Extreme water levels along the central Red Sea coast of Saudi Arabia: processes and frequency analysis
KAUST DepartmentApplied Mathematics and Computational Science Program
Beacon Development Company
Biological and Environmental Sciences and Engineering (BESE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Red Sea Research Center (RSRC)
KAUST Grant NumberREP/1/3268-01-01
Embargo End Date2021-10-19
Permanent link to this recordhttp://hdl.handle.net/10754/665688
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AbstractKnowledge about extreme water levels is essential for efficient planning and design of coastal infrastructure. This study uses a high-resolution (~ 60 m) coupled advanced circulation + simulating waves nearshore modeling system to estimate extreme water levels in the coastal waters of King Abdullah Economic City (KAEC), Saudi Arabia, located on the central eastern coast of the Red Sea. High spatial (5 km) and temporal (hourly) resolution meteorological fields are generated to drive the model, along with open ocean tides. The characteristics of extreme water levels in the region are subsequently described based on the validated model simulations. The central Red Sea is characterized by a low-tidal regime, and meteorological events contribute significantly to total water levels: meteorological surges cause water level increases of up to 75 cm inside the KAEC lagoon. An extreme value analysis based on annual maxima of hindcast water level data is conducted and the results suggest that the inferred 100-year water levels are about 80 cm inside the KAEC lagoon. It is also shown that projected sea level rise would reduce the average recurrence intervals of extreme water levels along the KAEC coastline.
CitationAntony, C., Langodan, S., Dasari, H. P., Knio, O., & Hoteit, I. (2020). Extreme water levels along the central Red Sea coast of Saudi Arabia: processes and frequency analysis. Natural Hazards. doi:10.1007/s11069-020-04377-y
SponsorsThis research was supported by funds from the Office of Sponsored Research (OSR) under the virtual Red Sea Initiative (Grant #REP/1/3268-01-01) at King Abdullah University of Science and Technology (KAUST). The study made use of the Supercomputing Laboratory resources at KAUST. Data used in this study can be made available on request. Graphs and maps were drawn using GMT (Generic Mapping Tools, Wessel et al. 2013) and FigureGen (Dietrich et al. 2013). The authors would like to thank the ADCIRC+SWAN model developers for making the model freely available.