The integrated green–gray–blue (IGGB) system is considered to be a new way of stormwater management, and a comprehensive evaluation of the green–gray–blue infrastructure layout mode under different return periods is the key to the implementation decision-making of stormwater management. In this study, a blue–green synergism evaluation model is established to optimize the layout of blue–green infrastructure. An evaluation framework combining the evaluation indicator system and the hydrology model is established. Stormwater storage, peak flow reduction, and life cycle cost are selected as evaluation indicators. On this basis, seven optimal scenarios, including green, blue, gray, green–blue, green–gray, blue–gray, and green–gray–blue, are established. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to analyze these seven scenarios under different return periods. The results indicate that (1) when the drainage infrastructures are arranged in combination, the peak flow reduction is significantly improved compared to that of a single drainage. (2) TOPSIS results show that green–gray and blue–gray perform better when the cost weight is 0–0.35, and green–gray–blue performs best when the cost weight is 0.35–1. (3) The integrated green–gray–blue system has obvious synergistic effects. This study can provide support for planning department workers for the urban stormwater management strategy.

  • The integrated green–gray–blue system shows an obvious synergistic effect.

  • Blue and gray infrastructures are more cost-effective than green infrastructure.

  • The construction of the blue–green synergism degree model is beneficial to the ability of the integrated green–gray–blue system to face heavy rainfall events.

  • The green–gray–blue strategy has the best comprehensive effect in dealing with heavy rainfall.

In the context of climate change, large-scale urbanization and urban expansion that have brought enormous pressure on ecosystems, dramatic changes in land use and land cover (LULC), and increased impervious areas have altered the microclimate in urban areas, especially in developing countries. Environmental problems such as increased runoff and rising temperatures are becoming prominent, and the frequency and intensity of floods caused by heavy rainfall are expected to continue to increase (Nagendra et al. 2018; Kong et al. 2021). In order to alleviate the problem of increased stormwater runoff in urban areas caused by climate change and LULC changes, developed countries have proposed various stormwater management strategies since 1980s, including best management practices (BMPs), low impact development (LID) in the United States and Canada, Australia's water sensitive urban design (WSUD), and the UK's sustainable drainage system (SuDS) (Fletcher et al. 2014; Yin et al. 2021). In recent years, China has proposed the construction of Sponge City (SC), and SC has, therefore, become a new strategy for urban stormwater management in China. SC facilitates the integration of green–gray–blue infrastructure for sustainable urban stormwater management. Traditional urban stormwater management mainly relies on gray infrastructure using stormwater pipes and gutters to transport stormwater out. The limitations of gray infrastructure prevent it from adapting to changing social and ecological systems. Green infrastructure can promote the discharge, storage, and evaporation of stormwater, restore the natural circulation of urban water systems, provide many useful supplements to gray infrastructure, and play a key role in improving the adaptability to climate change (Elmqvist et al. 2015; Leng et al. 2021). Due to the limited storage capacity of green infrastructure, in some heavy rainfall events, green infrastructure cannot completely replace gray infrastructure (Xu et al. 2019). The optimization method of the integrated green–gray (IGG) system has attracted the attention of researchers. The integrated green–gray system should be combined with blue infrastructure to improve a city's stormwater resilience (Alves et al. 2019). Blue infrastructure refers to large wetlands, ponds, or water bodies in cities, which are larger in scale and usually located at the end of catchment areas. They are used to store excess stormwater runoff that stormwater pipes cannot handle, thereby alleviating urban flooding. The integrated green–gray–blue (IGGB) system maintains natural water circulation and improves regional flood drainage capacity through synergy in a spatial layout (Ghofrani et al. 2017). The main features of SC and other new stormwater management approaches are the combination of green infrastructure and gray infrastructure, and the introduction of blue infrastructure to handle stormwater. Therefore, SC is a typical IGGB system (Yin et al. 2021).

Reliability, resilience, and sustainability are considered as three key factors for evaluating the robustness of urban drainage systems (Wang et al. 2022). The IGGB system has the resilience and sustainability of green infrastructure and blue infrastructure and also has the reliability of gray infrastructure for stormwater discharge. The comprehensive evaluation of the IGGB system is the basis for selecting the best option for SC planning and decision-making. Many researches have focused on the hydrology and water quality assessment of the IGGB system. Alves et al.(2019) evaluated several IGGB combinations using an optimization framework and identified the IGGB combination as the best strategy based on reduced costs, reduced flood losses, and enhanced synergies. Bakhshipour et al. (2019) constructed a framework for optimizing urban drainage systems, which evaluated hybrid green–gray–blue infrastructures (HGBGIs) and different degrees of centralization. The results show that the green-blue-gray coupled system can compete economically with pure gray infrastructure, which is also confirmed by the research results of Sun et al. (2020). Leng et al. (2021) evaluated the impact of different types and scales of green infrastructure on regional runoff control and river environment improvement, and the results showed that the IGGB system had a positive effect on runoff control and river environment improvement. Wang et al. (2022) proposed a framework combining hydrology models and optimization algorithms, and the optimization targets are flood risk reduction rate, life cycle cost (LCC), and land occupancy, confirming the synergistic effect between green–gray–blue infrastructures. Yin et al. (2023) constructed a framework combining the evaluation index system and the coupled model, and quantitatively evaluated the flood resistance ability of the IGGB system under the background of climate change and urbanization. In addition to evaluating the IGGB system from a hydrology perspective, the evaluation of the IGGB system can be further supported by social and ecological benefits. Alves et al. (2019) incorporated a monetary analysis of these synergies, such as energy savings from reduced cooling use, improved air quality, and carbon sequestration, into a cost–benefit analysis of flood risk mitigation measures to assess the importance of synergies when determining the best adaptation strategies to improve urban flood risk management. Ruangpan et al. (2021), when constructing a multi-criteria analysis framework, incorporated stakeholders' preferences for assessment criteria and measures into the framework, is important for long-term flood risk reduction and effective water resource management. Dong et al. (2023) incorporated environmental benefits related to greenhouse gas emissions into the evaluation framework of the IGG system, and the results showed that the IGG system can achieve an optimal balance between hydrology and environmental benefits. First, these studies assessed the performance of the IGGB systems under a given proportion of land occupied. However, in urbanized areas with scarce land resources, it is necessary to optimize the spatial layout to grasp the relationship between blue–green infrastructures and different land use types, and to rationally plan the IGGB system according to the scale of infrastructure storage capacity. Second, few existing studies have evaluated the performance of the IGGB systems from the perspective of stormwater storage. The use of only one set of weight values is usually limited in multi-criteria assessment and cannot provide multidimensional support for decision-makers.

