Chapter VI: Conclusion remark
Bioretention 1 Bioretention 2 Infiltration trench
4.1 Results and Discussion 4.3.1 Model sensitivity analysis
Fig. 4.4 (a) presents the sensitivity analysis for the simulation of the hydrology module in LID. In the hydrology module, the saturated hydraulic conductivity (𝐾𝑠) and wetting front suction head (ψ) had the largest mean EE values among all parameters (Fig. 4.4(a)). The standard deviation of the EE values for both parameters were also greater than those of the other parameters. This implies that these parameters strongly influenced not only the model output but also the interaction effect of the parameters (Saltelli et al, 2008). The results can also be attributed to the substantial influence of 𝐾𝑠 and ψ on the infiltration rate into soil based on Eq 1. Since these parameters are related to the surface layer (Eq 1), we can infer that surface layer parameters are more sensitive than those related to the soil layer (Eq 2). In the figure, the thickness of soil mixing zone (𝐷𝑚𝑖𝑥) and the attachment rates at soil (𝑘𝑎 ) were shown to have larger means and standard deviations of EEs compared to other parameters. This implies that these parameters had considerable effects on the model output and more interactions with other parameters (Pyo et al., 2017). Since both parameters are related to the mechanism of pollutant attachment, we can presume that this mechanism has a strong influence on the water quality module in LID at the soil mixing zone. The straining coefficient (𝐾𝑠𝑡𝑟) has the lowest mean and standard deviation of EEs. 𝐾𝑠𝑡𝑟 was one of the factors used to describe the straining effect of pollutants via vegetation during infiltration. The low values indicate that the straining effect of pollutants is not crucial to the water quality module.
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Figure 4.4. Sensitivity of the hydrology and water quality parameters of LID.
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4.3.2 Model evaluation in the pilot-scale LID experiment 4.3.2.1 Hydrology module
Fig. 4.5 presents the temporal variation in the observed surface runoff and inflow of each LID (e.g.
bioretention 1, bioretention 2 and infiltration trench). In the figure, both simulated and observed surface runoff values were at zero when the ponding depths were below the berm heights of the respective LIDs. Surface runoff was initiated when the ponding depth reached the berm height which can be observed by the sudden increase of surface runoff values in the plots. Afterwhich, the trend of the surface runoff in every plot was notably similar to the inflow, indicateing that the inflow can influence surface runoff. RSR values of the simulation were calculated to evaluate the performance of each LID. Bioretention 1 and 2 yielded RSR values of less than 0.6, which is within the “good”
criterion, while the simulation for the infiltration trench was 0.7, implying that model performance was “satisfactory” (Moriasi et al., 2007). These results signify acceptable performances and a good agreement between the observed and simulated surface flow (Moriasi et al., 2007).
The optimal parameters for hydrology module are presented in Table 4.1. For bioretention 1 and 2, the hydrology module optimized similar values for all parameters. These values are similar to the soil properties of sandy loam and loam (Rossman, 2015). On the other hand, infiltration trench has different parameter values compared to the bioretention LIDs. Infiltration trench has larger porosity and saturated hydraulic conductivity values and smaller field capacity, wilting point, and suction head.
These results entail that the soil in the infiltration trench had different properties compared to bioretention 1 and 2. The parameter values were similar to the soil properties of gravel which has high permeability and porosity (Lewis et al., 2006).
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Figure 4.5. Observed surface runoff and predicted surface runoff of three LIDs: Bioretention 1, Bioretention 2, and Infiltration trench.
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4.3.2.2 Water quality module
Fig 4.6-4.8 show the comparison between the observed pollutant values and the pollutant simulations of the SWMM and modified SWMM. Simulated pollutants of modified SWMM showed a good agreement with the observations while the SWMM overestimated its pollutant simulation. The figures show that the three LIDs have similar simulation trends of the load variation with their respective load inflow. This indicates that the load inflow has a high influence on the load outflow from bioretention and infiltration trench. RSR values of the SWMM for biorientation 1 were above 0.7 which is within the “unsatisfactory” performance range (>0.7) while biorientation 2 performances were “satisfactory” (0.6 to 0.7) except TP (2.52). The COD and TN simulations for infiltration trench can be regarded as “satisfactory” (0.6 to 0.7) while TSS and TP were “unsatisfactory” (>0.7).
