Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management
Abstract
:1. Introduction
2. Overview of Decision Support Tools in Stormwater Management
3. Methodology
3.1. Hydrology Module
3.1.1. Selection of the Rainfall-Runoff Model
3.1.2. Event-Based and Continuous Simulation
3.1.3. Estimation of Runoff Depth and Hydrograph
3.2. BMP Selection Module-Optimization Approach
3.2.1. Multi-Objective Optimization (MOO)
3.2.2. The Objective Function
3.2.3. Constraints
3.3. BMP Sizing Module
3.3.1. Green Roof System
- (a)
- (b)
- (c)
- (d)
3.3.2. Infiltration-Based BMPs
- (a)
- Infiltration-based BMPs with both soil layer and storage layers: Porous pavement, Grass swale (with surface layer), Bioretention (with surface layer), Sand filter (non-surface);
- (b)
- Infiltration-based BMPs with storage layer but without soil layer: Dry well (with surface layer), Infiltration trench, Sand filter (surface);
- (c)
- Infiltration-based BMPs with soil layer but without storage layer: Vegetated filter strip, Rain garden (with surface layer), Box tree (with surface layer);
- (d)
- Infiltration-based BMPs with neither soil layer nor storage layer: Infiltration basin, wetland (surface/ponding layer only).
3.3.3. Storage-Based BMPs
3.4. Cost Module
4. Verification of Component Modules
4.1. Hydrology Module
4.2. BMP Selection Scenario Analysis
4.3. BMP Sizing Module
4.3.1. Green Roof System
4.3.2. Infiltration-Based BMPs
4.3.3. Storage-Based BMPs
5. The User Interface
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Detailed Procedures
Appendix A1. Hydrology Module
Appendix A1.1. Green-Ampt Method:
- Step 1: Calculate precipitation intensity, i (mm/h)
- Step 2: Calculate cumulative infiltration, [37]
- Step 3: Calculate infiltration rate, [32]
- Step 4: Update soil moisture account [20]
- Case 1: If there is precipitation input (P ≠ 0), the soil moisture deficit (IMD) is updated using Equation (A5) below.
- Case 2: If there is no precipitation input (P = 0), then the soil moisture deficit (IMD) is updated using Equation (A6) below.
- Step 5: Calculate evapotranspiration
- Step 6: Calculate the rainfall excess
- Step 7: Calculate ponding depth [20]
- Step 8: Calculate overland flow/runoff [20]
Appendix A1.2. Curve Number Method:
- Step 1: Calculate precipitation intensity, i (mm/h)
- Step 2: Calculate cumulative infiltration F (mm) [20]
- Step 3: Calculate infiltration rate f (mm/h)
- Step 4: Update retention parameter [20]
- Case 1: If there is precipitation input (P ≠ 0), the retention parameter is updated using Equation (A36) below.
- Case 2: If there is no precipitation input (P = 0), then the retention parameter is updated using Equation (A37) below.
Appendix A2. BMP Selection Module
Appendix A2.1. Input Water Quality
Appendix A2.2. Target Water Quality
Appendix A2.3. BMP Removal Efficiency
Appendix A3. BMP Sizing Module
Appendix A3.1. Green-Roof System
Updating water content of media:
Calculate runoff flow from green roof:
Appendix A3.2. Infiltration-Based BMPs
- , this suggest that suction is greater than gravity and water is held tightly with the soil, thus, percolation does not occur.
- where is saturated hydraulic conductivity, is decay constant typically in the range of 5 to 15, is soil moisture content at saturation, is soil moisture content at filed capacity, and is soil moisture content during the time interval [20].
Appendix A3.3. Storage-Based BMPs
Appendix B. Demonstration of Operation of the Tool
- (a)
- Green roof system: In the “Green roof system”, the goal is to determine how much area has to be under the green roof to achieve a specific percent flow reduction. The area of the watershed, percent imperviousness and the runoff hydrograph come from the hydrology module. As shown in Figure A16, by running the tool for 20 percent flow reduction, the surface area that has to be under a green roof is calculated. User may also view the hydrographs from the graphical output. In the Figure A16 the blue line shows the hydrograph without green roof and the red line shows the hydrograph with green roof.