Multi-criteria decision analysis (MCDA) is increasingly used in the evaluation of urban stormwater management. Gogate et al. (2017) selected 11 indicators from four criteria, including technology, economy, society, and environment, and evaluated four green infrastructure combinations using the TOPSIS method. Xu et al. (2017) used the TOPSIS method to evaluate scenarios including total runoff, peak discharge, pollutant load, and construction cost, and selected the most cost-effective scenario. Luan et al. (2019) selected indicators such as total runoff control, peak flow reduction, pollutant load, and construction cost, and studied the cost-effectiveness of different green infrastructure combinations (distributed, centralized, and distribution–centralized) in terms of hydrology and economy through the TOPSIS method. In the above MCDA, weight and comprehensive performance are determined under a specific rainfall intensity, and the comprehensive performance of the IGGB system under multiple rainfall intensities is rarely evaluated. When the rainfall intensity changes, the synergies between green, blue, and gray infrastructures and their changing relationships have not been fully discussed. When evaluating the comprehensive performance of an infrastructure, it is necessary to classify and evaluate the optimal performance according to different rainfall intensities. It is still insufficient to discuss the comprehensive performance of the IGG system and the IGGB system under limited rainfall intensities (Leng et al. 2021; Lu et al. 2023). In addition, some research results show the impact of cost weights on the final assessment results, but the relationship between cost weights, hydrology weights, and final results deserves further study.

Therefore, the novelty of this study lies in (1) analyzing the degree of blue–green cooperation in the study area and optimizing the spatial layout of blue–green infrastructure. (2) Evaluating the comprehensive performance of coping with heavy rainfall under the condition that all scenarios maintain the same storage capacity. (3) Assessing synergies between green, blue, and gray infrastructures at different rainfall intensities. (4) Exploring the relationship between hydrology and cost weight and evaluation results under separate and combined layouts of green, blue, and gray infrastructures.

Overall framework

The overall framework of this study is shown in Figure 1. First, an optimization method for the spatial layout of the blue–green infrastructure is proposed. Second, the strategy of the IGGB system is developed, and the criteria and indicators for evaluating the comprehensive performance of the strategy are determined. Then, the TOPSIS method is used to analyze the comprehensive performance of each scenario under the evaluation framework and the synergies between green, blue, and gray infrastructures.
Figure 1

Overall framework.

Figure 1

Overall framework.

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Given the land availability of the city, the spatial layout of blue infrastructure is a key factor that decision-makers must face. In order to effectively control site stormwater runoff and reduce the pressure on downstream drainage pipes, this study selected a stormwater pond as blue infrastructure and established a green–blue synergism model (Zuo et al. 2022). The layout of green infrastructure and blue infrastructure is determined according to the results of green–blue synergism in the study area and combined with the planning land use type.

The study area adopts the stormwater and sewage diversion system and relies on the stormwater pipe along the road for drainage, and the drainage standard of the rainwater pipe network is once a year or less. The storage tank is selected as the gray infrastructure in this study, and it is set at the end of the pipeline to play a role in regulating the peak flow and increasing stormwater storage (Gogate et al. 2017). After the location of drainage infrastructure is determined, according to the requirement of 75% of the annual runoff volume control rate in the study area, the stormwater detention volume of each scenario is 100,000 m3, so the scale of green, gray, and blue infrastructures under the seven scenarios is also determined.

The main advantages of green infrastructure are peak flow reduction and stormwater runoff control, and the annual runoff volume control rate is also the main evaluation indicator of China's SC strategy (Li et al. 2018). Compared with the construction costs of green, gray, and blue infrastructures, the operation and maintenance costs should not be ignored. Although it is advocated to implement green and blue infrastructures as much as possible in urban areas, the cost-effectiveness of the LCC of infrastructure is still the primary consideration of most decision-makers. When cities experience extreme rainfall events, peak flow reduction can help relieve pressure on drainage pipes and reduce pipeline overflows. Therefore, the evaluation objectives of this study are to maximize stormwater storage, maximize peak flow reduction, and minimize LCC. The TOPSIS method is adopted to comprehensively evaluate scenarios under different rainfall intensification.

Study area

The research area is located in the Yantai Economic and Technological Development Area, Yantai City, Shandong Province, China, with a total area of 1,405.8 ha, and the newly added construction land area accounts for 66.46%. Among the planned construction land, green land and square land account for 25.1%. The research area has a good layout foundation of green, gray, and blue infrastructures. Yantai has a continental monsoon climate with an average annual rainfall of 691.5mm, with moderate temperature and humid air. 70% of the city's precipitation is concentrated in the rain events from July to September. The Bajiao River in the study area divides the whole area into two drainage zones, the north and the south: the north drains into the Yellow Sea through the Bajiao River and the south drains into the Yellow Sea. The Digital Elevation Model (DEM) and land use type of the study area are shown in Figure 2, and the data source is shown in Table 1.
Table 1

Data sources of this study

TypeYearResolutionData sources
DEM 2020 30 m Geospatial Data Cloud (http://www.gscloud.cn
Land use (2030) 2022 – Construction and Transportation Bureau of the Yantai Economic and Technological Development Area 
Stormwater pipe network 2022 – Construction and Transportation Bureau of the Yantai Economic and Technological Development Area 
TypeYearResolutionData sources
DEM 2020 30 m Geospatial Data Cloud (http://www.gscloud.cn
Land use (2030) 2022 – Construction and Transportation Bureau of the Yantai Economic and Technological Development Area 
Stormwater pipe network 2022 – Construction and Transportation Bureau of the Yantai Economic and Technological Development Area 
Figure 2

DEM (a) and land use (b) of the study.

