Author(s): Ji Li; Zhixian Cao
Linked Author(s):
Keywords: No Keywords
Abstract: The Yangtze River is one of the longest rivers in the world and plays a crucial role in waterborne freight transportation in China. However, the waterway conditions in the middle and lower reaches of the Yangtze River may not be able to meet the increasingly stringent requirements of cost-effective navigation necessary for the rapid economic development in the region. An improved understanding of the water flow, sediment transport and bed evolution is important for the regulation and maintenance of the waterway, especially since the operation of Three-Gorges Reservoir. A one-dimensional shallow water hydro-sediment-morphodynamic (SHSM) model has been widely used for river fluvial process. However, modelling hydro-sediment-morphodynamic (SHSM) processes is subject to multiple sources of uncertainty arising from input data and incomplete understanding of the underlying physics. A stochastic SHSM model with multiple uncertainties has yet to be developed as most SHSM models still concern deterministic problems. Here we first present a new probabilistic SHSM model incorporating multiple uncertainties within the stochastic Galerkin framework using a multidimensional tensor product of Haar wavelet expansion to capture local, nonlinear variations in joint probability distributions and an operator-splitting-based method to ensure the modelling system remain hyperbolic. Then, we verify the proposed model via benchmark probabilistic numerical tests with joint uncertainties introduced in initial and boundary conditions, matching a spectrum of established experiments of flow-sediment-bed evolutions. Then, it is applied to the reach from Yichang to Gong’an in the middle Changjiang Waterway. The present work facilitates a promising modelling framework for quantifying multiple uncertainties in practical shallow water hydro-sediment-morphodynamic modelling applications.
Year: 2024