# Site-Scale Integrated Decision Support Tool (i-DSTss) for Stormwater Management

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}value of 0.77 and 0.74, respectively. The BMP selection module recommends a BMP type appropriate for a site based on economic, technical, social and environmental criteria using a multi-criteria optimization approach. The BMP sizing module includes sizing options for green roofs, infiltration-based BMPs, and storage-based BMPs. A mass balance approach is implemented for all types of BMPs. The tool predicted outflow rates from a permeable pavement with R

^{2}value of 0.89. A cost module is included where capital, operation and maintenance, and rehabilitation costs are estimated based on BMP size obtained from the sizing module. The i-DSTss is built on an accessible platform (Microsoft Excel VBA) and can be operated with a basic skillset. The i-DSTss is intended for designers, regulators, and municipalities for quick analysis of scenarios involving interaction among several factors.

## 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

_{2}and D

_{1}are depth of runoff in time step 1 and 2. Equation (4) is computationally less intensive than Equation (2). Additional details describing calculation of runoff are provided in Appendix A.

#### 3.2. BMP Selection Module-Optimization Approach

#### 3.2.1. Multi-Objective Optimization (MOO)

#### 3.2.2. The Objective Function

_{i}is weight assigned by the user to criterion i, R

_{i}is expert assigned ranking to a BMP based on criterion i. i varies from 1 to m where m is the number of criteria (listed in Table 2). S (calculated for each BMP) refers to the sum-product obtained by multiplying the weights and ranks and summing up the products. If two BMPs meet the target water quality, a BMP that has a lower combined score S is preferred to a BMP with relatively high total score. The objective function is defined as the sum of scores (ΣS) where S is a score for one BMP. Thus, the multi-objective optimization problem is defined as:

_{j}is combined score calculated for one BMP using Equation (5). X

_{j}is a binary decision variable with a value of 0 or 1 and n is number of BMPs. During the optimization process, X

_{j}becomes zero if a BMP is not selected and 1 if selected. The goal is to minimize Obj Val (e.g., cost if cost is the only criterion). Thus, a BMP type is selected based on achieving minimum Obj Val. Note that this is subject to constraints that are chosen by the user, discussed below.

#### 3.2.3. Constraints

_{3}, PO

_{4}and Bacteria (Total Coliform). The target concentration can be set to water quality standard from EPA or user input values. The tool automatically selects a BMP type with treatment performance (removal efficiency) that exceeds the required removal efficiency calculated using Equation (7). The treatment performance is obtained from the International Stormwater BMP database.

#### 3.3. BMP Sizing Module

#### 3.3.1. Green Roof System

_{soil}is depth of soil/media (mm). Multiplying the rates by the time step we get:

- (a)
- $\mathrm{If}\theta {\theta}_{WP}\to \mathrm{no}\text{}\mathrm{transpiration},ET\text{'}=ET\mathrm{calculated}\text{}\mathrm{using}\mathrm{Equation}\text{}\left(3\right),OF\text{'}=0,\mathrm{and}\text{}UD\text{'}=0$
- (b)
- $\mathrm{If}{\theta}_{WP}\le \theta \le {\theta}_{FC}\to ET\text{'}=Min\left(ET\mathrm{calculated}\text{}\mathrm{using}\text{}\mathrm{Equation}\text{}\left(3\right),\left(\theta -{\theta}_{WP}\right)\times {d}_{soil}\right),OF\text{'}=0,\mathrm{and}UD\text{'}=0$
- (c)
- $\mathrm{If}{\theta}_{FC}\theta {\theta}_{s}\to E{T}^{\prime}=Min\left(ET\text{}\mathrm{calculated}\text{}\mathrm{using}\text{}\mathrm{Equation}\text{}\left(3\right)\text{}\mathrm{and}\left({\theta}_{FC}-{\theta}_{WP}\right)\times {d}_{soil}\right),O{F}^{\prime}=0,\mathrm{and}UD\text{'}=\left(\theta -{\theta}_{FC}\right)\times {d}_{soil}$
- (d)
- $\mathrm{If}i{k}_{s},\theta ={\theta}_{s}\to \mathrm{no}\text{}\mathrm{transpiration},E{T}^{\prime}=ET\mathrm{calculated}\text{}\mathrm{using}\text{}\mathrm{Equation}\text{}\left(3\right)\to O{F}^{\prime}=\left(i-{k}_{s}\right)\times \Delta t,\mathrm{and}UD\text{'}=\left({\theta}_{s}-{\theta}_{FC}\right)\times {d}_{soil}$

_{s}is saturate hydraulic conductivity, Δt is time step and d

_{soil}is depth of soil.

_{s}). Based on this assumption, the overflow can be estimated as OF’ = (i − k

_{s}) × ∆t. Based on Feddes model the transpiration ceases when soil is saturated while evaporation may occur, which is calculated using Equation (3) [45].

