Numerical modelling of wave evolution over variable coastal bathymetry
File(s)
Author(s)
Al Khalili, Umniya Khalil Ahmed
Type
Thesis or dissertation
Abstract
Accurate modelling of coastal wave transformation is critical for safe, efficient design of coastal and offshore infrastructure, yet remains challenging due to nonlinear amplification, wave–wave interactions, bathymetric effects, and wave breaking. This thesis presents a numerical investigation of unidirectional random waves over planar bathymetry, focusing on how bed slope controls nearshore wave statistics and kinematics.
An extensive dataset is generated using the non-hydrostatic model swash, com- prising over two million waves simulated across a wide range of offshore conditions and bed slopes. Model predictions are validated against laboratory experiments, with normalised wave and crest heights reproduced within 2% for moderate sea states.
Analysis shows that bed slope governs shoaling, breaking, and nonlinear energy transfer. Steeper slopes lead to larger wave and crest heights and higher near-surface velocities, while milder slopes enhance nonlinear evolution, broaden spectra, and promote stronger energy redistribution. These processes produce systematic spectral shifts and differences in wave asymmetry and higher-order statistics.
Existing engineering formulations for sea-state parameter evolution have limitations, including underestimation of mean energy period by up to 50%. New data- driven parameterisations are developed, incorporating sea-state steepness and bed slope. These improve wave height predictions by up to 21% and predict mean energy period, mean period, and mean zero-crossing period with accuracies of 13.8%, 6.9%, and 5.6%, respectively.
Phase-resolved analysis of wave kinematics shows that bed slope can alter shallow- water crest velocities by up to 48%. Regular wave methods perform well in inter- mediate depths but degrade in shallow water, with errors in the non-dimensional depth-averaged velocity profile of order O(1). Random wave methods achieve improved accuracy (±5%) in intermediate conditions but underestimate velocities by up to 40% in shallow water, highlighting the complexities of the processes in this region.
An extensive dataset is generated using the non-hydrostatic model swash, com- prising over two million waves simulated across a wide range of offshore conditions and bed slopes. Model predictions are validated against laboratory experiments, with normalised wave and crest heights reproduced within 2% for moderate sea states.
Analysis shows that bed slope governs shoaling, breaking, and nonlinear energy transfer. Steeper slopes lead to larger wave and crest heights and higher near-surface velocities, while milder slopes enhance nonlinear evolution, broaden spectra, and promote stronger energy redistribution. These processes produce systematic spectral shifts and differences in wave asymmetry and higher-order statistics.
Existing engineering formulations for sea-state parameter evolution have limitations, including underestimation of mean energy period by up to 50%. New data- driven parameterisations are developed, incorporating sea-state steepness and bed slope. These improve wave height predictions by up to 21% and predict mean energy period, mean period, and mean zero-crossing period with accuracies of 13.8%, 6.9%, and 5.6%, respectively.
Phase-resolved analysis of wave kinematics shows that bed slope can alter shallow- water crest velocities by up to 48%. Regular wave methods perform well in inter- mediate depths but degrade in shallow water, with errors in the non-dimensional depth-averaged velocity profile of order O(1). Random wave methods achieve improved accuracy (±5%) in intermediate conditions but underestimate velocities by up to 40% in shallow water, highlighting the complexities of the processes in this region.
Version
Open Access
Date Issued
2026-04-15
Date Awarded
2026-08-01
Copyright Statement
Attribution 4.0 International Licence (CC BY)
License URL
Advisor
Christou, Marios
Karmpadakis, Ioannis
Publisher Department
Department of Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)