Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance
Abstract
:1. Introduction
- How can dual-drainage modeling help determine the effect of GSI networks on the depth, flooded extent, and spatial distribution of roadway flooding?
- How do GSI networks affect the performance of the traffic system during a storm event?
- What are the limitations of dual-drainage modeling for characterizing the effects of GSI networks on roadway flooding?
2. Materials and Methods
2.1. Study Area and Data Sources
2.2. Stormwater Model Application
2.2.1. 1D Minor System Application
2.2.2. Stormwater Network Data Completeness
2.2.3. 2D Major System Application
2.2.4. Minor and Major System Connection
2.3. Stormwater Simulations
2.4. GSI Scenario Application
2.5. Traffic Performance Analysis Framework
2.5.1. GIS Spatial Analysis
2.5.2. Traffic Analysis
3. Results
3.1. Calibration and Validation
3.2. GSI Scenarios
3.3. Traffic Analysis
4. Discussion
5. Conclusions
- How can 1D–2D dual-drainage modeling help determine the effect of GSI networks on the depth, flooded extent, and spatial distribution of roadway flooding?
- Results of the major system flood model showed localized variation in flooding that indicates the value of a dual-drainage model in understanding the structure-scale interactions between GSI, stormwater structures, and runoff, given that these results could be verified by observation of urban flooding (Figure 3 and Figure 8).
- How do GSI networks affect the performance of the traffic system during a storm event?
- What are the limitations of dual-drainage 1D–2D modeling for characterizing the effects of GSI networks on roadway flooding?
- The model developed lacks spatially-specific GSI network implementation as the model methods to represent GSIs in space were not scalable to large watersheds. Future work to improve the incorporation of spatially-specific GSIs efficiently into stormwater models will clarify the importance of this challenge.
- Although we knew that the specific storm event modeled produced roadway flooding, there was not spatially distributed data on the depth and timing of roadway flooding that could be used to compare to modeled roadway flooding model results. Crowdsourced science and resident reporting have the potential to provide critically needed calibration data for urban flooding, but a significant increase in the quantity and distribution of these reports is needed.
- Additional computational capacity is needed to calibrate a 1D–2D dual-drainage model of this size and complexity using a continuous long-term precipitation and streamflow record.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Data Gap Filling Approach
- 1.
- If a junction is missing attribute data, the value may be recorded in one of the connected conduits;
- 2.
- If a junction is missing invert details:
- a
- If connected conduits have invert data, use the lowest connected conduit invert;
- b
- If connected conduits do not have invert data:
- i
- Use length and slope attributes from connected conduits and known junction invert to compute;
- ii
- If connected conduits have missing slope attribute:
- For inlets:
- (1)
- If inlet type is known, use default inlet depth based on inlet type from the city and county of Denver standard specifications;
- (2)
- If the inlet type is unknown, use the default connected conduit slope of 2% (based on Denver standard specification).
- For manholes, assume the nearest known slope is continuous and extrapolate from the nearest known junction attribute:
- (1)
- Some junctions in the stormwater network data represent a pipe-to-pipe connection. The invert of these is interpolated from the nearest known junction invert using the conduit slope.
- 3.
- If the ground elevation of an inlet or manhole is unknown, use the 1 m DEM elevation;
- 4.
- Conduit offsets were computed from the difference between known conduit inverts and junction inverts:
- If only the upstream or downstream conduit invert was known, the missing invert was computed using the slope and length, and then the offset was computed;
- If neither upstream or downstream conduit inverts, or the slope were known, the offset was assumed to be zero;
- If a zero offset resulted in a negative conduit slope, the slope was assumed to be the same as a neighboring in-line conduit, and the offset was recomputed.
- 5.
- If conduit geometry is unknown, it was assumed to be the same as the nearest known conduit geometry.
