Hydrodynamic modelling of a flood-prone tidal river using the ID model MIKE HYDRO River: calibration and sensitivity analysis

摘要

Hydrodynamic modelling is a powerful tool to gain understanding of river conditions. However, as widely known, models vary in terms of how they respond to changes and uncertainty in their input parameters. A hydrodynamic river model (MIKE HYDRO River) was developed and calibrated for a flood-prone tidal river located in South East Queensland, Australia. The model was calibrated using Manning's roughness coefficient for the normal dry and flood periods. The model performance was assessed by comparing observed and simulated water level, and estimating performance indices. Results indicated a satisfactory agreement between the observed and simulated results. The hydrodynamic modelling results revealed that the calibrated Manning's roughness coefficient ranged between 0.011 and 0.013. The impacts of tidal variation at the river mouth and the river discharge from upstream are the major driving force for the hydrodynamic process. To investigate the impacts of the boundary conditions, a new sensitivity analysis approach, based on adding stochastic terms (random noise) to the time series of boundary conditions, was conducted. The main purpose of such new sensitivity analysis was to impose changes in magnitude and time of boundary conditions randomly, which is more similar to the real and natural water level variations compared to impose constant changes of water level. In this new approach, the possible number of variations in simulated results was separately evaluated for both downstream and upstream boundaries under 5%, 10%, and 15% perturbation. The sensitivity analysis results revealed that in the river under study, the middle parts of the river were shown to be more sensitive to downstream boundary condition as maximum water level variations can reach 8%, 12%, and 15% under 5%, 10%, and 15% changes in the downstream boundary, respectively. The outcomes of the present paper will benefit future modelling efforts through provision of a robust tool to enable prediction of water levels at ungauged points of the river under various scenarios of flooding and climate change for the purpose of city planning and decision-making.