- (b)
- Infiltration-based BMPs: The next group of BMPs are infiltration-based BMPs. For the infiltration-based BMPs, the user inputs the area of the BMP and the tool will calculate the percent capture and drawdown time. The tool also shows the graphical outputs of inflow and outflow hydrographs as shown in Figure A17.The tool also tracks the water level inside the surface (ponding) layer (Figure A18).
- (c)
- Storage-based BMPs: The third group of BMPs are storage-based BMPs such as detention pond and retention pond. Since ponds usually are used for stormwater management for bigger area, the area of watershed has been increased in this scenario. The input is percent peak flow reduction and the output is geometry of the pond and geometry of the outlet structures. By inputting 50 percent peak flow reduction and other input parameters, the tool determines optimal size/geometries of pond and outlet structures through an optimization process. By minimizing the size/volume of the pond, the cost will be minimized. Users can view inflow and outflow hydrographs and the peak flow reduction in the graphical output section as shown in Figure A19.
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Tool | Capabilities/Features | |||||||
---|---|---|---|---|---|---|---|---|
Developing Environment | Runoff Volume | Peak Flow | Pollutant Loads/Concentration 1 | BMP/GI | Cost/LCCA 2 | Integrated Optimization 3 | Water Quality Analysis 4 | |
WERF SELECT [18] | Excel-VBA | √ | √ | √ | √ | √ | ||
Green Values [19] | JavaScript | √ | √ | √ | √ | |||
STEPL [1] | Excel-VBA | √ | √ | √ | √ | |||
EPA SWMM 5 [20] | C language | √ | √ | √ | √ | √ | ||
WinSLAMM [21] | Fortran | √ | √ | √ | √ | √ | √ | |
EPA SUSTAIN [22] | C language | √ | √ | √ | √ | √ | √ | √ |
L-THIA [23] | Excel-VBA | √ | √ | √ | √ | |||
MUSIC [24] | Fortran | √ | √ | √ | √ | √ | ||
LIDRA [13] | Visual studio, C# language | √ | √ | |||||
Stormwater calculator [25] | C language | √ | √ | √ | ||||
MIDS calculator [26] | Excel-VBA | √ | √ | √ | √ | |||
California LID Sizing Tool [27] | Visual basic, JavaScript | √ | √ | √ | ||||
BMP Checker [28] | Python | √ | √ | √ | √ | √ | √ | |
RSWMM-Cost [29] | R language | √ | √ | √ | √ | √ | √ | √ |
i-DSTss | Excel-VBA | √ | √ | √ | √ | √ | √ |
Category | Criteria | Description |
---|---|---|
Economic | LCA | Life cycle cost of a BMP |
Capital, O&M costs | Cost of installation, operation and maintenance for a BMP | |
Property value | Land cost and related property value of a BMP | |
Environmental | Flow reduction | Potential for runoff volume captured or peak flow reduction |
Pollutant reduction | Pollutant load reduction potential | |
Green space | Potential for creating green space covered with grass or trees | |
Social | Aesthetics | Potential for creation of scenic values |
Community Acceptance | Acceptance by the community, affected populations including local stakeholders and authorities | |
Nuisances | Creating inconvenience or annoyances | |
Technical | Material availability | Relative ease of obtaining construction materials |
Feasibility | Likelihood that projects can be easily implemented | |
