International low impact development conference china 2016 LID applications in sponge city projects

D EVELOPMENT C ONFERENCE C HINA 2016 PROCEEDINGS OF THE INTERNATIONAL LOW IMPACT DEVELOPMENT CONFERENCE CHINA 2016 June 26–29, 2016 Beijing, China SPONSORED BY Chinese Civil Engineer

Proceedings of the International

Low Impact Development Conference China 2016

International Low Impact

Development Conference China 2016

EDITED BY

Haifeng Jia, Ph.D., P.E., D.WRE; Shaw L Yu, Ph.D.;

Beijing, China June 26–29, 2016

Applications in Sponge City Construction

D EVELOPMENT C ONFERENCE C HINA

2016

PROCEEDINGS OF THE INTERNATIONAL LOW IMPACT

DEVELOPMENT CONFERENCE CHINA 2016

June 26–29, 2016 Beijing, China

SPONSORED BY Chinese Civil Engineering Society Chinese Water Industry Society Chinese Academy of Engineering—Division of Civil, Hydraulic, and

Architecture Engineering Environmental and Water Resources Institute

Published by American Society of Civil Engineers

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Preface

The 2016 International Low Impact Development (LID) Conference was successfully held

at the China National Conference Center in Beijing, China during June 26-29, 2016 The conference brought together experts and scholars from more than 23 countries and regions

to Beijing, China A total of nearly 800 papers were submitted, of which 576, through rounds

of peer reviews, were selected and presented at the conference There were 6 topical tracks,

4 special sessions and 4 keynote presentations The major theme of the conference was theory and practice of LID and green infrastructure (GI) application, which provided timely and valuable information for the implementation of the “Sponge City” projects, a major urban water management initiative, in China

The conference papers were reviewed by members of the program committee and selected authors were invited to submit their papers for possible publication in the ASCE Proceedings Manuscripts submitted were reviewed by proceeding editors listed below:

Haifeng Jia, Tsinghua University Shaw L Yu, University of Virginia Robert Traver, Villanova University Huapeng Qin, Peking University Shenzhen Graduate School Junqi Li, Beijing University of Civil Engineering and Architecture Mike Clar, Ecosite Inc

The papers approved for inclusion in the Proceedings are grouped into the following major tracks:

 LID and Urban Planning & Design

 LID/GI Research & Development

 Urban Water Infrastructure System Design & Optimization

 LID/GI Practices – Case Studies and Recent Advances

Acknowledgements

We acknowledge the sponsorship and financial support provided for the conference Efforts

by all the authors, editors and assistance by EWRI and the ASCE Publications are greatly appreciated

Contents

Urban Hydrology and Water Systems

Temporal and Spatial Variations of Extreme Precipitation and Flood

Thresholds in Qinghe Basin in Beijing, China 1

Li Lu, Xuebiao Pan, Lizhen Zhang, and Xingyao Pan

The Effects of Low Impact Development Practices on Urban Stormwater

Management 12

Na Li, Qian Yu, Jing Wang, and Xiaohe Du

The Impact of Focused Recharge with LID Devices on Groundwater

Dynamics and Water Quality under Natural Rainfall Conditions 21

Zhonghua Jia, Qing Xu, Wan Luo, and Shuangcheng Tang

Assessment of Stormwater Management and Storage Capacity for

Urban Green Space in Shanghai City 27

Bingqin Yu, Shengquan Che, and Jiankang Guo

Index System of Urban Rainwater Collection and Utilization in

Beijing City under Low Impact Development 37

Anping Shu, Xing Zhou, Donglian Kong, Lu Tian, and Li Huang

Verification of the Effectiveness of BMP Techniques in a Long Time

Period Using Trend Analysis 45

Zijing Liu and Yuntao Guan

Application of LID Attribute Index Evaluation Method in the Design

of Urban Stormwater Control 57

Jiangyun Li, Wang Sheng, Qing Chang, and Yi Zhou

Comparative Analysis of Different Evapotranspiration Estimation

Methods Used in a Raingarden in Auckland, New Zealand 66

Tingting Hao, Asaad Shamseldin, Keith Adams, and Bruce Melville

Concurrent Potential for Flooding Risk Reduction of Decentralized

Rainwater Management System 76

Donggeun Kwak, Minju Lee, Soyoung Baek, and Mooyoung Han

Urban Runoff Simulation and Analysis

Modeling of Streamflow in an Underdrain System of Vegetated

Dry Swales 85

Sidian Chen, Huapeng Qin, and Shuxiao Li

Stochastic Long Time Series Rainfall Generation Method 92

Yi Zhou, Yu Shang, Jiangyun Li, and Qiufeng Tang

Effects of Low Impact Development Practices on the Mitigation of

Nutrient Pollution in Deep Bay, China 100

Sidian Chen, Mingfeng Zheng, Huapeng Qin, and Xueran Li

Modeling of Bioretention Systems’ Hydrologic Performance: A

Case Study in Beijing 108

Meishui Li, Xiaohua Yang, Lei Chen, and Zhenyao Shen

Estimating Water Quality Capture Volume for LID Designs

Using a Mechanical Wash-Off Model 118

Qi Zhang, Fang Yang, and Zhijie Zhao

Distribution Analysis for Non-Point Source Pollution Control

Programs Using Multivariate Statistical Analysis Methods 126

Zijing Liu and Yuntao Guan

Study on Spatial Characteristics and Load of Urban Non-Point

Source Pollution Based on Geostatistical Model 137

Sheng Xie, Kai Yang, Yong Peng Lyu, Chen Zhang, Yue Che, and Lei Ding

Rainfall-Storage-Pump-Discharge (RSPD) Model for Sustainable

and Resilient Flood Mitigation 152

Duc Canh Nguyen and Moo Young Han

Runoff Characteristics on LID Combination Type in the New

Development Site Using XPSWMM 162

Donggeun Kwak, Hyunwoo Kim, and Mooyoung Han

Runoff LID Control Technology

Isolation and Characterization of a PYR-Degrading Bacterial

Consortium for Bioaugmentation in Bioretention Systems 172

Dongqi Wang, Zhangjie Yang, Jiaqi Shan, Enyu Liu, Guodong Chai, Chan Li,

Xiaohua Lin, Wen Dong, Huaien Li, and Jiake Li

Evaluation of the Effects of Low Impact Development on Base

Flow in an Urbanized Watershed Using HSPF 179

Qi Zhang, Zhijie Zhao, and Huapeng Qin

Groundwater Replenishment Analysis of Rainfall Collected via

an Ecological Detention Facility 186

Fengqing Guo, Yuntao Guan, and Tanaka Hiroaki

Reinvent of a New Public Toilet Wastewater Treatment System

Using Forward Osmosis as the Key Unit: A Resources Close-Loop

Model in Urban LID 194

Yangyu Xu, Lu Zhou, and Qibo Jia

LID-Based Ecological Planting Groove for Road Runoff Purification

Research 204

Xuexin Liu, Xueping Chen, Shaoyong Lu, Xinzhu Xiong, Shuohan Gao,

and Yaping Kong

Green Building and Green Roofs

How to Construct Green Roofs on the Tops of Existing Buildings:

