Please use this identifier to cite or link to this item: http://hdl.handle.net/11607/2639
Title: Calibration of Van genuchten unsaturated hydraulic conductivity parameters by regression technique
Authors: Kutlu, Turgut
Erşahin, Sabit
TR104286
TR2090
Arslanbey Vocational School, Kocaeli University
Keywords: RETC
Van Genuchten Parameters
Water Retention Curve
Unsaturated Hydraulic Conductivity
Estimation
Modeling
Issue Date: 2008
Publisher: Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi
Citation: Kutlu, T., Erşahin, S. (2008). Calibration of Van genuchten unsaturated hydraulic conductivity parameters by regression technique. International Meeting on Soil Fertility Land Management and Agroclimatology, Special Issue, 175-181.
Abstract: Unsaturated hydraulic conductivity is the mainstay, modulating water and chemical transport in the field. Measurements of parameters take place in the processes are difficult and require time, labor and finance. Thus, correct estimation of these parameters is very important to save valuable sources. The purposes of the study was to estimate van Genuchten unsaturated hydraulic conductivity parameters with RETC-ROSETTA program and calibrating the estimations by regression technique using easily measured soil physical properties, such as components of texture, bulk density and water holding capacity. Total, 168 soil and bulk density samples were collected from 0-30 cm soil depth in an alluvial area located over young river terraces of Yesilırmak near Tokat city. The soil samples were analyzed for clay, silt, sand, and organic matter, and saturated hydraulic conductivities of each sample was measured. Soil water content of each soil sample was determined for -10, -20, -33, -50, -75, -100, -300, -500, -700 and -1000 KPa soil water pressure. van Genuchten’s water retention curve parameters, a and n, were determined inversely using water retention data with RETC program. In addition to estimation of a and n parameters using RETC program, regression technique was used to develop equations to predict a and n parameters using basic soil parameters. Performance of regression-model was judged by correlation of estimations with observed values of validation data set.
URI: http://hdl.handle.net/11607/2639
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