特性量化丹麦土壤中的大孔隙流量

摘要 允许大孔隙流动的较大孔隙可以将污染物和养分从土壤表面快速输送到底土和地下水。由于需要大量参数，所涉及的大孔流的建模很复杂。解决这一困难的一种方法是对近饱和部分使用稳健的土壤传递函数来精确评估土壤水力传导率函数。为了开发 pedotransfer 函数来估计饱和和非饱和水力传导率 [Ks 和 K(h)] 以及特定压头下的水含量，我们使用高斯过程回归 (GPR)，一种非参数机器学习模型，来获得 van Genuchten (1980) 基于土壤质地和有机质的丹麦土壤保留和电导率参数。我们将 K10 定义为 - 10 cm 压头下的 K(h)。Ks 和 K10 的对数之间的差值 [log(Ks) - log(K10)] 被命名为 log(Kjump)，并假设仅考虑土壤水力特性来表达大孔隙流动的潜在程度。在通过自举获得的置信区间下，通过开发的 log(Ks) 和 log(K10) 的 PTF 评估了丹麦的大孔隙流量，平均 RMSE 为 0.635 和 0.594（对于 K in cm d-1）。使用 Hydrus-1D 模型模拟了具有零通量顶部边界条件和 2 m 深地下水位的初始饱和土柱中的压头动力学。该模型的结果表明，丹麦东部的土壤更有可能经历大孔隙流。最后，利用气象数据的空间分布，制作了丹麦大孔隙流量图。与砂质和壤质砂土相比，降水是砂质粘壤土和砂质壤土中大孔隙流动的主要因素。最后，大孔隙流的相对风险等级地图定性区分了丹麦土地区域的养分流失和地下水污染的脆弱性。