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Predicting the soil moisture retention curve, from soil particle size distribution and bulk density data using a packing density scaling factor

机译:使用填充密度缩放因子,根据土壤粒度分布和堆积密度数据预测土壤保水曲线

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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 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 particle packing density was considered as a metric of soil structure and used to define a soil particle packing scaling factor. This factor was subsequently integrated in the conceptual SMC prediction model. The model was tested on 82 soils, selected from the UNSODA database. The results show that the scaling approach properly estimates the SMC for all soil samples. In comparison to the original conceptual SMC model without scaling, the scaling approach improves the model estimations on average by 30 %. Improvements were particularly significant for the fine- and medium-textured soils. Since the scaling approach is parsimonious and does not rely on additional empirical parameters, we conclude that this approach may be used for estimating SMC at the larger field scale from basic soil data.
机译:从粒径分布(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,我们应用了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小SMC干燥范围内模型的偏差。土壤颗粒堆积密度被认为是土壤结构的度量,并用于定义土壤颗粒堆积比例因子。该因素随后被整合到概念性SMC预测模型中。该模型在选自UNSODA数据库的82种土壤上进行了测试。结果表明,缩放方法可以正确估计所有土壤样品的SMC。与没有缩放的原始概念SMC模型相比,缩放方法将模型估计平均提高了30%。对于质地细密和中等质地的土壤,改进特别重要。由于缩放方法是简约的,并且不依赖于其他经验参数,因此我们得出结论,该方法可用于根据基本土壤数据在较大的野外规模下估计SMC。

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