Integrating hydro-climatic drivers, flood frequency, and dynamic perception for sustainability assessment of flood vulnerability in Lagos, Nigeria

Lagos State faces escalating flood vulnerability due to interacting hydro-climatic changes, yet the integration of flood drivers and community perceptions with sustainability remains limited. This study examined rainfall trends and probability estimation, focusing on the dynamic perception–sustainability assessment. The study used a mixed-method approach, combining hydro-climatic temporal analysis, geospatial rainfall mapping, and household perception surveys. Hydro-climatic data (1983–2023) were analyzed to estimate trend, exceedance probability, and return periods. A structured household survey ( n = 397) was integrated within a sustainability framework using binary logistic regression. The results of spatial rainfall mapping established a significant relationship between rainfall months and flood vulnerability. Coastal and slum areas were highly vulnerable to monthly rainfall-flooding, particularly in June (312.2 mm), July (256.9 mm), and September (167.1 mm). The regression coefficient of ground-based stations (0.82) and CHIRPS satellite rainfall (0.71) established a strong relationship of rainfall-induced floods. Similarly, moderate return period (30–60% Exceedance) coincided with the 2019 flash flood. Additionally, Dynamic Flood Sustainability Index (0.50) revealed that hazard growth outpaced adaptation, rendering long-term resilience unsustainable. Logistic regression identified previous flood experience (β = 0.91, p < 0.01) and rainfall intensity (β = 0.68, p < 0.01) as the dominant risk factors, providing strong model precision (AUC = 0.89). Despite high vulnerability (V = 0.71), preparedness remains low (PP = 0.49). The findings underscore practical implications, including climate adaptation planning, improved flood early warning systems, and sustainability-focused urban resilience policies in coastal megacities.