Chapter VI: Conclusion remark
4.2 Materials and methods .1. Study sites
4.2.1.1 Pilot-scale LID experiment for model evaluation (Fig. 4.1(a))
We selected two different bioretentions and one infiltration trench to evaluate the hydrology and water quality simulation of the LID model. These LIDs were constructed in a new town at Pyeongtaek, South Korea (37° 03' 00.7''N, 127°03'04.3''E) (Fig. 4.1(a)). For bioretention 1, the depth of soil layer, storage layer, and berm height were 20.0 cm, 40.0 cm, and 10.0 cm, respectively. Bioretention 1 has a width of 2.00 m and length of 2.24 m. On the other hand, bioretention 2 has a soil layer depth, storage layer depth, and berm height of 20.0 cm, 20.0 cm, and 10.0 cm, respectively. It has dimensions of 1.0 m wide and 1.24 m long. Meanwhile, the infiltration trench has a 40.0 cm storage layer depth and 10.0 cm berm height with dimensions of 2.0 m and 2.24 m for width and length, respectively.
Bioretentions 1 and 2 have vegetable soil and gravel as the media for the soil and storage layers while infiltration trench has gravel for the storage layer only. In this study, we conducted the infiltration experiment for the native soil using the Guelph Permeameter which can measure the saturated hydraulic conductivity of the soil. Artificial inflow was collected from the road during rainfall events was then used for model evaluation. Runoff, total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were monitored in the inflow and outflow.
Flow measurements of the inflow and outflow were manually conducted based on the bucket method (Trimmer, 1994). Water samples were also collected at the sampling sites and were stored in polyethylene bottles for water quality analysis.
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Figure 4.1. Schematic diagram of the model evaluation and scenario analysis under climate change: (a) Illustration of the pilot-scale LID experiment, (b) Map of the urban subbasin, (c) Model evaluation flow chart, and (d) Scenario analysis under climate change flow chart.
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4.2.1.2 Urban subbasin for scenarios analysis under climate change (Fig. 4.1(b))
We selected an urban area located in Yongin City (37°14′07.6″N, 127°12′22.2″E) (Fig. 4.1(b)), one of the small and medium cities in South Korea. This site has an area of 0.0237 km2 with commercial and residential as the dominant land use, which consists of offices, residential buildings, and restaurants. Runoff and total suspended soils (TSS) from the urban area were monitored at the end of the urban drainage pipe. We measured the runoff volume during the rainfall events on June 18, 2013 and August 29, 2013 using a Flo-Tote 3 flowmeter (USA). Water samples were manually collected at the sampling sites and stored in polyethylene bottles. For this study site, the SWMM model was calibrated to be used as a representative area without LID installation. The SWMM model was calibrated for this study site to be used as a representative of the current status of the area without LID installation. The effects of the LID system on the changes in the runoff volume and water quality were evaluated using the original and modified water quality modules, which included the optimized water flow and water quality parameters from the urban subbasin (Fig. 4.1(b)).
4.2.2 LID-water quality simulation model (modified SWMM) (Fig. 4.1(c))
A schematic diagram of the methodology for the urban watershed and LID simulation is shown in Fig. 4.1. The urban watershed simulation in EPA SWMM (version 5.1.012) produced surface runoff, rainfall, and pollutant from the urban area (Fig. 4.2 (a)). Fig. 4.2 (b) shows that the LID simulation was divided into two modules: hydrology module and water quality module. The input for the hydrology module includes the simulated surface runoff and rainfall values from the area (Fig. 4.2(c)) while the water quality module incorporates with the rainfall and the pollutant from the urban area (Fig. 4.2(d)). The hydrology module produces the surface runoff, infiltration, and ponding depth from the LID (Fig. 4.2(c)). This information is then transferred into the water quality module simulating the outflow concentration from LID (Fig. 4.2(d)). After that, the surface runoff and outflow concentration from LID are fed into the conduit in the urban watershed simulation. The results of the modified and original LID modules were then evaluated by simulating two different bioretentions and one infiltration trench in which the flow and pollutant concentration were observed.
