Discussion of: Steven H. Hill and Patrick McLaren, 2001. A comparison between log-hyperbolic and model-independent grain size distributions in Sediment Trend Analysis (STA®). Journal of Coastal Research, 17 (4), 931-935

Journal of Coastal Research

Volumn 18, Issue 3, 2002, Pages 592-595

  Hartmann, Daniel ^a   Flemming, Burg ^a  

^a UNIVERSITY OF BREMEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

GRAIN SIZE;

EID: 0036616031     PISSN: 07490208     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Note

References (19)

1. The pattern of natural size distributions
2. Erosion, deposition and size distributions of sand
3. Distributional shape triangles with some applications in sedimentology
4. On using the log-hyperbolic distribution to describe the textural characteristics of eolian sediments
5. SAHARA: A package of PC-computer programs for estimating both log-hyperbolic grain size parameters and standard moments
6. The hyperbolic distribution
7. A new method for environmental analysis of particle size distribution data from shoreline sediments
8. The goodness-of-fit to ideal Gauss and Rosin distributions: A new grain-size parameter
9. Cross-shore selective sorting processes and grain-size distributional shape
10. Efficiency of the log-hyperbolic distribution - A case study: Pattern of sediment sorting in a small tidal-inlet - Het Zwin, The Netherlands
11. Settling-velocity distributions and sorting processes on a longitudinal dune: A case study
12. The hyperbolic shape triangle as a tool for discrimination of populations of sediment samples from closely connected origins
13. A comparison between Log-hyperbolic and Model-independent Grain Size Distributions in Sediment Trend Analysis (STA®)
14. Size frequency distributions and the normal phi curve
15. The effects of sediment transport on grainsize distributions
16. Sediment pathways in a British Columbia fjord and their relationship with particle-associated contaminants
17. Shelf sediment transport: A probability model