Figure 2

DEM (a) and land use (b) of the study.

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Hydrology modeling

Model settings

The stormwater management model (SWMM), first developed in 1971 by the US EPA, has been widely applied to simulate the runoff amount and water quality in urban watersheds, to evaluate different stormwater control strategies, and to provide cost-effective stormwater control solutions. The study area is divided into 100 sub-catchments according to the planned plots, which constitute the framework of the SWMM. The stormwater pipe network is simplified to 31 pipes, 39 pipe nodes, and 16 stormwater discharge outlets. The stormwater pipe network only retains pipe sections with a diameter greater than 200 mm, and the stormwater pipe network sets nodes at the joints of pipe sections with different diameters and road elbows. The sub-catchments, pipes, nodes, and outfalls are shown in Figure 3.
Figure 3

Sub-catchments and pipe network (a), and the location of gray infrastructure (storage tank) (b).

Figure 3

Sub-catchments and pipe network (a), and the location of gray infrastructure (storage tank) (b).

Close modal

The area of the subcatchment is directly extracted by ArcGIS, and the characteristic width refers to the definition in the SWMM user manual. According to the calculation of the flow length, the ratio of the area of the subcatchment to the flow length is the characteristic width. The slope distribution of the subcatchment is uniform, and the average slope is obtained directly from the average slope data in DEM. The percentage of impervious area is closely related to the land use type, and the subcatchment is weighted to obtain the percentage of impervious area according to the runoff coefficients of different land use types included.

Rain gardens and permeable pavement are common green infrastructures, which are an ecologically sustainable stormwater control and rainwater utilization facilities with the characteristic of high efficiency. In the area, rain gardens and permeable pavement are selected as green infrastructure. Stormwater ponds are selected as blue infrastructure and storage tanks are selected as gray infrastructure. The parameters of surface, pavement, soil, storage, drain, and drainage mat of green infrastructure are shown in Table 2. The rainfall hydrograph of the SWMM model adopts the Chicago rainfall pattern, which is combined with the rainstorm intensity formula of Yantai City, and calculates the 2-h rainfall sequence with a return period of 0.3a, 0.5a, 1a, 5a, 10a, and 30a. The rainstorm intensity formula in Yantai is as follows:
formula
(1)
where q represents the design rainstorm intensity [L/(s hm2)], P represents the design return period (years), t represents the duration of rainfall (min), and the scope of application of the formula is 5 min ≤ t ≤ 180 min, P = 1–100 years.
Table 2

Parameter settings of various green infrastructures

LayersParametersRain gardenPermeable pavement
Surface Berm height (mm) 200 
Vegetation volume fraction 0.2 
Surface roughness (Manning's n0.1 0.1 
Surface slope (%) 1.0 1.0 
Pavement Thickness (mm) – 100 
Void ratio (voids/solids) – 0.1 
Impervious surface fraction – 
Permeability (mm/h) – 200 
Clogging factor – 
Regeneration interval (days) – 
Regeneration fraction – 
Soil Thickness (mm) 300 
Porosity (volume fraction) 0.4 0.5 
Field capacity (volume fraction) 0.2 0.2 
Wilting point (volume fraction) 0.1 0.1 
Conductivity (mm/h) 10 0.5 
Conductivity slope 10 10 
Suction head (mm) 90 3.5 
Storage Thickness (mm) 500 300 
Void ratio (voids/solids) 0.75 0.75 
Seepage rate (mm/h) 10 10 
Clogging factor 
Drain Flow coefficient – 
Flow exponent – 0.5 
Offset (mm) – 
Drainage mat Thickness (mm) – – 
Void fraction – – 
Roughness (Manning's n– – 
LayersParametersRain gardenPermeable pavement
Surface Berm height (mm) 200 
Vegetation volume fraction 0.2 
Surface roughness (Manning's n0.1 0.1 
Surface slope (%) 1.0 1.0 
Pavement Thickness (mm) – 100 
Void ratio (voids/solids) – 0.1 
Impervious surface fraction – 
Permeability (mm/h) – 200 
Clogging factor – 
Regeneration interval (days) – 
Regeneration fraction – 
Soil Thickness (mm) 300 
Porosity (volume fraction) 0.4 0.5 
Field capacity (volume fraction) 0.2 0.2 
Wilting point (volume fraction) 0.1 0.1 
Conductivity (mm/h) 10 0.5 
Conductivity slope 10 10 
Suction head (mm) 90 3.5 
Storage Thickness (mm) 500 300 
Void ratio (voids/solids) 0.75 0.75 
Seepage rate (mm/h) 10 10 
Clogging factor 
Drain Flow coefficient – 
Flow exponent – 0.5 
Offset (mm) – 
Drainage mat Thickness (mm) – – 
Void fraction – – 
Roughness (Manning's n– – 
Table 3

