SEA LEVEL RISE AND THE ARABIAN GULF: ASSESSING COASTAL VULNERABILITY AND SOCIOECONOMIC EXPOSURE TO SEA LEVEL RISE IN QATAR

Abstract

Sea level rise (SLR) is one of the most pressing manifestations of global climate change, posing severe challenges for low-lying, arid nations whose economies and populations are concentrated along the coast. This study develops a comprehensive GIS-based framework for assessing coastal vulnerability and socioeconomic exposure to SLR in Qatar, which is one of the most physically and economically susceptible countries in the Arabian Gulf. The study integrates physical and socioeconomic drivers through a Coastal Vulnerability Index (CVI), combining physical and socioeconomic variables using Analytic Hierarchy Process (AHP) structure to ensure internal consistency and weighting transparency. Results reveal that over 90% of Qatar’s coastline falls within the high to very high vulnerability categories under present conditions, largely driven by flat geomorphology, low elevation, and concentrated infrastructure. Northern municipalities such as Al Shamal and Al Khor display the greatest physical exposure, while Doha and Al-Wakra exhibit the highest socioeconomic risk due to dense populations and critical assets. Sensitivity and uncertainty analyses confirm that physical parameters, particularly, shoreline erosion rate, mean tide range, and wave height dominate total model sensitivity, whereas socioeconomic parameters exert localized influence in highly urbanized areas. The model demonstrates statistical robustness, with cumulative propagated uncertainty below ±0.12% of the mean CVI. The findings highlight that while physical exposure defines Qatar’s baseline vulnerability, socioeconomic resilience has the potential to mitigate overall coastal risk. By examining the socioeconomic impacts through the Shared Socioeconomic Pathway (SSP), Socioeconomic Exposure Index (SEI), Economic Assets at Risk (EAR), and Population Displacement (PDIS). This study provides an evidence-based foundation for national adaptation and coastal management. Recommendations include strengthening tide and wave data collection, advancing regional modeling capacity through a Gulf-optimized Regional Climate Model (RCM), and developing a Coastal Risk Management (CRM) framework to guide infrastructure planning and policy interventions. The proposed methodology offers a replicable model for other arid, data-limited regions and contributes to the growing body of regional research on climate adaptation and sustainable coastal development in the Arabian Gulf.

Date of Award	2025
Original language	American English
Awarding Institution	HBKU College of Science and Engineering