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Prediction of the Soil Water Characteristic from Soil Particle Volume Fractions

机译:从土壤颗粒体积分数预测土壤水分特征

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Modeling water distribution and flow in partially saturated soils requires knowledge of the soil water characteristic (SWC). However, measurement of the SWC is challenging and time-consuming and, in some cases, not feasible. This study introduces two predictive models (FW–model and AW–model) for the SWC, derived from readily available soil properties such as texture and bulk density. A total of 46 undisturbed soils from different horizons at 15 locations across Denmark were used for model evaluation. The FW–model predicts the volumetric water content as a function of volumetric fines content (organic matter and clay). It performed reasonably well for the dry-end of SWC (above a pF value of 2.0; pF = log(|ψ|), where ψ is the matric potential in cm), but did not do as well closer to saturated conditions. The AW–model predicts the volumetric water content as a function of volumetric content of different particle size fractions (organic matter, clay, silt, and fine and coarse sands). The volumetric content of a particular soil particle size fraction was considered if it contributed to the pore size fraction still occupied with water at the given pF value. Hereby, the AW–model implicitly assumes that a given particle size fraction creates an analogue pore size fraction and further this pore size fraction filled with water is corresponding to a certain pF value according to the well-known capillary rise equation. The AW–model was found to be quite robust, and it performed exceptionally well for pF values ranging from 0.4 to 4.2 for different soil types. For prediction of the continuous SWC, it is recommended to parameterize the van Genuchten model based on the SWC data points predicted by the AW–model.
机译:对部分饱和土壤中的水分布和流量进行建模需要了解土壤水分特征(SWC)。但是,SWC的测量具有挑战性且耗时,在某些情况下不可行。这项研究介绍了SWC的两个预测模型(FW模型和AW模型),这些模型是从易于获得的土壤特性(例如质地和堆积密度)中得出的。在丹麦15个地点的不同水平的总共46种原状土壤用于模型评估。 FW模型预测体积水含量随体积细粉含量(有机物和粘土)的变化而变化。对于SWC的干燥端,它表现良好(pF值为2.0以上; pF = log(|ψ|),其中ψ是单位为cm的基质势),但在接近饱和条件下却表现不佳。 AW模型可预测体积水含量随不同粒径分数(有机物,粘土,粉砂,细砂和粗砂)的体积含量的变化。如果给定的pF值导致特定的土壤粒径级分的体积含量仍被水占据,则应考虑其体积含量。因此,AW-模型隐含地假定给定的粒度分数会产生类似的孔径分数,并且根据众所周知的毛细管上升方程,该充满水的孔径分数对应于某个pF值。发现AW模型非常健壮,并且在不同土壤类型的pF值介于0.4到4.2之间的情况下,它表现出色。对于连续SWC的预测,建议根据AW模型预测的SWC数据点对van Genuchten模型进行参数化。

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