【正文】 he methods of Komura and Simmons (1967), Han et al (1981), or is specified by the user according to available measurement data。 ? Abk / ? t is the bed deformation rate of size class k. The bed material is divided into several layers. The variation of bed material gradation pbk at the mixing layer (surface layer) is determined by the following equation (Wu and Li, 1992) (4)where Am is the crosssectional area of the mixing layer。 ? Ab / ? t is the total bed deformation rate, defined as ? Ab/ ? t =k=1N? Ab/ ? t。 N is the total number of size classes。 p*bk is pbk of the mixing layer when ?Am/ ? t ? ? Ab/?t≤0 , and p*bk is the percentage of the kth size class of bed material in subsurface layer (under mixing layer) when ?Am/ ? t ? ? Ab/?t 0. Eq. (1) is discretized using the Preissmann implicit scheme, with its source term being discretized by the same formulation as that for the righthand term of Eq. (3) in order to satisfy the sediment continuity. Eq. (4) is discretized by a difference scheme that satisfies mass conservation. A coupled method for the calculations of sediment transport, bed change and bed material sorting is established by implicitly treating the pbk in Eq. (2) as pbkn+1and simultaneously solving the set of algebraic equations corresponding to Eqs. (1)(4) by using the direct method proposed by Wu and Li (1992). This coupled method is more stable and can more easily eliminate the occurrence of the puted negative bed material gradation, when pared to the decoupled method, in which the pbk in Eq. (2) is treated explicitly. However, the aforementioned coupling procedure for sediment transport, bed change and bed material sorting putations is still decoupled from the flow calculation. Model Parameters to be Analyzed The parameters in numerical models of flow and sediment transport in rivers can be classified into two groups: numerical parameters and physical parameters. The numerical parameters result from the discretization and solution procedures, while the physical parameters represent the physical properties of flow and sediment, or the quantities derived from the modeling of flow and sediment transport. In the CCHE1D channel network model, the numerical parameters include putation time step and grid length, and the physical parameters are the Manning’s roughness coefficient, nonequilibrium adaptation length of sediment transport, mixing layer thickness, bed material porosity, etc. Usually, the numerical parameters can be more easily handled than the physical parameters. Some of these physical parameters, such as the Manning’s roughness coefficient and bed material porosity, have been studied by many investigators and may be determined by measurement. However, the nonequilibrium adaptation length and the mixing layer thickness are less understood and must be prescribed empirically. Therefore, the main concern in this paper is to analyze the influence of these two physical parameters on the simulation results. The nonequilibrium adaptation length Ls characterizes the distance for sediment to adjust from a nonequilibrium state to an equilibrium state. Wu, Rodi and Wenka (2000) and Wu and Vieira (2000) reviewed in detail those empirical and semiempirical methods for determining Ls published in the literature, such as Bell and Sutherland’s (1981), Armanini and di Silvio’s (1988), etc. It was found that those methods provide significantly different estimations of Ls. In CCHE1D, the adaptation length for wash load transport is set as infinitely large because the net exchange between wash load and channel bed is usually negligible. The adaptation length for suspended load transport is calculated with Ls=uh/αωs, in which u is the sectionaveraged velocity, h is the flow depth, ωs is the settling velocity of sediment particles, and α is the adaptation coeficient which can be calculated with Armanini and di Silvio’s (1988) method, or specified as a constant value by the user. The adaptation length for bed load transport is suggested to set as the length of the dominant bed forms, such as , the length of sand dunes (van Rijn, 1984), or , the length of alternate sand bars in the channel (Yalin, 1972). Here, B is the average channel width. The mixing layer thickness is a key parameter in the determination of bed material gradation, which in turn influences the whole simulation. However, the evaluation methods for this parameter found in the literature are highly empirical. Physically, it is related to bedform movements. Therefore, in CCHE1D, the mixing layer thickness is set as half the sand dune height, which is calculated with van Rijn’s (1984)formula.Case Studies Case A: Channel Degradation. The experiment of bed degradation and armoring processes performed by Ashida and Michiue (1971) was used to test the CCHE1D model in a previous study (Wu, Vieira and Wang, 2000), and it is here adopted to conduct the sensitivity analysis of the model. The experimental flume was wide and 20m long. The flume bed was filled with nonuniform sediment with a median size of and a standard deviation of . In experimental run 6, the inlet flow discharge was m3/s, and the initial bed slope was . In this sensitivity analysis, only one parameter’s value is changed at a time, while all other parameters are kept the same as those used in the previous test. In order to examine the influence of Ls, several functions, such as Ls=, Ls=t and Ls=1+, have been used. Here, t is the time in hours. Figure 1 shows the parison of the measured and calculated bed scour depths at 7m, 10m and 13m upstream from the weir at the end of the flume. The function Ls= provides the best result for the bed scour process, especially the time to reach equilibrium state. The results from Ls=t and Ls=1+ are also very close to the measured data. It appears that the calculated scour depth is insensitive to Ls. It is also found that the calculated