Vulnerability of the low-lying cities of the East Gulf of Guinea to the combined effect of land subsidence and sea level rise: case of Cameroon

Low-lying coastal cities of the eastern Gulf of Guinea, particularly Douala in Cameroon, are increasingly exposed to the compounded hazards of land subsidence and sea-level rise (SLR). Despite growing awareness of human- and climate-driven coastal risks, the interaction between vertical land motion and oceanic processes, their magnitude, spatial variability, and long-term implications remain insufficiently quantified and poorly integrated into coastal risk assessment frameworks. This limits effective adaptation, resilience, and urban planning for these rapidly growing cities. This PhD research not only quantifies the coastal exposure of major, rapidly growing coastal cities along the eastern Gulf of Guinea, such as Douala, Bata, Libreville, PortGentil, Pointe-Noire, Cabinda, and Luanda, to sea level rise (SLR) and low elevation, but also models and projects the combined impacts of land subsidence and SLR to assess the long-term vulnerability of the Douala coastland. A geospatial and random forest analysis that combined global digital elevation models (DEMs), world population density datasets, Global Human Settlement Layer (GHSL) data, and infrastructure datasets from GLOBUS was used to establish a novel regional synthesis of coastal exposure and development patterns across the eastern Gulf of Guinea, focusing on low-lying and densely populated cities. Multi-temporal InSAR analysis was applied for the first time to Douala, providing high-resolution estimates of vertical land motion and identifying zones of active deformation associated with anthropogenic and natural drivers. A comprehensive analysis of land-use/land-cover (LULC) change from 1992 to 2022 was conducted to investigate correlations between urban expansion, surface sealing, and increased subsidence susceptibility. A neural network integration of remote sensing data (InSAR), building loads, and geological soil data was used to assess urbanisation-induced land subsidence. Statistical best-fit models combining linear and exponential decay functions were developed to project subsidence trajectories from InSAR time series, introducing predictive capability into the assessment of future deformation. The combined effects of projected SLR (IPCC SSP scenarios) and modelled vertical land motion were then used to compute relative sea-level rise (RSLR) and future inundation risk up to 2050. Results reveal that much of the Douala coastal plain lies below 5 m above mean sea level, making it moderately to highly vulnerable to the compounded effects of land subsidence and projected sea-level rise. InSAR analysis (2018–2023) indicates an average subsidence rate of 3.0 mm/year, with hotspots exceeding 18 mm/year in recently urbanised or reclaimed floodplain zones. Land-use analysis (1992–2022) shows major conversions from vegetated and wetland areas to built-up surfaces, directly correlating with accelerated subsidence. The deep-learning model successfully simulates urbanisation-induced deformation, linking anthropogenic pressures, geology, and hydrological stressors; however, further data are required to enhance accuracy in future applications. Projections to 2050 under SSP5–8.5 scenarios demonstrate that RSLR could exceed 0.3 m when compounded by subsidence, drastically expanding flood exposure and saline intrusion zones.