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Assessing the effects of sea level rise on urban floods, a 1D2D satellite-based flood inundation modelling approach in Accra coastal zone

Welbeck, Ransford Nii Ayitey (2021) Assessing the effects of sea level rise on urban floods, a 1D2D satellite-based flood inundation modelling approach in Accra coastal zone.

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Abstract:Accra, Ghana’s regional capital, has experienced devastating impacts of urban floods since the late 1930s. The recurrent flooding of the coastal urban area of the Densu River Basin is an example of such events. The findings of a prior study of the area identified the upstream reservoir spillage and the backwater effect as a result of coastal water intrusion as the cause of the floods. The rapid unplanned urbanisation, the operation of the Weija reservoir and sea level rise make this low-lying coastal zone susceptible to severe flooding in the future. Therefore, the objective of this study was to evaluate the likely impacts of sea level rise by climate change on the recurrent floods in the area. The study employed hydrodynamic modelling to simulate flow processes in the area to achieve the study objective. Due to the lack of field observed data on recent inundations, satellite-based surface water maps were tested to serve model calibration. SAR and optical satellite images, namely Sentinel-1 and PlanetScope images, were sourced to produce surface water maps of a flood event that occurred in 2017. The Edge Otsu algorithm, an automatic threshold-based algorithm that integrates the Canny edge detection method, was applied to map surface water bodies in the satellite images. The surface water mapping operations were executed using Google Earth Engine (GEE), the GEE python API and the HYDRAFloods open-source python package. Surface water bodies in the Sentinel-1 images were mapped using the VV polarization bands. NDWI maps were computed using the green and near-infrared bands of the PlanetScope image to detect surface water bodies before applying the unsupervised surface water mapping algorithm chosen for the study. The evaluation of the surface water maps produced in the study was performed using visual inspection and the metrics, namely, the overall classification accuracy and Kappa coefficient. The overall classification accuracy recorded for the maps ranged from 84.16% to 90.10%, with Kappa coefficients also ranging from 0.69 to 0.80. With the aim of improving the satellite-based surface water maps produced from the individual images, the feature-level image fusion method was applied to fuse the SAR and optical satellite images using the random forest classifier. Overall classification accuracies of 97% and 98% with Kappa coefficients of 0.93 and 0.97 were achieved for the two fusion operations executed. Despite the results of the quantitative assessments performed, some causes of uncertainties were identified within the maps. Misclassification of water pixels was identified in the surface water maps produced from the optical images, while the maps produced from the SAR images showed dry patches along the course of the river channel. The cause of the former was attributed to the similarities of NDWI values of regions covered with water and built-up areas, while the latter was due to vegetation along the river channel. The schematization of the 1D2D SOBEK hydrodynamic model was designed to account for tidal behaviour at the downstream end of the model domain. Model tests performed proved that the model was able to replicate real-world flow processes affecting inundations in the study area. An attempt was made to calibrate the 1D2D SOBEK hydrodynamic model by means of the satellite-based surface water maps, and the corresponding model simulated inundation extents. The comparison results were not satisfactory, and as such, the model could not be calibrated. The assessment of the impacts of sea level rise on the flooding in the model domain was executed by comparing inundation area and average water depth of two scenarios with the flood event of 2017. These scenarios were based on the sea level rise projections for the years 2060 and 2100 that were obtained from literature. Overall, the results revealed that the inundation area at the downstream section of the model domain increased in all the scenarios. Also, the average water depth of the two scenarios also increased when compared to the flood event of 2017.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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