#### 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

^{2}, to measure the quality of calibration, which describes the degree of co-linearity between simulated and observed values and varies from 0 to 1. The model fit obtained in this work is relatively good (R

^{2}= 0.77) given that R

^{2}values greater than 0.5 are considered acceptable [54]. Although R

^{2}has been widely used for model evaluation, this statistic is oversensitive to outliers and insensitive to additive and proportional differences between simulated values and observed data [55]. Hence, measures such as the Nash–Sutcliffe coefficient of efficiency (NSE) [56] and Root Mean Square Error (RMSE) are often considered to be more appropriate [57]. Thus, the NSE was also calculated. The limitation of NSE is that it does not include weighting, thus ignores differences in uncertainty in observations [58]. NSE determines the model efficiency as a fraction of the observed flow variance reproduced by the model as:

#### 4.2. BMP Selection Scenario Analysis

#### 4.3. BMP Sizing Module

#### 4.3.1. Green Roof System

#### 4.3.2. Infiltration-Based BMPs

^{2}, NSE and PBIAS were used as performance measures.

^{2}, NSE, and PBIAS values for observed and simulated volumes from the permeable pavement are shown in Figure 17.

#### 4.3.3. Storage-Based BMPs

## 5. The User Interface

## 6. Conclusions

^{2}and NSE values greater than 0.7 in each case. The BMP sizing module includes sizing options for green roofs, infiltration-based BMPs and storage-based BMPs. The tool predicted outflow rate from a permeable pavement with R

^{2}and NSE values greater than 0.8. It was demonstrated through scenario evaluation that the BMP selection module recommended cost effective BMPs. A significant merit of the tool is that complex approaches were translated into easy to use, computationally less intensive, yet rigorous physically based modules that could be verified with observations. The tool can aid decision makers in analyzing various stormwater scenarios at the site-scale level.

## 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)$$i=\frac{P}{\Delta t}$$
- Step 2: Calculate cumulative infiltration, ${F}_{t}\text{}\left(\mathrm{mm}\right)$ [37]$${F}_{t}=\Psi \times IMD\times \left\{-1-\left[\frac{{t}^{*}+\mathrm{ln}\left[1+{t}^{*}+\frac{\sqrt{2{t}^{*}}}{\left(1+\sqrt{2{t}^{*}}/6\right)}\right]}{\frac{1}{1+{t}^{*}+\frac{\sqrt{2{t}^{*}}}{\left(1+\sqrt{2{t}^{*}}/6\right)}}-1}\right]\right\}$$$$IMD={\theta}_{s}-{\theta}_{i}$$

_{t}at time t depends on initial soil moisture deficit (IMD), and other soil physical properties such as suction head (ψ), and saturate hydraulic conductivity (K

_{s}). Higher IMD and ψ values suggest a higher cumulative infiltration F

_{t}at time t.

- Step 3: Calculate infiltration rate, $f\left(\mathrm{mm}/\mathrm{h}\right)$ [32]$$f={K}_{s}\left(1+\frac{\Psi \times IMD}{{F}_{t}}\right)$$

_{t}), and infiltration rate (f). Thus, IMD has to be updated each time step based on water input to account for the effect of IMD on infiltration rate. Equation for updating IMD is given in Step 4 below.

- 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.$$IM{D}_{{t}_{i}}=Max\left(IM{D}_{{t}_{i-1}}-\frac{f\times \Delta t}{4\sqrt{{K}_{s}}},0\right)$$

- Case 2: If there is no precipitation input (P = 0), then the soil moisture deficit (IMD) is updated using Equation (A6) below.$$IM{D}_{{t}_{i}}=Min\left(IM{D}_{{t}_{i-1}}+IM{D}_{max}\times {K}_{r}\times \Delta t,{\theta}_{s}-{\theta}_{r}\right)$$

_{r}) is a parameter that describes how fast the soil moisture changes in the absence of precipitation input and it is dependent on saturate hydraulic conductivity of the soil.

- Step 5: Calculate evapotranspiration

_{max}= maximum air temperature (°C), T

_{min}= minimum air temperature (°C), R

_{a}= extraterrestrial radiation (MJ·m

^{−2}), and 0.408 is a unit conversion factor. Extraterrestrial radiation, R

_{a}, is estimated based on latitude and the calendar day of the year given as:

_{sc}= solar constant (0.0820 MJ·m

^{−2}·min

^{−1}), φ = latitude (radians), converted from degrees latitude to radians (radians = degrees(π/180)), and the term 24 $\times $ 60 is a factor to convert minute to day.

- Step 6: Calculate the rainfall excess ${i}^{*}$

- 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)$$i=\frac{P}{\Delta t}$$
- Step 2: Calculate cumulative infiltration F (mm) [20]$$F=p-\frac{{p}^{2}}{p+S}$$$$S=\frac{1000}{C{N}_{composite}}-10$$$$C{N}_{composite}=\frac{{{\displaystyle \sum}}_{i=1}^{n}C{N}_{i}\times {A}_{i}}{{{\displaystyle \sum}}_{i=1}^{n}{A}_{i}}$$
- Step 3: Calculate infiltration rate f (mm/h)$$f=\frac{{F}_{{t}_{i+1}}-{F}_{{t}_{i}}}{\Delta t}$$
- Step 4: Update retention parameter [20]

