Channel Flow Time of Concentration
The channel flow time of concentration, , can be computed using the equation
2:1.3.7
where is the average flow channel length for the subbasin (km), is the average channel velocity (m s), and 3.6 is a unit conversion factor.
The average channel flow length can be estimated using the equation
2:1.3.8
where is the channel length from the most distant point to the subbasin outlet (km), and is the distance along the channel to the subbasin centroid (km). Assuming , the average channel flow length is
2:1.3.9
The average velocity can be estimated from Manning’s equation assuming a trapezoidal channel with 2:1 side slopes and a 10:1 bottom width-depth ratio.
2:1.3.10
where is the average channel velocity (m s), is the average channel flow rate (), is the channel slope (m m), and is Manning’s roughness coefficient for the channel. To express the average channel flow rate in units of mm/hr, the following expression is used
2.1.3.11
where is the average channel flow rate (mm hr), is the subbasin area (km), and 3.6 is a unit conversion factor. The average channel flow rate is related to the unit source area flow rate (unit source area = 1 ha)
2:1.3.12
where is the unit source area flow rate (mm hr), is the subbasin area (km), and 100 is a unit conversion factor. Assuming the unit source area flow rate is 6.35 mm/hr and substituting equations 2:1.3.11 and 2:1.3.12 into 2:1.3.10 gives
2:1.3.13
Substituting equations 2:1.3.9 and 2:1.3.13 into 2:1.3.7 gives
2:1.3.14
where is the time of concentration for channel flow (hr), is the channel length from the most distant point to the subbasin outlet (km), n is Manning’s roughness coefficient for the channel, Area is the subbasin area (km), and is the channel slope (m m).
Although some of the assumptions used in developing equations 2:1.3.6 and 2:1.3.14 may appear liberal, the time of concentration values obtained generally give satisfactory results for homogeneous subbasins. Since equations 2:1.3.6 and 2:1.3.14 are based on hydraulic considerations, they are more reliable than purely empirical equations.
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