Integrating the cellular vortex method with remote sensing and geographical information systems in the modelling of coastal flooding around Niger Delta
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Abstract
Coastal areas are increasingly vulnerable to flooding, necessitating accurate simulation methods to understand flood dynamics and their potential impacts. This study employed a Lagrangian framework integrating the cellular vortex method with remote sensing and GIS to simulate flood height distribution in a coastal region. Leveraging climatic and remotely sensed data, alongside ArcMap 10.6.1 for map processing, the research estimated flood magnitude and frequency using the L-moment approach, applied to a forty-year tidal record dataset. Essential input parameters, such as the roughness coefficient and curve number, were derived from land use and land cover characteristics. Additionally, river flow velocity was observed at 0.12m/s, with measured wind speed and direction recorded at 4m/s in the northwest direction. Notably, analysis of the initial flood height distribution map revealed a significant expansion of wetland areas, attributed to observed land use changes between May 2002 and July 2005. Projections for flood height distribution in 2025 and 2050 highlighted the emergence of tidal floods, emphasizing the critical role of considering future climate and land use scenarios in flood dynamics assessment. This research contributes to advancing understanding of flood modeling techniques and underscores the urgency of adaptive measures to mitigate the potential impacts of coastal flooding.
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References
Aja, D., Elias, E., & Obiahu, O. H. (2019). Flood risk zone mapping using rational model in a highly weathered Nitisols of Abakaliki Local Government Area South-eastern Nigeria. Geology, Ecology, and Landscape, 4(2), 131–139.
Alho, P., Sane, M., Huokuna, M., Käyhkö, J., Lotsari, E., & Lehtiö, L. (2008). Mapping of floods. Environmental Administration Guidelines, 2, 59-101.
Ali, S. A., Khatami, R., Ahmad, A., & Ahmad, S. N. (2019). Application of GIS based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region India. Model Earth Systems and Environment, 5(3), 1083–1102.
Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., & Niu, C. (2016). Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability, 8, 948.
Chen, W. B., & Liu, W. C. (2014). Artificial neural network modeling of dissolved oxygen in reservoir. Environmental Monitoring and Assessment, 186(11), 7469-7483.
Ehiorobo, J. O., Izinyon, O. C., & Ilaboya, I. R. (2012). Effects of climate change on river flow regimes in the mangrove and tropical rain forest region of West Africa [Conference presentation]. International Workshop on Exchange of Experience in Water Resources Management between Europe/China/Africa/Latin America.
Fohrer, N., Haverkamp, S., Eckhardt, K., & Frede, H. G. (2001). Hydrologic response to land use changes on the catchment scale. Phys. Chem. Earth., 26, 577–582.
Han, Y., Huang, Q., He, C., Fang, Y., Wen, J., Gao, J., & Du, S. (2020). The growth mode of built-up land in floodplains and its impacts on flood vulnerability. Science of the Total Environment, 700, 134462.
Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A. X., Chen, W., Kougias, I., & Kazakis, N. (2018a). Flood susceptibility assessment in Hengfeng area using coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci. Total Environ, 621, 1124–1141
Izinyon, O. C. (2018). Flood hazard modelling and management [Conference presentation]. The technical sections of the Nigeria Society of Engineers.
Klijn, F. (2009). Flood risk assessment and flood risk management: An introduction and guidance based on experience and findings of FLOOD site (an EU-funded Integrated Project). Delft: Deltares, Delft Hydraulics.
Kun, Y., Angelica, T., Gabor, B., Tommaso, M., & Di-Baldassarre, G. (2014). Exploring the potential of topography and radar altimetry to support flood propagation modeling: Danube case study. Journal of Hydrologic Engineering, 20(2), 04014048.
Kundzewicz, Z.W., Pinskwar, I., & Brakenridge, G. R. (2018). Large floods in Europe 1985–2009. Hydrological Science Journal, 58, 1–7.
Lichter, M., Vafeidis, A.T., Nicholls, R. J., & Kaiser, G. (2010). Exploring data related uncertainties in analyses of land area and population in the low elevation coastal zone (LECZ). Coastal Res., 6, 757–768.
Nkwunanwo, U. C., Malcolm W., & Brain, B. (2015). Flooding and flood risk reduction in Nigeria. Cardinal gaps J. Geogr. Nat. Disasters, 5, 136.
Ritter, J., Berenguer, M., Corral, C., Park, S., & Sempere-Torres, D. (2020). Real-time assessment of flash flood impacts: A regional high-resolution method. Environment International, 136, 105375.
Sofia, G., Roder, G., Dalla-Fontana, G., & Tarolli, P. (2017). Flood dynamics in urbanized landscapes: 100 years of climate and human’s interaction. Sci. Rep., 7, 40527.
Suyeon, S., Sung-Ik, S., & Woonjae, H. (2018). Vortex simulations of Kelvin-Helmholtz instability with surface tension in density-stratified flows. European Journal of Mechanics-B/Fluids, 67, 168-177.
Tomczyk, A. M., Ewertowski, M. W., & Carrivick, J. L. (2020). Geomorphological impacts of a glacier lake outburst flood in the high arctic Zackenberg River NE Greenland. Journal of Hydrology, 591, 125300.
Viganò, D., & Maddalena, L. (2018). A numerical model for supersonic vortex dynamics. In 22nd AIAA International Space Planes and Hypersonics Systems and Technologies Conference (p. 5162).
Wei, C., Haoyuan, H., Shaojun, L., Himan, S., Yi, W., Xiaojing, W., & Baharin, B. A. (2019). Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology, 575, 864–873.
Zeynab, S., Mehdi, D., & Hossein, H. Z. (2018). Numerical solution of 2D Navier – Stokes equation discretized via boundary elements method and finite difference approximation. Engineering Analysis with Boundary Elements, 96, 64–77.