Search Results (3)

The category 5-equivalent tropical Cyclone Gonu (2007) was the strongest cyclone to enter the northern Arabian Sea and Gulf of Oman. The impact of this cyclone on the sea surface temperature (SST) cooling and deepening of the mixed layer was investigated herein using an optimally interpolated (OI) cloud-free sea surface temperature (SST) dataset, climatological profiles of water temperature, and data from Argo profilers. SST data showed a maximum cooling of 1.7–6.5 °C during 1–7 June 2007 over the study area, which is similar to that of slow- to medium-moving cyclones in previous studies. The oceanic heat budget equation with the assumptions of the dominant turbulent mixing effect was used to establish relationships between SST and mixed layer depth (MLD) for regions that were directly affected by cyclone-induced turbulent mixing. The relationships were applied to the SST maps from satellite to obtain maps of MLD for 1–7 June, when Gonu was over the study area. Comparing with the measured MLD from Argo data showed that this approach estimated the MLDs with an average error of 15%, which is an acceptable amount considering the convenience of this approach in estimating MLD and the simplifications applied in the heat budget equation. Some inconsistencies in calculating MLD were attributed to use of climatological temperature profiles that may not have appropriately represented the pre-cyclone conditions due to pre-existing cold/warm core eddies. Estimation of the diapycnal diffusion that quantified the turbulent mixing across the water column showed consistent temporal and spatial variations with the calculated MLDs. Full article

Tropical cyclones may be destructive in the coastal region, such as the Gonu tropical cyclone, which affected the Arabian Peninsula and parts of southern Iran in 2007. In this study, a coupled MIKE 21/3 HD/SW (hydrodynamic/spectral wave) model was used to simulate the inland flooding inside the Sur port during the Gonu tropical cyclone. The MIKE 21 Cyclone Wind Generation (CWG) tool was utilized to generate the cyclone’s wind and pressure field. The required input data were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS) and imported into the CWG tool. In this study, the wind and pressure fields were compared between the analytical vortex model and European Centre for Medium-Range Weather Forecasts (ECMWF) data during the Gonu cyclone passage. Moreover, by developing a new model, artificial Mangroves’ effect on inland flooding was investigated. The results show that, contrary to the ECMWF data, the analytical vortex models well captured the storm event’s wind and pressure field. Furthermore, the flood hazard is calculated based on the inundation depth, flow velocity, and area’s vulnerability. The flood hazard map shows that 5% of the coast is at high-risk, 49% is at medium-risk, and 46% is at low-risk class in the Sur port. By applying Mangroves as flood risk reduction, the high-risk area is almost completely removed. However, medium and low-risk zones increase by 50% and 50%, respectively. This information could be helpful in disaster risk reduction and coastal management in the future. Full article

Floods are among the most common natural hazards around the world. Mapping and evaluating potential flood hazards are essential for flood risk management and mitigation strategies, particularly in coastal areas. Several factors play significant roles in flooding and recognizing the role of these flood-related factors may enhance flood disaster prediction and mitigation strategies. This study focuses on using Shannon’s entropy model to predict the role of seven factors in causing floods in the Governorate of Muscat, Sultanate of Oman, and mapping coastal flood-prone areas. The seven selected factors (including ground elevation, slope degree, hydrologic soil group (HSG), land use, distance from the coast, distance from the wadi, and distance from the road) were initially prepared and categorized into classes based on their contribution to flood occurrence. In the next step, the entropy model was used to determine the weight and contribution of each factor in overall susceptibility. Finally, results from the previous two steps were combined using ArcGIS software to produce the final coastal flood susceptibility index map that was categorized into five susceptibility zones. The result indicated that land use and HSG are the most causative factors of flooding in the area, and about 133.5 km² of the extracted area is threatened by coastal floods. The outcomes of this study can provide decision-makers with essential information for identifying flood risks and enhancing adaptation and mitigation strategies. For future work, it is recommended to evaluate the reliability of the obtained result by comparing it with a real flooding event, such as flooding during cyclones Gonu and Phet. Full article