An empirical method for estimating the soil hydraulic conductivity using particle size distribution curve, Case study: Isfahan city

Author(s):  
Rassoul Ajalloeian ◽  
Farid Fazileh ◽  
Gholam Hossein Karami
Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 23 ◽  
Author(s):  
Mohammad Reza Neyshabouri ◽  
Mehdi Rahmati ◽  
Claude Doussan ◽  
Boshra Behroozinezhad

Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.


2003 ◽  
Vol 68 (11) ◽  
pp. 903-907 ◽  
Author(s):  
Konstantin Popov ◽  
Predrag Zivkovic ◽  
Snezana Krstic

The relation between the specific surface and apparent density of copper powders electrodeposited from acid copper sulfate solutions is established. It is shown that the apparent density is inversely proportional to the specific surface of copper powder. The shape of the particle size distribution curve is also discussed.


2009 ◽  
Vol 421-422 ◽  
pp. 550-553
Author(s):  
Athipong Ngamjarurojana ◽  
Rattikorn Yimnirun ◽  
Supon Ananta

Zinc niobate, ZnNb2O6, nanopowders was synthesized by a solid-state reaction via a rapid vibro-milling technique. The effect of milling time on the phase formation and particle size of ZnNb2O6 powder was investigated. The formation of the ZnNb2O6 phase investigated as a function of calcination conditions by DTA and XRD. The particle size distribution of the calcined powders was determined by laser diffraction technique, while morphology, crystal structure and phase composition were determined via a SEM techniques. In addition, by employing an appropriate choice of milling time, a narrow particle size distribution curve was also observed.


2014 ◽  
Vol 28 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Hossein Bayat ◽  
Naser Davatgar ◽  
Mohsen Jalali

Abstract The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.


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