A Case Study in Shanghai 214

Tianqing Luo, Yining Su, and Libin Chen

Behavior of Soil Moisture in a Retentive Green Roof System 223

Saerom Yoon, Juyoung Lee, and Mooyoung Han

Impact Study of Thermal Environment on Integration of Extensive

Green Roof Techniques in Northwestern Arid Regions of China 231

Yajun Wang, Rajendra Prasad Singh, Dafang Fu, and Junyu Zhang

Sponge Cities and Landscapes

Traditional Pattern of Mountain-Water-City and Its Contemporary

Enlightenment: Changshou District of Chongqing as a Case 241

Lu Guo

Landscape-Scale Simulation Analysis of Waterlogging and Sponge

City Planning for a Central Urban Area in Fuzhou City, China 251

Shaoqing Dai, Jiajia Li, Shudi Zuo, Yin Ren, and Huixian Jiang

Adaptation to Water: A Study on Bamboo Landscape System with

Low Impact Development 261

Renwu Wu, Jun Zheng, Yan Shi, Fan Yang, and Zhiyi Bao

A Balance of Landscape Architectural Planning and Design among

Antiterrorism Concern with Nature, Cultural, or Socio-Economic

Ecosystem Services 267

Kaitai Lin

Case Studies

Optimization Study of Urban Stormwater Runoff Control BMPs

Scheme Based on SUSTAIN 278

Yifan Zeng, Xiaodong Long, Zimu Jia, Weihua Zeng, and Jianbin Shi

Comparison of Stormwater Management in the Community Park

between China and Singapore: A Case Study of Hillside Eco Park

and Crescent and Pioneer Hall 289

Mo Wang, Dong Qing Zhang, Ya Wang, Jin Su, Jian Wen Dong,

and Soon Keat Tan

Effects of Land Use and Rainfall Characteristics on River Pollutions:

A Case Study of Xili Reservoir Watershed in Shenzhen, China 304

Lixun Zhang, Bo Zhao, and Yuntao Guan

Low Impact Stormwater Management Development at Rutgers

University 318

Seth Richter, Christian Roche, and Qizhong Guo

Sponge City Construction and Management Strategies

Low Impact Thinking of the Spongy City Construction in Built-Up

Areas from the Perspective of Sustainable Urban Design 328

Xili Han, Wenqiang Zhao, Linus Zhang, and Peter Siostrom

Challenges and Future Improvements to China’s Sponge City

Construction 339

Hong Wang, Xiaotao Cheng, Li Man, Na Li, Jing Wang, and Qian Yu

A CFD-Based Level Sensor Location Optimization Method for

Overflow Discharge Estimation in CSOs 352

Hexiang Yan, Kangqian Zhao, Gislain Lipeme Kouyi, Tao Tao, Kunlun Xin,

and Shuping Li

Value and Rational Use of Landform Resources in Low Impact

Development 363

Dehua Mao, Wen Liu, and Min Yang

The Application of Adaptive Design Strategies in Urban Green

Stormwater Infrastructure Development 372

Wei Zhang, Jack Ahern, and Xiaoming Liu

Hydrologic Design and Economic Benefit Analysis of Rainwater

Harvesting Systems in Shanghai, China 381

Shouhong Zhang and Xueer Jing

A New Approach to Urban Water Environment Protection: Leasing

Mode and Its Risk Management of Urban Rivers and Lakes Pollution

Control Projects under Public-Private Partnership Model 390

Zhixuan Wu, Lu Zhou, Yi Zhou, and You Zhou

Temporal and Spatial Variations of Extreme Precipitation and Flood Thresholds in Qinghe

Basin in Beijing, China

Li Lu1; Xuebiao Pan2; Lizhen Zhang3; and Xingyao Pan4

1Agricultural Meteorological Dept., College of Resources and Environmental Sciences, China

Agriculture Univ., P.O Box 100193, Yuanmingyuan Xi Rd No 2, Haidian District, Beijing;

Dept of Beijing East-to-West Water Diversion Project, Beijing Water Authorities Bureau, P.O

Box 100192, Qinghe Rd No 189, Haidian District, Beijing E-mail: lillylug@163.com

2Agricultural Meteorological Dept., College of Resources and Environmental Sciences, China

Agriculture Univ., P.O Box 100193, Yuanmingyuan Xi Rd No 2, Haidian District, Beijing

(corresponding author) E-mail: panxb@cau.edu.cn

3Agricultural Meteorological Dept., College of Resources and Environmental Sciences, China

Agriculture Univ., P.O Box 100193, Yuanmingyuan Xi Rd No 2, Haidian District, Beijing

E-mail: zhanglizhen@cau.edu.cn

4Beijing Water Sciences and Technology Institute, P.O Box10004, Chegongzhuang Xi Rd No

21, Haidian District, Beijing E-mail: 041087@163.com

ABSTRACT

Extreme weather frequently causes torrential rains and flooding in modern cities, e.g., Beijing, which are much sensitive and fragile to flooding disasters because of high population

density In this study, we aimed to quantify the temporal and spatial distribution of extreme

precipitation in Qinghe Basin in Beijing and to develop optimal flood management thresholds by

using precipitation records from 1986 to 2014 in two sites of the region The time that maximum

precipitation occurs in a year differed temporally and spatially and mainly concentrated in July

and August Extreme precipitation amount covered 41.7% of total precipitation in a month

during flood season Rain days of rainstorms were on average 1.7 d and 87% of them

concentrated in July and August and were more in upstream than that in downstream

Precipitation intensity (SDII) during flood season was on average 11.7 mm d1 and highest (15.1

mm d1) in July SDII during critical flood control period increased in upstream during recent 30

years and implied a high flood risk in the future The spatial distribution of precipitation intensity

was significantly different Our results at basin level would help city authorities designing

optimal flood control constructions, drainage facilities, and warning systems

KEY WORDS: climate variation; flood control; precipitation intensity; rain events; urban

area

INTRODUCTION

Meteorological and secondary disasters happened frequently due to the extreme weather under climate change in the world especially during 21 century Under climate change, the

maximum of total precipitation and extreme rain events from 1950 to 2014 occurred in 1990s

and 2000s, and the extreme rain events would continuously increase according to the report of

Intergovernmental Panel on Climate Change (IPCC) (2014) Since meteorological disasters

cause significant social and economic losses, governments, civil societies, organizations and the

public therefore pay great concern to the managements of the disasters for the alleviation of the

negative influences of climate changes

Extreme weathers frequently cause torrential rains and flooding in modern cities, e.g Beijing

and Shanghai, which are more sensitive and fragile to flooding disasters because of high

population density The average annual cost of natural disasters was 200 to 400 billion Yuans

from 1949 to 1989 and gradually increased due to the climate changes The safety of big cities,

including managements, lives and properties, is greatly threatened by seeping in streets,

rainwater intrusion into underground facilities and other damages caused by extreme

precipitation events “Metropolis Disease” due to extreme precipitations were frequently

reported by public media For example, a heavy rain of 170 mm in one day, with a maximum

precipitation of 541 mm in Hebeizhen in Fangshan District, attacked Beijing on July 21, 2012,

which broke a historical record of single rain station in Beijing Nearly 600 million m3 rainwater

concentrated in a 2000 km2 area in Fangshan District during 10 hours, which equaled that the

Kunming Lake in Summer Palace was poured down once every 3 minutes The highest rainstorm

warning grade with “Orange Degree” and “Level II” of Flood Control Emergency were

announced The direct economic losses were as high as 11.8 billion Yuan, and 119.28 million

populations were greatly affected Total 9.48 million people were transferred to safe regions in

emergency, and 79 people were died during this terrible event More than 10 thousands of houses

collapsed, 940 enterprises were discontinued, and 361 kilometers embankments were damaged

The huge losses from this extreme precipitation event were partially due to the limitation

knowledge on the relationship between extreme rain and flood occurrence in a big city

Temporal and spatial distribution of precipitation intensity in relation to the land use types and

population density would significantly affect the alarm threshold However, such important

studies are lagged

Average annual rainstorm days in China showed a slight but not significant increasing trend

in the past half century (Zhi et al., 2006; Min and Qian, 2008; Feng et al., 2008; Zou et al., 2009;

