Modelling the Effects of Green Infrastructures on Water Quantity

Under Different Rainfall Characteristics

Qian Yu

1,2

, Xiaohe Du

1,2

, Na Li

1,2

, Yuting Meng

and Jing Wang

1,2

China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, Beijing, 100038, China

PowerChina Zhongnan Engineering Corporation Limited, Changsha, 410014, China

Keywords: Green Infrastructures, Water Quantity Control, Rainfall Characteristics, Pluvial Flooding.

Abstract: Under the dual impacts of climate change and rapid urbanization, urban pluvial flooding disasters in China

are increasingly serious, which cause huge economic losses and even serious casualties. Green infrastructure

(GI), a kind of resilient measure, can control rainfall runoffs and improve water quality. Modelling the effects

of GIs on controlling stormwater runoff under different rainfall characteristics plays an important role in

planning and designing GIs that are adapted to both local conditions and future climate change. In this paper,

we set nine rainfall scenarios with varying rainfall characteristics (intensity-duration-frequency, IDF) and

then study the effects of the combined GIs on water quantity in the Jinan pilot area. The results show that GIs

have good control effects on the inundation areas and runoff coefficients under rainfalls with small return

periods. With the increases in return periods and rainfall intensities, the control effects of GIs on inundation

areas decrease significantly. However, with the increases in rainfall intensities, the control effects on runoff

coefficients are not that obvious. In addition, rainfall duration variations have little impact on reducing rates

of controlling inundation areas and runoff coefficients.

1 INTRODUCTION

In the past few decades, China's urbanization

construction has developed rapidly. By the end of

2020, the urbanization rate in China has increased

from 17.9% in 1978 in the early stage of reform and

opening up to more than 60% (NBS, 2021), followed

by the increases in urban densities, changes in land

use and increases of the rate of surface

impermeability. However, the development of urban

flood control and drainage systems lags behind the

rate of urbanization, and rivers and lakes lose their

ability to regulate and store water. In addition, short-

term heavy rainfall events occur more frequently with

climate change (Min et al., 2011). Therefore, under

the dual pressures of urbanization developments and

climate change, the urban flooding problems in China

are increasingly severe. According to the China

Flood and Drought Disaster Bulletin, since 2008, an

average of 158 cities in China have experienced

fluvial or pluvial flooding, most of which is caused

by heavy rainfalls.

To simultaneously alleviate the urban flooding

problems and solve water environmental and

ecological problems, Sponge City Construction (SCC)

has been put forward in China since 2013 (Li et al.,

2017). A total of 30 national pilot cities have been

chosen to construct sponge cities. The core concept of

a sponge city is low impact development (LID) or in

other words green infrastructure (GI). LID/GI is a

kind of resilient practice, including grass swales,

bioretentions, green roofs, vegetated filter strips, etc.,

to reduce negative impacts caused by urbanization

(Ghodsi et al., 2016). GIs are designed to capture,

hold, and permeate urban runoff (Elliott et al., 2007),

and improve water quality. Previous studies have

revealed that the GIs are effective in controlling water

quantity under small to medium rainfalls (ATKINS,

2015; Yu et al., 2021). However, research on effects

of GIs on water quantity under different rainfall

characteristics, e.g., rainfall intensities and durations,

are rarely studied. According to the latest report

published by IPCC (IPCC, 2021), the frequency and

intensity of heavy precipitation events have increased

over the most land area since the 1950s. In recent

years, several heavy rainfall events have caused

serious economic loss and casualties, such as the

“7.21” storm in Beijing, in 2012, and the “7.20” storm

222

Yu, Q., Du, X., Li, N., Meng, Y. and Wang, J.

Modelling the Effects of Green Infrastructures on Water Quantity Under Different Rainfall Characteristics.