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Parameter | Initial Value(s) | Calibration Uncertainty | Calibrated Value(s) | Data Source |
---|---|---|---|---|
Subcatchment area (km2) | 2.0 × 10−4–0.22 | NA | No change | GIS area |
Subcatchment slope (%) | 0.04–1.82 | 25% | 0.043–2.14 | 3 m DEM |
Subcatchment width (m) | 1.02–1030.35 | 200% | 1.28–2041.64 | PCSWMM calculation |
Impervious (%) | 3.59–100 | NA | No change | City and County of Denver |
N-Impervious Roughness | 0.012 | 20% | 0.011 | PCSWMM documentation |
N-Pervious Roughness | 0.15 | NA | No change | PCSWMM documentation |
Depression storage—Impervious (mm) | 1.9 | 20% | 2.08 | PCSWMM documentation |
Depression storage—Pervious (mm) | 3.81 | 50% | 3.12 | PCSWMM documentation |
% Zero Depression storage Impervious | 25 | NA | No change | PCSWMM default |
% Routed to Pervious | 6–97.5 | NA | No change | Alley and Veenhuis [28] |
Suction head (cm) | 22 | NA | No change | SSURGO web soil survey [30] |
Conductivity (mm/hr) | 3.81 | 50% | 1.35 | SSURGO web soil survey [30] |
Initial Deficit (fract.) | 0.262 | 25% | 0.237 | Rawls et al. [33] |
Layer | Parameter | Input Value |
---|---|---|
Surface | Berm height (cm) | 20.32 |
Surface roughness | 0.1 | |
Surface slope (%) | 1.0 | |
Surface area (m2) | 22.3 | |
Soil | Soil thickness (cm) | 50.8 |
Porosity (volume fraction) | 0.453 | |
Field Capacity (volume fraction) | 0.19 | |
Wilting point | 0.085 | |
Conductivity (cm/hr) | 1.1 | |
Suction head (cm) | 11 | |
Storage | Thickness (cm) | 25.4 |
Void ratio (voids/solids) | 0.75 | |
Seepage rate (cm/hr) | 0.25 | |
Clogging factor | 0.1 | |
Underdrain | Drain Coefficient (cm/hr) | 222.5 |
Drain exponent | 0.5 | |
Drain offset height (cm) | 0 |
GSI Scenario (DCIA Converted) | Total Study Area Converted (km2) | Impervious Area Converted (%) | GSI Units | DCIA Mitigated (%) | Total Impervious Area Mitigated (%) | Total Watershed Area Mitigated (%) |
---|---|---|---|---|---|---|
1% | 0.014 | 0.47 | 566 | 13.1 | 5.9 | 2.1 |
2.5% | 0.034 | 1.14 | 1572 | 36.9 | 16.6 | 6.0 |
3.5% | 0.048 | 1.61 | 2213 | 52.2 | 23.5 | 8.5 |
5% | 0.068 | 2.29 | 3178 | 73.8 | 33.2 | 12.0 |
Statistic | Calibration | Validation |
---|---|---|
R2 | 0.848 | 0.52 |
RSR | 0.046 | 0.063 |
NSE | 0.80 | 0.45 |
%BIAS | −36.5% | −48.6% |
Scenario | Peak (m3/s) | Peak Percent Reduction (%) | Time of Peak | Total Volume (m3) | Volume Percent Reduction (%) |
---|---|---|---|---|---|
Pre-GSI | 21.86 | NA | 17:50 | 391.33 | NA |
1% GSI | 20.94 | 4.28 | 17:40 | 385.62 | 1.46 |
2.5% GSI | 20.58 | 5.85 | 17:40 | 377.07 | 3.64 |
3.5% GSI | 20.49 | 6.27 | 17:45 | 375.50 | 4.05 |
5% GSI | 20.47 | 6.36 | 17:45 | 363.03 | 7.23 |
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Knight, K.L.; Hou, G.; Bhaskar, A.S.; Chen, S. Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance. Water 2021, 13, 1563. https://doi.org/10.3390/w13111563
Knight KL, Hou G, Bhaskar AS, Chen S. Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance. Water. 2021; 13(11):1563. https://doi.org/10.3390/w13111563
Chicago/Turabian StyleKnight, Kathryn L., Guangyang Hou, Aditi S. Bhaskar, and Suren Chen. 2021. "Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance" Water 13, no. 11: 1563. https://doi.org/10.3390/w13111563