近饱和导电性与氚浸出的关系

IF 4 2区 农林科学 Q2 SOIL SCIENCE
Ping Xin, Charles Pesch, Trine Norgaard, Goswin Heckrath, Lis W. de Jonge, Bo V. Iversen
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引用次数: 0

摘要

结构土大孔流动是决定土壤中水分、污染物和养分运移的重要过程。因此,我们也期望饱和附近的水力导电性(k(h))与大孔隙流势之间存在密切联系。结合土壤水力特性(SHPs)的测量,示踪剂突破特性可用于深入了解结构性土壤中的大孔流动。在本研究中,我们旨在探讨示踪剂突破特征与结构性土壤SHPs之间是否存在直接联系,这可能部分解释土壤中溶质运移的动力学和空间变异。我们假设突破曲线的特征与土壤的近饱和k(h)之间存在直接关系。我们对来自丹麦8个不同地点的71个未受干扰的表土柱(20厘米高,20厘米直径)使用了SHPs和示踪剂突破特征。我们将k[10](近饱和水力导电性)定义为k(h)在−10 cm的基质电位(h)。在相同的土柱上,基于示踪剂突破实验,我们计算了5%、25%和50%的到达时间(ATs)作为氚示踪剂通过土柱淋滤的累积相对质量的百分比。线性混合模型(lmm)有效地捕捉了变量之间的线性关系。然而,应用机器学习方法(梯度增强决策树,GBDT)通过捕获非线性阈值效应和土壤水力特性之间的关键相互作用,进一步阐明了预测因子的重要性。尽管GBDT的总体预测精度略低于LMM,但两种方法都强调k[10]是最具影响力的预测因子,强调其在优先流动力学中的关键作用。我们得出结论,将SHPs与大型完整柱上的示踪剂突破特征联系起来对于表征土壤大孔功能非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Linkage Between Near-Saturated Hydraulic Conductivity and Tritium Leaching

Macropore flow in structured soils is an important process determining the transport of water, contaminants, and nutrients in the soil. Therefore, we also expect a close connection between hydraulic conductivity (k(h)) near saturation and the potential of macropore flow. In combination with measurements of soil hydraulic properties (SHPs), tracer breakthrough characteristics can be used to get an insight into the understanding of macropore flow in structured soils. In this study, we aim to investigate if a direct link exists between tracer breakthrough characteristics and SHPs of structured soils, which may partly explain the dynamics and the spatial variation of solute transport in soils. We hypothesize that a direct relationship exists between the characteristics of breakthrough curves (BTCs) and the near-saturated k(h) of the soil. We used SHPs and tracer breakthrough characteristics for 71 undisturbed topsoil columns (20 cm height, 20 cm diameter) sampled from eight different sites in Denmark. We defined k[10] (near-saturated hydraulic conductivity) as k(h) at a matric potential (h) of −10 cm. On the same soil columns, based on the tracer breakthrough experiment, we calculated the 5%, 25%, and 50% arrival times (ATs) as the percentage of the cumulative relative mass of the tritium tracer leaching through the soil column. Linear mixed models (LMMs) effectively captured the linear relationships among variables. However, applying a machine learning method (Gradient Boosting Decision Trees, GBDT) further clarified the importance of predictors by capturing nonlinear threshold effects and key interactions among soil hydraulic properties. Although the overall predictive accuracy of GBDT was slightly lower compared to LMM, both methods consistently highlighted k[10] as the most influential predictor, emphasizing its key role in preferential flow dynamics. We conclude that linking SHPs with tracer breakthrough characteristics on large intact columns is highly useful for characterizing soil macropore functions.

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来源期刊
European Journal of Soil Science
European Journal of Soil Science 农林科学-土壤科学
CiteScore
8.20
自引率
4.80%
发文量
117
审稿时长
5 months
期刊介绍: The EJSS is an international journal that publishes outstanding papers in soil science that advance the theoretical and mechanistic understanding of physical, chemical and biological processes and their interactions in soils acting from molecular to continental scales in natural and managed environments.
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