Although few simulation results showed acceptable performance ratings, the results of bioretention 1 and 2 presented considerable overestimations of the pollutant. On the other hand, the results of the infiltration trench were relatively close to the observed values albeit showing a slightly exaggerated simulation. These results may indicate that SWMM does not have enough functions on the pollutant reduction effect by LID since this model only used the dilution effect by rainfall.
The modified SWMM results for bioretention 1 were acceptable with RSR values of less than 0.6, excluding TN (0.73) (Moriasi et al., 2007). Fig 4.6 shows that the modified model underestimated the TN values when compared to the observations. The water quality simulation showed a peak load when the load inflow was high and a decreasing load outflow when the load inflow was low. For bioretention 2 in Fig 4.7, the RSR values obtained by the model were less than 0.4, implying that the model performance rating can be regarded as “very good” (Moriasi et al., 2007). The load outflow computed by the model showed instantaneous changes in the early stage of surface runoff since the load of the inflow was high before surface runoff began. After which, a decrease in the load outflow can be observed with decreasing load inflow after the start of surface runoff. In Fig 4.8, infiltration trench yielded RSR values of less than 0.7 which is within the “satisfactory” performance ratings for all pollutant loads (Moriasi et al., 2007). The TSS and TN simulations by the modified model were slightly overestimated while COD and TP were slightly underestimated. This may be due to the limitation of the modified model which focused on the straining effect of the pollutant by vegetation during infiltration and the attachment of pollutants on particles at the soil mixing zone. These effects are closer to the bioretention function rather than the infiltration trench, which rely on the rapid infiltration system (Maniquiz et al., 2010; Heilweil and Watt, 2011). Henceforth, developing an
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additional function for infiltration trench is recommended for future research work.
Table 4.1 summarizes the parameters of the water quality module for bioretention 1, bioretention 2, and infiltration trench. The water quality module for bioretention 1 and 2 had similar values depending on the type of pollutant; however, a different set of optimized parameter values were obtained for infiltration trench. This was induced by applying a larger 𝐾𝑠𝑡𝑟 and smaller 𝑘𝑎 to the infiltration trench while bioretention LIDs applied smaller 𝐾𝑠𝑡𝑟 and larger 𝑘𝑎 values. In addition, the 𝐷𝑚𝑖𝑥 of the infiltration trench was much smaller than those of bioretention LIDs. This parameter can determine that bioretention was strongly affected by the attachment of pollutants in particles at the soil mixing zone while infiltration trench was sensitive to the straining effect on the pollutant. In constrast, the values of the decay rate were low considering that all LID types had similar values.
This reveals that the decay effect was slow in reducing pollutants in LID.
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Figure 4.6. Observed and predicted pollutant loads in bioretention 1: total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP).
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Figure 4.7. Observed and predicted pollutant loads in bioretention 2: total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP).
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Figure 4.8. Observed and predicted pollutant loads in infiltration trench: total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP).
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4.3.3 Scenario analysis under the climate change 4.3.3.1 Model calibration
Fig. 4.9 shows the observed and simulated flow rate computed by model and Fig. 4.10 shows the observed and simulated suspended solids computed by the model. The flowrates and suspended solids simulations for both rainfall events showed “good” performances with RSR values of less than 0.6.
The peak flow and load simulations were slightly lower than their respective observation values.
Previous studies have presented a similar limitation in accurately simulating the peak flow and pollutant using the model (Barco et al., 2008; Baek et al., 2015).
Figure 4.9. Observed and simulated flow rate in calibration and validation.
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Figure 4.10. Observed and simulated suspended solids in calibration and validation.
