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Abstract <p>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&#xa0;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.</p> Graphical abstract <p></p> <p><i>Graphical Abstract Description</i>: The graphical abstract provides a visual and textual summary of a study focused on the potential consequences of sea level rise (SLR) on the coastal regions of the western Arabian Gulf, particularly along the Saudi Arabian coastline. On the left side, the image outlines the key objectives of the research, including forecasting sea level rise effects on major coastal cities, analyzing historic shoreline changes, and examining the implications of SLR on future land use, land cover (LULC), and economic zones. A small inset map highlights the geographical scope of the study, showing vulnerable coastal areas in Saudi Arabia. The center of the image emphasizes the methodology, which includes gathering technical data, performing geoprocessing operations, and simulating predictive models to assess risk levels. On the right side, the results and implications are presented: significant coastal belt alteration is expected, and cities such as Jubail, Qatif, and Ras Tanura are identified as high-risk zones under worst-case scenarios. Additionally, economic hubs like Ad Dammam and Al Qatif face severe inundation risks. The study underscores the urgency of proactive planning and adaptive strategies to address the anticipated ecological and economic challenges. Overall, the image integrates scientific analysis, spatial modeling, and policy-relevant findings to highlight the critical need for regional preparedness in the face of climate-induced sea level rise.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月26日

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  • Azher Hussain Syed,
  • Bijoy Mitra,
  • Mohammad Shahedur Rahman,
  • Omer Rehman Reshi,
  • Syed Masiur Rahman,
  • Asif Raihan

摘要

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.

Graphical abstract

Graphical Abstract Description: The graphical abstract provides a visual and textual summary of a study focused on the potential consequences of sea level rise (SLR) on the coastal regions of the western Arabian Gulf, particularly along the Saudi Arabian coastline. On the left side, the image outlines the key objectives of the research, including forecasting sea level rise effects on major coastal cities, analyzing historic shoreline changes, and examining the implications of SLR on future land use, land cover (LULC), and economic zones. A small inset map highlights the geographical scope of the study, showing vulnerable coastal areas in Saudi Arabia. The center of the image emphasizes the methodology, which includes gathering technical data, performing geoprocessing operations, and simulating predictive models to assess risk levels. On the right side, the results and implications are presented: significant coastal belt alteration is expected, and cities such as Jubail, Qatif, and Ras Tanura are identified as high-risk zones under worst-case scenarios. Additionally, economic hubs like Ad Dammam and Al Qatif face severe inundation risks. The study underscores the urgency of proactive planning and adaptive strategies to address the anticipated ecological and economic challenges. Overall, the image integrates scientific analysis, spatial modeling, and policy-relevant findings to highlight the critical need for regional preparedness in the face of climate-induced sea level rise.