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For a given open channel, solving the kinematic wave equation with flow depth-independent kinematic wave parameters is very common due to its simplicity. The kinematic wave parameters cannot be analytically estimated independently of the flow depth, except for some special cross-sections. In this research, approximate depth-independent kinematic parameters are derived, based on Manning equation for trapezoidal, rectangular, and parabolic channels. The estimation method is based on the kinematic point-sensitivity indicator and regression fitting methods. The estimated parameters were verified and graphical aids for practical applications are presented. Since the presented kinematic wave parameters are independent of the flow depth, they are useful for kinematic wave modeling. For system-specific conditions, such as a desired range of flow depth, the effective-sensitivity method would be preferable to the full range of kinematic wave parameters presented by other researchers as it would easily produce local and more relevant parameters.

Abstract: For a given open channel, solving the kinematic wave equation with flow depth-independent kinematic wave parameters is very common due to its simplicity. The kinematic wave parameters cannot be analytically estimated independently of the flow depth, except for some special cross-sections. In this research, approximate depth-independent kinematic parameters are derived, based on Manning equation for trapezoidal, rectangular, and parabolic channels. The estimation method is based on the kinematic point-sensitivity indicator and regression fitting methods. The estimated parameters were verified and graphical aids for practical applications are presented. Since the presented kinematic wave parameters are independent of the flow depth, they are useful for kinematic wave modeling. For system-specific conditions, such as a desired range of flow depth, the effective-sensitivity method would be preferable to the full range of kinematic wave parameters presented by other researchers as it would easily produce local and more relevant parameters. [Copyright &y& Elsevier]