Predicting the Soil Moisture Characteristic Curve from Particle Size Distribution with a Simple Conceptual Model

2011 ◽  
Vol 10 (2) ◽  
pp. 594-602 ◽  
Author(s):  
Mohammad Hossein Mohammadi ◽  
Marnik Vanclooster
Keyword(s):  
Particle Size ◽  
Soil Moisture ◽  
Particle Size Distribution ◽  
Conceptual Model ◽  
Size Distribution ◽  
Characteristic Curve ◽  
Moisture Characteristic

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

2013 ◽  
Vol 10 (11) ◽  
pp. 14305-14329 ◽  
Author(s):  
F. Meskini-Vishkaee ◽  
M. H. Mohammadi ◽  
M. Vanclooster
Keyword(s):  
Particle Size ◽  
Soil Moisture ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Characteristic Curve ◽  
Soil Samples ◽  
Continuous Form ◽  
Packing Parameter ◽  
Physically Based ◽  
Moisture Characteristic

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 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 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.

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

2014 ◽  
Vol 18 (10) ◽  
pp. 4053-4063 ◽  
Author(s):  
F. Meskini-Vishkaee ◽  
M. H. Mohammadi ◽  
M. Vanclooster
Keyword(s):  
Particle Size ◽  
Soil Moisture ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Packing Density ◽  
Characteristic Curve ◽  
Scaling Factor ◽  
Particle Packing ◽  
Soil Particle ◽  
Physically Based

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.

scholarly journals Bayesian Approach for Predicting Soil-Water Characteristic Curve from Particle-Size Distribution Data

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2992 ◽  
Author(s):  
Lin Wang ◽  
Wengang Zhang ◽  
Fuyong Chen
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Soil Water ◽  
Test Data ◽  
Bayesian Approach ◽  
Unsaturated Soils ◽  
Characteristic Curve ◽  
Scaling Parameter ◽  
Soil Water Characteristic Curve

Soil-water characteristic curve (SWCC) is a significant prerequisite for slope stability analysis involving unsaturated soils. However, it is difficult to measure an entire SWCC over a wide suction range using in-situ or laboratory tests. As an alternative, the Arya and Paris (AP) model provides a feasible way to predict SWCC from the routinely available particle-size distribution (PSD) data by introducing a scaling parameter. The accuracy of AP model is generally dependent on the calibrated database which contains test data collected from other sites. How to use the available test data to determine the scaling parameter and to predict the SWCC remains an unresolved problem. This paper develops a Bayesian approach to predict SWCC from PSD. The proposed approach not only determines the scaling parameter, but also identifies fitting parameters of the parametric SWCC model. Finally, the proposed approach is illustrated using real data in Unsaturated Soil Database (UNSODA). Results show that the proposed approach provides a proper prediction of SWCC by making use of the available test data. Additionally, the proposed approach is capable of predicting SWCC in the high suction range, allowing engineers to obtain a complete SWCC in practice with reasonable accuracy.

Sign in / Sign up

Export Citation Format

Share Document