- Case 1: If there is precipitation input (P ≠ 0), the retention parameter is updated using Equation (A36) below.$${S}_{{t}_{i}}=Max\left({S}_{{t}_{i-1}}-f\times \Delta t,{S}_{min}\right)$$

- Case 2: If there is no precipitation input (P = 0), then the retention parameter is updated using Equation (A37) below.$${S}_{{t}_{i}}=Min\left({S}_{{t}_{i-1}}+{S}_{max}\times {K}_{r}\times \Delta t,{S}_{max}\right)$$

#### 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

- $If\theta {\theta}_{WP}\to \mathrm{no}\text{}\mathrm{transpiration},ET\text{'}=ETc\mathrm{alculated}\text{}\mathrm{by}\text{}\mathrm{Hargreaves}\text{}\mathrm{Equation},OF\text{'}=0,\mathrm{and}UD\text{'}=0$
- $If\text{}{\theta}_{WP}\le \theta \le {\theta}_{FC}\to E{T}^{\prime}=Min\left(ET\mathrm{calculated}\text{}\mathrm{by}\text{}\mathrm{Hargreaves}\text{}\mathrm{Equation}\text{}\mathrm{and}\text{}\left(\theta -{\theta}_{WP}\right)\times {d}_{soil}\right),OF\text{'}=0,\text{}\mathrm{and}UD\text{'}=0$
- $If{\theta}_{FC}\theta {\theta}_{s}\to E{T}^{\prime}=Min\left(ET\mathrm{calculated}\text{}\mathrm{by}\text{}\mathrm{Hargreaves}\text{}\mathrm{Equation}\text{}\mathrm{and}\left({\theta}_{FC}-{\theta}_{WP}\right)\times {d}_{soil}\right),OF\text{'}=0,\mathrm{and}UD\text{'}=\left(\theta -{\theta}_{FC}\right)\times {d}_{soil}$
- $Ifi{k}_{s},\theta ={\theta}_{s}\to \mathrm{no}\text{}\mathrm{transpiration},E{T}^{\prime}=ET\text{}\mathrm{calculated}\text{}\mathrm{by}\text{}\mathrm{by}\text{}\mathrm{Hargreaves}\text{}\mathrm{Equation}\to O{F}^{\prime}=\left(i-{k}_{s}\right)\times \Delta t,\mathrm{and}\text{}UD\text{'}=\left({\theta}_{s}-{\theta}_{FC}\right)\times {d}_{soil}$

_{s}is saturate hydraulic conductivity.

#### Updating water content of media:

#### Calculate runoff flow from green roof:

#### Appendix A3.2. Infiltration-Based BMPs

- $if\text{}\theta \le {\theta}_{FC}\text{}\to \text{}PER=0$, this suggest that suction is greater than gravity and water is held tightly with the soil, thus, percolation does not occur.
- $if\text{}\theta {\theta}_{FC}\text{}\to \text{}PER={k}_{s}{e}^{-HCO\left({\theta}_{s}-\theta \right)}$ where ${k}_{s}$ is saturated hydraulic conductivity, $HCO$ is decay constant typically in the range of 5 to 15, ${\theta}_{s}$ is soil moisture content at saturation, ${\theta}_{FC}$ is soil moisture content at filed capacity, and $\theta $ is soil moisture content during the time interval [20].

#### Appendix A3.3. Storage-Based BMPs

^{3}) is volume at specific depth, D (m) is elevation or depth, L (m) is length of pond at bottom, W (m) is width of pond at bottom and Z is side slope.

^{3}) are storage volumes at the end and at the beginning of the time step, and ${Q}_{{t}_{i+1}},\text{}{Q}_{{t}_{i}}$ (m

^{3}/h) are discharge rates at the end and at the beginning of the time step.

## 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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**Figure 10.**(

**a**) Comparison of observed and simulated runoff for Yellowstone Drive catchment (

**b**) Scatter diagram of observed and simulated data.

**Figure 11.**(

**a**) Comparison of observed and simulated runoff for a small parking lot (

**b**) Scatter diagram of observed and simulated data.

**Figure 18.**Inflow and outflow hydrograph for (

**a**) 25 percent peak flow reduction and (

**b**) 50 percent peak flow reduction.

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 | √ | √ | √ | √ | √ | √ |

^{1}Pollutant load/Concentration refers to the load or concentration of pollutants discharged into the outlet or into a BMP from the catchment/watershed;

^{2}LCCA is a cost estimation method for incorporating all phases of project’s life useful in selecting between mutually exclusive options;

^{3}Integrated optimization refers to an optimization approach where multiple criteria including environmental, social, technical and economic factors are used in the optimization process;

^{4}Water quality analysis refers to BMP treatment performance evaluation.

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 | |

NO_{3} (mgL) | 0.05 | 0.64 | 1.26 | |

PO_{4} (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 | |

NO_{3} (mgL) | 0.13 | 0.51 | 1.24 | |

PO_{4} (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 | |

NO_{3} (mgL) | 0.03 | 0.51 | 2.20 | |

PO_{4} (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 (m^{3}) | 170 | 64 |

Pond surface area (m^{2}) | 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 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Shojaeizadeh, 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