Quantitative Characterization of the Roughness of Four Artificially Prepared Gravel Surfaces

  • Jie QinEmail author
  • Teng Wu
  • Deyu Zhong
Conference paper
Part of the GeoPlanet: Earth and Planetary Sciences book series (GEPS)


Two-dimensional second-order structure functions (2DSSFs) have been widely used to investigate the roughness characteristics of gravel surfaces. Because of the complex patterns of the 2DSSFs, the interpretation of the 2DSSFs results remains challenging, and the explanations are mostly qualitative. Recently, a novel quantitative method for the description of the roughness of gravel-beds was proposed by Qin et al. (J Hydraul Res 57:90–106, 2019), which determines the statistical significance of structure functions based on Monte Carlo simulations. This new method is evaluated by four artificially prepared gravel surfaces to test the capability of the method in quantitatively differentiating predefined gravel structures. The four surfaces were prepared according to dissimilar arrangements of gravels and tried to represent gravel structures of different scales. The results successfully differentiated the roughness characteristics of the four surfaces and demonstrated the promising capability of the new method.


Gravel surfaces Roughness scales Structure functions 



This work is supported by the National Key R&D Program of China (2016YFC0402506), the CRSRI Open Research Program (CKWV2019724/KY), and the Open Funding of the Key Laboratory of Port, Waterway and Sedimentation Engineering of Ministry of Transport (Yn918004).


