Abstract

Urban watershed hydrologic and water quality models, and stormwater control measure (SCM) designs, rely on accurate determination of stormwater runoff volume and impervious areas contributing to runoff. This research assesses the impacts of data availability and spatial resolution on two of the main indicators of urban impervious cover: total impervious area (TIA) and directly connected impervious area (DCIA). The research also explores the relationship between TIA, DCIA, and the effective impervious area (EIA), another important indicator of urban impervious cover. Using spatial programming, surface runoff was tracked, from originated to discharged locations, in nine urban catchments (ranging from 16 to 2035 ha) in Minnesota, USA, under various scenarios combining geospatial data types and resolutions. TIA fractions derived from low-resolution, nationally available imperviousness data were not statistically different than those from high-resolution land cover (LC) data modified by “unshading” to remove tree canopies overlaying impervious surfaces. Comparison of DCIA and EIA values indicated that modified LC is preferable for accurate DCIA delineation in ungauged catchments. EIA was 65 % of DCIA under the assumption that all rooftops were connected to sewer systems, while it exceeded DCIA by 34 % assuming all rooftops were unconnected, underscoring the importance of rooftop connectivity information in urban areas. The assumption of 0 % to 40 % rooftop connectivity resulted in comparable DCIA and EIA values in the study catchments. Using roads as the runoff collection system (instead of sewer inlets) did not significantly alter DCIA values at 5 % level, when unshaded LC was used. Given the absence of observed runoff data in most urban areas, DCIA serves as an appropriate imperviousness indicator for determining runoff in ungauged urban watersheds. However, achieving accurate DCIA delineation necessitates high-resolution LC data, emphasizing the need for relevant urban management organizations to develop such data.

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