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In almost all of the present mathematical models, the upstream subbasins, with overland flow as the dominant type of flow, are

simulated as a rectangular plane. However, the converging plane is the closest shape to an actual upstream subbasin. The

intricate nature of the governing equations of the overland flow on a converging plane is the cause of prolonged absence of an

analytical or semi analytical solution to define the rising limb of the resulted hydrograph. In the present research, a new

geomorphologic semi analytical method was developed that tries to establish a relationship between the parallel and converging

flows to reduce the complexity of the equations. The proposed method uses the principals of the Time Area method modified to

apply the kinematic wave theory and then by applying a correction factor finds the actual discharge. The correction factor, which

is based on the proportion of the effective drained area to the analytically calculated one, introduces the convergence effect of

the flow in reducing the potentially available discharge in a parallel flow. The proposed method was applied to a case study and

the result was compared with that of Woolhiser's numerical method that showed the reliability of the new method.

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In this study digital image correlation (DIC) technique combined with a high speed video system was used to predict movement of particles in a water model. Comparing with Particle-image velocimetry (PIV) technique, it provides a low cost alternative approach to visualize flow fields and was successfully employed to predict the movement of particles in a water model at different submergence depth using gas injection. As the submergence depth increases, the number of the exposed eye is reduced accordingly. At 26.4 cm submergence depth, an exposed eye was found at 1/3 of the submergence depth, whereas two exposed eyes were observed at 1/2 depth and near the bottom wall at 24 cm submergence depth.

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The unsteady flow in porous medium of a viscous incompressible fluid bounded by two parallel porous plates is studied with heat transfer. A uniform and constant pressure gradient is applied in the axial direction whereas a uniform suction and injection are applied in the direction normal to the plates. The two plates are kept at constant and different temperatures and the viscous dissipation is not ignored in the energy equation. The effect of the porosity of the medium and the uniform suction and injection velocity on both the velocity and temperature distributions are investigated.

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Due to the nonlinear relationship between storage and discharge existing in the nonlinear forms of the Muskingum model, the model parameters and outflow cannot be directly determined. The traditional routing procedure has been widely applied to model calibration and flood routing. However, most studies have focused only on the accuracy of parameter estimation methods which adopt the traditional routing procedure, ignored the correctness and effectiveness of routing procedure itself. In this study, three routing schemes of traditional routing procedure are evaluated by simulation experiment and the results demonstrate that the routing scheme 1 is the best, and scheme 3 is followed, the worst one is scheme 2. But the scheme 1 and 3 yield parameters estimates and corresponding outflow hydrographs lead to violation of the routing equations in terms of residuals. The scheme 2 is legitimate, however, the accuracy is not high enough. As an alternative, a new routing procedure based on iterative method is proposed for parameter estimation and flood routing of the nonlinear Muskingum models. The proposed routing procedure is applied to model calibration and flood routing for three examples involving single-peak, multi-peak, and non-smooth hydrographs. The results show that the proposed routing procedure is not only satisfying the routing equations for all time stages in the routing process, but also superior to the routing scheme 2. Therefore, it can confidently be applied to parameter estimation and flood routing for the nonlinear Muskingum models.