Ease of Maintenance and operation | Relative ease of operation and maintenance if failure occurs due to clogging or other factors |
Cost Category | Description | |
---|---|---|
Capital costs | Construction cost | BMP construction cost |
Land cost (user input value) | Cost of acquiring the land | |
Cost contingency, % of construction cost (~7%) | Unexpected cost | |
O&M costs, % of construction cost (~4%) | Costs incurred each year for maintenance and operation | |
Rehabilitation costs, % of construction cost (~70%) | Cost for replacing a BMP | |
Administrative and inspection costs, % of construction cost (~0.5%) | Inspection cost |
Land Cover Type | Percent Area |
---|---|
Streets | 17% |
Driveways | 6% |
Roofs | 17% |
Sidewalks | 5% |
Other Impervious | <1% |
Lawns/Open | 55% |
Land Use | Water Quality Parameter (unit) | Level of Concentration | ||
---|---|---|---|---|
Min | Median | Max | ||
Residential | TSS (mgL) | 0.25 | 38.00 | 2380.52 |
TP (mgL) | 0.01 | 0.23 | 21.20 | |
TN (mgL) | 0.20 | 1.51 | 10.30 | |
Bacteria (100 mL) | 25.50 | 1870.00 | 48,392.00 | |
Pb (ugL) | 0.15 | 5.00 | 368.00 | |
Zn (ugL) | 1.00 | 74.00 | 2077.40 | |
Cu (mgL) | 0.50 | 10.00 | 7270.00 | |
NO3 (mgL) | 0.05 | 0.64 | 1.26 | |
PO4 (mgL) | 0.00 | 0.10 | 6.00 | |
Optimal BMP | Rain garden | Bioretention | VFS-Bioretention |
Land Use | Water Quality Parameter (unit) | Level of Concentration | ||
---|---|---|---|---|
Min | Median | Max | ||
Industrial | TSS (mgL) | 0.50 | 48.00 | 1130.00 |
TP (mgL) | 0.01 | 0.19 | 2.14 | |
TN (mgL) | 0.21 | 2.03 | 8.01 | |
Bacteria (100 mL) | 25.50 | 1870.00 | 48,392.00 | |
Pb (ugL) | 0.10 | 6.80 | 370.00 | |
Zn (ugL) | 1.00 | 101.00 | 7700.00 | |
Cu (mgL) | 1.00 | 10.00 | 950.00 | |
NO3 (mgL) | 0.13 | 0.51 | 1.24 | |
PO4 (mgL) | 0.00 | 0.10 | 6.00 | |
Optimal BMP | Wetland | Wetland | Forebay-Wetland |
Land Use | Water Quality Parameter (unit) | Level of Concentration | ||
---|---|---|---|---|
Min | Median | Max | ||
Freeways | TSS (mgL) | 0.50 | 33.00 | 823.00 |
TP (mgL) | 0.01 | 0.25 | 3.35 | |
TN (mgL) | 0.20 | 0.84 | 8.14 | |
Bacteria (100 mL) | 25.50 | 1870.00 | 48,392.00 | |
Pb (ugL) | 0.15 | 5.20 | 230.00 | |
Zn (ugL) | 1.00 | 48.00 | 1000.00 | |
Cu (mgL) | 1.00 | 6.00 | 122.00 | |
NO3 (mgL) | 0.03 | 0.51 | 2.20 | |
PO4 (mgL) | 0.00 | 0.10 | 6.00 | |
Optimal BMP | Grass swale | Grass swale | Forebay-Grass swale |
Peak Flow Reduction (%) | 50 | 25 |
---|---|---|
Pond depth (m) | 1.8 | 1.3 |
Pond storage (m3) | 170 | 64 |
Pond surface area (m2) | 95 | 49 |
Length at bottom (m) | 6.1 | 4.2 |
Width at bottom (m) | 2.0 | 1.4 |
Total discharge by weir and orifice (cms) | 2.73 | 2.57 |
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Shojaeizadeh, A.; Geza, M.; McCray, J.; Hogue, T.S. Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management. Water 2019, 11, 2022. https://doi.org/10.3390/w11102022
Shojaeizadeh A, Geza M, McCray J, Hogue TS. Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management. Water. 2019; 11(10):2022. https://doi.org/10.3390/w11102022
Chicago/Turabian StyleShojaeizadeh, Ali, Mengistu Geza, John McCray, and Terri S. Hogue. 2019. "Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management" Water 11, no. 10: 2022. https://doi.org/10.3390/w11102022