A direct three-parameter Muskingum procedure incorporating lateral inflow

Hydrodynamic derivation of a variable parameter Muskingum method: 1. Theory and solution procedure

Abstract An approach is presented for directly deriving a variable parameter Muskingum method from the St. Venant equations for routing floods in channels having any shape of prismatic cross-section and flow following either Manning's or Chezy's friction law. The approach also allows the simultaneous computation of the stage hydrograph corresponding to a given inflow or the routed hydrograph. T...

Hydrodynamic derivation of a variable parameter Muskingum method: 2. Verification

The hydrodynamic derivation of a variable parameter Muskingum method and its solution procedure for estimating a routed hydrograph were presented in Part I of this series (Perumal, 1994a). In this paper, the limitations of the method, the criterion for its applicability and its accuracy are discussed based on the assumptions used. The method is verified by routing a given hypothetical inflow hy...

Fitting the Three-parameter Weibull Distribution by using Greedy Randomized Adaptive Search Procedure

The Weibull distribution is widely employed in several areas of engineering because it is an extremely flexible distribution with different shapes. Moreover, it can include characteristics of several other distributions. However, successful usage of Weibull distribution depends on estimation accuracy for three parameters of scale, shape and location. This issue shifts the attentions to the requ...

parameter estimation of the nonlinear muskingum model using simulated annealing

abstract the muskingum method is frequently used to route floods in hydrology. however, application of the model is still difficult because of the parameter estimation’s. recently, some of heuristic methods have been used in order to estimate the nonlinear muskingum model. this paper presents a efficient heuristic algorithm, simulated annealing, which has been used to estimate the three paramet...

Incorporating Parameter Constraints in GLMs