Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

  • Print ISSN 0367-8245

  • Online ISSN 0976-058X

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Indian Journal of Agricultural Research, volume 49 issue 6 (december 2015) : 528-533

Prediction of unsaturated soil hydraulic conductivity using air permeability: Regression approach

Mohammad Reza Neyshaboury1, Mehdi Rahmati*, Seyed Alireza Rafiee Alavi1, Hosein Rezaee2, Amirhosein Nazemi3
1<p>Department of Soil Science, Faculty of Agriculture,&nbsp;<br /> University of Maragheh, Maragheh, Iran.</p>
Cite article:- Neyshaboury1 Reza Mohammad, Rahmati* Mehdi, Alavi1 Rafiee Alireza Seyed, Rezaee2 Hosein, Nazemi3 Amirhosein (2015). Prediction of unsaturated soil hydraulic conductivity usingair permeability: Regression approach . Indian Journal of Agricultural Research. 49(6): 528-533. doi: 10.18805/ijare.v49i6.6680.

A close correlation between water conductivity (K(q)) and air permeability (Ka), measured at various water contents, is expected due to tight dependence of water filled porosity to air filled porosity of soils. Finding such a relation will greatly facilitate the prediction of unsaturated water conductivity (K(q)). So, the purpose of the current investigation was to find out if a reliable relation or function between the two permeabilities can be established. In this regard, K(q) and Ka were measured by pressure plate outflow and variable head methods, respectively, at the range of 0 to -100 kPa matric potential (ym). A linear regression function between relative water conductivity (Kr(q)) and Ka  with the correlation coefficient (R) from 0.884 to 0.999 were established for the 22 examined soils. The overall R for 128 data pairs (Kr(q) and Ka) became 0.821 (being significant at P<0.01) with the slope (b) of -2.54 and intercept (a) of -10.93. For the comparison propose Kr(q) were also predicted from RETC using experimental SMC data and van Genuchten and Brooks-Corey models. The reliability of the Kr(q) prediction from Ka based on root mean square error (RMSE), geometric mean error ratio (GMER), and geometric standard deviation of error ratio (GSDER) criteria became considerable greater than those predicted from the two mentioned models. 

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