A novel geospatial framework for projecting sea level rise impacts on the Western Arabian Gulf: insights from nighttime light and cellular automata modeling

Abstract: Sea-level rise (SLR) poses a major global risk to densely populated coastal zones, with recent assessments indicating significant acceleration due to climate change. This study advances conventional SLR vulnerability analysis by integrating Nighttime Light (NTL) data as a proxy for economic and industrial activity, enhancing the spatial representation of socio-economic exposure beyond traditional Land Use and Land Cover (LULC) or population-based models. We combine NTL, LULC, and Cellular Automata (CA) modeling within a unified geospatial framework to forecast the spatiotemporal impacts of SLR on six major coastal cities in Saudi Arabia’s Eastern Province along the Arabian Gulf. Historical sea-level records (1979–2020) reveal an annual mean rise of 7.9 mm, consistent with global trends. Future inundation was simulated using a GIS-based “bathtub” model, recognizing it as a first-order approximation that assumes static hydrodynamic conditions. Concurrently, the CA-based LULC simulations (1973–2020) project urban expansion to 2130, while NTL data identify evolving economic hotspots vulnerable to inundation. Results indicate heterogeneous but intensifying flood exposure, with Jubail, Qatif, and Ras Tanura emerging as high-risk zones—where up to 40% of coastal lands may be submerged under worst-case SLR scenarios. Rapidly expanding reclaimed and built-up areas in Dammam and Khobar show critical exposure, underscoring the need for differentiated adaptation strategies: protective infrastructure for high-economic-value reclaimed zones and ecological restoration or managed retreat for natural coastlines. The integrative framework presented here offers a scalable, data-driven approach for anticipating coastal vulnerability in arid urban environments, supporting region-specific climate adaptation and resilience planning.