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Figure 4.2. Model framework of LID simulation.
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4.2.2.1 Hydrology module for LID simulation (Fig 4.2(c))
The hydrology module adopted the LID module in EPA SWMM (version 5.1.012) for simulating the hydrologic performance of LID (Rossman and Huber, 2016). LID simulation includes a surface layer, soil layer, and storage layer that have different hydrological behaviors (Qin et al., 2013). For the surface layer, the infiltration and surface runoff from LID were calculated using the Green–Ampt equation. Soil percolation was then calculated according to variations in the soil moisture. Finally, the exfiltration rate was simulated for the storage layer (Rossman and Huber, 2016).
For the surface layer, the infiltration flux (f1) at the topsoil was estimated (Rossman and Huber, 2016). A modified Green–Ampt equation proposed by Mein and Larson (1973) was used to estimate f1:
𝑓1 = Ks(1 + (ϕ−θ1)(d1+ψ)
F ), (1)
where, f1 is the infiltration flux [cm/min], Ks is the saturated hydraulic conductivity of the soil in LID [cm/min], ϕ is the soil porosity [-], θ1 is the moisture content of the soil [-], ψ is the suction head at the infiltration wetting front [cm], d1 is the ponding depth on the surface [cm], and F is the cumulative infiltration volume per unit area [cm]. Mein and Larson’s modified Green–Ampt equation suggests that the infiltration rate is equal to rainfall intensity when the soil has not reached its saturation point (Almedeij and Esen, 2013).
In the soil layer, the rate of soil percolation (f2) through the soil into the storage layer was simulated.
The soil percolation can be calculated based on Darcy’s law which followed the same method employed in the groundwater module of the SWMM (Rossman and Huber, 2016):
𝑓2 = Ksexp(−𝐻𝐶𝑂(ϕ − θ2)) , θ2 > θFC , (2)
𝑓2 = 0, θ2 ≤ θFC , (3)
where, f2 is the soil percolation rate [cm/min], HCO is the decay constant derived from the moisture retention curve [-], and θ𝐹𝐶 is the field capacity moisture content of the soil [-].
The exfiltration rate (f3) from the storage zone into the native soil was estimated for the storage layer. The following equation was used to calculate f3 (Rossman and Huber, 2016):
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𝑓3 = 𝑓2− ϕ2∂d2
𝜕𝑡 , 𝑓3 < 𝐾3 (4)
𝑓3 = K3, 𝑓3 ≥ 𝐾3 (5)
where, f3 is the exfiltration rate [cm/min], ϕ2 is the void fraction of the storage layer [-], d2 is the water depth in the storage layer [cm], and 𝐾3 is the saturated hydraulic conductivity of the native soil [cm/min].
4.2.2.2 Modified water quality module for LID simulation (Fig 4.2(d))
In the EPA SWMM, the LID effect in terms of water quality is calculated using the following equation (Rossman and Huber, 2016).
𝐶𝑜𝑢𝑡 = [(𝐶𝑜𝑢𝑡𝑄𝑜𝑢𝑡)𝑛𝑜𝑛−𝐿𝐼𝐷+𝐶𝑟𝑎𝑖𝑛𝑖𝐴𝐿𝐼𝐷]
𝑄𝑜𝑢𝑡,𝑛𝑜𝑛−𝐿𝐼𝐷+𝑖𝐴𝐿𝐼𝐷 (6)
where, 𝐶𝑜𝑢𝑡 is the concentration of pollutant after LID treatment [mg/L], 𝐶𝑜𝑢𝑡,𝑛𝑜𝑛−𝐿𝐼𝐷 is the concentration of a pollutant before LID treatment [mg/L], 𝐶𝑜𝑢𝑡,𝑛𝑜𝑛−𝐿𝐼𝐷 is the surface runoff before LID treatment [mg/L], 𝐶𝑟𝑎𝑖𝑛 is the concentration of the pollutant in rainfall [mg/L], 𝑖 is the rainfall intensity [cm/min], and 𝐴𝐿𝐼𝐷 is the total surface area of LID [m2].
