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This paper presents a rapid forecast model for simulating hyperconcentrated sediment-laden floods in the Lower Yellow River. The model is a hybrid of a conventional one-dimensional mathematical model for unsteady sediment-laden flow and an artificial neural networks model for encapsulation of numerical results. The former provides detailed river flood routing information under typical scenarios, whereas the latter extracts modeling outputs from the former and establishes a station-specific model for efficient flood forecasting. Three typical floods that occurred in the Lower Yellow River in 1977, 1982, and 1996 are simulated. Not only the hybrid model predictions are found to be in close agreement with measured data, but also the computational speed is significantly enhanced. It is found that sediment transport is of significance with regard to the flooding behavior of hyperconcentrated flows. Therefore, the model presented herein is of particular use for rivers with high sediment concentration. [ABSTRACT FROM AUTHOR]

The major obstacles to simulating flood flow in the Yellow River are its high sediment concentration, complex compound cross section, and rapid change in channel planform. This paper presents an improved one-dimensional numerical model that takes into account the effect of sediment concentration and bed change on mass and momentum conservation of flood flow in the Yellow River. The model is calibrated and then validated by simulating three individual flood events. Results show that an increase in sediment concentration leads to a reduction in flood wave celerity and peak discharge. The generalized likelihood uncertainty estimation (GLUE) method is used to evaluate the uncertainty of modeling results. A sensitivity index, analogous to the Nash-Sutcliffe efficiency factor, is adopted to quantify the sensitivity of calibration parameters. The modeling results are sensitive to the choice of Manning's roughness coefficient and the empirical recovery coefficient for suspended sediment transport at reaches of transitional channel planform. [ABSTRACT FROM AUTHOR]