Search

The predictive capability of a model is dependent on the parameter uncertainty involved in it. This study examines the effect of predictive uncertainty and parameter sensitivity in the application of the wellknown HEC-RAS 1D hydrodynamic CSU (Colorado State University) scour prediction model. Correlationbased technique was used for carrying out the sensitivity analysis. Monte Carlo method was adopted for uncertainty quantification. The methodology suggested in the present study drastically improved the predictive capability of the model, by reducing the model error from 26.6% to 0.07%. In general, it improved the predictive capability of any scour model when tested on 19 datasets. [ABSTRACT FROM AUTHOR]

A semi-coupled 1D numerical model is presented to compute transient bed and water levels of aggrading channels due to the overloading of sediments. The numerical model solves mass and momentum equations (i.e. de Saint-Venant equations) for water and continuity equations for sediments simultaneously, using explicit finite difference scheme while considering upstream and downstream boundary conditions in the channel. Series of experimental studies are reported for measurements of bed and water levels in an aggrading channel due to the overloading of uniform sediments, in a flume installed at the Advanced Hydraulics Laboratory of SVNIT. The performance of bed level variation models, with different sediment transport functions, has been validated using the laboratory measurements. The performance of the numerical model is dependent on sediment transport functions. In addition, the performance of the proposed numerical model has been verified with existing numerical models on prediction of bed level variations. The proposed numerical model with recommended sediment transport function has been found to perform better than the existing numerical models on bed level variations of uniform sediment beds. [ABSTRACT FROM AUTHOR]