Effects of stormwater infrastructure data completeness and model resolution on urban flood modeling

• Infrastructure data gaps are common and challenge urban flood modeling. • SWMM 1D and SWMM 1D-2D models are developed to assess impact of missing data. • Sampling algorithm quantifies error and uncertainty from missing attribute data. • Relation between error and percentage of missing data varies by parameter. • Model accuracy depends on the interaction of data completeness and model resolution. The accuracy of hydrologic and hydrodynamic models, used to study urban hydrology and predict urban flooding, depends on the availability of high-resolution terrain and infrastructure data. Unfortunately, cities often do not have or cannot release complete infrastructure data, and high-resolution terrain data products are not available everywhere. In this study, we quantify how the accuracy and precision of urban hydrologic-hydrodynamic models vary as a function of data completeness and model resolution. For this aim, we apply the one-dimensional (1D) and coupled one- and two-dimensional (1D-2D) versions of the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) in an urban catchment in the city of Phoenix, Arizona. Here, we have collected detailed infrastructure data, a high-resolution 0.3-m LiDAR-based digital elevation model, and catchment properties data. We tested several model configurations assuming different levels of (i) availability of stormwater infrastructure data (ranging from 5% to 75% of attribute-values missing) and (ii) terrain aggregation (i.e., 4.6 m and 9.7 m). These configurations were generated through random Monte Carlo sampling for SWMM 1D and selective sampling with four cases for SWMM 1D-2D. We ran simulations under the 50-year return period design storm and compared simulated flood metrics assuming the highest-resolution and complete data model configuration as a reference. The study found that the model may over or underestimate flood volume and duration with different levels of missing data depending on the parameters — roughness, diameter or depth, and that model performance is more sensitive to missing data that is downstream and closer to the outfall as opposed to missing data upstream. Errors in flood depth, area and volume estimation are functions of both the data completeness and model resolution. Missing feature data leads to overestimation of flood depth, while lower model resolution results in underestimating flood depth and overestimating flood extent and volume.