Design of urban runoff pollution control based on the Sponge City concept in a large-scale high-plateau mountainous watershed: a case study in Yunnan, China

China recently commenced the Sponge City initiative for the effective management of urban stormwater runoff. Numerous studies have been carried out to evaluate the cost-effectiveness of low impact development (LID) practices in Sponge City planning and implementation. However, most of the studies were at the siteor subcatchment scale, and few were conducted at the watershed scale, given the dramatically increased routing complexity and number of decision variables. This study demonstrates the cost-effective Sponge City planning process for a 25.90 km highplateau watershed in southwest China. The Stormwater Management Model was coupled with the System for Urban Stormwater Treatment and Analysis Integrated (SUSTAIN) model to perform both continuous simulations and watershed-level optimization analyses, using the reduction of 85% annual runoff volume as the optimization target. Based on over 11,000 optimization runs, a nearoptimal aggregated LID scenario was identified for each subcatchment. The aggregated LID size was first converted into a generic LID storage volume for individual subcatchments, and the storage volume was then disaggregated into site-level LID layouts regarding specific site conditions. The disaggregated LID layout yielded an annual average runoff volume reduction of 87.61% and close to 85% reduction for the annual average total suspended solids, total nitrogen, and total phosphorus loads. The systematic approach outlined in this study could be used for watershed-level Sponge City planning and implementation analyses in other cities. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/wcc.2019.120 ://iwaponline.com/jwcc/article-pdf/12/1/201/851813/jwc0120201.pdf Zhenyu Zhang Junjie Gu (corresponding author) Ping Ning Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China E-mail: junjie_gu@sina.com Zhenyu Zhang Jian Shen School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Guoshun Zhang Wenjing Ma Nanjing Innowater Environmental Technology Ltd., Nanjing 210039, China Lei Zhao School of Information Science and Technology, Yunnan Normal University, Kunming 650092, China


INTRODUCTION
Urbanization converts the natural landscape into impervious surfaces and adversely alters the hydrologic cycle, resulting in increased peak flow rates, runoff volume, and pollutant load, as well as decreased groundwater infiltration (Schueler  Low impact development (LID) represents a paradigm shift in stormwater control, as it seeks to preserve the predevelopment hydrology using decentralized, microscale control measures (Baek et al. ). Commonly used LID practices include bioretention, grass swales, porous pavement, and green roofs (Roy et al. ; Newcomer et al. ; Shao et al. ). In the UK, a similar framework of sustainable urban drainage systems was used to address water pollution and flood hazards (Fletcher et al. ). Australia, in the meantime, proposed water sensitivity urban design to protect degrading urban water resources through a range of practices, including stormwater recycling and reuse, and the goal is to make cities more sustainable, livable, and resilient (Brown et al. ; Ashley et al. ).
In rapidly urbanizing China, urban flooding, the deterioration of surface water quality, urban heat island impacts, as well as climate change pose substantial challenges to stormwater management (Li et  This study proposes a comprehensive approach that bridges the watershed-scale optimization analysis with sitescale implementation for the Sponge City planning analysis. The objectives of this study are to: (1) evaluate pre-and postdevelopment runoff conditions at the watershed scale using the SWMM; (2) use the SUSTAIN model to perform the watershed-scale optimization of LID implementation using aggregated LID representation and to obtain the near-optimal aggregated LID sizes for each subcatchment. The near-optimal LID storage volume is then calculated for each subcatchment using the effective depth and the total area of the aggregated LID (i.e. bioretention area); (3) disaggregate the near-optimal LID storage volume for each subcatchment into the site-specific LID layout with regard to unique site characteristics; and (4) re-evaluate the watershed-scale hydrologic and water quality benefits using sitescale representations of disaggregated LIDs and verify both the hydrologic and water quality control benefits from the Sponge City implementation.

Study area
The case study watershed, Haidong New District (HND), is  Table 1. According to the Sponge City Construction Technology Guide, Dali is located in Zone II, where the annual average runoff volume control target is specified as 80-85% (MOHURD ).
The HND watershed has the subtropical monsoon climate, with an annual average rainfall depth of 620 mm and an annual average temperature of 15.6 C. Over 80% of the annual rainfall occurs between May and October.
Soils on the hilltops and hillslopes (slopes greater than 25%) are mostly weathered limestones with good drainage, and soils in valley areas (slopes less than 8%) of low elevations are typically loamy soils with poor drainage and a high groundwater table. With its close vicinity to the Erhai Lake, stormwater runoff from the post-development HND watershed is of grave concern to local authorities. The implementation of the Sponge City framework in the HND watershed is not only important to the protection of the Erhai Lake but also serves as an example for other urbanizing watersheds that are close to lakes or reservoirs.
Using the digital elevation model data, natural ditches, as well as the stormwater pipe network information, the HND watershed is divided into 76 subcatchments   based on the nonlinear reservoir representation: where d is the water depth (m), t is the time ( Table 2.