Chen et al., 2010) The frequency and intensity of extreme precipitation over total rainfall events

increased in most of China, while the rainfall days tended to be decreasing, and annual rainstorm

days slightly increased with high differences in temporal and spatial distribution (Zhai et al.,

2005; Wang and Zhai, 2008) Heavy rainfall in summer reduced in the north of China (Wang and

Yan, 2009) The frequency and intensity of extreme precipitation events decreased in North

China (Alexander et al., 2006; Wang et al., 2012) The frequency of precipitations during 1954 to

2006 reduced in North China; however, that of heavy rain did not too (Tu et al., 2010) The

extreme precipitation intensity and frequency of big cities in north of China were increased more

than in surrounding agricultural areas (Wang and Zhai, 2009) Although the extreme

precipitation amount, days and intensity in Beijing showed a downward trend from 1981 to 2010

(You et al (2009), the highest precipitation intensity occurred in 2012 That implies increased

variations of precipitations in Beijing, thus, it is necessary to explore the temporal and spatial

variations of precipitations in relation to flood control based on the capability of flood discharges

at a basin level

The objectives of this study therefore were to (a) quantify the temporal and spatial distribution of extreme precipitations with frequency, amount and intensity in the basin of

Qinghe River in north of Beijing city, where is one of four rivers in the capital urban center with

a drainage area of 175 km2, a length of 28.7 km, an elevation range from 24.4 m to 500.3 m, and

a stream length of 23.7 km; and (b) develop an extreme precipitation threshold (index) for the

flood control of Beijing city in relation to the real basin situation, in which the hydraulic

structures and embankment of Qinghe River are 20 years of flood recurrence period Considering

natural and social factors, the study would help to design an optimal construction of Sponge

Cities and provide scientific support to emergency warning and response activities

MATERIALS AND METHODS

Study sites

The study sites are Qinghe (40o01'N, 116o20'E) and Yangfang (40o02'N, 116o24'E) located in north of Beijing city (Fig 1), where are main regions for rain water collection in Qinghe basin

Ten flood discharge gates are distributed along Qinghe river, i.e Anhe gate, Xiaojiahe gate,

Shucun gate, Jingbao gate, Qinghe gate, Xiaqinghe gate, Yangfang gate, Waihuan gate,

Shenjiafen gate and Shaziying gate (Fig 1) The basin lies in semi-humid continental monsoon

climate, affected by the high-pressure Mongolia with prevailing northerly winds in winter, and

by the continental thermal low-pressure system with prevailing southerly winds in summer

Qinghe site is located at upstream of Qinghe basin in the front terrain of Jundu mountain, and the

climate is characterized as a strong air convection current, which often causes rainstorms

Yangfang site is located at downstream of Qinghe basin and affected by urban heat island effect,

by which short and partly rainstorm often occurs The two sites therefore could well present the

precipitation situation of studied basin

Figure 1 Locations of studied sites (red color crosses) and water discharge gates (blue filled

circles) for controlling flood of Beijing

Data source

The precipitation data of studied sites of Qinghe and Yangfang was from local meteorological and hydrological stations Data was recorded from 1986 to 2014 During 1986 to

2004, the precipitation data was measured by a 0.5 mm resolution manual rain gauge During

2005 to 2014, the data was measured by a 0.1 mm resolution automatic rain gauge All data was

manually re-checked by local hydraulic station to ensure the accuracy

Data analysis

Extreme weather events are rare weather events in specific areas and time (Solomon et al., 2007) Extreme weather event is defined as a weather event of a certain region when it seriously

deviates from its average Since “abnormal weather” is relative meaning that is not same for

different regions and seasons, World Meteorological Organization Commission for Climatology

(CCI/ WMO) recommends to divide climate extremes index into two categories, one is depended

Beijing

Anhe gate Xiaojiahe gate

Shucun gate

Jingbao gate Qinghe gate

Yangfang gate

Waihuan gate Shenjiafen gate

Shaziying gate

Xiaqinghe gate

on absolute physical boundaries and another is relative extreme index, which extreme events

have statistical probability of extreme low or high values, i.e less than 10 percentile or greater

than 90 percentile in accumulative distribution function (Wang and Wang, 2007)

Seasonal distribution of precipitations in Beijing is uneven, especially in the studied region where the precipitations during flood season (June to September) account for 64% of the total

annual precipitations and most daily precipitations in winter (from November to January) are

zero We therefore only focused the period of flood season (1 June to 30 September) All

calculations were done only during this period The absolute values for categorizing extreme

precipitations were used in this study

In order to classify precipitations into categories, i.e middle rain, heavy rain and rainstorm,

we used absolute thresholds which are commonly used in Beijing region We categorized a

precipitation greater than 10 mm as a middle rain, 25 mm as a heavy rain and 50 mm as a

rainstorm The days of each rain category were calculated accordingly

SDII defined as index of precipitation intensity is the total precipitation amount divided by rain days

Figure 2 Distribution of occurrence time (Calendar days) of maximum precipitation per year from 1986 to 2014 in Qinghe and Yangfang, Beijing Color filled areas indicate flood

season and months

June July August

September

Figure 3 Trends of the ratio of extreme precipitation amount over total precipitation

during a period from 1986 to 2014 in Qinghe and Yangfang, Beijing RESULTS

Extreme precipitations

The time that maximum precipitation occurs in a year was distributed almost all within June

to September (flood season), except for an exclusion of 1997 in Yangfang (Fig 2) The highest

frequency of the occurring time was in July, while that rarely distributed in June and September

y = -0.0022x + 4.84

R2 = 0.0203 (QH)

y = -0.0026x + 5.6517

R2 = 0.0243 (YF) 0

0.2 0.4 0.6 0.8 1

0.2 0.4 0.6 0.8 1

0.1 0.2 0.3 0.4 0.5

Comparing to a concentrated distribution of occurring times of maximum precipitation events in

Qinghe, the occurring times in June and September in Yangfang were much more, where there

were also not much maximum precipitations in these two months

The ratios of extreme precipitation over total precipitation in June for both Qinghe and Yangfang were slightly decreased from 1986 to 2014 (Fig 3a), while that in July, August and

September showed increasing trends (Fig 3b,c,d) In June, the extreme precipitations and their

variations before 1991 were much higher, comparing to a stable trend after 1991 However, the

variations of the ratio of extreme precipitation over total amount of rainfall in July during 2000

to 2014 were much higher than before 1994 That indicates an increased risk of flood in Beijing

in July

Days of extreme precipitations

Rain days of middle precipitations (R10 mm) during flood season averaged from 1986 to

2014 were 13.1 d and 16.7% more in 1980s than the average of 1990s and 2000s in Qinghe,

while that were 13.8 d and 22.7% more in 1980s The middle rain events mostly occurred in July

and August Rain days of heavy precipitations (R25 mm) during flood season averaged from

1986 to 2014 were 5.2 d in Qinghe and 5.9 d in Yangfang The 36-44% heavy rain events

concentrated in July in both sites, while it rarely happened in September Rain days of rainstorm

(R50 mm) during flood season on average of 1986 to 2014 were 1.8 d in Qinghe and 1.6 d in

Yangfang (Table 1) Events of rainstorms concentrated in July and August with a proportion of

80% over flood season for both sites, which indicated a critical period to control the flood in

Beijing city

Table 1 Rain days of threshold precipitation events (d) during flood season (June to