DOI: 10.5220/0011999400003536

In Proceedings of the 3rd Inter national Symposium on Water, Ecology and Environment (ISWEE 2022), pages 222-228

ISBN: 978-989-758-639-2; ISSN: 2975-9439

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

in Zhengzhou, in 2021. Hence, it is necessary to study

the effects of GI on water quantity under different

rainfall characteristics (intensity-duration-frequency,

IDF), which will provide more practical experiences

and technical support for the SCC under future

climate changes.

Given the above considerations, the present study

employed the Flood Risk Analysis Software (FRAS),

independently developed by the China Institute of

Water Resources and Hydropower Research, to

model and analyse the effects of GIs on water

quantity under different rainfall characteristics.

2 STUDY AREA

Jinan, one of the first batch of pilot SCC cities, is

located in the east of China with an average annual

rainfall of 672.8mm. Our study area is located in the

main urban areas of Jinan with a total area of about

39km

. The overall terrain of the study area is higher

in the south, lower in the north, higher in the east, and

lower in the west (see Figure 1 left). Rainstorms in

the summer result in serious flood disasters. The

eastern and southern regions are mountainous areas,

and the central area is the piedmont slope. There are

four small watersheds within the study area, including

Guangchangxigou, Guangchangdonggou, Xingji

River, and Shiliuli River (see Figure 1 right).

According to the Implementation Plan of Sponge

City Construction Pilot Project in Jinan, Shandong

Province (2015-2017) (hereinafter referred to as the

implementation plan). Five kinds of GIs are designed

within the study area: green roofs (0.26km

), sunken

greenbelt (0.31km

), retained greenbelt (1.32km

intensified infiltration greenbelt (0.46km

), and

permeable pavement (0.18km

) (see Figure 1 right).

Figure 1: Topography (left) and distributions of GIs (right).

3 DATA AND METHODS

3.1 Data

The following basic geographic data is used for

scenario modelling: 1:2000 DLG data and 1:1000000

soil data (1995). DLG data includes roads, residential

areas, and land use data. The aforementioned data are

adopting CGCS2000 coordinate system, Gauss

Kruger projection, and the coordinate unit is a meter.

The 1985 national elevation datum is adopted.

In addition, the following hydrological and

meteorological data are also collected: the river

systems and the main cross-section data, the

monitoring precipitation data at Guishan, Xinglong,

and other precipitation stations and hydrological

stations, and the design storm data as well, including

5-year return period, 10-year return period and 20-

year return period. Furthermore, data on drainage

systems are also collected. At present, the drainage

Modelling the Effects of Green Infrastructures on Water Quantity Under Different Rainfall Characteristics

223

pipe network within the study area is designed

according to the standard of 2-year or 3-year return

period.

3.2 Rainfall Scenarios

3.2.1 Rainfall Frequency

In this study, the return period is used to represent the

frequency. Three rainfall frequency scenarios (see

Figure 2) based on typical rainfall processes are set

for modelling: 5-year return period, 10-year return

period and 20-year return period.

Figure 2: The 24h design rainfall process under three different rainfall frequency scenarios.

3.2.2 Rainfall Duration

According to the actual conditions of Jinan city, the

inundation within the study area after rainfall events

is usually drained within 3~6h. Based on the usual 5-

year, 24-hour rainfall process in Jinan, we design

three possible scenarios for rainfall duration. As the

rainfall-runoff at the early stage (2~7h) is usually

drained within the first 12h (see Figure 2). Therefore,

we selected the last 12h continuous rainfall process

with a rainfall volume of 96.84 mm (see Figure 2).

The three rainfall duration scenarios are shown in

Figure 3.

Figure 3: 24h design rainfall process under 3 different rainfall duration scenarios.

3.2.3 Rainfall Intensity

Based on the short-duration rainstorm intensity

formula in Jinan (see equation 1), the design

rainstorm intensity with different return periods is

calculated.

123456789101112131415161718192021222324

Rainfall volume (mm)

Time (h)

5 year 10 year 20 year

123456789101112131415161718

Rianfall volume (mm)

Time (h)

12h 6h 18h

ISWEE 2022 - International Symposium on Water, Ecology and Environment

224

()

0.9973

35.0185 1 1.6868lg

27.7543

(1)

Where i is rainfall intensity, mm/min, T is the return

period, a, t is rainfall duration, min. The design

rainfall intensity under different return periods is

shown in Table 1.