Calibration of the SWMM
After the HND SWMM was established, the model was calibrated against monitored data. Currently, the HND watershed is still under rapid development, and runoff data were collected from a monitoring station in Subcatchment #7 ( Figure 5(a)). Runoff and water quality (total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP)) samples from two rainfall events, 03 August 2018 (with a total rainfall depth of 19.2 mm) and 22 August 2018 (with a total rainfall depth of 27.18 mm), were collected.  Subcatchment #7 is the campus of a vocational school and has a total area of 40.71 ha ( Figure 5(a)). The land-use compositions for the subcatchment are summarized in Table 3. In order to assist with the calibration process, Subcatchment #7 is further delineated with regard to individual land-use types and the flow is routed according to the elevation and stormwater pipe network ( Figure 5 The optimal simulation value occurs when R NS is close to 1, and the value is usually considered as acceptable model performance when R NS is greater than 0.5 (Moriasi et al.
The R NS is formulated as follows: where Q obs t is the observed value at time t, Q sim t is the simulation value at time t, Q avr is the average observed value, t is the time, and n is the total number of time steps.    Table 4, and the site requirements for LIDs are summarized in Table 5. The design parameters used for the LID implementation scenario setup in the HND SUSTAIN model are also shown in Table 4.   Table 6.

The USEPA SUSTAIN model
In SUSTAIN, pollutant removal in LIDs is simulated in each time step using the first-order decay function, along with an optional background pollutant concentration: where C* is the background pollutant concentration (mg/L), C in is the input concentration (mg/L), C out is the output concentration (mg/L), q is the hydraulic loading or overflow rate (m/yr), k 0 ¼ kh and is the rate constant (m/yr), k is the first-order decay rate (1/yr), and h is the pond depth (m).
Cost-effective analyses are performed in the SUSTAIN model after the user specifies the assessment point, and the optimization target could be annual average runoff volume reduction, peak flow exceedance frequency, or annual average load reduction. The multi-objective problem can be expressed as follows:  (Table 7) during the optimization analysis, and the search space was 100 76 or 10 152 . The optimization process identifies the tradeoff between aggregated LID sizes (in hectares) in each subcatchment and the annual average runoff volume from the watershed.
Disaggregation of the near-optimal LID sizes to the sitescale layout The SUSTAIN optimization analysis identifies the tradeoff between LID sizes and the annual average runoff volume reduction for the HND watershed. The near-optimal solution consists of aggregated LID sizes (in hectares) in each subcatchment. The composite LID sizes need to be disaggregated into the site-scale layout to assist with LID implementation. The disaggregation process in a subcatchment takes six steps: (1) Identify the near-optimal LID size (in hectares), A LID , for the subcatchment from SUSTAIN optimization results; (2) Calculate the effective LID volume for the subcatchment, Q e , by multiplying A LID with the effective depth (d e-bioretention ) of the bioretention area (from Table 4); (3) Calculate the LID to the imperviousness ratio, R, by dividing the near-optimal LID size (A LID ) with the total impervious area, A IMP-total , in the subcatchment; (4) For each impervious land use in the subcatchment, assign a corresponding type of LID and then calculate the relative LID to the impervious ratio, R r , by dividing the effective depth of the bioretention area (d e-bioretention ) with that of the chosen LID type; (5) Calculate the LID area to the impervious area ratio for LIDs other than the bioretention area in the subcatchment as R*R r ; (6) Implement site-scale LIDs following the LID area to the impervious area ratio of R*R r for each piece of impervious land use.
The disaggregation steps can be further explained through an example. Suppose a subcatchment has 30 ha of impervious surfaces (A IMP-total ) (roof, roads, parking lots, and public squares), and the near-optimal LID size (A LID )    Table 8.
The example in Table 8 shows that the sum of the sitescale disaggregated LID area is 29,183 m 2 (2.91 ha), and that is larger than the near-optimal LID size (A LID ) for the subcatchment (2.1 ha). This is because the effective depths for green roof and porous pavement are less than those of the bioretention area, and thus larger LID areas are needed for the same LID volume. The LID to the impervious area ratio in A typical site-scale layout of LID implementation in a high-elevation subcatchment is shown in Figure 8. As shown, green roofs are implemented on rooftops, and porous pavements are implemented for parking lots, sidewalks, and public squares. The runoff is then routed to bioretention areas through grass swales, and the overflow  from bioretention areas is routed to downstream stormwater pipes and ditches. Runoff from nearby impervious surfaces, such as roads and public squares, can also be routed to grass swales and the bioretention area along the flow path. One special arrangement is that porous pavement, grass swale, and the bioretention area are all implemented with the lined bottom. This is to discourage infiltration at the highelevation subcatchments, mainly to protect the stability of the karst topography.
The LID site-scale layout for medium-elevation subcatchments is similar to that for the high-elevation subcatchments, with the difference being that no lined bottom is used for porous pavement, grass swale, and the bioretention area.
A typical site-scale LID layout for low-elevation subcatchments is shown in Figure 9. As shown, the routing schemes are similar to those for the medium-elevation subcatchments (porous pavement, grass swale, and the bioretention area without the lined bottom), except that dry/wet ponds are used at the downstream of the bioretention area. The detention/retention facilities could help further improve stormwater quality through the settling process, and the detained stormwater can also be used for municipal purposes (e.g. irrigation and sprinkling). Excess runoff from the dry/wet ponds is routed to stormwater pipes and eventually discharged into the Erhai Lake.