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4.3.3.2 LID implementation on hydrology
Under the historical scenarios and RCP 8.5, the total volume and peak flow of the base case (without LIDs) and the seven LID scenarios (10 ~ 70%) are presented in Fig 4.11-4.14. Table 4.4 summarizes the conditions of each rainfall scenario (Kim et al., 2005; Lima et al., 2016). The historical rainfall volume of a 2-year return period was slightly larger than RCP scenarios. In contrast, the RCP 8.5 has a larger rainfall volume for the 5-year and 10-year return period as compared to the historical scenario. These discrepancies might be caused by the extreme precipitation events caused by climate change (Kunkel et al., 2013). These extreme events made a difference in the rainfall volumes between the historical and RCP 8.5 scenarios. In the base case, as the return period increased, the total volume and peak flow steadily increased as well. The historical and RCP 8.5 under 2-year return period produced similar values of the total volume and peak flow while the RCP 8.5 under 5- year and 10-year return period produced higher values of the total volume and peak flow than those from the historical scenarios.
Among LIDs under the historical scenarios and RCP 8.5, the infiltration trench showed a high reduction capacity in terms of total volume because the infiltration trench has high Ks and K-slope values that can increase the infiltration rate (Table 4.2). The peak flow and the reduction trend of bioretention 1 and 2 showed similar patterns since both bioretention LIDs have similar Ks and K- slope values that are lower than those of infiltration trench (Table 4.2). In this regard, the reduction effect of bioretention LIDs under all scenarios are lower compared to infiltration trench. Both historical scenarios and RCP 8.5 for the 2-year return period generated similar trends and values of the total volume and peak flow for all LID sizes. Meanwhile, RCP 8.5 generated higher values of the total volume and peak flow than those of the historical scenarios for the 5-year and 10-year return periods. These discrepancies were caused by the differences in rainfall volumes between the three return periods; wherein, the 2-year return period has the smallest difference between the scenarios, followed respectively by the 5-year and 10-year return periods. Although the rainfall volume and duration obtained by Huff's method (Huff, 1967) were the same, the Huff 3rd and 4th quartiles showed a higher increased in total flow and peak flow from LID than the Huff 1st and 2nd. However, the differences of the total flow between the Huff’s quartiles were not significant. This may indicate that the peak flow was considerably influenced by the rainfall temporal variability.
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Figure 4.11. Total volume and peak flow according to LID size, rainfall scenario, and Huff 1st rainfall distribution (the straight line is the result under historical scenarios and the dash line is the result under RCP scenarios 8.5).
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Figure 4.12. Total volume and peak flow according to LID size, rainfall scenario, and Huff 2nd rainfall distribution (the straight line is the result under historical scenarios and the dash line is the result under RCP scenarios 8.5).
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Figure 4.13. Total volume and peak flow according to LID size, rainfall scenario, and Huff 3rd rainfall distribution (the straight line is the result under historical scenarios and the dash line is the result under RCP scenarios 8.5).
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Figure 4.14. Total volume and peak flow according to LID size, rainfall scenario, and Huff 4th rainfall distribution (the straight line is the result under historical scenarios and the dash line is the result under RCP scenarios 8.5).
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Table 4.4. Information of rainfall scenarios (Kim et al., 2005; Lima et al., 2016).
Return period Scenarios Rainfall volume (mm)
2 year Historical 131.18
RCP scenarios 8.5. 128.66
5 year Historical 194.32
RCP scenarios 8.5. 225.82
10 year Historical 255.64
RCP scenarios 8.5. 262.5 Rainfall duration(hours) 14 antecedent dry hours
(hour) 80.5
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4.3.3.3 LID implementation on water quality
Fig 4.15-4.20 present the total load and peak load of the base case (without LIDs) and the seven LID scenarios (10 ~ 70%) under the historical scenarios and RCP 8.5. In contrast to the result of the total volume and peak flow, the total load and peak load in the base case showed a negligible difference between historical scenarios and RCP 8.5. This is due to the constant amount of pollutants present in the urban area even when the rainfall volume increased with respect to each scenario. The surface pollutants in the urban area accumulated logarithmically on dry days (Rossman and Huber, 2016). Using the build-up function in SWMM, the maximum amount of pollutant was calculated by estimating the urban surface accumulation of dust and dirt (Rossman and Huber, 2016). The results of the water quality simulations of LID revealed that the total and peak load from the historical and RCP 8.5 scenarios did not show significant differences. This was because the maximum amount of the pollutant may not have been influenced by rainfall properties.
