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

摘要

A substantial number of models predicting the soil moisture characteristiccurve (SMC) from particle size distribution (PSD) data underestimate thedry range of the SMC especially in soils with high clay and organic mattercontents. In this study, we applied a continuous form of the PSD model topredict the SMC, and subsequently we developed a physically based scalingapproach to reduce the model's bias at the dry range of the SMC. The soilparticle packing density was considered as a metric of soil structure andused to define a soil particle packing scaling factor. This factor wassubsequently integrated in the conceptual SMC prediction model. The modelwas tested on 82 soils, selected from the UNSODA database. Theresults show that the scaling approach properly estimates the SMC for allsoil samples. In comparison to the original conceptual SMC model withoutscaling, the scaling approach improves the model estimations on average by30%. Improvements were particularly significant for the fine- and medium-textured soils. Since the scaling approach is parsimonious and does not relyon additional empirical parameters, we conclude that this approach may beused for estimating SMC at the larger field scale from basic soil data.