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Quantification of Soil Surface Roughness Evolution under Simulated Rainfall

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Transactions of the ASABE. 56(2): 505-514. (doi: 10.13031/2013.42670) @2013
Authors:   Jan Vermang, L. Darrell Norton, Jan M. Baetens, Chi-hua Huang, Wim M. Cornelis, Donald Gabriels
Keywords:   Erosion Fractal dimension Laser scanner Microrelief Rainfall simulator Random roughness Revised triangular prism surface area method Soil surface roughness Variogram.
<italic>Abstract.</italic>

Soil surface roughness is commonly identified as one of the dominant factors governing runoff and interrill erosion. The objective of this study was to compare several existing soil surface roughness indices and to test the use of the revised triangular prism surface area method (RTPM) to calculate the fractal dimension as a roughness index. A silty clay loam soil was sampled, sieved to four aggregate sizes, and each size was packed in soil trays in order to derive four different soil surface roughness classes. Rainfall simulations using an oscillating nozzle simulator were conducted for 90 min at 50.2 mm h-1 average intensity. The surface microtopography was digitized by an instantaneous profile laser scanner before and after the rainfall application. Calculated roughness indices included random roughness, variogram sill and range, fractal dimension and fractal length using a fractional Brownian motion (fBm) model, variance and correlation length according to a Markov-Gaussian (MG) model, and fractal dimension using the RTPM. Random roughness is shown to be the best estimator to significantly distinguish soil surface roughness classes. When taking spatial dependency into account, the variogram sill was the best alternative. The fractal dimension calculated from the fBm model did not yield good results, as only short-range variations were incorporated. The MG variance described the large-scale roughness better than the parameters of the fBm model did. The fractal dimension from the RTPM performed well, although it could not significantly discriminate between all roughness classes. Since it covered a greater range of scales, we believe that it is a good estimator of the overall roughness.

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