Effect of Input Variables on Predictability of Soil Water Content through Different Soil Water Retention Curve Models

Document Type : Original Article

Authors

1 Former MSc Student of Soil Science, Department of Soil Science, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran.

2 Assistant Professor, Department of Soil Science, Faculty of Agriculture, Bu-Ali Sina University, Hamadan, Iran

3 Associate Professor, Department of Irrigation, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran

Abstract

Soil water retention curve (SWRC) is one of the main soil characteristics with many applications. Its direct measurement is costly and time-consuming. Therefore, it is often predicted through indirect methods such as pedotransfer functions (PTFs). Many models have been developed for quantitative description of SWRC and also a lot of PTFs has been developed for their estimation. However, predictability of soil water content through different SWRC models by using different input variables and artificial neural networks have not been investigated, so far. In this study, 75 soil samples were taken from Guilan province and basic soil properties have been measured. Water contents were measured at 12 matric potentials (0, 1, 2, 5, 10, 25, 50, 100, 200, 500, 1000 and 1500 kPa). Ten well known and frequently applied SWRC models were fitted to the measured data. The Perrier model was fitted on the particles and aggregates size distributions and fractal parameters were obtained. The fractal parameters of particles and aggregates size distributions along with clay, sand and bulk density were used to estimate water content in two input levels by different SWRC models. Results showed that the models of Seki, Fermi and Gardner were predicted more accurately, in comparison with other models. In spite of the expectation, the models of Dexter and Durner were not predicted accurately and according to the cluster analysis were classified in different groups. It was observed that the prediction capabilities of different models were changed and their arrangements were altered in the tables by changing input variables. Overall, Fermi and Dexter models had the highest and the least predictability with the first input levels, respectively. Gardner and Tani models had the highest and the least predictability with the second input levels, respectively.

Keywords

References

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Applied Soil Research
Volume 2, Issue 1 - Serial Number 1
Vol: 2, No: 1
August 2014
Pages 26-42

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History

  • Receive Date: 30 October 2013
  • Revise Date: 08 March 2016
  • Accept Date: 08 April 2014

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