Estimation of soil water retention curve in semi-arid areas using fractal dimension

The soil water retention curve (SWRC) is one of the important hydraulic functions in water flow modeling and solute transport in the porous medium. Direct measurement of SWRC is time consuming and expensive, therefore different models have been developed to describe it. In this study, a model based on fractal theory was derived to estimate water retention curve. The fractal dimension of SWRC (DSWRC) for 130 soil samples (with a spread range of soil texture) were determined and tried to find out a simple relation between this parameter and easily available soil properties such as clay, silt and sand contents, lime percent and bulk density by applying multiple linear regression analysis. The measured DSWRC for 110 soil samples used for regression analysis and 20 soil samples was used for model validation. The regression analysis showed a linear relationship between DSWRC, with clay, silt contents and soil bulk density with the goodness of fit, R2 = 0.909, but lime content did not show any significant effect on SWRC prediction improvement. Therefore, it can be concluded that estimating SWRC in calcareous soil using DSWRC obtained from soil easily measured properties will be a good, rapid and reliable alternative for reliable estimation of soil hydraulic properties of these areas.Keywords: Fractal model; lime percent; Regression analysis; Soil water retention curv

Estimation of soil water retention curve using fractal dimension

The soil water retention curve (SWRC) is a fundamental hydraulic property majorly used to study flow transport in soils and calculate plant-available water. Since, direct measurement of SWRC is time-consuming and expensive, different models have been developed to estimate SWRC. In this study, a fractal-based model was developed to predict SWRC. A wide range of soil textures (130 soil samples) was used to determine the fractal dimension of SWRC (DSWRC). Moreover, the SWRC pedotransfer functions were established based on easily available soil properties such as particle size  distribution and bulk density by applying multiple linear regression analysis. The measured DSWRC for 110 soil samples was considered for function parameterization and the remaining was used for model validation. The results illustrated that the DSWRC linearly correlates with clay and silt contents and soil bulk density (r2 = 0.909). The SWRC can, therefore, be easily and concisely estimated by the proposed fractal-based functions. Key words: Fractal model; Pedotransfer functions; Regression analysis; Soil water retention curv