In the SWMM simulations of LID scenarios, the total load and peak load significantly decreased with increasing LID coverage, from the base case at 0% until 20%. From the LID coverage of 20%
to 70%, the reduction effect for total load and peak load remained unchanged. The simulation results did not also present a significant difference between LID types as the percentage of each LID area increased and was weakly affected by the temporal rainfall distribution depending on Huff’s quantile.
This is may be due to the SWMM limitation which lack appropriate reduction functions for water quality in LID
For the modified SWMM simulation results, the total load and peak load decreased with increasing LID size (Fig 4.21-4.24). The bioretention 1 and 2 had a greater reduction effect for the total load and peak load compared to the relatively weak reduction effect of the infiltration trench. These discrepancies were due to the bioretention LIDs having higher values of the soil mixing zone thickness and the attachment rates of pollutants in the soil (Table 4.3). The total load and peak load of bioretention 1 and 2 decreased as the number of the Huff’s quartile and LID proportion increased.
On the other hand, the discharge from urban area decreased while the pollutant concentration from urban area increased when the number of Huff’s quartile was increased in the initial period of rainfall event (Sansalone Buchberger, 1997; Obermann et al., 2009). Therefore, the reduction effect can be greater in Huff’s 3rd and 4th quartiles compared to the other quartiles. This is because a small discharge causes to extend the retention time of pollutants in the LID, thereby increasing the reduction effect for pollutants in the LID. All LIDs showed a substantial reduction effect in terms of total load and peak load although the LID size was only 10% of the study site. In this regard, it was able to
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demonstrate that the reduction effect for urban pollutants might require a smaller LID size compared to reducing the total volume and peak flow. As well, some researchers suggested that a relatively small facility size can improve water quality (Guo and Urbonas, 1996, Kim and Han 2010; Baek et al., 2015). The result of the modified SWMM was sensitive to the types and size of LID as well as the temporal distribution of rainfall while the SWMM results were relatively insensitive. This indicates that the SWMM model has limitations in analyzing a detailed and an accurate response of pollutants with respect to various LID conditions and rainfall patterns. Hence, we can conclude that the modified SWMM can provide a more accurate pollutant simulation in terms of LID effects compared to SWMM.
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Figure 4.15. Total load and peak load of SWMM according to LID size, Huffs quantile and climate change scenario, LID type: bioretention 1
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Figure 4.16. Total load and peak load of modified SWMM according to LID size, Huffs quantile and climate scenario, LID type: bioretention 1
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Figure 4.17. Total load and peak load of SWMM according to LID size, Huffs quantile and climate scenario, LID type: bioretenti on 2
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Figure 4.18. Total load and peak load of SWMM according to LID size, Huffs quantile and climate scenario, LID type: Infiltration trench
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Figure 4.19. Total load and peak load of modified SWMM according to LID size, Huffs quantile and climate scenario, LID type: bioretention 2
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Figure 4.20. Total load and peak load of modified SWMM according to LID size, Huffs quantile and climate scenario, LID type: Infiltration trench.
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Figure 4.21. Total load and peak load according to LID size, rainfall scenario, and Huff 1st rainfall distribution
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Figure 4.22. Total load and peak load according to LID size, rainfall scenario, and Huff 2nd rainfall distribution.
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Figure 4.23. Total load and peak load according to LID size, rainfall scenario, and Huff 3rd rainfall distribution.
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Figure 4.24. Total load and peak load according to LID size, rainfall scenario, and Huff 4th rainfall distribution.
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