عنوان مقاله [English]
Due to defects or any inefficiency of urban drainage systems, flooding in urban areas causes significant damage to buildings and other public and private infrastructure. Therefore, researchers in recent years have tried to establish a more accurate relationship between rainfall and runoff. Various hydrological models have made significant contributions, and the SWMM model is one of the models with acceptable accuracy in this field (Zoppou, 2001). The SWMM model is a dynamic model for simulating runoff precipitation (event-based or continuous) and can simulate the quality and quantity of runoff for urban areas.
Moreover, District 10 of Tehran Municipality is chosen as the study area, with an area of 807 hectares, the north-south and east-west slopes are approximately 1.2% and 1%, respectively. The primary land use in this region is high-density residential. The severe population density in the region and the lack of green spaces are the most critical environmental problems in the region.
Therefore, the purpose of this study is to model quantitatively and qualitatively urban runoff modeling in District 10 of Tehran Municipality using the EPA SWMM model and the application of Low Impact Development methods (LID) in this area to reduce quantitative parameters such as flow rate, depth, velocity, and volume as well as qualitative parameters including the concentration of total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP).
The subcatchments of the study area are shown in Figure 3. Also, the characteristics of all subcatchments modeled in SWMM are presented in Table 1. According to Figure 3, the study area is divided into 60 subcatchments, 13 nodes (including the output node), and 12 links to create the drainage network. In this figure, the bold lines and dash lines indicate the flow channels and the route of runoff transfer from the subcatchments to the outlet nodes, respectively. In addition, Equation 1 is derived from intensity-duration-frequency curves having an estimate of the precipitation intensity based on the duration and frequency of the precipitation.
In this study, to take advantage of Low Impact Development methods, the Bioretention Cell method (Scenario 2) and the Permeable Pavement method (Scenario 3) are defined individually as well as combined (Scenario 4) in the modeling process and compared these scenarios with Scenario 1, which did not use the LID methods. These LID methods are selected due to the possibility of their implementation in the study area. Also, considering the available spaces for implementing these methods, it is assumed that 20-30% and 30-40% of the land uses for each subcatchment are allocated to bioretention cell (Scenario 2) and permeable pavement (Scenario 3), respectively. Then, by quantitative and qualitative modeling, the results from Scenario 1 are presented for rainfall with return periods of 2, 5, and 10 years. Finally, the results of other scenarios are compared with Scenario 1 to discuss the effectiveness of each LID method.
Based on the results of quantitative modeling, Figures 6-8 indicate the critical flood status in subcatchments, manholes, and flow channels with precipitation with different return periods. In this section, in terms of area, the vast subcatchments have critical flood conditions. Also, at this time, with increasing rainfall intensity (10-year return period), the amount of water depth in the links and nodes that are close to the output node increases, which is shown in light blue and yellow. These figures can help relevant decision-makers focus more on the critical subcatchments and manholes to reduce the discharge and depth of flooding in the region. In addition, if the budget for implementation of the LID methods is limited, decision-makers can benefit from LID methods in the presented critical location to reduce the depth of flooding.
By implementing scenarios 2-4, the critical parameters (Tables 2-4) for quantitative modelling are compared in the channel leading to the output node (C12), including 1- maximum flow rate (cubic meter per second), 2- maximum flow depth (meter), 3- maximum flow velocity (meter per second), and 4- maximum flow volume (cubic meter). According to these tables, with increasing rainfall intensity, flow rate, velocity, depth, and runoff volume in the channel increase. On the other hand, the use of permeable pavement (S3) is more effective than bioretention cells (S2), while using a combination of these methods (S4), we will see a significant reduction in the flow parameters in which the peak of the flow depth and flow depth are reduced by approximately 50 and 55%, respectively. It should be noted that the effectiveness of these methods in controlling the flow velocity compared to other parameters such as flow rate, depth and volume is lower. With increasing rainfall intensity (10-year return period), the effectiveness of these methods decreases compared to precipitation with less intensity (about 4-5%). In contrast, in the flow depth and volume parameters, we will see higher efficiency in applying LID methods (about 7%) with increasing rainfall intensity.
Additionally, the qualitative modelling findings and comparison of Tables 5-7 indicate that the TSS, TN, and TP concentrations rise when rainfall intensity increases. Regarding the effectiveness of bioretention cell and permeable pavement methods, it is observed that the application of permeable pavement (S3) is more effective than bioretention cell (S2) in reducing the concentration of TSS and TN parameters. In contrast, for the TP parameter, the bioretention cell is more effective. Similar to the quantitative model, the combined application of these methods is more effective in reducing the concentration of parameters. It should be noted that LID methods to reduce the TP concentration were less effective (approximately 20 to 30%) than TN and TSS.
On the other hand, with increasing rainfall intensity (comparing Tables 5-7), the performance of each LID method in reducing the TSS concentration is almost constant (in a specific scenario). At the same time, it is more effective for TN and TP parameters. In general, the effectiveness of LID methods in reducing the quality parameters is greater than the quantitative one, so that by using the combination of bioretention cell and permeable pavement, the concentration of total suspended solids can be reduced by about 77%, while the effect of this scenario on the reducing peak flood discharge is about 55%. Also, if there is an executive constraint or limitation budgeting, the efficiency of the permeable pavement method is better than the bioretention cell in the quantitative and qualitative analysis.