Assessment of Qatar's road network under sea-level-rise scenarios using traffic simulation and graph theory

Sea-level rise (SLR) threatens every dimension of sustainable development, testing infrastructure resilience and adaptation. This research develops a multi-stage framework to evaluate road network vulnerability under varying SLR scenarios through geospatial analysis, traffic simulation, and graph theory. High-resolution maps derived from IPCC-AR6 and NOAA projections show that without adaptation measures, SLR may affect approximately 3–11% of land and 2–17% of roads in Qatar, potentially increasing trip durations up to 15 times. Importantly, the estimated impacts are indicative trends rather than definitive outcomes linked to specific emissions scenarios. In parallel, the paper examines the effectiveness of several graph metrics in evaluating road network performance under SLR-induced disruptions. The goal of this exercise is to identify metrics that strongly correlate with severity levels and simulation outcomes, supporting their utility in resilience assessment. Finally, the paper outlines a practical roadmap to advance SLR risk simulation and support the development of effective adaptation strategies to enhance Qatar's resilience. The adaptability of the proposed framework and roadmap also enables their application to other geographical contexts, with minimal refinement and appropriate localization. Future research may extend this work by incorporating localized and temporal dynamics and analyzing additional critical infrastructure systems​.