September) from 1986 to 2014 in Qinghe and Yangfang, Beijing

a Index of R10 mm, R25 mm and R50 mm indicates rain days of middle (>10 mm), heavy (>25 mm) and rainstorm

(>50 mm) precipitation events, respectively

b Average indicates the data is averaged from 1986 to 2014

From 1986 to 2014, R10 mm greatly decreased in two sites, especially in Yangfang (Fig 4b)

R50 mm showed only a slight decrease in both sites R25 mm had a similar decreasing trend

The yearly variations of R50 mm were smaller than that of R10 mm and R25 mm(Fig 4)

Figure 4 Trends of rain days of threshold precipitation events (d) during flood season (June to September) from 1986 to 2014 in Qinghe (a) and Yangfang (b), Beijing R10 mm, R25 mm and R50 mm indicate rain days of middle (>10 mm), heavy (>25 mm) and

rainstorm (>50 mm) precipitation events, respectively Precipitation intensity

SDII index, defined as an averaged precipitation intensity (mm d1) calculated by the ratio of total precipitation amount during a period divided by rain days, was similar in two sites on

average from 1986 to 2014 during flood period, i.e 11.8 mm d1 in Qinghe and 11.5 mm d1 in

Yangfang (Table 2) The highest SDII occurred in July with a value of 14.9 mm d1 in Qinghe

and 15.2 mm d1 in Yangfang SDIIs in June and September were low ranged from 7.1 mm d1 to

8.6 mm d1 on average from 1986 to 2014 The highest SDII occurred in July of 1990s ranged

from 16.5 mm d1 to 18.1 mm d1 in two sites

during three decades in Qinghe and Yangfang, Beijing

From 1986 to 2014, SDII showed a slight but not significant decreasing trend during flood period in Yangfan, however SDII in Qinghe during flood period increased(Fig 5) While SDIIs

in June and August slightly decreased in two sites, during July, which is the critical flood control

period, and September, these trends were increasing especially in Qinghe (Fig 5b)

Figure 5 Trends of precipitation intensity (SDII) during flood period from 1986 to 2014 in

Qinghe and Yangfang, Beijing

CONCLUSIONS AND DISCUSSION

The time that highest precipitation occurs in a year differed temporally and spatially and mainly concentrated in July and August Extreme precipitation amount covered, on average for

sites and months, 41.7% of total precipitation in a month during flood season Extreme

precipitation amount in July, August and September in Beijing increased from 1986 to 2014,

especially in July, which was consistent with Zhang et al (2008) and You et al (2009) That

indicates the flood risk in Beijing would increase due to the climate change or probably fast

urbanization

Rain days of heavy precipitations during flood season on average were 5.6 d and 40% of them concentrated in July Rain days of rainstorm, as critical events for flood control, were on

average 1.7 d and 80% of them distributed in July and August However, rainstorm events

showed the decreasing trends from 1986 to 2014 Rain days of rainstorms were more in upstream

of Qinghe basin than that in downstream, which were probably caused by the mountain effects

The results were consistent with previous studies (Alexander et al., 2006; You et al., 2009; Wang

et al., 2012)

Precipitation intensity (SDII) during flood season was on average 11.7 mm d1 and highest (15.1 mm d1) in July The spatial distribution of precipitation intensity was significantly

different SDII during critical flood control period (July) slightly but not significantly decreased

in downstream of Qinghe basin (Yangfan site), however, increased in upstream (Qinghe site)

during recent 30 years It implies the flood risk of upstream would increase and discharging

pressure of whole basin further increase

In Beijing city, the 5% extreme precipitation covers 30-38% of total amount of precipitation and critical flood control period is from 20 July to 10 August(You et al., 2009) However, our

study showed the extreme precipitation proportioned 38-47% of total precipitation during flood

season in Qinghe basin, which was 25% higher than the average of total Beijing The critical

flood control period based on the frequency and intensity of precipitation events was from 20

June to 16 August in Qinghe basin, which was 36 d longer than that in total Beijing

The temporal and spatial distribution of extreme precipitation in terms of occurrence time, days and the intensity in Beijing at a basin level would help city authorities designing an optimal

flood control constructions, drainage facilities and warning systems Due to the increasing trend

of extreme precipitation in Qinghe basin, the standards of flood prevention and pipe drainage

adapted to the sponge city might be necessarily researched In this study, we only focused on the

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Beijing), the studies in relation to the land use changes due to city expansion, vegetation, river

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The Effects of Low Impact Development Practices on Urban Stormwater Management

Na Li, Ph.D.1; Qian Yu, Ph.D.2; Jing Wang, Ph.D.3; and Xiaohe Du4

1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China

Institute of Water Resources and Hydropower Research, Beijing 100038, People’s Republic of

China; Research Center on Flood and Drought Disaster Reduction of the Ministry of Water

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

People’s Republic of China E-mail: lina@iwhr.com

2State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China

Institute of Water Resources and Hydropower Research, Beijing 100038, People’s Republic of

China; Research Center on Flood and Drought Disaster Reduction of the Ministry of Water

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

People’s Republic of China (corresponding author) E-mail: yqcherie@126.com

3State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China

Institute of Water Resources and Hydropower Research, Beijing 100038, People’s Republic of

China; Research Center on Flood and Drought Disaster Reduction of the Ministry of Water

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

People’s Republic of China E-mail: wangjing8585@126.com

4State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China

Institute of Water Resources and Hydropower Research, Beijing 100038, People’s Republic of

China; Research Center on Flood and Drought Disaster Reduction of the Ministry of Water

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

People’s Republic of China E-mail: duxh@iwhr.com

ABSTRACT

Low impact development (LID), which aims at either infiltrating, evapotranspiring or storing water at the source, plays an important role in managing urban rainwater This paper summarizes

the effects of four individual LIDs (i.e., bioretention, green roof, porous pavement, and grass

swales) and several combinations of those LID practices on rainfall-runoff management The

survey shows that both individual and combined LIDs are effective in controlling small and

medium rainfalls, and the performances are less obvious with increases of the rainfall depths

Hence, the individual or combined LIDs applied with noticeable effects on low or moderate

rainfalls might not be useful for heavy rain events which would probably cause urban floods in

cities in China Cities located in different regions show big differences in rainfall characteristics

Rainfall intensity is an even more important factor than rainfall depth that influences

performances of LID practices In the future, more studies should be directed to the effects of

LID measures on large storm runoff managements under different rainfall

intensity-duration-frequency (IDF), which would be helpful to select suitable LID practices for cities in China

INTRODUCTION

During the last decades, urbanization has almost swept across China The population growth, urban density changes, and land cover changes accompany with urbanizations and urban

developments The traditional developments leading to land cover changes will raise the high

proportions of imperviousness In addition, traditional developments will also result in increased

surface runoff volume, diminished infiltration and decreased baseflow in consequence

(Ahiablame et al 2012) The adverse impact is the increase of urban flood risks, which would

also bring other negative impacts such as water quality deterioration, economics loss and even

casualty

Low impact development (LID) is an effective tool to reduce negative impacts caused by urbanizations (Ghodsi et al 2016) LID measures include grass swales, bioretentions, green

roofs, vegetated filter strips, etc They are designed to store, detain, and infiltrate urban runoff

(Elliott and Trowsdale 2007) LID was first put forward and applied during 1990s in Maryland,

USA (Coffman and France 2002), which was called Best Management Practices (BMPs) at the

beginning BMPs were proposed to mitigate the negative impacts of urbanizations, and they

achieved good effects Since then, similar concepts have also been proposed in the UK, Australia

and other developed countries which also suffer from stormwater problems Previous studies

reveal that the LID practices are effective in improving runoff quality at both watershed and

household scales However, debates still exist in the effects of controlling storm runoff quantity

