Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model

Abstract

Abstract. Urban stormwater runoff quantity and quality are strongly dependent upon catchment properties. Models are used to simulate the runoff characteristics, but the output from a stormwater management model is dependent on how the catchment area is subdivided and represented as spatial elements. For green infrastructure modeling, we suggest a discretization method that distinguishes directly connected impervious area (DCIA) from the total impervious area (TIA). Pervious buffers, which receive runoff from upgradient impervious areas should also be identified as a separate subset of the entire pervious area (PA). This separation provides an improved model representation of the runoff process. With these criteria in mind, an approach to spatial discretization for projects using the US Environmental Protection Agency's Storm Water Management Model (SWMM) is demonstrated for the Shayler Crossing watershed (SHC), a well-monitored, residential suburban area occupying 100 ha, east of Cincinnati, Ohio. The model relies on a highly resolved spatial database of urban land cover, stormwater drainage features, and topography. To verify the spatial discretization approach, a hypothetical analysis was conducted. Six different representations of a common urbanscape that discharges runoff to a single storm inlet were evaluated with eight 24 h synthetic storms. This analysis allowed us to select a discretization scheme that balances complexity in model setup with presumed accuracy of the output with respect to the most complex discretization option considered. The balanced approach delineates directly and indirectly connected impervious areas (ICIA), buffering pervious area (BPA) receiving impervious runoff, and the other pervious area within a SWMM subcatchment. It performed well at the watershed scale with minimal calibration effort (Nash–Sutcliffe coefficient = 0.852; R2 = 0.871). The approach accommodates the distribution of runoff contributions from different spatial components and flow pathways that would impact green infrastructure performance. A developed SWMM model using the discretization approach is calibrated by adjusting parameters per land cover component, instead of per subcatchment and, therefore, can be applied to relatively large watersheds if the land cover components are relatively homogeneous and/or categorized appropriately in the GIS that supports the model parameterization. Finally, with a few model adjustments, we show how the simulated stream hydrograph can be separated into the relative contributions from different land cover types and subsurface sources, adding insight to the potential effectiveness of planned green infrastructure scenarios at the watershed scale.

Cumulative Effects of Low Impact Development on Watershed Hydrology in a Mixed Land-Cover System

“…The primary land uses consist of 64.1% urban or developed area (including 37% lawn, 12% building, 6.5% street, 6.4% sidewalk, and 2.1% parking lot and driveway), 23% agriculture, and 13% deciduous forest (Table 1). Total imperviousness covers approximately 27% of the watershed area, the majority of which is directly connected to a storm sewer system without any intermediary controls [30]. The watershed was chosen for this study because it is part of the East Fork Little Miami River Watershed, where a long-term monitoring and focused modeling effort is being conducted by the US Environmental Protection Agency (EPA), Office of Research and Development (ORD), Ohio Environmental Protection Agency (Ohio EPA), and Clermont County (Ohio) Stormwater Division.…”

“…A clear need exists for an understanding of the extent to which LID approaches are effective in mixed land cover watersheds, i.e., those with urban and suburban land cover in addition to others (e.g., forest and agriculture). Furthermore, a spatially explicit approach toward representing LID practices and associated hydrological processes to analyze the effects of varying patterns of mixed land use and land cover under different management practices is critical [30], as most approaches are challenged with representing spatial landscape heterogeneities [31].…”

Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

“…SHC has an area of around 1 km 2 , and its land-use type is primarily residential. The drainage system of the SHC consists of conduits and channels, detention ponds, dry ponds, and wet ponds (Lee et al, 2018a). In SHC, the stormwater runoffs generated by the indirectly connected impervious areas (such as the sidewalks) are treated by the nearby pervious areas, which are termed as buffering pervious areas (BPA) and function as SuDS (Lee et al, 2018b).…”

“…In SHC, the stormwater runoffs generated by the indirectly connected impervious areas (such as the sidewalks) are treated by the nearby pervious areas, which are termed as buffering pervious areas (BPA) and function as SuDS (Lee et al, 2018b). An SWMM model was built in Lee et al (2018a),…”

“…The feature engineering and XGBoost hyperparameters are the same as WS, except that _ and _ are always set to be zero (see explanations in Section 2.2.2). The hydrographs predicted by the XGBoost models and the SWMM model built in Lee et al (2018a) for the test period are shown in Fig. 12.…”