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<br />FIG. if <br />> <br />- .... <br /> <br />.< <br /> <br />fico. ~ <br />)t <br />-- <br /> <br />This implies that the channel roughness associated with streambed- <br /> <br />material size can be evaluated in terms of the more easily obtained <br /> <br />friction slope. The relation of Manning's roughness coefficient to <br /> <br />friction slope, which indicates that roughness increases with slope, Is <br /> <br />shown in figure 4. The scatter is due to the at-a-site decrease of <br /> <br />roughness with increasing depth of flow. There is much greater scatter, <br /> <br />or change in the roughness coefficient, on steeper gradient streams. <br /> <br />The method for predicting channel roughness uses multiple-regression <br /> <br />analysis, which relates Manning's roughness coefficient to the easily <br /> <br />measured hydraulic characteristics sh9wn in tables 1 and 2. Multiple- <br /> <br />regression analyses were performed using several different types of <br /> <br /> <br />equations (arithmetic, polynomial, semilogarithmic, and logarithmic or <br /> <br />power) . <br /> <br />The three highest measurements for Cottonwood Creek, South Fork Rio <br /> <br />Grande, and Trout Creek were not used to develop the equation because of <br /> <br />the extreme effect of bank vegetation. The effect of bank vegetation <br /> <br />should be evaluated using equation 5. <br /> <br />I <br /> <br />The resul ting eqlJation from the mul tiple regression analyses <br /> <br />developed for predicting Manning's n in steep natural channels is: <br />n = 0.39 SO.38R-O.16 . and <br /> <br />(10) <br /> <br />is graphically depicted in figure 5. <br /> <br />31 <br />