In addition, different cities have quite distinct rainfall intensity-duration-frequency (IDF) The

already proved effective LID techniques in cities with low rainfall depths abroad may not be that

effective in cities which are vulnerable to heavy storms in China Hence, a literature review on

the performances of LID practices under different rainfall characteristics is needed

The objectives of this paper are to (1) review and compare the effects of both individual LID practices and combined LID measures on rainwater runoff managements (2) compare rainfall

IDF in foreign cities with those in China

EFFECTS OF LID PRACTICES ON STORMWATER RUNOFF

Field observations, model simulations, and laboratory experiments are often used to study the effects of LID practices on stormwater runoff managements (Chapman and Horner 2010;

DeBusk and Wynn 2011) Although LID practices have been introduced into China in recent

decades, available long-term field observations are still lacking Instead, researchers in China

usually use urban stormwater models such as SWMM Most of LID practices are effective to

reduce rainfall runoff volumes, decrease peak runoff and postpone flow peak appearance time

Bioretention systems

Bioretention systems, or rain gardens, are beneficial to control rainwater runoff In general, bioretentions can reduce runoff volume by 47%~97% and reduce peak runoff by 3%~84.3% (see

Table 1) The filler materials and soil thickness laid in bioretention systems have influences on

the effectiveness Pan et al (2012) found that compared to the bioretentions with grass inside, the

bioretentions with buxus inside are better at controlling larger runoff volumes and peak runoff

Yin et al (2015) found that bioretentions with larger-sized particular fillers inside are better in

infiltration than those with traditional fillers inside Brown et al (2012) found that the effects of

controlling runoff volumes is better when the soil depths are thicker (depths between 0.6~0.9 m),

although the effect of increasing soil depth is not evident

Green roofs

Green roofs have been widely used abroad for decades (Rowe 2011; Stovin et al 2012), which are proved to be effective in controlling runoff volume and peak runoff (see Table 1)

However, the water storage ability of green roofs will decrease as rainfall intensity increases

Green roofs are divided into two types based on the depth of substrate layer, i.e., “extensive”

green roofs and “intensive” green roofs (Mentens et al 2006; Ahiablame et al 2012) The depth

of substrate layer of extensive green roofs are usually thinner than 150 mm while that of

intensive green roofs are usually thicker than 150 mm Extensive green roofs are suitable for

single household or residential buildings, while intensive green roofs are widely used in

commercial buildings with grass, flowers, shrubs, etc (Ahiablame et al 2012; Stovin et al 2012)

Debates still exist in the impacts of thickness and slope of green roofs VanWoert et al

(2005) found that the rainwater storage capacity increases only a little with the increase of the

soil thickness, especially when the thickness is between 2 and 12 cm If so, we can appropriately

decrease the soil depth to reduce the construction cost However, Dunnett et al (2008) took the

opposite view and reported that the water storage capacity would increase with the increase of

soil depth Villarreal and Bengtsson (2005) found that the changes of slope (2°~14°) would not

influence the performances on runoff volume and peak runoff On the contrary, VanWoert et al

(2005) found that simultaneously increasing soil thickness and decreasing the slope would

significantly reduce the rainfall runoff volume

Porous pavements

Porous pavements are designed for temporary storage of surface runoff (Ahiablame et al

2012) Porous pavements using different media would reduce runoff volumes by 23%~93% (see

Table 1) Rushton (2001) found that the runoff coefficient of porous pavements is only 0.20

while those of asphalt pavements and concrete pavements are 0.35 and 0.30, respectively Bean

et al (2007) found that the porous pavements can not only reduce runoff but also impede the

generation of surface runoff

Grass swales

Grass swales are usually designed to control runoff velocity and improve water quality Grass swales are categorized into dry grass swales and wet grass swales Among them, dry swales is

more effective to reduce runoff volumes (Huang et al 2015) Davis et al (2012) found that the

grass swales could significantly reduce runoff volumes when rainfall volumes were lower than

30 mm As a whole, grass swales are less effective compared with other LID devices on

controlling stormwater runoffs

LID combinations

The effects of a train of several LID measures are more significant than individual LID on stormwater runoff managements At present, modeling simulation is the most used method to

study the effects of LID combinations while field observations and laboratory experiments are

less employed With the increase of rainfall volumes and rainfall intensity, the effects of LID

combinations will also decrease

In conclusion, the storm characteristics (depth and intensity) is one common and main factor that influence the performances of individual and combined LID devices

EFFECTS OF LID PRACTICES UNDER DIFFERENT RAINFALL

CHARACTERISTICS

Only a given rainfall volume is retained by a specific LID for all storms, no matter how heavy the storm is Hence, most of the LID practices are effective in reducing low to moderate

rainfall runoffs (see Table 1) On the contrary, the performances of LIDs on alleviating the

impacts of large storm events are usually speculated but seldom studied However, the rainfall

depths, which would cause urban floods in China, are usually large

Table 1 Summary of Rainfall Runoff Reductions by LID Practices

LID practices

Runoff volume reduction (%)

Liu et al 2009

Porous pavements

12.7 and 50.8 mm (Atkins 2015) Carpenter and Kaluvakolanu (2010) divided 21 rainfall events

studied into three levels: small with rainfall sizes between 4~12.7 mm, medium with rainfall

sizes between 12.7~25.4 mm, and large with rainfall sizes larger than 25.4 mm The

corresponding volumetric runoff coefficients were 0.044, 0.131 and 0.591, respectively Hence,

noticeable effects have been found on small rainfall runoff reductions while the effect isn’t

significant under heavy rainfalls Guo et al (2015) observed that grassed swales reduced the

runoff by 96.3% under the precipitation with 47.3 mm In contrast, the swales only reduced the

runoff by 13% under the rainfall depth of 350.9 mm Yan et al (2014) found that the bioretention

systems could only reduce peak runoff by 3% under 100 year design storms in Jinan

Table 2 Summary of Rainfall Characteristics in Previous Studies

(mm)

Return period (year)

Rainfall duration (h)

Rainfall intensity

Bioretention systems

Campus, Foshan, China

Campus, Foshan, China

Jia et al 2015

2015 LID

Rainfall intensity and duration are even more important than rainfall depth in the performances of LID practices Hathaway et al (2008) found that individual storm in summer

with higher rainfall intensity would be retained less than the storm with lower intensity and

longer duration However, there are few studies of rainfall IDF on the performances of LIDs

Most of the studies only use rainfall depth to describe rainfall characteristics

RAINFALL CHARACTERISTICS

As can be seen in Table 2 and Table 3, different cities have clearly different rainfall characteristics Hong Kong is located in a subtropical climate region with an average annual

rainfall of around 2400 mm (Chui et al 2016) The 2-year and 50-year design rainfalls (a

duration of 200 min) are 122 mm and 260 mm, respectively (Chui et al 2016) In contrast, the

climate of Seattle is temperate marine with an average annual rainfall of approximately 950 mm

The 2-year and 50-year design storms (a duration of 200 min) are only 20 mm and 37 mm,

respectively (Chui et al 2016) As aforementioned, Maryland in USA first proposed LID devices

to control urban stormwater runoff Take Dorchester County in Maryland as an example, the

100-year design storm depth is 198.12 mm (see Table 3) In contrast, the amount of 100-year

design storm in Jinan is approximately 247 mm (Hou 2010) According to Table 1, the observed

nine rainfalls in Athens, Georgia, are between 3 and 18.5 mm Although the storm sizes are small, they constitute about 90% of storm events in Athens, even during non-drought years (Dreelin et

al 2006)

