Assessing Urban Flood Risk with Lidar-Enhanced GIS: City of Lagos Case Study

Thesis Abstract

Urban flood risk in Lagos has intensified due to rapid urbanisation, informal settlement expansion, and climate-driven rainfall variability, underscoring the need for high-resolution, spatially explicit risk assessments. This study aims to quantify and map urban flood risk by integrating LiDAR-derived topography with high-resolution GIS, enabling robust scenario analysis and targeted flood mitigation strategies for Lagos metropolis. The specific objectives are to (1) generate a 0.5–1.0 m LiDAR-derived digital elevation model (DEM) and derive hydrologically conditionable surfaces; (2) develop a GIS-based flood exposure model that integrates drainage network data, land-use/land-cover (LULC) change, and population distribution; (3) calibrate and validate hydrological and hydraulic flood models using historical flood extents (2012–2020) and radar-based rainfall data; (4) assess flood vulnerability by coupling exposure with a social resilience index derived from household survey responses; (5) identify priority flood risk zones and evaluate the effectiveness of proposed mitigation alternatives under three climate-augmented rainfall scenarios. The study adopts a mixed-methods research design under a pragmatic paradigm, drawing from a population comprising Lagos residents, urban planners, and municipal engineers. A stratified random sample of 1,200 households across Lagos Island, Surulere, Lekki, and Mushin is surveyed to collect socio-economic indicators, housing characteristics, and adaptive capacity measures, while semi-structured key informant interviews (n=25) with city planners, civil engineers, and NGO stakeholders provide qualitative insights into governance and policy gaps. Data collection instruments include LiDAR-derived DEMs and LiDAR-derived point clouds, high-resolution satellite imagery (Sentinel-2 and PlanetScope), survey questionnaires, interview guides, drainage network inventories, and rainfall/runoff records from the Lagos State Water Corporation and the Nigerian Meteorological Agency. Validity and reliability are ensured through triangulation, pilot testing (n=60 households), Cronbach’s alpha for resilience indices (? ? 0.80), and inter-rater reliability in qualitative coding (? ? 0.70). Data analysis employs a sequence of advanced spatial and statistical techniques (i) LiDAR processing for DEM generation, pit removal, and hydrological conditioning using TauDEM and ArcGIS Pro; (ii) flood hazard mapping via 2D hydraulic modelling with HEC-RAS 2D and a raster-based intensity-duration-frequency approach; (iii) exposure assessment through overlay analysis of population, housing, and critical infrastructure against flood extents; (iv) vulnerability estimation combining socio-economic indicators with exposure to produce a composite index validated by regression-based sensitivity analysis; (v) scenario analysis under climate projections using CMIP6-based rainfall intensification factors; (vi) regression analysis and ANOVA to examine determinants of vulnerability and to compare risk across districts; and (vii) thematic analysis of qualitative data to contextualize model outputs within governance and urban planning regimes. The anticipated findings include high-resolution flood risk maps identifying Lagos Island and parts of Lekki as recurrent high-exposure zones, with vulnerability driven by informal housing density and limited drainage capacity. The study is expected to show a strong positive relationship between exposure and socio-economic vulnerability, moderated by adaptive capacity and governance quality. The integration of LiDAR-enhanced DEMs with GIS-based hydrological modelling will demonstrate improved accuracy in flood delineation and risk prioritisation compared with conventional DEMs. The research contributes to knowledge by presenting a replicable framework for LiDAR-supported urban flood risk assessment in megacities with informal settlements, offering methodological advances in combining physical and social dimensions of risk. It also provides actionable guidance for Lagos State policymakers on prioritising drainage upgrades, green infrastructure, and land-use regulation under climate-resilient planning. The main conclusion is that LiDAR-enhanced GIS substantially improves urban flood risk assessment accuracy and decision-support capabilities, but its effectiveness hinges on integrated governance, timely data sharing, and community engagement. Recommendations include scaling the LiDAR-based workflow to adjacent coastal cities, establishing open data platforms for flood information, prioritising high-exposure informal settlements for retrofitting, and incorporating flood risk indicators into zoning and infrastructure development plans.