
By Parsons A.J., Abrahams A.D. (eds.)
Encompassing geomorphology, hydrology and agricultural engineering, this gives an interdisciplinary overview of a subject very important in either medical And functional phrases - With the explicit objective of selling interplay among modellers, box employees and laboratory experimentalists
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Extra info for Overland flow. Hydraulics and erosion mechanics
Example text
For most overland-flow conditions, rainfall would be expected to have a minimal effect on total hydraulic resistance. Laboratory measurements of roughness coefficients on surfaces covered with sand or gravel were made by Woo & Brater (1961), Emmett (1970), Phelps (1975) and Savat (1980). Similar tests were performed on natural landscapes by Dunne & Dietrich (1980), Roels (1984), Abrahams et al. (1986) and Parsons et al. (1990). In these studies, a significant correlation was established between Reynolds number and roughness coefficient.
4%. The experimental design for the study consisted of two randomized complete blocks, with the first block being located immediately upslope from the second. 7 Regression equation for roughness coefficients for surface residue on croplands versus percent cover and Reynolds number. 648 where fcr is the roughness coefficient for surface residue on croplands and Re is Reynolds number. 7, while Reynolds number varied from approximately 500 to 16,000. Each experimental block consisted of six tillage operations performed at random locations within the block.
1 Darcy-Weisbach roughness coefficients vs. 81 cm. regression equation. 8. Information on the size distribution of surface material obtained on the basis of mass may be more readily available and easier to obtain. Gilley et al. (1992) made measurements of the mass of gravel or cobble material corresponding to a given surface cover. This data was used to develop regression equations for relating surface cover for a given size class to gravel or cobble mass. 4, which contained multiple size classes, were used to test the reliability of the regression equations.