Evaluating Objective Functions for SWMM Calibration in Urban Catchments

Urban hydrological models require robust calibration strategies to accurately simulate rainfall–runoff processes, yet parameter sensitivity and the choice of objective functions remain key challenges. This study applied the SWMM nonlinear reservoir approach to an urban catchment in Pavia, Italy, using high-resolution rainfall–runoff data from multiple events. Sensitivity analysis showed that surface storage and hydraulic resistance dominated runoff response, while infiltration had limited influence. Calibration was performed with a genetic algorithm, testing six objective functions. The results indicated that the Index of Agreement and NSE-sqrt provided the most consistent performance across calibration and validation, supporting their use in urban drainage modeling.