Table 1: Design rainfall intensity.

Return period

(a)

Duration

(180min)

i (mm/min)

1 180 0.171

2 180 0.258

5 180 0.373

10 180 0.459

20 180 0.546

50 180 0.661

100 180 0.748

200 180 0.835

On "7.18", 2007 in Jinan, an extreme rainfall

event caused huge economic loss and serious

casualties. Hence, we select the heaviest 3h process

(16:00-18:00) of the natural rainstorm at the rainfall

station of the flood control office of Shizhong District

on “7.18” as the typical rainfall process. The rainfall

volume of 3h was 142.3mm. We set three different

rainfall intensity scenarios (see Figure 4).

Figure 4: The 24h design rainfall process under three different rainfall intensity scenarios.

3.3 Hydro-Hydraulic Model

FRAS is used to simulate the inundation areas under

different rainfall characteristics. FRAS is an

integrated software that can simulate the whole flood

process, mainly including 1D-2D coupling hydraulic

model, hydrological model, and drainage model (Li

et al., 2018). The hydraulic model can simulate the

surface flow well, including flow simulations in wide

or narrow rivers and the flow spreading along streets.

In this software, the 1D hydraulic model is coupled

with the 2D hydraulic model by calculating the flow

exchanges between the passage and grids on both

sides. The SCS-CN model is selected in this research

to simulate the runoff production. In addition, the

equivalent pipe network model is used to simulate

underground drainage.

A total of 26659 irregular grids are divided with

an average grid area of 1500m

(38m×38m). The

rivers and roads are set as special passages. The

roughness coefficients are set according to the soil

types. Four kinds of GIs are modified by changing

parameters, i.e., elevations, roughness, and values of

CN (see table 2). Retained greenbelt is also

considered an intensified infiltration greenbelt.

The calibration and validation details of the model

can be found in our previous paper (Li et al., 2018),

which is then not elaborated in this paper.

123

Rainfall volume (mm)

Time (h)

5 year 10 year 20 year

Modelling the Effects of Green Infrastructures on Water Quantity Under Different Rainfall Characteristics

225

Table 2: Parameters used for GIs in the FRAS model.

Measures Elevation Roughness

Value

of CN

Sunken

reenbelt

Lower

20c

0.06 61

Increased

infiltration

greenbelt

Unchanged 0.06 39

Porous

avement

Unchanged 0.035 66

Green roof Unchanged 0.07 61

4 RESULTS AND DISCUSSION

4.1 Effects of GIs on Inundation Areas

under Different Rainfall

Characteristics

Table 3 and Figure 5 show the effects of the

implementation of GIs on inundation areas under

different rainfall characteristics. The results show that

the inundation areas are reduced after the

implementation of GIs under different rainfall

frequency-duration-intensity, which suggests that GIs

have control effects on reducing the inundation areas

to some extent. In addition, GIs have the most

obvious control effect under 5-year events. With the

increases of rainfall volumes varying with the return

periods, the control effects of inundation areas

decrease. Moreover, with the increases of rainfall

intensities, the reduction rate of controlling

inundation areas decreases as well. Compared to the

obvious impacts of rainfall return periods and

intensity on the control effects, rainfall duration

variations have little effect on the reduction rates of

controlling inundation areas. The simulation results

are consistent with the field observations conducted

by Carpenter and Kaluvakolanu (2010) and Lewellyn

et al. (2015). In fact, grey infrastructures play a more

important role in controlling urban pluvial flooding.

GIs can help grey infrastructures control rainfall

runoff at the sources. Although the relatively weak

control effects compared to grey infrastructures, GIs

have more comprehensive benefits, such as

improving surface water quality and enhancing public

awareness on water security, which is very important

in view of many water problems facing to urban areas

(Yu et al., 2020).

