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Quantification of soil surface roughness evolution under simulated rainfall

(2013) TRANSACTIONS OF THE ASABE. 56(2). p.505-514
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Abstract
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 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.
Keywords
Rainfall simulator, Soil surface roughness, Random roughness, Laser scanner, Microrelief, Erosion, Fractal dimension, INDEX, EROSION, PARAMETERS, MICROTOPOGRAPHY, MICRORELIEF, TILLAGE, LASER SCANNER, FRACTAL DIMENSION, CUMULATIVE RAINFALL, MULTIFRACTAL ANALYSIS, Revised triangular prism surface area method, Variogram

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Citation

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Chicago
Vermang, Jan, L Darrell Norton, Jan Baetens, Chihua Huang, Wim Cornelis, and Donald Gabriëls. 2013. “Quantification of Soil Surface Roughness Evolution Under Simulated Rainfall.” Transactions of the Asabe 56 (2): 505–514.
APA
Vermang, J., Norton, L. D., Baetens, J., Huang, C., Cornelis, W., & Gabriëls, D. (2013). Quantification of soil surface roughness evolution under simulated rainfall. TRANSACTIONS OF THE ASABE, 56(2), 505–514.
Vancouver
1.
Vermang J, Norton LD, Baetens J, Huang C, Cornelis W, Gabriëls D. Quantification of soil surface roughness evolution under simulated rainfall. TRANSACTIONS OF THE ASABE. 2013;56(2):505–14.
MLA
Vermang, Jan, L Darrell Norton, Jan Baetens, et al. “Quantification of Soil Surface Roughness Evolution Under Simulated Rainfall.” TRANSACTIONS OF THE ASABE 56.2 (2013): 505–514. Print.
@article{3159380,
  abstract     = {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 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.},
  author       = {Vermang, Jan and Norton, L Darrell and Baetens, Jan and Huang, Chihua and Cornelis, Wim and Gabri{\"e}ls, Donald},
  issn         = {2151-0032},
  journal      = {TRANSACTIONS OF THE ASABE},
  language     = {eng},
  number       = {2},
  pages        = {505--514},
  title        = {Quantification of soil surface roughness evolution under simulated rainfall},
  volume       = {56},
  year         = {2013},
}

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