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The Scientific World Journal
Volume 2014 (2014), Article ID 569851, 12 pages
Research Article

Performance Evaluation of Four-Parameter Models of the Soil-Water Characteristic Curve

1Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
2Civil Engineering Program, School of Engineering and Information Technology, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
3Department of Civil Engineering, Polytechnic Negeri Semarang, Jl. Professor Soedarto, SH, Tembalang, Semarang 50275, Indonesia

Received 20 January 2014; Revised 2 May 2014; Accepted 2 May 2014; Published 21 May 2014

Academic Editor: Pu-yan Nie

Copyright © 2014 Siti Jahara Matlan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Soil-water characteristic curves (SWCCs) are important in terms of groundwater recharge, agriculture, and soil chemistry. These relationships are also of considerable value in geotechnical and geoenvironmental engineering. Their measurement, however, is difficult, expensive, and time-consuming. Many empirical models have been developed to describe the SWCC. Statistical assessment of soil-water characteristic curve models found that exponential-based model equations were the most difficult to fit and generally provided the poorest fit to the soil-water characteristic data. In this paper, an exponential-based model is devised to describe the SWCC. The modified equation is similar to those previously reported by Gardner (1956) but includes exponential variable. Verification was performed with 24 independent data sets for a wide range of soil textures. Prediction results were compared with the most widely used models to assess the model’s performance. It was proven that the exponential-based equation of the modified model provided greater flexibility and a better fit to data on various types of soil.