In China, the problem is that mainly heavy storms cause urban floods or water-logging The average rainfall depth of the “7.21” storm in 2012 in the whole Beijing city is about 170 mm and

the amount is 215 mm in the urban area (Sun 2014) According to Table 1, most of the studied

rainfall sizes are smaller than 70.9 mm That is, the rainfalls which bring threatens to the whole

city in China are usually larger than those studied in previous works Cities located in different

regions in China show big differences in rainfall characteristics In general, the annual average

rainfall depth in the south-east cities in China is much larger than that in the north-west cities

(Yuan et al 2014) In addition, storms with high intensities, which exceed the capacities of

drainage systems, are responsible for urban floods and water-logging in China, such as Shanghai

(Fang et al 2012) In view of different city characteristics, different rainfall characteristics (IDF)

are responsible for urban floods in different cities

Table 3 Summary of Design Rainfall Characteristics in Different Cities

Location Design storm depth

(mm)

Return period (year)

those of individual LID However, the performances of LIDs under heavy storms, which might

cause urban floods in China, are usually surmised while seldom studied According to previous

studies, only a given rainfall depths will be retained by a specific LID, no matter how heavy the

rainfall is Hence, LID practices, improved urban drainage systems, deep tunnels and other

conventional flood control measures should be comprehensively considered to manage large

storms in China

In the future, performances of LID practices under heavy rain events in China should be studied In addition, effects of combined LID devices, urban drainage systems, deep tunnels and

other measures under heavy storms should also be studied, which is meaningful for urban

stormwater management in China Besides, rainfall intensity and duration are even more

important than rainfall depth that influences the performances LID practices on controlling the

rainfall runoffs in cities in China Hence, performances of LIDs under different rainfall IDF

should also be studied

ACKNOWLEDGEMENTS

We acknowledge the financial support of the IWHR Scientific Research Projects (No

JZ0145B322016) and Public Welfare Scientific Research Projects of Ministry of Water

Resources (No 201401038)

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The Impact of Focused Recharge with LID Devices on Groundwater Dynamics and Water

Quality under Natural Rainfall Conditions

Zhonghua Jia1; Qing Xu2; Wan Luo3; and Shuangcheng Tang4

1College of Water Resources and Hydropower Engineering, Yangzhou Univ., Yangzhou, China,

been found effective in restoring some predevelopment hydrology through focused recharge to

groundwater with enhanced infiltration However, the focused recharge from LID devices may

bring pollutants down to the groundwater and poses potential threat to water quality The

magnitude of such negative impact is subject to several environmental factors, including rainfall

pattern, water table depth, and the soil properties This research presents a field monitoring study

on the effect of rain garden infiltration on local groundwater dynamics and water quality in

Xi’an, China The preliminary results showed that even sparsely distributed rain gardens had

certain effect on groundwater dynamics, forming a groundwater mound underneath the recharge

point; the effect of focused recharge on groundwater quality was even more significant Further

investigation is needed to determine the regional effect of more densely constructed LID

measures on groundwater dynamics

KEY WORDS: LID; Focused recharge; Groundwater; Water quality; Rain gardens INTRODUCTION

One negative impact of urbanization is the reduced groundwater recharge due to expansion

of impervious area Many approaches have been proposed to mitigate such adverse impact

through the low impact development (LID) practices Rain gardens have been widely accepted as

an effective measure in mitigating negative hydrological and water quality impacts of

urbanization; and they can compensate for groundwater depletion through focused recharge But

the focused recharge may bring unwanted pollutants down to groundwater, causing negative

impact on groundwater quality The magnitude of such impact is influenced by several

environmental factors, including precipitation pattern, groundwater depth, and soil properties etc

(Hudak, 2000) As stated by Gessner et al (2014), the urban water interface is heterogeneous and

dynamic The groundwater recharge with rain gardens has not been extensively studied, but

similar research can be found in managed aquifer recharge (MAR) In a review study on

groundwater recharge using reclaimed municipal wastewater, Asano and Cotruvo (2004)

concluded that the traditional surface spreading is a favorable option because the vadose zone

can provide pollutant assimilation The assimilation of pollutants in vadose zone can be

substantial and extensive Abel et al (2014) tested the impact of intermittent applications versus

the continuous pattern on pollutants reductions using a 4.2 m high soil columns; they found that

the difference is insignificant for suspended solids and dissolved organics when the hydraulic

loading rates varied from 0.625 to 1.25 m/d., but NH4-N and pathogens were significantly

reduced Bekele et al (2011) reported substantial reduction in pollutants; including 30% in P,

66% in fluoride, 62% in iron, and 51% in total organic carbon; they concluded that water quality

improvements through infiltration is more favorable than direct injection for groundwater

recharge

During the rain garden infiltration process, water travels from surface to groundwater through the vadose zone; the process is complex and requires both empirical research and new

modeling approaches Estimating the volume of groundwater recharge is not straightforward due

to the complex processes involving the vadose zone interception It is particularly important in

arid and semi-arid regions, where vadose zone is relatively thick, and the net recharge to

groundwater may be substantially smaller than the infiltration volume Existing calculation

methods include surface water balance, groundwater balance and solute tracing methods based

on variation of salinity, temperature or other indices The surface water balance method

calculates the water volume infiltrated, and the groundwater balance method calculates the net

recharge excluding intercepted water in the vadose zone In practice, the surface water balance

method is easier to implement than the groundwater method Results from different studies vary

considerably due to the differences in climatic, soil and groundwater conditions Boisson et al

(2014) compared surface and groundwater approaches to evaluate MAR structures; they found

that the storage in vadose zone contributed to the delay of water infiltration, the surface water

balance approach was found simple and easy to adopt, but ignoring the vadose storage led to

error in estimating the delay; they pointed out that the actual recharge to groundwater may be

limited due to the vadose zone storage Sharda et al (2006) found that the estimation of recharge

using the chloride methods is comparable to mass balance methods, and that 1 mm net recharge

required more than 100 mm rainfall at a site in India But their estimate did not count the

temporary storage in the vadose zone, which may eventually either be evaporated or reach

groundwater at a much delayed time Barron et al (2013) reported that more than 40% of the

annual rainfall recharged groundwater due to highly permeable sandy soil and shallow

groundwater table in a Mediterranean region Under managed conditions, the infiltration from a

storage facility can be much higher For instance, Massuel et al (2014) found that the infiltrated

water accounted for more than half (57-63%) of the tank storage；and the influenced area is

limited to 100 m from the tank

While LID measures are highly advocated for mitigating urban stormwater problems, there have been few studies concerning the potential negative effect of focused recharge on

groundwater quality The objective of this study was to investigate the effect of focused recharge

through a rain garden on groundwater dynamics and water quality based on a field monitoring

study

STUDY SITE AND EXPERIMENT SETUP

The rain garden was built on the campus of the Xi’an University of Technology in Xi’an, China (Jia et al., 2016; Tang et al., 2015) It has a surface area of 26.7 m2 as well as a storage

depth of 15 cm, receiving runoff from a nearby laboratory roof that is about 605 m2 in area And

runoff from the roof was diverted to the rain garden through a cement gutter on the ground Two

V-notch weirs were installed at the inlet and outlet of the rain garden to measure the inflow and

outflow/overflow of the rain garden, one is 45-degree while the other is 30-degree A staff gauge

was installed in the garden to measure water level change with time Rainfall was recorded by a

weather station about 100 m from the site Hydraulic heads over the weir crest were measured by

pressure transducers The infiltration process was recorded for eight of the 29 storm events

monitored between 2011 and 2014 The city Xi’an is situated on the vast Loess plateau with very

deep and uniform loamy soil (normally >50 m) (E107°40′～109°49′ and N33°39′～34°45′) The

annual average temperature in Xi’an is 13°C, rainfall 551 mm and evaporation 990 mm