  1. Aberle J, Nikora VI (2006) Statistical properties of armored gravel bed surfaces. Water Resour Res 42:W11414ADSCrossRefGoogle Scholar
  2. Aberle J, Nikora VI, Henning M, Ettmer B, Hentschel B (2010) Statistical characterization of bed roughness due to bed forms: a field study in the Elbe River at Aken, Germany. Water Resour Res 46:W03521ADSCrossRefGoogle Scholar
  3. Ballio F, Guadagnini A (2004) Convergence assessment of numerical Monte Carlo simulations in groundwater hydrology. Water Resour Res 40:W04603ADSCrossRefGoogle Scholar
  4. Bertin S, Friedrich H (2014) Measurement of gravel-bed topography: evaluation study applying statistical roughness analysis. J Hydraul Eng ASCE 140:269–279CrossRefGoogle Scholar
  5. Bertin S, Friedrich H (2016) Field application of close-range digital photogrammetry (CRDP) for grain-scale fluvial morphology studies. Earth Surf Process Landf 41:1358–1369ADSCrossRefGoogle Scholar
  6. Bertin S, Groom J, Friedrich H (2017) Isolating roughness scales of gravel-bed patches. Water Resour Res 53:6841–6856ADSCrossRefGoogle Scholar
  7. Brayshaw AC (1985) Bed microtopography and entrainment thresholds in gravel-bed rivers. Bull Geol Soc Am 96:218–223CrossRefGoogle Scholar
  8. Butler JB, Lane SN, Chandler JH (2001) Characterization of the structure of river-bed gravels using two-dimensional fractal analysis. Math Geol 33:301–330CrossRefGoogle Scholar
  9. Church M, Hassan MA, Wolcott JF (1998) Stabilizing self-organized structures in gravel-bed stream channels: field and experimental observations. Water Resour Res 34:3169–3179ADSCrossRefGoogle Scholar
  10. Clifford NJ, Robert A, Richards KS (1992) Estimation of flow resistance in gravel-bedded rivers: a physical explanation of the multiplier of roughness length. Earth Surf Process Landf 17:111–126ADSCrossRefGoogle Scholar
  11. Cooper JR, Aberle J, Koll K, McLelland SJ, Murphy BM, Tait SJ, Marion A (2008) Observation of the near-bed flow field over gravel bed surfaces with different roughness length scales. In: Altinakar M, Kokpinar MA, Aydin I, Cokgor S, Kubaba SK (eds) Proceedings of the international conference on fluvial hydraulics river flow 2008, Cesme, Turkey, pp 739–746Google Scholar
  12. Curran JC, Waters KA (2014) The importance of bed sediment sand content for the structure of a static armor layer in a gravel bed river. J Geophys Res 119:1484–1497CrossRefGoogle Scholar
  13. Guala M, Singh A, BadHeartBull N, Foufoula-Georgiou E (2014) Spectral description of migrating bed forms and sediment transport. J Geophys Res 119:123–137CrossRefGoogle Scholar
  14. L’Amoreaux P, Gibson S (2013) Quantifying the scale of gravel-bed clusters with spatial statistics. Geomorphology 197:56–63ADSCrossRefGoogle Scholar
  15. Lane SN, Chandler JH, Porfiri K (2001) Monitoring river channel and flume surfaces with digital photogrammetry. J Hydraul Eng ASCE 127:871–877CrossRefGoogle Scholar
  16. Millane RP, Weir MI, Smart GM (2006) Automated analysis of imbrication and flow direction in alluvial sediments using laser-scan data. J Sediment Res 76:1049–1055CrossRefGoogle Scholar
  17. Nikora VI, Goring DG, Biggs BJF (1998) On gravel-bed roughness characterization. Water Resour Res 34:517–527ADSCrossRefGoogle Scholar
  18. Nikora VI, Koll K, McEwan IK, McLean S, Dittrich A (2004) Velocity distribution in the roughness layer of rough-bed flows. J Hydraul Eng ASCE 130:1036–1042CrossRefGoogle Scholar
  19. Powell DM, Ockelford AM, Rice SP, Hillier JK, Nguyen T, Reid I, Tate NJ, Ackerley D (2016) Structural properties of mobile armors formed at different flow strengths in gravel-bed rivers. J Geophys Res 121:1–21Google Scholar
  20. Qin J, Ng SL (2011) Multifractal characterization of water-worked gravel surfaces. J Hydraul Res 49:345–351CrossRefGoogle Scholar
  21. Qin J, Ng SL (2012) Estimation of effective roughness for water-worked gravel surfaces. J Hydraul Eng ASCE 138:923–934CrossRefGoogle Scholar
  22. Qin J, Zhong D, Wu T, Wu L (2017) Evolution of gravel surfaces in a sediment-recirculating flume. Earth Surf Process Landf 42:1397–1407ADSCrossRefGoogle Scholar
  23. Qin J, Aberle J, Henry PY, Wu T, Zhong D (2019) Statistical significance of spatial correlation patterns in armoured gravel beds. J Hydraul Res 57:90–106CrossRefGoogle Scholar
  24. Richards K, Clifford N (2016) Fluvial geomorphology: structured beds in gravelly rivers. Prog Phys Geogr 15:407–422CrossRefGoogle Scholar
  25. Robert A (1988) Statistical properties of sediment bed profiles in alluvial channels. Math Geol 20:205–224CrossRefGoogle Scholar
  26. Smart GM, Duncan MJ, Walsh JM (2002) Relatively rough flow resistance equations. J Hydraul Eng ASCE 128:568–578CrossRefGoogle Scholar
  27. Strom KB, Papanicolaou AN (2008) Morphological characterization of cluster microforms. Sedimentology 55:137–153Google Scholar
  28. Theiler J, Eubank S, Longtin A, Galdrikian B, Doyne Farmer J (1992) Testing for nonlinearity in time series: the method of surrogate data. Physica D 58:77–94ADSCrossRefGoogle Scholar
  29. Wu C (2013) Towards linear-time incremental structure from motion. In: 2013 international conference on 3D vision (3DV). IEEE, pp 127–134Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Harbour, Coastal and Offshore Engineering, Hohai UniversityNanjingChina
  2. 2.Key Laboratory of Port, Waterway and Sedimentation Engineering of Ministry of TransportNanjingChina
  3. 3.State Key Laboratory of Hydroscience and EngineeringTsinghua UniversityBeijingChina

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