Datensatz

Zusammenfassung

Rapid urbanization necessitates reliable assessments of its impact on flooding. The two major challenges in data-sparse basins are the equifinality of the hydrological parameters and their dynamic updating with respect to changing land use and land cover. Existing calibration approaches typically address only one of these challenges. The existing approaches that allow for dynamic updating of parameters with changing land use and account for parametric uncertainty are referred to as Dyn_CN and uStat_CN, respectively. We propose a novel ‘urbanization-informed uncertainty-based calibration approach’, referred to as uDyn_CN, that combines the advantages of both these approaches. The existing and proposed calibration approaches were applied to India's data-limited, frequently flooded, urbanizing Adyar river basin comprising Chennai city. The performance assessment of the methodologies was done in an externally coupled hydrological-hydraulic modelling framework. In that light, the proposed hydrological calibration approach is more accurate than the existing approaches in both the hydrological and hydraulic simulations. For instance, the RMSE values for the simulated maximum water depths for uDyn_CN, uStat_CN, and Dyn_CN approaches are 0.22, 0.36, and 0.6 m, respectively. The study also demonstrates its subsequent application to analyze the impact of urbanization on flood hazard in the Adyar basin by 2050.