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Soil texture fractions and fractal dimension of particle size distribution as predictors of interrill erodibility

Choosing a particular textural fraction as an erodibility predictor is often confusing because various fractions of soil particles have been introduced as erodibility index by many researchers. Recently, advances in fractal theory have introduced a scaling parameter for characterizing soil fragments. | Research Article Turk J Agric For 35 (2011) 95-102 © TÜBİTAK doi:10.3906/tar-0911-30 Soil texture fractions and fractal dimension of particle size distribution as predictors of interrill erodibility Mohammad Reza NEYSHABOURI1, Abbas AHMADI1,*, Hassan ROUHIPOUR2, Hossein ASADI3, Mehdi IRANNAJAD4 1Department of Soil Science, Faculty of Agriculture, Tabriz University, Tabriz - IRAN 2Research Institute of Forests and Rangelands, Tehran - IRAN 3Department of Soil Science, Faculty of Agriculture, University of Guilan, Rasht - IRAN 4Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran - IRAN Received: 05.11.2009 Abstract: Choosing a particular textural fraction as an erodibility predictor is often confusing because various fractions of soil particles have been introduced as erodibility index by many researchers. Recently, advances in fractal theory have introduced a scaling parameter for characterizing soil fragments. The objectives of this study were (i) to test the applicability of fractal dimension of particle size distribution (PSD) for estimation of interrill erodibility and (ii) to study the relationship between interrill erodibility and soil texture components. Samples from 36 soil series with contrasting characters were collected from northwest Iran. The sand fractions were obtained by sieving, while silt and clay fractions were determined by hydrometer. Fractal dimension (DB) of PSD was estimated. A rainfall simulator with drainable tilting 2 -1 flume (1 × 0.5 m ) at a slope of 9% was used and interrill erodibility (Ki) was calculated for 20, 37, and 47 mm h rainfall intensities. The results showed a positive correlation between Ki and clay content. The degree of dependence of Ki to soil texture fractions (sand, silt, and clay contents) was greatly affected by the rainfall intensity level. Using either texture fractions (sand, silt, very fine sand and sand) or DB did not affect the accuracy of the Ki- predicting .