Numerical models are used to enhance the understanding of the behavior of real world systems. With increasing complexity of numerical models and their applications, there is a need of more computational power. State of the art processors contain many cores on one single chip. As such, new programming techniques are required if all these cores are to be utilized during model simulation runs. This manuscript reviews the runtime and speedup behavior of parallel model analysis software (e.g. Calimero and Achilles) applied to simulation tools for urban water management (e.g. CityDrain3, EPANET2, SWMM5, par-SWMM). The potential of using a parallel programming environment for ‘coordinating’ tasks of multiple runs of commonly used modeling software is analyzed. This is especially interesting as the modeling software itself can be implanted sequentially or parallel. Performance tests are performed on a set of real-world case studies. Additionally, a benchmark set of 2,280 virtual case studies is used to investigate performance improvement in relation to the size of the system. It was found that speedup depends on the system size and the time spent in critical code sections with increasing number of used cores. Applying parallelism only at the level of the model analysis software performs best.