Water and Soil Conservation
Shiva Mohammadian Khorasani
Abstract
Improving water and soil productivity and its management by considering soil structure, soil textures and soil physics parameters are an important criterion for the suitable management of soil and water resources. One of the relatively new methods proposed to explain soil structure in a quantitative ...
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Improving water and soil productivity and its management by considering soil structure, soil textures and soil physics parameters are an important criterion for the suitable management of soil and water resources. One of the relatively new methods proposed to explain soil structure in a quantitative manner is the so-called fractal geometry concept. In this concept, by determining the fractal dimension of bulk soil, the stability of aggregates can be quantitatively analyzed at different scales. The objective of this study has been to quantify the soil structure stability using some classic indicators and also fractal approach in a large scale. Consequently, 41 intact soil samples were taken from an agricultural area and their particle size distribution, soil bulk density and aggregate bulk density, were measured. The weighted mean diameter and geometric mean diameter of both dry and wet aggregates were measured using the dry and wet sieving method. The fractal dimensions of all dry and wet aggregates were obtained using the fractal models of Mandelbrot, Tyler-Wheatcraft and Rieu-Sposito. The results indicated that fractal dimensions of the number-size model of Mandelbrot for dry sieve series and the number-size model of Rieu-Sposito in the wet sieve series perform quite well (R2=0.82). These two models could have the suitable determination coefficient with classical geometric mean and weighted mean diameters of aggregates (R2=0.69).
Water and Soil Conservation
Shiva Mohammadian Khorasani; Mehdi Homaee; Ebrahim Pazira
Abstract
Soil water retention curve is among the most important properties needed for many soil and water management purposes. Due to high spatial and temporal variability in soils, its direct measurement is rather difficult, time consuming and expensive. Consequently, it would be more feasible to estimate it ...
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Soil water retention curve is among the most important properties needed for many soil and water management purposes. Due to high spatial and temporal variability in soils, its direct measurement is rather difficult, time consuming and expensive. Consequently, it would be more feasible to estimate it by using some indirect mathematical methods. The objectives of this investigation are to (1) determine the fractal dimension of the soil retention curve by fitting fractal models to the measurements and (2) investigate the relationship between the fractal dimension and other physical/textural/hydraulic parameters such assoil particle fractions of clay, silt, and sand in large scale. For this purpose, 190 soil samples with broad range of textures from four large agricultural areas were collected, and their particle size distribution, bulk density, organic carbon, salinity, pH, and retention curves were measured. To evaluate the performance of examined fractal models, three statistical parameters including RMSE, RMSD and R2 were used. Results indicated that the fractal dimension has an inverse relationship with soil texture; the finer the soil texture, the greater the fractal dimension. The lowest and greatest fractal dimensions of the Tyler-Wheatcraft model in loamy sand and clay textures were obtained to be 2.38 and 2.74, respectively. These were significant at 1% level based on the Duncan’s multiple range tests. Results further showed that the most accuracy of estimating retention curve in different soil textures by using van Genuchten, Brooks-Corey, and Tyler-Wheatcraft with normalized errors average obtained were 0.06, 1.09, and 3.27, respectively. Furthermore, the obtained R2 values were ranged from 0.88 to 0.99 for Tyler-Wheatcraft and van Genuchten models, respectively. Compares to Brooks-Corey model, the van Genuchten retention model provided better accuracy in estimating retention curve for different soil textures.
