Hydrodynamic modelling of a flood-prone tidal river using the 1D model MIKE HYDRO River: calibration and sensitivity analysis

Abstract

Hydrodynamic modeling 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 0.011-0.013. 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

Minimisation of Overestimation of River Flows in 1D- Hydrodynamic Modeling

“…The river width at K. Agraharam was divided in 100 m intervals 2). A similar kind of trend has been observed in previous studies (Tehrani et al, 2020;Timbadiya et al, 2014;. The underestimation of peak values can be due to negligence of the confluence of the Krishna River with the Tungabhadra River.…”

Improved method for identifying Manning’s roughness coefficients in plain looped river network area

“…The model structure and boundary conditions can be used to explain this spatial difference. Some studies indicated that boundary conditions strongly impact the measured data of gauging stations close to the boundary conditions (Dung et al, 2010;Jahandideh-Tehrani et al, 2020;Yang et al, 2014). The weakest sensitivity region can be found in the north of the study area close to the water boundary.…”

“…Detailed measurement data can be used to identify the distribution of n-values by hydrodynamic model calibration (Orlandini, 2002). In practice, global (Parhi et al, 2012;Timbadiya et al, 2011) and spatially varying n values (Attari & Hosseini, 2019;Jahandideh-Tehrani et al, 2020;Ong et al, 2017) are commonly used in model calibration. It is simple and effective to take a global roughness value for a few rivers having similar cross-sections without floodplains under the condition of limited water level change.…”

A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

“…Natural hazards refer to the frequency and intensity of hazards, and vulnerability denotes the susceptibility of the exposed elements to hazards, while exposure indicates population or assets located in flood-prone areas [11,14]. Many effective approaches have been used in FIR, such as expertise-based methods like analytic hierarchy process [15][16][17][18], probability-based evaluations like Bayesian networks [13,19], machine-learning-based approaches like neural networks [20][21][22][23][24], and hydrological and hydraulic models [25][26][27].…”