Articles | Volume 18, issue 10
Hydrol. Earth Syst. Sci., 18, 4053–4063, 2014
https://doi.org/10.5194/hess-18-4053-2014
Hydrol. Earth Syst. Sci., 18, 4053–4063, 2014
https://doi.org/10.5194/hess-18-4053-2014

Research article 15 Oct 2014

Research article | 15 Oct 2014

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

F. Meskini-Vishkaee1, M. H. Mohammadi1,2, and M. Vanclooster2 F. Meskini-Vishkaee et al.
  • 1Department of Soil Science, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
  • 2Earth and Life Institute, Environmental Sciences, Univ. Catholique de Louvain, Croix du Sud 2, Bte 2, 1348 Louvain-la-Neuve, Belgium

Abstract. 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.

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