Impacts of Spatial Discretization Approach and Resolution on Urban Flood Modeling: A Review and Meta-Analysis Focused on Storm Water Management Model Applications

Flooding is one of the most dangerous natural hazards and is expected to occur more frequently due to the impacts of climate change. By altering the hydrologic characteristics of basins, urbanization exacerbates flooding risk. The Storm Water Management Model (SWMM) is a widely used urban hydrology model. Discretization of sub-catchments is a critical phase in constructing an SWMM model because it influences the effort of model construction and accuracy of the results. In the current review, we examine sub-catchment delineation in SWMM-based modeling by analyzing information from 43 journal papers encompassing 123 modeling scenarios. A novel categorization of sub-catchment delineation techniques with five primary and three secondary methods is proposed. Despite the hydrologic significance of external routing, where runoff is routed between sub-catchments, and internal routing, where runoff is routed from impervious to pervious portion of a sub-catchment or vice versa, such connections were considered in only 27% and 15% of the reviewed scenarios, respectively. Results also show that 69% of the scenarios used sub-catchments that were coarser than parcel (CP) resolution, but they were outperformed in calibration and validation by scenarios that used sub-catchments that were finer than or equal to parcel (FEP) resolution. Calibration was attempted in only 40% of the scenarios. Depression storage and surface roughness were commonly calibrated model parameters, but the tuned values suggest that in many cases calibration was achieved at the expense of physical plausibility. Future SWMM modeling applications can be improved by (i) delineating sub-catchments at FEP scale, (ii) utilizing SWMM routing options to better represent the physical flow pathways, and (iii) avoiding physically unrealistic calibration scenarios. Continued research on secondary delineation methods, which combine multiple primary methods, is recommended to offset the limitations of primary methods.