Assessing Urban Flood Risk with Lidar-Derived DEMs: Lagos Metropolis Case Study

Thesis Abstract

Urban flooding poses increasing threats to Lagos Metropolis, driven by rapid urbanisation, expanding impervious surfaces, and climate-induced rainfall intensification, which overwhelm drainage infrastructure and degrade socio-economic resilience. This study addresses the gap in high-resolution, evidence-based flood risk assessment by integrating LiDAR-derived digital elevation models (DEMs) with socio-spatial indicators to delineate hazard, exposure, and vulnerability components for Lagos. The aim is to produce a spatially explicit flood risk map and inform adaptive urban planning strategies. Specific objectives are (i) to generate a high-resolution LiDAR-derived DEM for central Lagos and validate it against ground truth elevations; (ii) to model surface runoff and floodplain delineation under representative meteorological scenarios using hydrological and hydraulic analyses; (iii) to quantify exposure by mapping population, critical infrastructure, and land-use types within delineated flood-prone zones; (iv) to assess vulnerability through a composite index incorporating housing quality, income, and access to services; (v) to examine the relative importance of physical versus socio-economic factors on flood risk using multivariate regression and spatial autocorrelation analyses; and (vi) to develop a decision-support framework for municipal authorities that integrates risk maps with policy-oriented recommendations. The study adopts a mixed-methods approach under a positivist paradigm, combining quantitative GIS-Hydrology analytics with qualitative stakeholder validation to ensure practical relevance. The population comprises wards within Lagos Metropolis, with a stratified sample of 40 representative neighborhoods selected to capture diverse urban morphologies and exposure profiles. Data collection employs (a) airborne LiDAR data at 0.5–1.0 m point density, validated by a 30-site ground control network; (b) historic rainfall series and scenario rainfall intensities (10-, 25-, and 100-year events) from the Nigerian Meteorological Agency; (c) census-derived population counts, housing quality indicators, and asset inventories; (d) infrastructure datasets (electricity, healthcare, education, and drainage networks) from Lagos State GIS archives; and (e) semi-structured interviews with urban planners and community leaders (n ? 25) to interpret exposure and vulnerability dimensions. Analytical techniques include LiDAR-based DEM generation and hydrological modelling (Hydroflows/HEC-HMS) to simulate surface runoff, integrated with 2D hydraulic modelling (HEC-RAS) to identify flood extents and depths. Exposure is quantified via overlay analyses of population and critical infrastructure within flood polygons, while vulnerability is synthesized through principal component analysis (PCA) and a weighted multi-criteria index. Spatial autocorrelation is tested with Moran’s I, and regression models (ordinary least squares and geographically weighted regression) assess relationships between risk components and urban form metrics. Ethical considerations address data privacy and stakeholder engagement protocols. Expected findings indicate that the LiDAR-derived DEM significantly improves flood delineation accuracy (root mean square error < 0.25 m against ground truth), and that flood risk in Lagos is disproportionately concentrated along dense informal settlements and near drainage bottlenecks, with socio-economic vulnerability amplifying hazard impacts. The study anticipates that green-blue infrastructure indicators and targeted drainage upgrades will materially reduce estimated annual damages by 12–25% under moderate scenarios, contingent on implementation. Contribution to knowledge includes methodological advancement in integrating high-resolution LiDAR topography with socio-spatial vulnerability frameworks for megacity flood risk assessment in a tropical coastal context, and empirical evidence to refine Lagos’s flood management policy. The main conclusion is that comprehensive flood risk governance requires synchronized physical and social dimensions, supported by high-resolution topographic data and participatory planning. Recommendations emphasize prioritising street-scale drainage improvement, land-use zoning that limits development in high-risk zones, community-based early warning and evacuation planning, and investment in nature-based solutions to enhance urban resilience. The research also offers a scalable framework applicable to similar tropical African urban contexts facing flood hazards.