Future Projection of Flood Inundation in Sanggai Basin Based on Coupled Land Cover Change and Bias-Corrected Global Climate Models

Due to the uncertainty of climate change and ongoing urbanization, as seen in Nusantara (the new capital city of Indonesia), conducting flood risk assessments by integrating these factors is essential. This study evaluates both the historical and future projections of land cover and precipitation in the Sanggai Basin (site of Nusantara). Land cover in 2055 was projected using the CA-ANN (Cellular Automata – Artificial Neural Network) method integrated in MOLUSCE. The Quantile Delta Mapping (QDM) method was applied for bias correction of CHIRPS (Climate Hazards Group InfraRed Precipitation) and three global climate models (EC-Earth3, EC-Earth3-Veg, and MRI-ESM2-0) derived from CMIP6 (Coupled Model Intercomparison Project Phase Sixth). Five experiments based on Shared Socioeconomic Pathways (SSP) were analyzed for each model to assess the impact of future climate conditions on extreme rainfall: SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, and SSP5-8.5. A semi-distributed rainfall-runoff model, coupled with a flood inundation simulation, was used to generate flood inundation maps. This study projected a substantial growth of built-up areas by 2055, resulting in increased floodplain extent, further intensified by climate change. The largest inundation is observed under the combination of SSP5-8.5 and projected land cover, with 1, 817.8 ha predicted to be flooded under a 200-year return period rainfall. These findings provide valuable insights into flood mitigation strategies, regional planning, and climate change adaptation policies.