Existing, high-fidelity models for sewer network modelling are accurate but too slow and inflexible for modern applications such as optimisation or scenario analysis. Reduced complexity surrogate modelling has been applied in response to this, however, current approaches are expensive to set up and still require high-fidelity simulations to derive parameters. In this study, we compare and develop graph partitioning algorithms to automatically group sections of sewer networks into semi-distributed compartments. These compartments can then be simulated using sewer network information only in the integrated modelling framework, CityWat-SemiDistributed (CWSD), which has been developed for application to sewer network modelling in this study. We find that combining graph partitioning with CWSD can produce accurate simulations 100-1,000x faster than existing high-fidelity modelling. Because we anticipate that many CWSD users will not have high-fidelity models available, we demonstrate that the approach provides reasonable simulations even under significant parametric uncertainty through a sensitivity analysis. We compare multiple graph partitioning techniques enabling users to specify the spatial aggregation of the partitioned network, also enabling them to preserve key locations for simulation. We test the impact of temporal resolution, finding that accurate simulations can be produced with timesteps up to one hour. Our experiments show a log-log relationship between temporal/spatial resolution and simulation time, enabling users to pre-specify the efficiency and accuracy needed for their applications. We expect that the efficiency and flexibility of our approach may facilitate novel applications of sewer network models ranging from continuous simulations for long-term planning to spatially optimising the placement of network sensors.