RELATIONSHIP BETWEEN MANNING ROUGHNESS COEFFICIENT AND FLOW DEPTH IN BANGLADESH RIVERS
Keywords:Manning’s n, water depth, natural channel/waterway, Bangladesh river, regression equation.
AbstractManning's n is the most widely used resistance coefficient for open channel flows. There are several factors that affecting the variation of roughness coefficient in open channels such as surface roughness, bed material, channel alignment, shape irregularity and vegetation. The prediction of the variation of the roughness coefficient in a natural waterway becomes more complex and challenging task to hydraulics engineers until now. The main emphasis of this research is the assessment of the Manning coefficient of riverside roughness, which is used in hydraulic simulations and to explore the link between the coefficient of Manning and water depth The aim of this study was to investigate the correlation/relationship between flow depth and Manning's n for several selected rivers in Bangladesh. This research represents graphically the connection between roughness coefficient of Manning and water depth of year 2019 based on the collected data's (cross section, discharge, stream width) from Bangladesh Water Development Board (BWDB). The main focus of this research was to establish the regression equations by graphically plotting calculated Manning's n versus flow depth. The relationship between the two variables in the stations is shown to be directly proportional, while some are inversely proportional, by changing water depth and computing Manning roughness coefficient. It can be seen that most stations have more than one behavior, i.e., the connection between these parameters is directly related in certain periods all through the year, while it is inversely proportional with others. The findings prevail that the Manning's n varied from 0.01 s/m1/3 to 0.14 s/m1/3 for a comparable depth of 1m to 20m at all the stations being studied here and 6th order polynomial equation observed R2 is between 0.9288 and 0.9943 for most of the stations being studied here which may provide an efficient prediction evaluation in estimating Manning roughness coefficient.
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