SENSITIVITY OF SEABED MORPHOLOGY TO HYDRODYNAMIC AND SEDIMENT TRANSPORT PARAMETERS IN TIDAL SYSTEMS
DOI:
https://doi.org/10.55197/qjoest.v6i3.254Keywords:
sediment transport, seabed evolution, hydrodynamic modeling, coastal morphologyAbstract
Sediment transport and seabed evolution are key processes influencing the morphology and stability of coastal and estuarine systems. This study uses a one-dimensional (1D) coupled hydrodynamic–sediment transport model to simulate shallow water flow and sediment dynamics in the Kuala Terengganu region. The model integrates the Saint-Venant shallow water equations with the Exner equation for bed evolution and the advection–diffusion equation for suspended sediment concentration. Numerical implementation using the finite volume method ensures mass conservation and numerical stability, allowing accurate representation of flow–sediment feedback. The objectives are: (1) to simulate the temporal evolution of seabed morphology and quantify long-term bed elevation changes; (2) to evaluate the effects of hydrodynamic parameters, particularly tidal range and bed friction coefficient, on sediment entrainment, deposition, and erosion; and (3) to assess the influence of sediment transport parameters, especially the Grass coefficient, on seabed stability and erosion–deposition dynamics. Results show a non-linear decline in bed elevation with a 38% reduction over three years, indicating rapid early sediment mobility followed by stabilization. Increasing tidal range enhances sediment removal, while friction and Grass coefficients govern the transition between erosion and deposition. The findings provide insights for predicting coastal morphological change and improving sediment management.
References
[1] Amoudry, L.O., Souza, A.J. (2011): Deterministic coastal morphological and sediment transport modeling: A review and discussion. – Reviews of Geophysics 49(2): 1-21.
[2] Cheng, Y., Wang, Y., Jiang, C. (2007): A coupling model of nonlinear wave and sandy seabed dynamic interaction. – China Ocean Engineering 21(1): 77-89.
[3] Díaz, M.C., Fernández-Nieto, E.D., Ferreiro, A.M. (2008): Sediment transport models in shallow water equations and numerical approach by high order finite volume methods. – Computers & Fluids 37(3): 299-316.
[4] Dong, Y., Jiang, J., Liu, X., Wang, D., Zhang, J. (2023): An empirical formula of bottom friction coefficient with a dependence on the current speed for the tidal models. – Frontiers in Marine Science 10: 16p.
[5] Fincham, J.I., Barry, J. (2025): The value of broadscale semi-autonomous seabed monitoring from the perspective of a marine fisheries monitoring programme. – ICES Journal of Marine Science 82(6): 1-6.
[6] Grass, A.J. (1981): Sediment transport by waves and currents. – University College, London, Department of Civil Engineering 52p.
[7] Hou, J., Kang, Y., Hu, C., Tong, Y., Pan, B., Xia, J. (2020): A GPU-based numerical model coupling hydrodynamical and morphological processes. – International Journal of Sediment Research 35(4):
[8] Huai, W., Yang, L., Guo, Y. (2020): Analytical solution of suspended sediment concentration profile: Relevance of dispersive flow term in vegetated channels. – Water Resources Research 56(7): 20p.
[9] Jelti, M., Serghini, A. (2023): Numerical modeling of non-capacity model for sediment transport in open channel hydraulics by Roe scheme with a new discretization of the source term. – Advanced Mathematical Models & Applications 12p.
[10] Jiang, L., Gerkema, T., Idier, D., Slangen, A., Soetaert, K. (2020): Effects of sea-level rise on tides and sediment dynamics in a Dutch tidal bay. – Ocean Science 16(2): 307-321.
[11] Khojasteh, D., Chen, S., Felder, S., Heimhuber, V., Glamore, W. (2021). Estuarine tidal range dynamics under rising sea levels. – PLoS One 16(9): 25p.
[12] Li, S., Tran, H. Q., McCarroll, R. J., Ierodiaconou, D., Babanin, A. V. (2025): Estimation of bottom friction in modelling tidal dynamics of Port Phillip Bay. – Journal of Atmospheric and Oceanic Technology 42(8):1085-1098.
[13] Li, X., Cai, Y., Liu, Z., Mo, X., Zhang, L., Zhang, C., Cui, B., Ren, Z. (2023): Impacts of river discharge, coastal geomorphology, and regional sea level rise on tidal dynamics in Pearl River Estuary. – Frontiers in Marine Science 10: 12p.
[14] Paola, C., Voller, V.R. (2005): A generalized Exner equation for sediment mass balance. – Journal of Geophysical Research: Earth Surface 110(F4): 8p.
[15] Papanicolaou, A., Diplas, P., Dancey, C., Balakrishnan, M. (2001): Surface roughness effects in near-bed turbulence: Implications to sediment entrainment. – Journal of Engineering Mechanics 127(3): 211-218.
[16] Pareta, K. (2024): 1D-2D hydrodynamic and sediment transport modelling using MIKE models. – Discover Water 4(1): 28p.
[17] Penna, N., Coscarella, F., D’Ippolito, A., Gaudio, R. (2020): Bed roughness effects on the turbulence characteristics of flows through emergent rigid vegetation. – Water 12(9): 17p.
[18] Rasyif, T.M., Kato, S., Syamsidik, Okabe, T. (2017): Preliminary study on performance of a coupled hydrodynamic and sediment transport model on small domain. – AIP Conference Proceedings 1892(1): 10p.
[19] Salheddine, M., André, P., Mahmoud, H. (2020): A coupled 1-D/2-D model for simulating river sediment transport and bed evolution. – Journal of Hydroinformatics 22(5): 1122-1137.
[20] Simpson, G., Castelltort, S. (2006): Coupled model of surface water flow, sediment transport and morphological evolution. – Computers & Geosciences 32(10): 1600-1614.
[21] Sun, Z., Li, Y., Wu, N., Fan, Z., Li, K., Sun, Z., Song, X., Xue, L., Jia, Y. (2025): Dynamic Analysis of Subsea Sediment Engineering Properties Based on Long-Term In Situ Observations in the Offshore Area of Qingdao. – Journal of Marine Science and Engineering 13(4): 25p.
[22] Wu, Y., Zhao, E., Li, X., Zhang, S. (2025): Application of wave–current coupled sediment transport models with variable grain properties for coastal morphodynamics: A case study of the Changhua River, Hainan. – Ocean Science 21(1): 473-495.
[23] Yildiz, I., Stanev, E.V., Staneva, J. (2025): Advancing bathymetric reconstruction and forecasting using deep learning. – Ocean Dynamics 75(4): 17p.
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