Scenarios for comparison

ScenariosTypeDescription
S1 Green strategy 20% Rain garden + 50% permeable pavement 
S2 Blue strategy 3%/5% stormwater pond (total area of stormwater pond: 7.91 ha) 
S3 Gray strategy Storage tank 
S4 Green–blue strategy 10% Rain garden + 50% permeable pavement + 1.5% stormwater pond (total area of stormwater pond: 4.03 ha) 
S5 Green–gray strategy 10% Rain garden + 50% permeable pavement + storage tank 
S6 Gray–blue strategy 1.5% Stormwater pond (total area of stormwater pond: 4.03 ha) + storage reservoir 
S7 Green–gray–blue strategy 5% Rain garden + 50% permeable pavement + 1.5% stormwater pond (total area of stormwater pond: 4.03 ha) + storage tank 
ScenariosTypeDescription
S1 Green strategy 20% Rain garden + 50% permeable pavement 
S2 Blue strategy 3%/5% stormwater pond (total area of stormwater pond: 7.91 ha) 
S3 Gray strategy Storage tank 
S4 Green–blue strategy 10% Rain garden + 50% permeable pavement + 1.5% stormwater pond (total area of stormwater pond: 4.03 ha) 
S5 Green–gray strategy 10% Rain garden + 50% permeable pavement + storage tank 
S6 Gray–blue strategy 1.5% Stormwater pond (total area of stormwater pond: 4.03 ha) + storage reservoir 
S7 Green–gray–blue strategy 5% Rain garden + 50% permeable pavement + 1.5% stormwater pond (total area of stormwater pond: 4.03 ha) + storage tank 

Model calibration

This study refers to the parameter calibration method of an urban stormwater model based on a runoff coefficient (Liu 2009; Xu & Gao 2022). The larger value of the reference runoff coefficient is selected for calculation. Finally, the comprehensive runoff coefficient of the area is obtained as 0.36. After establishing the model of the site, the rainfall of 38.89 mm under the 1-year return period rainfall scenario is used for simulation verification. The runoff coefficient of the site is 0.36, which verified the rationality of the model parameters set in this study.

Green–blue space layout optimization method

‘Green–blue synergy’ refers to the synergy relationship between rainwater runoff and green space after the layout coupling. Excessive surface runoff can be absorbed by wetlands and the impact of flooding can be effectively alleviated by retaining surface runoff (Myers & Pezzaniti 2019; Wu et al. 2023). Green–blue space layout optimization means that green infrastructure and blue infrastructure are located in locations with high green–blue synergy, which can effectively absorb runoff (Zuo et al. 2022).

According to the green–blue synergy degree model, the analysis on the green–blue space synergy effect of the research area is carried out. First, the planned land use type of the research area is analyzed to obtain the green area ratio. Second, ArcGIS10.2 is used to analyze the hydrology of DEM to obtain the surface flow data, and after the surface flow data are normalized, statistics are made on the plots of the research area. On the basis of referring to and combining with the reality of this research, the green–blue space coupling degree model is constructed, and the calculation formula is as follows (Jin et al. 2019):
formula
(2)
where C is the coupling degree between the functions of the blue–green space, and the value range is [0,1]. The value of C is determined by the coupling degree of the blue–green space; the larger the value, the stronger the interaction between the blue space and the green space.
Although the degree of coupling can reflect the degree of interaction in the green–blue space, it cannot represent the level of synergism between the two systems in the blue–green space. Therefore, this study constructs a blue–green synergism model to represent the degree of blue–green synergism. The specific calculation formula is as follows (Jin et al. 2019):
formula
(3)
formula
(4)
where C is the coupling degree of the blue–green space system; D is the synergy degree of the green–blue space system; T is a function of the normalized values of the coupling factors U1 and U2; U1 and U2 are the area ratios of water bodies and vegetation green land in the site; and a and b are adjustment coefficients; this study adopts the composite perspective of the blue–green space, a = b = 0.5.

Based on the analysis of the blue–green coupling index D, combined with land use types, the arrangement of green infrastructure and blue infrastructure in locations with a high degree of synergism can effectively play the role of absorbing stormwater runoff and reducing peak flow.

Indicator determination

Making reasonable standards is the key to MCDA (Gogate et al. 2017). In the stormwater management strategy, peak flow reduction for large rainfall events and runoff volume control for small and medium rainfall events are the main objectives (MOHURD 2018). The LCC not only considers the investment cost but also includes various costs incurred during the life cycle. Taking the time of completion and delivery as the division time point, the LCC can be divided into construction cost and operation cost (including demolition) according to the cost generation time (Gluch & Baumann 2004; Cao & Dong 2012). This study chooses stormwater storage, peak flow reduction rate, and LCC as the analysis criteria.

Determination of the scenarios for comparison

According to the SC planning of the Yantai Economic and Technological Development Area, this study developed seven scenarios including green, blue, gray, green–blue, green–gray, gray–blue, and green–gray–blue. The scenarios are shown in Table 3. In accordance with the requirement of a 75% control rate of total annual runoff in the Yantai Economic Development Area, the stormwater detention volume of each scenario is determined to be 100,000 m3. According to the proportion of permeable pavement and rain garden in the study area, the calculated area of rain garden, permeable pavement, and rain pond is as follows:
formula
(5)
formula
(6)
where Arain garden, Asubcatchment area, and Apermeable pavement represent the rain garden area, subcatchment area, and permeable pavement area, respectively; Rgreen, Rrain garden, Rroad, and Rpermeable pavement represent the green area ratio, rain garden ratio, road and square ratio, and permeable pavement ratio, respectively.
  • (1)

    Scenario 1 (green strategy): Rain garden provided for 20% green space in the sub-catchments remained + permeable pavement provided for 50% road area in the sub-catchments remained;

  • (2)

    Scenario 2 (blue strategy): Stormwater pond provided for 3%/5% green space in the sub-catchments remained (total area of the stormwater pond: 7.91 ha);

  • (3)

    Scenario 3 (gray strategy): storage tank;

  • (4)

    Scenario 4 (green–blue strategy): rain garden provided for 10% green space in the sub-catchments remained + permeable pavement provided for 50% road area in the sub-catchments remained + stormwater pond provided for 1.5% green space in the sub-catchments remained (total area of the stormwater pond: 4.03 ha);

  • (5)

    Scenario 5 (green–gray strategy): rain garden provided for 10% green space in the sub-catchments remained + permeable pavement provided for 50% road area in the sub-catchments remained + storage tank;

  • (6)

    Scenario 6 (gray–blue strategy): stormwater pond provided for 1.5% green space in the sub-catchments remained (total area of the stormwater pond: 4.03 ha) + storage tank;

  • (7)

    Scenario 7 (green–gray–blue strategy): rain garden provided for 5% green space in the sub-catchments remained + permeable pavement provided for 50% road area in the sub-catchments remained + stormwater pond provided for 1.5% green space in the sub-catchments remained (total area of the stormwater pond: 4.03 ha) + storage reservoir.

