Reliable real-time probabilistic flood forecasting is critical for effective water management and flood protection all over the world. In this study, we develop a real-time probabilistic channel flood-forecasting model by combining a channel hydraulic model with the Bayesian particle filter approach. The new model is tested in Highlights • A real-time flood-forecasting model is proposed by assimilating real-time stage observations into a hydraulic model.
By employing the theory of propagation of columnar disturbance, general criteria for selective withdrawal in sedimentation basins are derived. The stratified fluid is assumed to be two‐layered with different density distribution in each layer. The effects of a free surface are also included in the analysis, but the viscosity is neglected as in most other studies of stratified flow behavior. The criteria for different density profiles are established in terms of the densimetric Froude number. If the actual densimetric Froude number is smaller than its critical value, selective withdrawal will occur. Comparison of the theoretical results with the experimental data obtained by Bohan and Grace [1973] indicate an excellent agreement as long as the thickness of the bottom layer, s, is small compared to that of the top layer. Results obtained by assuming rigid upper boundary instead of free surface are also compared with the results derived in this paper. For weak stratification and small values of s they show excellent agreement. As s increases for strong stratification, the effect of free surface will become increasingly pronounced.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.