This study added the new water quality function to the original LID module. In the modified water quality module, the pollutant is subjected to the straining effect caused by vegetation during infiltration, decay effect, and its attachment to particles at the soil mixing zone (Negev, 2012). This module also includes the dilution effect by rainfall collected in the LID system. Crank-Nicolson method was utilized to implement the modified water quality module of LID:
𝜕𝐶ℎ
𝜕𝑡 = 𝐶𝑖𝑛ℎ𝑖𝑛− 𝐶ℎ𝑜𝑢𝑡 − 𝐶ℎ𝑖𝑛𝑓𝑖𝑙(1 − 𝑘𝑠𝑡𝑟) − 𝜇𝐶 − 𝐷𝑚𝑖𝑥(ϕ𝑘𝑎𝐶) (7)
Cin = [(𝐶𝑜𝑢𝑡𝑄𝑜𝑢𝑡)𝑛𝑜𝑛−𝐿𝐼𝐷+𝐶𝑟𝑎𝑖𝑛𝑖𝐴𝐿𝐼𝐷
𝑄𝑜𝑢𝑡,𝑛𝑜𝑛−𝐿𝐼𝐷+𝑖𝐴𝐿𝐼𝐷 (8) where, ℎ is the ponding depth on the LID surface [cm], ℎ𝑖𝑛 is the water depth before LID treatment [cm], ℎ𝑜𝑢𝑡 is the water depth after LID treatment [cm], ℎ𝑖𝑛𝑓𝑖𝑙 is the infiltration through LID [cm], 𝐶 is the concentration of the pollutant after LID treatment [mg/L], 𝐾𝑠𝑡𝑟 is the straining coefficient
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[-], 𝑢 is the decay rate [min-1], 𝐷𝑚𝑖𝑥 is the thickness of soil mixing zone [cm], and 𝑘𝑎 is the attachment rates at soil [-].
4.2.3 Model performance evaluation 4.2.3.1 Sensitivity analysis.
The sensitivity analysis of the model was conducted using the elementary effects (EEs) analysis (Morris et al., 1994). This method has been established as an effective approach in terms of quantifying the effect of the interactions between parameters for nonlinear models (Campolongo and Braddock, 1999; Saltelli et al, 2008; Morris et al., 2014). The EEs of the ith simulation is expressed as follows (Morris et al., 2014):
EEi = [𝑌̅(𝑋1,𝑋2,….,𝑋𝑖−1,𝑋𝑖+Δ,…𝑋𝑘)−𝑌(𝑋1,𝑋2,…,𝑋𝑘)]
Δ (9)
where, 𝑌̅ is the new outcome, Y is the original outcome, and Δ is the increment depending on the range of Xi.
The mean value of EEs describes the level of influence by each parameter on the model output while standard deviation shows the existing interaction between parameters (Saltelli et al., 2008).
Detailed explanations of the EEs analysis are published in Morris et al. (2014) and Saltelli et al.
(2008). The ranges of parameter values for the auto calibration and sensitivity analysis of hydrology and water quality modules were obtained from previous literatures (Rossman, 2015; Negev, 2012;
Yang et al., 2010; Debele et al., 2008; Almasri et al., 2007; Simunek et al., 2005) (Table 4.1-2). In this study, we conducted the EEs sensitivity analysis using the SAFE toolbox (Sensitivity Analysis for Everybody) in MATLAB (Pianosi et al. (2015)).
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Table 4.1. Description and range of parameters for LID-hydrology module.
*Adopted from (Rawl et al., 1983; Rossman, 2015)
Parameters Description Min* Max*
Value