A multi-strategy percolation framework for road network robustness under projected sea-level rise

Climate change, particularly sea-level rise (SLR), poses a significant long-term threat to transportation networks, necessitating their robustness as an essential part of long-term resilience. This study utilizes different percolation strategies (random, proximity-guided, and targeted) to evaluate and compare road network robustness under various SLR scenarios. Applied to Qatar's road network, the analysis reveals strong baseline robustness as evidenced by the need to remove over 23% of edges to meet the established threshold of reducing the Giant Weakly Connected Component (GWCC) to 50%. Meanwhile, in targeted percolation, Node Strength (NS) outperformed betweenness-based strategies in identifying critical elements, reinforcing its utility for prioritizing protective interventions. Proximity-guided removal showed that Qatar's road network can maintain structural cohesion even under considerable SLR scenarios. The decline in robustness exceeds the direct impact of inundated roads, implying that SLR weakens the network beyond the apparently water-logged areas. The framework's flexibility supports diverse applications, from gradual SLR scenarios to comparative assessments across geographic scales, from individual cities to entire nations. Actionable outputs include ranked critical segments and scenario-based robustness benchmarks to guide targeted upgrades (e.g., protection/elevation, redundancy), maintenance prioritization, and coastal adaptation planning. This transferable methodology provides policymakers, engineers, and researchers with a practical tool for benchmarking infrastructure resilience strategies and climate-adaptive planning in coastal regions and beyond.