Table 3: Effects on inundation areas under different rainfall characteristics.

No. Rainfall characteristics Scenarios

Inundation area (km

)

Reduction

(km

)

Reduction

ratio (%)

Before Afte

Frequency

5 yea

2.003 1.782 0.221 11.02

2 10

3.000 2.720 0.279 9.32

3 20

3.927 3.613 0.315 8.02

Duration

1.823 1.588 0.235

12.89

12h

1.241 1.104 0.137

11.08

18h

1.303 1.114 0.189

14.47

Intensity

5 year

1.176 0.939 0.237

20.14

10 year

1.919 1.643 0.276

14.37

20 year

2.797 2.501 0.296

10.58

Figure 5: Reduction ratios of inundation areas under different rainfall characteristics.

11,02

9,32

8,02

12,89

11,08

14,47

20,14

14,37

10,58

5 year 10 year 20 year 6h 12h 18h 5 year 10 year 20 year

Frequency Duration Intensity

Reduction ratio (%)

ISWEE 2022 - International Symposium on Water, Ecology and Environment

226

4.2 Effects of GIs on Comprehensive

Runoff Coefficients under Different

Rainfall Characteristics

Table 4 and Figure 6 show the effects of GIs on runoff

coefficient control under different rainfall

characteristics. The results show that the runoff

coefficients after the implementation of GIs are

smaller than those before the implementation under

different rainfall characteristic scenarios, which

means that GI can control rainfall runoffs. In addition,

with the increases of rainfall return periods, rainfall

intensities and durations, the corresponding runoff

control coefficients of GIs decrease. The simulation

results are consistent with the field investigations

conducted by Carpenter and Kaluvakolanu (2010).

They found that the average runoff coefficient on

green roofs is 0.044 under small rainfalls (<12.7 mm),

0.131 under middle rainfalls (12.7~25.4mm), and

0.591 under heavy rainfalls (>25.4mm), based on 21

rainfall events. According to the study of US EPA

(2015), the LID and related measures can only reduce

rainfall runoffs with 12.7~50.8 mm. With regard to

rainfall intensity, the heavier the rainfall is, the more

prone to generate runoffs. Lewellyn et al. (2015)

found that the study area generated runoffs even

though the total rainfall volume did not reach the

design storage volume.

With the increases of rainfall return periods, the

reduction ratios of control effects decrease obviously.

The reduction ratios of control effects under 5-year

and 20-year events are 10.148% and 6.611%,

respectively. The simulation results are consistent

with Yin et al. (2021), which found that the runoff

control rate is 98.1% under a small rainfall (<10 mm),

73.8% under a middle rainfall (10~15 mm) and 52.9%

under a heavy rainfall (>25 mm), respectively.

However, compared to the obvious control effects on

runoff coefficients with the increases of return

periods, the reduction ratios of control effects are not

that obvious with the increases of rainfall intensities

and durations. Yin et al. (2021) also observed that the

control effects of rainfall durations on control effects

are not obvious.

Table 4: Effects on runoff coefficient under different rainfall characteristics.

No. Rainfall characteristics Scenarios

Runoff coefficient

Reduction ratio

(%)

Before Afte

Frequency

5 years

0.517 0.465 10.148

10 years

0.559 0.512 8.406

20 years

0.578 0.540 6.611

Duration

0.372 0.323 13.364

12h

0.453 0.403 10.977

18h

0.460 0.409 11.135

Intensity

5 years

0.281 0.236 16.053

10 years

0.319 0.258 19.082

20 years

0.350 0.291 17.066

Figure 6: Reduction ratio of runoff coefficient under different rainfall characteristics.

