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A scaling approach, predicting the continuous form of soil moisture characteristics curve, from soil particle size distribution and bulk density data

机译:一种缩放方法,根据土壤粒径分布和堆积密度数据预测土壤水分特征曲线的连续形式

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摘要

A substantial number of models, predicting the Soil Moisture Characteristic Curve (SMC) from Particle Size Distribution (PSD) data, underestimate the dry range of the SMC especially in soils with high clay and organic matter contents. In this study, we 5 applied a continuous form of the PSD model to predict the SMC and subsequently, we developed a physically based scaling approach to reduce the model’s bias at the dry range of the SMC. The soil particles packing parameter, obtained from the porosity was considered as a characteristic length. The model was tested by using eighty-two soil samples, selected from the UNSODA database. The result showed that the scaling 10 approach properly estimate the SMC for all soil samples. In comparison to the formerly used physically based SMC model, the proposed approach improved the model estimations by an average of 30% for all soil samples. However, the advantage of this new approach was larger for the fine and medium textured soils than that for the coarse textured soil. In view that in this approach there is no further need for empirical parameters, we conclude that this approach could become applicable for estimating SMC at the larger field scale.
机译:从粒度分布(PSD)数据预测土壤水分特征曲线(SMC)的大量模型都低估了SMC的干燥范围,尤其是在粘土和有机物含量高的土壤中。在这项研究中,我们5运用了PSD模型的连续形式来预测SMC,随后,我们开发了一种基于物理的缩放方法来减少模型在SMC干燥范围内的偏差。由孔隙率获得的土壤颗粒堆积参数被认为是特征长度。使用从UNSODA数据库中选择的82个土壤样本对模型进行了测试。结果表明,尺度10方法可以正确估计所有土壤样品的SMC。与以前使用的基于物理的SMC模型相比,该方法对所有土壤样本的模型估计平均提高了30%。但是,这种新方法的优势对于中,细纹理土壤比粗纹理土壤更大。鉴于此方法不再需要经验参数,因此我们得出结论,该方法可能适用于在更大的领域中估算SMC。

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