Study of Runoff and Pollution Control in Sponge Cities based on Storm Water Management Model

: To better solve the problems of water environment degradation and water shortage caused by urbanization, sponge cities have become a hot research topic. How to evaluate the effect of sponge facilities on runoff and pollution control and conduct benefit analysis is an urgent problem. In this paper, we simulate the runoff control, SS and COD control effects of low impact development (LID) facilities for 0.5a, 1a, 2a, 5a, 10a and 20a rainfall return periods before and after the construction of a sponge city in the southern part of Licang District, Qingdao, China. The results show that the model Nash efficiency coefficient is more than 0.8, which is in line with the reality, and the LID facility can play the role of runoff and pollutants control. 68.2% of the runoff volume is reduced and 70.4% of SS is removed at the return period of P=0.5a, which is the best effect. As the return period increases, the control rates of runoff and pollutants gradually become lower. It can provide a basis for future sponge city effect evaluation.


Introduction
In recent years, the urbanization process has been accelerated, leading to the change of urban sub-bedding types, such as a large number of natural sub-bedding surfaces such as vegetation, woodland and grassland cultivated land are gradually developed into impervious sub-bedding surfaces such as roofs, roads and squares. This further triggers the reduction of rainwater infiltration and the acceleration of flow production and confluence, which greatly increases surface runoff and makes it easy for water to accumulate in the rainy season. At the same time, the enhanced surface runoff leads to a much higher concentration of pollutants in the stormwater, which reduces the quality of the receiving water body and increases pollution, greatly affecting the urban water environment 1 .
In order to solve this problem, many countries have proposed Best Management Practices (BMPs), Low Impact Development (LID), Sustainable Drainage Systems (SDS), Water Sensitive Urban Design (WSUD), in-depth scientific research on urban stormwater management and water management has been carried out [2][3][4][5]. In order to implement the concept of low-carbon and ecological urban planning and construction, to control rainfall runoff, reduce runoff pollution and contain water, China has been vigorously promoting a new type of urban stormwater management system called sponge city [6].
By constructing a SWMM model to evaluate the impact of various sponge facilities on the urban hydrological system, it can provide relevant theoretical basis and data support for more extensive promotion of sponge city construction in the future and design guidance for planning and construction, thus greatly improving the science and reliability of sponge city construction [5].

Study area overview
The study area is located in Licang District, Qingdao, Shandong Province, with a temperate maritime climate, mild and humid with four distinct seasons. The average annual precipitation is 755.6 mm, the maximum annual precipitation is 1227.6 mm, the minimum annual precipitation is 386.3 mm, and the maximum daily precipitation is 182.6 mm. The total area of the study area is 180.552 ha, with a greening rate of about 35%. The northwestern part of the study area is hilly with high terrain, while the central southern part has low terrain. In general, the study area has a high north and low south trend. The highest elevation of the ground surface is 116m and the lowest elevation is 26m.

SWMM introduction
SWMM model is a precipitation-runoff simulation software based on a single precipitation event or longterm rainfall sequence, the main function is to simulate the water quantity or water quality dynamically in the city. SWMM can use a series of sub-catchments as the source of rainfall runoff and pollutants, through the pipeline, channel, pump, storage facilities, etc. to achieve hydrological hydraulic and water quality simulation, and support a variety of output forms. In each simulation, SWMM can record regional and pipeline system runoff flow, velocity, depth and pollutant yield in real time [7].
The SWMM consists of a calculation module and a service module. The calculation module is the core of the SWMM operational algorithm, which mainly includes runoff, storage, conveyance, extended conveyance, treatment, and stowed water bodies. The service module includes rainfall, conjunctive, statistical, mapping, operational, and implementation modules. Through these modules, the model can realize the functions of simulation data input, hydrological and water quality simulation, and simulation result output.

