Integrating hydro-climatic drivers, flood frequency, and dynamic perception for sustainability assessment of flood vulnerability in Lagos, Nigeria
5 affiliations
- 1Institute of Ecology and Environmental Studies, Obafemi Awolowo University, Ile-Ife, Nigeria
- 2Department of Project Management, University of Northampton, United Kingdom
- 3Department of Mathematical Sciences, Olabisi Onabajo University, Ago-Iwoye, Nigeria
- 4Department of Geography and GIS, University of Cincinnati, United States
- 5Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria
Abstract
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.
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BibTeX
@article{aniramu2026integrating,
title = {{Integrating hydro-climatic drivers, flood frequency, and dynamic perception for sustainability assessment of flood vulnerability in Lagos, Nigeria}},
author = {Opeyemi Aniramu and Raphael Odelola and Joseph Okebugwu and Ugochukwu Ojimadu and Oluwagbenga Orimoogunje},
journal = {Progress in Disaster Science},
year = {2026},
month = jun,
doi = {10.1016/j.pdisas.2026.100631},
url = {https://doi.org/10.1016/j.pdisas.2026.100631},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Integrating hydro-climatic drivers, flood frequency, and dynamic perception for sustainability assessment of flood vulnerability in Lagos, Nigeria
AU - Opeyemi Aniramu
AU - Raphael Odelola
AU - Joseph Okebugwu
AU - Ugochukwu Ojimadu
AU - Oluwagbenga Orimoogunje
PY - 2026
DA - 2026/06/18
JO - Progress in Disaster Science
DO - 10.1016/j.pdisas.2026.100631
UR - https://doi.org/10.1016/j.pdisas.2026.100631
AB - 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.
SN - 2590-0617
ER -