10,148

8,406

6,611

13,364

10,977

11,135

16,053

19,082

17,066

5 year 10 year 20 year 6h 12h 18h 5 year 10 year 20 year

Frequency Duration Intensity

Reduction ratio (%)

Modelling the Effects of Green Infrastructures on Water Quantity Under Different Rainfall Characteristics

227

5 CONCLUSIONS

Hydrological performances of combined GIs under

nine rainfall scenarios are modeled using FRAS. The

results showed that GIs have good control effects on

water quantity in the study area under rainfalls with

small return periods. As the rainfall volumes grow

with the return periods, the control effects of GIs on

inundation areas and runoff coefficients decrease

significantly. In addition, with the increases in rainfall

intensities, the control effects on inundation areas

decrease obviously. However, the reduction ratios of

GIs on controlling runoff coefficients are not that

obvious with the increases in rainfall intensity. The

rainfall duration variations have little impact on the

reduction ratios of controlling rainfall runoffs.

GIs can design and accompany grey

infrastructures, such as deep tunnels and drainage

pipe networks, together to control rainfall runoffs

effectively. In order to provide technical assistance

for GIs to better adapt to climate change and improve

urban resilience, more studies should be conducted to

examine the control impacts of combined green and

gray infrastructures on rainwater runoff under future

climate change scenarios.

ACKNOWLEDGEMENTS

This work was supported by the National Natural

Science Foundation of China [No. 51909273] and

Talent Innovation Team for the Strategic Research on

Flood and Drought Disaster Prevention of the

Ministry of Water Resources [No.

WH0145B042021].

REFERENCES

National Bureau of Statistics 2021 Statistical bulletin of the

People's Republic of China on national economic and

social development in 2020

Min S K, Zhang X, Zwiers F W, et al 2011 Human

contribution to more-intense precipitation extremes.

Nature, 470(7334) 378-381.

Li H, Ding L Q, Ren M L, et al. 2017 Sponge city

construction in China: a survey of the challenges and

opportunities. Water, 9(9), 594.

Ghodsi S H, Kerachian R, Zahmatkesh Z. 2016 A multi-

stakeholder framework for urban runoff quality

management: application of social choice and

bargaining techniques. Sci. Total Environ. 500 574-

585.

Elliott A H, Trowsdale S A 2007 A review of models for

low impact urban stormwater drainage. Environ.

Modell. Softw. 22(3) 394-405.

ATKINS 2015 Flood loss avoidance benefits of green

infrastructure for stormwater management. Maryland.

Yu Q, Li N 2021 Research Progress and Perspective of

Effects of Low Impact Development on Rainfall Runoff

Reduction under Different Rainfall Characteristics.

Water Resources and Power, 39(08), 18-21+9. (in

Chinese)

IPCC, 2021. Summary for Policymakers. In: Climate

Change 2021: The Physical Science Basis. Contribution

of Working Group I to the Sixth Assessment Report of

the Intergovernmental Panel on Climate Change

[Masson-Delmotte, V., P. et al (eds.)]. (Cambridge

University Press. In Press)

Li N, Meng Y T, Wang J, et al 2018 Effect of low impact

development measures on inundation reduction-Taking

Jinan pilot area as example. Journal of Hydraulic

Engineering. 49(12) 1489-1502. (in Chinese)

Carpenter D D, Kaluvakolanu P 2010 Effect of roof surface

type on storm-water runoff from full-scale roofs in a

temperate climate. J. Irrig. Drain. E-ASCE, 137(3) 161-

169.

Lewellyn C, Lyons C E, Traver R G, et al. 2015 Evaluation

of seasonal and large storm runoff volume capture of an

infiltration green infrastructure system. J. Hydrol. Eng.,

21(1) 04015047.

Yu Q, Li N, Wang S, et al. 2020 Study on comprehensive

benefit assessment systems for low impact

development practices. Journal of Hydroelectric

Engineering. 39(12), 94-103. (in Chinese)

Yin D K, Chen Z X, Li Q. et al. 2021 Influence of rainfall

characteristics on runoff control of a sponge

reconstructed community in a rainy city. Journal of

Tsinghua University (Science and Technology). 61(01)

50-56. (in Chinese)

ISWEE 2022 - International Symposium on Water, Ecology and Environment

228