Life cycle cost calculation

The LCC is calculated by formula (7):
formula
(7)
where LCC(A) represents the LCC of scenario A, LCCGreen(A) represents the LCC of green infrastructure under scenario A, LCCBlue(A) represents the LCC of blue infrastructure under scenario A, and LCCGray(A) represents the LCC of gray infrastructure under scenario A.
The LCC of green infrastructure, blue infrastructure, and gray infrastructure is calculated by the following formula:
formula
(8)
where ULCCi represents the LCC per unit area of i green infrastructure, Aij represents the area occupied by i green infrastructure in the j plot; ULCCSt represents the LCC per unit volume of the storage tank, Vk represents the storage tank with a volume of k; ULCCp represents the LCC per unit area of the stormwater pond, and Ap represents the area of the stormwater pond.
This study considers all costs related to the implementation of the project from a systematic point of view. The calculation methods for the calculation of LCC and ULCC refer to Wang et al. (2022) and Mei et al. (2018). Rain garden and permeable pavement are incorporated into the existing and prescribed infrastructure, and land costs incurred by green infrastructure are not considered. LCC and ULCC are calculated using the following formula:
formula
(9)
where ICi represents the initial construction cost of i green infrastructure, fr,t is the present value coefficient of the discount rate r in year t, and ICO&Mt is the operation and maintenance cost in year t, fr,n is the present value coefficient of the discount rate r at the end of the design life of n years, and SVn is the residual value (SV) of i green infrastructure at the end of the n year of the design life.
The SV of green infrastructure is calculated using the following formula:
formula
(10)
where i represents the interval between the last year of facility operation and maintenance, and the design life of the facility. In this study, i is taken as 1; P represents the ratio of operation and maintenance costs to construction costs.
fr, t and fr, n are calculated using the following formula, where r is set to 0.05 (Liao et al. 2014):
formula
(11)

The construction cost of different infrastructures is referenced (MOHURD 2018), and the LCC of the selected infrastructures is shown in Table 4.

Table 4

Parameter settings of various green infrastructure

Device typeSpecificationUnit construction cost (CNY)PLife cycle (years)Discount rateULCC (CNY)
Rain garden 1 m2 450 0.05 20 0.05 946.70 
Permeable pavement 1 m2 150 0.02 0.05 1,120.55 
Stormwater pond 1 m2 300 0.05 20 0.05 995.05 
Storage reservoir 1 m3 1,500 0.05 20 0.05 2,794.37 
Device typeSpecificationUnit construction cost (CNY)PLife cycle (years)Discount rateULCC (CNY)
Rain garden 1 m2 450 0.05 20 0.05 946.70 
Permeable pavement 1 m2 150 0.02 0.05 1,120.55 
Stormwater pond 1 m2 300 0.05 20 0.05 995.05 
Storage reservoir 1 m3 1,500 0.05 20 0.05 2,794.37 

TOPSIS assessment method

MCDA encompasses various evaluation methods such as the analytic hierarchy process, analytical network process, and Multi-Attribute Utility Theory (Huang et al. 2011). TOPSIS, as a MCDA, has been widely used in the past few decades and has been significantly developed in the field of water environment (Huang et al. 2011; Behzadian et al. 2012; Zavadskas et al. 2016; Gogate et al. 2017; Zyoud & Fuchs-Hanusch 2017; Luan et al. 2019). For urban flood protection, some scholars have created a decision-making tool based on the MCDM method, and TOPSIS has been used to comprehensively rank alternatives to select the best option for flood resistance (Mishra & Satapathy 2020). Compared with other evaluation methods, the TOPSIS method makes full use of attribute information, provides a comprehensive ranking of scenarios, does not require attribute preferences to be independent, is easy to calculate, and is easy to use. Therefore, TOPSIS is chosen to evaluate the programs based on this study (Huang et al. 2011). The TOPSIS model is a ‘ranking method for approaching ideal solutions.’ By defining a measure in the target space and calculating the degree of the target approaching/deviating from the positive and negative ideal solutions, the comprehensive ranking of the scenario can be evaluated, and the dynamics and changing trends of the comprehensive target in the research area can be fully and objectively reflected (Li et al. 2013; Lei & Qiu 2016).

Indicator weight is a key issue in TOPSIS evaluation. The focus of this study is to evaluate the effects of stormwater storage weight, peak flow reduction weight, and LCC weight on the outcome of scenario decisions under different rainfall return periods. The weight is set as follows: for example, when studying the impact of LCC weight (Wc) on scenarios ranking, the cost weight is assigned a value ranging from 0 to 1, and equal weight ((1 − Wc)/3) is assigned to the remaining two hydrology indicators, the stormwater storage and the peak flow reduction.

Green–blue infrastructure layout optimization

According to the technical route, the green–blue synergism degree is calculated for the study area, and the green–blue synergism index D is classified by the natural break point classification method. As shown in Figure 4(a), statistical analysis shows that plots with extremely high degrees of green–blue synergism account for 12.64%, those with high degrees of green–blue synergism account for 32.33%, those with medium degrees of green–blue synergism account for 15.45%, those with low degrees of green–blue synergism account for 26.05%, and those with extremely low degrees of green–blue synergism account for 13.52%. According to the planned land use type of the study area, green infrastructure is arranged in each subcatchment area, and the location of blue infrastructure is determined by combining the green–blue synergism degree analysis diagram (Figure 4(b)).
Figure 4

The result of green–blue synergism (a), and the location of the subcatchment where the stormwater pond is located (b).

