Proceedings of Indian Geotechnical Conference December 15-17, 2011, Kochi (Paper No. B-021) ESTIMATION OF THE SOIL MOISTURE RETENTION CURVE IN COSTAL KARNATAKA USING PEDOTRANSFER FUNCTION K. Varija, Associate Professor, Department of Applied Mechanics, NITK, Surathkal, India, varija.nitk@gmail.com P. Shwetha, Research Scholar, Department of Applied Mechanics, NITK, Surathkal, India, shwethaprasanna@gmail.com ABSTRACT: Estimation of soil moisture retention curve is very much required for the simulation studies of water and solute transport in unsaturated or vadose zone. The soil moisture retention curve and hydraulic conductivity function are two basic hydraulic properties of soils. Because of the complicated structure of unsaturated soil, direct measurement of the soil moisture retention curve and hydraulic conductivity are laborious and time-consuming. It is quite well known that pedotransfer functions are being used for determining the water retention curves from basic soil properties. The objective of this study is to develop and validate point PTFs for the estimation of water retention curve from basic soil properties such as particle-size distribution, bulk density, and organic matter content using multiple-linear regression technique. Fifty soil samples were collected from the different locations at different depths in the coastal area of Karnataka and divided as forty for derivation and ten for validation of the PTFs. Two PTFs were derived for the point estimation of soil moisture retention curve. The results show that, the PTFs for point estimation of soil moisture retention curve could be used appropriately for the soils with loamy sand and sandy loam textures. INTRODUCTION Soil moisture retention curve (SMRC), as one of the most important soil hydraulic properties, is widely used in simulation of water flow in saturated and unsaturated zones and solute transport. Direct measurement of the SMRC is costly and time consuming. Therefore, an alternative to measurement is to estimate this property using more easily available soil properties such as particle size distribution curve, particle density, bulk density, pore size distribution, mineralogy, and soil morphology [1, 2, 3, 4, 5, 6]. Most of these methods can be called pedotransfer functions (PTFs) [7], because they translate existing surrogate data into soil hydraulic data. Pedotransfer procedures are classified as point pedotransfers and function pedotransfers [8]. Point pedotransfer estimates the water content of the soil at certain matric potentials. This kind of PTFs can be used for most conditions based on linear or nonlinear regressions. For an example, Givi et al [9] evaluated the PTFs for `predicting the soil water contents at field capacity and wilting point for 16 soil samples of fine clay or clay loam soil profiles in a semiarid region in Iran. Also, Fooladmand [10] used soil specific surface area and mean geometric soil diameter for estimating the water content of the soil at different matric potentials for some Iranian soils. Furthermore, many studies have done about point PTFs such as the proposed PTFs by Gupta and Larson and, Ahuja et al [11.12]. Function pedotransfer predicts the parameters of a closed-form analytical equation, such as the model of Brooks and Corey or the van Genuchten equation [13, 14]. Also, this kind of PTFs can be used for most conditions based on linear or nonlinear regressions. Vereecken et al. and Scheinost et al. [15, 16] developed PTFs for van Genuchten parameters, separately. Minasny et al. [17] presented both parametric and point PTFs using different approaches such as multiple linear regression, extended nonlinear regression and artificial neural network for estimating SWRC. Those authors found extended nonlinear regression and multiple linear regression to be the most appropriate for parametric and point PTFs, respectively. Tomasella et al [18] compared two techniques, point based method and a parametric approach, to develop a PTFs for water retention of Brazilian soils using the group method of data handling (GMDH) and soil properties such as coarse sand, fine sand, silt, clay, organic carbon content, moisture equivalent, and bulk density. Those authors indicated that the point-based method provided better results. They explained the obtained results by the fact that water content is controlled by different independent variables at different matric potentials in soils, and the point-based method provided a more proper combination of the independent variables. Recently, Børgesen and Schaap [19] developed a point model to estimate water content at -1, -10, -100, and -1500 kPa, and a parametric model to estimate van Genuchten retention model parameters using neural networks and Bootsrap method for a large database of Danish soils. Those authors found that adding organic matter and bulk density as the input parameters of neural networks could improve the estimation of SMRC. They also found that point PTF models over- come parametric PTF models. The objective of this study is to develop and validate the PTFs for point estimation of soil moisture retention curve at nine different soil matric potentials for loamy sand and sandy loam textures, in the coastal area of Karnataka, based on clay, sand fraction of soil, bulk density and organic matte content. MATERIALS AND METHODS For this study, fifty soil samples were collected from the different locations at different depths in coastal area of Mangalore, Karnataka. Five borrow pits were dug out and 145