Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model
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 gr...
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doaj-99c4da1c1504417dac6d7c58851113532020-11-24T22:40:14ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382018-05-01222615263510.5194/hess-22-2615-2018Drainage area characterization for evaluating green infrastructure using the Storm Water Management ModelJ. G. Lee0C. T. Nietch1S. Panguluri2Center for Urban Green Infrastructure Engineering (CUGIE Inc), Cincinnati, OH 45255, USAOffice of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USAIndependent Consultant, Olney, MD 20832, USAUrban 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; <i>R</i><sup>2</sup> = 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.https://www.hydrol-earth-syst-sci.net/22/2615/2018/hess-22-2615-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. G. Lee C. T. Nietch S. Panguluri |
spellingShingle |
J. G. Lee C. T. Nietch S. Panguluri Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model Hydrology and Earth System Sciences |
author_facet |
J. G. Lee C. T. Nietch S. Panguluri |
author_sort |
J. G. Lee |
title |
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model |
title_short |
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model |
title_full |
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model |
title_fullStr |
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model |
title_full_unstemmed |
Drainage area characterization for evaluating green infrastructure using the Storm Water Management Model |
title_sort |
drainage area characterization for evaluating green infrastructure using the storm water management model |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2018-05-01 |
description |
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; <i>R</i><sup>2</sup> = 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. |
url |
https://www.hydrol-earth-syst-sci.net/22/2615/2018/hess-22-2615-2018.pdf |
work_keys_str_mv |
AT jglee drainageareacharacterizationforevaluatinggreeninfrastructureusingthestormwatermanagementmodel AT ctnietch drainageareacharacterizationforevaluatinggreeninfrastructureusingthestormwatermanagementmodel AT spanguluri drainageareacharacterizationforevaluatinggreeninfrastructureusingthestormwatermanagementmodel |
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1725705208622743552 |