Figure 4

The result of green–blue synergism (a), and the location of the subcatchment where the stormwater pond is located (b).

Close modal

Effects of stormwater management under different scenarios

In order to analyze the stormwater storage and peak flow reduction in SC construction, this study uses hydrology models to simulate the performance of each scenario and evaluate the performance effect of 2 h designed rainfall events. Due to the different rainfall return periods, there are significant differences in the performance of various rainfall events. In order to assess the drainage system capacity in the face of extreme rainfall events, it is necessary to consider multiple rainfall intensities for analysis. In this study, 0.3a, 0.5a, 1a, 5a, 10a, and 30a are used for simulation, and then, the simulation results are obtained for further analysis.

Stormwater storage

Final stormwater storage is expressed as the amount of storage that is eventually collected and retained on the surface under each scenario. The rain garden's final stormwater storage includes the storage volume and the surface volume. The final stormwater storage of permeable pavement includes the storage volume and the pavement volume. The final storage of the stormwater pond and the stormwater storage tank is the collection and detention volumes. Figure 5(a) compares stormwater storage in different scenarios under different rainfall return periods. As can be seen from Figure 5(a), the green strategy has the best stormwater storage performance, and with the increase in rainfall intensity, the advantage of the green strategy becomes more obvious. The second-best performance is better than the green–gray strategy and the green–blue strategy. When the rainfall return period is 0.3a, 0.5a, 1a, 5a, and 10a, the green–gray strategy is better than green–blue, and when the rainfall return period is 30a, the green–blue strategy is better than green–gray.
Figure 5

Stormwater storage of scenarios (a) and percent reduction of the peak flow of scenarios (b) under different return periods.

Figure 5

Stormwater storage of scenarios (a) and percent reduction of the peak flow of scenarios (b) under different return periods.

Close modal

First of all, these results show that the green strategy has a good stormwater storage effect, and the performance of the green strategy is significantly better than that of the blue strategy and the gray strategy. The difference in effect is mainly due to spatial arrangement. When the storage capacity of a strategy is the same, the effect of the dispersed arrangement of water storage infrastructures is better than that of the centralized arrangement. The higher the rainfall return period, the more obvious the difference. Second, gray facility and blue facility have a certain upper limit of water storage due to their centralized arrangement. When the rainfall intensity exceeds the upper limit and keeps increasing, the change in the water storage volume of gray infrastructure and blue infrastructure no longer increases, which can be seen in Figure 5(a). In terms of stormwater storage, the IGGB system does not show significant synergies.

Reduction of peak flow

The reduction rates of peak flow in different scenarios under different rainfall return periods are shown in Figure 5(b). The value corresponding to each scenario represents the percentage reduction in the peak traffic reduction of the corresponding scenario compared to the undeveloped state, and the higher the percentage, the better the effect. The simulation results show that the green–gray–blue strategy has the best reduction of peak flow performance. Under the rainfall return periods of 0.3a, 0.5a, 1a, 5a, 10a, and 30a, the reduction rates of the peak flow of the green–gray–blue strategy are 76.73, 76.21, 76.37, 70.78, 62.84, and 58.72%, respectively. Besides the green–gray–blue strategy, the effect of green–gray strategy is also very significant, and the reduction rates of the peak flow under six rainfall return periods are 75.48, 75.33, 75.91, 66.04, 58.74, and 54.28%, respectively. On the other hand, the reduction of the peak flow effects of the blue strategy and the gray strategy are limited. Under the six rainfall return periods, the peak flow reduction rates of the blue strategy are 15.60, 17.66, 19.43, 14.15, 14.54, and 15.49%, respectively.

On top of that, there are significant synergies among green, blue, and gray infrastructures. The peak flow reduction rates of the green–blue strategy under six rainfall return periods are 61.30, 63.06, 66.23, 55.41, 44.34, and 36.11%, and the performance under conditions of 0.3a, 0.5a, and 1a is better than that of the green strategy and the blue strategy. The peak flow reduction rates of the green–gray strategy under six rainfall return periods are 75.48, 75.33, 75.91, 66.04, 58.74, and 54.28%, respectively, which are better than those of the green strategy and the gray strategy. The peak flow reduction rates of the gray–blue strategy under six rainfall return periods are 45.53, 45.43, 49.39, 46.47, 46.46, and 47.06%, respectively, which are also better than that of the blue strategy and the gray strategy. Therefore, the green–gray–blue strategy in this study has a synergistic effect in terms of peak flow reduction.

LCC and benefit evaluation and analysis

Considering construction costs, operation maintenance costs, and damage costs, the LCC of the seven scenarios from low to high is followed by blue strategy, gray–blue strategy, gray strategy, green–gray–blue strategy, green–blue strategy, green–gray strategy, and green strategy.

The cost-effectiveness values of the scenario for stormwater storage and the reduction of peak flow in different rainfall return periods are shown in Figures 6 and 7 and can be obtained when (i) the blue strategy has the lowest LCC of stormwater storage, ranging from 817.77 to 6,389.83 CNY/m3, and the gray–blue strategy has the lowest cost of peak flow reduction, ranging from 1,844.88 to 5,192.73 CNY/(m3/s). (ii) When the rainfall return period is 0.3a, 0.5a, 10a, and 30a, and the LCC of green–gray–blue stormwater storage is the highest; when the rainfall return period is 1a, the LCC of green–blue strategy is the highest; when the rainfall return period is 5a, the LCC of green strategy is the highest. The green strategy has the highest peak flow reduction cost, with the cost ranging from 1,670.86 to 12,119.81 CNY/(m3/s). (iii) The LCC of peak flow reduction is lower for the gray–blue strategy compared with the blue strategy and the gray strategy, and the gray–blue strategy performs better than blue and gray strategies under the six rainfall return periods. As the rainfall intensity increases, the LCC of stormwater storage and peak flow reduction for each scenario decreases. This shows that the proper arrangement of green infrastructure, blue infrastructure, and gray infrastructure in terms of space and scale is critical to responding to urban heavy rainfall events and reducing LCC.
Figure 6

Cost of the stormwater storage of different scenarios under return periods.