Establishment of SWMM model in the study area
Sub-catchment division. The sub-bedding structure of the study area is divided into woodland, road (including surrounding green belt), impervious surface-based parking lot and commercial area. According to the subbedding surface and elevation in ArcGIS to divide subcatchment area, a total of 123 sub-catchment areas, the division results are shown in Fig. 1.
In the modeling process, the principle of not changing the original dynamic characteristics of the whole pipe network, delete unnecessary nodes and branch pipes, in ArcGIS to establish the well and pipe network structure map. The distribution of rainwater pipes and rainwater well nodes are shown in Fig. 1. Node coordinates, pipe lengths and other information are obtained using the computational geometry function. In the structure map, it is also necessary to add specific information of wells and pipes, including well depth, well top elevation, rainwater pipe cross-section shape, pipe diameter, etc., which needs to be manually entered into the attribute table of relevant layers. When the data of well top elevation and well bottom elevation are lacked, the data of well bottom elevation is estimated by the surrounding surface elevation and the pipe is constructed by means of flat connection at the top of the pipe. Since it is easy to have the situation that the downstream well depth is higher than the upstream well depth due to inappropriate selection, the logical structure of the topology was checked after it was completed to ensure that the downstream rainfall well is lower than the upstream rainfall well. The above information needs to be organized into SWMM inp files in a specific format. Model parameter values. In this study, the infiltration process was simulated using the SCS runoff curve number method, which was determined based on the empirical parameters provided in the SWMM user manual. The Manning roughness coefficient of impervious area, Manning roughness coefficient of permeable area, initial loss filling depth of impervious area and initial loss filling depth of permeable area of various sub-bedding surfaces are uncertain parameters, which are reasonably determined according to the range of parameter values given in the SWMM User Manual and combined with experience, and the final values are shown in Table 1  Rainfall sequence. The Chicago rain type was chosen and the rain scenario was set for the cases with return periods of 0.5a, 1a, 2a, 5a, 10a, and 20a. Since the study area is a small watershed, 2h is used as the rainfall duration, and the design storm intensity of the study area is shown in Equation (1).
P and t represent the design storm return period (a) and rainfall duration (min), respectively.
LID facility layout. Based on the information of the sub-bedding surface of the community and the preliminary field research work, the area for arranging LID facilities was determined, as shown in Table 2.
， ， ， ， represent the Nash efficiency coefficient, the measured value, the simulated value, the mean value of the measured value and the length of the data series, respectively. In general, an NSE greater than 0.7 indicates a good model fit, and the NSEs of runoff and SS fitted by this model are 0.86 and 0.97, respectively, which shows that the model is more accurate [8].

Runoff control effect
The runoff control effect was expressed as the total runoff reduction rate. The total runoff flow generated in the study area before and after the sponge retrofit is shown in Fig. 3 for 0.5a, 1a, 2a, 5a, 10a, and 20a return periods, with an average reduction of about 52.5% for the different return periods. When the return period p=0.5a, the runoff volume before and after the construction of the sponge city was 2.8×10 6 L and 8.8×10 6 L respectively, and the runoff volume reduction rate was 68.2%. With the increase of the return period, the reduction rate of runoff volume was 60.0%, 59.6%, 56.1%, 46.4%, and 44.2% for the return periods p=1a, p=2a, p=5a, p=10a, and p=20a, respectively. The reduction rate of runoff decreases as the return period increases, which is due to the fact that a larger return period brings a greater intensity of rainfall, which is converted into runoff when the intensity of rainfall exceeds the infiltration rate of the LID facility [9].

Pollutant control effect
According to the overall goal of sponge city construction in Qingdao city is as follows: urban surface source pollution control is measured by SS removal rate, and the specific requirement is to reach more than 65% by 2030. Among urban runoff pollutants, SS often has a certain correlation with other pollutant indicators, therefore, SS can generally be used as a runoff pollutant control indicator [10,11]. The annual SS removal rate for lowimpact development stormwater systems can generally reach 40%-60%. The pollutant control effect indicators mainly consider SS removal rate and COD removal rate. The simulation results are shown in Table 3. As can be seen from Table 3, the removal effect of the 2 pollutants gradually decreased with the increase of the rainfall return period. The SS removal rate decreased from 70.74% to 30.93% and COD removal rate decreased from 63.77% to 27.88% as the return period increased. SS removal rate was higher than COD removal rate in each return period because SS removal is a physical interception process, while COD requires a series of biochemical reactions that take longer time.

Conclusion
By constructing a SWMM model of the study area, the removal effects of runoff and pollutants were simulated before and after the construction of the sponge city. The LID facilities could play a role in controlling runoff and pollutants, and the control rate of runoff and pollutants gradually became lower with the increase of the return period. In terms of pollutant control, the SS removal effect is slightly better than the COD removal effect. More LID arrangement options can be simulated in the future, and the optimal arrangement can be selected by combining the evaluation methods.