Re-evaluation of site-scale hydrologic and water quality performances
After the watershed-scale near-optimal LID sizes were disaggregated into site-scale LID layouts for each subcatchment,

SWMM calibration
Hydrologic and water quality calibration results of Subcatchment #7 for the two monitored events are shown in

Disaggregation of the near-optimal LID solution
The six-step methodology for disaggregating the watershedscale near-optimal LID solution to site-scale layouts was applied to each of the 76 subcatchments. The effective LID volume, LID to imperviousness ratio, and relative LID to impervious ratio were estimated for each subcatchment and site-level impervious land use. Engineering judgments were made when necessary regarding slight adjustments of LID sizes to accommodate individual site characteristics. The disaggregated site-level LIDs in each subcatchment are summarized in Table 11.
The disaggregation of the near-optimal LID to site-scale layouts shows that the bioretention is the largest LID used in many subcatchments. This is mainly due to two reasons.
First, the bioretention area has been reported to be highly

Limitations
There are several limitations to this study. Firstly, the aggregated LID representation ignored land-use layouts in the individual subcatchment, and this could potentially result in mismatch during the disaggregation process. For an unlikely scenario, a subcatchment consisting of mainly buildings may find an insufficient rooftop to implement green roofs, as the required LID sizes are much larger. Engineering judgments are crucial in such circumstances to appropriate size LIDs or even balance the LID volume among neighboring subcatchments if necessary. Secondly, while the SWMM was calibrated using monitored data, the SUSTAIN model was not calibrated to local data, and thus the actual LID hydrological and water quality benefits could differ from what is reported in this study. Lastly, the long-term maintenance operation and management (O&M) costs are not included in this analysis.

Future research directions
This study shows that LID practices can effectively reduce the post-development stormwater peak flow rate and the annual total runoff volume. However, for high-plateau mountainous watersheds such as the HND watershed, heavy rainfall events during summer time could cause severe peak flow rates and flooding, and the steep slope could only worsen the situation. Therefore, efforts are needed to investigate the potential combination of traditional stormwater control measures (e.g. regional stormwater ponds) and the LID practices in achieving flooding prevention during the extreme events. The real-time control technologies could also be used to achieve better management of stormwater. In addition, stormwater management studies need to consider climate change impacts and its influences on receiving water quantity and quality (Zhang et al. ).
As the HND watershed is still under development and the site conditions are constantly changing, no LID performance data are available at this time. It is recommended that monitoring stations be established in the HND watershed, and future studies need to include the calibration of LID practices using local data.

CONCLUSIONS
In this study, we developed a SUSTAIN model for a 25.90 km 2 watershed in the HND in southwest China.
Composite LID volumes were identified for each subcatch-