Figure 6

Cost of the stormwater storage of different scenarios under return periods.

Close modal
Figure 7

Cost of the peak flow reduction of different scenarios under return periods.

Figure 7

Cost of the peak flow reduction of different scenarios under return periods.

Close modal

TOPSIS method to evaluate the results

The TOPSIS method is used to make a comprehensive ranking of seven scenarios. After data standardization and normalization, index weights such as stormwater storage, peak flow reduction, and LCC are established, and the comprehensive evaluation results of seven scenarios are obtained. Figure 8 plots the relationship between the comprehensive ranking of seven scenarios and the change of index weights under different rainfall return periods.
Figure 8

Relations between weight and TOPSIS scores of various scenarios.

Figure 8

Relations between weight and TOPSIS scores of various scenarios.

Close modal

It can be seen from the overall trend of comprehensive scores and stormwater storage weights that (i) the scores of green, green–blue, and green–gray strategy continuously increase with the increase in weight values, while the scores of blue, gray, gray–blue, and green–gray–blue strategies continuously decrease. This indicates that green, green–blue, and green–gray strategies are positive scenarios for stormwater storage, and blue, gray, gray–blue, and green–gray–blue strategies are negative scenarios for stormwater storage. (ii) The gray–blue strategy performs better when the weight value is between 0 and 0.35, and the green strategy performs the best when the weight value is between 0.35 and 1. (iii) The performance of the green–gray–blue strategy is the most stable, with a composite score between 0.46 and 0.55, which is a stable scenario.

It can be seen from the overall trend of comprehensive scores and the reduction of peak flow weights that (i) the scores of green, green–gray, green–blue, and green–gray–blue strategies continue to increase with the increase in weight values, while the scores of blue, gray, and gray–blue strategies continue to decrease. This indicates that green, green–gray, green–blue, and green–gray–blue strategies are positive scenarios for the reduction in peak flow, and blue, gray, and gray–blue strategies are negative scenarios for the reduction in peak flow. (ii) When the rainfall return periods are 0.3a and 0.5a, the gray–blue strategy performs the best, and the weight value is 0–0.43. Green–gray and green–gray–blue strategies perform better when the weight value is 0.43–1. When the rainfall return period is 1a, the gray strategy performs the best with weight values between 0 and 0.12, the gray–blue strategy with weight values between 0.12 and 0.37, and the green–gray–blue strategy with weight values between 0.37 and 1. When rainfall return periods are 5a and 10a, the gray–blue strategy performs the best with weight values 0–0.32, and the green–gray–blue strategy performs the best with weight values 0.32–1. When the rainfall return period is 30a, the advantage of the green–gray–blue strategy is obvious, and the comprehensive effect is the best.

It can be seen from the overall trend of comprehensive scores and LCC weights that (i) the scores of gray–blue strategy, gray strategy, and blue strategy continue to increase with the increase in weight values, while the scores of green–gray–blue strategy, green–gray strategy, green–blue strategy, and green strategy continue to decrease, which indicates that blue strategy, gray strategy, and gray–blue strategy are cost-type weights, and green–gray–blue strategy, green–gray strategy, green–blue strategy, and the green strategy are effective-type weights. (ii) When the rainfall return period is 0.3a and 0.5a, the green–gray strategy is the best scenario with weight values between 0 and 0.28, and the gray–blue strategy is the best scenario with weight values between 0.28 and 1. When the rainfall return period is 1a, the green–gray strategy is the best scenario with weight values between 0 and 0.21, the green–gray–blue strategy is the best scenario with weight values between 0.21 and 0.31, and the gray–blue strategy is the best scenario with weight values between 0.31 and 1. When the rainfall return period is 5a, the performance of the green–gray–blue strategy is the best when the weight value is less than 0.17, and the performance of the gray–blue strategy is the best when the weight value is greater than 0.17. When the rainfall return periods are 10a and 30a, the performance of the green–gray–blue strategy is the best when the weight value is less than 0.33, and the performance of the gray–blue strategy is the best when the weight value is more than 0.33.

In summary, under the six rainfall return periods, green–gray–blue and green–gray strategies are the best choices. The results show that the green–gray–blue strategy has a strong synergistic effect. As the urban green–gray–blue scale continues to expand, the system is extremely resilient in dealing with heavy rainfalls.

Based on the multi-rainfall intensity as the research perspective

In this study, an evaluation model including stormwater storage, peak flow reduction, and LCC is established. Through the case analysis of the study area, it is proved that the IGGB system has an obvious synergistic effect. Green infrastructure contributes to the connectivity of the blue–green space (Malekmohammadi & Jahanishakib 2017), blue infrastructure can collect stormwater within the catchment zone, and gray infrastructure arranged at the pipe outlet can play a role in regulating the peak flow. The IGGB system can effectively cope with short-duration heavy rainfall and alleviate urban waterlogging. However, the results of stormwater storage show that the performance of the IGGB system is not outstanding. Green infrastructure has a good rainwater storage effect, which can maximize the efficiency of stormwater storage. These conclusions have been verified by earlier studies (WWAP/UN-Water 2018; Yin et al. 2023). Rain garden has a good function of storing stormwater, which is confirmed by the study of Zhang et al. (2019). In addition to the stormwater detention volume, the final storage of the rain garden also includes the surface volume, which is about 2.2 times the stormwater detention volume. Moreover, when determining the stormwater detention volume of each scenario, permeable pavement does not have a detention volume, but its pavement volume and storage volume are large. Therefore, with the increase in rainfall intensity, the final storage of green infrastructure gradually increases. Blue infrastructures are located at the end of the stormwater collection area and rely on the vertical design of the surrounding road site to collect stormwater. Under the condition of green–blue, stormwater is first absorbed into green infrastructure for consumption and storage, while blue infrastructure, as a receiving stormwater pond, cannot quickly collect stormwater under short-duration heavy rainfall. The stormwater storage effect of the blue strategy is not as good as that of the green strategy, and the final storage volume of the green strategy is about 3–6 times that of the blue strategy. Gray infrastructure is located at the outlet of the pipeline, and its main function is to adjust the peak flow. When the amount of stormwater stored reaches a certain level, stormwater discharges into the river through the orifice, and so, the amount of stormwater stored by gray infrastructure has a certain limit. The stormwater storage effect of the green strategy is better than that of the gray strategy, and the final storage volume is about 5–6 times than that of the gray strategy.

