Sewer Network Data Completeness: Implications for Urban Pluvial Flood Modelling

Abstract

[Abstract]: 2D/1D dual drainage models are one of the most useful tools for studying urban pluvial flooding. However, the accuracy of these models depends on data quality and completeness. This study assesses the effects of sewer network data completeness on the results of the 2D/1D free distribution model Iber-SWMM. The research is conducted in two case studies: Differdange (Luxembourg) and Osuna (Spain), considering six different return period storms. Different scenarios of data completeness were generated by simplifying the original sewer network, based on two characteristics of the conduit segments: the Strahler Order number and the length. Each scenario was evaluated by comparing the maximum flood extent maps obtained. The results indicate that the lower the degree of data completeness, the higher the overestimation of the maximum flood extent. For 80% completeness, the False Alarm Ratio is less than 0.05, but it can increase exponentially to over 0.30 when network completeness drops to 20%. However, if the available information includes the most important conduits, such as the main collectors, errors are minimal. Furthermore, if the data on surface elements (inlets) is also complete, the accuracy of flood modeling is maintained compared to the complete data scenario. These results can contribute to the simplification of flood model setup in large urban areas, where not always complete sewer network data sets are available and information preprocessing can be complex and time-consuming, and the computation of the network in SWMM can become a bottleneck in the simulation.