Physically based semidistributed rainfall-runoff models are an important tool for assessing decentralized green infrastructure alternatives in controlling combined sewer overflows (CSO). Few studies have analyzed reliable calibration methods of highly detailed rainfall-runoff models at the subcatchment level. This research presents calibration of the storm water management model version 5 (SWMM5) using high temporal resolution rainfall and flow data to perform a 10-month continuous simulation on a physically distributed urban catchment. Detailed analyses of parameters that physically represent the area in the SWMM5 model were done using remote sensing and geographical information systems techniques. Parallel computing and a multi-search driver were implemented along with a model-independent parameter estimation (PEST) method, to find global optima and accelerate the calibration process. PEST internal settings were tuned for the continuous SWMM5 estimation problem using one month of rainfall data. Subsequently, parameter calibration was carried out with 10 months of rainfall data and model validation was performed with 5 months of data. The overall calibration and validation of the SWMM5 model was good with a Nash-Sutcliffe efficiency coefficient greater than 0.5 and a total volume runoff error of less than 5%. Time offset bias from the rainfall data affected negatively the Nash-Sutcliffe efficiency because of highly variable storm cells. This study shows it is possible to reproduce accurately the urban runoff from a highly detailed, semidistributed SWMM5 model, even when the calibration process is restricted to physical parameters and not allowed to distort the geometric representation of the project area.