It is worth noting that while the green strategy performs well in terms of stormwater storage and peak flow reduction, blue strategy and gray strategy are more advantageous in terms of the LCC of stormwater storage and peak flow reduction. The LCC weight has an important impact on the decision-making process. Considering the impact of future urbanization and climate change, the study of the IGGB system should be considered to optimize under the condition of heavy rainfall. Taking 30a of the rainfall return period as an example, if LCC weight is the main factor (0.25–0.33), the green–gray–blue strategy is the best. Therefore, in the planning of future urban land use, a balanced layout combining green, blue, and gray infrastructures should be considered. The purpose of this research framework is to enable decision-makers to make full use of urban land resources in urban planning, coordinate the regulation volume of stormwater infrastructures with the LCC, and pay attention to the scale and space matchings of green, blue, and gray infrastructures.

Since the hydrology indicators selected in this study are only peak flow reduction and stormwater storage, water quality improvement is not included in them, and land cost is very critical to the cost-effectiveness of the LCC, more indicators should be considered in the future analysis of cost and hydrology weights, so as to further improve the assessment of the ecological environment and social impact.

Improve the effectiveness of green–gray–blue infrastructure by optimizing the spatial layout

Some studies have explored the hydrology and configuration parameters of the IGGB system to determine the best choice for extreme rainfall, but few studies have focused on the spatial arrangement of blue infrastructure (Alves et al. 2020; Wang et al. 2022). In our study, the concept of blue–green synergism degree is applied. The newly added blue infrastructure in the study is located at a position with a high blue–green synergism degree, so as to achieve the purpose of effectively absorbing stormwater runoff and improving the application effect of blue infrastructure in coping with urban floods. The optimization of blue and green space layout has a positive effect on stormwater runoff and storage. This method can provide guidance for the layout of green and blue infrastructures in the early planning stage and provide a new solution for the problem of flood disaster and stormwater storage caused by frequent heavy rainfall in cities.

In the natural state of low artificial interference, plants often thrive rich in water, and blue–green infrastructure is usually naturally coupled and synergistic. The research area is located in the urban economic development zone, which belongs to the urban–rural interlace zone. The area with a low blue–green synergism degree accounts for 39.57%, and the blue–green synergism degree is generally low, indicating that the distribution of surface runoff and its interaction with green space have not been fully integrated into the urban planning policy. In a new development area of the city, future planning and design should avoid destroying the original ecological functions as much as possible, and green space planning should consider the blue–green synergism degree, which is essentially the practice of integrating the principle of urban hydrology, green space distribution, and interaction into urban planning.

However, the blue–green synergism degree model in this study is based on the plots in the study area, ignoring the distinction between the areas with high green land rates and the areas with low green land rates. Terrain slope, rainfall intensity, and the location of traffic dump points are also important for optimizing the spatial layout of blue infrastructure (Zuo et al. 2022). In a large degree of built areas, less stormwater runoff can also be achieved through phased planning schemes (Galen et al. 2022). In the future, when evaluating the blue–green space layout in areas with a large degree of built area, the key issues behind urban green space planning based on the blue–green synergism principle lie in how to control the spatial scale of blue–green synergism degree division, how to evaluate the blue–green synergism degree in areas with a large intensity of urban development, and how to coordinate urban development infrastructure needs with ecological conservation needs.

Taking a new development district in China as the research object, this study establishes a framework combining the urban hydrology model and data statistics under the multi-objective criteria. While ensuring the consistency of the adjusted volume of each scenario, the performance of green infrastructure, blue infrastructure, and gray infrastructure under a single arrangement scenario is evaluated, and the conclusions can be drawn as follows.

The blue–green synergism degree can reflect the capacity of a subcatchment to absorb runoff. The subcatchment with a high blue–green synergism degree has a strong capacity, while the subcatchment with a low blue–green synergism degree has an insufficient capacity of green space to absorb runoff. The layout of blue infrastructure (rain pond) can be determined by the results of the blue–green synergism degree and land type classification, and the layout of blue infrastructure in the area with a high blue–green synergism degree can produce the best benefits.

In terms of stormwater storage, the green strategy has the best performance. In terms of peak flow reduction, the green–gray–blue strategy performs the best. TOPSIS evaluation shows that blue strategy, gray strategy, and gray–blue strategy are cost-type scenarios, green–gray–blue strategy, green–gray strategy, green–blue strategy, and green strategy are effective-type scenarios. The IGGB system shows significant synergism effects. With the increase in rainfall intensity, the synergism effect of the IGGB system becomes more and more significant.

We would like to thank the Construction and Transportation Bureau of the Yantai Economic and Development Area for their support.

This work was funded by the National Key R&D Program of China (Grant No. 2022YFC3800500), the 2023 Shandong Province Housing and Urban Rural Construction Science and Technology Plan Project (Research on waterlogging risk threshold technology for the underground space of Yantai City) and the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions (BPHR20220108).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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