Over-pumping of groundwater in Xi’an has caused great depression of ground water table in the city

range Considering the moderate rainfall, deep soil profile and low water table, there is a great

potential to retain urban stormwater runoff with LID measures to increase groundwater recharge

in Xi’an For this study, the groundwater monitoring was conducted at the edge of the rain

garden, and a reference point was located 200 m from the rain garden The reference point was

assumed unaffected by the rain garden

Figure 1 The experimental rain garden during the construction (at left) and the operation

(at right) RESULTS AND DISCUSSIONS

Table 1 lists the measured annual rainfall during the monitoring period from 2011 to 2014

The rainfall in 2011 and 2014 were slightly above the average, while the rainfall in 2012 and

2013 were below the average Nearly all inflow to the rain garden was infiltrated Using a flow

contributing areal ratio of 20:1 and a runoff coefficient of 0.5, the total inflow/infiltrated water

depth is computed as listed in Table 1, the average infiltrated water depth is about 6483 mm

annually

Table 1 Measured annual rainfall depth and computed rain garden inflow

† It is assumed that all inflow is infiltrated, and the depth is over the rain garden surface area

The rain garden is situated in a generally impervious area concentrated with a complex of tennis and volleyball courts, lab and office buildings Though infiltration depth was large (Table

1), the effect of the focused recharge over such a small ‘point’ area is not obvious as shown in

Fig 2, which plots groundwater dynamics near the rain garden and the reference point

Figure 2 Measured daily rainfall and groundwater depth near the rain garden and

reference point

Table 2 lists the statistics for the groundwater table depth in the two measured locations The averages are 3.47 and 3.66 cm, with variations of 0.02 and 0.23 cm While we cannot exclude the

effect of the infiltration from other permeable areas, it is obvious the installation of the rain

garden elevated the groundwater table, and also stabilized the process This is perhaps due to

slower releasing or consumption of the storage in the vadose zone, which is deeper than 3 m in

the study area Sparsely installed LIDs may form small islands of groundwater mound, which

may provide much needed water supply regions for plants; and more facilities may eventually

have overlapped influenced areas and elevated the regional water table

Table 2 Statistics of measured groundwater table depth

Fig 3 shows the changes in measured TP and TN concentrations, and Table 3 lists the statistics for the measured TP and TN concentrations at the two groundwater monitoring

locations For TP, the averages are 0.62 and 0.20 mg/L at the rain garden site and the reference

point It is obvious that the TP concentrations are much higher with much greater peak values

and variations For TN, the averages are 2.43 and 1.89 mg/L at the rain garden site and reference

point For TP the average concentration at the rain garden site is 3.10 times of that at the

reference point; for TN it is 1.31 It is obvious that the increase in TP is much higher than the

mobile TN This is somehow different from previous findings that the less mobile substances

such as TP is more likely intercepted in the vadose zone than the mobile ones (TN) We

postulated the following potential causes: 1) the fast response of TP in the groundwater is due to

preferential flow developed in soil matrix and along the plants roots, the loess soil can support

very developed macro-pores, and the plants in the rain garden can quickly re-establish the paths

following completion through root development; and 2) the background concentration of TP in

groundwater is much lower than that in the infiltrated water, and the infiltrated water resulted in

greater increase; while the situations for TN may be different Thus, the specific impact of

infiltrated stormwater runoff may be much dependent on the environment factors

40 50

Figure 3 Measured daily rainfall, TP and TN concentrations

Table 3 Statistics of TP and TN in groundwater

(TP), indicating the flow path may be dominated by preferential flows While the lateral scale of

an individual facility is hard to determine, the observed localized effect indicate that much

densely distributed LID facilities may generate potential regional impact on groundwater

recharge While the elevated groundwater table is beneficial, the increase pollutant inputs should

be further investigated, especially under more densely distributed LID facilities

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sub-humid loess region of China Water Resources Management 30 (3): 983–1000

Assessment of Stormwater Management and Storage Capacity for Urban Green Space in

Shanghai City

Bingqin Yu, Ph.D.1; Shengquan Che, Ph.D.2; and Jiankang Guo3

1School of Agriculture and Biology, Dept of Landscape Architecture, Shanghai Jiao Tong Univ.,

P.O Box 76, City, Shanghai 200240 E-mail: yubingchin1983@sjtu.edu.cn

2Prof, School of Agriculture and Biology, Dept of Landscape Architecture, Shanghai Jiao Tong

Univ., P.O Box 76, City, Shanghai 200240 E-mail: chsq@sjtu.edu.cn

3Ph.D Candidate, School of Agriculture and Biology, Dept of Landscape Architecture,

Shanghai Jiao Tong Univ., P.O Box 76, City, Shanghai 200240 E-mail:

jiankang0317@hotmail.com

ABSTRACT

In order to relieve urban environmental problems due to stormwater runoff, approaches involving green space planning for sponge city construction was previously proposed In the

current study, water retention characteristics of 168 green space was surveyed to develop

estimates of stormwater retention in Shanghai City’s center, suburbs, and outskirts

Multidisciplinary methods of community investigation, soil tests, artificial rainfall simulation

experiments, and simulations in Autodesk’s Storm and Sanitary Analysis Storm Water

Management Model were used The factors affecting the capacity of stormwater management

were identified and used to calculate storage estimates The relationships among the rainfall

interception capacity, runoff, soil water storage properties, and vegetative cover were analyzed,

which provided the theoretical foundation for the assessment of the water-holding capacity in

urban green spaces A criterion for the selection of low impact development (LID) techniques for

the Shanghai area can be developed based on the data from this study

KEY WORDS: landscape architecture; Sponge City; storm water management; green space;

water interception capacity

INTRODUCTION

Due to rapid urbanization, many environmental problems are emerging in cities, such as, urban inland inundation To solve stormwater management issues in the urban ecological

environment, “Sponge City” was previously proposed as a city with the capacity for natural

storage, infiltration, and purification of stormwater In 2014, the Ministry of Housing and

Urban-Rural Development of the People’s Republic of China (MOHURD) published recommendations

in ‘Guidance on Sponge City Construction Techniques’ MOHURD studied current practices in

the Beijing, Nanning, and Shanghai areas A standard technique suitable for the Shanghai area is

being formulated for use in construction and development An important and necessary element

of green space construction for Sponge City is to quantify the existing rainfall patterns,

stormwater management, and runoff storage capacity, from which the construction goals and

adaptive low impact development techniques can be selected Currently, sufficient data is not

available to quantitatively assess existing rainfall management and storage capacity of urban

green spaces The current study investigated the existing runoff storage capacity of green spaces

in Shanghai city’s center, suburbs, and outskirts through a detailed analysis of rainfall and runoff

patterns, soil water storage properties, and vegetative cover An approach to assess rainfall

management and storage capacity was then established using simulations from Autodesk’s Storm

and Sanitary Analysis Storm Water Management Model (SSA-SWMM) Future work can utilize

the results for developing low impact development (LID) techniques for the eventual

construction of Sponge City in the Yangtze River Delta, China

INVESTIGATION OF GREEN SPACE

Sample plot selection: Green spaces of Shanghai were categorized according to urbanization,

time of construction, type of land use, and the service function of a green space; 168 green

spaces with diverse functions were selected for investigation at the center (built before 1990),

suburbs (built between 1990 and 2000), and outskirts (built between 2000 and 2010) of Shanghai

