Fine sediment plays crucial and multiple roles in the hydrological, ecological and geomorphological functioning of river systems. This study employs a two-dimensional (2D) numerical model to track the hydro-morphological processes dominated by fine suspended sediment, including the prediction of sediment concentration in flow bodies, and erosion and deposition caused by sediment transport. The model is governed by 2D full shallow water equations with which an advection-diffusion equation for fine sediment is coupled. Bed erosion and sedimentation are updated by a bed deformation model based on local sediment entrainment and settling flux in flow bodies. The model is initially validated with the three laboratory-scale experimental events where suspended load plays a dominant role. Satisfactory simulation results confirm the model’s capability in capturing hydro-morphodynamic processes dominated by fine suspended sediment at laboratory-scale. Applications to sedimentation in a stormwater pond are conducted to develop the process-based understanding of fine sediment dynamics over a variety of flow conditions. Urban flows with 5-year, 30-year and 100-year return period and the extreme flood event in 2012 are simulated. The modelled results deliver a step change in understanding fine sediment dynamics in stormwater ponds. The model is capable of quantitatively simulating and qualitatively assessing the performance of a stormwater pond in managing urban water quantity and quality.
Funding
The work is supported by the UK EPSRC grant (No. EP/K013661/1) and the open fund grant (No. SKHL1607) from Sichuan University.
History
School
Social Sciences
Department
Geography and Environment
Published in
Journal of Hydrology
Citation
GUAN, M. ... et al, 2018. Numerical modelling of hydro-morphological processes dominated by fine suspended sediment in a stormwater pond. Journal of Hydrology, 556, pp.87-99.
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Acceptance date
2017-11-03
Publication date
2017-11-07
Notes
This paper was published by Elsevier as Open Access under a CC BY 4.0 licence.