City Green spaces at urban communities, parks, roads, and squares were chosen, as well as

commercial, educational, and industrial areas (see Figure 1)

Figure 1 Sample plot locations

Investigation methods: In order to determine soil infiltration curves, the double loop

permeameter (IN-8W) was used to measure the infiltration rate of the soil, which was tested

every 10 minutes over a period of at least one hour Darcy’s Law was then applied, with

hydraulic slope approximately equal to one, meaning the permeability coefficient equals the soil

infiltration rate measured by the permeameter The soil water storage was determined using mass

Vo = volume of FeSO4 used for blank titration (mL),

V = volume of FeSO4 used for sample titration (mL), 3.0 is ¼ molar mass of carbon atom (g.mol1),

103 is conversion from mL to L, 1.33 is oxidation adjusting coefficient, and 1.724 is the average conversion factor for changing soil organic carbon to soil organic matter

Based on the investigation of vegetation communities, 70 kinds of landscape plants were selected with occurrence frequency higher than 10% Unit leaf water storage capacity was

determined using the soaking method and leaf area index (LAI) was estimated Rainfall canopy

interception capacity (RCIC) of landscape plants was then determined by

K = unit leaf water storage capacity (g·m2),

M1 = weight of plants before soaking (g),

M2 = weight after soaking (g), and

A = blade area (m2)

The rainfall and runoff of urban green space was simulated using SSA-SWMM on AutoCAD Civil 3D (version 2015) Time-varying rainfall, ground surface evaporation, green infrastructures, and regional hydrological processes were used to estimate the flow and pollutant concentration

of runoff in sub-catchments Scale of drainage was also analyzed, along with water storage and

shunting infrastructures, in order to evaluate opportunities for reducing the runoff by LID

techniques

ANALYSIS OF CHARACTERISTICS

Analysis of rainfall characteristics: According to weather bureau data, the average annual

rainfall is 1150.6 mm in the Shanghai area From 1991 to 2014, the average rainfall increased

11% from the previous thirty years Rainfall distribution was not found to be uniform, with more

rainfall occurring in the center of the city than in the suburbs During the same period, short-term

rainfall intensity was 9.2 mm·day1, 12% higher than the previous 30 years Increases in

short-term rainfall intensity will result in negative impacts on urban transportation, drainage networks,

and water quality Compared with suburbs and outskirts, urban communities are confronted with

more challenges from the ecological water environment, leading to the urgent requirement for

green infrastructure in Shanghai City

Analysis of soil characteristics: Soil infiltration rates varied greatly by existing green space

functional type in the Shanghai area (see Figure 2) For example, the green space in educational

areas and residential communities has greater permeability (510 3 mm·s1), due to lower impact

from human activities Conversely, the soil in the road green belt has higher soil bulk density,

lower porosity, and lower permeability due to compaction and tread Since soil infiltration

capacity affects the water storage capacity, adding appropriate material to the soil can improve

the physicochemical property to meet the soil infiltration requirement for promoting green space

(5.5610 3 to 7.0610 2 mm·s1), which is a key point for storm water management and Sponge

City construction

Figure 2 Comparison of soil infiltration rates for green space functional types

Soil in green spaces of the outskirts had higher natural and saturation moisture than the ones

in the suburbs and the city center However, there was greater potential of water storage in terms

of soil space in the city center than any other place on average (see Figure 3) For instance, green

spaces at educational areas in Shanghai City center have the potential to capture 13% amount of

water storage space in soil, while the one in the suburbs and outskirts can only occupy 8%

However, in the city center, the soil in squares, roads, commercial areas, and urban communities

had the lowest capacity due to human activities and careless maintenance

Figure 3 Comparison of potential soil water storage space by green space locations and

Recidential Area Public Green Space Commercial Area Educational Area Road Green Belt

City Center Suburbs Outskirts

roads Since the storage capacity of green spaces is proportional to their organic matter content,

the infiltration capacity of soil may be improved by adding soil conditioners and organic matter,

such as straw or spherical bio-ceramic Adjusting the organic content is in line with soil

reclamation goals for urban green spaces

Analysis of runoff characteristics: The factors having influence on rainfall canopy

interception capacity (RCIC) of plant communities include vegetation form, area, inset pattern,

multiple-layer structure, and RCIC for a single plant The canopy interception process involves

the two aspects of leaf absorption and attachment, with the latter playing a more significant role

Submersion tests on leaf samples showed that the average content of canopy rainfall interception

is about 3.6 mm, much higher than evergreen broadleaf plants (2.2 mm), deciduous broadleaf

plants (1.8 mm), shrubs (2.1 mm), and herbaceous plants (1.3 mm) Because the plant canopy

interception has little impact on space-time rainfall distribution, the canopy interception was

ignored in the process of runoff simulation in SSA-SWMM, but was considered when assessing

the rainfall storage capacity for unit area green space

Figure 4 Comparison of runoff in green spaces before and after improvement for the city

center, suburbs, and outskirts

Rui Jin Community, Xin Cheng Community, and Fang Song Community were identified as

three sub-catchments, and were located in the center, suburbs and outskirts, respectively, of

Shanghai City A 1-year rainfall event (36 mm/h) was used The SSA-SWMM simulation

produced comprehensive runoff coefficients of 0.83, 0.76, and 0.68 for the three existing

sub-catchments In order to verify the effect of storm water management for improved or added green

infrastructures, the spatial pattern of green spaces was adjusted in SSA-SWMM Land use and

land cover was adjusted in each sub-catchment by adding LID options, such as green roof,

bio-swale, rain garden, and permeable pavement for reducing runoff coefficients in urban green

space (see Figure 4)

Assuming 5% to 7% of the sub-catchment area to be green infrastructure, as proposed by the environmental consultant department of Prince George County, Maryland, USA (L.A Rossman,

2009), the area of green infrastructure in urban communities was estimated at 87.5 hato 114.8 ha Green infrastructure includes bioswales, rain gardens, multi-functional storage ponds, and

artificial wetlands The built environment could further improve infiltration by building roofs

with proper load and slope, and by using permeable pavement in bicycle and car lanes Climate,

soil, and hydrologic condition need to be considered when selecting LID infrastructure for a

given location

Using SSA-SWMM to simulate the effect of 5 to 7% green infrastructure, the scale was modulated by effect feedback until the runoff coefficient of urban green space in a sub-

catchment reached a given number Using a runoff coefficient cutoff close to 0.37, which was

taken from hydrological data before site development, the area of green infrastructure dedicated

to stormwater management had to account for 11 to 15% of the total sub-catchment Since the

scale of green infrastructure had to be 1/8 to 1/4 of green space, the area of urban green space

had to be 40% to 60% of the total sub-catchment

ASSESSMENT OF GREEN SPACE RAINFALL STORAGE CAPACITY

To calculate rainfall storage capacity of urban green spaces comprehensively, plant canopy interception and soil water-holding capacity had to beassessed simultaneously, taking into

consideration canopy density, tree type, shrub type, shrub area, soil texture, and slope Rainfall

storage capacity was therefore calculated as

where, for i type of plant community,

Li = rainfall storage amount (m3),

Ai = annual average rainfall interception percentage due to tree canopy density (%),

Bi = annual average rainfall interception percentage due to tree form (%),

H = annual average rainfall (mm),

Ci = shrub cover rate (%),

Di = annual average rainfall interception percentage by shrub (%), E=soil water storage capacity (mm), and Si=area of community (m2)

The total rainfall interception capacity for a given green space was then computed by taking the sum over all types of plant communities, and adding a term to account for the storage

capacity of eaters The final value was normalized by area and rainfall amount Results are